The power of genomic control

Although association analysis is a useful tool for uncovering the genetic u nderpinnings of complex traits, its utility is diminished by population sub structure, which can produce spurious association between phenotype and gen otype within population-based samples. Because family-based designs are rob ust against substructure, they have risen to the fore of association analys is. Yet, if population substructure could be ignored, this robustness can c ome at the price of power. Unfortunately it is rarely evident when populati on substructure can be ignored. Devlin and Roeder recently have proposed a method, termed "genomic control" (GC), which has the robustness of family-b ased designs even though it uses population-based data. GC uses the genome itself to determine appropriate corrections for population-based associatio n tests. Using the GC method, we contrast the power of two study designs, f amily trios (i.e., father, mother, and affected progeny) versus case-contro l. For analysis of trios, we use the TDT test. When population substructure is absent, we find GC is always more powerful than TDT; furthermore, contr ary to previous results, we show that as a disease becomes more prevalent t he discrepancy in power becomes more extreme. When population substructure is present, however, the results are more complex: TDT is more powerful whe n population substructure is substantial, and GC is more powerful otherwise . We also explore general issues of power and implementation of GC within t he case-control setting and find that, economically, CC is at least compara ble to and often less expensive than family-based methods. Therefore, CC me thods should prove a useful complement to family-based methods for the gene tic analysis of complex traits.