title: A likelihood ratio-based Mann-Whitney approach finds novel replicable joint gene action for type 2 diabetes.
abstract: The potential importance of the joint action of genes, whether modeled with or without a statistical interaction term, has long been recognized. However, identifying such action has been a great challenge, especially when millions of genetic markers are involved. We propose a likelihood ratio-based Mann-Whitney test to search for joint gene action either among candidate genes or genome-wide. It extends the traditional univariate Mann-Whitney test to assess the joint association of genotypes at multiple loci with disease, allowing for high-order statistical interactions. Because only one overall significance test is conducted for the entire analysis, it avoids the issue of multiple testing. Moreover, the approach adopts a computationally efficient algorithm, making a genome-wide search feasible in a reasonable amount of time on a high performance personal computer. We evaluated the approach using both theoretical and real data. By applying the approach to 40 type 2 diabetes (T2D) susceptibility single-nucleotide polymorphisms (SNPs), we identified a four-locus model strongly associated with T2D in the Wellcome Trust (WT) study (permutation P-value < 0.001), and replicated the same finding in the Nurses' Health Study/Health Professionals Follow-Up Study (NHS/HPFS) (P-value = 3.03×10-11). We also conducted a genome-wide search on 385,598 SNPs in the WT study. The analysis took approximately 55 hr on a personal computer, identifying the same first two loci, but overall a different set of four SNPs, jointly associated with T2D (P-value = 1.29×10-5). The nominal significance of this same association reached 4.01×10-6 in the NHS/HPFS.
authors: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA.
authors: Qing
authors: Q
authors: Lu
authors: Changshuai
authors: C
authors: Wei
authors: Chengyin
authors: C
authors: Ye
authors: Ming
authors: M
authors: Li
authors: Robert C
authors: RC
authors: Elston
mesh_terms: D000465:Algorithms; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide
chemical_list: D005819:Genetic Markers