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Arnab SP, Campelo dos Santos AL, Fumagalli M, DeGiorgio M. Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning. Mol Biol Evol 2025; 42:msaf094. [PMID: 40341942 PMCID: PMC12062966 DOI: 10.1093/molbev/msaf094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 04/16/2025] [Accepted: 04/17/2025] [Indexed: 05/11/2025] Open
Abstract
Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pretrained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.
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Affiliation(s)
- Sandipan Paul Arnab
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | | | - Matteo Fumagalli
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
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2
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Bressan P. Why humans evolved blue eyes. Front Psychol 2025; 16:1442500. [PMID: 40309207 PMCID: PMC12041803 DOI: 10.3389/fpsyg.2025.1442500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 02/03/2025] [Indexed: 05/02/2025] Open
Abstract
A surprising number of humans are equipped with a subpar eye model-featuring pale, colorful irides that are nowhere as good as the original dark ones at guarding the retina from sunlight and do, in fact, raise one's risk of eye disease. Here I apply evolutionary theory to understand why. I propose that the allele for human blue eyes, which arose just once, managed to spread from one individual to millions at an astonishing speed because it is a greenbeard. "Greenbeards"-imaginary genes, or groups of genes, that produce both a green beard and a behavior that favors other bearers of a green beard-have been deemed exceedingly unlikely to show up in the real world. And yet, as individuals who prefer blue eyes are more inclined to mate with blue-eyed partners and invest in blue-eyed offspring, any blue-eye preference (whether random or arising from the bias for colorful stimuli shared by all recognition systems) becomes rapidly linked to the blue-eye trait. Thus, blue eyes gain an edge by working like a peacock's colorful tail and a nestling's colorful mouth: twice self-reinforcing, "double runaway" evolution via sexual and parental selection. The blue-eye ornament gene, by binding to a behavior that favors other bearers of the blue-eye ornament gene, is ultimately recognizing and helping copies of itself in both kin and strangers-and greatly prospering, just like theory predicts.
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Affiliation(s)
- Paola Bressan
- Dipartimento di Psicologia Generale, University of Padova, Padua, Italy
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3
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Wong ZQ, Deng L, Cengnata A, Abdul Rahman T, Mohd Ismail A, Hong Lim RL, Xu S, Hoh BP. Expression quantitative trait loci (eQTL): from population genetics to precision medicine. J Genet Genomics 2025; 52:449-459. [PMID: 39986349 DOI: 10.1016/j.jgg.2025.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Revised: 02/11/2025] [Accepted: 02/12/2025] [Indexed: 02/24/2025]
Abstract
Evidence has shown that differential transcriptomic profiles among human populations from diverse ancestries, supporting the role of genetic architecture in regulating gene expression alongside environmental stimuli. Genetic variants that regulate gene expression, known as expression quantitative trait loci (eQTL), are primarily shaped by human migration history and evolutionary forces, likewise, regulation of gene expression in principle could have been influenced by these events. Therefore, a comprehensive understanding of how human evolution impacts eQTL offers important insights into how phenotypic diversity is shaped. Recent studies, however, suggest that eQTL is enriched in genes that are selectively constrained. Whether eQTL is minimally affected by selective pressures remains an open question and requires comprehensive investigations. In addition, such studies are primarily dominated by the major populations of European ancestry, leaving many marginalized populations underrepresented. These observations indicate there exists a fundamental knowledge gap in the role of genomics variation on phenotypic diversity, which potentially hinders precision medicine. This article aims to revisit the abundance of eQTL across diverse populations and provide an overview of their impact from the population and evolutionary genetics perspective, subsequently discuss their influence on phenomics, as well as challenges and opportunities in the applications to precision medicine.
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Affiliation(s)
- Zhi Qi Wong
- Faculty of Applied Sciences, UCSI University, Kuala Lumpur 56000, Malaysia
| | - Lian Deng
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Alvin Cengnata
- Faculty of Applied Sciences, UCSI University, Kuala Lumpur 56000, Malaysia
| | - Thuhairah Abdul Rahman
- Clinical Pathology Diagnostic Centre Research Laboratory, Faculty of Medicine, Universiti Teknologi MARA, 47000, Malaysia
| | - Aletza Mohd Ismail
- Clinical Pathology Diagnostic Centre Research Laboratory, Faculty of Medicine, Universiti Teknologi MARA, 47000, Malaysia
| | - Renee Lay Hong Lim
- Faculty of Applied Sciences, UCSI University, Kuala Lumpur 56000, Malaysia
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200433, China; Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China; Jiangsu Key Laboratory of Phylogenomics and Comparative Genomics, School of Life Sciences, Jiangsu Normal University, Xuzhou, Jiangsu 221008, China; Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Boon-Peng Hoh
- Division of Applied Biomedical Sciences and Biotechnology, School of Health Sciences, IMU University, Kuala Lumpur 57000, Malaysia.
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Arnab SP, Dos Santos ALC, Fumagalli M, DeGiorgio M. Efficient detection and characterization of targets of natural selection using transfer learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.05.641710. [PMID: 40093065 PMCID: PMC11908262 DOI: 10.1101/2025.03.05.641710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 03/19/2025]
Abstract
Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.
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Affiliation(s)
- Sandipan Paul Arnab
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | | | - Matteo Fumagalli
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
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Mpofana N, Mlambo ZP, Makgobole MU, Dlova NC, Naicker T. Association of Genetic Polymorphisms in SLC45A2, TYR, HERC2, and SLC24A in African Women with Melasma: A Pilot Study. Int J Mol Sci 2025; 26:1158. [PMID: 39940926 PMCID: PMC11818098 DOI: 10.3390/ijms26031158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2024] [Revised: 01/20/2025] [Accepted: 01/26/2025] [Indexed: 02/16/2025] Open
Abstract
Melasma is a chronic skin disorder characterized by hyperpigmentation, predominantly affecting women with darker skin types, including those of African descent. This study investigates the association between genetic variants in SLC45A2, TYR, HERC2, and SLC24A5 genes and the severity of melasma in women of reproductive age. Forty participants were divided into two groups: twenty with facial melasma and twenty without. Deoxyribonucleic acid (DNA) was extracted from blood samples and genotyped using TaqMan assays to identify allele frequencies and genotype distributions. Significant associations were observed for the TYR gene (rs1042602), HERC2 gene (rs1129038), and SLC24A5 gene (rs1426654) polymorphisms, highlighting their potential roles in melasma susceptibility. For example, the rs1042602 Single Nucleotide Polymorphisms (SNP) in the TYR gene showed a strong association with melasma, with the AA genotype conferring a markedly increased risk. Similarly, the rs1129038 SNP in the HERC2 gene and the rs1426654 SNP in the SLC24A5 gene revealed significant genetic variations between groups in women of African descent. These findings underscore the influence of genetic polymorphisms on melasma's pathogenesis, emphasizing the need for personalized approaches to its treatment, particularly for women with darker skin types.
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Affiliation(s)
- Nomakhosi Mpofana
- Dermatology Department, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban 4000, South Africa; (M.U.M.); (N.C.D.)
- Department of Somatology, Durban University of Technology, Durban 4000, South Africa
| | - Zinhle Pretty Mlambo
- Discipline of Optics and Imaging, Doris Duke Medical Research Institute, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa; (Z.P.M.); (T.N.)
| | - Mokgadi Ursula Makgobole
- Dermatology Department, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban 4000, South Africa; (M.U.M.); (N.C.D.)
| | - Ncoza Cordelia Dlova
- Dermatology Department, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban 4000, South Africa; (M.U.M.); (N.C.D.)
| | - Thajasvarie Naicker
- Discipline of Optics and Imaging, Doris Duke Medical Research Institute, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa; (Z.P.M.); (T.N.)
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Davidović S, Aleksić JM, Tanasković M, Erić P, Stevanović M, Kovačević-Grujičić N. Origin and Genealogy of Rare mtDNA Haplotypes Detected in the Serbian Population. Genes (Basel) 2025; 16:106. [PMID: 39858653 PMCID: PMC11765032 DOI: 10.3390/genes16010106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Revised: 01/14/2025] [Accepted: 01/15/2025] [Indexed: 01/27/2025] Open
Abstract
Background: The Balkan Peninsula has served as an important migration corridor between Asia Minor and Europe throughout humankind's history and a refugium during the Last Glacial Maximum. Past migrations such as the Neolithic expansion, Bronze Age migrations, and the settlement of Slavic tribes in the Early Middle Ages, are well known for their impact on shaping the genetic pool of contemporary Balkan populations. They have contributed to the high genetic diversity of the region, especially in mitochondrial DNA (mtDNA) lineages. Serbia, located in the heart of the Balkans, reflects this complex history in a broad spectrum of mtDNA subhaplogroups. Methods: To explore genetic diversity in Serbia and the wider Balkan region, we analyzed rare mtDNA subclades-R0a, N1a, N1b, I5, W, and X2-using publicly available data. Our dataset included already published sequences from 3499 HVS-I/HVS-II and 1426 complete mitogenomes belonging to West Eurasian and African populations, containing both contemporary and archaeological samples. We assessed the parameters of genetic diversity found in different subclades across the studied regions and constructed detailed phylogeographic trees and haplotype networks to determine phylogenetic relationships. Results: Our analyses revealed the observable geographic structure and identified novel mtDNA subclades, some of which may have originated in the Balkan Peninsula (e.g., R0a1a5, I5a1, W1c2, W3b2, and X2n). Conclusions: The geographic distribution of rare subclades often reveals patterns of past population movements, routes, and gene flows. By tracing the origin and diversity of these subclades, our study provided new insights into the impact of historical migrations on the maternal gene pool of Serbia and the wider Balkan region, contributing to our understanding of the complex genetic history of this important European crossroads.
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Affiliation(s)
- Slobodan Davidović
- Group for Human Molecular Genetics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, 11042 Belgrade, Serbia; (M.S.); (N.K.-G.)
- Department of Genetics of Populations and Ecogenotoxicology, Institute for Biological Research “Siniša Stanković”—National Institute of the Republic of Serbia, University of Belgrade, Bulevar Despota Stefana 142, 11060 Belgrade, Serbia; (M.T.); (P.E.)
| | - Jelena M. Aleksić
- Group for Cardiovascular Physiology, Institute for Medical Research—National Institute of the Republic of Serbia, University of Belgrade, Dr. Subotića 4, P.O. Box 39, 11129 Belgrade, Serbia;
| | - Marija Tanasković
- Department of Genetics of Populations and Ecogenotoxicology, Institute for Biological Research “Siniša Stanković”—National Institute of the Republic of Serbia, University of Belgrade, Bulevar Despota Stefana 142, 11060 Belgrade, Serbia; (M.T.); (P.E.)
| | - Pavle Erić
- Department of Genetics of Populations and Ecogenotoxicology, Institute for Biological Research “Siniša Stanković”—National Institute of the Republic of Serbia, University of Belgrade, Bulevar Despota Stefana 142, 11060 Belgrade, Serbia; (M.T.); (P.E.)
| | - Milena Stevanović
- Group for Human Molecular Genetics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, 11042 Belgrade, Serbia; (M.S.); (N.K.-G.)
- Serbian Academy of Sciences and Arts, Kneza Mihaila 35, 11000 Belgrade, Serbia
| | - Nataša Kovačević-Grujičić
- Group for Human Molecular Genetics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, 11042 Belgrade, Serbia; (M.S.); (N.K.-G.)
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7
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Omdahl AR, Weinstock JS, Keener R, Chhetri SB, Arvanitis M, Battle A. Sparse matrix factorization robust to sample sharing across GWAS reveals interpretable genetic components. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.12.623313. [PMID: 39651140 PMCID: PMC11623536 DOI: 10.1101/2024.11.12.623313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2024]
Abstract
Complex trait-associated genetic variation is highly pleiotropic. This extensive pleiotropy implies that multi-phenotype analyses are informative for characterizing genetic associations, as they facilitate the discovery of trait-shared and trait-specific variants and pathways ("genetic factors"). Previous efforts have estimated genetic factors using matrix factorization (MF) applied to numerous GWAS. However, existing methods are susceptible to spurious factors arising from residual confounding due to sample-sharing in biobank GWAS. Furthermore, MF approaches have historically estimated dense factors, loaded on most traits and variants, that are challenging to map onto interpretable biological pathways. To address these shortcomings, we introduce "GWAS latent embeddings accounting for noise and regularization" (GLEANR), a MF method for detection of sparse genetic factors from summary statistics. GLEANR accounts for sample sharing between studies and uses regularization to estimate a data-driven number of interpretable factors. GLEANR is robust to confounding induced by shared samples and improves the replication of genetic factors derived from distinct biobanks. We used GLEANR to evaluate 137 diverse GWAS from the UK Biobank, identifying 58 factors that decompose the genetic architecture of input traits and have distinct signatures of negative selection and degrees of polygenicity. These sparse factors can be interpreted with respect to disease, cell-type, and pathway enrichment. We highlight three such factors capturing platelet measure phenotypes and enriched for disease-relevant markers corresponding to distinct stages of platelet differentiation. Overall, GLEANR is a powerful tool for discovering both trait-specific and trait-shared pathways underlying complex traits from GWAS summary statistics.
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Boyd R, Richerson PJ. Cultural evolution: Where we have been and where we are going (maybe). Proc Natl Acad Sci U S A 2024; 121:e2322879121. [PMID: 39556734 PMCID: PMC11621844 DOI: 10.1073/pnas.2322879121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2024] Open
Abstract
The study of cultural evolution using ideas from population biology began about 50 y ago, with the work of L.L. Cavalli-Sforza, Marcus Feldman, and ourselves. It has grown from this small beginning into a vital field with many publications and its own scientific society. In this essay, we give our perspective on the origins of the field and current unanswered questions.
