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Yi H, Yang Q, Repaci C, Lee CM, Heo G, Timsina J, Gorijala P, Yang C, Budde J, Wang L, Cruchaga C, Sung YJ. TOPMed imputed genomics enhances genomic atlas of the human proteome in brain, cerebrospinal fluid, and plasma. Sci Data 2024; 11:387. [PMID: 38627416 PMCID: PMC11021418 DOI: 10.1038/s41597-024-03140-3] [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: 08/22/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024] Open
Abstract
Comprehensive expression quantitative trait loci studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein quantitative trait loci in brain, cerebrospinal fluid, and plasma. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in cerebrospinal fluid, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 loci in brain, 30 loci in cerebrospinal fluid, and 22 loci in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These resources are accessible through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer for use by the scientific community.
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Affiliation(s)
- Heng Yi
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Qijun Yang
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Charlie Repaci
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Cheolmin Matthew Lee
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA
| | - Gyujin Heo
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John Budde
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
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2
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Kalnapenkis A, Jõeloo M, Lepik K, Kukuškina V, Kals M, Alasoo K, Mägi R, Esko T, Võsa U. Genetic determinants of plasma protein levels in the Estonian population. Sci Rep 2024; 14:7694. [PMID: 38565889 PMCID: PMC10987560 DOI: 10.1038/s41598-024-57966-3] [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: 06/22/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.
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Affiliation(s)
- Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.
| | - Maarja Jõeloo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Kaido Lepik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Viktorija Kukuškina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
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James C, Pemberton JM, Navarro P, Knott S. Investigating pedigree- and SNP-associated components of heritability in a wild population of Soay sheep. Heredity (Edinb) 2024; 132:202-210. [PMID: 38341521 PMCID: PMC10997785 DOI: 10.1038/s41437-024-00673-6] [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: 05/29/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
Estimates of narrow sense heritability derived from genomic data that contain related individuals may be biased due to the within-family effects such as dominance, epistasis and common environmental factors. However, for many wild populations, removal of related individuals from the data would result in small sample sizes. In 2013, Zaitlen et al. proposed a method to estimate heritability in populations that include close relatives by simultaneously fitting an identity-by-state (IBS) genomic relatedness matrix (GRM) and an identity-by-descent (IBD) GRM. The IBD GRM is identical to the IBS GRM, except relatedness estimates below a specified threshold are set to 0. We applied this method to a sample of 8557 wild Soay sheep from St. Kilda, with genotypic information for 419,281 single nucleotide polymorphisms. We aimed to see how this method would partition heritability into population-level (IBS) and family-associated (IBD) variance for a range of genetic architectures, and so we focused on a mixture of polygenic and monogenic traits. We also implemented a variant of the model in which the IBD GRM was replaced by a GRM constructed from SNPs with low minor allele frequency to examine whether any additive genetic variance is captured by rare alleles. Whilst the inclusion of the IBD GRM did not significantly improve the fit of the model for the monogenic traits, it improved the fit for some of the polygenic traits, suggesting that dominance, epistasis and/or common environment not already captured by the non-genetic random effects fitted in our models may influence these traits.
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Affiliation(s)
- Caelinn James
- Institute of Ecology and Evolution, School of Biological Sciences, The University of Edinburgh, Edinburgh, UK.
- Scotland's Rural College (SRUC), The Roslin Institute Building, Easter Bush, Midlothian, UK.
| | - Josephine M Pemberton
- Institute of Ecology and Evolution, School of Biological Sciences, The University of Edinburgh, Edinburgh, UK
| | - Pau Navarro
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Sara Knott
- Institute of Ecology and Evolution, School of Biological Sciences, The University of Edinburgh, Edinburgh, UK
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4
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Feldmann MJ, Pincot DDA, Cole GS, Knapp SJ. Genetic gains underpinning a little-known strawberry Green Revolution. Nat Commun 2024; 15:2468. [PMID: 38504104 PMCID: PMC10951273 DOI: 10.1038/s41467-024-46421-6] [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: 03/03/2023] [Accepted: 02/26/2024] [Indexed: 03/21/2024] Open
Abstract
The annual production of strawberry has increased by one million tonnes in the US and 8.4 million tonnes worldwide since 1960. Here we show that the US expansion was driven by genetic gains from Green Revolution breeding and production advances that increased yields by 2,755%. Using a California population with a century-long breeding history and phenotypes of hybrids observed in coastal California environments, we estimate that breeding has increased fruit yields by 2,974-6,636%, counts by 1,454-3,940%, weights by 228-504%, and firmness by 239-769%. Using genomic prediction approaches, we pinpoint the origin of the Green Revolution to the early 1950s and uncover significant increases in additive genetic variation caused by transgressive segregation and phenotypic diversification. Lastly, we show that the most consequential Green Revolution breeding breakthrough was the introduction of photoperiod-insensitive, PERPETUAL FLOWERING hybrids in the 1970s that doubled yields and drove the dramatic expansion of strawberry production in California.
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Affiliation(s)
- Mitchell J Feldmann
- Department of Plant Sciences, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Dominique D A Pincot
- Department of Plant Sciences, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Steven J Knapp
- Department of Plant Sciences, University of California Davis, One Shields Avenue, Davis, CA, 95616, USA.
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5
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Mapel XM, Kadri NK, Leonard AS, He Q, Lloret-Villas A, Bhati M, Hiltpold M, Pausch H. Author Correction: Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle. Nat Commun 2024; 15:1506. [PMID: 38374319 PMCID: PMC10876946 DOI: 10.1038/s41467-024-45727-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024] Open
Affiliation(s)
- Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Naveen Kumar Kadri
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Qiongyu He
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | | | - Meenu Bhati
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- Roslin Institute, The University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK
| | - Maya Hiltpold
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland.
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6
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Zhang Q, Liu J, Liu H, Ao L, Xi Y, Chen D. Genome-wide epistasis analysis reveals gene-gene interaction network on an intermediate endophenotype P-tau/Aβ 42 ratio in ADNI cohort. Sci Rep 2024; 14:3984. [PMID: 38368488 PMCID: PMC10874417 DOI: 10.1038/s41598-024-54541-8] [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: 10/22/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.
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Affiliation(s)
- Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China
| | - Lang Ao
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Dandan Chen
- School of Automation Engineering, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China.
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China.
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7
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Sun Q, Rowland BT, Chen J, Mikhaylova AV, Avery C, Peters U, Lundin J, Matise T, Buyske S, Tao R, Mathias RA, Reiner AP, Auer PL, Cox NJ, Kooperberg C, Thornton TA, Raffield LM, Li Y. Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI. Nat Commun 2024; 15:1016. [PMID: 38310129 PMCID: PMC10838303 DOI: 10.1038/s41467-024-45135-z] [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: 10/07/2022] [Accepted: 01/16/2024] [Indexed: 02/05/2024] Open
Abstract
Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Bryce T Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Anna V Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Tara Matise
- Department of Genetics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Steve Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Nancy J Cox
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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8
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Mapel XM, Kadri NK, Leonard AS, He Q, Lloret-Villas A, Bhati M, Hiltpold M, Pausch H. Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle. Nat Commun 2024; 15:674. [PMID: 38253538 PMCID: PMC10803364 DOI: 10.1038/s41467-024-44935-7] [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: 07/15/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking in cattle, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 414,667 molecular phenotypes and 21,501,032 genome-wide variants to identify 41,156 regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.
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Affiliation(s)
- Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Naveen Kumar Kadri
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Qiongyu He
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | | | - Meenu Bhati
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- Roslin Institute, The University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK
| | - Maya Hiltpold
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland.
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9
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Huang L, Tang J, Zhu B, Chen G, Chen L, Bu S, Zhu H, Liu Z, Li Z, Meng L, Liu G, Wang S. QTL epistasis plays a role of homeostasis on heading date in rice. Sci Rep 2024; 14:373. [PMID: 38172169 PMCID: PMC10764746 DOI: 10.1038/s41598-023-50786-x] [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: 10/23/2023] [Accepted: 12/25/2023] [Indexed: 01/05/2024] Open
Abstract
If there was no gene interaction, the gene aggregation effect would increase infinitely with the increase of gene number. Epistasis avoids the endless accumulation of gene effects, playing a role of homeostasis. To confirm the role, QTL epistases were analyzed by four single-segment substitution lines with heading date QTLs in this paper. We found that QTLs of three positive effects and one negative effect generated 62.5% negative dual QTL epistatic effects and 57.7% positive triple QTL epistatic effects, forming the relationship "positive QTLs-negative one order interactions-positive two order interactions". In this way, the aggregation effect of QTLs was partially neutralized by the opposite epistatic effect sum. There also were two exceptions, QTL OsMADS50 and gene Hd3a-2 were always with consistent effect directions with their epistases, implying they could be employed in pyramiding breeding with different objectives. This study elucidated the mechanism of epistatic interactions among four QTLs and provided valuable genetic resources for improving heading date in rice.
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Affiliation(s)
- Lilong Huang
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Jichun Tang
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
- Kunpeng Institute of Modern Agriculture at Foshan, Foshan, 528200, People's Republic of China
| | - Bihuang Zhu
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Guodong Chen
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Leyi Chen
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Suhong Bu
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Haitao Zhu
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Zupei Liu
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Zhan Li
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China
| | - Lijun Meng
- Kunpeng Institute of Modern Agriculture at Foshan, Foshan, 528200, People's Republic of China.
| | - Guifu Liu
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China.
| | - Shaokui Wang
- Guangdong Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou, 510642, People's Republic of China.
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10
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Davoudi P, Do DN, Colombo S, Rathgeber B, Sargolzaei M, Plastow G, Wang Z, Hu G, Valipour S, Miar Y. Genome-wide association studies for economically important traits in mink using copy number variation. Sci Rep 2024; 14:24. [PMID: 38167844 PMCID: PMC10762091 DOI: 10.1038/s41598-023-50497-3] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
Copy number variations (CNVs) are structural variants consisting of duplications and deletions of DNA segments, which are known to play important roles in the genetics of complex traits in livestock species. However, CNV-based genome-wide association studies (GWAS) have remained unexplored in American mink. Therefore, the purpose of the current study was to investigate the association between CNVs and complex traits in American mink. A CNV-based GWAS was performed with the ParseCNV2 software program using deregressed estimated breeding values of 27 traits as pseudophenotypes, categorized into traits of growth and feed efficiency, reproduction, pelt quality, and Aleutian disease tests. The study identified a total of 10,137 CNVs (6968 duplications and 3169 deletions) using the Affymetrix Mink 70K single nucleotide polymorphism (SNP) array in 2986 American mink. The association analyses identified 250 CNV regions (CNVRs) associated with at least one of the studied traits. These CNVRs overlapped with a total of 320 potential candidate genes, and among them, several genes have been known to be related to the traits such as ARID1B, APPL1, TOX, and GPC5 (growth and feed efficiency traits); GRM1, RNASE10, WNT3, WNT3A, and WNT9B (reproduction traits); MYO10, and LIMS1 (pelt quality traits); and IFNGR2, APEX1, UBE3A, and STX11 (Aleutian disease tests). Overall, the results of the study provide potential candidate genes that may regulate economically important traits and therefore may be used as genetic markers in mink genomic breeding programs.
