ATRIUM: testing untyped SNPs in case-control association studies with related individuals.
In genome-wide association studies, only a subset of all genomic variants are typed by current, high-throughput, SNP-genotyping platforms. However, many of the untyped variants can be well predicted from typed variants, with linkage disequilibrium (LD) information among typed and untyped variants available from an external reference panel such as HapMap. Incorporation of such external information can allow one to perform tests of association between untyped variants and phenotype, thereby making more efficient use of the available genotype data. When related individuals are included in case-control samples, the dependence among their genotypes must be properly addressed for valid association testing. In the context of testing untyped variants, an additional analytical challenge is that the dependence, across related individuals, of the partial information on untyped-SNP genotypes must also be assessed and incorporated into the analysis for valid inference. We address this challenge with ATRIUM, a method for case-control association testing with untyped SNPs, based on genome screen data in samples in which some individuals are related. ATRIUM uses LD information from an external reference panel to specify a one-degree-of-freedom test of association with an untyped SNP. It properly accounts for dependence in the partial information on untyped-SNP genotypes across related individuals. We demonstrate that ATRIUM is robust in that it maintains the nominal type I error rate even when the external reference panel is not well matched to the case-control sample. We apply the method to detect association between type 2 diabetes and variants on chromosome 10 in the Framingham SHARe data.
Wang
Zuoheng
Z
Department of Statistics, The University of Chicago, Chicago, IL 60637, USA.
McPeek
Mary Sara
MS
D000483:Alleles; D044466:Asian People / Q000235:genetics; D044383:Black People / Q000235:genetics; D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000379:methods; D002879:Chromosomes, Human, Pair 10; D015331:Cohort Studies; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D005787:Gene Frequency; D005819:Genetic Markers; D014644:Genetic Variation; D015894:Genome, Human; D055106:Genome-Wide Association Study; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D016015:Logistic Models; D008137:Longitudinal Studies; D008957:Models, Genetic; D015233:Models, Statistical; D009679:Nuclear Family; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015203:Reproducibility of Results; D012984:Software; D044465:White People / Q000235:genetics
D005819:Genetic Markers