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Giollabhui NM, Slaney C, Hemani G, Foley E, van der Most P, Nolte I, Snieder H, Davey Smith G, Khandaker G, Hartman C. Role of Inflammation in Depressive and Anxiety Disorders, Affect, and Cognition: Genetic and Non-Genetic Findings in the Lifelines Cohort Study. RESEARCH SQUARE 2024:rs.3.rs-4379779. [PMID: 39149475 PMCID: PMC11326402 DOI: 10.21203/rs.3.rs-4379779/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Inflammation is associated with a range of neuropsychiatric symptoms; however, the nature of the causal relationship is unclear. We used complementary non-genetic, genetic risk score (GRS), and Mendelian randomization (MR) analyses to examine whether inflammatory markers are associated with affect, depressive and anxiety disorders, and cognition. We tested in ≈ 55,098 (59% female) individuals from the Dutch Lifelines cohort the concurrent/prospective associations of C-reactive protein (CRP) with: depressive and anxiety disorders; positive/negative affect; and attention, psychomotor speed, episodic memory, and executive functioning. Additionally, we examined the association between inflammatory GRSs (CRP, interleukin-6 [IL-6], IL-6 receptor [IL-6R and soluble IL-6R (sIL-6R)], glycoprotein acetyls [GlycA]) on these same outcomes (Nmax=57,946), followed by MR analysis examining evidence of causality of CRP on outcomes (Nmax=23,268). In non-genetic analyses, higher CRP was associated with a depressive disorder, lower positive/higher negative affect, and worse executive function, attention, and psychomotor speed after adjusting for potential confounders. In genetic analyses, CRPgrs was associated with any anxiety disorder (β = 0.002, p = 0.037) whereas GlycAGRS was associated with major depressive disorder (β = 0.001, p = 0.036). Both CRPgrs (β = 0.006, p = 0.035) and GlycAGRS (β = 0.006, p = 0.049) were associated with greater negative affect. Inflammatory GRSs were not associated with cognition, except slL-6RGRS which was associated with poorer memory (β=-0.009, p = 0.018). There was weak evidence for a CRP-anxiety association using MR (β = 0.12; p = 0.054). Genetic and non-genetic analyses provide consistent evidence for an association between CRP and negative affect. These results suggest that dysregulated immune physiology may impact a broad range of trans-diagnostic affective symptoms.
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Affiliation(s)
| | | | | | | | | | - Ilja Nolte
- University of Groningen, University Medical Center Groningen
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Cui D, Li L, Yu N, Xiong S, Xiao S, Zheng H, Huang Z, Guo Y, Huang L. Phenotypic correlations of carpal gland diverticular number with production traits and its genome-wide association analysis in multiple pig populations. Anim Genet 2024; 55:396-403. [PMID: 38380686 DOI: 10.1111/age.13407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 01/30/2024] [Accepted: 02/01/2024] [Indexed: 02/22/2024]
Abstract
Pig carpal glands play crucial roles in territorial recognition, reproductive behavior, and information exchange; however, their effects on production traits and underlying genetic mechanisms remain unclear. In this study, 1028 pigs from six populations were counted for the carpal gland diverticular numbers (CGDNs) on the left (CGDNL) and right (CGDNR) legs, and their carcass and meat quality traits were assessed. The CGDNs were significantly different among the populations, and Licha Black pigs had a lower CGDN than the Bama Xiang breed. It was also significantly different between sexes, with males having more diverticula than females (p ≤ 0.0391). Moreover, the number was asymmetric, with CGDNR being significantly higher than CGDNL. Notably, CGDNs was significantly correlated with each other in phenotype and genetics and with 24-h pH, 24-h meat color score, 24-h marbling score, fat content, moisture content, sodium salt content, and saturated fatty acid content in phenotype. Furthermore, genome-wide association analyses identified seven SNPs in association with CGDNs at a 5% genome-wide significance level, all of which were located in a 1.78-Mb (35.347-37.129 Mb) region on chromosome 1. CNC10010837 and CNC10010840 were the top SNPs: both had an additive effect of 0.789 ± 0.120 on CGDNR with p = 8.31E-10. These findings provide important insights into the functions and underlying genetic mechanisms of swine carpal glands.
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Affiliation(s)
- Dengshuai Cui
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Longyun Li
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Naibiao Yu
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Sanya Xiong
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Shijun Xiao
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Hao Zheng
- Jiangxi Shanxia Huaxi Pig Breeding Company Limited, Ganzhou, Jiangxi, China
| | - Zhiyong Huang
- Jiangxi Shanxia Huaxi Pig Breeding Company Limited, Ganzhou, Jiangxi, China
| | - Yuanmei Guo
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi, China
| | - Lusheng Huang
- National Key Laboratory of Pig Genetic Improvement and Germplasm Innovation, Ministry of Science and Technology of China, Jiangxi Agricultural University, Nanchang, Jiangxi, China
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Giollabhui NM, Slaney C, Hemani G, Foley ÉM, van der Most PJ, Nolte IM, Snieder H, Smith GD, Khandaker G, Hartman CA. Role of Inflammation in Depressive and Anxiety Disorders, Affect, and Cognition: Genetic and Non-Genetic Findings in the Lifelines Cohort Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.17.24305950. [PMID: 38699368 PMCID: PMC11065023 DOI: 10.1101/2024.04.17.24305950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Background Low-grade systemic inflammation is implicated in the pathogenesis of various neuropsychiatric conditions affecting mood and cognition. While much of the evidence concerns depression, large-scale population studies of anxiety, affect, and cognitive function are scarce. Importantly, causality remains unclear. We used complementary non-genetic, genetic risk score (GRS), and Mendelian randomization (MR) analyses to examine whether inflammatory markers are associated with affect, depressive and anxiety disorders, and cognitive performance in the Lifelines Cohort; and whether associations are likely to be causal. Methods Using data from up to 55,098 (59% female) individuals from the Dutch Lifelines cohort, we tested the cross-sectional and longitudinal associations of C-reactive protein (CRP) with (i) depressive and anxiety disorders; (ii) positive and negative affect scores, and (iii) five cognitive measures assessing attention, psychomotor speed, episodic memory, and executive functioning (figural fluency and working memory). Additionally, we examined the association between inflammatory marker GRSs (CRP, interleukin-6 [IL-6], IL-6 receptor [IL-6R and soluble IL-6R (sIL-6R)], glycoprotein acetyls [GlycA]) on these same outcomes (Nmax=57,946), followed by MR analysis examining evidence of causality of CRP on outcomes (Nmax=23,268). In genetic analyses, all GRSs and outcomes were z-transformed. Results In non-genetic analyses, higher CRP was associated with diagnosis of any depressive disorder, lower positive and higher negative affect scores, and worse performance on tests of figural fluency, attention, and psychomotor speed after adjusting for potential confounders, although the magnitude of these associations was small. In genetic analyses, CRPGRS was associated with any anxiety disorder (β=0.002, p=0.037, N=57,047) whereas GlycAGRS was associated with major depressive disorder (β=0.001, p=0.036; N=57,047). Both CRPGRS (β=0.006, p=0.035, N=57,946) and GlycAGRS (β=0.006, p=0.049; N=57,946) were associated with higher negative affect score. Inflammatory marker GRSs were not associated with cognitive performance, except sIL-6RGRS which was associated with poorer memory performance (β=-0.009, p=0.018, N=36,783). Further examination of the CRP-anxiety association using MR provided some weak evidence of causality (β=0.12; p=0.054). Conclusions Genetic and non-genetic analyses provide consistent evidence for an association between CRP and negative affect. Genetic analyses suggest that IL-6 signaling could be relevant for memory, and that the association between CRP and anxiety disorders could be causal. These results suggest that dysregulated immune physiology may impact a broad range of trans-diagnostic affective symptoms. However, given the small effect sizes and multiple tests conducted, future studies are required to investigate whether effects are moderated by sub-groups and whether these findings replicate in other cohorts.
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Affiliation(s)
- Naoise Mac Giollabhui
- Depression Clinical & Research Program, Department of Psychiatry, Massachusetts General Hospital, USA
| | - Chloe Slaney
- MRC Integrative Epidemiology Unit at the University of Bristol, UK; Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit at the University of Bristol, UK
| | - Éimear M. Foley
- MRC Integrative Epidemiology Unit at the University of Bristol, UK; Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Ilja M. Nolte
- University of Groningen, University Medical Center Groningen, the Netherlands
| | - Harold Snieder
- University of Groningen, University Medical Center Groningen, the Netherlands
| | | | - Golam Khandaker
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK; Centre for Academic Mental Health, Bristol Medical School, University of Bristol, Bristol, UK; NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK; Avon and Wiltshire Mental Health Partnership NHS Trust, Bristol, UK
| | - Catharina A. Hartman
- Interdisciplinary Center Psychopathology and Emotion Regulation, Department of Psychiatry, University Medical Center Groningen, University of Groningen, the Netherlands
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Haque MA, Alam MZ, Iqbal A, Lee YM, Dang CG, Kim JJ. Genome-Wide Association Studies for Body Conformation Traits in Korean Holstein Population. Animals (Basel) 2023; 13:2964. [PMID: 37760364 PMCID: PMC10526087 DOI: 10.3390/ani13182964] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/15/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
The objective of this study was to identify quantitative trait loci (QTL) and nearby candidate genes that influence body conformation traits. Phenotypic data for 24 body conformation traits were collected from a population of 2329 Korean Holstein cattle, and all animals were genotyped using the 50 K Illumina bovine SNP chip. A total of 24 genome-wide significant SNPs associated with 24 body conformation traits were identified by genome-wide association analysis. The selection of the most promising candidate genes was based on gene ontology (GO) terms and the previously identified functions that influence various body conformation traits as determined in our study. These genes include KCNA1, RYBP, PTH1R, TMIE, and GNAI3 for body traits; ANGPT1 for rump traits; MALRD1, INHBA, and HOXA13 for feet and leg traits; and CDK1, RHOBTB1, and SLC17A1 for udder traits, respectively. These findings contribute to our understanding of the genetic basis of body conformation traits in this population and pave the way for future breeding strategies aimed at enhancing desirable traits in dairy cattle.
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Affiliation(s)
- Md Azizul Haque
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea; (M.A.H.); (M.Z.A.); (A.I.); (Y.-M.L.)
| | - Mohammad Zahangir Alam
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea; (M.A.H.); (M.Z.A.); (A.I.); (Y.-M.L.)
| | - Asif Iqbal
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea; (M.A.H.); (M.Z.A.); (A.I.); (Y.-M.L.)
| | - Yun-Mi Lee
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea; (M.A.H.); (M.Z.A.); (A.I.); (Y.-M.L.)
| | - Chang-Gwon Dang
- Animal Breeding and Genetics Division, National Institute of Animal Science, Cheonan 31000, Chungcheongnam-do, Republic of Korea
| | - Jong-Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Gyeongbuk, Republic of Korea; (M.A.H.); (M.Z.A.); (A.I.); (Y.-M.L.)
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Valente BD, de los Campos G, Grueneberg A, Chen CY, Ros-Freixedes R, Herring WO. Using residual regressions to quantify and map signal leakage in genomic prediction. Genet Sel Evol 2023; 55:57. [PMID: 37550618 PMCID: PMC10405418 DOI: 10.1186/s12711-023-00830-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/12/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Most genomic prediction applications in animal breeding use genotypes with tens of thousands of single nucleotide polymorphisms (SNPs). However, modern sequencing technologies and imputation algorithms can generate ultra-high-density genotypes (including millions of SNPs) at an affordable cost. Empirical studies have not produced clear evidence that using ultra-high-density genotypes can significantly improve prediction accuracy. However, (whole-genome) prediction accuracy is not very informative about the ability of a model to capture the genetic signals from specific genomic regions. To address this problem, we propose a simple methodology that detects chromosome regions for which a specific model (e.g., single-step genomic best linear unbiased prediction (ssGBLUP)) may fail to fully capture the genetic signal present in such segments-a phenomenon that we refer to as signal leakage. We propose to detect regions with evidence of signal leakage by testing the association of residuals from a pedigree or a genomic model with SNP genotypes. We discuss how this approach can be used to map regions with signals that are poorly captured by a model and to identify strategies to fix those problems (e.g., using a different prior or increasing marker density). Finally, we explored the proposed approach to scan for signal leakage of different models (pedigree-based, ssGBLUP, and various Bayesian models) applied to growth-related phenotypes (average daily gain and backfat thickness) in pigs. RESULTS We report widespread evidence of signal leakage for pedigree-based models. Including a percentage of animals with SNP data in ssGBLUP reduced the extent of signal leakage. However, local peaks of missed signals remained in some regions, even when all animals were genotyped. Using variable selection priors solves leakage points that are caused by excessive shrinkage of marker effects. Nevertheless, these models still miss signals in some regions due to low linkage disequilibrium between the SNPs on the array used and causal variants. Thus, we discuss how such problems could be addressed by adding sequence SNPs from those regions to the prediction model. CONCLUSIONS Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.
