title: Detection of disease genes by use of family data. I. Likelihood-based theory.
abstract: We present a class of likelihood-based score statistics that accommodate genotypes of both unrelated individuals and families, thereby combining the advantages of case-control and family-based designs. The likelihood extends the one proposed by Schaid and colleagues (Schaid and Sommer 1993, 1994; Schaid 1996; Schaid and Li 1997) to arbitrary family structures with arbitrary patterns of missing data and to dense sets of multiple markers. The score statistic comprises two component test statistics. The first component statistic, the nonfounder statistic, evaluates disequilibrium in the transmission of marker alleles from parents to offspring. This statistic, when applied to nuclear families, generalizes the transmission/disequilibrium test to arbitrary numbers of affected and unaffected siblings, with or without typed parents. The second component statistic, the founder statistic, compares observed or inferred marker genotypes in the family founders with those of controls or those of some reference population. The founder statistic generalizes the statistics commonly used for case-control data. The strengths of the approach include both the ability to assess, by comparison of nonfounder and founder statistics, the potential bias resulting from population stratification and the ability to accommodate arbitrary family structures, thus eliminating the need for many different ad hoc tests. A limitation of the approach is the potential power loss and/or bias resulting from inappropriate assumptions on the distribution of founder genotypes. The systematic likelihood-based framework provided here should be useful in the evaluation of both the relative merits of case-control and various family-based designs and the relative merits of different tests applied to the same design. It should also be useful for genotype-disease association studies done with the use of a dense set of multiple markers.
authors.affiliation: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, 94305, USA. alicesw@leland.stanford.edu
authors.forename: A S
authors.initials: AS
authors.lastname: Whittemore
authors.forename: I P
authors.initials: IP
authors.lastname: Tu
mesh_terms: D000483:Alleles; D015982:Bias; D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000379:methods* / Q000706:statistics & numerical data*; D005192:Family Health; D018703:Founder Effect; D005799:Genes, Dominant / Q000235:genetics; D005808:Genes, Recessive / Q000235:genetics; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D008040:Genetic Linkage / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006239:Haplotypes / Q000235:genetics; D006579:Heterozygote; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium / Q000235:genetics; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D019683:Penetrance; D011110:Polymorphism, Genetic / Q000235:genetics
chemical_list: D005819:Genetic Markers