Abstract
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.
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Acknowledgements
This research work was funded by the FiDiPro program of the Academy of Finland, Grants MH84995, MH59490 and RR017515 from the National Institutes of Health, the Helsingin Sanomat Centennial Foundation, Biomedicum Helsinki Foundation, Emil Aaltonen Foundation, Otto A. Malm Foundation, Jenny and Antti Wihuri Foundation, Finnish Cultural Society, and the SBC Foundation are gratefully acknowledged. This research was supported in part by the Intramural Research Program of the NIH, NLM (AAS). Maija Wessman, Verneri Anttila, Mari Kaunisto, and Tiina Paunio are acknowledged for providing Finnish migraine and schizophrenia pedigree structures for simulation studies. Markus Perola and Leena Peltonen-Palotie are greatly acknowledged for their guidance and support over the years. The great majority of simulations were executed on the Linux-based supercomputers of the Finnish IT Center for Science (CSC). CSC is greatly acknowledged. Thanks to three reviewers for numerous helpful suggestions to improve the manuscript.
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Hiekkalinna, T., Göring, H., Lambert, B. et al. On the statistical properties of family-based association tests in datasets containing both pedigrees and unrelated case–control samples. Eur J Hum Genet 20, 217–223 (2012). https://doi.org/10.1038/ejhg.2011.173
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DOI: https://doi.org/10.1038/ejhg.2011.173
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