Windfalls and pitfalls: Applications of population genetics to the search for disease genes.
Association mapping can be viewed as an application of population genetics and evolutionary biology to the problem of identifying genes causally connected to phenotypes. However, some population-genetic principles important to the design and analysis of association studies have not been widely understood or have even been generally misunderstood. Some of these principles underlie techniques that can aid in the discovery of genetic variants that influence phenotypes ('windfalls'), whereas others can interfere with study design or interpretation of results ('pitfalls'). Here, considering examples involving genetic variant discovery, linkage disequilibrium, power to detect associations, population stratification and genotype imputation, we address misunderstandings in the application of population genetics to association studies, and we illuminate how some surprising results in association contexts can be easily explained when considered from evolutionary and population-genetic perspectives. Through our examples, we argue that population-genetic thinking-which takes a theoretical view of the evolutionary forces that guide the emergence and propagation of genetic variants-substantially informs the design and interpretation of genetic association studies. In particular, population-genetic thinking sheds light on genetic confounding, on the relationships between association signals of typed markers and causal variants, and on the advantages and disadvantages of particular strategies for measuring genetic variation in association studies.
Department of Biology, Stanford University, Stanford, CA 94305-5020, USA and Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
Michael D
MD
Edge
Prakash
P
Gorroochurn
Noah A
NA
Rosenberg
genome-wide association studies; imputation; linkage disequilibrium; population stratification