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Affiliation(s)
- Robert Boyd
- School of Human Evolution and Social Change, Institute for Human Origins, Arizona State University, Tempe, AZ85281
| | - Peter J. Richerson
- Department of Environmental Science and Policy, University of California, Davis, AZ95616
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9
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Pandey D, Harris M, Garud NR, Narasimhan VM. Leveraging ancient DNA to uncover signals of natural selection in Europe lost due to admixture or drift. Nat Commun 2024; 15:9772. [PMID: 39532856 PMCID: PMC11557891 DOI: 10.1038/s41467-024-53852-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/23/2024] [Indexed: 11/16/2024] Open
Abstract
Large ancient DNA (aDNA) studies offer the chance to examine genomic changes over time, providing direct insights into human evolution. While recent studies have used time-stratified aDNA for selection scans, most focus on single-locus methods. We conducted a multi-locus genotype scan on 708 samples spanning 7000 years of European history. We show that the G12 statistic, originally designed for unphased diploid data, can effectively detect selection in aDNA processed to create 'pseudo-haplotypes'. In simulations and at known positive control loci (e.g., lactase persistence), G12 outperforms the allele frequency-based selection statistic, SweepFinder2, previously used on aDNA. Applying our approach, we identified 14 candidate regions of selection across four time periods, with half the signals detectable only in the earliest period. Our findings suggest that selective events in European prehistory, including from the onset of animal domestication, have been obscured by neutral processes like genetic drift and demographic shifts such as admixture.
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Affiliation(s)
- Devansh Pandey
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Mariana Harris
- Department of Computational Medicine, University of California, Los Angeles, CA, USA
| | - Nandita R Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, CA, USA.
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA.
- Department of Statistics and Data Science, The University of Texas at Austin, Austin, TX, USA.
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10
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Vaughn AH, Nielsen R. Fast and Accurate Estimation of Selection Coefficients and Allele Histories from Ancient and Modern DNA. Mol Biol Evol 2024; 41:msae156. [PMID: 39078618 PMCID: PMC11321360 DOI: 10.1093/molbev/msae156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 07/02/2024] [Accepted: 07/10/2024] [Indexed: 07/31/2024] Open
Abstract
We here present CLUES2, a full-likelihood method to infer natural selection from sequence data that is an extension of the method CLUES. We make several substantial improvements to the CLUES method that greatly increases both its applicability and its speed. We add the ability to use ancestral recombination graphs on ancient data as emissions to the underlying hidden Markov model, which enables CLUES2 to use both temporal and linkage information to make estimates of selection coefficients. We also fully implement the ability to estimate distinct selection coefficients in different epochs, which allows for the analysis of changes in selective pressures through time, as well as selection with dominance. In addition, we greatly increase the computational efficiency of CLUES2 over CLUES using several approximations to the forward-backward algorithms and develop a new way to reconstruct historic allele frequencies by integrating over the uncertainty in the estimation of the selection coefficients. We illustrate the accuracy of CLUES2 through extensive simulations and validate the importance sampling framework for integrating over the uncertainty in the inference of gene trees. We also show that CLUES2 is well-calibrated by showing that under the null hypothesis, the distribution of log-likelihood ratios follows a χ2 distribution with the appropriate degrees of freedom. We run CLUES2 on a set of recently published ancient human data from Western Eurasia and test for evidence of changing selection coefficients through time. We find significant evidence of changing selective pressures in several genes correlated with the introduction of agriculture to Europe and the ensuing dietary and demographic shifts of that time. In particular, our analysis supports previous hypotheses of strong selection on lactase persistence during periods of ancient famines and attenuated selection in more modern periods.
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Affiliation(s)
- Andrew H Vaughn
- Center for Computational Biology, University of California, Berkeley, CA 94720, USA
| | - Rasmus Nielsen
- Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720, USA
- Center for GeoGenetics, University of Copenhagen, Copenhagen DK-1350, Denmark
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11
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Weller RB. Sunlight: Time for a Rethink? J Invest Dermatol 2024; 144:1724-1732. [PMID: 38661623 DOI: 10.1016/j.jid.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 04/26/2024]
Abstract
UVR is a skin carcinogen, yet no studies link sun exposure to increased all-cause mortality. Epidemiological studies from the United Kingdom and Sweden link sun exposure with reduced all-cause, cardiovascular, and cancer mortality. Vitamin D synthesis is dependent on UVB exposure. Individuals with higher serum levels of vitamin D are healthier in many ways, yet multiple trials of oral vitamin D supplementation show little benefit. Growing evidence shows that sunlight has health benefits through vitamin D-independent pathways, such as photomobilization of nitric oxide from cutaneous stores with reduction in cardiovascular morbidity. Sunlight has important systemic health benefit as well as risks.
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Affiliation(s)
- Richard B Weller
- Centre for Inflammation Research, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, United Kingdom; Department of Dermatology, The University of Edinburgh, Edinburgh, United Kingdom.
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12
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Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Kripke CM, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Shakt G, Sun YV, Tsao N, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao P, O'Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano JM, Madduri RK, Damrauer S, Liao KP. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science 2024; 385:eadj1182. [PMID: 39024449 DOI: 10.1126/science.adj1182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/10/2024] [Indexed: 07/20/2024]
Abstract
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.
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Affiliation(s)
- Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA 94304, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University's Mailman School of Public Health, New York, NY 10032, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - David A Heise
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lindsay Guare
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Franciel Linares
- R&D Systems Engineering, Information Technology Services Directorate, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lauren Costa
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Ian Goethert
- Data Management and Engineering, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Ryan Tipton
- Knowledge Discovery Infrastructure, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Laura Davies
- Computing and Computational Sciences Dir PMO, PMO, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Stacey Whitbourne
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Jeremy Cohen
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Rahul Sangar
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Michael Murray
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Daniel R Dochtermann
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Poornima Devineni
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Yunling Shi
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | | | - Charles A Brunette
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Research Service, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37211, USA
| | - Royce Clifford
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Otolaryngology, UCSD San Diego, La Jolla, CA 92093, USA
| | - Scott Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Joel Gelernter
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Adriana Hung
- Medicine, Nephrology & Hypertension, VA Tennessee Valley Healthcare System & Vanderbilt University, Nashville, TN 37232, USA
| | - Sudha K Iyengar
- Departments of Population and Quantitative Health Sciences, Genetics and Genome Sciences, and Ophthalmology and Visual Sciences and the Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob Joseph
- Medicine, Cardiology Section, VA Providence Healthcare System, Providence, RI 02908, USA
- Department of Medicine, Brown University, Providence, RI, 02908, USA
| | - Rachel Kember
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Henry Kranzler
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel Levey
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, OR 97239, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Victoria C Merritt
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
| | - Cassie Overstreet
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
| | - Joseph D Deak
- Psychiatry, Yale University, New Haven, CT 06520, USA
- Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Panos Roussos
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Gabrielle Shakt
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Noah Tsao
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Sanan Venkatesh
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Georgios Voloudakis
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Amy Justice
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
- Internal Medicine, General Medicine, Yale University, New Haven, CT 06520, USA
- Health Policy, Yale School of Public Health, New Haven, CT 06520, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Georgia Tourassi
- National Center for Computational Sciences, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Saiju Pyarajan
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Philip Tsao
- Medicine, Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA 94304, USA
- Department of Medicine, Stanford University, Palo Alto, CA, 94304, USA
| | | | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Jennifer Moser
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Alexander G Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37325, USA
| | - Wei Zhou
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Cambridge, MA 02142, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kelly Cho
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - J Michael Gaziano
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Scott Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA 02115, USA
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13
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Liu J, Bitsue HK, Yang Z. Skin colour: A window into human phenotypic evolution and environmental adaptation. Mol Ecol 2024; 33:e17369. [PMID: 38713101 DOI: 10.1111/mec.17369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/13/2024] [Accepted: 04/17/2024] [Indexed: 05/08/2024]
Abstract
As modern humans ventured out of Africa and dispersed around the world, they faced novel environmental challenges that led to geographic adaptations including skin colour. Over the long history of human evolution, skin colour has changed dramatically, showing tremendous diversity across different geographical regions, for example, the majority of individuals from the expansive lands of Africa have darker skin, whereas the majority of people from Eurasia exhibit lighter skin. What adaptations did lighter skin confer upon modern humans as they migrated from Africa to Eurasia? What genetic mechanisms underlie the diversity of skin colour observed in different populations? In recent years, scientists have gradually gained a deeper understanding of the interactions between pigmentation gene and skin colour through population-based genomic studies of different groups around the world, particularly in East Asia and Africa. In this review, we summarize our current understanding of 26 skin colour-related pigmentation genes and 48 SNPs that influence skin colour. Important pigmentation genes across three major populations are described in detail: MFSD12, SLC24A5, PDPK1 and DDB1/CYB561A3/TMEM138 influence skin colour in African populations; OCA2, KITLG, SLC24A2, GNPAT and PAH are key to the evolution of skin pigmentation in East Asian populations; and SLC24A5, SLC45A2, TYR, TYRP1, ASIP, MC1R and IRF4 significantly contribute to the lightening of skin colour in European populations. We summarized recent findings in genomic studies of skin colour in populations that implicate diverse geographic environments, local adaptation among populations, gene flow and multi-gene interactions as factors influencing skin colour diversity.
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Affiliation(s)
- Jiuming Liu
- Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Habtom K Bitsue
- Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Zhaohui Yang
- Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
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14
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Simon A, Coop G. The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change. Proc Natl Acad Sci U S A 2024; 121:e2312377121. [PMID: 38363870 PMCID: PMC10907250 DOI: 10.1073/pnas.2312377121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/09/2024] [Indexed: 02/18/2024] Open
Abstract
Genomic time series from experimental evolution studies and ancient DNA datasets offer us a chance to directly observe the interplay of various evolutionary forces. We show how the genome-wide variance in allele frequency change between two time points can be decomposed into the contributions of gene flow, genetic drift, and linked selection. In closed populations, the contribution of linked selection is identifiable because it creates covariances between time intervals, and genetic drift does not. However, repeated gene flow between populations can also produce directionality in allele frequency change, creating covariances. We show how to accurately separate the fraction of variance in allele frequency change due to admixture and linked selection in a population receiving gene flow. We use two human ancient DNA datasets, spanning around 5,000 y, as time transects to quantify the contributions to the genome-wide variance in allele frequency change. We find that a large fraction of genome-wide change is due to gene flow. In both cases, after correcting for known major gene flow events, we do not observe a signal of genome-wide linked selection. Thus despite the known role of selection in shaping long-term polymorphism levels, and an increasing number of examples of strong selection on single loci and polygenic scores from ancient DNA, it appears to be gene flow and drift, and not selection, that are the main determinants of recent genome-wide allele frequency change. Our approach should be applicable to the growing number of contemporary and ancient temporal population genomics datasets.
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Affiliation(s)
- Alexis Simon
- Center for Population Biology, University of California, Davis, CA95616
- Department of Evolution and Ecology, University of California, Davis, CA95616
| | - Graham Coop
- Center for Population Biology, University of California, Davis, CA95616
- Department of Evolution and Ecology, University of California, Davis, CA95616
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15
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Simon A, Coop G. The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.11.548607. [PMID: 37503227 PMCID: PMC10370008 DOI: 10.1101/2023.07.11.548607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Genomic time series from experimental evolution studies and ancient DNA datasets offer us a chance to directly observe the interplay of various evolutionary forces. We show how the genome-wide variance in allele frequency change between two time points can be decomposed into the contributions of gene flow, genetic drift, and linked selection. In closed populations, the contribution of linked selection is identifiable because it creates covariances between time intervals, and genetic drift does not. However, repeated gene flow between populations can also produce directionality in allele frequency change, creating covariances. We show how to accurately separate the fraction of variance in allele frequency change due to admixture and linked selection in a population receiving gene flow. We use two human ancient DNA datasets, spanning around 5,000 years, as time transects to quantify the contributions to the genome-wide variance in allele frequency change. We find that a large fraction of genome-wide change is due to gene flow. In both cases, after correcting for known major gene flow events, we do not observe a signal of genome-wide linked selection. Thus despite the known role of selection in shaping long-term polymorphism levels, and an increasing number of examples of strong selection on single loci and polygenic scores from ancient DNA, it appears to be gene flow and drift, and not selection, that are the main determinants of recent genome-wide allele frequency change. Our approach should be applicable to the growing number of contemporary and ancient temporal population genomics datasets.
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Affiliation(s)
- Alexis Simon
- Center for Population Biology, University of California, Davis, CA 95616
- Department of Evolution and Ecology, University of California, Davis, CA 95616
| | - Graham Coop
- Center for Population Biology, University of California, Davis, CA 95616
- Department of Evolution and Ecology, University of California, Davis, CA 95616
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16
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Gao Z. Unveiling recent and ongoing adaptive selection in human populations. PLoS Biol 2024; 22:e3002469. [PMID: 38236800 PMCID: PMC10796035 DOI: 10.1371/journal.pbio.3002469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2024] Open
Abstract
Genome-wide scans for signals of selection have become a routine part of the analysis of population genomic variation datasets and have resulted in compelling evidence of selection during recent human evolution. This Essay spotlights methodological innovations that have enabled the detection of selection over very recent timescales, even in contemporary human populations. By harnessing large-scale genomic and phenotypic datasets, these new methods use different strategies to uncover connections between genotype, phenotype, and fitness. This Essay outlines the rationale and key findings of each strategy, discusses challenges in interpretation, and describes opportunities to improve detection and understanding of ongoing selection in human populations.
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Affiliation(s)
- Ziyue Gao
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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17
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Irving-Pease EK, Refoyo-Martínez A, Barrie W, Ingason A, Pearson A, Fischer A, Sjögren KG, Halgren AS, Macleod R, Demeter F, Henriksen RA, Vimala T, McColl H, Vaughn AH, Speidel L, Stern AJ, Scorrano G, Ramsøe A, Schork AJ, Rosengren A, Zhao L, Kristiansen K, Iversen AKN, Fugger L, Sudmant PH, Lawson DJ, Durbin R, Korneliussen T, Werge T, Allentoft ME, Sikora M, Nielsen R, Racimo F, Willerslev E. The selection landscape and genetic legacy of ancient Eurasians. Nature 2024; 625:312-320. [PMID: 38200293 PMCID: PMC10781624 DOI: 10.1038/s41586-023-06705-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/03/2023] [Indexed: 01/12/2024]
Abstract
The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.
