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Home / Equine Research Bank / G3 (Bethesda) / GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factor...
G3 (Bethesda, Md.)2018; 8(7); 2301-2308; doi: 10.1534/g3.118.200336

GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait.

Abstract: Bayesian models for genomic prediction and association mapping are being increasingly used in genetics analysis of quantitative traits. Given a point estimate of variance components, the popular methods SNP-BLUP and GBLUP result in joint estimates of the effect of all markers on the analyzed trait; single and multiple marker frequentist tests (EMMAX) can be constructed from these estimates. Indeed, BLUP methods can be seen simultaneously as Bayesian or frequentist methods. So far there is no formal method to produce Bayesian statistics from GBLUP. Here we show that the Bayes Factor, a commonly admitted statistical procedure, can be computed as the ratio of two normal densities: the first, of the estimate of the marker effect over its posterior standard deviation; the second of the null hypothesis (a value of 0 over the prior standard deviation). We extend the BF to pool evidence from several markers and of several traits. A real data set that we analyze, with ours and existing methods, analyzes 630 horses genotyped for 41711 polymorphic SNPs for the trait "outcome of the qualification test" (which addresses gait, or ambling, of horses) for which a known major gene exists. In the horse data, single marker EMMAX shows a significant effect at the right place at Bonferroni level. The BF points to the same location although with low numerical values. The strength of evidence combining information from several consecutive markers increases using the BF and decreases using EMMAX, which comes from a fundamental difference in the Bayesian and frequentist schools of hypothesis testing. We conclude that our BF method complements frequentist EMMAX analyses because it provides a better pooling of evidence across markers, although its use for primary detection is unclear due to the lack of defined rejection thresholds.
Copyright © 2018 Legarra et al.
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Publication Date: 2018-07-02 PubMed ID: 29748199PubMed Central: PMC6027892DOI: 10.1534/g3.118.200336Google Scholar: Lookup
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  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article presents a method for producing Bayesian statistics from a Genomic Best Linear Unbiased Predictors (GBLUP) model with an emphasis on highlighting its applicability in the genetics analysis of quantitative traits. The method designed to examine a major gene for horse gait uses Bayes Factor to quantify evidence from multiple markers and traits.

Introduction to Bayesian Models in Genetic Analysis

  • The study emphasizes the growing use of Bayesian models in genetic analysis of quantitative traits. These models bear a unique approach – it holds certain parameters as random variables backed with a probability distribution.
  • It revolves around SNP-BLUP (Single Nucleotide Polymorphism Best Linear Unbiased Predictor) and GBLUP, both of which offer combined effect estimates of all analyzed markers on a specific trait.
  • The study establishes the dual characteristic of BLUP methods – their ability to adapt to both Bayesian and frequentist statistical techniques.

Bayes Factor Computation

  • The researchers introduced a fresh approach that permits generating Bayesian statistics from GBLUP models. The method involves calculating the Bayes Factor as per the ratio of two normal densities.
  • The mathematical aspect involves the estimation of the marker effect over its subsequent standard deviation and value 0 over the prior standard deviation in regard to the null hypothesis.
  • The researchers also broadened the Bayes Factor to merge evidence obtained from several markers and more than one trait which is not possible through conventional methods.

Empirical Evidence from Horse Data

  • This study employs real data from horse genotypes and exhibits the practical implementation of the introduced method using EMMAX (Efficient Mixed-Model Association Expedited).
  • The data comprised of 630 horses genotyped for 41711 polymorphic SNPs concerning the trait “outcome of the qualification test” (which pertains to the gait, or ambling of horses).
  • With the help of EMMAX, a significant effect was recorded correctly at the Bonferroni level. Furthermore, the Bayes Factor was also able to point to the same location although with a lesser numerical value.

Comparison of Bayesian and Frequentist Methods

  • There’s a fundamental difference in how the Bayesian and frequentist schools view hypothesis testing. The strength of evidence combining information from several markers increased using the Bayes Factor and decreased with EMMAX.
  • However, it is important to note that although BF method offers a better evidence pooling across markers, its use for primary detection remains unclear due to undefined rejection thresholds.
  • Thus, it is concluded that the presented BF method can serve as a complementary tool to frequentist EMMAX analysis in genetic studies.