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Affiliation(s)
| | - Gustavo de los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI USA
- Department of Statistics and Probability, Michigan State University, East Lansing, MI USA
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI USA
| | - Alexander Grueneberg
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI USA
| | - Ching-Yi Chen
- The Pig Improvement Company, Genus Plc, Hendersonville, TN USA
| | - Roger Ros-Freixedes
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland, UK
- Departament de Ciència Animal, Universitat de Lleida-Agrotecnio-CERCA Center, Lleida, Spain
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Hou Z, Ochoa A. Genetic association models are robust to common population kinship estimation biases. Genetics 2023; 224:iyad030. [PMID: 36843304 PMCID: PMC10474929 DOI: 10.1093/genetics/iyad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/08/2022] [Accepted: 02/17/2023] [Indexed: 02/28/2023] Open
Abstract
Common genetic association models for structured populations, including principal component analysis (PCA) and linear mixed-effects models (LMMs), model the correlation structure between individuals using population kinship matrices, also known as genetic relatedness matrices. However, the most common kinship estimators can have severe biases that were only recently determined. Here we characterize the effect of these kinship biases on genetic association. We employ a large simulated admixed family and genotypes from the 1000 Genomes Project, both with simulated traits, to evaluate key kinship estimators. Remarkably, we find practically invariant association statistics for kinship matrices of different bias types (matching all other features). We then prove using statistical theory and linear algebra that LMM association tests are invariant to these kinship biases, and PCA approximately so. Our proof shows that the intercept and relatedness effect coefficients compensate for the kinship bias, an argument that extends to generalized linear models. As a corollary, association testing is also invariant to changing the reference ancestral population of the kinship matrix. Lastly, we observed that all kinship estimators, except for popkin ratio-of-means, can give improper non-positive semidefinite matrices, which can be problematic although some LMMs handle them surprisingly well, and condition numbers can be used to choose kinship estimators. Overall, we find that existing association studies are robust to kinship estimation bias, and our calculations may help improve association methods by taking advantage of this unexpected robustness, as well as help determine the effects of kinship bias in related problems.
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Affiliation(s)
- Zhuoran Hou
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA
| | - Alejandro Ochoa
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA
- Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC 27705, USA
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Defo J, Awany D, Ramesar R. From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? Brief Bioinform 2023; 24:6972298. [PMID: 36611240 DOI: 10.1093/bib/bbac600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.
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Affiliation(s)
- Joel Defo
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
| | - Denis Awany
- South African Tuberculosis Vaccine Initiative (SATVI), University of Cape Town, 7925, South Africa
| | - Raj Ramesar
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
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Udine E, Jain A, van Blitterswijk M. Advances in sequencing technologies for amyotrophic lateral sclerosis research. Mol Neurodegener 2023; 18:4. [PMID: 36635726 PMCID: PMC9838075 DOI: 10.1186/s13024-022-00593-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/23/2022] [Indexed: 01/14/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is caused by upper and lower motor neuron loss and has a fairly rapid disease progression, leading to fatality in an average of 2-5 years after symptom onset. Numerous genes have been implicated in this disease; however, many cases remain unexplained. Several technologies are being used to identify regions of interest and investigate candidate genes. Initial approaches to detect ALS genes include, among others, linkage analysis, Sanger sequencing, and genome-wide association studies. More recently, next-generation sequencing methods, such as whole-exome and whole-genome sequencing, have been introduced. While those methods have been particularly useful in discovering new ALS-linked genes, methodological advances are becoming increasingly important, especially given the complex genetics of ALS. Novel sequencing technologies, like long-read sequencing, are beginning to be used to uncover the contribution of repeat expansions and other types of structural variation, which may help explain missing heritability in ALS. In this review, we discuss how popular and/or upcoming methods are being used to discover ALS genes, highlighting emerging long-read sequencing platforms and their role in aiding our understanding of this challenging disease.
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Affiliation(s)
- Evan Udine
- grid.417467.70000 0004 0443 9942Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road S, Jacksonville, FL 32224 USA ,grid.417467.70000 0004 0443 9942Mayo Clinic Graduate School of Biomedical Sciences, 4500 San Pablo Road S, Jacksonville, FL 32224 USA
| | - Angita Jain
- grid.417467.70000 0004 0443 9942Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road S, Jacksonville, FL 32224 USA ,grid.417467.70000 0004 0443 9942Mayo Clinic Graduate School of Biomedical Sciences, 4500 San Pablo Road S, Jacksonville, FL 32224 USA ,grid.417467.70000 0004 0443 9942Center for Clinical and Translational Sciences, Mayo Clinic, 4500 San Pablo Road S, Jacksonville, FL 32224 USA
| | - Marka van Blitterswijk
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road S, Jacksonville, FL, 32224, USA.
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Alamin M, Sultana MH, Lou X, Jin W, Xu H. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS. PLANTS (BASEL, SWITZERLAND) 2022; 11:3277. [PMID: 36501317 PMCID: PMC9739826 DOI: 10.3390/plants11233277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.
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Affiliation(s)
- Md. Alamin
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Xiangyang Lou
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
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Zhao Y, Gao J, Guo X, Su B, Wang H, Yang R, Jiang L. Gene-Based Genome-Wide Association Study Identified Genes for Agronomic Traits in Maize. BIOLOGY 2022; 11:1649. [PMID: 36421363 PMCID: PMC9687540 DOI: 10.3390/biology11111649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/05/2022] [Accepted: 11/08/2022] [Indexed: 07/05/2024]
Abstract
A gene integrates the effects of all SNPs in its sequence span, which benefits the genome-wide association study. To explore gene-level variations affecting economic traits in maize, we extended the SNP-based GWAS analysis software Single-RunKing developed by our team to gene-based GWAS, which used the FaST-LMM algorithm to convert the linear mixed model into simple linear model association analysis. An F-test statistic was formulated to test and identify candidate genes. We compared the statistical efficiency of using 80% principal components (EPC), the first principal component (FPC), and all SNP markers (ALLSNP) as independent variables, which predecessors commonly used to integrate SNPs and represent genes. With a Huazhong Agricultural University (HAU) genomic dataset of 2.65M SNPs from 540 maize plants, 34,774 genes were annotated across the whole genome. Genome-wide association studies with 20 agronomic traits were performed using the software developed here. Another maize dataset from the Ames panel (AP) was also analyzed. The EPC method fits the model well and has good statistical efficiency. It not only overcomes the false negative problem when using all SNP markers for analysis (ALLSNP) but also solves the false positive problem of its corresponding simple linear model method EPCLM. Compared with FPC, the EPC method has higher statistical efficiency. A total of 132 quantitative trait genes (QTG) were identified for the 20 traits from HAU maize dataset and one trait of AP maize.
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Affiliation(s)
- Yunfeng Zhao
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China
- General Education College, Weifang University of Science and Technology, Weifang 262700, China
| | - Jin Gao
- Hainan Academy of Ocean and Fisheries Sciences, Haikou 571126, China
| | - Xiugang Guo
- General Education College, Weifang University of Science and Technology, Weifang 262700, China
| | - Baofeng Su
- School of Fisheries, Aquaculture and Aquatic Sciences, Auburn University, Auburn, AL 36849, USA
| | - Haijie Wang
- General Education College, Weifang University of Science and Technology, Weifang 262700, China
| | - Runqing Yang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China
| | - Li Jiang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, China
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Benaouda S, Dadshani S, Koua P, Léon J, Ballvora A. Identification of QTLs for wheat heading time across multiple-environments. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2833-2848. [PMID: 35776141 PMCID: PMC9325850 DOI: 10.1007/s00122-022-04152-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
KEY MESSAGE The genetic response to changing climatic factors selects consistent across the tested environments and location-specific thermo-sensitive and photoperiod susceptible alleles in lower and higher altitudes, respectively, for starting flowering in winter wheat. Wheat breeders select heading date to match the most favorable conditions for their target environments and this is favored by the extensive genetic variation for this trait that has the potential to be further explored. In this study, we used a germplasm with broad geographic distribution and tested it in multi-location field trials across Germany over three years. The genotypic response to the variation in the climatic parameters depending on location and year uncovered the effect of photoperiod and spring temperatures in accelerating heading date in higher and lower latitudes, respectively. Spring temperature dominates other factors in inducing heading, whereas the higher amount of solar radiation delays it. A genome-wide scan of marker-trait associations with heading date detected two QTL: an adapted allele at locus TaHd102 on chromosome 5A that has a consistent effect on HD in German cultivars in multiple environments and a non-adapted allele at locus TaHd044 on chromosome 3A that accelerates flowering by 5.6 days. TaHd102 and TaHd044 explain 13.8% and 33% of the genetic variance, respectively. The interplay of the climatic variables led to the detection of environment specific association responding to temperature in lower latitudes and photoperiod in higher ones. Another locus TaHd098 on chromosome 5A showed epistatic interactions with 15 known regulators of flowering time when non-adapted cultivars from outside Germany were included in the analysis.
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Affiliation(s)
- Salma Benaouda
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, Rheinische Friedrich-Wilhelms-University, Katzenburgweg 5, 53115, Bonn, Germany
| | - Said Dadshani
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, Rheinische Friedrich-Wilhelms-University, Katzenburgweg 5, 53115, Bonn, Germany
| | - Patrice Koua
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, Rheinische Friedrich-Wilhelms-University, Katzenburgweg 5, 53115, Bonn, Germany
| | - Jens Léon
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, Rheinische Friedrich-Wilhelms-University, Katzenburgweg 5, 53115, Bonn, Germany
- Field Lab Campus Klein-Altendorf, Rheinische Friedrich-Wilhelms-University, Bonn, Germany
| | - Agim Ballvora
- Institute for Crop Science and Resource Conservation, Chair of Plant Breeding, Rheinische Friedrich-Wilhelms-University, Katzenburgweg 5, 53115, Bonn, Germany.
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12
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Chakrabarty S, Mufumbo R, Windpassinger S, Jordan D, Mace E, Snowdon RJ, Hathorn A. Genetic and genomic diversity in the sorghum gene bank collection of Uganda. BMC PLANT BIOLOGY 2022; 22:378. [PMID: 35906543 PMCID: PMC9335971 DOI: 10.1186/s12870-022-03770-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/21/2022] [Indexed: 06/01/2023]
Abstract
BACKGROUND The Plant Genetic Resources Centre at the Uganda National Gene Bank houses has over 3000 genetically diverse landraces and wild relatives of Sorghum bicolor accessions. This genetic diversity resource is untapped, under-utilized, and has not been systematically incorporated into sorghum breeding programs. In this study, we characterized the germplasm collection using whole-genome SNP markers (DArTseq). Discriminant analysis of principal components (DAPC) was implemented to study the racial ancestry of the accessions in comparison to a global sorghum diversity set and characterize the sub-groups present in the Ugandan (UG) germplasm. RESULTS Population structure and phylogenetic analysis revealed the presence of five subgroups among the Ugandan accessions. The samples from the highlands of the southwestern region were genetically distinct as compared to the rest of the population. This subset was predominated by the caudatum race and unique in comparison to the other sub-populations. In this study, we detected QTL for juvenile cold tolerance by genome-wide association studies (GWAS) resulting in the identification of 4 markers associated (-log10p > 3) to survival under cold stress under both field and climate chamber conditions, located on 3 chromosomes (02, 06, 09). To our best knowledge, the QTL on Sb09 with the strongest association was discovered for the first time. CONCLUSION This study demonstrates how genebank genomics can potentially facilitate effective and efficient usage of valuable, untapped germplasm collections for agronomic trait evaluation and subsequent allele mining. In face of adverse climate change, identification of genomic regions potentially involved in the adaptation of Ugandan sorghum accessions to cooler climatic conditions would be of interest for the expansion of sorghum production into temperate latitudes.