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Affiliation(s)
- Pourya Davoudi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Duy Ngoc Do
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Stefanie Colombo
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Bruce Rathgeber
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Mehdi Sargolzaei
- Department of Pathobiology, University of Guelph, Guelph, ON, Canada
- Select Sires Inc., Plain City, OH, USA
| | - Graham Plastow
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Zhiquan Wang
- Livestock Gentec, Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Guoyu Hu
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Shafagh Valipour
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Younes Miar
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada.
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11
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Chhotaray S, Vohra V, Uttam V, Santhosh A, Saxena P, Gahlyan RK, Gowane G. TWAS revealed significant causal loci for milk production and its composition in Murrah buffaloes. Sci Rep 2023; 13:22401. [PMID: 38104199 PMCID: PMC10725422 DOI: 10.1038/s41598-023-49767-x] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023] Open
Abstract
Milk yield is the most complex trait in dairy animals, and mapping all causal variants even with smallest effect sizes has been difficult with the genome-wide association study (GWAS) sample sizes available in geographical regions with small livestock holdings such as Indian sub-continent. However, Transcriptome-wide association studies (TWAS) could serve as an alternate for fine mapping of expression quantitative trait loci (eQTLs). This is a maiden attempt to identify milk production and its composition related genes using TWAS in Murrah buffaloes (Bubalus bubalis). TWAS was conducted on a test (N = 136) set of Murrah buffaloes genotyped through ddRAD sequencing. Their gene expression level was predicted using reference (N = 8) animals having both genotype and mammary epithelial cell (MEC) transcriptome information. Gene expression prediction was performed using Elastic-Net and Dirichlet Process Regression (DPR) model with fivefold cross-validation and without any cross-validation. DPR model without cross-validation predicted 80.92% of the total genes in the test group of Murrah buffaloes which was highest compared to other methods. TWAS in test individuals based on predicted gene expression, identified a significant association of one unique gene for Fat%, and two for SNF% at Bonferroni corrected threshold. The false discovery rates (FDR) corrected P-values of the top ten SNPs identified through GWAS were comparatively higher than TWAS. Gene ontology of TWAS-identified genes was performed to understand the function of these genes, it was revealed that milk production and composition genes were mainly involved in Relaxin, AMPK, and JAK-STAT signaling pathway, along with CCRI, and several key metabolic processes. The present study indicates that TWAS offers a lower false discovery rate and higher significant hits than GWAS for milk production and its composition traits. Hence, it is concluded that TWAS can be effectively used to identify genes and cis-SNPs in a population, which can be used for fabricating a low-density genomic chip for predicting milk production in Murrah buffaloes.
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Affiliation(s)
- Supriya Chhotaray
- Division of Animal Genetics and Breeding, ICAR-Central Institute for Research on Buffaloes, Hisar, Haryana, 125001, India
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Vikas Vohra
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India.
| | - Vishakha Uttam
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Ameya Santhosh
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Punjika Saxena
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Rajesh Kumar Gahlyan
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
| | - Gopal Gowane
- Animal Genetics and Breeding Division, ICAR-National Dairy Research Institute, Karnal, Haryana, 132001, India
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12
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Megat S, Mora N, Sanogo J, Roman O, Catanese A, Alami NO, Freischmidt A, Mingaj X, De Calbiac H, Muratet F, Dirrig-Grosch S, Dieterle S, Van Bakel N, Müller K, Sieverding K, Weishaupt J, Andersen PM, Weber M, Neuwirth C, Margelisch M, Sommacal A, Van Eijk KR, Veldink JH, Lautrette G, Couratier P, Camuzat A, Le Ber I, Grassano M, Chio A, Boeckers T, Ludolph AC, Roselli F, Yilmazer-Hanke D, Millecamps S, Kabashi E, Storkebaum E, Sellier C, Dupuis L. Author Correction: Integrative genetic analysis illuminates ALS heritability and identifies risk genes. Nat Commun 2023; 14:8026. [PMID: 38049418 PMCID: PMC10696059 DOI: 10.1038/s41467-023-43710-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023] Open
Affiliation(s)
- Salim Megat
- Université de Strasbourg, Inserm, Mécanismes Centraux et Périphériques de la Neurodégénérescence, UMR-S1118, Centre de Recherches en Biomédecine, Strasbourg, France.
| | - Natalia Mora
- Department of Molecular Neurobiology, Donders Institute for Brain, Cognition and Behaviour and Faculty of Science, Radboud University, Nijmegen, Netherlands
| | - Jason Sanogo
- Université de Strasbourg, Inserm, Mécanismes Centraux et Périphériques de la Neurodégénérescence, UMR-S1118, Centre de Recherches en Biomédecine, Strasbourg, France
| | - Olga Roman
- Université de Strasbourg, Inserm, Mécanismes Centraux et Périphériques de la Neurodégénérescence, UMR-S1118, Centre de Recherches en Biomédecine, Strasbourg, France
| | - Alberto Catanese
- Institute of Anatomy and Cell Biology, Ulm University, Ulm, Germany
- German Center for Neurodegenerative Diseases (DZNE) Ulm, Ulm, Germany
| | - Najwa Ouali Alami
- Clinical Neuroanatomy, Department of Neurology, Ulm University, Ulm, Germany
| | - Axel Freischmidt
- German Center for Neurodegenerative Diseases (DZNE) Ulm, Ulm, Germany
- Department of Neurology, Ulm University, Ulm, Germany
| | - Xhuljana Mingaj
- Laboratory of Translational Research for Neurological Disorders, Imagine Institute, Université de Paris, INSERM UMR 1163, 75015, Paris, France
| | - Hortense De Calbiac
- Laboratory of Translational Research for Neurological Disorders, Imagine Institute, Université de Paris, INSERM UMR 1163, 75015, Paris, France
| | - François Muratet
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Sylvie Dirrig-Grosch
- Université de Strasbourg, Inserm, Mécanismes Centraux et Périphériques de la Neurodégénérescence, UMR-S1118, Centre de Recherches en Biomédecine, Strasbourg, France
| | - Stéphane Dieterle
- Université de Strasbourg, Inserm, Mécanismes Centraux et Périphériques de la Neurodégénérescence, UMR-S1118, Centre de Recherches en Biomédecine, Strasbourg, France
| | - Nick Van Bakel
- Department of Molecular Neurobiology, Donders Institute for Brain, Cognition and Behaviour and Faculty of Science, Radboud University, Nijmegen, Netherlands
| | - Kathrin Müller
- German Center for Neurodegenerative Diseases (DZNE) Ulm, Ulm, Germany
- Institute of Human Genetics, Ulm University, Ulm, Germany
| | | | - Jochen Weishaupt
- Division for Neurodegenerative Diseases, Neurology Department, University Medicine Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Markus Weber
- Neuromuscular Disease Unit/ALS Clinic, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Christoph Neuwirth
- Neuromuscular Disease Unit/ALS Clinic, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Markus Margelisch
- Institute for Pathology, Kanstonsspital St. Gallen, St. Gallen, Switzerland
| | - Andreas Sommacal
- Institute for Pathology, Kanstonsspital St. Gallen, St. Gallen, Switzerland
| | - Kristel R Van Eijk
- Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Jan H Veldink
- Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Géraldine Lautrette
- Service de Neurologie, Centre de Référence SLA et autres maladies du neurone moteur, CHU Dupuytren 1, Limoges, France
| | - Philippe Couratier
- Service de Neurologie, Centre de Référence SLA et autres maladies du neurone moteur, CHU Dupuytren 1, Limoges, France
| | - Agnès Camuzat
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Isabelle Le Ber
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Maurizio Grassano
- ALS Center "Rita Levi Montalcini" Department of Neuroscience, University of Turin, Turin, Italy
| | - Adriano Chio
- ALS Center "Rita Levi Montalcini" Department of Neuroscience, University of Turin, Turin, Italy
| | - Tobias Boeckers
- Institute of Anatomy and Cell Biology, Ulm University, Ulm, Germany
- German Center for Neurodegenerative Diseases (DZNE) Ulm, Ulm, Germany
| | - Albert C Ludolph
- German Center for Neurodegenerative Diseases (DZNE) Ulm, Ulm, Germany
- Department of Neurology, Ulm University, Ulm, Germany
| | - Francesco Roselli
- German Center for Neurodegenerative Diseases (DZNE) Ulm, Ulm, Germany
- Department of Neurology, Ulm University, Ulm, Germany
| | | | - Stéphanie Millecamps
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Edor Kabashi
- Laboratory of Translational Research for Neurological Disorders, Imagine Institute, Université de Paris, INSERM UMR 1163, 75015, Paris, France
| | - Erik Storkebaum
- Department of Molecular Neurobiology, Donders Institute for Brain, Cognition and Behaviour and Faculty of Science, Radboud University, Nijmegen, Netherlands
| | - Chantal Sellier
- Université de Strasbourg, Inserm, Mécanismes Centraux et Périphériques de la Neurodégénérescence, UMR-S1118, Centre de Recherches en Biomédecine, Strasbourg, France
| | - Luc Dupuis
- Université de Strasbourg, Inserm, Mécanismes Centraux et Périphériques de la Neurodégénérescence, UMR-S1118, Centre de Recherches en Biomédecine, Strasbourg, France.
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13
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Goovaerts S, Hoskens H, Eller RJ, Herrick N, Musolf AM, Justice CM, Yuan M, Naqvi S, Lee MK, Vandermeulen D, Szabo-Rogers HL, Romitti PA, Boyadjiev SA, Marazita ML, Shaffer JR, Shriver MD, Wysocka J, Walsh S, Weinberg SM, Claes P. Joint multi-ancestry and admixed GWAS reveals the complex genetics behind human cranial vault shape. Nat Commun 2023; 14:7436. [PMID: 37973980 PMCID: PMC10654897 DOI: 10.1038/s41467-023-43237-8] [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: 12/07/2022] [Accepted: 11/01/2023] [Indexed: 11/19/2023] Open
Abstract
The cranial vault in humans is highly variable, clinically relevant, and heritable, yet its genetic architecture remains poorly understood. Here, we conduct a joint multi-ancestry and admixed multivariate genome-wide association study on 3D cranial vault shape extracted from magnetic resonance images of 6772 children from the ABCD study cohort yielding 30 genome-wide significant loci. Follow-up analyses indicate that these loci overlap with genomic risk loci for sagittal craniosynostosis, show elevated activity cranial neural crest cells, are enriched for processes related to skeletal development, and are shared with the face and brain. We present supporting evidence of regional localization for several of the identified genes based on expression patterns in the cranial vault bones of E15.5 mice. Overall, our study provides a comprehensive overview of the genetics underlying normal-range cranial vault shape and its relevance for understanding modern human craniofacial diversity and the etiology of congenital malformations.