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Affiliation(s)
- Evan K Irving-Pease
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
| | - Alba Refoyo-Martínez
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - William Barrie
- GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Andrés Ingason
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark
| | - Alice Pearson
- Department of Genetics, University of Cambridge, Cambridge, UK
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Anders Fischer
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden
- Sealand Archaeology, Kalundborg, Denmark
| | - Karl-Göran Sjögren
- Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden
| | - Alma S Halgren
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Ruairidh Macleod
- GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- UCL Genetics Institute, University College London, London, UK
| | - Fabrice Demeter
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Eco-anthropologie, Muséum national d'Histoire naturelle, CNRS, Université Paris Cité, Musée de l'Homme, Paris, France
| | - Rasmus A Henriksen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Tharsika Vimala
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Hugh McColl
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Andrew H Vaughn
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Leo Speidel
- UCL Genetics Institute, University College London, London, UK
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Aaron J Stern
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Gabriele Scorrano
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Abigail Ramsøe
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
| | - Anders Rosengren
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark
| | - Lei Zhao
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Kristiansen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden
| | - Astrid K N Iversen
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Lars Fugger
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
- MRC Human Immunology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Peter H Sudmant
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Daniel J Lawson
- Institute of Statistical Sciences, School of Mathematics, University of Bristol, Bristol, UK
| | - Richard Durbin
- Department of Genetics, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Thorfinn Korneliussen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Werge
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Center Sct Hans, Copenhagen University Hospital, Copenhagen, Denmark
| | - Morten E Allentoft
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Science, Curtin University, Perth, Western Australia, Australia
| | - Martin Sikora
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Nielsen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
- Departments of Integrative Biology and Statistics, UC Berkeley, Berkeley, CA, USA.
| | - Fernando Racimo
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
| | - Eske Willerslev
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
- GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK.
- MARUM Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Bremen, Germany.
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18
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Mo Z, Siepel A. Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. PLoS Genet 2023; 19:e1011032. [PMID: 37934781 PMCID: PMC10655966 DOI: 10.1371/journal.pgen.1011032] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 11/17/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023] Open
Abstract
Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.
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Affiliation(s)
- Ziyi Mo
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Adam Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
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19
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Amin MR, Hasan M, Arnab SP, DeGiorgio M. Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data. Mol Biol Evol 2023; 40:msad216. [PMID: 37772983 PMCID: PMC10581699 DOI: 10.1093/molbev/msad216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 08/10/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023] Open
Abstract
Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under nonconvex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data although preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.
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Affiliation(s)
- Md Ruhul Amin
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Mahmudul Hasan
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Sandipan Paul Arnab
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
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20
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Mo Z, Siepel A. Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.01.529396. [PMID: 36909514 PMCID: PMC10002701 DOI: 10.1101/2023.03.01.529396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does not adequately resemble data from the real world. Here, we show that this "simulation mis-specification" problem can be framed as a "domain adaptation" problem, where a model learned from one data distribution is applied to a dataset drawn from a different distribution. By applying an established domain-adaptation technique based on a gradient reversal layer (GRL), originally introduced for image classification, we show that the effects of simulation mis-specification can be substantially mitigated. We focus our analysis on two state-of-the-art deep-learning population genetic methods-SIA, which infers positive selection from features of the ancestral recombination graph (ARG), and ReLERNN, which infers recombination rates from genotype matrices. In the case of SIA, the domain adaptive framework also compensates for ARG inference error. Using the domain-adaptive SIA (dadaSIA) model, we estimate improved selection coefficients at selected loci in the 1000 Genomes CEU population. We anticipate that domain adaptation will prove to be widely applicable in the growing use of supervised machine learning in population genetics.
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Affiliation(s)
- Ziyi Mo
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
| | - Adam Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
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21
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Konner M, Eaton SB. Hunter-gatherer diets and activity as a model for health promotion: Challenges, responses, and confirmations. Evol Anthropol 2023; 32:206-222. [PMID: 37417918 DOI: 10.1002/evan.21987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 07/27/2022] [Accepted: 04/17/2023] [Indexed: 07/08/2023]
Abstract
Beginning in 1985, we and others presented estimates of hunter-gatherer (and ultimately ancestral) diet and physical activity, hoping to provide a model for health promotion. The Hunter-Gatherer Model was designed to offset the apparent mismatch between our genes and the current Western-type lifestyle, a mismatch that arguably affects prevalence of many chronic degenerative diseases. The effort has always been controversial and subject to both scientific and popular critiques. The present article (1) addresses eight such challenges, presenting for each how the model has been modified in response, or how the criticism can be rebutted; (2) reviews new epidemiological and experimental evidence (including especially randomized controlled clinical trials); and (3) shows how official recommendations put forth by governments and health authorities have converged toward the model. Such convergence suggests that evolutionary anthropology can make significant contributions to human health.
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Affiliation(s)
- Melvin Konner
- Department of Anthropology, Program in Anthropology and Human Biology, Emory University, Atlanta, Georgia, USA
| | - S Boyd Eaton
- Department of Radiology, Emory University School of Medicine (Emeritus), Adjunct Lecturer, Department of Anthropology, Emory University, Atlanta, Georgia, USA
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22
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Elkin J, Martin A, Courtier-Orgogozo V, Santos ME. Analysis of the genetic loci of pigment pattern evolution in vertebrates. Biol Rev Camb Philos Soc 2023; 98:1250-1277. [PMID: 37017088 DOI: 10.1111/brv.12952] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 04/06/2023]
Abstract
Vertebrate pigmentation patterns are amongst the best characterised model systems for studying the genetic basis of adaptive evolution. The wealth of available data on the genetic basis for pigmentation evolution allows for analysis of trends and quantitative testing of evolutionary hypotheses. We employed Gephebase, a database of genetic variants associated with natural and domesticated trait variation, to examine trends in how cis-regulatory and coding mutations contribute to vertebrate pigmentation phenotypes, as well as factors that favour one mutation type over the other. We found that studies with lower ascertainment bias identified higher proportions of cis-regulatory mutations, and that cis-regulatory mutations were more common amongst animals harbouring a higher number of pigment cell classes. We classified pigmentation traits firstly according to their physiological basis and secondly according to whether they affect colour or pattern, and identified that carotenoid-based pigmentation and variation in pattern boundaries are preferentially associated with cis-regulatory change. We also classified genes according to their developmental, cellular, and molecular functions. We found a greater proportion of cis-regulatory mutations in genes implicated in upstream developmental processes compared to those involved in downstream cellular functions, and that ligands were associated with a higher proportion of cis-regulatory mutations than their respective receptors. Based on these trends, we discuss future directions for research in vertebrate pigmentation evolution.
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Affiliation(s)
- Joel Elkin
- Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
| | - Arnaud Martin
- Department of Biological Sciences, The George Washington University, 800 22nd St. NW, Suite 6000, Washington, DC, 20052, USA
| | | | - M Emília Santos
- Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
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23
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Whitehouse LS, Schrider DR. Timesweeper: accurately identifying selective sweeps using population genomic time series. Genetics 2023; 224:iyad084. [PMID: 37157914 PMCID: PMC10324941 DOI: 10.1093/genetics/iyad084] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/25/2022] [Accepted: 04/25/2023] [Indexed: 05/10/2023] Open
Abstract
Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been developed to tackle this task, few are designed to leverage the potential of genomic time-series data. This is because in most population genetic studies of natural populations, only a single period of time can be sampled. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for more direct analysis of recent evolutionary dynamics. Serial sampling of organisms with shorter generation times has also become more feasible due to improvements in the cost and throughput of sequencing. With these advances in mind, here we present Timesweeper, a fast and accurate convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper analyzes population genomic time-series data by first simulating training data under a demographic model appropriate for the data of interest, training a one-dimensional convolutional neural network on said simulations, and inferring which polymorphisms in this serialized data set were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is accurate under multiple simulated demographic and sampling scenarios, identifies selected variants with high resolution, and estimates selection coefficients more accurately than existing methods. In sum, we show that more accurate inferences about natural selection are possible when genomic time-series data are available; such data will continue to proliferate in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times, as well as experimentally evolved populations where time-series data are often generated. Methodological advances such as Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper as a Python package for use by the community.
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Affiliation(s)
- Logan S Whitehouse
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
| | - Daniel R Schrider
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27514, USA
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24
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Arnab SP, Amin MR, DeGiorgio M. Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics. Mol Biol Evol 2023; 40:msad157. [PMID: 37433019 PMCID: PMC10365025 DOI: 10.1093/molbev/msad157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 06/28/2023] [Accepted: 07/06/2023] [Indexed: 07/13/2023] Open
Abstract
Natural selection leaves a spatial pattern along the genome, with a haplotype distribution distortion near the selected locus that fades with distance. Evaluating the spatial signal of a population-genetic summary statistic across the genome allows for patterns of natural selection to be distinguished from neutrality. Considering the genomic spatial distribution of multiple summary statistics is expected to aid in uncovering subtle signatures of selection. In recent years, numerous methods have been devised that consider genomic spatial distributions across summary statistics, utilizing both classical machine learning and deep learning architectures. However, better predictions may be attainable by improving the way in which features are extracted from these summary statistics. We apply wavelet transform, multitaper spectral analysis, and S-transform to summary statistic arrays to achieve this goal. Each analysis method converts one-dimensional summary statistic arrays to two-dimensional images of spectral analysis, allowing simultaneous temporal and spectral assessment. We feed these images into convolutional neural networks and consider combining models using ensemble stacking. Our modeling framework achieves high accuracy and power across a diverse set of evolutionary settings, including population size changes and test sets of varying sweep strength, softness, and timing. A scan of central European whole-genome sequences recapitulated well-established sweep candidates and predicted novel cancer-associated genes as sweeps with high support. Given that this modeling framework is also robust to missing genomic segments, we believe that it will represent a welcome addition to the population-genomic toolkit for learning about adaptive processes from genomic data.
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Affiliation(s)
- Sandipan Paul Arnab
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Md Ruhul Amin
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
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25
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Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Sun YV, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao PS, O’Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano MJ, Madduri RK, Damrauer SM, Liao KP. Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291975. [PMID: 37425708 PMCID: PMC10327290 DOI: 10.1101/2023.06.28.23291975] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P < 4.6 × 10 - 11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.
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Affiliation(s)
- Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA, 94304, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University’s Mailman School of Public Health, New York, NY, 10032, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - David A Heise
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Lindsay Guare
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | | | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Franciel Linares
- R&D Systems Engineering, Information Technology Services Directorate, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Lauren Costa
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Ian Goethert
- Data Management and Engineering, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Ryan Tipton
- Knowledge Discovery Infrastructure, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Laura Davies
- Computing and Computational Sciences Dir PMO, PMO, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Stacey Whitbourne
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Jeremy Cohen
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Rahul Sangar
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Michael Murray
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Daniel R Dochtermann
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Poornima Devineni
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Yunling Shi
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | | | - Charles A Brunette
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Research Service, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37211, USA
| | - Royce Clifford
- Research Department, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Surgery, Otolaryngology, UCSD San Diego, La Jolla, California, 92093, USA
| | - Scott Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, 84148, USA
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA
| | - Joel Gelernter
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Adriana Hung
- Medicine, Nephrology & Hypertension, VA Tennessee Valley Healthcare System & Vanderbilt University, Nashville, TN, 37232, USA
| | - Sudha K Iyengar
- Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA
| | - Jacob Joseph
- Medicine, Cardiology Section, VA Providence Healthcare System, Providence, RI, 02908, USA
- Department of Medicine, Brown University, Providence, RI, 02908, USA
| | - Rachel Kember
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Henry Kranzler
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Daniel Levey
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, OR, 97239, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Victoria C Merritt
- Research Department, VA San Diego Healthcare System, San Diego, CA, 92161, USA
| | - Cassie Overstreet
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
| | - Joseph D Deak
- Psychiatry, Yale University, New Haven, CT, 06520, USA
- Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | | | - Panos Roussos
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY, 10468, USA
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, 30322, USA
| | - Sanan Venkatesh
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY, 10468, USA
| | - Georgios Voloudakis
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY, 10468, USA
| | - Amy Justice
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
- Internal Medicine, General Medicine, Yale University, New Haven, CT, 06520, USA
- Health Policy, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Georgia Tourassi
- National Center for Computational Sciences, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Saiju Pyarajan
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Philip S Tsao
- Medicine, Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA, 94304, USA
- Department of Medicine, Stanford University, Palo Alto, CA, 94304, USA
| | | | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Jennifer Moser
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Alexander G Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37325, USA
| | - Wei Zhou
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA, 02142, USA
- Program in Medical and Population Genetics, Cambridge, MA, 02142, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Kelly Cho
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Michael J Gaziano
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Scott M Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Katherine P Liao
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA, 02115, USA
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26
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Gu L, Xia C, Yang S, Yang G. The adaptive evolution of cancer driver genes. BMC Genomics 2023; 24:215. [PMID: 37098512 PMCID: PMC10131384 DOI: 10.1186/s12864-023-09301-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 04/08/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Cancer is a life-threatening disease in humans; yet, cancer genes are frequently reported to be under positive selection. This suggests an evolutionary-genetic paradox in which cancer evolves as a secondary product of selection in human beings. However, systematic investigation of the evolution of cancer driver genes is sparse. RESULTS Using comparative genomics analysis, population genetics analysis and computational molecular evolutionary analysis, the evolution of 568 cancer driver genes of 66 cancer types were evaluated at two levels, selection on the early evolution of humans (long timescale selection in the human lineage during primate evolution, i.e., millions of years), and recent selection in modern human populations (~ 100,000 years). Results showed that eight cancer genes covering 11 cancer types were under positive selection in the human lineage (long timescale selection). And 35 cancer genes covering 47 cancer types were under positive selection in modern human populations (recent selection). Moreover, SNPs associated with thyroid cancer in three thyroid cancer driver genes (CUX1, HERC2 and RGPD3) were under positive selection in East Asian and European populations, consistent with the high incidence of thyroid cancer in these populations. CONCLUSIONS These findings suggest that cancer can be evolved, in part, as a by-product of adaptive changes in humans. Different SNPs at the same locus can be under different selection pressures in different populations, and thus should be under consideration during precision medicine, especially for targeted medicine in specific populations.
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Affiliation(s)
- Langyu Gu
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China.
| | - Canwei Xia
- Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing, 100875, China
| | - Shiyu Yang
- The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, 510180, Guangdong, China
| | - Guofen Yang
- Department of Gynecology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510060, Guangdong, China.
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27
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Pandey D, Harris M, Garud NR, Narasimhan VM. Understanding natural selection in Holocene Europe using multi-locus genotype identity scans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538113. [PMID: 37163039 PMCID: PMC10168228 DOI: 10.1101/2023.04.24.538113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Ancient DNA (aDNA) has been a revolutionary technology in understanding human history but has not been used extensively to study natural selection as large sample sizes to study allele frequency changes over time have thus far not been available. Here, we examined a time transect of 708 published samples over the past 7,000 years of European history using multi-locus genotype-based selection scans. As aDNA data is affected by high missingness, ascertainment bias, DNA damage, random allele calling, and is unphased, we first validated our selection scan, G 12 a n c i e n t , on simulated data resembling aDNA under a demographic model that captures broad features of the allele frequency spectrum of European genomes as well as positive controls that have been previously identified and functionally validated in modern European datasets on data from ancient individuals from time periods very close to the present time. We then applied our statistic to the aDNA time transect to detect and resolve the timing of natural selection occurring genome wide and found several candidates of selection across the different time periods that had not been picked up by selection scans using single SNP allele frequency approaches. In addition, enrichment analysis discovered multiple categories of complex traits that might be under adaptation across these periods. Our results demonstrate the utility of applying different types of selection scans to aDNA to uncover putative selection signals at loci in the ancient past that might have been masked in modern samples.
