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000 (100K) to 1 million single nucleotide polymor-phisms (SNPs), the rst step is usually a genome-wide scanto identify candidate chromosome regions for further anal-yses. The goal of the genome-wide scan is to rank all theSNPs based on their association tests or p-values and selectthe top SNPs. A good ranking procedure ranks the SNPswith true associations as near to the top as possible. Thisenhances the probability of selecting at least one SNP witha true association. However, if the disease-associated SNPshave moderate genetic eects, the probability that a largenumber of null SNPs will have extremely small p-values(or large test statistics) is high when screening more than300K SNPs. Therefore, when selecting a small fraction oftop SNPs (usually less than 5%), the probability of select-ing at least one SNP with a true association is usually lessthan 80% unless the sample size is large. Robust statisticshave been proposed to rank all the SNPs (e.g., MAX3 andMIN2). In this article we consider genome-wide scans with agenetic model selection and compare this proposed methodto the existing approaches. Results from simulation studiesare presented.AMS 2000 subject classifications: Primary 62G35,62G10; secondary 62G30, 62P10.Keywords and phrases: Case-control design, Eciencyrobustness, Genetic model selection, Genome-wide studies,MAX.

Robust genome-wide scans with genetic model selection using case-control design