An adaptive gene-level association test for pedigree data

Jun Young Park,Wei Pan,Chong Wu

doi:10.1186/s12863-018-0639-2

Abstract

BackgroundWe propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines the results across a class of score-based tests, only requiring fitting a single null model (under the null hypothesis) for the whole genome, thereby being computationally efficient.ResultsWe applied our approach to test for association with the high-density lipoprotein (HDL) ratio of post- and pretreatments in GAW20 data. Using the LMM similar to that used by Aslibekyan et al. (PLos One, 7:48663, 2012), our method identified 2 nearly significant genes (APOA5 and ZNF259) near rs964184, whereas neither the other gene-level tests nor the standard test on each individual single-nucleotide polymorphism (SNP) detected any significant gene in a genome-wide scan.ConclusionsGene-level association testing can be a complementary approach to the SNP-level association testing and our method is adaptive and efficient compared to several other existing gene-level association tests.

Highlights

We propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs)
Based on the 95 loci drawn from previous studies based on single-nucleotide polymorphism (SNP)-level association testing [1], and found SNP rs964184 to be strongly associated with the high-density lipoprotein (HDL) ratio of post- and pretreatments
Note that the Adaptive sum of powered score (aSPU) test was originally proposed for generalized linear models, and extended to generalized estimating equations and generalized linear mixed models (GLMM) [3–5]

Summary

Introduction

We propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines the results across a class of score-based tests, only requiring fitting a single null model (under the null hypothesis) for the whole genome, thereby being computationally efficient. The goal of this work is to perform a gene-level association test to detect genes significantly associated with a single trait using the GAW20 data while effectively controlling for the false-positive rate. We apply the adaptive sum of powered score (aSPU) test [2], which is motivated to account for unknown and varying association patterns (eg, varying numbers or proportions of associated SNPs) across the genes, maintaining higher power than other nonadaptive gene-level tests. The aSPU test is computationally feasible as it does not require to fit separate models for each SNP or gene, and it satisfactorily controls false-positive rates.

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