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Affiliation(s)
| | - Raphael Mufumbo
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
- Uganda National Gene Bank, National Agricultural Research Laboratories, Kampala, Uganda
| | | | - David Jordan
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Warwick, QLD, 4370, Australia
| | - Emma Mace
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Warwick, QLD, 4370, Australia
| | - Rod J Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany.
| | - Adrian Hathorn
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Warwick, QLD, 4370, Australia
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13
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Kenny D, Sleator RD, Murphy CP, Evans RD, Berry DP. Detection of Genomic Imprinting for Carcass Traits in Cattle Using Imputed High-Density Genotype Data. Front Genet 2022; 13:951087. [PMID: 35910233 PMCID: PMC9334527 DOI: 10.3389/fgene.2022.951087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/16/2022] [Indexed: 12/03/2022] Open
Abstract
Genomic imprinting is an epigenetic phenomenon defined as the silencing of an allele, at least partially, at a given locus based on the sex of the transmitting parent. The objective of the present study was to detect the presence of SNP-phenotype imprinting associations for carcass weight (CW), carcass conformation (CC) and carcass fat (CF) in cattle. The data used comprised carcass data, along with imputed, high-density genotype data on 618,837 single nucleotide polymorphisms (SNPs) from 23,687 cattle; all animal genotypes were phased with respect to parent of origin. Based on the phased genotypes and a series of single-locus linear models, 24, 339, and 316 SNPs demonstrated imprinting associations with CW, CC, and CF, respectively. Regardless of the trait in question, no known imprinted gene was located within 0.5 Mb of the SNPs demonstrating imprinting associations in the present study. Since all imprinting associations detected herein were at novel loci, further investigation of these regions may be warranted. Nonetheless, knowledge of these associations might be useful for improving the accuracy of genomic evaluations for these traits, as well as mate allocations systems to exploit the effects of genomic imprinting.
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Affiliation(s)
- David Kenny
- Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
- Department of Biological Sciences, Munster Technological University, Bishopstown Campus, Co. Cork, Ireland
| | - Roy D. Sleator
- Department of Biological Sciences, Munster Technological University, Bishopstown Campus, Co. Cork, Ireland
| | - Craig P. Murphy
- Department of Biological Sciences, Munster Technological University, Bishopstown Campus, Co. Cork, Ireland
| | - Ross D. Evans
- Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland
| | - Donagh P. Berry
- Animal and Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
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14
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Pacheco HA, Rossoni A, Cecchinato A, Peñagaricano F. Deciphering the genetic basis of male fertility in Italian Brown Swiss dairy cattle. Sci Rep 2022; 12:10575. [PMID: 35732705 PMCID: PMC9217806 DOI: 10.1038/s41598-022-14889-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Improving reproductive performance remains a major goal in dairy cattle worldwide. Service sire has been recognized as an important factor affecting herd fertility. The main objective of this study was to reveal the genetic basis of male fertility in Italian Brown Swiss dairy cattle. Dataset included 1102 Italian Brown Swiss bulls with sire conception rate records genotyped with 454k single nucleotide polymorphisms. The analysis included whole-genome scans and gene-set analyses to identify genomic regions, individual genes and genetic mechanisms affecting Brown Swiss bull fertility. One genomic region on BTA1 showed significant additive effects. This region harbors gene RABL3 which is implicated cell proliferation and motility. Two genomic regions, located on BTA6 and BTA26, showed marked non-additive effects. These regions harbor genes, such as WDR19 and ADGRA1, that are directly involved in male fertility, including sperm motility, acrosome reaction, and embryonic development. The gene-set analysis revealed functional terms related to cell adhesion, cellular signaling, cellular transport, immune system, and embryonic development. Remarkably, a gene-set analysis also including Holstein and Jersey data, revealed significant processes that are common to the three dairy breeds, including cell migration, cell-cell interaction, GTPase activity, and the immune function. Overall, this comprehensive study contributes to a better understanding of the genetic basis of male fertility in cattle. In addition, our findings may guide the development of novel genomic strategies for improving service sire fertility in Brown Swiss cattle.
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Affiliation(s)
- Hendyel A Pacheco
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Attilio Rossoni
- Italian Brown Breeders Association, Bussolengo, 37012, Verona, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, 35020, Legnaro, Padua, Italy
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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15
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Odell SG, Hudson AI, Praud S, Dubreuil P, Tixier MH, Ross-Ibarra J, Runcie DE. Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci. G3 (BETHESDA, MD.) 2022; 12:6509518. [PMID: 35100382 PMCID: PMC8895984 DOI: 10.1093/g3journal/jkac011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022]
Abstract
The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.
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Affiliation(s)
- Sarah G Odell
- Department of Plant Sciences, University of California, Davis, CA 95616, USA.,Department of Evolution and Ecology, University of California, Davis, CA 95616, USA
| | - Asher I Hudson
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA
| | - Sébastien Praud
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | - Pierre Dubreuil
- Limagrain, Centre de Recherche de Chappes, Chappes 63720, France
| | | | - Jeffrey Ross-Ibarra
- Department of Evolution and Ecology, University of California, Davis, CA 95616, USA.,Center for Population Biology, University of California, Davis, CA 95616, USA.,Genome Center, University of California, Davis, CA 95616, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
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16
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Novo LC, Cavani L, Pinedo P, Melendez P, Peñagaricano F. Genomic Analysis of Visceral Fat Accumulation in Holstein Cows. Front Genet 2022; 12:803216. [PMID: 35058972 PMCID: PMC8764383 DOI: 10.3389/fgene.2021.803216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/15/2021] [Indexed: 11/30/2022] Open
Abstract
Visceral fat is related to important metabolic processes, including insulin sensitivity and lipid mobilization. The goal of this study was to identify individual genes, pathways, and molecular processes implicated in visceral fat deposition in dairy cows. Data from 172 genotyped Holstein cows classified at slaughterhouse as having low (n = 77; omental fold <5 mm in thickness and minimum fat deposition in omentum) or high (n = 95; omental fold ≥20 mm in thickness and marked fat deposition in omentum) omental fat were analyzed. The identification of regions with significant additive and non-additive genetic effects was performed using a two-step mixed model-based approach. Genomic scans were followed by gene-set analyses in order to reveal the genetic mechanisms controlling abdominal obesity. The association mapping revealed four regions located on BTA19, BTA20 and BTA24 with significant additive effects. These regions harbor genes, such as SMAD7, ANKRD55, and the HOXB family, that are implicated in lipolysis and insulin tolerance. Three regions located on BTA1, BTA13, and BTA24 showed marked non-additive effects. These regions harbor genes MRAP, MIS18A, PRNP and TSHZ1, that are directly implicated in adipocyte differentiation, lipid metabolism, and insulin sensitivity. The gene-set analysis revealed functional terms related to cell arrangement, cell metabolism, cell proliferation, cell signaling, immune response, lipid metabolism, and membrane permeability, among other functions. We further evaluated the genetic link between visceral fat and two metabolic disorders, ketosis, and displaced abomasum. For this, we analyzed 28k records of incidence of metabolic disorders from 14k cows across lactations using a single-step genomic BLUP approach. Notably, the region on BTA20 significantly associated with visceral fat deposition was also associated with the incidence of displaced abomasum. Overall, our findings suggest that visceral fat deposition in dairy cows is controlled by both additive and non-additive effects. We detected at least one region with marked pleiotropic effects affecting both visceral fat accumulation and displaced abomasum.
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Affiliation(s)
- Larissa C Novo
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Ligia Cavani
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
| | - Pablo Pinedo
- Department of Animal Sciences, Colorado State University, Fort Collins, CO, United States
| | - Pedro Melendez
- School of Veterinary Medicine, Texas Tech University, Amarillo, TX, United States
| | - Francisco Peñagaricano
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, United States
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17
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Wang L, Liu Y, Gao L, Yang X, Zhang X, Xie S, Chen M, Wang YH, Li J, Shen Y. Identification of Candidate Forage Yield Genes in Sorghum ( Sorghum bicolor L.) Using Integrated Genome-Wide Association Studies and RNA-Seq. FRONTIERS IN PLANT SCIENCE 2022; 12:788433. [PMID: 35087554 PMCID: PMC8787639 DOI: 10.3389/fpls.2021.788433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/06/2021] [Indexed: 05/26/2023]
Abstract
Genetic dissection of forage yield traits is critical to the development of sorghum as a forage crop. In the present study, association mapping was performed with 85,585 SNP markers on four forage yield traits, namely plant height (PH), tiller number (TN), stem diameter (SD), and fresh weight per plant (FW) among 245 sorghum accessions evaluated in four environments. A total of 338 SNPs or quantitative trait nucleotides (QTNs) were associated with the four traits, and 21 of these QTNs were detected in at least two environments, including four QTNs for PH, ten for TN, six for SD, and one for FW. To identify candidate genes, dynamic transcriptome expression profiling was performed at four stages of sorghum development. One hundred and six differentially expressed genes (DEGs) that were enriched in hormone signal transduction pathways were found in all stages. Weighted gene correlation network analysis for PH and SD indicated that eight modules were significantly correlated with PH and that three modules were significantly correlated with SD. The blue module had the highest positive correlation with PH and SD, and the turquoise module had the highest negative correlation with PH and SD. Eight candidate genes were identified through the integration of genome-wide association studies (GWAS) and RNA sequencing. Sobic.004G143900, an indole-3-glycerol phosphate synthase gene that is involved in indoleacetic acid biosynthesis, was down-regulated as sorghum plants grew in height and was identified in the blue module, and Sobic.003G375100, an SD candidate gene, encoded a DNA repair RAD52-like protein 1 that plays a critical role in DNA repair-linked cell cycle progression. These findings demonstrate that the integrative analysis of omics data is a promising approach to identify candidate genes for complex traits.
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Affiliation(s)
- Lihua Wang
- College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing, China
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Yanlong Liu
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Li Gao
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Xiaocui Yang
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Xu Zhang
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Shaoping Xie
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Meng Chen
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Yi-Hong Wang
- Department of Biology, University of Louisiana at Lafayette, Lafayette, LA, United States
| | - Jieqin Li
- College of Agriculture, Anhui Science and Technology University, Fengyang, China
| | - Yixin Shen
- College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing, China
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18
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Li J, Zhang M, Li X, Khan A, Kumar S, Allan AC, Lin-Wang K, Espley RV, Wang C, Wang R, Xue C, Yao G, Qin M, Sun M, Tegtmeier R, Liu H, Wei W, Ming M, Zhang S, Zhao K, Song B, Ni J, An J, Korban SS, Wu J. Pear genetics: Recent advances, new prospects, and a roadmap for the future. HORTICULTURE RESEARCH 2022; 9:uhab040. [PMID: 35031796 PMCID: PMC8778596 DOI: 10.1093/hr/uhab040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 06/14/2023]
Abstract
Pear, belonging to the genus Pyrus, is one of the most economically important temperate fruit crops. Pyrus is an important genus of the Rosaceae family, subfamily Maloideae, and has at least 22 different species with over 5000 accessions maintained or identified worldwide. With the release of draft whole-genome sequences for Pyrus, opportunities for pursuing studies on the evolution, domestication, and molecular breeding of pear, as well as for conducting comparative genomics analyses within the Rosaceae family, have been greatly expanded. In this review, we highlight key advances in pear genetics, genomics, and breeding driven by the availability of whole-genome sequences, including whole-genome resequencing efforts, pear domestication, and evolution. We cover updates on new resources for undertaking gene identification and molecular breeding, as well as for pursuing functional validation of genes associated with desirable economic traits. We also explore future directions for "pear-omics".
