pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies

Abstract

Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.

mrMLM v4.0: An R Platform for Multi-locus Genome-wide Association Studies

“…To confirm the correctness of our software mrMLM v4.0, the same simulation datasets in Zhang et al [20] (File S4) were re-analyzed by the above six methods and three current methods (GEMMA [21], FarmCPU [14] and EMMAX [4]). As a result, our six methods were better than the three current methods (Tables S9 to S11; Figs.…”

“…The new package works well for continuous variables in plant, animal and human GWAS, although the current version doesn't work for the case-control datasets in human genetics. In addition, we corrected one mistake in the determination of the potentially associated SNPs in the Monte Carlo simulation studies of Zhang et al [20].…”

“…Using mrMLM v4.0, individual parameters may be changed in order to obtain the best results (Files S5 and S6). For example, the number of potentially associated SNPs for each chromosome in Zhang et al [20] is set at 50, and the search radius in mrMLM [16] and FASTmrMLM [17] is set at 20 kb in real data analysis. In addition, users should understand some parameter settings.…”

“…Thereafter, Liu et al [14] developed FarmCPU. Based on the advantages of the random model of QTN effect over the fixed model [15], recently, we have developed six multi-locus methods: mrMLM [16], FASTmrMLM [17], FASTmrEMMA [18], ISIS EM-BLASSO [19], pLARmEB [20] and pKWmEB [21] (Files S1 and S2). These methods include two stages.…”

Distinct characteristics of genes associated with phenome-wide variation in maize (Zea mays)

“…Given a gene, we consider all the SNPs as the multi-responses. For analysis of the given gene on each chromosome, a separate principal component analysis (PCA) was conducted using markers solely from the other 9 chromosomes to reduce the endogenous correlations between genes and principal components 54 . A subset of 1.24 million SNPs distributed across both intragenic and intergenic regions on all 10 chromosomes was used to perform PCA for both GPWAS and GWAS.…”