Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle

BackgroundSingle-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population.MethodsP-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects.ResultsComputation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10−5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency.ConclusionsThe use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.

[1]  P. Hansen,et al.  Genome-Wide Association Mapping for Identification of Quantitative Trait Loci for Rectal Temperature during Heat Stress in Holstein Cattle , 2013, PloS one.

[2]  W. Muir,et al.  Genome-wide association mapping including phenotypes from relatives without genotypes. , 2012, Genetics research.

[3]  K A Weigel,et al.  Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency. , 2018, Journal of dairy science.

[4]  J. Cole,et al.  A Genome-Wide Association Study for Clinical Mastitis in First Parity US Holstein Cows Using Single-Step Approach and Genomic Matrix Re-Weighting Procedure , 2015, PloS one.

[5]  Tad S Sonstegard,et al.  Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows , 2011, BMC Genomics.

[6]  Stephen D. Turner,et al.  qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots , 2014, bioRxiv.

[7]  M. Suzuki,et al.  Application of supernodal sparse factorization and inversion to the estimation of (co)variance components by residual maximum likelihood. , 2014, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[8]  M. Goddard,et al.  Genomic selection. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[9]  P M VanRaden,et al.  Derivation, calculation, and use of national animal model information. , 1991, Journal of dairy science.

[10]  Jack Dekkers,et al.  Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach , 2014 .

[11]  Ignacy Misztal,et al.  Inexpensive Computation of the Inverse of the Genomic Relationship Matrix in Populations with Small Effective Population Size , 2015, Genetics.

[12]  David R. Kelley,et al.  A whole-genome assembly of the domestic cow, Bos taurus , 2009, Genome Biology.

[13]  I Misztal,et al.  Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements. , 2015, Journal of animal science.

[14]  P Madsen,et al.  Single-step methods for genomic evaluation in pigs. , 2012, Animal : an international journal of animal bioscience.

[15]  Dorian Garrick,et al.  Bayesian methods applied to GWAS. , 2013, Methods in molecular biology.

[16]  Rohan L Fernando,et al.  A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses , 2014, Genetics Selection Evolution.

[17]  Juan P Steibel,et al.  Genome-Wide Association Analyses Based on Broadly Different Specifications for Prior Distributions, Genomic Windows, and Estimation Methods , 2017, Genetics.

[18]  M. Goddard,et al.  Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits , 2010, PLoS genetics.

[19]  L. Varona,et al.  GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait , 2018, G3: Genes, Genomes, Genetics.

[20]  Huiyu Wang.  Genome-Wide Association Mapping Using Single-Step GBLUP , 2013 .

[21]  James M. Reecy,et al.  AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond , 2006, Nucleic Acids Res..

[22]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[23]  P. Visscher,et al.  Common SNPs explain a large proportion of heritability for human height , 2011 .

[24]  M Quinton,et al.  Estimation of effects of single genes on quantitative traits. , 1992, Journal of animal science.

[25]  F. Peñagaricano,et al.  Unravelling the genomic architecture of bull fertility in Holstein cattle , 2016, BMC Genetics.

[26]  Juan P. Steibel,et al.  Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations , 2014, BMC Bioinformatics.

[27]  B. Guldbrandtsen,et al.  Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle. , 2014, Journal of dairy science.

[28]  Andrés Legarra,et al.  Genetic evaluation with major genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP , 2015, Genetics Selection Evolution.

[29]  O. F. Christensen,et al.  Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation , 2012, Genetics Selection Evolution.

[30]  S. D. Shackelford,et al.  Meta‐analysis of genome‐wide association from genomic prediction models , 2015, Animal genetics.

[31]  D. Garrick,et al.  Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. , 2009, Journal of dairy science.

[32]  Pavel Senin,et al.  A Point Mutation in Suppressor of Cytokine Signalling 2 (Socs2) Increases the Susceptibility to Inflammation of the Mammary Gland while Associated with Higher Body Weight and Size and Higher Milk Production in a Sheep Model , 2015, PLoS genetics.

[33]  I Misztal,et al.  Bias in genomic predictions for populations under selection. , 2011, Genetics research.

[34]  C. R. Henderson,et al.  Best linear unbiased estimation and prediction under a selection model. , 1975, Biometrics.

[35]  Miguel Pérez-Enciso,et al.  Sparse Matrix Inversion for Restricted Maximum Likelihood Estimation of Variance Components by Expectation-Maximization , 1993 .

[36]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.

[37]  H. Kang,et al.  Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.

[38]  C. R. Henderson.  Applications of linear models in animal breeding , 1984 .

[39]  A Legarra,et al.  Computational strategies for national integration of phenotypic, genomic, and pedigree data in a single-step best linear unbiased prediction. , 2012, Journal of dairy science.

[40]  P. Visscher,et al.  Advantages and pitfalls in the application of mixed-model association methods , 2014, Nature Genetics.

[41]  I Misztal,et al.  Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. , 2010, Journal of dairy science.

[42]  A. Legarra,et al.  Computation of deregressed proofs for genomic selection when own phenotypes exist with an application in French show-jumping horses. , 2013, Journal of animal science.

[43]  E. J. Pollak,et al.  QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies , 2014, BMC Genomics.

[44]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[45]  Simon Teyssèdre,et al.  Statistical distributions of test statistics used for quantitative trait association mapping in structured populations , 2012, Genetics Selection Evolution.

[46]  R. Fernando,et al.  Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens , 2014, Front. Genet..

[47]  I Misztal,et al.  Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. , 2015, Journal of animal science.

[48]  D. Lourenço.  Tuning indirect predictions based on SNP effects from single-step GBLUP , 2018 .

[49]  Ignacy Misztal,et al.  Single Step, a general approach for genomic selection , 2014 .

[50]  S. Wild,et al.  Localising Loci underlying Complex Trait Variation Using Regional Genomic Relationship Mapping , 2012, PloS one.

[51]  Dorian J. Garrick,et al.  Bayesian genome-wide association analysis of growth and yearling ultrasound measures of carcass traits in Brangus heifers , 2012 .

[52]  Yutaka Masuda,et al.  BLUPF90 suite of programs for animal breeding with focus on genomics , 2018 .

[53]  A. Legarra,et al.  Single-Step Genomic Evaluations with 570K Genotyped Animals in US Holsteins , 2015 .

[54]  R. Fernando,et al.  Deregressing estimated breeding values and weighting information for genomic regression analyses , 2009, Genetics Selection Evolution.

[55]  M. Stephens,et al.  Genome-wide Efficient Mixed Model Analysis for Association Studies , 2012, Nature Genetics.

[56]  M. Lund,et al.  Genomic prediction when some animals are not genotyped , 2010, Genetics Selection Evolution.