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Affiliation(s)
- Jiaming Li
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Mingyue Zhang
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai-An, Shandong 271018, China
| | - Xiaolong Li
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Awais Khan
- Plant Pathology & Plant-Microbe Biology Section, Cornell University, Geneva, NY 14456, USA
| | - Satish Kumar
- Hawke’s Bay Research Centre, The New Zealand Institute for Plant and Food Research Limited, Havelock North 4157, New Zealand
| | - Andrew Charles Allan
- The New Zealand Institute for Plant and Food Research Limited, Auckland 1142, New Zealand
| | - Kui Lin-Wang
- The New Zealand Institute for Plant and Food Research Limited, Auckland 1142, New Zealand
| | - Richard Victor Espley
- The New Zealand Institute for Plant and Food Research Limited, Auckland 1142, New Zealand
| | - Caihong Wang
- College of Horticulture, Qingdao Agricultural University, Qingdao, 266109, China
| | - Runze Wang
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Cheng Xue
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai-An, Shandong 271018, China
| | - Gaifang Yao
- School of Food and Biological Engineering, Hefei University of Technology, 230009 Hefei, China
| | - Mengfan Qin
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Manyi Sun
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Richard Tegtmeier
- Plant Pathology & Plant-Microbe Biology Section, Cornell University, Geneva, NY 14456, USA
| | - Hainan Liu
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Weilin Wei
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Meiling Ming
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Shaoling Zhang
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Kejiao Zhao
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Bobo Song
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Jiangping Ni
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Jianping An
- State Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai-An, Shandong 271018, China
| | - Schuyler S Korban
- Department of Natural Resources & Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jun Wu
- Center of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
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Vukasovic S, Alahmad S, Christopher J, Snowdon RJ, Stahl A, Hickey LT. Dissecting the Genetics of Early Vigour to Design Drought-Adapted Wheat. FRONTIERS IN PLANT SCIENCE 2022; 12:754439. [PMID: 35046971 PMCID: PMC8763316 DOI: 10.3389/fpls.2021.754439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
Due to the climate change and an increased frequency of drought, it is of enormous importance to identify and to develop traits that result in adaptation and in improvement of crop yield stability in drought-prone regions with low rainfall. Early vigour, defined as the rapid development of leaf area in early developmental stages, is reported to contribute to stronger plant vitality, which, in turn, can enhance resilience to erratic drought periods. Furthermore, early vigour improves weed competitiveness and nutrient uptake. Here, two sets of a multi-reference nested association mapping (MR-NAM) population of bread wheat (Triticum aestivum ssp. aestivum L.) were used to investigate early vigour in a rain-fed field environment for 3 years, and additionally assessed under controlled conditions in a greenhouse experiment. The normalised difference vegetation index (NDVI) calculated from red/infrared light reflectance was used to quantify early vigour in the field, revealing a correlation (p < 0.05; r = 0.39) between the spectral measurement and the length of the second leaf. Under controlled environmental conditions, the measured projected leaf area, using a green-pixel counter, was also correlated to the leaf area of the second leaf (p < 0.05; r = 0.38), as well as to the recorded biomass (p < 0.01; r = 0.71). Subsequently, genetic determination of early vigour was tested by conducting a genome-wide association study (GWAS) for the proxy traits, revealing 42 markers associated with vegetation index and two markers associated with projected leaf area. There are several quantitative trait loci that are collocated with loci for plant developmental traits including plant height on chromosome 2D (log10 (P) = 3.19; PVE = 0.035), coleoptile length on chromosome 1B (-log10 (P) = 3.24; PVE = 0.112), as well as stay-green and vernalisation on chromosome 5A (-log10 (P) = 3.14; PVE = 0.115).
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Affiliation(s)
- Stjepan Vukasovic
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
| | - Samir Alahmad
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Jack Christopher
- Leslie Research Facility, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD, Australia
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
| | - Andreas Stahl
- Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Julius Kühn-Institute, Quedlinburg, Germany
| | - Lee T. Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
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20
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Ahmadi N. Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction. Methods Mol Biol 2022; 2467:1-44. [PMID: 35451771 DOI: 10.1007/978-1-0716-2205-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.
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Affiliation(s)
- Nourollah Ahmadi
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
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21
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Gangurde SS, Xavier A, Naik YD, Jha UC, Rangari SK, Kumar R, Reddy MSS, Channale S, Elango D, Mir RR, Zwart R, Laxuman C, Sudini HK, Pandey MK, Punnuri S, Mendu V, Reddy UK, Guo B, Gangarao NVPR, Sharma VK, Wang X, Zhao C, Thudi M. Two decades of association mapping: Insights on disease resistance in major crops. FRONTIERS IN PLANT SCIENCE 2022; 13:1064059. [PMID: 37082513 PMCID: PMC10112529 DOI: 10.3389/fpls.2022.1064059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/10/2022] [Indexed: 05/03/2023]
Abstract
Climate change across the globe has an impact on the occurrence, prevalence, and severity of plant diseases. About 30% of yield losses in major crops are due to plant diseases; emerging diseases are likely to worsen the sustainable production in the coming years. Plant diseases have led to increased hunger and mass migration of human populations in the past, thus a serious threat to global food security. Equipping the modern varieties/hybrids with enhanced genetic resistance is the most economic, sustainable and environmentally friendly solution. Plant geneticists have done tremendous work in identifying stable resistance in primary genepools and many times other than primary genepools to breed resistant varieties in different major crops. Over the last two decades, the availability of crop and pathogen genomes due to advances in next generation sequencing technologies improved our understanding of trait genetics using different approaches. Genome-wide association studies have been effectively used to identify candidate genes and map loci associated with different diseases in crop plants. In this review, we highlight successful examples for the discovery of resistance genes to many important diseases. In addition, major developments in association studies, statistical models and bioinformatic tools that improve the power, resolution and the efficiency of identifying marker-trait associations. Overall this review provides comprehensive insights into the two decades of advances in GWAS studies and discusses the challenges and opportunities this research area provides for breeding resistant varieties.
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Affiliation(s)
- Sunil S. Gangurde
- Crop Genetics and Breeding Research, United States Department of Agriculture (USDA) - Agriculture Research Service (ARS), Tifton, GA, United States
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
| | - Alencar Xavier
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | | | - Uday Chand Jha
- Indian Council of Agricultural Research (ICAR), Indian Institute of Pulses Research (IIPR), Kanpur, Uttar Pradesh, India
| | | | - Raj Kumar
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
| | - M. S. Sai Reddy
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
| | - Sonal Channale
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
| | - Dinakaran Elango
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Reyazul Rouf Mir
- Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST), Sopore, India
| | - Rebecca Zwart
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
| | - C. Laxuman
- Zonal Agricultural Research Station (ZARS), Kalaburagi, University of Agricultural Sciences, Raichur, Karnataka, India
| | - Hari Kishan Sudini
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Manish K. Pandey
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Somashekhar Punnuri
- College of Agriculture, Family Sciences and Technology, Dr. Fort Valley State University, Fort Valley, GA, United States
| | - Venugopal Mendu
- Department of Plant Science and Plant Pathology, Montana State University, Bozeman, MT, United States
| | - Umesh K. Reddy
- Department of Biology, West Virginia State University, West Virginia, WV, United States
| | - Baozhu Guo
- Crop Genetics and Breeding Research, United States Department of Agriculture (USDA) - Agriculture Research Service (ARS), Tifton, GA, United States
| | | | - Vinay K. Sharma
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
| | - Xingjun Wang
- Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences (SAAS), Jinan, China
| | - Chuanzhi Zhao
- Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences (SAAS), Jinan, China
- *Correspondence: Mahendar Thudi, ; Chuanzhi Zhao,
| | - Mahendar Thudi
- Dr. Rajendra Prasad Central Agricultural University (RPCAU), Bihar, India
- Crop Health Center, University of Southern Queensland (USQ), Toowoomba, QLD, Australia
- Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences (SAAS), Jinan, China
- *Correspondence: Mahendar Thudi, ; Chuanzhi Zhao,
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22
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Vollrath P, Chawla HS, Alnajar D, Gabur I, Lee H, Weber S, Ehrig L, Koopmann B, Snowdon RJ, Obermeier C. Dissection of Quantitative Blackleg Resistance Reveals Novel Variants of Resistance Gene Rlm9 in Elite Brassica napus. FRONTIERS IN PLANT SCIENCE 2021; 12:749491. [PMID: 34868134 PMCID: PMC8636856 DOI: 10.3389/fpls.2021.749491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/29/2021] [Indexed: 05/15/2023]
Abstract
Blackleg is one of the major fungal diseases in oilseed rape/canola worldwide. Most commercial cultivars carry R gene-mediated qualitative resistances that confer a high level of race-specific protection against Leptosphaeria maculans, the causal fungus of blackleg disease. However, monogenic resistances of this kind can potentially be rapidly overcome by mutations in the pathogen's avirulence genes. To counteract pathogen adaptation in this evolutionary arms race, there is a tremendous demand for quantitative background resistance to enhance durability and efficacy of blackleg resistance in oilseed rape. In this study, we characterized genomic regions contributing to quantitative L. maculans resistance by genome-wide association studies in a multiparental mapping population derived from six parental elite varieties exhibiting quantitative resistance, which were all crossed to one common susceptible parental elite variety. Resistance was screened using a fungal isolate with no corresponding avirulence (AvrLm) to major R genes present in the parents of the mapping population. Genome-wide association studies revealed eight significantly associated quantitative trait loci (QTL) on chromosomes A07 and A09, with small effects explaining 3-6% of the phenotypic variance. Unexpectedly, the qualitative blackleg resistance gene Rlm9 was found to be located within a resistance-associated haploblock on chromosome A07. Furthermore, long-range sequence data spanning this haploblock revealed high levels of single-nucleotide and structural variants within the Rlm9 coding sequence among the parents of the mapping population. The results suggest that novel variants of Rlm9 could play a previously unknown role in expression of quantitative disease resistance in oilseed rape.
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Affiliation(s)
- Paul Vollrath
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
| | - Harmeet S. Chawla
- Department of Plant Sciences, Crop Development Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Dima Alnajar
- Plant Pathology and Crop Protection Division, Department of Crop Sciences, Georg August University of Göttingen, Göttingen, Germany
| | - Iulian Gabur
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
- Department of Plant Sciences, Faculty of Agriculture, Iasi University of Life Sciences, Iaşi, Romania
| | - HueyTyng Lee
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
| | - Sven Weber
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
| | - Lennard Ehrig
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
| | - Birger Koopmann
- Plant Pathology and Crop Protection Division, Department of Crop Sciences, Georg August University of Göttingen, Göttingen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
| | - Christian Obermeier
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, Giessen, Germany
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23
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Yue K, Ma J, Thornton T, Shojaie A. REHE: Fast variance components estimation for linear mixed models. Genet Epidemiol 2021; 45:891-905. [PMID: 34658056 DOI: 10.1002/gepi.22432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/11/2021] [Accepted: 10/04/2021] [Indexed: 11/07/2022]
Abstract
Linear mixed models are widely used in ecological and biological applications, especially in genetic studies. Reliable estimation of variance components is crucial for using linear mixed models. However, standard methods, such as the restricted maximum likelihood (REML), are computationally inefficient in large samples and may be unstable with small samples. Other commonly used methods, such as the Haseman-Elston (HE) regression, may yield negative estimates of variances. Utilizing regularized estimation strategies, we propose the restricted Haseman-Elston (REHE) regression and REHE with resampling (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide nonnegative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.
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Affiliation(s)
- Kun Yue
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Jing Ma
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Timothy Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
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24
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Xiao J, Zhou Y, He S, Ren WL. An Efficient Score Test Integrated with Empirical Bayes for Genome-Wide Association Studies. Front Genet 2021; 12:742752. [PMID: 34659362 PMCID: PMC8517403 DOI: 10.3389/fgene.2021.742752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/13/2021] [Indexed: 11/30/2022] Open
Abstract
Many methods used in multi-locus genome-wide association studies (GWAS) have been developed to improve statistical power. However, most existing multi-locus methods are not quicker than single-locus methods. To address this concern, we proposed a fast score test integrated with Empirical Bayes (ScoreEB) for multi-locus GWAS. Firstly, a score test was conducted for each single nucleotide polymorphism (SNP) under a linear mixed model (LMM) framework, taking into account the genetic relatedness and population structure. Then, all of the potentially associated SNPs were selected with a less stringent criterion. Finally, Empirical Bayes in a multi-locus model was performed for all of the selected SNPs to identify the true quantitative trait nucleotide (QTN). Our new method ScoreEB adopts the similar strategy of multi-locus random-SNP-effect mixed linear model (mrMLM) and fast multi-locus random-SNP-effect EMMA (FASTmrEMMA), and the only difference is that we use the score test to select all the potentially associated markers. Monte Carlo simulation studies demonstrate that ScoreEB significantly improved the computational efficiency compared with the popular methods mrMLM, FASTmrEMMA, iterative modified-sure independence screening EM-Bayesian lasso (ISIS EM-BLASSO), hybrid of restricted and penalized maximum likelihood (HRePML) and genome-wide efficient mixed model association (GEMMA). In addition, ScoreEB remained accurate in QTN effect estimation and effectively controlled false positive rate. Subsequently, ScoreEB was applied to re-analyze quantitative traits in plants and animals. The results show that ScoreEB not only can detect previously reported genes, but also can mine new genes.