Cite This Article

APA
Legarra A, Ricard A, Varona L. (2018). GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait. G3 (Bethesda), 8(7), 2301-2308. https://doi.org/10.1534/g3.118.200336

Publication

G3 (Bethesda, Md.)
ISSN: 2160-1836
NlmUniqueID: 101566598
Country: England
Language: English
Volume: 8
Issue: 7
Pages: 2301-2308

Researcher Affiliations

Legarra, Andres
  • INRA (Institut National de la Recherche Agronomique), UMR 1388 GenPhySE, F-31326 Castanet-Tolosan, France andres.legarra@inra.fr.
Ricard, Anne
  • INRA (Institut National de la Recherche Agronomique), UMR 1313 GABI, 78352 Jouy-en-Josas, France.
  • IFCE (Institut Francais du Cheval et de l'Equitation), Recherche et Innovation, 61310 Exmes, France.
Varona, Luis
  • Departamento de Anatomía, Embriología y Genética, Universidad de Zaragoza, 50013 Zaragoza, Spain.
  • Instituto Agroalimentario de Aragón (IA2), 50013 Zaragoza, Spain.

MeSH Terms

  • Algorithms
  • Animals
  • Bayes Theorem
  • Databases, Genetic
  • Gait
  • Genetic Markers
  • Genome-Wide Association Study / methods
  • Genomics / methods
  • Horses
  • Models, Genetic
  • Quantitative Trait Loci
  • Quantitative Trait, Heritable
  • Selection, Genetic

