The Power of Genomic Control

Abstract

Although association analysis is a useful tool for uncovering the genetic underpinnings of complex traits, its utility is diminished by population substructure, which can produce spurious association between phenotype and genotype within population-based samples. Because family-based designs are robust against substructure, they have risen to the fore of association analysis. Yet, if population substructure could be ignored, this robustness can come at the price of power. Unfortunately it is rarely evident when population substructure can be ignored. Devlin and Roeder recently have proposed a method, termed "genomic control" (GC), which has the robustness of family-based designs even though it uses population-based data. GC uses the genome itself to determine appropriate corrections for population-based association tests. Using the GC method, we contrast the power of two study designs, family trios (i.e., father, mother, and affected progeny) versus case-control. For analysis of trios, we use the TDT test. When population substructure is absent, we find GC is always more powerful than TDT; furthermore, contrary to previous results, we show that as a disease becomes more prevalent the discrepancy in power becomes more extreme. When population substructure is present, however, the results are more complex: TDT is more powerful when population substructure is substantial, and GC is more powerful otherwise. We also explore general issues of power and implementation of GC within the case-control setting and find that, economically, GC is at least comparable to and often less expensive than family-based methods. Therefore, GC methods should prove a useful complement to family-based methods for the genetic analysis of complex traits.

The effects of human population structure on large genetic association studies

“…The method is based on the observation that population structure changes the null distribution of the χ 2 statistic by a simple multiplicative factor, which may be estimated by a collection of L anonymous markers. Here we extend previous assessments of Genomic Control 3,16,20,26,27 to consider large sample sizes and small P values.…”

“…As in ref. 27, we estimated λ as the median value of the trend statistic divided by 0.456, where values below 1 are changed to 1.…”

“…Here, a nonlinearity means that over-and underestimation of λ have different effects on the true P value, so that increasing the variance of the estimator (decreasing L) gives a less conservative test. Earlier studies 16,20,27 seem to have considered the former effect, but the latter may be more relevant for large studies.…”

The Pattern of Polymorphism on Human Chromosome 21

“…Although such data can serve as a "genomic control" for population structure, so that traces of selection may be distinguished from the background (analogously to what has been proposed for association mapping; see Bacanu et al 2000;Pritchard et al 2000), the problem of multiple comparisons is very serious.…”

Admixture-matched case-control study: a practical approach for genetic association studies in admixed populations

“…To control population stratification confounding, a previous study reported several analytical strategies for matching cases and controls as part of association tests in admixed populations (Hinds et al 2004). In addition, several groups have proposed to detect and control population stratification confounding in case-control association tests by using two powerful approaches: 1) identifying and including ancestry in the analysis; 2) using genomic control to adjust for potential existing population stratification (Bacanu et al 2000;Devlin et al 2001;Freedman et al 2004;Hoggart et al 2003;Parra et al 2004;Pritchard and Donnelly 2001).…”