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Affiliation(s)
- Jing Xiao
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
| | - Yang Zhou
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
| | - Shu He
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
| | - Wen-Long Ren
- Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, Nantong, China
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25
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Song Y, Yang L, Jiang L, Hao Z, Yang R, Xu P. Optimizing genomic control in mixed model associations with binary diseases. Brief Bioinform 2021; 23:6394993. [PMID: 34643219 DOI: 10.1093/bib/bbab426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/09/2021] [Accepted: 09/18/2021] [Indexed: 11/14/2022] Open
Abstract
Complex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then reduced polygenic effects by regulating downward genomic heritability to control false negative errors produced in the association tests. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygenic effects and genomic controls could be evaluated using the fewer sampling markers, which extremely simplified GLMM-based association analysis in large-scale data. Further, joint association analysis for quantitative trait nucleotide (QTN) candidates chosen by multiple testing offered significant improved statistical power to detect QTNs over existing methods.
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Affiliation(s)
- Yuxin Song
- Wuxi Fisheries College, Nanjing Agricultural University, People's Republic of China
| | - Li'ang Yang
- College of Life Science, Northeast Agricultural University, People's Republic of China
| | - Li Jiang
- Research Centre for Aquatic biotechnology, Chinese Academy of Fishery Sciences, People's Republic of China
| | - Zhiyu Hao
- Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, People's Republic of China
| | - Runqing Yang
- Research Centre for Aquatic biotechnology, Chinese Academy of Fishery Sciences, People's Republic of China
| | - Pao Xu
- Wuxi Fisheries College, Nanjing Agricultural University, People's Republic of China
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26
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Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Al Khleifat A, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Al Chalabi A, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Panico S, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell CE, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Davey Smith G, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, Relton CL. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet 2021; 53:1311-1321. [PMID: 34493871 PMCID: PMC7612069 DOI: 10.1038/s41588-021-00923-x] [Citation(s) in RCA: 194] [Impact Index Per Article: 64.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 07/12/2021] [Indexed: 12/25/2022]
Abstract
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
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Affiliation(s)
- Josine L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eilis Hannon
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Koen F Dekkers
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | - René Luijk
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Elena Carnero-Montoro
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Daniel J Lawson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrew D Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Johanna Klughammer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | | | - Gemma Sharp
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Aleksey Shatunov
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Wendy L McArdle
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Karen M Ho
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ashish Kumar
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Epidemiology unit, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Cilla Söderhäll
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Carolina Soriano-Tárraga
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Eva Giralt-Steinhauer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Nabila Kazmi
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Karen Sugden
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Silva Kasela
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alexia Cardona
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giovanni Cugliari
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Clara Viberti
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Simonetta Guarrera
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Michael Lerro
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Richa Gupta
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sailalitha Bollepalli
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Pooja Mandaviya
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yanni Zeng
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Vanessa Schmoll
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Carlos Ruiz-Arenas
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Faisal I Rezwan
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tian Lin
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Yvonne Awaloff
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Marine Germain
- INSERM UMR_S 1219, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France
| | - Dylan Aïssi
- Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Ramona Zwamborn
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kristel van Eijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Annelot Dekker
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Cheng-Jian Xu
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
- CiiM and TWINCORE, Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany
| | - Guillermo Barturen
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Francesc Català-Moll
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme, Bellvitge Biomedical Research Institute, Barcelona, Spain
| | - Martin Kerick
- Instituto de Parasitología y Biomedicina López Neyra, CSIC, Granada, Spain
| | - Carol Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Phillip Melton
- Menzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, Australia
- School of Global Population Health, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Hannah R Elliott
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jean Shin
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Manon Bernard
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Idil Yet
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Bioinformatics, Institute of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Melissa Smart
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | | | - Chris Shaw
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
| | - Ammar Al Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
- United Kingdom Dementia Research Institute, King's College London, London, UK
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Jordi Jiménez-Conde
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Jaume Roquer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford, UK
| | | | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Terrie E Moffitt
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - John R B Perry
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giuseppe Matullo
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Carlotta Sacerdote
- Italian Institute for Genomic Medicine, Turin, Italy
- Piemonte Centre for Cancer Prevention, Turin, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia, Federico II University, Naples, Italy
| | - Avshalom Caspi
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Miina Ollikainen
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Marinus H van IJzendoorn
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Department of Clinical, Educational and Health Psychology, Division on Psychology and Language Sciences, Faculty of Brain Sciences, University College London, London, UK
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Chris Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alison Murray
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ellen A Nohr
- Research Unit for Gynaecology and Obstetrics, Institute of Clinical research, University of Southern Denmark, Odense, Denmark
- Centre of Women's, Family and Child Health, University of South-Eastern Norway, Kongsberg, Norway
| | - Thorkild I A Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health (Section of Epidemiology), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Camilla S Morgen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Susanne Lucae
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Juan Ramon Gonzalez
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Center for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Wilfried Karmaus
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Hongmei Zhang
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Diana van Heemst
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | | | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Eco J C de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Judith M Vonk
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences, Universiteit Utrecht, Utrecht, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerard H Koppelman
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Marta E Alarcón-Riquelme
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Rae-Chi Huang
- Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | | | | | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Tomas Paus
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Jonathan Mill
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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27
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Hao Z, Gao J, Song Y, Yang R, Liu D. Genome-wide hierarchical mixed model association analysis. Brief Bioinform 2021; 22:6342938. [PMID: 34368830 PMCID: PMC8575042 DOI: 10.1093/bib/bbab306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/05/2021] [Accepted: 07/17/2021] [Indexed: 11/14/2022] Open
Abstract
In genome-wide mixed model association analysis, we stratified the genomic mixed model into two hierarchies to estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and statistically infer the association of GBVs with each SNP using the generalized least square. The hierarchical mixed model (Hi-LMM) can correct confounders effectively with polygenic effects as residuals for association tests, preventing potential false-negative errors produced with genome-wide rapid association using mixed model and regression or an efficient mixed-model association expedited (EMMAX). Meanwhile, the Hi-LMM performs the same statistical power as the exact mixed model association and the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, the Hi-LMM can detect more quantitative trait nucleotides (QTNs) than existing methods. Especially under the Hi-LMM framework, joint association analysis can be made straightforward to improve the statistical power of detecting QTNs.
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Affiliation(s)
- Zhiyu Hao
- Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences
| | - Jin Gao
- Wuxi Fisheries College, Nanjing Agricultural University
| | - Yuxin Song
- Wuxi Fisheries College, Nanjing Agricultural University
| | - Runqing Yang
- Corresponding authors: Runqing Yang, Research Center for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, People's Republic of China. E-mail: ; Di Liu, Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, People's Republic of China. E-mail:
| | - Di Liu
- Corresponding authors: Runqing Yang, Research Center for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100141, People's Republic of China. E-mail: ; Di Liu, Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, People's Republic of China. E-mail:
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28
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Çelikeloğlu K, Tekerli M, Erdoğan M, Koçak S, Hacan Ö, Bozkurt Z. An investigation of the effects of BMPR1B, BMP15, and GDF9 genes on litter size in Ramlıç and Dağlıç sheep. Arch Anim Breed 2021; 64:223-230. [PMID: 34159253 PMCID: PMC8209503 DOI: 10.5194/aab-64-223-2021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/27/2021] [Indexed: 11/11/2022] Open
Abstract
This study was carried out to determine the presence of polymorphisms in genes affecting litter size. The SNPs in bone morphogenetic protein receptor type 1B (BMPR1B), bone morphogenetic protein 15 (BMP15), and growth differentiation factor 9 (GDF9) genes were detected in 60 uniparous and 60 multiparous ewes from Ramlıç and Dağlıç breeds. The ewes are maintained in nine public herds at the breeding station of the Afyonkarahisar Sheep and Goats Breeders' Association and lambed in two consecutive breeding seasons. PCR and DNA sequencing analyses were conducted, and 36, 4, and 11 SNPs in Ramlıç and 40, 3, and 11 SNPs in Dağlıç were detected in BMPR1B, BMP15, and GDF9 genes, respectively. A total of 16 SNPs in Ramlıç and 10 SNPs in Dağlıç breeds for three genes were found to be significant (P<0.05). The resulting analyses showed that four SNPs (g.49496G>A, c.1658A>C, c.2037C>T, c.2053C>T) of the BMPR1B gene and one deletion mutation (c.28_30delCTT) in the BMP15 gene of the Ramlıç breed as well as five SNPs (c.1487C>A, c.2492C>T, c.2523G>A, c.2880A>G, and c.2763G>A) of the BMPR1B gene of the Dağlıç breed have significant positive regression coefficients in the desired direction of the rare allele. The observed mutations have potential to be used as genetic markers in the selection of prolific animals for both breeds.
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Affiliation(s)
- Koray Çelikeloğlu
- Department of Animal Science, Faculty of Veterinary Medicine, Afyonkarahisar, Turkey
| | - Mustafa Tekerli
- Department of Animal Science, Faculty of Veterinary Medicine, Afyonkarahisar, Turkey
| | - Metin Erdoğan
- Department of Veterinary Biology and Genetics, Faculty of Veterinary Medicine, Afyonkarahisar, Turkey
| | - Serdar Koçak
- Department of Animal Science, Faculty of Veterinary Medicine, Afyonkarahisar, Turkey
| | - Özlem Hacan
- Department of Animal Science, Faculty of Veterinary Medicine, Afyonkarahisar, Turkey
| | - Zehra Bozkurt
- Department of Animal Science, Faculty of Veterinary Medicine, Afyonkarahisar, Turkey
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29
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Vollrath P, Chawla HS, Schiessl SV, Gabur I, Lee H, Snowdon RJ, Obermeier C. A novel deletion in FLOWERING LOCUS T modulates flowering time in winter oilseed rape. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1217-1231. [PMID: 33471161 PMCID: PMC7973412 DOI: 10.1007/s00122-021-03768-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 01/06/2021] [Indexed: 05/05/2023]
Abstract
A novel structural variant was discovered in the FLOWERING LOCUS T orthologue BnaFT.A02 by long-read sequencing. Nested association mapping in an elite winter oilseed rape population revealed that this 288 bp deletion associates with early flowering, putatively by modification of binding-sites for important flowering regulation genes. Perfect timing of flowering is crucial for optimal pollination and high seed yield. Extensive previous studies of flowering behavior in Brassica napus (canola, rapeseed) identified mutations in key flowering regulators which differentiate winter, semi-winter and spring ecotypes. However, because these are generally fixed in locally adapted genotypes, they have only limited relevance for fine adjustment of flowering time in elite cultivar gene pools. In crosses between ecotypes, the ecotype-specific major-effect mutations mask minor-effect loci of interest for breeding. Here, we investigated flowering time in a multiparental mapping population derived from seven elite winter oilseed rape cultivars which are fixed for major-effect mutations separating winter-type rapeseed from other ecotypes. Association mapping revealed eight genomic regions on chromosomes A02, C02 and C03 associating with fine modulation of flowering time. Long-read genomic resequencing of the seven parental lines identified seven structural variants coinciding with candidate genes for flowering time within chromosome regions associated with flowering time. Segregation patterns for these variants in the elite multiparental population and a diversity set of winter types using locus-specific assays revealed significant associations with flowering time for three deletions on chromosome A02. One of these was a previously undescribed 288 bp deletion within the second intron of FLOWERING LOCUS T on chromosome A02, emphasizing the advantage of long-read sequencing for detection of structural variants in this size range. Detailed analysis revealed the impact of this specific deletion on flowering-time modulation under extreme environments and varying day lengths in elite, winter-type oilseed rape.
