Simple and efficient analysis of disease association with missing genotype data.
Missing genotype data arise in association studies when the single-nucleotide polymorphisms (SNPs) on the genotyping platform are not assayed successfully, when the SNPs of interest are not on the platform, or when total sequence variation is determined only on a small fraction of individuals. We present a simple and flexible likelihood framework to study SNP-disease associations with such missing genotype data. Our likelihood makes full use of all available data in case-control studies and reference panels (e.g., the HapMap), and it properly accounts for the biased nature of the case-control sampling as well as the uncertainty in inferring unknown variants. The corresponding maximum-likelihood estimators for genetic effects and gene-environment interactions are unbiased and statistically efficient. We developed fast and stable numerical algorithms to calculate the maximum-likelihood estimators and their variances, and we implemented these algorithms in a freely available computer program. Simulation studies demonstrated that the new approach is more powerful than existing methods while providing accurate control of the type I error. An application to a case-control study on rheumatoid arthritis revealed several loci that deserve further investigations.
Lin
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Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA. lin@bios.unc.edu
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Huang
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D000465:Algorithms; D016022:Case-Control Studies; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics