Generalized correlation coefficient for genome-wide association analysis of cognitive ability in twins.

Afsaneh Mohammadnejad,Shuxia Li,Jesper Lund,Chunsheng Xu,Jan Baumbach,Kaare Christensen,Marianne Nygaard,Lene Christiansen,Weilong Li,Dongfeng Zhang,Jacob V B Hjelmborg,Qihua Tan

doi:10.18632/aging.104198

Abstract

Despite a strong genetic background in cognitive function only a limited number of single nucleotide polymorphisms (SNPs) have been found in genome-wide association studies (GWASs). We hypothesize that this is partially due to mis-specified modeling concerning phenotype distribution as well as the relationship between SNP dosage and the level of the phenotype. To overcome these issues, we introduced an assumption-free method based on generalized correlation coefficient (GCC) in a GWAS of cognitive function in Danish and Chinese twins to compare its performance with traditional linear models. The GCC-based GWAS identified two significant SNPs in Danish samples (rs71419535, p = 1.47e-08; rs905838, p = 1.69e-08) and two significant SNPs in Chinese samples (rs2292999, p = 9.27e-10; rs17019635, p = 2.50e-09). In contrast, linear models failed to detect any genome-wide significant SNPs. The number of top significant genes overlapping between the two samples in the GCC-based GWAS was higher than when applying linear models. The GCC model identified significant genetic variants missed by conventional linear models, with more replicated genes and biological pathways related to cognitive function. Moreover, the GCC-based GWAS was robust in handling correlated samples like twin pairs. GCC is a useful statistical method for GWAS that complements traditional linear models for capturing genetic effects beyond the additive assumption.

Highlights

Cognitive function is an important phenotype involving multiple mental abilities including learning, thinking, reasoning, remembering, problem-solving, decisionmaking, and attention
We introduced an assumptionfree method based on generalized correlation coefficient (GCC) in a genome-wide association studies (GWASs) of cognitive function in Danish and Chinese twins to compare its performance with traditional linear models
The type I error rates estimated for GCC, kinship model (Kinship) and linear model (LME) were 0.052, 0.052 and 0.050, respectively

Summary

Introduction

Cognitive function is an important phenotype involving multiple mental abilities including learning, thinking, reasoning, remembering, problem-solving, decisionmaking, and attention. Recent twin and family studies have shown that the heritability of general cognitive function is more than 50% in adolescence through adulthood to older age [1], www.aging-us.com suggesting a substantial genetic contribution to the phenotype. Despite the relatively large number of genome-wide association studies (GWASs) of cognition performed to date, a lot of the heritability is still unexplained. Different limitations in current GWASs might be the reason for the situation due to the distribution of cognitive measurements as well as the complex relationship between SNP genotypes and cognitive performance. One of the assumptions in the popular linear models used for GWAS is normality of the phenotype of interest. The additive genetic effect has been the most popular assumption in current genetic association studies and the only genetic model addressed in studies using linear models. The pattern of genetic associations is likely more complex, including both linear (additive) and non-linear (non-additive) relationships [4, 5]

Objectives

Methods

Results

Conclusion