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Affiliation(s)
- Seppe Goovaerts
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
| | - Hanne Hoskens
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Ryan J Eller
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Noah Herrick
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Anthony M Musolf
- Statistical Genetics Section, Computational and Statistical Genomics Branch, NHGRI, NIH, MD, Baltimore, USA
| | - Cristina M Justice
- Genometrics Section, Computational and Statistical Genomics Branch, Division of Intramural Research, NHGRI, NIH, Baltimore, MD, USA
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meng Yuan
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Myoung Keun Lee
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dirk Vandermeulen
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Heather L Szabo-Rogers
- Department of Anatomy, Physiology and Pharmacology, University of Saskatchewan, Saskatchewan, Canada
| | - Paul A Romitti
- Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA, USA
| | - Simeon A Boyadjiev
- Department of Pediatrics, University of California Davis, Sacramento, CA, USA
| | - Mary L Marazita
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - John R Shaffer
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Indianapolis, Indianapolis, IN, USA
| | - Seth M Weinberg
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, VIC, Australia.
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14
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Sallam AM, Abou-Souliman I, Reyer H, Wimmers K, Rabee AE. New insights into the genetic predisposition of brucellosis and its effect on the gut and vaginal microbiota in goats. Sci Rep 2023; 13:20086. [PMID: 37973848 PMCID: PMC10654701 DOI: 10.1038/s41598-023-46997-x] [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: 12/09/2022] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
Goats contribute significantly to the global food security and industry. They constitute a main supplier of meat and milk for large proportions of people in Egypt and worldwide. Brucellosis is a zoonotic infectious disease that causes a significant economic loss in animal production. A case-control genome-wide association analysis (GWAS) was conducted using the infectious status of the animal as a phenotype. The does that showed abortion during the last third period of pregnancy and which were positive to both rose bengal plate and serum tube agglutination tests, were considered as cases. Otherwise, they were considered as controls. All animals were genotyped using the Illumina 65KSNP BeadChip. Additionally, the diversity and composition of vaginal and fecal microbiota in cases and controls were investigated using PCR-amplicone sequencing of the V4 region of 16S rDNA. After applying quality control criteria, 35,818 markers and 66 does were available for the GWAS test. The GWAS revealed a significantly associated SNP (P = 5.01 × 10-7) located on Caprine chromosome 15 at 29 megabases. Four other markers surpassed the proposed threshold (P = 2.5 × 10-5). Additionally, fourteen genomic regions accounted for more than 0.1% of the variance explained by all genome windows. Corresponding markers were located within or in close vicinity to several candidate genes, such as ARRB1, RELT, ATG16L2, IGSF21, UBR4, ULK1, DCN, MAPB1, NAIP, CD26, IFIH1, NDFIP2, DOK4, MAF, IL2RB, USP18, ARID5A, ZAP70, CNTN5, PIK3AP1, DNTT, BLNK, and NHLRC3. These genes play important roles in the regulation of immune responses to the infections through several biological pathways. Similar vaginal bacterial community was observed in both cases and controls while the fecal bacterial composition and diversity differed between the groups (P < 0.05). Faeces from the control does showed a higher relative abundance of the phylum Bacteroidota compared to cases (P < 0.05), while the latter showed more Firmicutes, Spirochaetota, Planctomycetota, and Proteobacteria. On the genus level, the control does exhibited higher abundances of Rikenellaceae RC9 gut group and Christensenellaceae R-7 group (P < 0.05), while the infected does revealed higher Bacteroides, Alistipes, and Prevotellaceae UCG-003 (P < 0.05). This information increases our understanding of the genetics of the susceptibility to Brucella in goats and may be useful in breeding programs and selection schemes that aim at controlling the disease in livestock.
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Affiliation(s)
- Ahmed M Sallam
- Animal and Poultry Breeding Department, Desert Research Center, Cairo, Egypt.
| | | | - Henry Reyer
- Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Klaus Wimmers
- Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196, Dummerstorf, Germany
| | - Alaa Emara Rabee
- Animal and Poultry Nutrition Department, Desert Research Center, Cairo, Egypt
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15
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Tayade R, Imran M, Ghimire A, Khan W, Nabi RBS, Kim Y. Molecular, genetic, and genomic basis of seed size and yield characteristics in soybean. Front Plant Sci 2023; 14:1195210. [PMID: 38034572 PMCID: PMC10684784 DOI: 10.3389/fpls.2023.1195210] [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] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans' genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits' genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.
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Affiliation(s)
- Rupesh Tayade
- Upland Field Machinery Research Center, Kyungpook National University, Daegu, Republic of Korea
| | - Muhammad Imran
- Division of Biosafety, National Institute of Agriculture Science, Rural Development Administration, Jeonju, Jeollabul-do, Republic of Korea
| | - Amit Ghimire
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
| | - Waleed Khan
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
| | - Rizwana Begum Syed Nabi
- Department of Southern Area Crop Science, National Institute of Crop Science, Rural Development Administration, Miryang, Republic of Korea
| | - Yoonha Kim
- Upland Field Machinery Research Center, Kyungpook National University, Daegu, Republic of Korea
- Department of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea
- Department of Integrative Biology, Kyungpook National University, Daegu, Republic of Korea
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16
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Zhou F, Soremekun O, Chikowore T, Fatumo S, Barroso I, Morris AP, Asimit JL. Leveraging information between multiple population groups and traits improves fine-mapping resolution. Nat Commun 2023; 14:7279. [PMID: 37949886 PMCID: PMC10638399 DOI: 10.1038/s41467-023-43159-5] [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: 05/31/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.
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Affiliation(s)
- Feng Zhou
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Opeyemi Soremekun
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Segun Fatumo
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
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17
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Middleton KM, Duren DL, McNulty KP, Oh H, Valiathan M, Sherwood RJ. Cross-sectional data accurately model longitudinal growth in the craniofacial skeleton. Sci Rep 2023; 13:19294. [PMID: 37935807 PMCID: PMC10630296 DOI: 10.1038/s41598-023-46018-x] [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: 05/11/2023] [Accepted: 10/26/2023] [Indexed: 11/09/2023] Open
Abstract
Dense, longitudinal sampling represents the ideal for studying biological growth. However, longitudinal samples are not typically possible, due to limits of time, prohibitive cost, or health concerns of repeat radiologic imaging. In contrast, cross-sectional samples have few such drawbacks, but it is not known how well estimates of growth milestones can be obtained from cross-sectional samples. The Craniofacial Growth Consortium Study (CGCS) contains longitudinal growth data for approximately 2000 individuals. Single samples from the CGCS for individuals representing cross-sectional data were used to test the ability to predict growth parameters in linear trait measurements separately by sex. Testing across a range of cross-sectional sample sizes from 5 to the full sample, we found that means from repeated samples were able to approximate growth rates determined from the full longitudinal CGCS sample, with mean absolute differences below 1 mm at cross-sectional sample sizes greater than ~ 200 individuals. Our results show that growth parameters and milestones can be accurately estimated from cross-sectional data compared to population-level estimates from complete longitudinal data, underscoring the utility of such datasets in growth modeling. This method can be applied to other forms of growth (e.g., stature) and to cases in which repeated radiographs are not feasible (e.g., cone-beam CT).
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Affiliation(s)
- Kevin M Middleton
- Division of Biological Sciences, University of Missouri, Columbia, MO, USA.
| | - Dana L Duren
- Department of Orthopaedic Surgery, University of Missouri School of Medicine, Columbia, MO, USA
- Department of Pathology and Anatomical Sciences, University of Missouri School of Medicine, Columbia, MO, USA
| | - Kieran P McNulty
- Department of Anthropology, University of Minnesota, Minneapolis, MN, USA
| | - Heesoo Oh
- Department of Orthodontics, Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, CA, USA
| | - Manish Valiathan
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Richard J Sherwood
- Department of Orthopaedic Surgery, University of Missouri School of Medicine, Columbia, MO, USA
- Department of Pathology and Anatomical Sciences, University of Missouri School of Medicine, Columbia, MO, USA
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, OH, USA
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18
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Lopez-Cruz M, Aguate FM, Washburn JD, de Leon N, Kaeppler SM, Lima DC, Tan R, Thompson A, De La Bretonne LW, de Los Campos G. Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America. Nat Commun 2023; 14:6904. [PMID: 37903778 PMCID: PMC10616096 DOI: 10.1038/s41467-023-42687-4] [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: 05/08/2023] [Accepted: 10/18/2023] [Indexed: 11/01/2023] Open
Abstract
Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set's genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.
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Affiliation(s)
- Marco Lopez-Cruz
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
| | - Fernando M Aguate
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Jacob D Washburn
- United States Department of Agriculture, Agricultural Research Service, University of Missouri, Columbia, MO, 65211, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, 53706, USA
- Wisconsin Crop Innovation Center, University of Wisconsin, Middleton, WI, 53562, USA
| | | | - Ruijuan Tan
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
| | - Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, 48824, USA
- Plant Resilience Institute, Michigan State University, East Lansing, MI, 48824, USA
| | | | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA.
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19
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Bouidghaghen J, Moreau L, Beauchêne K, Chapuis R, Mangel N, Cabrera-Bosquet L, Welcker C, Bogard M, Tardieu F. Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field. Nat Commun 2023; 14:6603. [PMID: 37857601 PMCID: PMC10587076 DOI: 10.1038/s41467-023-42298-z] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023] Open
Abstract
Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.
