title: Exact family-based association tests for biallelic data.
abstract: Family-based study designs have an important role in the search for association between disease phenotypes and genetic markers. Unlike traditional case-control methods, family-based tests use within-family data to avoid identification of spurious associations that may result from population admixture. Many family-based association tests have been proposed to accommodate a variety of ascertainment schemes and patterns of missing data. In this report, we describe exact family-based association tests for biallelic data. Specifically, we discuss test of the null hypotheses "no linkage and no association" and "linkage, but no association". These tests, which are valid under various models for inheritance and patterns of missingness, utilize the procedure proposed by Rabinowitz and Laird [2000: Hum Hered 50:211-223] that provides a unified framework for family based association testing (FBAT). The conditioning approach implemented in FBAT makes an exact test conceptually straightforward, but computationally difficult since the minimum sufficient statistics upon which we condition do not have a conventional form. An exact test may be especially critical when accurate computation of the extreme area of the FBAT statistic is needed, such as when the study design necessitates multiple comparisons adjustments. We describe the exact approach as a useful alternative to the asymptotic test and show that the exact tests for biallelic data may be most useful for the recessive disease model.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. kschneit@hsph.harvard.edu
authors.forename: Kady
authors.initials: K
authors.lastname: Schneiter
authors.forename: Nan
authors.initials: N
authors.lastname: Laird
authors.forename: Chris
authors.initials: C
authors.lastname: Corcoran
mesh_terms: D000465:Algorithms; D000483:Alleles; D005190:Family; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D013223:Statistics as Topic / Q000379:methods*