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A common approach to genetic mapping of loci for complex diseases is to perform a genome-wide association study (GWAS) by analyzing a vast number of SNP markers in cohorts of unrelated cases and controls. A direct motivation for the case-control design is that unrelated, affected individuals can be easier to collect than large families with multiple affected persons in the Western world. Despite its higher potential power, investigators have not actively pursued family ascertainment in part because of a dearth of methods for analyzing such correlated data on a large scale. We examine the statistical properties of several commonly used family-based association tests, as to their performance using real-life mixtures of families and singletons taken from our own migraine and schizophrenia studies, as well as population-based data for a complex trait simulated with the evolutionary phenogenetic simulator, ForSim. In virtually every situation, the full likelihood-based methods in the PSEUDOMARKER program outperformed those implemented in FBAT, GENEHUNTER TDT, PLINK (family-based options), HRR/HHRR, QTDT, TRANSMIT, UNPHASED, MENDEL, and LAMP. We further show that GWAS is much more powerful when family samples are used rather than unrelateds, on a genotype-by-genotype basis.

On the statistical properties of family-based association tests in datasets containing both pedigrees and unrelated case–control samples