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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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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

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