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Affiliation(s)
- Jugurta Bouidghaghen
- LEPSE, Univ Montpellier, INRAE, Montpellier, France
- ARVALIS, Chemin de la côte vieille, Baziège, France
| | - Laurence Moreau
- GQE-Le Moulon, INRAE, Université Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Katia Beauchêne
- ARVALIS, 45 Voie Romaine, Ouzouer-Le-Marché, Beauce La Romaine, France
| | | | - Nathalie Mangel
- ARVALIS, Station de recherche et d'expérimentation, Boigneville, France
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20
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Höllinger I, Wölfl B, Hermisson J. A theory of oligogenic adaptation of a quantitative trait. Genetics 2023; 225:iyad139. [PMID: 37550847 PMCID: PMC10550320 DOI: 10.1093/genetics/iyad139] [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: 04/20/2023] [Revised: 04/20/2023] [Accepted: 07/13/2023] [Indexed: 08/09/2023] Open
Abstract
Rapid phenotypic adaptation is widespread in nature, but the underlying genetic dynamics remain controversial. Whereas population genetics envisages sequential beneficial substitutions, quantitative genetics assumes a collective response through subtle shifts in allele frequencies. This dichotomy of a monogenic and a highly polygenic view of adaptation raises the question of a middle ground, as well as the factors controlling the transition. Here, we consider an additive quantitative trait with equal locus effects under Gaussian stabilizing selection that adapts to a new trait optimum after an environmental change. We present an analytical framework based on Yule branching processes to describe how phenotypic adaptation is achieved by collective changes in allele frequencies at the underlying loci. In particular, we derive an approximation for the joint allele-frequency distribution conditioned on the trait mean as a comprehensive descriptor of the adaptive architecture. Depending on the model parameters, this architecture reproduces the well-known patterns of sequential, monogenic sweeps, or of subtle, polygenic frequency shifts. Between these endpoints, we observe oligogenic architecture types that exhibit characteristic patterns of partial sweeps. We find that a single compound parameter, the population-scaled background mutation rate Θbg, is the most important predictor of the type of adaptation, while selection strength, the number of loci in the genetic basis, and linkage only play a minor role.
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Affiliation(s)
- Ilse Höllinger
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
| | - Benjamin Wölfl
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
- Vienna Graduate School of Population Genetics, University of Vienna and Veterinary Medical University of Vienna, Vienna, Austria
- Vienna Doctoral School of Ecology and Evolution, University of Vienna, Vienna, Austria
| | - Joachim Hermisson
- Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
- Max Perutz Labs, Vienna Biocenter Campus (VBC), Dr.-Bohr-Gasse 9, 1030 Vienna, Austria
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21
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Tang S, Buchman AS, Wang Y, Avey D, Xu J, Tasaki S, Bennett DA, Zheng Q, Yang J. Differential gene expression analysis based on linear mixed model corrects false positive inflation for studying quantitative traits. Sci Rep 2023; 13:16570. [PMID: 37789141 PMCID: PMC10547771 DOI: 10.1038/s41598-023-43686-7] [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: 03/09/2023] [Accepted: 09/27/2023] [Indexed: 10/05/2023] Open
Abstract
Differential gene expression (DGE) analysis has been widely employed to identify genes expressed differentially with respect to a trait of interest using RNA sequencing (RNA-Seq) data. Recent RNA-Seq data with large samples pose challenges to existing DGE methods, which were mainly developed for dichotomous traits and small sample sizes. Especially, existing DGE methods are likely to result in inflated false positive rates. To address this gap, we employed a linear mixed model (LMM) that has been widely used in genetic association studies for DGE analysis of quantitative traits. We first applied the LMM method to the discovery RNA-Seq data of dorsolateral prefrontal cortex (DLPFC) tissue (n = 632) with four continuous measures of Alzheimer's Disease (AD) cognitive and neuropathologic traits. The quantile-quantile plots of p-values showed that false positive rates were well calibrated by LMM, whereas other methods not accounting for sample-specific mixed effects led to serious inflation. LMM identified 37 potentially significant genes with differential expression in DLPFC for at least one of the AD traits, 17 of which were replicated in the additional RNA-Seq data of DLPFC, supplemental motor area, spinal cord, and muscle tissues. This application study showed not only well calibrated DGE results by LMM, but also possibly shared gene regulatory mechanisms of AD traits across different relevant tissues.
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Affiliation(s)
- Shizhen Tang
- Department of Human Genetics, Center for Computational and Quantitative Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Denis Avey
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Jishu Xu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Qi Zheng
- Department of Bioinformatics and Biostatistics, University of Louisville, 485 E. Gray St, Louisville, KY, 40202, USA.
| | - Jingjing Yang
- Department of Human Genetics, Center for Computational and Quantitative Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA.
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22
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Eldjarn GH, Ferkingstad E, Lund SH, Helgason H, Magnusson OT, Gunnarsdottir K, Olafsdottir TA, Halldorsson BV, Olason PI, Zink F, Gudjonsson SA, Sveinbjornsson G, Magnusson MI, Helgason A, Oddsson A, Halldorsson GH, Magnusson MK, Saevarsdottir S, Eiriksdottir T, Masson G, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Melsted P, Saemundsdottir J, Norddahl GL, Thorleifsson G, Ulfarsson MO, Gudbjartsson DF, Thorsteinsdottir U, Sulem P, Stefansson K. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 2023; 622:348-358. [PMID: 37794188 PMCID: PMC10567571 DOI: 10.1038/s41586-023-06563-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 08/22/2023] [Indexed: 10/06/2023]
Abstract
High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.
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Affiliation(s)
| | | | - Sigrun H Lund
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hannes Helgason
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Bjarni V Halldorsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavik, Iceland
| | | | | | | | | | | | - Agnar Helgason
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Department of Anthropology, University of Iceland, Reykjavik, Iceland
| | | | | | - Magnus K Magnusson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Saedis Saevarsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Ingileif Jonsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | | | - Pall Melsted
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Kari Stefansson
- deCODE Genetics/Amgen, Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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23
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Dhindsa RS, Burren OS, Sun BB, Prins BP, Matelska D, Wheeler E, Mitchell J, Oerton E, Hristova VA, Smith KR, Carss K, Wasilewski S, Harper AR, Paul DS, Fabre MA, Runz H, Viollet C, Challis B, Platt A, Vitsios D, Ashley EA, Whelan CD, Pangalos MN, Wang Q, Petrovski S. Rare variant associations with plasma protein levels in the UK Biobank. Nature 2023; 622:339-347. [PMID: 37794183 PMCID: PMC10567546 DOI: 10.1038/s41586-023-06547-x] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 08/15/2023] [Indexed: 10/06/2023]
Abstract
Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1-4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype-protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene-protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.
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Affiliation(s)
- Ryan S Dhindsa
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, US.
| | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Benjamin B Sun
- Translational Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Bram P Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dorota Matelska
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Eleanor Wheeler
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Jonathan Mitchell
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Erin Oerton
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Ventzislava A Hristova
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, US
| | - Katherine R Smith
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Keren Carss
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Sebastian Wasilewski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Andrew R Harper
- Clinical Development, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dirk S Paul
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Margarete A Fabre
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Heiko Runz
- Translational Sciences, Research & Development, Biogen Inc., Cambridge, MA, US
| | - Coralie Viollet
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Benjamin Challis
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Adam Platt
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Dimitrios Vitsios
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Euan A Ashley
- Division of Cardiology, Department of Medicine, Stanford University, Palo Alto, CA, USA
| | | | | | - Quanli Wang
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, US
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.
- Department of Medicine, Austin Health, University of Melbourne, Melbourne, Victoria, Australia.
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24
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Hebbar P, Nizam R, John SE, Antony D, Dashti M, Channanath A, Shaltout A, Al-Khandari H, Koistinen HA, Tuomilehto J, Alsmadi O, Thanaraj TA, Al-Mulla F. Linkage analysis using whole exome sequencing data implicates SLC17A1, SLC17A3, TATDN2 and TMEM131L in type 1 diabetes in Kuwaiti families. Sci Rep 2023; 13:14978. [PMID: 37696853 PMCID: PMC10495342 DOI: 10.1038/s41598-023-42255-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/07/2023] [Indexed: 09/13/2023] Open
Abstract
Type 1 diabetes (T1D) is characterized by the progressive destruction of pancreatic β-cells, leading to insulin deficiency and lifelong dependency on exogenous insulin. Higher estimates of heritability rates in monozygotic twins, followed by dizygotic twins and sib-pairs, indicate the role of genetics in the pathogenesis of T1D. The incidence and prevalence of T1D are alarmingly high in Kuwait. Consanguineous marriages account for 50-70% of all marriages in Kuwait, leading to an excessive burden of recessive allele enrichment and clustering of familial disorders. Thus, genetic studies from this Arab region are expected to lead to the identification of novel gene loci for T1D. In this study, we performed linkage analyses to identify the recurrent genetic variants segregating in high-risk Kuwaiti families with T1D. We studied 18 unrelated Kuwaiti native T1D families using whole exome sequencing data from 86 individuals, of whom 37 were diagnosed with T1D. The study identified three potential loci with a LOD score of ≥ 3, spanning across four candidate genes, namely SLC17A1 (rs1165196:pT269I), SLC17A3 (rs942379: p.S370S), TATDN2 (rs394558:p.V256I), and TMEM131L (rs6848033:p.R190R). Upon examination of missense variants from these genes in the familial T1D dataset, we observed a significantly increased enrichment of the genotype homozygous for the minor allele at SLC17A3 rs56027330_p.G279R accounting for 16.2% in affected children from 6 unrelated Kuwaiti T1D families compared to 1000 genomes Phase 3 data (0.9%). Data from the NephQTL database revealed that the rs1165196, rs942379, rs394558, and rs56027330 SNPs exhibited genotype-based differential expression in either glomerular or tubular tissues. Data from the GTEx database revealed rs942379 and rs394558 as QTL variants altering the expression of TRIM38 and IRAK2 respectively. Global genome-wide association studies indicated that SLC17A1 rs1165196 and other variants from SLC17A3 are associated with uric acid concentrations and gout. Further evidence from the T1D Knowledge portal supported the role of shortlisted variants in T1D pathogenesis and urate metabolism. Our study suggests the involvement of SLC17A1, SLC17A3, TATDN2, and TMEM131L genes in familial T1D in Kuwait. An enrichment selection of genotype homozygous for the minor allele is observed at SLC17A3 rs56027330_p.G279R variant in affected members of Kuwaiti T1D families. Future studies may focus on replicating the findings in a larger T1D cohort and delineate the mechanistic details of the impact of these novel candidate genes on the pathophysiology of T1D.
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Affiliation(s)
- Prashantha Hebbar
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, 15462, Kuwait City, Kuwait
| | - Rasheeba Nizam
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, 15462, Kuwait City, Kuwait
| | - Sumi Elsa John
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, 15462, Kuwait City, Kuwait
| | - Dinu Antony
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, 15462, Kuwait City, Kuwait
| | - Mohammad Dashti
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, 15462, Kuwait City, Kuwait
| | - Arshad Channanath
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, 15462, Kuwait City, Kuwait
| | - Azza Shaltout
- Department of Population Health, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Hessa Al-Khandari
- Department of Population Health, Dasman Diabetes Institute, Kuwait City, Kuwait
- Department of Pediatrics, Farwaniya Hospital, Ministry of Health, Kuwait City, Kuwait
| | - Heikki A Koistinen
- Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jaakko Tuomilehto
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | | | - Fahd Al-Mulla
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, 15462, Kuwait City, Kuwait.
