Robust Tests in Genome-Wide Scans under Incomplete Linkage Disequilibrium

TL;DR

This paper evaluates the effectiveness of robust statistical tests in genome-wide association studies under incomplete linkage disequilibrium, comparing their performance to traditional tests through simulations and real data applications.

Contribution

It extends the analysis of robust tests to incomplete LD models, providing insights into their efficiency and robustness in GWAS with many markers.

Findings

Robust tests outperform Pearson's chi-square under incomplete LD.

Simulations show robust tests maintain power across various genetic models.

Applications to real GWAS data demonstrate practical utility.

Abstract

Under complete linkage disequilibrium (LD), robust tests often have greater power than Pearson's chi-square test and trend tests for the analysis of case-control genetic association studies. Robust statistics have been used in candidate-gene and genome-wide association studies (GWAS) when the genetic model is unknown. We consider here a more general incomplete LD model, and examine the impact of penetrances at the marker locus when the genetic models are defined at the disease locus. Robust statistics are then reviewed and their efficiency and robustness are compared through simulations in GWAS of 300,000 markers under the incomplete LD model. Applications of several robust tests to the Wellcome Trust Case-Control Consortium [Nature 447 (2007) 661--678] are presented.