Linear mixed models for association analysis of quantitative traits with next‐generation sequencing data

Abstract

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene‐based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F‐statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F‐distributed statistics provide a good control of the type I error rate. The F‐test statistics of the LMMs have similar or higher power than the FLMMs, kernel‐based famSKAT (family‐based sequence kernel association test), and burden test famBT (family‐based burden test). The F‐statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05. For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Bi-Level Structured Functional Analysis for Genome-Wide Association Studies

“…Significantly different from the existing functional genetic analysis, such as Y. Li et al (2022) and Chiu et al (2018), we explore multiple structures of SNP data, including the group structure, bi-level sparsity, and group-level network structure. This allows the proposed analysis to more accurately and more deeply describe disease biology.…”

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A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation

“…Well-known examples include Hotelling’s T 2 statistic ( Fan & Knapp, 2003 ), genomic-distance based regression ( Wessel and Schork, 2006 ), variance component (VC) or kernel machine regression based tests ( Tzeng and Zhang 2007 ; Pan 2009 ; Wu et al, 2010 ; Wu et al, 2011 ), the use of weighted genetic risk scores ( Li et al, 2009 ; Iribarren et al, 2018 ), and the C-alpha test ( Neale et al, 2011 ). These tests have also been extended using the framework of functional or mixed effects models ( Fan et al, 2013 ; Chen et al, 2019 ; Chiu et al, 2019 ). In the third group, each test assesses associations between the phenotype variable and some form of aggregated genotype data.…”

Gene‐based analysis of bi‐variate survival traits via functional regressions with applications to eye diseases

“…For gene‐based analysis of sequencing data which contain a large number of rare variants, the available methods can only analyze univariate traits (Chein et al, 2017; Chen et al, 2014; Chiu, Yuan, et al, 2019; Chiu, Zhang, et al, 2019; Fan, Wang, Qi, et al, 2016). To analyze univariate survival traits, Fan, Wang, Qi, et al (2016) has developed gene‐based Cox models and related test statistics to analyze the sequencing data.…”