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Affiliation(s)
- Paul Vollrath
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Harmeet S Chawla
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Sarah V Schiessl
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Iulian Gabur
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - HueyTyng Lee
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
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30
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Sutera AM, Moscarelli A, Mastrangelo S, Sardina MT, Di Gerlando R, Portolano B, Tolone M. Genome-Wide Association Study Identifies New Candidate Markers for Somatic Cells Score in a Local Dairy Sheep. Front Genet 2021; 12:643531. [PMID: 33828586 PMCID: PMC8019815 DOI: 10.3389/fgene.2021.643531] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/01/2021] [Indexed: 12/13/2022] Open
Abstract
In the Mediterranean basin countries, the dairy sheep production is usually based on local breeds, which are very well-adapted to their production systems and environments and can indeed guarantee income, employment, and economic viability in areas where production alternatives are scarce or non-existent. Mastitis is still one of the greatest problems affecting commercial milk production. However, genetic evaluation of mastitis is particularly difficult because of its low heritability and the categorical nature of the trait. The aim of this study was to identify genomic regions putatively associated with somatic cells count (SCC) in the local economically important Valle del Belice sheep breed using of deregressed breeding values (DEBV) as response variables. All the samples were genotyped using the Illumina OvineSNP50K BeadChip. Genome-wide association analysis was carried out based on regression of DEBV. A total of eight markers were found to be significantly associated with log-transformed SCC. Several candidate genes associated with SCC were identified related to immunity system and udder conformation. The results can help improving the competitiveness of the local Valle del Belìce breed. Further studies considering a higher sample size or independent population will be needed to confirm our results.
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Affiliation(s)
- Anna Maria Sutera
- Dipartimento Scienze Veterinarie, University of Messina, Messina, Italy
| | - Angelo Moscarelli
- Dipartimento di Scienze Agrarie Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Salvatore Mastrangelo
- Dipartimento di Scienze Agrarie Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Maria Teresa Sardina
- Dipartimento di Scienze Agrarie Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Rosalia Di Gerlando
- Dipartimento di Scienze Agrarie Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Baldassare Portolano
- Dipartimento di Scienze Agrarie Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Marco Tolone
- Dipartimento di Scienze Agrarie Alimentari e Forestali, University of Palermo, Palermo, Italy
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31
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Tibbs Cortes L, Zhang Z, Yu J. Status and prospects of genome-wide association studies in plants. THE PLANT GENOME 2021; 14:e20077. [PMID: 33442955 DOI: 10.1002/tpg2.20077] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 11/18/2020] [Indexed: 05/22/2023]
Abstract
Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome-wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare-variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.
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Affiliation(s)
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, 50010, USA
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32
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Genome-wide association studies provide insights into the genetic determination of fruit traits of pear. Nat Commun 2021; 12:1144. [PMID: 33602909 PMCID: PMC7892570 DOI: 10.1038/s41467-021-21378-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 01/25/2021] [Indexed: 01/31/2023] Open
Abstract
Pear is a major fruit tree crop distributed worldwide, yet its breeding is a very time-consuming process. To facilitate molecular breeding and gene identification, here we have performed genome-wide association studies (GWAS) on eleven fruit traits. We identify 37 loci associated with eight fruit quality traits and five loci associated with three fruit phenological traits. Scans for selective sweeps indicate that traits including fruit stone cell content, organic acid and sugar contents might have been under continuous selection during breeding improvement. One candidate gene, PbrSTONE, identified in GWAS, has been functionally verified to be involved in the regulation of stone cell formation, one of the most important fruit quality traits in pear. Our study provides insights into the complex fruit related biology and identifies genes controlling important traits in pear through GWAS, which extends the genetic resources and basis for facilitating molecular breeding in perennial trees.
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33
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Wang M, Li R, Xu S. Deshrinking ridge regression for genome-wide association studies. Bioinformatics 2021; 36:4154-4162. [PMID: 32379866 DOI: 10.1093/bioinformatics/btaa345] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 04/21/2020] [Accepted: 04/29/2020] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) are still the primary steps toward gene discovery. The urgency is more obvious in the big data era when GWAS are conducted simultaneously for thousand traits, e.g. transcriptomic and metabolomic traits. Efficient mixed model association (EMMA) and genome-wide efficient mixed model association (GEMMA) are the widely used methods for GWAS. An algorithm with high computational efficiency is badly needed. It is interesting to note that the test statistics of the ordinary ridge regression (ORR) have the same patterns across the genome as those obtained from the EMMA method. However, ORR has never been used for GWAS due to its severe shrinkage on the estimated effects and the test statistics. RESULTS We introduce a degree of freedom for each marker effect obtained from ORR and use it to deshrink both the estimated effect and the standard error so that the Wald test of ORR is brought back to the same level as that of EMMA. The new method is called deshrinking ridge regression (DRR). By evaluating the methods under three different model sizes (small, medium and large), we demonstrate that DRR is more generalized for all model sizes than EMMA, which only works for medium and large models. Furthermore, DRR detect all markers in a simultaneous manner instead of scanning one marker at a time. As a result, the computational time complexity of DRR is much simpler than EMMA and about m (number of genetic variants) times simpler than that of GEMMA when the sample size is way smaller than the number of markers. CONTACT shizhong.xu@ucr.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Meiyue Wang
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Ruidong Li
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
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Ochoa A, Storey JD. Estimating FST and kinship for arbitrary population structures. PLoS Genet 2021; 17:e1009241. [PMID: 33465078 PMCID: PMC7846127 DOI: 10.1371/journal.pgen.1009241] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/29/2021] [Accepted: 11/02/2020] [Indexed: 12/20/2022] Open
Abstract
FST and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently-used estimators of FST and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we analyze the behavior of these estimators in the presence of arbitrarily-complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After generalizing the definition of FST to arbitrary population structures and establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing FST and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally-assumed models of structure. We then present our new approach, which consistently estimates kinship and FST when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and FST estimates. Kinship coefficients and FST, which measure relatedness and population structure, respectively, are important quantities needed to accurately perform various analyses on genetic data, including genome-wide association studies and heritability estimation. However, existing estimators require restrictive assumptions of independence that are not met by real human and other datasets. In this work we find that existing estimators can be severely biased under reasonable scenarios, first by theoretically determining their properties, and then using an admixture simulation to illustrate our findings. In particular, we find that existing FST estimators are downwardly biased, and that existing kinship matrix estimators have related biases that are on average downward and of similar magnitude but vary for every pair of individuals. These insights led us to a new estimation framework for kinship and FST that is practically unbiased for any population structure, as demonstrated by theory and simulations. Our new approaches—available as open-source R packages—are easy to use and are more widely applicable than existing approaches, and they are likely to improve downstream analyses that require accurate kinship and FST estimates.
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Affiliation(s)
- Alejandro Ochoa
- Duke Center for Statistical Genetics and Genomics, Duke University, Durham, North Carolina, United States of America
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, United States of America
| | - John D. Storey
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
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Sutera AM, Di Gerlando R, Mastrangelo S, Sardina MT, D’Alessandro E, Portolano B, Tolone M. Genome-wide association study for milk production traits in an economically important local dairy sheep breed. ITALIAN JOURNAL OF ANIMAL SCIENCE 2021. [DOI: 10.1080/1828051x.2021.1963865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Anna Maria Sutera
- Dipartimento di Scienze Veterinarie, Università di Messina, Messina, Italy
| | - Rosalia Di Gerlando
- Dipartimento Scienze Agrarie, Alimentari e Forestali, Università di Palermo, Palermo, Italy
| | - Salvatore Mastrangelo
- Dipartimento Scienze Agrarie, Alimentari e Forestali, Università di Palermo, Palermo, Italy
| | - Maria Teresa Sardina
- Dipartimento Scienze Agrarie, Alimentari e Forestali, Università di Palermo, Palermo, Italy
| | | | - Baldassare Portolano
- Dipartimento Scienze Agrarie, Alimentari e Forestali, Università di Palermo, Palermo, Italy
| | - Marco Tolone
- Dipartimento Scienze Agrarie, Alimentari e Forestali, Università di Palermo, Palermo, Italy
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Genetic consistency between gait analysis by accelerometry and evaluation scores at breeding shows for the selection of jumping competition horses. PLoS One 2020; 15:e0244064. [PMID: 33326505 PMCID: PMC7743953 DOI: 10.1371/journal.pone.0244064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 12/02/2020] [Indexed: 01/16/2023] Open
Abstract
The aim was to assess the efficiency of gaits characteristics in improving jumping performance of sport horses and confront accelerometers and judge scores for this purpose. A sample of 1,477 young jumping horses were measured using accelerometers for walk, trot, and canter. Of these, 702 were genotyped with 541,175 SNPs after quality control. Dataset of 26,914 horses scored by judges in breeding shows for gaits and dataset of 142,682 horses that performed in jumping competitions were used. Analysis of accelerometric data defined three principal components from 64% to 89% of variability explained for each gait. Animal mixed models were used to estimate genetic parameters with the inclusion to up 308,105 ancestors for the relationship matrix. Fixed effects for the accelerometric variables included velocity, gender, age, and event. A GWAS was performed on residuals with the fixed effect of each SNP. The GWAS did not reveal other QTLs for gait traits than the one related to the height at withers. The accelerometric principal components were highly heritable for the one linked to stride frequency and dorsoventral displacement at trot (0.53) and canter (0.41) and moderately for the one linked to longitudinal activities (0.33 for trot, 0.19 for canter). Low heritabilities were found for the walk traits. The genetic correlations of the accelerometric principal components with the jumping competition were essentially nil, except for a negative correlation with longitudinal activity at canter (-0.19). The genetic correlation between the judges’ scores and the jumping competition reached 0.45 for canter (0.31 for trot and 0.17 for walk). But these correlations turned negative when the scores were corrected for the known parental breeding value for competition at the time of the judging. In conclusion, gait traits were not helpful to select for jumping performances. Different gaits may be suitable for a good jumping horse.
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Milet J, Courtin D, Garcia A, Perdry H. Mixed logistic regression in genome-wide association studies. BMC Bioinformatics 2020; 21:536. [PMID: 33228527 PMCID: PMC7684894 DOI: 10.1186/s12859-020-03862-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 11/04/2020] [Indexed: 12/15/2022] Open
Abstract
Background Mixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved in 2016 that this method is inappropriate in some situations and proposed GMMAT, a score test for the mixed logistic regression (MLR). However, this test does not produces an estimation of the variants’ effects. We propose two computationally efficient methods to estimate the variants’ effects. Their properties and those of other methods (MLM, logistic regression) are evaluated using both simulated and real genomic data from a recent GWAS in two geographically close population in West Africa. Results We show that, when the disease prevalence differs between population strata, MLM is inappropriate to analyze binary traits. MLR performs the best in all circumstances. The variants’ effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p values inflation or deflation when population strata are not clearly identified in the sample. Conclusion The two proposed methods are implemented in the R package milorGWAS available on the CRAN. Both methods scale up to at least 10,000 individuals. The same computational strategies could be applied to other models (e.g. mixed Cox model for survival analysis).
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Affiliation(s)
| | - David Courtin
- Université de Paris, MERIT, IRD, 75006, Paris, France
| | - André Garcia
- Université de Paris, MERIT, IRD, 75006, Paris, France
| | - Hervé Perdry
- Université Paris-Saclay, UVSQ, Inserm, CESP, 94807, Villejuif, France.
