Multilocus Association Testing of Quantitative Traits Based on Partial Least-Squares Analysis

Abstract

Because of combining the genetic information of multiple loci, multilocus association studies (MLAS) are expected to be more powerful than single locus association studies (SLAS) in disease genes mapping. However, some researchers found that MLAS had similar or reduced power relative to SLAS, which was partly attributed to the increased degrees of freedom (dfs) in MLAS. Based on partial least-squares (PLS) analysis, we develop a MLAS approach, while avoiding large dfs in MLAS. In this approach, genotypes are first decomposed into the PLS components that not only capture majority of the genetic information of multiple loci, but also are relevant for target traits. The extracted PLS components are then regressed on target traits to detect association under multilinear regression. Simulation study based on real data from the HapMap project were used to assess the performance of our PLS-based MLAS as well as other popular multilinear regression-based MLAS approaches under various scenarios, considering genetic effects and linkage disequilibrium structure of candidate genetic regions. Using PLS-based MLAS approach, we conducted a genome-wide MLAS of lean body mass, and compared it with our previous genome-wide SLAS of lean body mass. Simulations and real data analyses results support the improved power of our PLS-based MLAS in disease genes mapping relative to other three MLAS approaches investigated in this study. We aim to provide an effective and powerful MLAS approach, which may help to overcome the limitations of SLAS in disease genes mapping.

Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis

“…To control family-wise type I error rate, SMA requires methods like Bonferroni correction for multiple testing. Such adjustments are usually too conservative, especially in a large scale SMA with extensive LD between linked markers, and thus they may cause true associations to be missed [ 26 , 94 – 96 ]. That is why for most complex polygenic traits, SMA only detects a very small proportion of genetic variants [ 97 , 98 ].…”

Prediction and association mapping of agronomic traits in maize using multiple omic data

“…In addition, single-locus methods usually require multiple test corrections for the P-value threshold, such as Bonferroni correction, to control the Type 1 error rate. This criterion is too stringent and many true associations may be missed (Zhang et al, 2011). In contrast, multi-locus associations can overcome these problems because these methods simultaneously use all genetic information of multiple loci and there is no need for multiple testing corrections due to the multi-locus nature (Zhang et al, 2011).…”

“…This criterion is too stringent and many true associations may be missed (Zhang et al, 2011). In contrast, multi-locus associations can overcome these problems because these methods simultaneously use all genetic information of multiple loci and there is no need for multiple testing corrections due to the multi-locus nature (Zhang et al, 2011). Multi-locus methods have shown to perform better than single-locus methods.…”

Applying genome-wide gene-based expression quantitative trait locus mapping to study population ancestry and pharmacogenetics

“…This study used a gene-based partial least squares (PLS) method to correlate the multiple transcript probes and multiple SNP markers. The original PLS method had been applied to gene expression data analysis for more than 10 years [ 63 - 65 ] and started to apply to genome-wide association study in the past few years [ 66 - 68 ]. However, the original PLS method did not consider gene information.…”