References

This article includes 45 references
  1. Aguilar I, Misztal I, Johnson D, Legarra A, Tsuruta S. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score1. J. Dairy Sci. 93: 743–752.
    doi: 10.3168/jds.2009-2730pubmed: 20105546google scholar: lookup
  2. Andersson L S, Larhammar M, Memic F, Wootz H, Schwochow D. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice. Nature 488: 642–646.
    doi: 10.1038/nature11399pmc: PMC3523687pubmed: 22932389google scholar: lookup
  3. Bernal Rubio Y L, Gualdrón Duarte J L, Bates R O, Ernst C W, Nonneman D. Meta-analysis of genome-wide association from genomic prediction models. Anim. Genet. 47: 36–48.
    doi: 10.1111/age.12378pmc: PMC4738412pubmed: 26607299google scholar: lookup
  4. Brard S, Ricard A. Should we use the single nucleotide polymorphism linked to in genomic evaluation of French trotter?. J. Anim. Sci. 93: 4651–4659.
    doi: 10.2527/jas.2015-9224pubmed: 26523557google scholar: lookup
  5. Casiró S, Velez-Irizarry D, Ernst C W, Raney N E, Bates R O. Genome-wide association study in an F2 Duroc x Pietrain resource population for economically important meat quality and carcass traits. J. Anim. Sci. 95: 545–558.
    doi: 10.2527/jas.2016.1003pubmed: 28380601google scholar: lookup
  6. Chen C, Steibel J P, Tempelman R J. Genome-Wide Association Analyses Based on Broadly Different Specifications for Prior Distributions, Genomic Windows, and Estimation Methods. Genetics 206: 1791–1806.
    doi: 10.1534/genetics.117.202259pmc: PMC5560788pubmed: 28637709google scholar: lookup
  7. Christensen O F. Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation. Genet. Sel. Evol. 44: 37.
    doi: 10.1186/1297-9686-44-37pmc: PMC3549765pubmed: 23206367google scholar: lookup
  8. Dikmen S, Cole J B, Null D J, Hansen P J. Genome-wide association mapping for identification of quantitative trait loci for rectal temperature during heat stress in Holstein cattle. PLoS One 8: e69202.
    doi: 10.1371/journal.pone.0069202pmc: PMC3720646pubmed: 23935954google scholar: lookup
  9. Endelman J B. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4: 250–255.
    doi: 10.3835/plantgenome2011.08.0024google scholar: lookup
  10. Fernando R L, Habier D, Stricker C, Dekkers J C M, Totir L R. Genomic selection. Acta Agric. Scand. A 57: 192–195.
  11. Fernando R L, Garrick D. Bayesian methods applied to GWAS. Genome-Wide Assoc. Stud. Genomic Predict. 237–274.
    doi: 10.1007/978-1-62703-447-0_10pubmed: 23756894google scholar: lookup
  12. Gualdrón Duarte J L, Cantet R J, Bates R O, Ernst C W, Raney N E. Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations. BMC Bioinformatics 15: 246.
    doi: 10.1186/1471-2105-15-246pmc: PMC4112210pubmed: 25038782google scholar: lookup
  13. Habier D, Fernando R L, Dekkers J C M. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177: 2389–2397.
    pmc: PMC2219482pubmed: 18073436
  14. Habier D, Fernando R L, Kizilkaya K, Garrick D J. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12: 186.
    doi: 10.1186/1471-2105-12-186pmc: PMC3144464pubmed: 21605355google scholar: lookup
  15. Hayes B J, Pryce J, Chamberlain A J, Bowman P J, Goddard M E. Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits. PLoS Genet. 6: e1001139.
    doi: 10.1371/journal.pgen.1001139pmc: PMC2944788pubmed: 20927186google scholar: lookup
  16. Heath S C. Markov chain Monte Carlo segregation and linkage analysis for oligogenic models. Am. J. Hum. Genet. 61: 748–760.
    doi: 10.1086/515506pmc: PMC1715966pubmed: 9326339google scholar: lookup
  17. Jarquín D, Crossa J, Lacaze X, Cheyron P D, Daucourt J. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor. Appl. Genet. 127: 595–607.
    doi: 10.1007/s00122-013-2243-1pmc: PMC3931944pubmed: 24337101google scholar: lookup
  18. Jia Y, Jannink J-L. Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy. Genetics 192: 1513–1522.
    doi: 10.1534/genetics.112.144246pmc: PMC3512156pubmed: 23086217google scholar: lookup
  19. Kang H M, Sul J H, Zaitlen N A, Kong S, Freimer N B. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42: 348–354.
    doi: 10.1038/ng.548pmc: PMC3092069pubmed: 20208533google scholar: lookup
  20. Kass R E, Raftery A E. Bayes factors. J. Am. Stat. Assoc. 90: 773–795.
    doi: 10.1080/01621459.1995.10476572google scholar: lookup
  21. Kennedy B, Quinton M, Van Arendonk J. Estimation of effects of single genes on quantitative traits. J. Anim. Sci. 70: 2000–2012.
    doi: 10.2527/1992.7072000xpubmed: 1644672google scholar: lookup
  22. Kruglyak L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat. Genet. 22: 139–144.
    doi: 10.1038/9642pubmed: 10369254google scholar: lookup
  23. Legarra A, Misztal I. Technical note: Computing strategies in genome-wide selection. J. Dairy Sci. 91: 360–366.
