Abstract
To account for population stratification in association studies, principal-components analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. Here, we use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs. Standardized height residuals from unrelated adults from the FHS Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individual's ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20-Mbp regions within each chromosome. We assessed the extent to which there were differences in association studies of height depending on whether PCs for global, local, or both global and local ancestry were included as covariates. The correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Genome-wide association tests without any ancestry adjustment demonstrated an inflated type I error rate that decreased with adjustment for local ancestry, global ancestry, or both. A known spurious association was replicated for SNPs within the lactase gene, and this false-positive association was abolished by adjustment with local or global ancestry PCs. Population stratification is a potential source of bias in this seemingly homogenous FHS population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.
Highlights
To account for population stratification in association studies, principalcomponents analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome
Population stratification is a potential source of bias in this seemingly homogenous Framingham Heart Study (FHS) population
Local and global principal components (PCs) derived from SNPs appear to provide adequate information about ancestry
Summary
To account for population stratification in association studies, principalcomponents analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. We use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs. Association studies, whether genome-wide or candidategene-based, have emerged as a popular tool to identify underlying genetic variants with small disease-related effects. Genome-wide association studies that adjust for PS using information from markers selected across the entire genome address the effect of global PS, which is mainly driven by the demographic history of a population. We use the Framingham Heart Study (FHS) data from the Genetic Analysis Workshop 16 (GAW16) to assess the differences in genome-wide association studies of height using adjustment for global versus local ancestry to account for PS
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