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Affiliation(s)
- Devansh Pandey
- Department of Integrative Biology, The University of Texas at Austin
| | - Mariana Harris
- Department of Computational Medicine, University of California, Los Angeles
| | - Nandita R Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles
- Department of Human Genetics, University of California, Los Angeles
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin
- Department of Statistics and Data Science, The University of Texas at Austin
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28
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Amin MR, Hasan M, Arnab SP, DeGiorgio M. Tensor decomposition based feature extraction and classification to detect natural selection from genomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.27.527731. [PMID: 37034767 PMCID: PMC10081272 DOI: 10.1101/2023.03.27.527731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx , which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.
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29
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Chen Y, Hysi P, Maj C, Heilmann-Heimbach S, Spector TD, Liu F, Kayser M. Genetic prediction of male pattern baldness based on large independent datasets. Eur J Hum Genet 2023; 31:321-328. [PMID: 36336714 PMCID: PMC9995341 DOI: 10.1038/s41431-022-01201-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/09/2022] Open
Abstract
Genetic prediction of male pattern baldness (MPB) is important in science and society. Previous genetic MPB prediction models were limited by sparse marker coverage, small sample size, and/or data dependency in the different analytical steps. Here, we present novel models for genetic prediction of MPB based on a large set of markers and large independent subsample sets drawn among 187,435 European subjects. We selected 117 SNP predictors within 85 distinct loci from a list of 270 previously MPB-associated SNPs in 55,573 males of the UK Biobank Study (UKBB). Based on these 117 SNPs with and without age as additional predictor, we trained, by use of different methods, prediction models in a non-overlapping subset of 104,694 UKBB males and tested them in a non-overlapping subset of 26,177 UKBB males. Estimates of prediction accuracy were similar between methods with AUC ranges of 0.725-0.728 for severe, 0.631-0.635 for moderate, 0.598-0.602 for slight, and 0.708-0.711 for no hair loss with age, and slightly lower without, while prediction of any versus no hair loss gave 0.690-0.711 with age and slightly lower without. External validation in an early-onset enriched MPB dataset from the Bonn Study (N = 991) showed improved prediction accuracy without considering age such as AUC of 0.830 for no vs. any hair loss. Because of the large number of markers and the large independent datasets used for the different analytical steps, the newly presented genetic prediction models are the most reliable ones currently available for MPB or any other human appearance trait.
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Affiliation(s)
- Yan Chen
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Pirro Hysi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Carlo Maj
- Institute for Genomic Statistics and Bioinformatics (IGSB), University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Fan Liu
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
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30
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Lucock MD. The evolution of human skin pigmentation: A changing medley of vitamins, genetic variability, and UV radiation during human expansion. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2023; 180:252-271. [PMID: 36790744 PMCID: PMC10083917 DOI: 10.1002/ajpa.24564] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/19/2022] [Accepted: 05/25/2022] [Indexed: 04/12/2023]
Abstract
This review examines putative, yet likely critical evolutionary pressures contributing to human skin pigmentation and subsequently, depigmentation phenotypes. To achieve this, it provides a synthesis of ideas that frame contemporary thinking, without limiting the narrative to pigmentation genes alone. It examines how geography and hence the quality and quantity of UV exposure, pigmentation genes, diet-related genes, vitamins, anti-oxidant nutrients, and cultural practices intersect and interact to facilitate the evolution of human skin color. The article has a strong focus on the vitamin D-folate evolutionary model, with updates on the latest biophysical research findings to support this paradigm. This model is examined within a broad canvas that takes human expansion out of Africa and genetic architecture into account. A thorough discourse on the biology of melanization is provided (includes relationship to BH4 and DNA damage repair), with the relevance of this to the UV sensitivity of folate and UV photosynthesis of vitamin D explained in detail, including the relevance of these vitamins to reproductive success. It explores whether we might be able to predict vitamin-related gene polymorphisms that pivot metabolism to the prevailing UVR exposome within the vitamin D-folate evolutionary hypothesis context. This is discussed in terms of a primary adaptive phenotype (pigmentation/depigmentation), a secondary adaptive phenotype (flexible metabolic phenotype based on vitamin-related gene polymorphism profile), and a tertiary adaptive strategy (dietary anti-oxidants to support the secondary adaptive phenotype). Finally, alternative evolutionary models for pigmentation are discussed, as are challenges to future research in this area.
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Affiliation(s)
- Mark D. Lucock
- School of Environmental & Life SciencesUniversity of NewcastleOurimbahNew South WalesAustralia
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31
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Puckett EE, Davis IS, Harper DC, Wakamatsu K, Battu G, Belant JL, Beyer DE, Carpenter C, Crupi AP, Davidson M, DePerno CS, Forman N, Fowler NL, Garshelis DL, Gould N, Gunther K, Haroldson M, Ito S, Kocka D, Lackey C, Leahy R, Lee-Roney C, Lewis T, Lutto A, McGowan K, Olfenbuttel C, Orlando M, Platt A, Pollard MD, Ramaker M, Reich H, Sajecki JL, Sell SK, Strules J, Thompson S, van Manen F, Whitman C, Williamson R, Winslow F, Kaelin CB, Marks MS, Barsh GS. Genetic architecture and evolution of color variation in American black bears. Curr Biol 2023; 33:86-97.e10. [PMID: 36528024 PMCID: PMC10039708 DOI: 10.1016/j.cub.2022.11.042] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/08/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022]
Abstract
Color variation is a frequent evolutionary substrate for camouflage in small mammals, but the underlying genetics and evolutionary forces that drive color variation in natural populations of large mammals are mostly unexplained. The American black bear, Ursus americanus (U. americanus), exhibits a range of colors including the cinnamon morph, which has a similar color to the brown bear, U. arctos, and is found at high frequency in the American southwest. Reflectance and chemical melanin measurements showed little distinction between U. arctos and cinnamon U. americanus individuals. We used a genome-wide association for hair color as a quantitative trait in 151 U. americanus individuals and identified a single major locus (p < 10-13). Additional genomic and functional studies identified a missense alteration (R153C) in Tyrosinase-related protein 1 (TYRP1) that likely affects binding of the zinc cofactor, impairs protein localization, and results in decreased pigment production. Population genetic analyses and demographic modeling indicated that the R153C variant arose 9.36 kya in a southwestern population where it likely provided a selective advantage, spreading both northwards and eastwards by gene flow. A different TYRP1 allele, R114C, contributes to the characteristic brown color of U. arctos but is not fixed across the range.
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Affiliation(s)
- Emily E Puckett
- Department of Biological Sciences, University of Memphis, Memphis, TN 38152, USA.
| | - Isis S Davis
- Department of Biological Sciences, University of Memphis, Memphis, TN 38152, USA
| | - Dawn C Harper
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Kazumasa Wakamatsu
- Institute for Melanin Chemistry, Fujita Health University, Toyoake, Japan
| | - Gopal Battu
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Jerrold L Belant
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA
| | - Dean E Beyer
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA
| | - Colin Carpenter
- West Virginia Division of Natural Resources, Beckley, WV 25801, USA
| | - Anthony P Crupi
- Division of Wildlife Conservation, Alaska Department of Fish and Game, Douglas, Juneau, AK 99824, USA
| | - Maria Davidson
- The Louisiana Department of Wildlife and Fisheries, Baton Rouge, LA 70898, USA
| | - Christopher S DePerno
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695-7646, USA
| | - Nicholas Forman
- New Mexico Department of Game and Fish, Santa Fe, NM 87507, USA
| | - Nicholas L Fowler
- Division of Wildlife Conservation, Alaska Department of Fish and Game, Douglas, Juneau, AK 99824, USA
| | - David L Garshelis
- Minnesota Department of Natural Resources, Grand Rapids, MN 55744, USA; IUCN SSC Bear Specialist Group
| | - Nicholas Gould
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695-7646, USA
| | - Kerry Gunther
- National Park Service, Yellowstone National Park, WY 82190-0168, USA
| | - Mark Haroldson
- U.S. Geological Survey, Northern Rocky Mountain Science Center, Interagency Grizzly Bear Study Team, Bozeman, MT 59715, USA
| | - Shosuke Ito
- Institute for Melanin Chemistry, Fujita Health University, Toyoake, Japan
| | - David Kocka
- Virginia Department of Wildlife Resources, Verona, VA 24482, USA
| | - Carl Lackey
- Nevada Department of Wildlife, Reno, NV 89512, USA
| | - Ryan Leahy
- National Park Service, Yosemite National Park Wildlife Management, Yosemite, CA 95389, USA
| | - Caitlin Lee-Roney
- National Park Service, Yosemite National Park Wildlife Management, Yosemite, CA 95389, USA
| | - Tania Lewis
- National Park Service, Glacier Bay National Park, Gustavus, AK 99826, USA
| | - Ashley Lutto
- U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK 99669, USA
| | - Kelly McGowan
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | | | - Mike Orlando
- Florida Fish and Wildlife Conservation Commission, Tallahassee, FL 32399, USA
| | - Alexander Platt
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew D Pollard
- Department of Biological Sciences, University of Memphis, Memphis, TN 38152, USA
| | - Megan Ramaker
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | | | - Jaime L Sajecki
- Virginia Department of Wildlife Resources, Verona, VA 24482, USA
| | - Stephanie K Sell
- Division of Wildlife Conservation, Alaska Department of Fish and Game, Douglas, Juneau, AK 99824, USA
| | - Jennifer Strules
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695-7646, USA
| | - Seth Thompson
- Virginia Department of Wildlife Resources, Verona, VA 24482, USA
| | - Frank van Manen
- U.S. Geological Survey, Northern Rocky Mountain Science Center, Interagency Grizzly Bear Study Team, Bozeman, MT 59715, USA
| | - Craig Whitman
- U.S. Geological Survey, Northern Rocky Mountain Science Center, Interagency Grizzly Bear Study Team, Bozeman, MT 59715, USA
| | - Ryan Williamson
- National Park Service, Great Smoky Mountains National Park, Gatlinburg, TN 37738, USA
| | | | - Christopher B Kaelin
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Michael S Marks
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Departments of Pathology and Laboratory Medicine and of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Gregory S Barsh
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA; Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA
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32
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Kataria S, Dabas P, Saraswathy KN, Sachdeva MP, Jain S. Investigating the morphology and genetics of scalp and facial hair characteristics for phenotype prediction. Sci Justice 2023; 63:135-148. [PMID: 36631178 DOI: 10.1016/j.scijus.2022.12.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Microscopic traits and ultrastructure of hair such as cross-sectional shape, pigmentation, curvature, and internal structure help determine the level of variations between and across human populations. Apart from cosmetics and anthropological applications, such as determining species, somatic origin (body area), and biogeographic ancestry, the evidential value of hair has increased with rapid progression in the area of forensic DNA phenotyping (FDP). Individuals differ in the features of their scalp hair (greying, shape, colour, balding, thickness, and density) and facial hair (eyebrow thickness, monobrow, and beard thickness) features. Scalp and facial hair characteristics are genetically controlled and lead to visible inter-individual variations within and among populations of various ethnic origins. Hence, these characteristics can be exploited and made more inclusive in FDP, thereby leading to more comprehensive, accurate, and robust prediction models for forensic purposes. The present article focuses on understanding the genetics of scalp and facial hair characteristics with the goal to develop a more inclusive approach to better understand hair biology by integrating hair microscopy with genetics for genotype-phenotype correlation research.
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Affiliation(s)
- Suraj Kataria
- Department of Anthropology, University of Delhi, India.
| | - Prashita Dabas
- Amity Institute of Forensic Sciences, Amity University, Noida, Uttar Pradesh, India.
| | | | - M P Sachdeva
- Department of Anthropology, University of Delhi, India.
| | - Sonal Jain
- Department of Anthropology, University of Delhi, India.
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Muktupavela RA, Petr M, Ségurel L, Korneliussen T, Novembre J, Racimo F. Modeling the spatiotemporal spread of beneficial alleles using ancient genomes. eLife 2022; 11:e73767. [PMID: 36537881 PMCID: PMC9767474 DOI: 10.7554/elife.73767] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/21/2022] [Indexed: 12/24/2022] Open
Abstract
Ancient genome sequencing technologies now provide the opportunity to study natural selection in unprecedented detail. Rather than making inferences from indirect footprints left by selection in present-day genomes, we can directly observe whether a given allele was present or absent in a particular region of the world at almost any period of human history within the last 10,000 years. Methods for studying selection using ancient genomes often rely on partitioning individuals into discrete time periods or regions of the world. However, a complete understanding of natural selection requires more nuanced statistical methods which can explicitly model allele frequency changes in a continuum across space and time. Here we introduce a method for inferring the spread of a beneficial allele across a landscape using two-dimensional partial differential equations. Unlike previous approaches, our framework can handle time-stamped ancient samples, as well as genotype likelihoods and pseudohaploid sequences from low-coverage genomes. We apply the method to a panel of published ancient West Eurasian genomes to produce dynamic maps showcasing the inferred spread of candidate beneficial alleles over time and space. We also provide estimates for the strength of selection and diffusion rate for each of these alleles. Finally, we highlight possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.