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25
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Pereira de Castro A, Breseghello F, Furtini IV, Utumi MM, Pereira JA, Cao TV, Bartholomé J. Population improvement via recurrent selection drives genetic gain in upland rice breeding. Heredity (Edinb) 2023; 131:201-210. [PMID: 37407693 PMCID: PMC10462700 DOI: 10.1038/s41437-023-00636-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/07/2023] Open
Abstract
One of the main challenges of breeding programs is to identify superior genotypes from a large number of candidates. By gradually increasing the frequency of favorable alleles in the breeding population, recurrent selection improves the population mean for target traits, increasing the chance to identify promising genotypes. In rice, population improvement through recurrent selection has been used very little to date, except in Latin America. At Embrapa (Brazilian Agricultural Research Corporation), the upland rice breeding program is conducted in two phases: population improvement followed by product development. In this study, the CNA6 population, evaluated over five cycles (3 to 7) of selection, including 20 field trials, was used to assess the realized genetic gain. A high rate of genetic gain was observed for grain yield, at 215 kg.ha-1 per cycle or 67.8 kg.ha-1 per year (3.08%). The CNA6 population outperformed the controls only for the last cycle, with a yield difference of 1128 kg.ha-1. An analysis of the product development pipeline, based on 29 advanced yield trials with lines derived from cycles 3 to 6, showed that lines derived from the CNA6 population had high grain yield, but did not outperform the controls. These results demonstrate that the application of recurrent selection to a breeding population with sufficient genetic variability can result in significant genetic gains for quantitative traits, such as grain yield. The integration of this strategy into a two-phase breeding program also makes it possible to increase quantitative traits while selecting for other traits of interest.
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Affiliation(s)
| | | | | | | | | | - Tuong-Vi Cao
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
| | - Jérôme Bartholomé
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
- Alliance Bioversity-CIAT, Cali, Colombia
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26
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Krichevski M, Calderon-Margalit R, Carmi S, Raz R. The heritability of weight gain in infancy: A population-based twin study. Paediatr Perinat Epidemiol 2023; 37:577-585. [PMID: 37282801 DOI: 10.1111/ppe.12991] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/08/2023]
Abstract
BACKGROUND Rapid weight gain during infancy is a strong predictor of childhood obesity and is affected by genetic and environmental factors. Identifying ages with low heritability will allow for targeted interventions that might be able to prevent the adverse effects of childhood obesity. OBJECTIVES The objective of the study is to estimate the heritability of weight gain from birth to defined ages during infancy, as well as during 6-month periods from birth to 18 months of age. We address this by leveraging large-scale computerised anthropometric data from the state-run network of well-baby clinics in Israel. METHODS We performed a population-based twin study. We extracted weight measurements recorded between birth to 24 months from well-baby clinics for 9388 twin pairs born in Israel between 2011 and 2015. The reported sexes of the twins were used as a proxy for their zygosity status. We estimated the heritability of the weight z-score change from birth to specific ages and during particular periods in infancy. To assess the validity of the results, we repeated the analysis in a sub-cohort of twin pairs with complete weight measurements. RESULTS During the first 2 years of life, heritability was lowest for birthweight (h 2 = 0.40 ± 0.11 ). Heritability for weight gain since birth was highest at 4 months (h 2 = 0.87 ± 0.13 ), and then gradually decreased until age 18 months (h 2 = 0.62 ± 0.13 ). Estimating the heritability in 6-month intervals from birth to 18 months, heritability was highest during the 6-12-month interval (h 2 = 0.84 ± 0.14 ), and was substantially lower during the subsequent 12-18-month interval (h 2 = 0.43 ± 0.16 ). CONCLUSIONS Heritability of weight gain decreases substantially in the second year of life, suggesting that this period could be an appropriate time for interventions for infants who are at an increased risk of childhood obesity.
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Affiliation(s)
- Masha Krichevski
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem - Hadassah, Jerusalem, Israel
- Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ronit Calderon-Margalit
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem - Hadassah, Jerusalem, Israel
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem - Hadassah, Jerusalem, Israel
| | - Raanan Raz
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem - Hadassah, Jerusalem, Israel
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27
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Zubiri-Gaitán A, Blasco A, Hernández P. Plasma metabolomic profiling in two rabbit lines divergently selected for intramuscular fat content. Commun Biol 2023; 6:893. [PMID: 37653068 PMCID: PMC10471702 DOI: 10.1038/s42003-023-05266-3] [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: 03/02/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023] Open
Abstract
This study provides a thorough comparison of the plasma metabolome of two rabbit lines divergently selected for intramuscular fat content (IMF). The divergent selection led to a correlated response in the overall adiposity, turning these lines into a valuable animal material to study also the genetics of obesity. Over 900 metabolites were detected, and the adjustment of multivariate models, both discriminant and linear, allowed to identify 322 with differential abundances between lines, which also adjusted linearly to the IMF content. The most affected pathways were those of lipids and amino acids, with differences between lines ranging from 0.23 to 6.04 standard deviations, revealing a limited capacity of the low-IMF line to obtain energy from lipids, and a greater branched-chain amino acids catabolism in the high-IMF line related to its increased IMF content. Additionally, changes in metabolites derived from microbial activity supported its relevant role in the lipid deposition. Future research will focus on the analysis of the metabolomic profile of the cecum content, and on the integration of the several -omics datasets available for these lines, to help disentangle the host and microbiome biological mechanisms involved in the IMF deposition.
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Affiliation(s)
- Agostina Zubiri-Gaitán
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain.
| | - Agustín Blasco
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain
| | - Pilar Hernández
- Institute for Animal Science and Technology, Universitat Politècnica de València, Valencia, Spain.
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28
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Di Scipio M, Khan M, Mao S, Chong M, Judge C, Pathan N, Perrot N, Nelson W, Lali R, Di S, Morton R, Petch J, Paré G. A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets. Nat Commun 2023; 14:5196. [PMID: 37626057 PMCID: PMC10457310 DOI: 10.1038/s41467-023-40913-7] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.
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Affiliation(s)
- Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Mohammad Khan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Shihong Mao
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
| | - Conor Judge
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Nazia Pathan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Nicolas Perrot
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Walter Nelson
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Robert Morton
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
| | - Jeremy Petch
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
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29
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Bozkurt B, Terlemez G, Sezgin E. Basidiomycota species in Drosophila gut are associated with host fat metabolism. Sci Rep 2023; 13:13807. [PMID: 37612350 PMCID: PMC10447447 DOI: 10.1038/s41598-023-41027-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/21/2023] [Indexed: 08/25/2023] Open
Abstract
The importance of bacterial microbiota on host metabolism and obesity risk is well documented. However, the role of fungal microbiota on host storage metabolite pools is largely unexplored. We aimed to investigate the role of microbiota on D. melanogaster fat metabolism, and examine interrelatedness between fungal and bacterial microbiota, and major metabolic pools. Fungal and bacterial microbiota profiles, fat, glycogen, and trehalose metabolic pools are measured in a context of genetic variation represented by whole genome sequenced inbred Drosophila Genetic Reference Panel (DGRP) samples. Increasing Basidiomycota, Acetobacter persici, Acetobacter pomorum, and Lactobacillus brevis levels correlated with decreasing triglyceride levels. Host genes and biological pathways, identified via genome-wide scans, associated with Basidiomycota and triglyceride levels were different suggesting the effect of Basidiomycota on fat metabolism is independent of host biological pathways that control fungal microbiota or host fat metabolism. Although triglyceride, glycogen and trehalose levels were highly correlated, microorganisms' effect on triglyceride pool were independent of glycogen and trehalose levels. Multivariate analyses suggested positive interactions between Basidiomycota, A. persici, and L. brevis that collectively correlated negatively with fat and glycogen pools. In conclusion, fungal microbiota can be a major player in host fat metabolism. Interactions between fungal and bacterial microbiota may exert substantial control over host storage metabolite pools and influence obesity risk.
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Affiliation(s)
- Berkay Bozkurt
- Bioengineering Program, Izmir Institute of Technology, Urla, Izmir, Turkey
| | - Gamze Terlemez
- Biotechnology Program, Izmir Institute of Technology, Urla, Izmir, Turkey
| | - Efe Sezgin
- Biotechnology Program, Izmir Institute of Technology, Urla, Izmir, Turkey.
- Department of Food Engineering, Izmir Institute of Technology, Urla, Izmir, Turkey.
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30
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Saripalli G, Adhikari L, Amos C, Kibriya A, Ahmed HI, Heuberger M, Raupp J, Athiyannan N, Wicker T, Abrouk M, Wallace S, Hosseinirad S, Chhuneja P, Livesay J, Rawat N, Krattinger SG, Poland J, Tiwari V. Integration of genetic and genomics resources in einkorn wheat enables precision mapping of important traits. Commun Biol 2023; 6:835. [PMID: 37573415 PMCID: PMC10423216 DOI: 10.1038/s42003-023-05189-z] [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: 01/24/2023] [Accepted: 07/26/2023] [Indexed: 08/14/2023] Open
Abstract
Einkorn wheat (Triticum monococcum) is an ancient grain crop and a close relative of the diploid progenitor (T. urartu) of polyploid wheat. It is the only diploid wheat species having both domesticated and wild forms and therefore provides an excellent system to identify domestication genes and genes for traits of interest to utilize in wheat improvement. Here, we leverage genomic advancements for einkorn wheat using an einkorn reference genome assembly combined with skim-sequencing of a large genetic population of 812 recombinant inbred lines (RILs) developed from a cross between a wild and a domesticated T. monococcum accession. We identify 15,919 crossover breakpoints delimited to a median and average interval of 114 Kbp and 219 Kbp, respectively. This high-resolution mapping resource enables us to perform fine-scale mapping of one qualitative (red coleoptile) and one quantitative (spikelet number per spike) trait, resulting in the identification of small physical intervals (400 Kb to 700 Kb) with a limited number of candidate genes. Furthermore, an important domestication locus for brittle rachis is also identified on chromosome 7A. This resource presents an exciting route to perform trait discovery in diploid wheat for agronomically important traits and their further deployment in einkorn as well as tetraploid pasta wheat and hexaploid bread wheat cultivars.
