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Freudiger A, Jovanovic VM, Huang Y, Snyder-Mackler N, Conrad DF, Miller B, Montague MJ, Westphal H, Stadler PF, Bley S, Horvath JE, Brent LJN, Platt ML, Ruiz-Lambides A, Tung J, Nowick K, Ringbauer H, Widdig A. Taking identity-by-descent analysis into the wild: Estimating realized relatedness in free-ranging macaques. bioRxiv 2024:2024.01.09.574911. [PMID: 38260273 PMCID: PMC10802400 DOI: 10.1101/2024.01.09.574911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent-offspring dyads, pedigrees capture relatedness imperfectly. The number and length of DNA segments that are identical-by-descent (IBD) yield the most precise estimates of relatedness. Here, we leverage novel methods for estimating locus-specific IBD from low coverage whole genome resequencing data to demonstrate the feasibility and value of resolving fine-scaled gradients of relatedness in free-living animals. Using primarily 4-6× coverage data from a rhesus macaque (Macaca mulatta) population with available long-term pedigree data, we show that we can call the number and length of IBD segments across the genome with high accuracy even at 0.5× coverage. The resulting estimates demonstrate substantial variation in genetic relatedness within kin classes, leading to overlapping distributions between kin classes. They identify cryptic genetic relatives that are not represented in the pedigree and reveal elevated recombination rates in females relative to males, which allows us to discriminate maternal and paternal kin using genotype data alone. Our findings represent a breakthrough in the ability to understand the predictors and consequences of genetic relatedness in natural populations, contributing to our understanding of a fundamental component of population structure in the wild.
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Affiliation(s)
- Annika Freudiger
- Behavioral Ecology Research Group, Faculty of Life Sciences, Institute of Biology, Leipzig University, Leipzig, Germany
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Vladimir M Jovanovic
- Human Biology and Primate Evolution, Institut für Zoologie, Freie Universität Berlin, Berlin, Germany
- Bioinformatics Solution Center, Freie Universität Berlin, Berlin, Germany
| | - Yilei Huang
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Bioinformatics Group, Institute of Computer Science, and Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Noah Snyder-Mackler
- Center for Evolution & Medicine, School of Life Sciences, Arizona State University, Tempe, USA
| | - Donald F Conrad
- Division of Genetics, Oregon National Primate Research Center, Portland, Oregon, USA
| | - Brian Miller
- Division of Genetics, Oregon National Primate Research Center, Portland, Oregon, USA
| | - Michael J Montague
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hendrikje Westphal
- Behavioral Ecology Research Group, Faculty of Life Sciences, Institute of Biology, Leipzig University, Leipzig, Germany
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Bioinformatics Group, Institute of Computer Science, and Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
| | - Peter F Stadler
- Bioinformatics Group, Institute of Computer Science, and Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
- Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
- Institute for Theoretical Chemistry, University of Vienna, Austria
- Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia
- Santa Fe Institute, Santa Fe, NM, USA
| | - Stefanie Bley
- Behavioral Ecology Research Group, Faculty of Life Sciences, Institute of Biology, Leipzig University, Leipzig, Germany
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Julie E Horvath
- Department of Biological and Biomedical Sciences, North Carolina Central University, North Carolina, Durham, USA
- Research and Collections Section, North Carolina Museum of Natural Sciences, North Carolina, Raleigh, USA
- Department of Biological Sciences, North Carolina State University, North Carolina, Raleigh, USA
- Department of Evolutionary Anthropology, Duke University, North Carolina, Durham, USA
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Lauren J N Brent
- Centre for Research in Animal Behaviour, University of Exeter, Exeter, UK
| | - Michael L Platt
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Marketing Department, the Wharton School of Business, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Angelina Ruiz-Lambides
- Cayo Santiago Field Station, Caribbean Primate Research Center, University of Puerto Rico, Punta Santiago, Puerto Rico
| | - Jenny Tung
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- Department of Evolutionary Anthropology, Duke University, North Carolina, Durham, USA
- Department of Biology, Duke University, Durham, North Carolina, USA
- Duke University Population Research Institute, Durham, North Carolina, USA
| | - Katja Nowick
- Human Biology and Primate Evolution, Institut für Zoologie, Freie Universität Berlin, Berlin, Germany
- Bioinformatics Solution Center, Freie Universität Berlin, Berlin, Germany
| | - Harald Ringbauer
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Anja Widdig
- Behavioral Ecology Research Group, Faculty of Life Sciences, Institute of Biology, Leipzig University, Leipzig, Germany
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
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2
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Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. Recombination map tailored to Native Hawaiians may improve robustness of genomic scans for positive selection. Hum Genet 2024; 143:85-99. [PMID: 38157018 PMCID: PMC10794367 DOI: 10.1007/s00439-023-02625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/25/2023] [Indexed: 01/03/2024]
Abstract
Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map) and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearman's rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score|> 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.