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Affiliation(s)
- Rasa A Muktupavela
- Lundbeck GeoGenetics Centre, GLOBE Institute, Faculty of HealthCopenhagenDenmark
| | - Martin Petr
- Lundbeck GeoGenetics Centre, GLOBE Institute, Faculty of HealthCopenhagenDenmark
| | - Laure Ségurel
- UMR5558 Biométrie et Biologie Evolutive, CNRS - Université Lyon 1VilleurbanneFrance
| | | | - John Novembre
- Department of Human Genetics, University of ChicagoChicagoUnited States
| | - Fernando Racimo
- Lundbeck GeoGenetics Centre, GLOBE Institute, Faculty of HealthCopenhagenDenmark
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Askapuli A, Vilar M, Garcia-Ortiz H, Zhabagin M, Sabitov Z, Akilzhanova A, Ramanculov E, Schamiloglu U, Martinez-Hernandez A, Contreras-Cubas C, Barajas-Olmos F, Schurr TG, Zhumadilov Z, Flores-Huacuja M, Orozco L, Hawks J, Saitou N. Kazak mitochondrial genomes provide insights into the human population history of Central Eurasia. PLoS One 2022; 17:e0277771. [PMID: 36445929 PMCID: PMC9707748 DOI: 10.1371/journal.pone.0277771] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 11/03/2022] [Indexed: 11/30/2022] Open
Abstract
As a historical nomadic group in Central Asia, Kazaks have mainly inhabited the steppe zone from the Altay Mountains in the East to the Caspian Sea in the West. Fine scale characterization of the genetic profile and population structure of Kazaks would be invaluable for understanding their population history and modeling prehistoric human expansions across the Eurasian steppes. With this mind, we characterized the maternal lineages of 200 Kazaks from Jetisuu at mitochondrial genome level. Our results reveal that Jetisuu Kazaks have unique mtDNA haplotypes including those belonging to the basal branches of both West Eurasian (R0, H, HV) and East Eurasian (A, B, C, D) lineages. The great diversity observed in their maternal lineages may reflect pivotal geographic location of Kazaks in Eurasia and implies a complex history for this population. Comparative analyses of mitochondrial genomes of human populations in Central Eurasia reveal a common maternal genetic ancestry for Turko-Mongolian speakers and their expansion being responsible for the presence of East Eurasian maternal lineages in Central Eurasia. Our analyses further indicate maternal genetic affinity between the Sherpas from the Tibetan Plateau with the Turko-Mongolian speakers.
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Affiliation(s)
- Ayken Askapuli
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
- National Center for Biotechnology, Astana, Kazakhstan
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Miguel Vilar
- The Genographic Project, National Geographic Society, Washington, DC, United States of America
- Department of Anthropology, University of Maryland, College Park, Maryland, United States of America
| | - Humberto Garcia-Ortiz
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Maxat Zhabagin
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
- National Center for Biotechnology, Astana, Kazakhstan
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
| | | | - Ainur Akilzhanova
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
| | - Erlan Ramanculov
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
- National Center for Biotechnology, Astana, Kazakhstan
| | - Uli Schamiloglu
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
| | - Angelica Martinez-Hernandez
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Cecilia Contreras-Cubas
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Francisco Barajas-Olmos
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Theodore G. Schurr
- Department of Anthropology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Zhaxybay Zhumadilov
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
- School of Medicine, Nazarbayev University, Astana, Kazakhstan
| | - Marlen Flores-Huacuja
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Lorena Orozco
- Immunogenomics and Metabolic Diseases Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico
| | - John Hawks
- Department of Integrative Biology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Anthropology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Naruya Saitou
- Population Genetics Laboratory, National Institute of Genetics, Mishima, Shizuoka, Japan
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan
- Advanced Medical Research Center, Faculty of Medicine, University of the Ryukyus, Okinawa Ken, Japan
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Lazaridis I, Alpaslan-Roodenberg S, Acar A, Açıkkol A, Agelarakis A, Aghikyan L, Akyüz U, Andreeva D, Andrijašević G, Antonović D, Armit I, Atmaca A, Avetisyan P, Aytek Aİ, Bacvarov K, Badalyan R, Bakardzhiev S, Balen J, Bejko L, Bernardos R, Bertsatos A, Biber H, Bilir A, Bodružić M, Bonogofsky M, Bonsall C, Borić D, Borovinić N, Bravo Morante G, Buttinger K, Callan K, Candilio F, Carić M, Cheronet O, Chohadzhiev S, Chovalopoulou ME, Chryssoulaki S, Ciobanu I, Čondić N, Constantinescu M, Cristiani E, Culleton BJ, Curtis E, Davis J, Demcenco TI, Dergachev V, Derin Z, Deskaj S, Devejyan S, Djordjević V, Duffett Carlson KS, Eccles LR, Elenski N, Engin A, Erdoğan N, Erir-Pazarcı S, Fernandes DM, Ferry M, Freilich S, Frînculeasa A, Galaty ML, Gamarra B, Gasparyan B, Gaydarska B, Genç E, Gültekin T, Gündüz S, Hajdu T, Heyd V, Hobosyan S, Hovhannisyan N, Iliev I, Iliev L, Iliev S, İvgin İ, Janković I, Jovanova L, Karkanas P, Kavaz-Kındığılı B, Kaya EH, Keating D, Kennett DJ, Deniz Kesici S, Khudaverdyan A, Kiss K, Kılıç S, Klostermann P, Kostak Boca Negra Valdes S, Kovačević S, Krenz-Niedbała M, Krznarić Škrivanko M, Kurti R, Kuzman P, Lawson AM, Lazar C, Leshtakov K, Levy TE, Liritzis I, Lorentz KO, Łukasik S, et alLazaridis I, Alpaslan-Roodenberg S, Acar A, Açıkkol A, Agelarakis A, Aghikyan L, Akyüz U, Andreeva D, Andrijašević G, Antonović D, Armit I, Atmaca A, Avetisyan P, Aytek Aİ, Bacvarov K, Badalyan R, Bakardzhiev S, Balen J, Bejko L, Bernardos R, Bertsatos A, Biber H, Bilir A, Bodružić M, Bonogofsky M, Bonsall C, Borić D, Borovinić N, Bravo Morante G, Buttinger K, Callan K, Candilio F, Carić M, Cheronet O, Chohadzhiev S, Chovalopoulou ME, Chryssoulaki S, Ciobanu I, Čondić N, Constantinescu M, Cristiani E, Culleton BJ, Curtis E, Davis J, Demcenco TI, Dergachev V, Derin Z, Deskaj S, Devejyan S, Djordjević V, Duffett Carlson KS, Eccles LR, Elenski N, Engin A, Erdoğan N, Erir-Pazarcı S, Fernandes DM, Ferry M, Freilich S, Frînculeasa A, Galaty ML, Gamarra B, Gasparyan B, Gaydarska B, Genç E, Gültekin T, Gündüz S, Hajdu T, Heyd V, Hobosyan S, Hovhannisyan N, Iliev I, Iliev L, Iliev S, İvgin İ, Janković I, Jovanova L, Karkanas P, Kavaz-Kındığılı B, Kaya EH, Keating D, Kennett DJ, Deniz Kesici S, Khudaverdyan A, Kiss K, Kılıç S, Klostermann P, Kostak Boca Negra Valdes S, Kovačević S, Krenz-Niedbała M, Krznarić Škrivanko M, Kurti R, Kuzman P, Lawson AM, Lazar C, Leshtakov K, Levy TE, Liritzis I, Lorentz KO, Łukasik S, Mah M, Mallick S, Mandl K, Martirosyan-Olshansky K, Matthews R, Matthews W, McSweeney K, Melikyan V, Micco A, Michel M, Milašinović L, Mittnik A, Monge JM, Nekhrizov G, Nicholls R, Nikitin AG, Nikolov V, Novak M, Olalde I, Oppenheimer J, Osterholtz A, Özdemir C, Özdoğan KT, Öztürk N, Papadimitriou N, Papakonstantinou N, Papathanasiou A, Paraman L, Paskary EG, Patterson N, Petrakiev I, Petrosyan L, Petrova V, Philippa-Touchais A, Piliposyan A, Pocuca Kuzman N, Potrebica H, Preda-Bălănică B, Premužić Z, Price TD, Qiu L, Radović S, Raeuf Aziz K, Rajić Šikanjić P, Rasheed Raheem K, Razumov S, Richardson A, Roodenberg J, Ruka R, Russeva V, Şahin M, Şarbak A, Savaş E, Schattke C, Schepartz L, Selçuk T, Sevim-Erol A, Shamoon-Pour M, Shephard HM, Sideris A, Simalcsik A, Simonyan H, Sinika V, Sirak K, Sirbu G, Šlaus M, Soficaru A, Söğüt B, Sołtysiak A, Sönmez-Sözer Ç, Stathi M, Steskal M, Stewardson K, Stocker S, Suata-Alpaslan F, Suvorov A, Szécsényi-Nagy A, Szeniczey T, Telnov N, Temov S, Todorova N, Tota U, Touchais G, Triantaphyllou S, Türker A, Ugarković M, Valchev T, Veljanovska F, Videvski Z, Virag C, Wagner A, Walsh S, Włodarczak P, Workman JN, Yardumian A, Yarovoy E, Yavuz AY, Yılmaz H, Zalzala F, Zettl A, Zhang Z, Çavuşoğlu R, Rohland N, Pinhasi R, Reich D, Davtyan R. A genetic probe into the ancient and medieval history of Southern Europe and West Asia. Science 2022; 377:940-951. [PMID: 36007020 PMCID: PMC10019558 DOI: 10.1126/science.abq0755] [Show More Authors] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Literary and archaeological sources have preserved a rich history of Southern Europe and West Asia since the Bronze Age that can be complemented by genetics. Mycenaean period elites in Greece did not differ from the general population and included both people with some steppe ancestry and others, like the Griffin Warrior, without it. Similarly, people in the central area of the Urartian Kingdom around Lake Van lacked the steppe ancestry characteristic of the kingdom's northern provinces. Anatolia exhibited extraordinary continuity down to the Roman and Byzantine periods, with its people serving as the demographic core of much of the Roman Empire, including the city of Rome itself. During medieval times, migrations associated with Slavic and Turkic speakers profoundly affected the region.
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Affiliation(s)
- Iosif Lazaridis
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.,Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Songül Alpaslan-Roodenberg
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Ayşe Acar
- Department of Anthropology, Faculty of Letters, Mardin Artuklu University, 47510 Artuklu, Mardin, Turkey
| | - Ayşen Açıkkol
- Department of Anthropology, Faculty of Letters, Sivas Cumhuriyet University, 58140 Sivas, Turkey
| | | | - Levon Aghikyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | - Uğur Akyüz
- Samsun Museum of Archeology and Ethnography, Kale Mahallesi, Merkez, İlkadım, 55030 Samsun, Turkey
| | | | | | | | - Ian Armit
- Department of Archaeology, University of York, York YO1 7EP, UK
| | - Alper Atmaca
- Amasya Archaeology Museum, Mustafa Kemal Paşa Caddesi, 05000 Amasya, Turkey
| | - Pavel Avetisyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | - Ahmet İhsan Aytek
- Department of Anthropology, Faculty of Arts and Science, Burdur Mehmet Akif University, 15100 Burdur, Turkey
| | - Krum Bacvarov
- National Institute of Archaeology and Museum, Bulgarian Academy of Sciences, 1000 Sofia, Bulgaria
| | - Ruben Badalyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | | | | | - Lorenc Bejko
- Department of Archaeology and Heritage Studies, University of Tirana, 1010 Tirana, Albania
| | - Rebecca Bernardos
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Andreas Bertsatos
- Department of Animal and Human Physiology, Faculty of Biology, School of Sciences, National and Kapodistrian University of Athens, 10679 Athens, Greece
| | - Hanifi Biber
- Department of Archaeology, Faculty of Humanities, Van Yüzüncü Yıl University, 65090 Tuşba, Van, Turkey
| | - Ahmet Bilir
- Department of Archaeology, Faculty of Science and Letters, Düzce University, 81620 Düzce, Turkey
| | | | | | - Clive Bonsall
- School of History, Classics and Archaeology, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Dušan Borić
- The Italian Academy for Advanced Studies in America, Columbia University, New York, NY 10027, USA
| | - Nikola Borovinić
- Center for Conservation and Archaeology of Montenegro, 81250 Cetinje, Montenegro
| | | | - Katharina Buttinger
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Kim Callan
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | | | - Mario Carić
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | - Olivia Cheronet
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Stefan Chohadzhiev
- Department of Archaeology, University of Veliko Tarnovo "St. Cyril and St. Methodius," 5003 Veliko Tarnovo, Bulgaria
| | - Maria-Eleni Chovalopoulou
- Department of Animal and Human Physiology, Faculty of Biology, School of Sciences, National and Kapodistrian University of Athens, 10679 Athens, Greece
| | - Stella Chryssoulaki
- Hellenic Ministry of Culture and Sports, Ephorate of Antiquities of Piraeus and the Islands, 10682 Piraeus, Greece
| | - Ion Ciobanu
- "Orheiul Vechi" Cultural-Natural Reserve, Institute of Bioarchaeological and Ethnocultural Research, 3552 Butuceni, Moldova.,National Archaeological Agency, 2012 Chișinău, Moldova
| | | | | | - Emanuela Cristiani
- Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, 00161 Rome, Italy
| | - Brendan J Culleton
- Institutes of Energy and the Environment, The Pennsylvania State University, University Park, PA 16802, USA
| | - Elizabeth Curtis
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Jack Davis
- Department of Classics, University of Cincinnati, Cincinnati, OH 45221, USA
| | | | - Valentin Dergachev
- Center of Archaeology, Institute of Cultural Heritage, Academy of Science of Moldova, 2001 Chișinău, Moldova
| | - Zafer Derin
- Department of Archaeology, Faculty of Letters, Ege University, 35100 Bornova-Izmir, Turkey
| | - Sylvia Deskaj
- Museum of Anthropological Archaeology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Seda Devejyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | | | | | - Laurie R Eccles
- Human Paleoecology and Isotope Geochemistry Lab, Department of Anthropology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Nedko Elenski
- Regional Museum of History - Veliko Tarnovo, 5000 Veliko Tarnovo, Bulgaria
| | - Atilla Engin
- Department of Archaeology, Faculty of Science and Letters, Gaziantep University, 27310 Gaziantep, Turkey
| | - Nihat Erdoğan
- Mardin Archaeological Museum, Şar, Cumhuriyet Meydanı üstü, 47100 Artuklu, Mardin, Turkey
| | | | - Daniel M Fernandes
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria.,Research Centre for Anthropology and Health (CIAS), Department of Life Sciences, University of Coimbra, 3000-456 Coimbra, Portugal
| | - Matthew Ferry
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Suzanne Freilich
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Alin Frînculeasa
- Prahova County Museum of History and Archaeology, 100042 Ploiești, Romania
| | - Michael L Galaty
- Museum of Anthropological Archaeology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Beatriz Gamarra
- Institut Català de Paleoecologia Humana i Evolució Social, 43007 Tarragona, Spain.,Departament d'Història i Història de l'Art, Universitat Rovira i Virgili, 43002 Tarragona, Spain.,School of Archaeology and Earth Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Boris Gasparyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | | | - Elif Genç
- Department of Archaeology, Faculty of Science and Letters, Çukurova University, 01330 Balçalı-Sarıçam-Adana, Turkey
| | - Timur Gültekin
- Department of Anthropology, Faculty of Humanities, Ankara University, 06100 Sıhhiye, Ankara, Turkey
| | - Serkan Gündüz
