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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Improved Lasso for genomic selection.

Andrés Legarra¹, Christèle Robert-Granié, Pascal Croiseau

¹INRA, UR 631 SAGA, F-31326 Castanet-Tolosan, France. andres.legarra@toulouse.inra.fr

Genetics Research

|December 15, 2010

Summary

This summary is machine-generated.

Bayesian Lasso with distinct variances (BL2Var) improved genomic prediction accuracy in dairy cattle by better estimating genetic variation. This method, and HetVar-GBLUP, outperformed models with common variance parameters.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Animal Genetics
Statistical Genomics
Dairy Cattle Breeding

Background:

Genomic selection in dairy cattle often assumes normal distributions for SNP effects, which may not hold true for all traits.
Bayesian Lasso (BL) offers an alternative to arbitrary priors, but standard implementations (BL1Var) use a common variance for residual and SNP effects.

Purpose of the Study:

To propose and evaluate a Bayesian Lasso model (BL2Var) with separate variances for residual and SNP effects.
To assess the performance of BL2Var and a derived method (HetVar-GBLUP) for genomic prediction in dairy cattle.

Main Methods:

Developed Bayesian Lasso with distinct residual and SNP effect variances (BL2Var).
Proposed HetVar-GBLUP, utilizing BL2Var-inferred SNP variances within a linear mixed model.
Tested models using cross-validation on Holstein and Montbéliarde French bulls with extensive SNP data for milk production traits.

Main Results:

BL2Var provided estimates of genetic variation closer to pedigree-based estimates compared to BL1Var.
BL1Var showed reduced accuracy due to excessive shrinkage from the common variance parameter.
BL2Var demonstrated the highest prediction accuracy and effectively handled major genes, particularly for fat percentage.

Conclusions:

BL2Var offers a more accurate approach to genomic prediction in dairy cattle by accommodating non-normal SNP effect distributions.
HetVar-GBLUP is a computationally efficient and nearly as accurate alternative to BL2Var, with potential for further extensions.