Genetic association tests for binary traits with an application

Genetic association studies aim to map causal variants for a trait by performing many association tests between each marker along a chromosomal region and a trait of interest. Therefore, valid and powerful association tests are essential for a genetic association study. In this dissertation, association tests are considered for binary traits using both unrelated and related individuals.;For unrelated data, this dissertation shows that prospective models may be developed that correspond conceptually to retrospective tests. Two single-marker tests and four two-marker tests are discussed. The true association models are derived, allowing us to understand the effects of marker association patterns. The power of the association tests was investigated by simulation using HapMap data. Among the single-marker tests, the allelic test has on average the most power in the case of an additive disease; but, for nonadditive diseases, the genotypic test has the most power. Among the four two-marker tests, the Allelic-LD contrast test provides the most reliable power overall for the cases studied.;The proposed methods were applied to Diabetic Nephropathy (DN) data. Two genes, Carnosine Dipeptidase 1 (CNDP1) and Engulfment and Cell Motility 1 (ELMO1) have previously shown association with DN. These two genes, along with eight other genes (HMCN1, CFH, AHSG, CASP3, HSPA1A, HSPB1, CASP12, and HMOX1) were examined in a new study of Mexican-Americans. There was no replication of the associations with either CNDP1 or ELMO1. Of the other eight candidate genes, association with DN was found with a SNP pair, rs2146098 and rs6659783, in HMCN1. Association with a rare haplotype in this region was subsequently identified.;Lastly, association tests for related individuals were considered, particularly with genome-wide data. Two versions of the quasi-likelihood score test using a generalized linear mixed model (GLMM-QLS) were proposed. For 100 nuclear families of the same structure, it was shown that the proposed methods maintain nominal Type I error and have power comparable previously published methods. The main strength of the GLMM-QLSs is their computational efficiency when applied to genome-wide association studies. Because they are based on the prospective model, it can easily incorporate other covariates.