- Department of Archaeology, Faculty of Science and Letters, Bursa Uludağ University, 16059 Görükle, Bursa, Turkey
| | - Tamás Hajdu
- Department of Biological Anthropology, Institute of Biology, Eötvös Loránd University, 1053 Budapest, Hungary
| | - Volker Heyd
- Department of Cultures, University of Helsinki, 00100 Helsinki, Finland
| | - Suren Hobosyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | - Nelli Hovhannisyan
- Department of Ecology and Nature Protection, Yerevan State University, 0025 Yerevan, Armenia
| | - Iliya Iliev
- Yambol Regional Historical Museum, 8600 Yambol, Bulgaria
| | - Lora Iliev
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | | | - İlkay İvgin
- Ministry of Culture and Tourism, İsmet İnönü Bulvarı, 06100 Emek, Ankara, Turkey
| | - Ivor Janković
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | - Lence Jovanova
- Museum of the City of Skopje, 1000 Skopje, North Macedonia
| | - Panagiotis Karkanas
- Malcolm H. Wiener Laboratory, American School of Classical Studies at Athens, 10676 Athens, Greece
| | - Berna Kavaz-Kındığılı
- Department of Archaeology, Faculty of Letters, Atatürk University, 25100 Erzurum, Turkey
| | - Esra Hilal Kaya
- Muğla Archaeological Museum and Yatağan Thermal Power Generation Company, Rescue Excavations, 48000 Muğla, Turkey
| | - Denise Keating
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Douglas J Kennett
- Institutes of Energy and the Environment, The Pennsylvania State University, University Park, PA 16802, USA.,Department of Anthropology, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Seda Deniz Kesici
- Bodrum Museum of Underwater Archeology, Çarşı Neighbourhood, 48400 Bodrum, Muğla, Turkey
| | | | - Krisztián Kiss
- Department of Biological Anthropology, Institute of Biology, Eötvös Loránd University, 1053 Budapest, Hungary.,Department of Anthropology, Hungarian Natural History Museum, 1117 Budapest, Hungary
| | - Sinan Kılıç
- Department of Archaeology, Faculty of Humanities, Van Yüzüncü Yıl University, 65090 Tuşba, Van, Turkey
| | - Paul Klostermann
- Department of Anthropology, Natural History Museum Vienna, 1010 Vienna, Austria
| | | | | | | | | | - Rovena Kurti
- Prehistory Department, Albanian Institute of Archaeology, Academy of Albanian Studies, 1000 Tirana, Albania
| | - Pasko Kuzman
- National Museum in Ohrid, 6000 Ohrid, North Macedonia
| | - Ann Marie Lawson
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Catalin Lazar
- ArchaeoSciences Division, Research Institute of the University of Bucharest, University of Bucharest, 050663 Bucharest, Romania
| | - Krassimir Leshtakov
- Department of Archaeology, St. Kliment Ohridski University of Sofia, 1504 Sofia, Bulgaria
| | - Thomas E Levy
- Department of Anthropology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ioannis Liritzis
- Key Research Institute of Yellow River Civilization and Sustainable Development and the Collaborative Innovation Center on Yellow River Civilization of Henan Province, Laboratory of Yellow River Cultural Heritage, Henan University, 475001 Kaifeng, China.,European Academy of Sciences and Arts, 5020 Salzburg, Austria
| | - Kirsi O Lorentz
- Science and Technology in Archaeology and Culture Research Center, The Cyprus Institute, 2121 Aglantzia, Nicosia, Cyprus
| | - Sylwia Łukasik
- Faculty of Biology, Adam Mickiewicz University in Poznań, 61-614 Poznań, Poland
| | - Matthew Mah
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Swapan Mallick
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Kirsten Mandl
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | | | - Roger Matthews
- Department of Archaeology, University of Reading, Reading RG6 6AB, UK
| | - Wendy Matthews
- Department of Archaeology, University of Reading, Reading RG6 6AB, UK
| | - Kathleen McSweeney
- School of History, Classics and Archaeology, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Varduhi Melikyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | - Adam Micco
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Megan Michel
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.,Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | | | - Alissa Mittnik
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.,Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
| | - Janet M Monge
- University of Pennsylvania Museum of Archaeology and Anthropology, Philadelphia, PA 19104, USA
| | - Georgi Nekhrizov
- National Institute of Archaeology and Museum, Bulgarian Academy of Sciences, 1000 Sofia, Bulgaria
| | - Rebecca Nicholls
- School of Archaeological and Forensic Sciences, Faculty of Life Sciences, University of Bradford, Bradford BD7 1DP, UK
| | - Alexey G Nikitin
- Department of Biology, Grand Valley State University, Allendale, MI 49401, USA
| | - Vassil Nikolov
- National Institute of Archaeology and Museum, Bulgarian Academy of Sciences, 1000 Sofia, Bulgaria
| | - Mario Novak
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | - Iñigo Olalde
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,BIOMICs Research Group, University of the Basque Country UPV/EHU, 01006 Vitoria-Gasteiz, Spain
| | - Jonas Oppenheimer
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Anna Osterholtz
- Department of Anthropology and Middle Eastern Cultures, Mississippi State University, Mississippi State, MS 39762, USA
| | - Celal Özdemir
- Amasya Archaeology Museum, Mustafa Kemal Paşa Caddesi, 05000 Amasya, Turkey
| | - Kadir Toykan Özdoğan
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Nurettin Öztürk
- Department of Archaeology, Faculty of Letters, Atatürk University, 25100 Erzurum, Turkey
| | | | - Niki Papakonstantinou
- Faculty of Philosophy, School of History and Archaeology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Anastasia Papathanasiou
- Ephorate of Paleoantropology and Speleology, Greek Ministry of Culture, 11636 Athens, Greece
| | | | | | - Nick Patterson
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.,Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Ilian Petrakiev
- Regional Museum of History - Veliko Tarnovo, 5000 Veliko Tarnovo, Bulgaria
| | - Levon Petrosyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
| | - Vanya Petrova
- Department of Archaeology, St. Kliment Ohridski University of Sofia, 1504 Sofia, Bulgaria
| | | | - Ashot Piliposyan
- Department of Armenian History, Armenian State Pedagogical University After Khachatur Abovyan, 0010 Yerevan, Armenia
| | | | - Hrvoje Potrebica
- Department of Archaeology, Faculty of Humanities and Social Sciences, University of Zagreb, 10000 Zagreb, Croatia
| | | | | | - T Douglas Price
- Laboratory for Archaeological Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Lijun Qiu
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Siniša Radović
- Institute for Quaternary Paleontology and Geology, Croatian Academy of Sciences and Arts, 10000 Zagreb, Croatia
| | - Kamal Raeuf Aziz
- Sulaymaniyah Directorate of Antiquities and Heritage, 46010 Sulaymaniyah, Iraq
| | - Petra Rajić Šikanjić
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | | | - Sergei Razumov
- Pridnestrovian University named after Taras Shevchenko, 3300 Tiraspol, Moldova
| | - Amy Richardson
- Department of Archaeology, University of Reading, Reading RG6 6AB, UK
| | - Jacob Roodenberg
- The Netherlands Institute for the Near East, 2311 Leiden, Netherlands
| | - Rudenc Ruka
- Prehistory Department, Albanian Institute of Archaeology, Academy of Albanian Studies, 1000 Tirana, Albania
| | - Victoria Russeva
- Institute of Experimental Morphology, Pathology and Archeology with Museum, Bulgarian Academy of Science, 1113 Sofia, Bulgaria
| | - Mustafa Şahin
- Department of Archaeology, Faculty of Science and Letters, Bursa Uludağ University, 16059 Görükle, Bursa, Turkey
| | - Ayşegül Şarbak
- Department of Anthropology, Faculty of Science and Letters, Hitit University, 19040 Çorum, Turkey
| | - Emre Savaş
- Bodrum Museum of Underwater Archeology, Çarşı Neighbourhood, 48400 Bodrum, Muğla, Turkey
| | - Constanze Schattke
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Lynne Schepartz
- School of Anatomical Sciences, The University of the Witwatersrand, 2193 Johannesburg, South Africa
| | - Tayfun Selçuk
- Bodrum Museum of Underwater Archeology, Çarşı Neighbourhood, 48400 Bodrum, Muğla, Turkey
| | - Ayla Sevim-Erol
- Department of Anthropology, Faculty of Language and History - Geography, Ankara University, 06100 Sıhhiye, Ankara, Turkey
| | - Michel Shamoon-Pour
- Department of Anthropology, Binghamton University, Binghamton, NY 13902, USA
| | | | - Athanasios Sideris
- Institute of Classical Archaeology, Charles University, 11636 Prague, Czechia
| | - Angela Simalcsik
- "Orheiul Vechi" Cultural-Natural Reserve, Institute of Bioarchaeological and Ethnocultural Research, 3552 Butuceni, Moldova.,"Olga Necrasov" Centre of Anthropological Research, Romanian Academy Iași Branch, 2012 Iaşi Romania
| | - Hakob Simonyan
- Scientific Research Center of the Historical and Cultural Heritage, 0010 Yerevan, Armenia
| | - Vitalij Sinika
- Pridnestrovian University named after Taras Shevchenko, 3300 Tiraspol, Moldova
| | - Kendra Sirak
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Ghenadie Sirbu
- Thracology Scientific Research Laboratory of the State University of Moldova, Department of Academic Management, Academy of Science of Moldova, 2009 Chișinău, Moldova
| | - Mario Šlaus
- Anthropological Center of the Croatian Academy of Sciences and Arts, 10000 Zagreb, Croatia
| | - Andrei Soficaru
- "Francisc I. Rainer" Institute of Anthropology, 050711 Bucharest, Romania
| | - Bilal Söğüt
- Department of Archaeology, Faculty of Science and Arts, Pamukkale University, 20070 Denizli, Turkey
| | | | - Çilem Sönmez-Sözer
- Department of Anthropology, Faculty of Language and History - Geography, Ankara University, 06100 Sıhhiye, Ankara, Turkey
| | - Maria Stathi
- Ephorate of Antiquities of East Attica, Ministry of Culture and Sports, 10682 Athens, Greece
| | - Martin Steskal
- Austrian Archaeological Institute at the Austrian Academy of Sciences, 1190 Vienna, Austria
| | - Kristin Stewardson
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Sharon Stocker
- Department of Classics, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Fadime Suata-Alpaslan
- Department of Anthropology, Faculty of Letters, Istanbul University, 34134 Istanbul, Turkey
| | - Alexander Suvorov
- Department of Cultures, University of Helsinki, 00100 Helsinki, Finland
| | - Anna Szécsényi-Nagy
- Institute of Archaeogenomics, Research Centre for the Humanities, Eötvös Loránd Research Network, 1097 Budapest, Hungary
| | - Tamás Szeniczey
- Department of Biological Anthropology, Institute of Biology, Eötvös Loránd University, 1053 Budapest, Hungary
| | - Nikolai Telnov
- Pridnestrovian University named after Taras Shevchenko, 3300 Tiraspol, Moldova
| | - Strahil Temov
- Archaeology Museum of North Macedonia, 1000 Skopje, North Macedonia
| | - Nadezhda Todorova
- Department of Archaeology, St. Kliment Ohridski University of Sofia, 1504 Sofia, Bulgaria
| | - Ulsi Tota
- Prehistory Department, Albanian Institute of Archaeology, Academy of Albanian Studies, 1000 Tirana, Albania.,Culture and Patrimony Department, University of Avignon, 84029 Avignon, France
| | - Gilles Touchais
- Department of the History of Art and Archaeology, Université Paris 1 Panthéon-Sorbonne, 75006 Paris, France
| | - Sevi Triantaphyllou
- Faculty of Philosophy, School of History and Archaeology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Atila Türker
- Department of Archaeology, Faculty of Science and Letters, Ondokuz Mayıs University, 55139 Atakum-Samsun, Turkey
| | | | - Todor Valchev
- Yambol Regional Historical Museum, 8600 Yambol, Bulgaria
| | | | - Zlatko Videvski
- Archaeology Museum of North Macedonia, 1000 Skopje, North Macedonia
| | | | - Anna Wagner
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Sam Walsh
- School of Natural Sciences, University of Central Lancashire, Preston PR1 2HE, UK
| | - Piotr Włodarczak
- Institute of Archaeology and Ethnology, Polish Academy of Sciences, 31-016 Kraków, Poland
| | - J Noah Workman
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Aram Yardumian
- Department of History and Social Sciences, Bryn Athyn College, Bryn Athyn, PA 19009, USA.,Penn Museum, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Evgenii Yarovoy
- History of the Ancient World and Middle Ages Department, Moscow Region State University, Moscow Region, 141014 Mytishi, Russia
| | - Alper Yener Yavuz
- Department of Anthropology, Burdur Mehmet Akif Ersoy University, Istiklal Campus, 15100 Burdur, Turkey
| | - Hakan Yılmaz
- Department of Archaeology, Faculty of Humanities, Van Yüzüncü Yıl University, 65090 Tuşba, Van, Turkey
| | - Fatma Zalzala
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Anna Zettl
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria
| | - Zhao Zhang
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Rafet Çavuşoğlu
- Department of Archaeology, Faculty of Humanities, Van Yüzüncü Yıl University, 65090 Tuşba, Van, Turkey
| | - Nadin Rohland
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Ron Pinhasi
- Department of Evolutionary Anthropology, University of Vienna, 1030 Vienna, Austria.,Human Evolution and Archaeological Sciences, University of Vienna, 1030 Vienna, Austria
| | - David Reich
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.,Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Howard Hughes Medical Institute, Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Ruben Davtyan
- Institute of Archaeology and Ethnography, NAS RA, 0025 Yerevan, Armenia
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36
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Behnamian S, Esposito U, Holland G, Alshehab G, Dobre AM, Pirooznia M, Brimacombe CS, Elhaik E. Temporal population structure, a genetic dating method for ancient Eurasian genomes from the past 10,000 years. CELL REPORTS METHODS 2022; 2:100270. [PMID: 36046618 PMCID: PMC9421539 DOI: 10.1016/j.crmeth.2022.100270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 06/17/2022] [Accepted: 07/19/2022] [Indexed: 11/21/2022]
Abstract
Radiocarbon dating is the gold standard in archeology to estimate the age of skeletons, a key to studying their origins. Many published ancient genomes lack reliable and direct dates, which results in obscure and contradictory reports. We developed the temporal population structure (TPS), a DNA-based dating method for genomes ranging from the Late Mesolithic to today, and applied it to 3,591 ancient and 1,307 modern Eurasians. TPS predictions aligned with the known dates and correctly accounted for kin relationships. TPS dating of poorly dated Eurasian samples resolved conflicting reports in the literature, as illustrated by one test case. We also demonstrated how TPS improved the ability to study phenotypic traits over time. TPS can be used when radiocarbon dating is unfeasible or uncertain or to develop alternative hypotheses for samples younger than 10,000 years ago, a limitation that may be resolved over time as ancient data accumulate.
