Generalized genetic association study with samples of related individuals

TL;DR

This paper introduces a generalized quasi-likelihood score (GQLS) test for genetic association studies involving related samples, applicable to both quantitative and binary traits, and demonstrates its improved power over existing methods.

Contribution

The paper presents a novel GQLS test that handles related samples and unspecified trait distributions, enhancing power and flexibility in genetic association analyses.

Findings

GQLS outperforms FBAT in power while controlling type I error.

Identifies significant SNPs consistent with previous studies.

Discovers new SNPs associated with complex traits.

Abstract

Genetic association study is an essential step to discover genetic factors that are associated with a complex trait of interest. In this paper we present a novel generalized quasi-likelihood score (GQLS) test that is suitable for a study with either a quantitative trait or a binary trait. We use a logistic regression model to link the phenotypic value of the trait to the distribution of allelic frequencies. In our model, the allele frequencies are treated as a response and the trait is treated as a covariate that allows us to leave the distribution of the trait values unspecified. Simulation studies indicate that our method is generally more powerful in comparison with the family-based association test (FBAT) and controls the type I error at the desired levels. We apply our method to analyze data on Holstein cattle for an estimated breeding value phenotype, and to analyze data from the Collaborative Study of the Genetics of Alcoholism for alcohol dependence. The results show a good portion of significant SNPs and regions consistent with previous reports in the literature, and also reveal new significant SNPs and regions that are associated with the complex trait of interest.