    doi: 10.3168/jds.2007-0403pubmed: 18096959google scholar: lookup
  24. Legarra A, Ricardi A, Filangi O. GS3: Genomic Selection, Gibbs Sampling, Gauss-Seidel (and BayesCp). .
  25. Legarra A, Croiseau P, Sanchez M P, Teyssèdre S, Sallé G. A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species. Genet. Sel. Evol. 47: 6.
    doi: 10.1186/s12711-015-0087-7pmc: PMC4324410pubmed: 25885597google scholar: lookup
  26. Legarra A, Vitezica Z G. Genetic evaluation with major genes and polygenic inheritance when some animals are not genotyped using gene content multiple-trait BLUP. Genet. Sel. Evol. 47: 89.
    doi: 10.1186/s12711-015-0165-xpmc: PMC5518164pubmed: 26576649google scholar: lookup
  27. Lourenco D L, Tsuruta S, Fragomeni B O, Masuda Y, Aguilar I. Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus. J. Anim. Sci. 93: 2653–2662.
    doi: 10.2527/jas.2014-8836pubmed: 26115253google scholar: lookup
  28. Maier R, Moser G, Chen G-B, Ripke S, Absher D. Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder. Am. J. Hum. Genet. 96: 283–294.
    doi: 10.1016/j.ajhg.2014.12.006pmc: PMC4320268pubmed: 25640677google scholar: lookup
  29. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T. BLUPF90 and related programs (BGF90), pp. 28–07 in: 7th World Congress on Genetics Applied to Livestock Production, CD-ROM Communication, Montpellier, France.. .
  30. Moser G, Lee S H, Hayes B J, Goddard M E, Wray N R. Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model. PLoS Genet. 11: e1004969.
    doi: 10.1371/journal.pgen.1004969pmc: PMC4388571pubmed: 25849665google scholar: lookup
  31. Nagamine Y, Pong-Wong R, Navarro P, Vitart V, Hayward C. Localising Loci underlying Complex Trait Variation Using Regional Genomic Relationship Mapping. PLoS One 7: e46501.
    doi: 10.1371/journal.pone.0046501pmc: PMC3471913pubmed: 23077511google scholar: lookup
  32. Parker Gaddis K L, Cole J B, Clay J S, Maltecca C. Genomic selection for producer-recorded health event data in US dairy cattle. J. Dairy Sci. 97: 3190–3199.
    doi: 10.3168/jds.2013-7543pubmed: 24612803google scholar: lookup
  33. Pérez-Enciso M, Varona L. Quantitative trait loci mapping in F2 crosses between outbred lines. Genetics 155: 391–405.
    pmc: PMC1461080pubmed: 10790412
  34. Ricard A. Does heterozygosity at the DMRT3 gene make French trotters better racers?. Genet. Sel. Evol. 47: 10.
    doi: 10.1186/s12711-015-0095-7pmc: PMC4340234pubmed: 25886871google scholar: lookup
  35. Strandén I, Garrick D J. Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J. Dairy Sci. 92: 2971–2975.
    doi: 10.3168/jds.2008-1929pubmed: 19448030google scholar: lookup
  36. Teyssèdre S, Elsen J-M, Ricard A. Statistical distributions of test statistics used for quantitative trait association mapping in structured populations. Genet. Sel. Evol. 44: 32.
    doi: 10.1186/1297-9686-44-32pmc: PMC3817592pubmed: 23146127google scholar: lookup
  37. Tsuruta S, Misztal I, Aguilar I, Lawlor T. Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins. J. Dairy Sci. 94: 4198–4204.
    doi: 10.3168/jds.2011-4256pubmed: 21787955google scholar: lookup
  38. VanRaden P M. Efficient Methods to Compute Genomic Predictions. J. Dairy Sci. 91: 4414–4423.
    doi: 10.3168/jds.2007-0980pubmed: 18946147google scholar: lookup
  39. Varona L, García-Cortés L A, Pérez-Enciso M. Bayes factors for detection of quantitative trait loci. Genet. Sel. Evol. 33: 133–152.
    doi: 10.1186/1297-9686-33-2-133pmc: PMC2705388pubmed: 11333831google scholar: lookup
  40. Varona L. Understanding the use of Bayes factor for testing candidate genes. J. Anim. Breed. Genet. 127: 16–25.
    doi: 10.1111/j.1439-0388.2009.00826.xpubmed: 20074183google scholar: lookup
  41. Wakefield J. Bayes factors for genome‐wide association studies: comparison with P‐values. Genet. Epidemiol. 33: 79–86.
    doi: 10.1002/gepi.20359pubmed: 18642345google scholar: lookup
  42. Wakefield J. Commentary: Genome-wide significance thresholds via Bayes factors. Int. J. Epidemiol. 41: 286–291.
    doi: 10.1093/ije/dyr241pmc: PMC3304534pubmed: 22345299google scholar: lookup
  43. Wang H, Misztal I, Aguilar I, Legarra A, Muir W. Genome-wide association mapping including phenotypes from relatives without genotypes. Genet. Res. 94: 73–83.
    doi: 10.1017/S0016672312000274pubmed: 22624567google scholar: lookup
  44. Wang T, Chen Y-P P, Bowman P J, Goddard M E, Hayes B J. A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping. BMC Genomics 17: 744.
    doi: 10.1186/s12864-016-3082-7pmc: PMC5031345pubmed: 27654580google scholar: lookup
  45. Yang J, Benyamin B, McEvoy B P, Gordon S, Henders A K. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42: 565–569.
    doi: 10.1038/ng.608pmc: PMC3232052pubmed: 20562875google scholar: lookup
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