Abstract

Author SummaryThe number of known associations between human diseases and common genetic variants has grown dramatically in the past decade, most being identified in large-scale genetic studies of people of Western European origin. But because the frequencies of genetic variants can differ substantially between continental populations, it's important to assess how well these associations can be extended to populations with different continental ancestry. Are the correlations between genetic variants, disease endpoints, and risk factors consistent enough for genetic risk models to be reliably applied across different ancestries? Here we describe a systematic analysis of disease outcome and risk-factor–associated variants (tagSNPs) identified in European populations, in which we test whether the effect size of a tagSNP is consistent across six populations with significant non-European ancestry. We demonstrate that although nearly all such tagSNPs have effects in the same direction across all ancestries (i.e., variants associated with higher risk in Europeans will also be associated with higher risk in other populations), roughly a quarter of the variants tested have significantly different magnitude of effect (usually lower) in at least one non-European population. We therefore advise caution in the use of tagSNP-based genetic disease risk models in populations that have a different genetic ancestry from the population in which original associations were first made. We then show that this differential strength of association can be attributed to population-dependent variations in the correlation between tagSNPs and the variant that actually determines risk—the so-called functional variant. Risk models based on functional variants are therefore likely to be more robust than tagSNP-based models.

Highlights

  • In the past six years, genome-wide association studies (GWAS)have revealed thousands of common polymorphisms associated with a wide variety of traits and diseases, as study sample sizes have increased from thousands to hundreds of thousands of subjects

  • GWAS analyses stratify on genetic ancestry, because many polymorphism allele frequencies differ by ancestral group, producing false positive associations for traits that correlate with genetic ancestry

  • A panel of 68 common polymorphisms previously reported to associate with body mass index (BMI) [13], type 2 diabetes (T2D) [14], or lipid levels [15] was genotyped in up to 14,492 self-reported African

Read more

Summary

Introduction

In the past six years, genome-wide association studies (GWAS). Have revealed thousands of common polymorphisms (tagSNPs) associated with a wide variety of traits and diseases, as study sample sizes have increased from thousands to hundreds of thousands of subjects. GWAS analyses stratify on genetic ancestry, because many polymorphism allele frequencies differ by ancestral group, producing false positive associations for traits that correlate with genetic ancestry. GWAS in Asian populations in particular are becoming more common [3,4,5,6], it remains important to understand the degree to which the magnitude and direction of allelic effects generalize across diverse populations [7,8,9,10]. Provides a unique opportunity to assess GWAS generalization across multiple non-EA populations and multiple traits

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call