Methodological implementation of mixed linear models in multi-locus genome-wide association studies

• Published in: Briefings in bioinformatics Vol. 19; no. 4; pp. 700 - 712
• Main Authors: Wen, Yang-Jun, Zhang, Hanwen, Ni, Yuan-Li, Huang, Bo, Zhang, Jin, Feng, Jian-Ying, Wang, Shi-Bo, Dunwell, Jim M, Zhang, Yuan-Ming, Wu, Rongling
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 20.07.2018; Oxford Publishing Limited (England)
• Subjects: Algorithms; Arabidopsis - genetics; Arabidopsis Proteins - genetics; Background noise; Bayes Theorem; Bayesian analysis; Computer Simulation; Covariance matrix; Eigenvalues; Empirical analysis; Genome-wide association studies; genome-wide association study; Genome-Wide Association Study - methods; Genomes; Linear Models; Loci; Models, Genetic; Multifactorial Inheritance; Nucleotides; Phenotype; Polygenic inheritance; Polymorphism; Polymorphism, Single Nucleotide; quantitative traits; Single-nucleotide polymorphism; Software Review; variance covariance matrix; mixed linear model; random effect; multi-locus model; genome-wide association study
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbw145

Abstract The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with ≤ 0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.