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Affiliation(s)
- Gautam Saripalli
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, 20783, USA
| | - Laxman Adhikari
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Cameron Amos
- Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
| | - Ashraf Kibriya
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Hanin Ibrahim Ahmed
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Matthias Heuberger
- Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland
| | - John Raupp
- Wheat Genetics Resource Center and Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA
| | - Naveenkumar Athiyannan
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Thomas Wicker
- Department of Plant and Microbial Biology, University of Zurich, Zurich, Switzerland
| | - Michael Abrouk
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Sydney Wallace
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, 20783, USA
| | - Seyedali Hosseinirad
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, 20783, USA
| | - Parveen Chhuneja
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, 141004, Punjab, India
| | - Janelle Livesay
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, 20783, USA
| | - Nidhi Rawat
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, 20783, USA
| | - Simon G Krattinger
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Jesse Poland
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
| | - Vijay Tiwari
- Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, 20783, USA.
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31
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Pavel AB, Garrison C, Luo L, Liu G, Taub D, Xiao J, Juan-Guardela B, Tedrow J, Alekseyev YO, Yang IV, Geraci MW, Sciurba F, Schwartz DA, Kaminski N, Beane J, Spira A, Lenburg ME, Campbell JD. Integrative genetic and genomic networks identify microRNA associated with COPD and ILD. Sci Rep 2023; 13:13076. [PMID: 37567908 PMCID: PMC10421936 DOI: 10.1038/s41598-023-39751-w] [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: 04/30/2022] [Accepted: 07/30/2023] [Indexed: 08/13/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) are clinically and molecularly heterogeneous diseases. We utilized clustering and integrative network analyses to elucidate roles for microRNAs (miRNAs) and miRNA isoforms (isomiRs) in COPD and ILD pathogenesis. Short RNA sequencing was performed on 351 lung tissue samples of COPD (n = 145), ILD (n = 144) and controls (n = 64). Five distinct subclusters of samples were identified including 1 COPD-predominant cluster and 2 ILD-predominant clusters which associated with different clinical measurements of disease severity. Utilizing 262 samples with gene expression and SNP microarrays, we built disease-specific genetic and expression networks to predict key miRNA regulators of gene expression. Members of miR-449/34 family, known to promote airway differentiation by repressing the Notch pathway, were among the top connected miRNAs in both COPD and ILD networks. Genes associated with miR-449/34 members in the disease networks were enriched among genes that increase in expression with airway differentiation at an air-liquid interface. A highly expressed isomiR containing a novel seed sequence was identified at the miR-34c-5p locus. 47% of the anticorrelated predicted targets for this isomiR were distinct from the canonical seed sequence for miR-34c-5p. Overexpression of the canonical miR-34c-5p and the miR-34c-5p isomiR with an alternative seed sequence down-regulated NOTCH1 and NOTCH4. However, only overexpression of the isomiR down-regulated genes involved in Ras signaling such as CRKL and GRB2. Overall, these findings elucidate molecular heterogeneity inherent across COPD and ILD patients and further suggest roles for miR-34c in regulating disease-associated gene-expression.
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Affiliation(s)
- Ana B Pavel
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA.
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA.
| | - Carly Garrison
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
| | - Lingqi Luo
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
| | - Gang Liu
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
| | - Daniel Taub
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
| | - Ji Xiao
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
| | - Brenda Juan-Guardela
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - John Tedrow
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Norman Regional Medical Center, Norman, Oklahoma, USA
| | - Yuriy O Alekseyev
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Ivana V Yang
- Department of Medicine, University of Colorado, Aurora, CO, USA
| | - Mark W Geraci
- Department of Medicine, University of Colorado, Aurora, CO, USA
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Frank Sciurba
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - David A Schwartz
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Naftali Kaminski
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Jennifer Beane
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA
| | - Avrum Spira
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA
| | - Marc E Lenburg
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Joshua D Campbell
- Department of Medicine, Boston University School of Medicine, 72 East Concord St, Boston, MA, 02118, USA.
- Bioinformatics Graduate Program, Boston University, Boston, MA, USA.
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32
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Drury AL, Gout JF, Dapper AL. Modeling Recombination Rate as a Quantitative Trait Reveals New Insight into Selection in Humans. Genome Biol Evol 2023; 15:evad132. [PMID: 37506266 PMCID: PMC10404793 DOI: 10.1093/gbe/evad132] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 06/01/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Meiotic recombination is both a fundamental biological process required for proper chromosomal segregation during meiosis and an important genomic parameter that shapes major features of the genomic landscape. However, despite the central importance of this phenotype, we lack a clear understanding of the selective pressures that shape its variation in natural populations, including humans. While there is strong evidence of fitness costs of low rates of recombination, the possible fitness costs of high rates of recombination are less defined. To determine whether a single lower fitness bound can explain the variation in recombination rates observed in human populations, we simulated the evolution of recombination rates as a sexually dimorphic quantitative trait. Under each scenario, we statistically compared the resulting trait distribution with the observed distribution of recombination rates from a published study of the Icelandic population. To capture the genetic architecture of recombination rates in humans, we modeled it as a moderately complex trait with modest heritability. For our fitness function, we implemented a hyperbolic tangent curve with several flexible parameters to capture a wide range of existing hypotheses. We found that costs of low rates of recombination alone are likely insufficient to explain the current variation in recombination rates in both males and females, supporting the existence of fitness costs of high rates of recombination in humans. With simulations using both upper and lower fitness boundaries, we describe a parameter space for the costs of high recombination rates that produces results consistent with empirical observations.
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Affiliation(s)
- Austin L Drury
- Department of Biological Sciences, Mississippi State University, Mississippi State, Mississippi, USA
| | - Jean-Francois Gout
- Department of Biological Sciences, Mississippi State University, Mississippi State, Mississippi, USA
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33
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D'Antonio M, Nguyen JP, Arthur TD, Matsui H, D'Antonio-Chronowska A, Frazer KA. Author Correction: Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease. Nat Commun 2023; 14:4370. [PMID: 37474502 PMCID: PMC10359269 DOI: 10.1038/s41467-023-40048-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023] Open
Affiliation(s)
- Matteo D'Antonio
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Timothy D Arthur
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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34
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Raben TG, Lello L, Widen E, Hsu SDH. Biobank-scale methods and projections for sparse polygenic prediction from machine learning. Sci Rep 2023; 13:11662. [PMID: 37468507 PMCID: PMC10356957 DOI: 10.1038/s41598-023-37580-5] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/23/2023] [Indexed: 07/21/2023] Open
Abstract
In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.
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Affiliation(s)
- Timothy G Raben
- Department of Physics and Astronomy, Michigan State University, Michigan, USA.
| | - Louis Lello
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Erik Widen
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Stephen D H Hsu
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
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35
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Schoeler T, Speed D, Porcu E, Pirastu N, Pingault JB, Kutalik Z. Participation bias in the UK Biobank distorts genetic associations and downstream analyses. Nat Hum Behav 2023; 7:1216-1227. [PMID: 37106081 PMCID: PMC10365993 DOI: 10.1038/s41562-023-01579-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [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: 09/27/2022] [Accepted: 03/07/2023] [Indexed: 04/29/2023]
Abstract
While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses (neffective = 94,643 to 102,215) with that from standard genome-wide association analyses (n = 263,464 to 283,749), we found that increasing representativeness led to changes in SNP effect sizes and identified novel SNP associations for 12 traits. While heritability estimates were less impacted by weighting (maximum change in h2, 5%), we found substantial discrepancies for genetic correlations (maximum change in rg, 0.31) and Mendelian randomization estimates (maximum change in βSTD, 0.15) for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.
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Affiliation(s)
- Tabea Schoeler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Department of Clinical, Educational and Health Psychology, University College London, London, UK.
| | - Doug Speed
- Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Eleonora Porcu
- Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nicola Pirastu
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
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36
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Li YJ, Nuytemans K, La JO, Jiang R, Slifer SH, Sun S, Naj A, Gao XR, Martin ER. Identification of novel genes for age-at-onset of Alzheimer's disease by combining quantitative and survival trait analyses. Alzheimers Dement 2023; 19:3148-3157. [PMID: 36738287 DOI: 10.1002/alz.12927] [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: 06/24/2022] [Revised: 12/06/2022] [Accepted: 12/19/2022] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Our understanding of the genetic predisposition for age-at-onset (AAO) of Alzheimer's disease (AD) is limited. Here, we sought to identify genes modifying AAO and examined whether any have sex-specific effects. METHODS Genome-wide association analysis were performed on imputed genetic data of 9219 AD cases and 10,345 controls from 20 cohorts of the Alzheimer's Disease Genetics Consortium. AAO was modeled from cases directly and as a survival outcome. RESULTS We identified 11 genome-wide significant loci (P < 5 × 10-8 ), including six known AD-risk genes and five novel loci, UMAD1, LUZP2, ARFGEF2, DSCAM, and 4q25, affecting AAO of AD. Additionally, 39 suggestive loci showed strong association. Twelve loci showed sex-specific effects on AAO including CD300LG and MLX/TUBG2 for females and MIR4445 for males. DISCUSSION Genes that influence AAO of AD are excellent therapeutic targets for delaying onset of AD. Several loci identified include genes with promising functional implications for AD.
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Affiliation(s)
- Yi-Ju Li
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Karen Nuytemans
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA
- John T. MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Jong Ok La
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Rong Jiang
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Psychiatry and Behavior Science, Duke University School of Medicine, Durham, North Carolina, USA
| | - Susan H Slifer
- John T. MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, Florida, USA
| | - Shuming Sun
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Adam Naj
- Department of Biostatistics, Epidemiology, and Informatics, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Xiaoyi Raymond Gao
- Department of Ophthalmology and Visual Sciences, Division of Human Genetics, The Ohio State University, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA
| | - Eden R Martin
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA
- John T. MacDonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, Florida, USA
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37
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Dahanayaka BA, Martin A. Multi-parental fungal mapping population study to detect genomic regions associated with Pyrenophora teres f. teres virulence. Sci Rep 2023; 13:9804. [PMID: 37328500 PMCID: PMC10275933 DOI: 10.1038/s41598-023-36963-y] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/13/2023] [Indexed: 06/18/2023] Open
Abstract
In recent years multi-parental mapping populations (MPPs) have been widely adopted in many crops to detect quantitative trait loci (QTLs) as this method can compensate for the limitations of QTL analyses using bi-parental mapping populations. Here we report the first multi-parental nested association mapping (MP-NAM) population study used to detect genomic regions associated with host-pathogenic interactions. MP-NAM QTL analyses were conducted on 399 Pyrenophora teres f. teres individuals using biallelic, cross-specific and parental QTL effect models. A bi-parental QTL mapping study was also conducted to compare the power of QTL detection between bi-parental and MP-NAM populations. Using MP-NAM with 399 individuals detected a maximum of eight QTLs with a single QTL effect model whilst only a maximum of five QTLs were detected with an individual bi-parental mapping population of 100 individuals. When reducing the number of isolates in the MP-NAM to 200 individuals the number of QTLs detected remained the same for the MP-NAM population. This study confirms that MPPs such as MP-NAM populations can be successfully used in detecting QTLs in haploid fungal pathogens and that the power of QTL detection with MPPs is greater than with bi-parental mapping populations.