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Affiliation(s)
- Sara Behnamian
- Department of Biology, Lund University, 22362 Lund, Sweden
| | - Umberto Esposito
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Grace Holland
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Ghadeer Alshehab
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
| | - Ann M. Dobre
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
| | - Mehdi Pirooznia
- National Heart, Lung, and Blood Institute (NHLBI), Bethesda, MD 20892, USA
| | - Conrad S. Brimacombe
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, UK
- Department of Anthropology and Archaeology, University of Bristol, Bristol BS8 1TH, UK
| | - Eran Elhaik
- Department of Biology, Lund University, 22362 Lund, Sweden
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37
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Översti S, Palo JU. Variation in the substitution rates among the human mitochondrial haplogroup U sublineages. Genome Biol Evol 2022; 14:6613373. [PMID: 35731946 PMCID: PMC9250076 DOI: 10.1093/gbe/evac097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 11/22/2022] Open
Abstract
Resolving the absolute timescale of phylogenetic trees stipulates reliable estimates for the rate of DNA sequence evolution. For this end, various calibration methods have been developed and studied intensively. Intraspecific rate variation among distinct genetic lineages, however, has gained less attention. Here, we have assessed lineage-specific molecular rates of human mitochondrial DNA (mtDNA) by performing tip-calibrated Bayesian phylogenetic analyses. Tip-calibration, as opposed to traditional nodal time stamps from dated fossil evidence or geological events, is based on sample ages and becoming ever more feasible as ancient DNA data from radiocarbon-dated samples accumulate. We focus on subhaplogroups U2, U4, U5a, and U5b, the data including ancient mtDNA genomes from 14C-dated samples (n = 234), contemporary genomes (n = 301), and two outgroup sequences from haplogroup R. The obtained molecular rates depended on the data sets (with or without contemporary sequences), suggesting time-dependency. More notable was the rate variation between haplogroups: U4 and U5a stand out having a substantially higher rate than U5b. This is also reflected in the divergence times obtained (U5a: 17,700 years and U5b: 29,700 years), a disparity not reported previously. After ruling out various alternative causes (e.g., selection, sampling, and sequence quality), we propose that the substitution rates have been influenced by demographic histories, widely different among populations where U4/U5a or U5b are frequent. As with the Y-chromosomal subhaplogroup R1b, the mitochondrial U4 and U5a have been associated with remarkable range extensions of the Yamnaya culture in the Bronze Age.
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Affiliation(s)
- Sanni Översti
- Transmission, Infection, Diversification and Evolution Group, Max-Planck Institute for the Science of Human History, Jena, Germany Kahlaische Straße 10, 07745, Jena, Germany.,Organismal and Evolutionary Biology Research Programme, Faculty of Biological Sciences, University of Helsinki, Helsinki, Finland P.O. Box 56, FI-00014, Helsinki, Finland
| | - Jukka U Palo
- Department of Forensic Medicine, Faculty of Medicine, University of Helsinki, Helsinki, Finland P.O. Box 40, FI-00014, Helsinki, Finland.,Forensic Chemistry Unit, Forensic Genetics Team, Finnish Institute for Health and Welfare, Helsinki, Finland P.O. Box 30, FI-00271, Helsinki, Finland
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38
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Galliano A, Guerin M, Lambert V, Favrot I, Seneca D, Lequeux F, Flament F, Sleurs A, Foster B, Phung E, Lee KM, Houghton J. Virtual approach of the aesthetical fit between hair colours and skin tones in women of different ethnical origin backgrounds. Skin Res Technol 2022; 28:455-464. [PMID: 35261091 PMCID: PMC9907600 DOI: 10.1111/srt.13146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 01/17/2022] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To determine the aesthetical accordance between a given skin tone and the 11 possible colours of head hairs, covered by a marketed hair colouration product. MATERIAL AND METHODS The photographs of professional top models, representing several ancestries (non-Hispanic European and Euro-American, East Asian, Hispanic Euro-American, and African-American ancestries), were used to virtually modify skin tones (from light, medium to dark) and hair colour by an artificial intelligence (AI)-based algorithm. Hence, 117 modified photographs were then assessed by five local panels of about 60 women each (one in China, one in France and three in US). The same questionnaire was given to the panels, written in their own language, asking which and how both skin tones and hair colours fit preferentially (or not appreciated), asking in addition the reasons of their choices, using fixed wordings. RESULTS Answers from the five panels differed according to origin or cultural aspects, although some agreements were found among both non-Hispanic European and Euro-American groups. The Hispanic American panel in US globally much appreciated darker hair tones (HTs). Two panels (East Asian in China and African American in US) and part of non-Hispanic European panel in France declared appreciating all HTs, almost irrespective with the skin tone (light, medium and dark). This surprising result is very likely caused by gradings (in %) that differ by too low values, making the establishment of a decisive or significant assessment. By nature highly subjective (culturally and/or fashion driven), the assessments should be more viewed as trends, an unavoidable limit of the present virtual approach. The latter offers nevertheless a full respect of ethical rules as such objective could hardly be conducted in vivo: applying 10 or 11 hair colourations on the same individual is an unthinkable option. CONCLUSION The virtual approach developed in the present study that mixes two major facial coloured phenotypes seems at the crossroad of both genetic backgrounds and the secular desire of a modified appearance. Nonetheless, this methodology could afford, at the individual level in total confidentiality, a great help to subjects exposed to some facial skin disorders or afflictions.
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Affiliation(s)
- Anthony Galliano
- L'Oréal Research and Innovation, Centre Charles Zviak, Saint-Ouen, France
| | - Myriam Guerin
- L'Oréal Research and Innovation, Centre Charles Zviak, Saint-Ouen, France
| | - Valerie Lambert
- L'Oréal Research and Innovation, Centre Charles Zviak, Saint-Ouen, France
| | - Ioanna Favrot
- L'Oréal Research and Innovation, Centre Charles Zviak, Saint-Ouen, France
| | - David Seneca
- L'Oréal Research and Innovation, Centre Charles Zviak, Saint-Ouen, France
| | - Fabien Lequeux
- L'Oréal Research and Innovation, Centre Charles Zviak, Saint-Ouen, France
| | - Frédéric Flament
- L'Oréal Research and Innovation, Centre Charles Zviak, Saint-Ouen, France
| | - Anna Sleurs
- L'Oréal Paris, Centre Eugene Schueller, Clichy, France
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39
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Saitou M, Masuda N, Gokcumen O. Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants. Mol Biol Evol 2022; 39:msab313. [PMID: 34718708 PMCID: PMC8896759 DOI: 10.1093/molbev/msab313] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 576 structural variants that show unusual population differentiation. Of these putatively adaptive structural variants, we further showed that 24 variants are multiallelic and overlap with coding sequences, and 20 variants are significantly associated with GWAS traits. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to: 1) population differentiation of rapidly evolving multiallelic variants, 2) incomplete sweeps, and 3) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.
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Affiliation(s)
- Marie Saitou
- Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Naoki Masuda
- Department of Mathematics, University at Buffalo, State University of New York, Buffalo, NY, USA
- Computational and Data-Enabled Science and Engineering Program, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Omer Gokcumen
- Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
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40
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Rashid MU, Lao Y, Spicer V, Coombs KM. Zika Virus Infection of Sertoli Cells Alters Protein Expression Involved in Activated Immune and Antiviral Response Pathways, Carbohydrate Metabolism and Cardiovascular Disease. Viruses 2022; 14:v14020377. [PMID: 35215967 PMCID: PMC8878972 DOI: 10.3390/v14020377] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/03/2022] [Accepted: 02/07/2022] [Indexed: 11/29/2022] Open
Abstract
Zika virus (ZIKV), a re-emerging virus, causes congenital brain abnormalities and Guillain–Barré syndrome. It is mainly transmitted by Aedes mosquitoes, but infections are also linked to sexual transmissions. Infectious ZIKV has been isolated, and viral RNA has been detected in semen over a year after the onset of initial symptoms, but the mode of long-term persistence is not yet understood. ZIKV can proliferate in human Sertoli cells (HSerC) for several weeks in vitro, suggesting that it might be a reservoir for persistent ZIKV infection. This study determined proteomic changes in HSerC during ZIKV infections by TMT-mass spectrometry analysis. Levels of 4416 unique Sertoli cell proteins were significantly altered at 3, 5, and 7 days after ZIKV infection. The significantly altered proteins include enzymes, transcription regulators, transporters, kinases, peptidases, transmembrane receptors, cytokines, ion channels, and growth factors. Many of these proteins are involved in pathways associated with antiviral response, antigen presentation, and immune cell activation. Several immune response pathway proteins were significantly activated during infection, e.g., interferon signaling, T cell receptor signaling, IL-8 signaling, and Th1 signaling. The altered protein levels were linked to predicted activation of immune response in HSerC, which was predicted to suppress ZIKV infection. ZIKV infection also affected the levels of critical regulators of gluconeogenesis and glycolysis pathways such as phosphoglycerate mutase, phosphoglycerate kinase, and enolase. Interestingly, many significantly altered proteins were associated with cardiac hypertrophy, which may induce heart failure in infected patients. In summary, our research contributes to a better understanding of ZIKV replication dynamics and infection in Sertoli cells.
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Affiliation(s)
- Mahamud-ur Rashid
- Department of Medical Microbiology and Infectious Diseases, The University of Manitoba, Room 543 Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, MB R3E 0J9, Canada;
- Manitoba Centre for Proteomics & Systems Biology, Room 799, 715 McDermot Avenue, Winnipeg, MB R3E 3P4, Canada; (Y.L.); (V.S.)
- Correspondence:
| | - Ying Lao
- Manitoba Centre for Proteomics & Systems Biology, Room 799, 715 McDermot Avenue, Winnipeg, MB R3E 3P4, Canada; (Y.L.); (V.S.)
| | - Victor Spicer
- Manitoba Centre for Proteomics & Systems Biology, Room 799, 715 McDermot Avenue, Winnipeg, MB R3E 3P4, Canada; (Y.L.); (V.S.)
| | - Kevin M. Coombs
- Department of Medical Microbiology and Infectious Diseases, The University of Manitoba, Room 543 Basic Medical Sciences Building, 745 Bannatyne Avenue, Winnipeg, MB R3E 0J9, Canada;
- Manitoba Centre for Proteomics & Systems Biology, Room 799, 715 McDermot Avenue, Winnipeg, MB R3E 3P4, Canada; (Y.L.); (V.S.)
- Children’s Hospital Research Institute of Manitoba, Room 513, John Buhler Research Centre, 715 McDermot Avenue, Winnipeg, MB R3E 3P4, Canada
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41
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Hejase HA, Mo Z, Campagna L, Siepel A. A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph. Mol Biol Evol 2022; 39:msab332. [PMID: 34888675 PMCID: PMC8789311 DOI: 10.1093/molbev/msab332] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.
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Affiliation(s)
- Hussein A Hejase
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ziyi Mo
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Leonardo Campagna
- Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Ithaca, NY, USA
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
| | - Adam Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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42
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Mathieson I, Terhorst J. Direct detection of natural selection in Bronze Age Britain. Genome Res 2022; 32:2057-2067. [PMID: 36316157 PMCID: PMC9808619 DOI: 10.1101/gr.276862.122] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 08/29/2022] [Indexed: 11/04/2022]
Abstract
We developed a novel method for efficiently estimating time-varying selection coefficients from genome-wide ancient DNA data. In simulations, our method accurately recovers selective trajectories and is robust to misspecification of population size. We applied it to a large data set of ancient and present-day human genomes from Britain and identified seven loci with genome-wide significant evidence of selection in the past 4500 yr. Almost all of them can be related to increased vitamin D or calcium levels, suggesting strong selective pressure on these or related phenotypes. However, the strength of selection on individual loci varied substantially over time, suggesting that cultural or environmental factors moderated the genetic response. Of 28 complex anthropometric and metabolic traits, skin pigmentation was the only one with significant evidence of polygenic selection, further underscoring the importance of phenotypes related to vitamin D. Our approach illustrates the power of ancient DNA to characterize selection in human populations and illuminates the recent evolutionary history of Britain.
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Affiliation(s)
- Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Jonathan Terhorst
- Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA
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43
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Colbran LL, Johnson MR, Mathieson I, Capra JA. Tracing the Evolution of Human Gene Regulation and Its Association with Shifts in Environment. Genome Biol Evol 2021; 13:evab237. [PMID: 34718543 PMCID: PMC8576593 DOI: 10.1093/gbe/evab237] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2021] [Indexed: 12/16/2022] Open
Abstract
As humans populated the world, they adapted to many varying environmental factors, including climate, diet, and pathogens. Because many of these adaptations were mediated by multiple noncoding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting PrediXcan to increase robustness to incomplete data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes proposed to be involved in diet-related local adaptation, and the predicted effects on regulation often suggest explanations for known signals of selection, for example, at FADS1, GPX1, and LEPR. In contrast, skin pigmentation genes show little regulatory change over a 38,000-year time series of 2,999 ancient Europeans, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates that combining aDNA with present-day genomes is informative about the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between genetic diversity and complex traits.
