Determinants of the success of whole-genome association testing
- Andrew G. Clark1,4,
- Eric Boerwinkle2,
- James Hixson2, and
- Charles F. Sing3
- 1 Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, USA
- 2 Human Genetics Center, University of Texas Health Science Center, Houston, Texas 77030, USA
- 3 Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA
This extract was created in the absence of an abstract.
The convergence of an aging population and spiraling health care costs have produced a perfect storm of urgency for understanding the causal basis for common chronic disorders such as diabetes and cardiovascular disease. At issue is the role of genetics in providing solid, practical answers to contemporary public health challenges. The International HapMap Project has staked out ambitious claims, and the time to deliver on these promises approaches. We anticipate that analyses of HapMap data will suggest better study designs for identifying genetic variants that predict risk of chronic complex disorders. These studies will entail enormous costs, yet the answers to many important questions about the optimal design remain unanswered. The brave and the wealthy will be able to proceed without answers to these questions, and, indeed, some have experienced success (Hinds et al. 2004; Klein et al. 2005). At a time when many research groups are about to leap into the enterprise of whole-genome association testing, it seems prudent to reflect on assumptions that are being made and challenges that lie ahead. Our intent is not to question the fundamental idea of linkage disequilibrium mapping (Weiss and Terwilliger 2000), but rather to point out the hidden and untested assumptions implicit in using tag SNPs for linkage disequilibrium mapping of common diseases using samples from human populations. Our hope is to avoid expensive mistakes that may emerge if we ignore the assumptions related to study design and analysis, and blindly generate genotypes for large population studies with the hope that somehow LD mapping analysis will produce useful results.
Some of the factors that will have an impact on the efficacy of HapMap for providing guidelines for whole-genome association testing can be summarized as follows:
1. What is the population of inference?
The first design issue in any study of complex disorders is to identify and characterize …