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Affiliation(s)
- Buddhika A Dahanayaka
- Centre for Crop Health, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Anke Martin
- Centre for Crop Health, University of Southern Queensland, Toowoomba, QLD, 4350, Australia.
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38
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Canal GB, Oliveira GF, de Almeida FAN, Péres MZ, Moro GLJ, Dos Santos Oliveira WB, Azevedo CF, Nascimento M, da Silva Ferreira MF, Ferreira A. Genomic studies of the additive and dominant genetic control on production traits of Euterpe edulis fruits. Sci Rep 2023; 13:9795. [PMID: 37328527 PMCID: PMC10276026 DOI: 10.1038/s41598-023-36970-z] [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: 10/28/2022] [Accepted: 06/13/2023] [Indexed: 06/18/2023] Open
Abstract
In forest genetic improvement programs for non-domesticated species, limited knowledge of kinship can compromise or make the estimation of variance components and genetic parameters of traits of interest unfeasible. We used mixed models and genomics (in the latter, considering additive and non-additive effects) to evaluate the genetic architecture of 12 traits in juçaizeiro for fruit production. A population of 275 genotypes without genetic relationship knowledge was phenotyped over three years and genotyped by whole genome SNP markers. We have verified superiority in the quality of the fits, the prediction accuracy for unbalanced data, and the possibility of unfolding the genetic effects into their additive and non-additive terms in the genomic models. Estimates of the variance components and genetic parameters obtained by the additive models may be overestimated since, when considering the dominance effect in the model, there are substantial reductions in them. The number of bunches, fresh fruit mass of bunch, rachis length, fresh mass of 25 fruits, and amount of pulp were strongly influenced by the dominance effect, showing that genomic models with such effect should be considered for these traits, which may result in selective improvements by being able to return more accurate genomic breeding values. The present study reveals the additive and non-additive genetic control of the evaluated traits and highlights the importance of genomic information-based approaches for populations without knowledge of kinship and experimental design. Our findings underscore the critical role of genomic data in elucidating the genetic control architecture of quantitative traits, thereby providing crucial insights for driving species' genetic improvement.
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Affiliation(s)
- Guilherme Bravim Canal
- Department of Agronomy, Federal University of Espírito Santo, Alegre, Espírito Santo, 29500-000, Brazil
| | | | | | - Marcello Zatta Péres
- Department of Agronomy, Federal University of Espírito Santo, Alegre, Espírito Santo, 29500-000, Brazil
| | | | | | | | - Moysés Nascimento
- Department of Statistics, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | - Adésio Ferreira
- Department of Agronomy, Federal University of Espírito Santo, Alegre, Espírito Santo, 29500-000, Brazil
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Yu J, Zhu C, Xuan W, An H, Tian Y, Wang B, Chi W, Chen G, Ge Y, Li J, Dai Z, Liu Y, Sun Z, Xu D, Wang C, Wan J. Genome-wide association studies identify OsWRKY53 as a key regulator of salt tolerance in rice. Nat Commun 2023; 14:3550. [PMID: 37321989 PMCID: PMC10272163 DOI: 10.1038/s41467-023-39167-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 01/10/2021] [Accepted: 06/01/2023] [Indexed: 06/17/2023] Open
Abstract
Salinity stress progressively reduces plant growth and productivity, while plant has developed complex signaling pathways to confront salt stress. However, only a few genetic variants have been identified to mediate salt tolerance in the major crop rice, and the molecular mechanism remains poorly understood. Here, we identify ten candidate genes associated with salt-tolerance (ST) traits by performing a genome-wide association analysis in rice landraces. We characterize two ST-related genes, encoding transcriptional factor OsWRKY53 and Mitogen-activated protein Kinase Kinase OsMKK10.2, that mediate root Na+ flux and Na+ homeostasis. We further find that OsWRKY53 acts as a negative modulator regulating expression of OsMKK10.2 in promoting ion homeostasis. Furthermore, OsWRKY53 trans-represses OsHKT1;5 (high-affinity K+ transporter 1;5), encoding a sodium transport protein in roots. We show that the OsWRKY53-OsMKK10.2 and OsWRKY53-OsHKT1;5 module coordinate defenses against ionic stress. The results shed light on the regulatory mechanisms underlying plant salt tolerance.
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Affiliation(s)
- Jun Yu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Chengsong Zhu
- Department of Immunology, The University of Texas Southwestern Medical Centre, Dallas, TX, 75390, USA
| | - Wei Xuan
- MOA Key Laboratory of Plant Nutrition and Fertilization in Lower-Middle Reaches of the Yangtze River, Nanjing Agricultural University, Nanjing, 210095, China
| | - Hongzhou An
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Yunlu Tian
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Baoxiang Wang
- Lianyungang Academy of Agricultural Science, Lianyungang, Jiangsu, 222006, China
| | - Wenchao Chi
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Gaoming Chen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Yuwei Ge
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Jin Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Zhaoyang Dai
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China
| | - Yan Liu
- Lianyungang Academy of Agricultural Science, Lianyungang, Jiangsu, 222006, China
| | - Zhiguang Sun
- Lianyungang Academy of Agricultural Science, Lianyungang, Jiangsu, 222006, China
| | - Dayong Xu
- Lianyungang Academy of Agricultural Science, Lianyungang, Jiangsu, 222006, China
| | - Chunming Wang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China.
- Southern Japonica Rice R&D Corporation Ltd, Key Laboratory of Biology, Genetics and Breeding of Japonica Rice in the Mid-lower Yangtze River, Ministry of Agriculture, Nanjing, 210095, China.
| | - Jianmin Wan
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Centre for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China.
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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Kang Y, Choi C, Kim JY, Min KD, Kim C. Optimizing genomic selection of agricultural traits using K-wheat core collection. Front Plant Sci 2023; 14:1112297. [PMID: 37389296 PMCID: PMC10303932 DOI: 10.3389/fpls.2023.1112297] [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: 11/30/2022] [Accepted: 02/02/2023] [Indexed: 07/01/2023]
Abstract
The agricultural traits that constitute basic plant breeding information are usually quantitative or complex in nature. This quantitative and complex combination of traits complicates the process of selection in breeding. This study examined the potential of genome-wide association studies (GWAS) and genomewide selection (GS) for breeding ten agricultural traits by using genome-wide SNPs. As a first step, a trait-associated candidate marker was identified by GWAS using a genetically diverse 567 Korean (K)-wheat core collection. The accessions were genotyped using an Axiom® 35K wheat DNA chip, and ten agricultural traits were determined (awn color, awn length, culm color, culm length, ear color, ear length, days to heading, days to maturity, leaf length, and leaf width). It is essential to sustain global wheat production by utilizing accessions in wheat breeding. Among the traits associated with awn color and ear color that showed a high positive correlation, a SNP located on chr1B was significantly associated with both traits. Next, GS evaluated the prediction accuracy using six predictive models (G-BLUP, LASSO, BayseA, reproducing kernel Hilbert space, support vector machine (SVM), and random forest) and various training populations (TPs). With the exception of the SVM, all statistical models demonstrated a prediction accuracy of 0.4 or better. For the optimization of the TP, the number of TPs was randomly selected (10%, 30%, 50% and 70%) or divided into three subgroups (CC-sub 1, CC-sub 2 and CC-sub 3) based on the subpopulation structure. Based on subgroup-based TPs, better prediction accuracy was found for awn color, culm color, culm length, ear color, ear length, and leaf width. A variety of Korean wheat cultivars were used for validation to evaluate the prediction ability of populations. Seven out of ten cultivars showed phenotype-consistent results based on genomics-evaluated breeding values (GEBVs) calculated by the reproducing kernel Hilbert space (RKHS) predictive model. Our research provides a basis for improving complex traits in wheat breeding programs through genomics assisted breeding. The results of our research can be used as a basis for improving wheat breeding programs by using genomics-assisted breeding.
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Affiliation(s)
- Yuna Kang
- Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
| | - Changhyun Choi
- Wheat Research Team, National Institution Crop Sciences, Wanju-gun, Republic of Korea
| | - Jae Yoon Kim
- Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Kyeong Do Min
- Department of Plant Resources, Kongju National University, Yesan, Republic of Korea
| | - Changsoo Kim
- Department of Crop Science, Chungnam National University, Daejeon, Republic of Korea
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon, Republic of Korea
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Hongo JA, de Castro GM, Albuquerque Menezes AP, Rios Picorelli AC, Martins da Silva TT, Imada EL, Marchionni L, Del-Bem LE, Vieira Chaves A, Almeida GMDF, Campelo F, Lobo FP. CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species. Patterns (N Y) 2023; 4:100728. [PMID: 37409050 PMCID: PMC10318336 DOI: 10.1016/j.patter.2023.100728] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 03/15/2023] [Indexed: 07/07/2023]
Abstract
Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture.
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Affiliation(s)
- Jorge Augusto Hongo
- Instituto de Computação, Universidade Estadual de Campinas, Campinas, Sao Paulo 13083-872, Brazil
| | - Giovanni Marques de Castro
- Department of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Alison Pelri Albuquerque Menezes
- Department of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Agnello César Rios Picorelli
- Department of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Thieres Tayroni Martins da Silva
- Department of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Eddie Luidy Imada
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Luiz-Eduardo Del-Bem
- Department of Botany, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Anderson Vieira Chaves
- Department of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
| | - Gabriel Magno de Freitas Almeida
- Faculty of Biosciences, Fisheries and Economics, Norwegian College of Fishery Science, UiT The Arctic University of Norway, 9019 Tromsø, Norway
| | - Felipe Campelo
- Department of Computer Science, College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK
| | - Francisco Pereira Lobo
- Department of Genetics, Ecology and Evolution, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-901, Brazil
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Hellwege JN, Stallings SC, Piekos JA, Jasper EA, Aronoff DM, Edwards TL, Velez Edwards DR. Association of genetically-predicted placental gene expression with adult blood pressure traits. J Hypertens 2023; 41:1024-1032. [PMID: 37016918 PMCID: PMC10287061 DOI: 10.1097/hjh.0000000000003427] [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] [Indexed: 04/06/2023]
Abstract
OBJECTIVE Blood pressure is a complex, polygenic trait, and the need to identify prehypertensive risks and new gene targets for blood pressure control therapies or prevention continues. We hypothesize a developmental origins model of blood pressure traits through the life course where the placenta is a conduit mediating genomic and nongenomic transmission of disease risk. Genetic control of placental gene expression has recently been described through expression quantitative trait loci (eQTL) studies which have identified associations with childhood phenotypes. METHODS We conducted a transcriptome-wide gene expression analysis estimating the predicted gene expression of placental tissue in adult individuals with genome-wide association study (GWAS) blood pressure summary statistics. We constructed predicted expression models of 15 154 genes from reference placenta eQTL data and investigated whether genetically-predicted gene expression in placental tissue is associated with blood pressure traits using published GWAS summary statistics. Functional annotation of significant genes was generated using FUMA. RESULTS We identified 18, 9, and 21 genes where predicted expression in placenta was significantly associated with systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP), respectively. There were 14 gene-tissue associations (13 unique genes) significant only in placenta. CONCLUSIONS In this meta-analysis using S-PrediXcan and GWAS summary statistics, the predicted expression in placenta of 48 genes was statistically significantly associated with blood pressure traits. Notable findings included the association of FGFR1 expression with increased SBP and PP. This evidence of gene expression variation in placenta preceding the onset of adult blood pressure phenotypes is an example of extreme preclinical biological changes which may benefit from intervention.