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Leal-Gutiérrez JD, Rezende FM, Reecy JM, Kramer LM, Peñagaricano F, Mateescu RG. Whole Genome Sequence Data Provides Novel Insights Into the Genetic Architecture of Meat Quality Traits in Beef. Front Genet 2020; 11:538640. [PMID: 33101375 PMCID: PMC7500205 DOI: 10.3389/fgene.2020.538640] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022] Open
Abstract
Tenderness is a major quality attribute for fresh beef steaks in the United States, and meat quality traits in general are suitable candidates for genomic research. The objectives of the present analysis were to (1) perform genome-wide association (GWA) analysis for marbling, Warner-Bratzler shear force (WBSF), tenderness, and connective tissue using whole-genome data in an Angus population, (2) identify enriched pathways in each GWA analysis; (3) construct a protein-protein interaction network using the associated genes and (4) perform a μ-calpain proteolysis assessment for associated structural proteins. An Angus-sired population of 2,285 individuals was assessed. Animals were transported to a commercial packing plant and harvested at an average age of 457 ± 46 days. After 48 h postmortem, marbling was recorded by graders' visual appraisal. Two 2.54-cm steaks were sampled from each muscle for recording of WBSF, and tenderness, and connective tissue by a sensory panel. The relevance of additive effects on marbling, WBSF, tenderness, and connective tissue was evaluated on a genome-wide scale using a two-step mixed model-based approach in single-trait analysis. A tissue-restricted gene enrichment was performed for each GWA where all polymorphisms with an association p-value lower than 1 × 10-3 were included. The genes identified as associated were included in a protein-protein interaction network and a candidate structural protein assessment of proteolysis analyses. A total of 1,867, 3,181, 3,926, and 3,678 polymorphisms were significantly associated with marbling, WBSF, tenderness, and connective tissue, respectively. The associate region on BTA29 (36,432,655-44,313,046 bp) harbors 13 highly significant markers for meat quality traits. Enrichment for the GO term GO:0005634 (Nucleus), which includes transcription factors, was evident. The final protein-protein network included 431 interations between 349 genes. The 42 most important genes based on significance that encode structural proteins were included in a proteolysis analysis, and 81% of these proteins were potential μ-Calpain substrates. Overall, this comprehensive study unraveled genetic variants, genes and mechanisms of action responsible for the variation in meat quality traits. Our findings can provide opportunities for improving meat quality in beef cattle via marker-assisted selection.
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Affiliation(s)
| | - Fernanda M. Rezende
- Department of Animal Sciences, University of Florida, Gainesville, FL, United States
- Faculdade de Medicina Veterinária, Universidade Federal de Uberlândia, Uberlândia, Brazil
| | - James M. Reecy
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Luke M. Kramer
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Francisco Peñagaricano
- Department of Animal Sciences, University of Florida, Gainesville, FL, United States
- University of Florida Genetics Institute, University of Florida, Gainesville, FL, United States
| | - Raluca G. Mateescu
- Department of Animal Sciences, University of Florida, Gainesville, FL, United States
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Osazuwa-Peters OL, Waken RJ, Schwander KL, Sung YJ, de Vries PS, Hartz SM, Chasman DI, Morrison AC, Bierut LJ, Xiong C, de Las Fuentes L, Rao DC. Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures. Genet Epidemiol 2020; 44:629-641. [PMID: 32227373 PMCID: PMC7717887 DOI: 10.1002/gepi.22292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/30/2019] [Accepted: 03/06/2020] [Indexed: 12/27/2022]
Abstract
Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.
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Affiliation(s)
| | - R J Waken
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Karen L Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Sarah M Hartz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Daniel I Chasman
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
| | - Lisa de Las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri
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Moualeu-Ngangué D, Dolch C, Schneider M, Léon J, Uptmoor R, Stützel H. Physiological and morphological responses of different spring barley genotypes to water deficit and associated QTLs. PLoS One 2020; 15:e0237834. [PMID: 32853269 PMCID: PMC7451664 DOI: 10.1371/journal.pone.0237834] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 08/04/2020] [Indexed: 12/03/2022] Open
Abstract
Water deficit is one of the major limitations to food production worldwide and most climate change scenarios predict an aggravation of the situation. To face the expected increase in drought stress in the coming years, breeders are working to elucidate the genetic control of barley growth and productivity traits under water deficit. Barley is known as a relatively drought tolerant crop and genetic variability was observed for drought tolerance traits. The objectives of the present study were the quantification of morphological and physiological responses in a collection of 209 spring barley genotypes to drought stress, and the genetic analysis by genome-wide association study to find quantitative trait loci (QTL) and the allele contributions for each of the investigated traits. In six pot experiments, 209 spring barley genotypes were grown under a well-watered and water-limited regime. Stress phases were initiated individually for each genotype at the beginning of tillering and spiking for the vegetative- and the generative stage experiments, respectively, and terminated when the transpiration rates of stress treatments reached 10% of the well-watered control. After the stress phase, a total of 42 productivity related traits such as the dry matter of plant organs, tiller number, leaf length, leaf area, amount of water soluble carbohydrates in the stems, proline content in leaves and osmotic adjustment of corresponding well-watered and stressed plants were analysed, and QTL analyses were performed to find marker-trait associations. Significant water deficit effects were observed for almost all traits and significant genotype x treatment interactions (GxT) were observed for 37 phenotypic traits. Genome-wide association studies (GWAS) revealed 77 significant loci associated with 16 phenotypic traits during the vegetative stage experiment and a total of 85 significant loci associated with 13 phenotypic traits during the generative stage experiment for traits such as leaf area, number of green leaves, grain yield, harvest index and stem length. For traits with significant GxT interactions, genotypic differences for relative values were analysed using one way ANOVA. More than 110 loci for GxT interaction were found for 17 phenotypic traits explaining in many cases more than 50% of the genetic variance.
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Affiliation(s)
- Dany Moualeu-Ngangué
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
- * E-mail:
| | - Christoph Dolch
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
| | - Michael Schneider
- Chair of Plant Breeding, Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
| | - Jens Léon
- Chair of Plant Breeding, Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
| | - Ralf Uptmoor
- Department of Agronomy, University of Rostock, Rostock, Germany
| | - Hartmut Stützel
- Institute of Horticultural Production Systems, Leibniz University Hannover, Hannover, Germany
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Jiang Y, Chiu CY, Yan Q, Chen W, Gorin MB, Conley YP, Lakhal-Chaieb ML, Cook RJ, Amos CI, Wilson AF, Bailey-Wilson JE, McMahon FJ, Vazquez AI, Yuan A, Zhong X, Xiong M, Weeks DE, Fan R. Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration. J Am Stat Assoc 2020; 116:531-545. [PMID: 34321704 PMCID: PMC8315575 DOI: 10.1080/01621459.2020.1799809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 07/09/2020] [Accepted: 07/17/2020] [Indexed: 10/23/2022]
Abstract
Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Affiliation(s)
- Yingda Jiang
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Qi Yan
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Wei Chen
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Michael B. Gorin
- Department of Ophthalmology, David Geffen School of Medicine, UCLA Stein Eye Institute, Los Angeles, CA
| | - Yvette P. Conley
- Department of Health Promotion and Development, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | | | - Richard J. Cook
- Department of Statistics and Actuarial Science, Waterloo, ON, Canada
| | | | - Alexander F. Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Joan E. Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Francis J. McMahon
- Human Genetics Branch and Genetic Basis of Mood and Anxiety Disorders Section, National Institute of Mental Health, NIH, Bethesda, MD
| | - Ana I. Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI
| | - Ao Yuan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Xiaogang Zhong
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Momiao Xiong
- Human Genetics Center, University of Texas, Houston, TX
| | - Daniel E. Weeks
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Ruzong Fan
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
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Igolkina AA, Meshcheryakov G, Gretsova MV, Nuzhdin SV, Samsonova MG. Multi-trait multi-locus SEM model discriminates SNPs of different effects. BMC Genomics 2020; 21:490. [PMID: 32723302 PMCID: PMC7385891 DOI: 10.1186/s12864-020-06833-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 06/16/2020] [Indexed: 11/21/2022] Open
Abstract
Background There is a plethora of methods for genome-wide association studies. However, only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes. Results We propose a multi-trait multi-locus model which employs structural equation modeling (SEM) to describe complex associations between SNPs and traits - multi-trait multi-locus SEM (mtmlSEM). The structure of our model makes it possible to discriminate pleiotropic and single-trait SNPs of direct and indirect effect. We also propose an automatic procedure to construct the model using factor analysis and the maximum likelihood method. For estimating a large number of parameters in the model, we performed Bayesian inference and implemented Gibbs sampling. An important feature of the model is that it correctly copes with non-normally distributed variables, such as some traits and variants. Conclusions We applied the model to Vavilov’s collection of 404 chickpea (Cicer arietinum L.) accessions with 20-fold cross-validation. We analyzed 16 phenotypic traits which we organized into five groups and found around 230 SNPs associated with traits, 60 of which were of pleiotropic effect. The model demonstrated high accuracy in predicting trait values.
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Brinke I, Große-Brinkhaus C, Roth K, Pröll-Cornelissen MJ, Henne H, Schellander K, Tholen E. Genomic background and genetic relationships between boar taint and fertility traits in German Landrace and Large White. BMC Genet 2020; 21:61. [PMID: 32513168 PMCID: PMC7282179 DOI: 10.1186/s12863-020-00865-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 05/28/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Due to ethical reasons, surgical castration of young male piglets in their first week of life without anesthesia will be banned in Germany from 2021. Breeding against boar taint is already implemented in sire breeds of breeding organizations but in recent years a low demand made this trait economically less important. The objective of this study was to estimate heritabilities and genetic relationships between boar taint compounds androstenone and skatole and maternal/paternal reproduction traits in 4'924 Landrace (LR) and 4'299 Large White (LW) animals from nucleus populations. Additionally, genome wide association analysis (GWAS) was performed per trait and breed to detect SNP marker with possible pleiotropic effects that are associated with boar taint and fertility. RESULTS Estimated heritabilities (h2) were 0.48 (±0.08) for LR (0.39 ± 0.07 for LW) for androstenone and 0.52 (±0.08) for LR (0.32 ± 0.07 for LW) for skatole. Heritabilities for reproduction did not differ between breeds except age at first insemination (LR: h2 = 0.27 (±0.05), LW: h2 = 0.34 (±0.05)). Estimates of genetic correlation (rg) between boar taint and fertility were different in LR and LW breeds. In LR an unfavorable rg of 0.31 (±0.15) was observed between androstenone and number of piglets born alive, whereas this rg in LW (- 0.15 (±0.16)) had an opposite sign. A similar breed-specific difference is observed between skatole and sperm count. Within LR, the rg of 0.08 (±0.13) indicates no relationship between the traits, whereas the rg of - 0.37 (±0.14) in LW points to an unfavorable relationship. In LR GWAS identified QTL regions on SSC5 (21.1-22.3 Mb) for androstenone and on SSC6 (5.5-7.5 Mb) and SSC14 (141.1-141.6 Mb) for skatole. For LW, one marker was found on SSC17 at 48.1 Mb for androstenone and one QTL on SSC14 between 140.5 Mb and 141.6 Mb for skatole. CONCLUSION Knowledge about such genetic correlations could help to balance conventional breeding programs with boar taint in maternal breeds. QTL regions with unfavorable pleiotropic effects on boar taint and fertility could have deleterious consequences in genomic selection programs. Constraining the weighting of these QTL in the genomic selection formulae may be a useful strategy to avoid physiological imbalances.
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Affiliation(s)
- Ines Brinke
- Institute of Animal Science, University of Bonn, 53115, Bonn, Germany
| | | | - Katharina Roth
- Institute of Animal Science, University of Bonn, 53115, Bonn, Germany
| | - Maren J Pröll-Cornelissen
- Institute of Animal Science, University of Bonn, 53115, Bonn, Germany.,Association for Bioeconomy Research (FBF e.V.), Adenauerallee 174, 53113, Bonn, Germany
| | - Hubert Henne
- BHZP GmbH, An der Wassermühle 8, 21368 Dahlenburg-Ellringen, Germany
| | - Karl Schellander
- Institute of Animal Science, University of Bonn, 53115, Bonn, Germany
| | - Ernst Tholen
- Institute of Animal Science, University of Bonn, 53115, Bonn, Germany
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Jeong CD, Islam M, Kim JJ, Cho YI, Lee SS. Reduction of slaughter age of Hanwoo steers by early genotyping based on meat yield index. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2020; 33:770-777. [PMID: 32054220 PMCID: PMC7206395 DOI: 10.5713/ajas.19.0503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 10/03/2019] [Indexed: 11/27/2022]
Abstract
Objective: This study was conducted to determine early hereditary endowment to establish a short-term feeding program.Methods: Hanwoo steers (n = 140) were equally distributed into four groups (35/group) based on genetic meat yield index (MYI) viz. the greatest, great, low, and the lowest at Jukam Hanwoo farm, Goheung. All animals were fed in group pens (5 animals/pen) with similar feed depending on the growth stage. Rice straw was provided ad libitum, whereas concentrate was fed at 5.71 kg during the growing period (6 to 13 mo) and 9.4 kg during the fattening period (13 to 28 mo). Body weight (BW) was measured at two-month intervals, whereas carcass weight was determined at slaughtering at about 31 months of age. The Affymetrix Bovine Axiom Array 640K single nucleotide polymorphism (SNP) chip was used to determine the meat quantity-related gene in the blood.Results: After 6 months, the highest (p<0.05) BW was observed in the greatest MYI group (190.77 kg) and the lowest (p<0.05) in the lowest MYI group (173.51 kg). The great MYI group also showed significantly (p<0.05) higher BW than the lowest MYI group. After 16 and 24 months, the greatest MYI group had the highest BW gain (p<0.05) and were therefore slaughtered the earliest. Carcass weight was significantly (p<0.05) higher in the greatest and the great MYI groups followed by the low and the lowest MYI groups. Back-fat thickness in the greatest MYI group was highly correlated to carcass weight and marbling score. The SNP array analysis identified the carcass-weight related gene BTB-01280026 with an additive effect. The steers with the allele increasing carcass weight had heavier slaughter weight of about 12 kg.Conclusion: Genetic MYI is a potential tool for calf selection, which will reduce the slaughter age while simultaneously increasing carcass weight, back-fat thickness, and marbling score.
