Efficient study designs for test of genetic association using sibship data and unrelated cases and controls.
Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.
Li
Mingyao
M
Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia; and; Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor. Electronic address: mli@cceb.med.upenn.edu.
Boehnke
Michael
M
Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor.
Abecasis
Gonçalo R
GR
Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor.
D016022:Case-Control Studies; D002874:Chromosome Mapping; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D005828:Genetics, Population; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D012107:Research Design; D035781:Siblings; D018709:Statistics, Nonparametric