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Affiliation(s)
- Jacklyn N Hellwege
- Department of Medicine, Division of Genetic Medicine
- Vanderbilt Genetics Institute
| | - Sarah C Stallings
- Department of Medicine, Division of Genetic Medicine
- Vanderbilt Genetics Institute
| | - Jacqueline A Piekos
- Vanderbilt Genetics Institute
- Department of Obstetrics and Gynecology, Division of Quantitative Sciences
| | - Elizabeth A Jasper
- Department of Obstetrics and Gynecology, Division of Quantitative Sciences
| | - David M Aronoff
- Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Todd L Edwards
- Vanderbilt Genetics Institute
- Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute
- Department of Obstetrics and Gynecology, Division of Quantitative Sciences
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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43
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Little P, Liu S, Zhabotynsky V, Li Y, Lin DY, Sun W. A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data. Nat Commun 2023; 14:3030. [PMID: 37231002 PMCID: PMC10212972 DOI: 10.1038/s41467-023-38795-w] [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: 04/21/2022] [Accepted: 05/16/2023] [Indexed: 05/27/2023] Open
Abstract
Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.
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Affiliation(s)
- Paul Little
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Si Liu
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Vasyl Zhabotynsky
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wei Sun
- Biostatistics Program, Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
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Jung H, Jung HU, Baek EJ, Chung JY, Kwon SY, Kang JO, Lim JE, Oh B. Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits. Front Genet 2023; 14:1150889. [PMID: 37229196 PMCID: PMC10203621 DOI: 10.3389/fgene.2023.1150889] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R 2 value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.
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Affiliation(s)
- Hyein Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | | | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
- Mendel, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
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45
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Larios E, Ramirez-Parada TH, Mazer SJ. Heritability and variance components of seed size in wild species: influences of breeding design and the number of genotypes tested. Heredity (Edinb) 2023; 130:251-258. [PMID: 36781978 PMCID: PMC10076259 DOI: 10.1038/s41437-023-00597-7] [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: 07/15/2021] [Revised: 01/25/2023] [Accepted: 02/01/2023] [Indexed: 02/15/2023] Open
Abstract
Seed size affects individual fitness in wild plant populations, but its ability to evolve may be limited by low narrow-sense heritability (h2). h2 is estimated as the proportion of total phenotypic variance (σ2P) attributable to additive genetic variance (σ2A), so low values of h2 may be due to low σ2A (potentially eroded by natural selection) or to high values of the other factors that contribute to σ2P, such as extranuclear maternal effects (m2) and environmental variance effects (e2). Here, we reviewed the published literature and performed a meta-analysis to determine whether h2 of seed size is routinely low in wild populations and, if so, which components of σ2P contribute most strongly to total phenotypic variance. We analyzed available estimates of narrow-sense heritability (h2) of seed size, as well as the variance components contributing to these parameters. Maternal and environmental components of σ2P were significantly greater than σ2A, dominance, paternal, and epistatic components. These results suggest that low h2 of seed size in wild populations (the mean value observed in this study was 0.13) is due to both high values of maternally derived and environmental (residual) σ2, and low values of σ2A in seed size. The type of breeding design used to estimate h2 and m2 also influenced their values, with studies using diallel designs generating lower variance ratios than nested and other designs. e2 was not influenced by breeding design. For some breeding designs, the number of genotypes included in a study also influenced the resulting h2 and e2 estimates, but not m2. Our data support the view that a diallel design is better suited than the alternatives for the accurate estimation of σ2A in seed size due to its factorial design and the inclusion of reciprocal crosses, which allows the independent estimation of both additive and non-additive components of variance.
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Affiliation(s)
- Eugenio Larios
- Department of Ecology Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA.
- Programa Educativo de Licenciado en Ecología, Universidad Estatal de Sonora, Hermosillo, Sonora, Mexico.
| | - Tadeo H Ramirez-Parada
- Department of Ecology Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Susan J Mazer
- Department of Ecology Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA
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Tellier LCAM, Treff NR, Hsu SDH, Lello L, Widen E. Scientific refutation of ESHG statement on embryo selection. Eur J Hum Genet 2023; 31:278. [PMID: 36503927 PMCID: PMC9995478 DOI: 10.1038/s41431-022-01237-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/13/2022] [Indexed: 12/14/2022] Open
Affiliation(s)
| | | | | | - Louis Lello
- Genomic Prediction, North Brunswick, NJ, USA
| | - Erik Widen
- Genomic Prediction, North Brunswick, NJ, USA
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47
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D'Antonio M, Nguyen JP, Arthur TD, Matsui H, D'Antonio-Chronowska A, Frazer KA. Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease. Nat Commun 2023; 14:1132. [PMID: 36854752 PMCID: PMC9975214 DOI: 10.1038/s41467-023-36638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 02/10/2023] [Indexed: 03/02/2023] Open
Abstract
The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion.
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Affiliation(s)
- Matteo D'Antonio
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Timothy D Arthur
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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48
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Zhang L, Wang Z, Zhu Z, Yang Q, Cheng C, Zhao S, Liu C, Zhao J. A genome-wide association study identified new variants associated with mathematical abilities in Chinese children. Genes Brain Behav 2023; 22:e12843. [PMID: 36811322 PMCID: PMC10067424 DOI: 10.1111/gbb.12843] [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] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 01/15/2023] [Accepted: 01/25/2023] [Indexed: 02/24/2023]
Abstract
Mathematical ability is moderately heritable, and it is a complex trait which can be evaluated in several different categories. A few genetic studies have been published on general mathematical ability. However, no genetic study focused on specific mathematical ability categories. In this study, we separately performed genome-wide association studies on 11 mathematical ability categories in 1146 students from Chinese elementary schools. We identified seven genome-wide significant single nucleotide polymorphisms (SNPs) with strong linkage disequilibrium among each other (all r2 > 0.8) associated with mathematical reasoning ability (top SNP: rs34034296, p = 2.01 × 10-8 , nearest gene: CUB and Sushi multiple domains 3, CSMD3). We replicated one SNP (rs133885) from 585 SNPs previously reported to be associated with general mathematical ability associated with division ability in our data (p = 1.053 × 10-5 ). In the gene- and gene-set enrichment analysis by MAGMA, we found three significant enrichments of associations with three mathematical ability categories for three genes (LINGO2, OAS1 and HECTD1). We also observed four significant enrichments of associations with four mathematical ability categories for three gene sets. Our results suggest new candidate genetic loci for the genetics of mathematical ability.
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Affiliation(s)
- Liming Zhang
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Zhengjun Wang
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Zijian Zhu
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Qing Yang
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Chen Cheng
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Shunan Zhao
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
| | - Chunyu Liu
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China.,Department of Psychiatry, SUNY Upstate Medical University, Syracuse, New York, USA
| | - Jingjing Zhao
- School of Psychology, Shaanxi Normal University, and Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, Xi'an, China
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49
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Gupta Y, Ernst AL, Vorobyev A, Beltsiou F, Zillikens D, Bieber K, Sanna-Cherchi S, Christiano AM, Sadik CD, Ludwig RJ, Sezin T. Impact of diet and host genetics on the murine intestinal mycobiome. Nat Commun 2023; 14:834. [PMID: 36788222 DOI: 10.1038/s41467-023-36479-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 02/01/2023] [Indexed: 02/16/2023] Open
Abstract
The mammalian gut is home to a diverse microbial ecosystem, whose composition affects various physiological traits of the host. Next-generation sequencing-based metagenomic approaches demonstrated how the interplay of host genetics, bacteria, and environmental factors shape complex traits and clinical outcomes. However, the role of fungi in these complex interactions remains understudied. Here, using 228 males and 363 females from an advanced-intercross mouse line, we provide evidence that fungi are regulated by host genetics. In addition, we map quantitative trait loci associated with various fungal species to single genes in mice using whole genome sequencing and genotyping. Moreover, we show that diet and its' interaction with host genetics alter the composition of fungi in outbred mice, and identify fungal indicator species associated with different dietary regimes. Collectively, in this work, we uncover an association of the intestinal fungal community with host genetics and a regulatory role of diet in this ecological niche.
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50
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Yu Q, Liu X, Keller MP, Navarrete-Perea J, Zhang T, Fu S, Vaites LP, Shuken SR, Schmid E, Keele GR, Li J, Huttlin EL, Rashan EH, Simcox J, Churchill GA, Schweppe DK, Attie AD, Paulo JA, Gygi SP. Sample multiplexing-based targeted pathway proteomics with real-time analytics reveals the impact of genetic variation on protein expression. Nat Commun 2023; 14:555. [PMID: 36732331 PMCID: PMC9894840 DOI: 10.1038/s41467-023-36269-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Targeted proteomics enables hypothesis-driven research by measuring the cellular expression of protein cohorts related by function, disease, or class after perturbation. Here, we present a pathway-centric approach and an assay builder resource for targeting entire pathways of up to 200 proteins selected from >10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput by 16-fold. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, a list of up to 200 proteins, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We apply GoDig to quantify the impact of genetic variation on protein expression in mice fed a high-fat diet. We create several GoDig assays to quantify the expression of multiple protein families (kinases, lipid metabolism- and lipid droplet-associated proteins) across 480 fully-genotyped Diversity Outbred mice, revealing protein quantitative trait loci and establishing potential linkages between specific proteins and lipid homeostasis.
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Affiliation(s)
- Qing Yu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Xinyue Liu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Mark P Keller
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Tian Zhang
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sipei Fu
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Laura P Vaites
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Steven R Shuken
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Ernst Schmid
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Edrees H Rashan
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Judith Simcox
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | | | - Devin K Schweppe
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Alan D Attie
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA.
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