On the analysis of a repeated measure design in genome-wide association analysis.

Young Lee,Juyoung Lee,Suyeon Park,Sungho Won,Sanghoon Moon,Woojoo Lee,Robert Elston

doi:10.3390/ijerph111212283

Abstract

Longitudinal data enables detecting the effect of aging/time, and as a repeated measures design is statistically more efficient compared to cross-sectional data if the correlations between repeated measurements are not large. In particular, when genotyping cost is more expensive than phenotyping cost, the collection of longitudinal data can be an efficient strategy for genetic association analysis. However, in spite of these advantages, genome-wide association studies (GWAS) with longitudinal data have rarely been analyzed taking this into account. In this report, we calculate the required sample size to achieve 80% power at the genome-wide significance level for both longitudinal and cross-sectional data, and compare their statistical efficiency. Furthermore, we analyzed the GWAS of eight phenotypes with three observations on each individual in the Korean Association Resource (KARE). A linear mixed model allowing for the correlations between observations for each individual was applied to analyze the longitudinal data, and linear regression was used to analyze the first observation on each individual as cross-sectional data. We found 12 novel genome-wide significant disease susceptibility loci that were then confirmed in the Health Examination cohort, as well as some significant interactions between age/sex and SNPs.

Highlights

Disease prognosis and personalized medicine require the identification of genetic and non-genetic risk factors and, with the rapid improvement of genotyping technology, more than ten thousand genome-wide association studies (GWAS) have been conducted to discover disease susceptibility loci
The analysis of multiple phenotypes can suffer from their inherent heterogeneity, but the analysis of the multiple measures of the same phenotype provided by longitudinal data may avoid this issue and, if measurement errors are substantial, GWAS with longitudinal data can be expected to mitigate the sample size problem
Results from the longitudinal GWAS were compared with those from GWAS using cross-sectional data, and our results showed that GWAS using longitudinal data provided more significant results

Summary

Introduction

Disease prognosis and personalized medicine require the identification of genetic and non-genetic risk factors and, with the rapid improvement of genotyping technology, more than ten thousand genome-wide association studies (GWAS) have been conducted to discover disease susceptibility loci. Since the first such successful study in 2005 [1], more than ten thousand disease susceptibility loci have been successfully identified and these findings have improved our understanding of the genetic background of human diseases. The analysis of multiple phenotypes can suffer from their inherent heterogeneity, but the analysis of the multiple measures of the same phenotype provided by longitudinal data may avoid this issue and, if measurement errors are substantial, GWAS with longitudinal data can be expected to mitigate the sample size problem

Methods

Results

Discussion

Conclusion