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Affiliation(s)
- Bryan L Dinh
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Kekoa Taparra
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | | | - Fei Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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3
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Shi M, Tanikawa C, Munter HM, Akiyama M, Koyama S, Tomizuka K, Matsuda K, Lathrop GM, Terao C, Koido M, Kamatani Y. Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels. Brief Bioinform 2023; 25:bbad509. [PMID: 38221906 PMCID: PMC10788679 DOI: 10.1093/bib/bbad509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024] Open
Abstract
Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value.
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Affiliation(s)
- Mingyang Shi
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Hans Markus Munter
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Satoshi Koyama
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Gregory Mark Lathrop
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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4
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Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. Recombination map tailored to Native Hawaiians improves robustness of genomic scans for positive selection. bioRxiv 2023:2023.07.12.548735. [PMID: 37503129 PMCID: PMC10370006 DOI: 10.1101/2023.07.12.548735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Recombination events establish the patterns of haplotypic structure in a population and estimates of recombination rates are used in several downstream population and statistical genetic analyses. Using suboptimal maps from distantly related populations may reduce the efficacy of genomic analyses, particularly for underrepresented populations such as the Native Hawaiians. To overcome this challenge, we constructed recombination maps using genome-wide array data from two study samples of Native Hawaiians: one reflecting the current admixed state of Native Hawaiians (NH map), and one based on individuals of enriched Polynesian ancestries (PNS map) with the potential to be used for less admixed Polynesian populations such as the Samoans. We found the recombination landscape to be less correlated with those from other continental populations (e.g. Spearman's rho = 0.79 between PNS and CEU (Utah residents with Northern and Western European ancestry) compared to 0.92 between YRI (Yoruba in Ibadan, Nigeria) and CEU at 50 kb resolution), likely driven by the unique demographic history of the Native Hawaiians. PNS also shared the fewest recombination hotspots with other populations (e.g. 8% of hotspots shared between PNS and CEU compared to 27% of hotspots shared between YRI and CEU). We found that downstream analyses in the Native Hawaiian population, such as local ancestry inference, imputation, and IBD segment and relatedness detections, would achieve similar efficacy when using the NH map compared to an omnibus map. However, for genome scans of adaptive loci using integrated haplotype scores, we found several loci with apparent genome-wide significant signals (|Z-score| > 4) in Native Hawaiians that would not have been significant when analyzed using NH-specific maps. Population-specific recombination maps may therefore improve the robustness of haplotype-based statistics and help us better characterize the evolutionary history that may underlie Native Hawaiian-specific health conditions that persist today.
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Affiliation(s)
- Bryan L Dinh
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Echo Tang
- Department of Quantitative and Computational Biology, University of Southern California
| | - Kekoa Taparra
- Department of Radiation Oncology, Stanford University, Palo Alto, California
| | | | - Fei Chen
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Charleston W K Chiang
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
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5
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Baldrighi GN, Nova A, Bernardinelli L, Fazia T. A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software. Life (Basel) 2022; 12:life12122030. [PMID: 36556394 PMCID: PMC9781110 DOI: 10.3390/life12122030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/09/2022]
Abstract
Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data.
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6
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Vihinen M. Individual Genetic Heterogeneity. Genes (Basel) 2022; 13:genes13091626. [PMID: 36140794 PMCID: PMC9498725 DOI: 10.3390/genes13091626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 08/25/2022] [Accepted: 09/08/2022] [Indexed: 11/28/2022] Open
Abstract
Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22184 Lund, Sweden
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7
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van Eeden G, Uren C, Pless E, Mastoras M, van der Spuy GD, Tromp G, Henn BM, Möller M. The recombination landscape of the Khoe-San likely represents the upper limits of recombination divergence in humans. Genome Biol 2022; 23:172. [PMID: 35945619 PMCID: PMC9361568 DOI: 10.1186/s13059-022-02744-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 08/01/2022] [Indexed: 11/10/2022] Open
Abstract
Background Recombination maps are important resources for epidemiological and evolutionary analyses; however, there are currently no recombination maps representing any African population outside of those with West African ancestry. We infer the demographic history for the Nama, an indigenous Khoe-San population of southern Africa, and derive a novel, population-specific recombination map from the whole genome sequencing of 54 Nama individuals. We hypothesise that there are no publicly available recombination maps representative of the Nama, considering the deep population divergence and subsequent isolation of the Khoe-San from other African groups. Results We show that the recombination landscape of the Nama does not cluster with any continental groups with publicly available representative recombination maps. Finally, we use selection scans as an example of how fine-scale differences between the Nama recombination map and the combined Phase II HapMap recombination map can impact the outcome of selection scans. Conclusions Fine-scale differences in recombination can meaningfully alter the results of a selection scan. The recombination map we infer likely represents an upper bound on the extent of divergence we expect to see for a recombination map in humans and would be of interest to any researcher that wants to test the sensitivity of population genetic or GWAS analysis to recombination map input. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02744-5.
