A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next‐generation sequencing

Abstract

In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i.e., scenarios in which some genetic variants have relatively large effects on the phenotypes); GAMuT has higher power and is more appropriate for analyzing polygenic effects (i.e., effects from a large number of genetic variants each of which contributes a small amount to the phenotypes). MFLM and MANOVA are very flexible and can be used to perform association analysis for: (i) rare variants, (ii) common variants, and (iii) a combination of rare and common variants. Although GAMuT was designed to analyze rare variants, it can be applied to analyze a combination of rare and common variants and it performs well when (1) the number of genetic variants is large and (2) each variant contributes a small amount to the phenotypes (i.e., polygenes). MFLM and MANOVA are fixed effect models which perform well for major gene association analysis. GAMuT can be viewed as an extension of sequence kernel association tests (SKAT). Both GAMuT and SKAT are more appropriate for analyzing polygenic effects and they perform well not only in the rare variant case, but also in the case of a combination of rare and common variants. Data analyses of European cohorts and the Trinity Students Study are presented to compare the performance of the two methods.

Discovery of rare variants implicated in schizophrenia using next-generation sequencing

“…Gene association with multiple traits is a variation of the sequence kernel association, called kernel distance covariance. This test uses non-parametric tests to test the association between rare variants and multiple phenotypes [ 53 ] . Similarity and dissimilarity are assessed for both genotype and phenotype and a matrix is formed for each variable.…”

“…Then, the similarity or dissimilarity matrices for each variable are tested for independence. The calculation of P -values does not require any permutations and the method can be utilized on WES or WGS [ 53 ] .…”

Multi‐SKAT: General framework to test for rare‐variant association with multiple phenotypes

“…Extending them, several groups have developed multiple‐phenotype tests for rare variants (Broadaway et al, ; Lee et al, ; Maity, Sullivan, & Tzeng, ; Sun et al, ; Wang et al, ; Wu & Pankow, ; Yan et al, ; Zhan et al, ). For example, Wang et al () proposed a multivariate functional linear model (MFLM); Broadaway et al () used a dual‐kernel‐based distance‐covariance approach to test for cross‐phenotype effects of rare variants by comparing similarity in multivariate phenotypes to similarity in genetic variants (GAMuT; Chiu et al, ); Wu and Pankow () developed a score‐based sequence kernel association test for multiple traits, MSKAT, which has been shown to be similar in performance to GAMuT (Broadaway et al, ); Zhan et al () proposed a dual kernel based association test (DKAT), which uses the dual‐kernel approach as in GAMuT but provides more robust performance when the dimension of phenotypes is high compared with the sample size.…”

Multi-SKAT: General framework to test multiple phenotype associations of rare variants

“…Extending them, several groups have developed multiple phenotype tests for rare variants Broadaway et al, 2016;Wu and Pankow, 2016;Lee et al, 2016;Sun et al, 2016;Maity et al, 2012;Yan et al, 2015;Zhan et al, 2017). For example, Wang et al (2015) proposed a multivariate functional linear model (MFLM); Broadaway et al (2016) used a dual-kernel based distancecovariance approach to test for cross phenotype effects of rare variants by comparing similarity in multivariate phenotypes to similarity in genetic variants (GAMuT) (Chiu et al, 2017); Wu et al (Wu and Pankow, 2016) developed a score based sequence kernel association test for multiple traits, MSKAT, which has been shown to be similar in performance to GAMuT (Broadaway et al, 2016); and Zhan et al (2017) proposed DKAT, which uses the dual kernel approach as in GAMuT but provides more robust performance when the dimension of phenotypes is high compared to the sample size.…”