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Robust genome-wide scans with genetic model selection using case-control design. (English) Zbl 1245.62167

Summary: In a genome-wide association study with more than 100,000 (100K) to 1 million single nucleotide polymorphisms (SNPs), the first step is usually a genome-wide scan to identify candidate chromosome regions for further analyses. The goal of the genome-wide scan is to rank all the SNPs based on their association tests or \(p\)-values and select the top SNPs. A good ranking procedure ranks the SNPs with true associations as near to the top as possible. This enhances the probability of selecting at least one SNP with a true association. However, if the disease-associated SNPs have moderate genetic effects, the probability that a large number of null SNPs will have extremely small \(p\)-values (or large test statistics) is high when screening more than 300K SNPs. Therefore, when selecting a small fraction of top SNPs (usually less than \(5\%\)), the probability of selecting at least one SNP with a true association is usually less than \(80\%\) unless the sample size is large. Robust statistics have been proposed to rank all the SNPs (e.g., MAX3 and MIN2). We consider genome-wide scans with a genetic model selection and compare this proposed method to the existing approaches. Results from simulation studies are presented.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
92C40 Biochemistry, molecular biology
65C60 Computational problems in statistics (MSC2010)
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