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Affiliation(s)
- Gerald van Eeden
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Evlyn Pless
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California (UC) Davis, Davis, CA, USA
| | - Mira Mastoras
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California (UC) Davis, Davis, CA, USA
| | - Gian D van der Spuy
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa.,SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, Cape Town, South Africa
| | - Gerard Tromp
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa.,SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, Cape Town, South Africa
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California (UC) Davis, Davis, CA, USA
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa. .,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, 7602, South Africa.
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Sun BB, Kurki MI, Foley CN, Mechakra A, Chen CY, Marshall E, Wilk JB, Chahine M, Chevalier P, Christé G, Palotie A, Daly MJ, Runz H; Biogen Biobank Team., FinnGen. Genetic associations of protein-coding variants in human disease. Nature 2022; 603:95-102. [PMID: 35197637 DOI: 10.1038/s41586-022-04394-w] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 12/20/2021] [Indexed: 12/11/2022]
Abstract
Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have so far remained largely underpowered in relation to identifying associations in the rare and low-frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes1. Here we combined whole-exome sequencing in 392,814 UK Biobank participants with imputed genotypes from 260,405 FinnGen participants (653,219 total individuals) to conduct association meta-analyses for 744 disease endpoints across the protein-coding allelic frequency spectrum, bridging the gap between common and rare variant studies. We identified 975 associations, with more than one-third being previously unreported. We demonstrate population-level relevance for mutations previously ascribed to causing single-gene disorders, map GWAS associations to likely causal genes, explain disease mechanisms, and systematically relate disease associations to levels of 117 biomarkers and clinical-stage drug targets. Combining sequencing and genotyping in two population biobanks enabled us to benefit from increased power to detect and explain disease associations, validate findings through replication and propose medical actionability for rare genetic variants. Our study provides a compendium of protein-coding variant associations for future insights into disease biology and drug discovery. A meta-analysis combining whole-exome sequencing data from UK Biobank participants and imputed genotypes from FinnGen participants enables identification of genetic associations with human disease in the rare and low-frequency allelic spectrum
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9
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Hao Z, Du P, Pan Y, Li H. Fine human genetic map based on UK10K data set. Hum Genet. [DOI: 10.1007/s00439-021-02415-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 12/03/2021] [Indexed: 11/04/2022]
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10
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van Eeden G, Uren C, van der Spuy G, Tromp G, Möller M. Local ancestry inference in heterogeneous populations-Are recent recombination events more relevant? Brief Bioinform 2021; 22:6337894. [PMID: 34343255 DOI: 10.1093/bib/bbab300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/29/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
To date, numerous software tools have been developed to infer recombination maps. Many of these software tools infer the recombination rate from linkage disequilibrium, and therefore they infer recombination many generations into the past. Other recently developed methods rely on the inference of recent recombination events to determine the recombination rate, such as identity by descent- and local ancestry inference (LAI)-based tools. Methods that mainly use recent recombination events to infer the recombination rate might be more relevant for certain analyses like LAI. We therefore describe a protocol for creating high-resolution, population-specific recombination maps using methods that mainly use recent recombination events and a method that uses recent and distant recombination events for recombination rate inference. Subsequently, we compared the effect of using maps inferred by these two paradigms on LAI accuracy.
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Affiliation(s)
| | | | - Gian van der Spuy
- Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa
| | - Gerard Tromp
- South African Tuberculosis Bioinformatics Initiative (SATBBI), South Africa
| | - Marlo Möller
- Department of Molecular Biology and Human Genetics, Stellenbosch University, South Africa
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11
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van Eeden G, Uren C, Möller M, Henn BM. Inferring recombination patterns in African populations. Hum Mol Genet 2021; 30:R11-R16. [PMID: 33445180 DOI: 10.1093/hmg/ddab020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/04/2021] [Accepted: 01/06/2021] [Indexed: 11/14/2022] Open
Abstract
Although several high-resolution recombination maps exist for European-descent populations, the recombination landscape of African populations remains relatively understudied. Given that there is high genetic divergence among groups in Africa, it is possible that recombination hotspots also diverge significantly. Both limitations and opportunities exist for developing recombination maps for these populations. In this review, we discuss various recombination inference methods, and the strengths and weaknesses of these methods in analyzing recombination in African-descent populations. Furthermore, we provide a decision tree and recommendations for which inference method to use in various research contexts. Establishing an appropriate methodology for recombination rate inference in a particular study will improve the accuracy of various downstream analyses including but not limited to local ancestry inference, haplotype phasing, fine-mapping of GWAS loci and genome assemblies.
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Affiliation(s)
- Gerald van Eeden
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa.,Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California Davis, Davis, CA 95616, USA
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