Increasing the power and efficiency of disease-marker case-control association studies through use of allele-sharing information.
Case-control disease-marker association studies are often used in the search for variants that predispose to complex diseases. One approach to increasing the power of these studies is to enrich the case sample for individuals likely to be affected because of genetic factors. In this article, we compare three case-selection strategies that use allele-sharing information with the standard strategy that selects a single individual from each family at random. In affected sibship samples, we show that, by carefully selecting sibships and/or individuals on the basis of allele sharing, we can increase the frequency of disease-associated alleles in the case sample. When these cases are compared with unrelated controls, the difference in the frequency of the disease-associated allele is therefore also increased. We find that, by choosing the affected sib who shows the most evidence for pairwise allele sharing with the other affected sibs in families, the test statistic is increased by >20%, on average, for additive models with modest genotype relative risks. In addition, we find that the per-genotype information associated with the allele sharing-based strategies is increased compared with that associated with random selection of a sib for genotyping. Even though we select sibs on the basis of a nonparametric statistic, the additional gain for selection based on the unknown underlying mode of inheritance is minimal. We show that these properties hold even when the power to detect linkage to a region in the entire sample is negligible. This approach can be extended to more-general pedigree structures and quantitative traits.
Fingerlin
Tasha E
TE
Department of Epidemiology, School of Public Health, and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. Tasha.Fingerlin@uchsc.edu
Boehnke
Michael
M
Abecasis
Gonçalo R
GR
D000483:Alleles; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D030342:Genetic Diseases, Inborn; D005819:Genetic Markers; D006801:Humans
D005819:Genetic Markers