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Mefford J, Park D, Zheng Z, Ko A, Ala-Korpela M, Laakso M, Pajukanta P, Yang J, Witte J, Zaitlen N. Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models. J Comput Biol 2020; 27:599-612. [PMID: 32077750 PMCID: PMC7185352 DOI: 10.1089/cmb.2019.0325] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.
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Affiliation(s)
| | - Danny Park
- School of Medicine, UCSF, San Francisco, California
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Arthur Ko
- Human Genetics, UCLA, Los Angeles, California
| | - Mika Ala-Korpela
- Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia
- University of Oulu Biocenter, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
- University of Bristol School of Medical Sciences, Population Health Science, Bristol, Bristol, United Kingdom
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland School of Medicine, Kuopio, Finland
| | | | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - John Witte
- Departments of Epidemiology and Biostatistics, and Urology, UCSF, San Francisco, California
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Gabur I, Chawla HS, Lopisso DT, von Tiedemann A, Snowdon RJ, Obermeier C. Gene presence-absence variation associates with quantitative Verticillium longisporum disease resistance in Brassica napus. Sci Rep 2020; 10:4131. [PMID: 32139810 PMCID: PMC7057980 DOI: 10.1038/s41598-020-61228-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 02/07/2020] [Indexed: 12/31/2022] Open
Abstract
Although copy number variation (CNV) and presence-absence variation (PAV) have been discovered in selected gene families in most crop species, the global prevalence of these polymorphisms in most complex genomes is still unclear and their influence on quantitatively inherited agronomic traits is still largely unknown. Here we analyze the association of gene PAV with resistance of oilseed rape (Brassica napus) against the important fungal pathogen Verticillium longisporum, as an example for a complex, quantitative disease resistance in the strongly rearranged genome of a recent allopolyploid crop species. Using Single Nucleotide absence Polymorphism (SNaP) markers to efficiently trace PAV in breeding populations, we significantly increased the resolution of loci influencing V. longisporum resistance in biparental and multi-parental mapping populations. Gene PAV, assayed by resequencing mapping parents, was observed in 23-51% of the genes within confidence intervals of quantitative trait loci (QTL) for V. longisporum resistance, and high-priority candidate genes identified within QTL were all affected by PAV. The results demonstrate the prominent role of gene PAV in determining agronomic traits, suggesting that this important class of polymorphism should be exploited more systematically in future plant breeding.
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Affiliation(s)
- Iulian Gabur
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, 35392, Giessen, Germany
| | - Harmeet Singh Chawla
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, 35392, Giessen, Germany
| | - Daniel Teshome Lopisso
- Section of General Plant Pathology and Crop Protection, Georg August University Göttingen, 37077, Göttingen, Germany
- College of Agriculture and Veterinary Medicine, Jimma University, Jimma, Ethiopia
| | - Andreas von Tiedemann
- Section of General Plant Pathology and Crop Protection, Georg August University Göttingen, 37077, Göttingen, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, 35392, Giessen, Germany
| | - Christian Obermeier
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University Giessen, 35392, Giessen, Germany.
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Pacheco HA, Rezende FM, Peñagaricano F. Gene mapping and genomic prediction of bull fertility using sex chromosome markers. J Dairy Sci 2020; 103:3304-3311. [PMID: 32063375 DOI: 10.3168/jds.2019-17767] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/09/2019] [Indexed: 12/29/2022]
Abstract
Service sire has been recognized as an important factor affecting dairy herd fertility. Our group has reported promising results on gene mapping and genomic prediction of dairy bull fertility using autosomal SNP markers. Little is known, however, about the genetic contribution of sex chromosomes, which are enriched in genes related to sexual development and reproduction. As such, the main goal of this study was to investigate the effect of SNP markers on X and Y chromosomes (BTAX and BTAY, respectively) on sire conception rate (SCR) in US Holstein bulls. The analysis included a total of 5,014 bulls with SCR records and genotypes for roughly 291k SNP located on the autosomes, 1.5k SNP located on the pseudoautosomal region (PAR), 13.7k BTAX-specific SNP, and 24 BTAY-specific SNP. We first performed genomic scans of the sex chromosomes, and then we evaluated the genomic prediction of SCR including BTAX SNP markers in the predictive models. Two markers located on PAR and 3 markers located on the X-specific region showed significant associations with sire fertility. Interestingly, these regions harbor genes, such as FAM9B, TBL1X, and PIH1D3, that are directly implicated in testosterone concentration, spermatogenesis, and sperm motility. On the other hand, BTAY showed very low genetic variability, and none of the segregating markers were associated with SCR. Notably, model predictive ability was largely improved by including BTAX markers. Indeed, the combination of autosomal with BTAX SNP delivered predictive correlations around 0.343, representing an increase in accuracy of about 7.5% compared with the standard whole autosomal genome approach. Overall, this study provides evidence of the importance of both PAR and X-specific regions in male fertility in dairy cattle. These findings may help to improve conception rates in dairy herds through accurate genome-guided decisions on bull fertility.
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Affiliation(s)
- Hendyel A Pacheco
- Department of Animal Sciences, University of Florida, Gainesville 32611
| | - Fernanda M Rezende
- Department of Animal Sciences, University of Florida, Gainesville 32611; Faculdade de Medicina Veterinária, Universidade Federal de Uberlândia, Uberlândia MG 38400-902, Brazil
| | - Francisco Peñagaricano
- Department of Animal Sciences, University of Florida, Gainesville 32611; University of Florida Genetics Institute, University of Florida, Gainesville 32610.
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Chen H, Hao Z, Zhao Y, Yang R. A fast-linear mixed model for genome-wide haplotype association analysis: application to agronomic traits in maize. BMC Genomics 2020; 21:151. [PMID: 32046650 PMCID: PMC7014697 DOI: 10.1186/s12864-020-6552-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 02/04/2020] [Indexed: 11/25/2022] Open
Abstract
Background Haplotypes combine the effects of several single nucleotide polymorphisms (SNPs) with high linkage disequilibrium, which benefit the genome-wide association analysis (GWAS). In the haplotype association analysis, both haplotype alleles and blocks are tested. Haplotype alleles can be inferred with the same statistics as SNPs in the linear mixed model, while blocks require the formulation of unified statistics to fit different genetic units, such as SNPs, haplotypes, and copy number variations. Results Based on the FaST-LMM, the fastLmPure function in the R/RcppArmadillo package has been introduced to speed up genome-wide regression scans by a re-weighted least square estimation. When large or highly significant blocks are tested based on EMMAX, the genome-wide haplotype association analysis takes only one to two rounds of genome-wide regression scans. With a genomic dataset of 541,595 SNPs from 513 maize inbred lines, 90,770 haplotype blocks were constructed across the whole genome, and three types of markers (SNPs, haplotype alleles, and haplotype blocks) were genome-widely associated with 17 agronomic traits in maize using the software developed here. Conclusions Two SNPs were identified for LNAE, four haplotype alleles for TMAL, LNAE, CD, and DTH, and only three blocks reached the significant level for TMAL, CD, and KNPR. Compared to the R/lm function, the computational time was reduced by ~ 10–15 times.
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Affiliation(s)
- Heli Chen
- Research Center for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, People's Republic of China
| | - Zhiyu Hao
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, China
| | - Yunfeng Zhao
- Research Center for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, People's Republic of China
| | - Runqing Yang
- Research Center for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, People's Republic of China. .,College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, China.
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Lichthardt C, Chen TW, Stahl A, Stützel H. Co-Evolution of Sink and Source in the Recent Breeding History of Winter Wheat in Germany. FRONTIERS IN PLANT SCIENCE 2020; 10:1771. [PMID: 32117340 PMCID: PMC7019858 DOI: 10.3389/fpls.2019.01771] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 12/18/2019] [Indexed: 05/24/2023]
Abstract
Optimizing the interplay between sinks and sources is of crucial importance for breeding progress in winter wheat. However, the physiological limitations of yield from source (e.g. green canopy duration, GCD) and sink (e.g. grain number) are still unclear. Furthermore, there is little information on how the source traits have been modified during the breeding history of winter wheat. This study analyzed the breeding progress of sink and source components and their relationships to yield components. Field trials were conducted over three years with 220 cultivars representing the German breeding history of the past five decades. In addition, genetic associations of QTL for the traits were assessed with genome-wide association studies. Breeding progress mainly resulted from an increase in grain numbers per spike, a sink component, whose variations were largely explained by the photosynthetic activity around anthesis, a source component. Surprisingly, despite significant breeding progress in GCD and other source components, they showed no direct influence on thousand grain weights, indicating that grain filling was not limited by the source strength. Our results suggest that, 1) the potential longevity of the green canopy is predetermined at the time point that the number of grains is fixed; 2) a co-evolution of source and sink strength during the breeding history contribute to the yield formation of the modern cultivars. For future breeding we suggest to choose parental lines with high grain numbers per spike on the sink side, and high photosynthetic activity around anthesis and canopy duration on the source side, and to place emphasis on these traits throughout selection.
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Affiliation(s)
- Carolin Lichthardt
- Vegetable Systems Modelling Section, Institute of Horticultural Production Systems, University of Hannover, Hannover, Germany
| | - Tsu-Wei Chen
- Vegetable Systems Modelling Section, Institute of Horticultural Production Systems, University of Hannover, Hannover, Germany
| | - Andreas Stahl
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Hartmut Stützel
- Vegetable Systems Modelling Section, Institute of Horticultural Production Systems, University of Hannover, Hannover, Germany
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Gao J, Zhou X, Hao Z, Jiang L, Yang R. Genome-wide barebones regression scan for mixed-model association analysis. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:51-58. [PMID: 31552442 DOI: 10.1007/s00122-019-03439-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 09/17/2019] [Indexed: 06/10/2023]
Abstract
Based on the simplified FaST-LMM, wherein genomic variance is replaced with heritability, we have significantly improved computational efficiency by implementing rapid R/fastLmPure to statistically infer the genetic effects of tested SNPs and focus on large or highly significant SNPs obtained using the EMMAX algorithm. For a genome-wide mixed-model association analysis, we introduce a barebones linear model fitting function called fastLmPure from the R/RcppArmadillo package for the rapid estimation of single nucleotide polymorphism (SNP) effects and the maximum likelihood values of factored spectrally transformed linear mixed models (FaST-LMM). Starting from the estimated genomic heritability of quantitative traits under a null model without quantitative trait nucleotides, maximum likelihood estimations of the polygenic heritabilities of candidate markers consume the same time as approximately four rounds of genome-wide regression scans. When focusing only on SNPs with large effects or high significance levels, as estimated by the efficient mixed-model association expedited algorithm, the run time of genome-wide mixed-model association analysis is reduced to at most two rounds of genome-wide regression scans. We have developed a novel software application called Single-RunKing to transform nonlinear mixed-model association analyses into barebones linear regression scans. Based on a realised relationship matrix calculated using genome-wide markers, Single-RunKing saves significantly computation time, as compared with the FaST-LMM that optimises the variance ratios of polygenic variances to residual variances using the R/lm function.
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Affiliation(s)
- Jin Gao
- Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China
| | - Xuefei Zhou
- Zhongbo International Business School, Guangzhou Zhongbo Education Corporation Limited, Guangzhou, 511458, China
| | - Zhiyu Hao
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, China
| | - Li Jiang
- Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China
| | - Runqing Yang
- Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China.
- Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China.
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