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Affiliation(s)
- Laura L Colbran
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, USA
| | - Maya R Johnson
- School for Science and Math at Vanderbilt, Vanderbilt University, USA
- Department of Computer Science, Bryn Mawr College, Pennsylvania, USA
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, USA
| | - John A Capra
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, USA
- Department of Biological Sciences, Vanderbilt University, USA
- Department of Biomedical Informatics, Vanderbilt University, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
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44
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Freilich S, Ringbauer H, Los D, Novak M, Pavičić DT, Schiffels S, Pinhasi R. Reconstructing genetic histories and social organisation in Neolithic and Bronze Age Croatia. Sci Rep 2021; 11:16729. [PMID: 34408163 PMCID: PMC8373892 DOI: 10.1038/s41598-021-94932-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/13/2021] [Indexed: 02/07/2023] Open
Abstract
Ancient DNA studies have revealed how human migrations from the Neolithic to the Bronze Age transformed the social and genetic structure of European societies. Present-day Croatia lies at the heart of ancient migration routes through Europe, yet our knowledge about social and genetic processes here remains sparse. To shed light on these questions, we report new whole-genome data for 28 individuals dated to between ~ 4700 BCE-400 CE from two sites in present-day eastern Croatia. In the Middle Neolithic we evidence first cousin mating practices and strong genetic continuity from the Early Neolithic. In the Middle Bronze Age community that we studied, we find multiple closely related males suggesting a patrilocal social organisation. We also find in that community an unexpected genetic ancestry profile distinct from individuals found at contemporaneous sites in the region, due to the addition of hunter-gatherer-related ancestry. These findings support archaeological evidence for contacts with communities further north in the Carpathian Basin. Finally, an individual dated to Roman times exhibits an ancestry profile that is broadly present in the region today, adding an important data point to the substantial shift in ancestry that occurred in the region between the Bronze Age and today.
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Affiliation(s)
- Suzanne Freilich
- Department of Evolutionary Anthropology, University of Vienna, 1090, Vienna, Austria.
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History, 07745, Jena, Germany.
| | - Harald Ringbauer
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History, 07745, Jena, Germany
- Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA
| | | | - Mario Novak
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000, Zagreb, Croatia
| | | | - Stephan Schiffels
- Department of Archaeogenetics, Max Planck Institute for the Science of Human History, 07745, Jena, Germany.
| | - Ron Pinhasi
- Department of Evolutionary Anthropology, University of Vienna, 1090, Vienna, Austria.
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45
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Irving-Pease EK, Muktupavela R, Dannemann M, Racimo F. Quantitative Human Paleogenetics: What can Ancient DNA Tell us About Complex Trait Evolution? Front Genet 2021; 12:703541. [PMID: 34422004 PMCID: PMC8371751 DOI: 10.3389/fgene.2021.703541] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/08/2021] [Indexed: 12/13/2022] Open
Abstract
Genetic association data from national biobanks and large-scale association studies have provided new prospects for understanding the genetic evolution of complex traits and diseases in humans. In turn, genomes from ancient human archaeological remains are now easier than ever to obtain, and provide a direct window into changes in frequencies of trait-associated alleles in the past. This has generated a new wave of studies aiming to analyse the genetic component of traits in historic and prehistoric times using ancient DNA, and to determine whether any such traits were subject to natural selection. In humans, however, issues about the portability and robustness of complex trait inference across different populations are particularly concerning when predictions are extended to individuals that died thousands of years ago, and for which little, if any, phenotypic validation is possible. In this review, we discuss the advantages of incorporating ancient genomes into studies of trait-associated variants, the need for models that can better accommodate ancient genomes into quantitative genetic frameworks, and the existing limits to inferences about complex trait evolution, particularly with respect to past populations.
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Affiliation(s)
- Evan K. Irving-Pease
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Rasa Muktupavela
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Michael Dannemann
- Center for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fernando Racimo
- Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
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46
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Estimating the age of single nucleotide polymorphic sites in humans. Genes Genomics 2021; 43:1179-1188. [PMID: 34245420 DOI: 10.1007/s13258-021-01135-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 06/28/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Determining the ages of polymorphic sites in human genomes needs to be carried out in a careful balance between the degree of complexity of the approach and the desired accuracy. OBJECTIVE We provide evidence that a simpler approach where age determination is based upon the degree to which the alternative allele is spread among the population can be competitive with more complex methods. METHODS The information contained in the vcf files of Phase 1 of the 1000 Genomes Project combined with the genomic sequences of seven non-human primate species was analyzed. The analyses were supplemented by a computer simulation of the mutational changes in 10,000 model chromosomes with a length of 10,000 nucleotides over a period of 16 million years. The list of the birth dates of the derived alleles of homozygous and heterozygous components of the derived alleles served as a yardstick to estimate the ages of human alternative alleles. RESULTS The age of the derived alleles born in Africa before the "Out of Africa" event and not brought to other continents are estimated to be 0.17 Ma, the derived alleles present simultaneously on all continents are estimated to be 1.3 Ma old and the age of alleles arising in Europe or Asia is 0.06 Ma. CONCLUSION Our approach functions with polymorphisms that respect the "more frequent means older" principle. However, this shortcoming only leads to disagreement with the Atlas of Variant Age in about 20% of cases.
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47
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De Lillo A, D'Antona S, Pathak GA, Wendt FR, De Angelis F, Fuciarelli M, Polimanti R. Cross-ancestry genome-wide association studies identified heterogeneous loci associated with differences of allele frequency and regulome tagging between participants of European descent and other ancestry groups from the UK Biobank. Hum Mol Genet 2021; 30:1457-1467. [PMID: 33890984 PMCID: PMC8283210 DOI: 10.1093/hmg/ddab114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 04/14/2021] [Accepted: 01/09/2021] [Indexed: 01/28/2023] Open
Abstract
To investigate cross-ancestry genetics of complex traits, we conducted a phenome-wide analysis of loci with heterogeneous effects across African, Admixed-American, Central/South Asian, East Asian, European and Middle Eastern participants of the UK Biobank (N = 441 331). Testing 843 phenotypes, we identified 82 independent genomic regions mapping variants showing genome-wide significant (GWS) associations (P < 5 × 10-8) in the trans-ancestry meta-analysis and GWS heterogeneity among the ancestry-specific effects. These included (i) loci with GWS association in one ancestry and concordant but heterogeneous effects among the other ancestries and (ii) loci with a GWS association in one ancestry group and an experiment-wide significant discordant effect (P < 6.1 × 10-4) in at least another ancestry. Since the trans-ancestry GWS associations were mostly driven by the European ancestry sample size, we investigated the differences of the allele frequency (ΔAF) and linkage disequilibrium regulome tagging (ΔLD) between European populations and the other ancestries. Within loci with concordant effects, the degree of heterogeneity was associated with European-Middle Eastern ΔAF (P = 9.04 × 10-6) and ΔLD of European populations with respect to African, Admixed-American and Central/South Asian groups (P = 8.21 × 10-4, P = 7.17 × 10-4 and P = 2.16 × 10-3, respectively). Within loci with discordant effects, ΔAF and ΔLD of European populations with respect to African and Central/South Asian ancestries were associated with the degree of heterogeneity (ΔAF: P = 7.69 × 10-3 and P = 5.31 × 10-3, ΔLD: P = 0.016 and P = 2.65 × 10-4, respectively). Considering the traits associated with cross-ancestry heterogeneous loci, we observed enrichments for blood biomarkers (P = 5.7 × 10-35) and physical appearance (P = 1.38 × 10-4). This suggests that these specific phenotypic classes may present considerable cross-ancestry heterogeneity owing to large allele frequency and LD variation among worldwide populations.
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Affiliation(s)
- Antonella De Lillo
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
| | - Salvatore D'Antona
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
| | - Frank R Wendt
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
| | - Flavio De Angelis
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
- VA CT Healthcare Center, West Haven, CT 06516, USA
| | - Maria Fuciarelli
- Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, CT 06516, USA
- VA CT Healthcare Center, West Haven, CT 06516, USA
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48
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Saupe T, Montinaro F, Scaggion C, Carrara N, Kivisild T, D'Atanasio E, Hui R, Solnik A, Lebrasseur O, Larson G, Alessandri L, Arienzo I, De Angelis F, Rolfo MF, Skeates R, Silvestri L, Beckett J, Talamo S, Dolfini A, Miari M, Metspalu M, Benazzi S, Capelli C, Pagani L, Scheib CL. Ancient genomes reveal structural shifts after the arrival of Steppe-related ancestry in the Italian Peninsula. Curr Biol 2021; 31:2576-2591.e12. [PMID: 33974848 DOI: 10.1016/j.cub.2021.04.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 11/28/2020] [Accepted: 04/09/2021] [Indexed: 12/30/2022]
Abstract
Across Europe, the genetics of the Chalcolithic/Bronze Age transition is increasingly characterized in terms of an influx of Steppe-related ancestry. The effect of this major shift on the genetic structure of populations in the Italian Peninsula remains underexplored. Here, genome-wide shotgun data for 22 individuals from commingled cave and single burials in Northeastern and Central Italy dated between 3200 and 1500 BCE provide the first genomic characterization of Bronze Age individuals (n = 8; 0.001-1.2× coverage) from the central Italian Peninsula, filling a gap in the literature between 1950 and 1500 BCE. Our study confirms a diversity of ancestry components during the Chalcolithic and the arrival of Steppe-related ancestry in the central Italian Peninsula as early as 1600 BCE, with this ancestry component increasing through time. We detect close patrilineal kinship in the burial patterns of Chalcolithic commingled cave burials and a shift away from this in the Bronze Age (2200-900 BCE) along with lowered runs of homozygosity, which may reflect larger changes in population structure. Finally, we find no evidence that the arrival of Steppe-related ancestry in Central Italy directly led to changes in frequency of 115 phenotypes present in the dataset, rather that the post-Roman Imperial period had a stronger influence, particularly on the frequency of variants associated with protection against Hansen's disease (leprosy). Our study provides a closer look at local dynamics of demography and phenotypic shifts as they occurred as part of a broader phenomenon of widespread admixture during the Chalcolithic/Bronze Age transition.
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Affiliation(s)
- Tina Saupe
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23B, Tartu 51010, Estonia.
| | - Francesco Montinaro
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23B, Tartu 51010, Estonia; Department of Biology-Genetics, University of Bari, Via E. Orabona, 4, Bari 70124, Italy
| | - Cinzia Scaggion
- Department of Geosciences, University of Padova, Via Gradenigo 6, Padova 35131, Italy
| | - Nicola Carrara
- Museum of Anthropology, University of Padova, Palazzo Cavalli, via Giotto 1, Padova 35121, Italy
| | - Toomas Kivisild
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23B, Tartu 51010, Estonia; Department of Human Genetics, KU Leuven, Leuven, Herestraat 49 3000, Belgium
| | - Eugenia D'Atanasio
- Institute of Molecular Biology and Pathology, CNR, Piazzale Aldo Moro 5, Rome 00185, Italy
| | - Ruoyun Hui
- McDonald Institute for Archaeological Research, University of Cambridge, Downing Street, Cambridge CB2 3ER, UK
| | - Anu Solnik
- Core Facility, Institute of Genomics, University of Tartu, Riia 23B, Tartu 51010, Estonia
| | - Ophélie Lebrasseur
- Department of Archaeology, Classics and Egyptology, University of Liverpool, 12-14 Abercromby Square, Liverpool L69 7WZ, UK; Palaeogenomics & Bio-Archaeology Research Network, School of Archaeology, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK
| | - Greger Larson
- Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Via Diocleziano 328, Naples 80125, Italy
| | - Luca Alessandri
- Groningen Institute of Archaeology, University of Groningen, Poststraat 6, Groningen 9712, the Netherlands
| | - Ilenia Arienzo
- Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Via Diocleziano 328, Naples 80125, Italy
| | - Flavio De Angelis
- Centre of Molecular Anthropology for Ancient DNA Studies, Department of Biology, University of Rome "Tor Vergata," Via della Ricerca Scientifica 1, Rome 00133, Italy
| | - Mario Federico Rolfo
- Department of History, Culture and Society, University of Rome "Tor Vergata," Via Columbia 1, Rome 00133, Italy
| | - Robin Skeates
- Department of Archaeology, Durham University, Lower Mountjoy, South Road, Durham DH1 3LE, UK
| | - Letizia Silvestri
- Department of History, Culture and Society, University of Rome "Tor Vergata," Via Columbia 1, Rome 00133, Italy
| | | | - Sahra Talamo
- Department of Chemistry "Giacomo Ciamician," University of Bologna, Via Selmi 2, Bologna 40126, Italy; Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, Leipzig 04103, Germany
| | - Andrea Dolfini
- School of History, Classics and Archaeology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Monica Miari
- Superintendency of Archeology, Fine Arts and Landscape for the metropolitan city of Bologna and the provinces of Modena, Reggio Emilia and Ferrara, Comune di Bologna, Sede Via Belle Arti n. 52, Bologna 40126, Italy
| | - Mait Metspalu
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23B, Tartu 51010, Estonia
| | - Stefano Benazzi
- Department of Cultural Heritage, University of Bologna, Via degli Ariani, 1, Ravenna 40126, Italy
| | - Cristian Capelli
- Department of Zoology, University of Oxford, 11a Mansfield Road, Oxford OX1 3SZ, UK; Dipartimento di Scienze Chimiche, della Vita e della Sostenibilità Ambientale, University of Parma, Parco Area delle Scienze 17/A, Parma 43124, Italy
| | - Luca Pagani
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23B, Tartu 51010, Estonia; Department of Biology, University of Padova, Via U. Bassi, 58/B, Padova 35122, Italy
| | - Christiana L Scheib
- Estonian Biocentre, Institute of Genomics, University of Tartu, Riia 23B, Tartu 51010, Estonia; St. John's College, University of Cambridge, St. John's Street, Cambridge CB2 1TP, UK.
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49
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Feng Y, McQuillan MA, Tishkoff SA. Evolutionary genetics of skin pigmentation in African populations. Hum Mol Genet 2021; 30:R88-R97. [PMID: 33438000 PMCID: PMC8117430 DOI: 10.1093/hmg/ddab007] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 12/14/2022] Open
Abstract
Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.
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Affiliation(s)
- Yuanqing Feng
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael A McQuillan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah A Tishkoff
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
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50
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Shi H, Gazal S, Kanai M, Koch EM, Schoech AP, Siewert KM, Kim SS, Luo Y, Amariuta T, Huang H, Okada Y, Raychaudhuri S, Sunyaev SR, Price AL. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat Commun 2021; 12:1098. [PMID: 33597505 PMCID: PMC7889654 DOI: 10.1038/s41467-021-21286-1] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 01/15/2021] [Indexed: 01/31/2023] Open
Abstract
Many diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.
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Affiliation(s)
- Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Steven Gazal
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Evan M Koch
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Armin P Schoech
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Katherine M Siewert
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuel S Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yang Luo
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Soumya Raychaudhuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Shamil R Sunyaev
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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