STA 290 Seminar Series
DATE: Friday, March 10th 2017, 3:10pm
LOCATION: MSB 1143, Statistics Seminar Room
SPEAKER: Indranil Mukhopadhyay, Human Genetics Unit, Indian Statistical Institute, Kolkata
TITLE: “Multi-loci association test in genetic association study using similarity between individuals”
ABSTRACT: Some possible explanations for the limited success of genome wide association study (GWAS) are that the current biostatistical analysis paradigm ignores all prior knowledge about disease pathobiology and/or the linear modeling framework of GWAS considers only one marker at a time. This in turn fails to exploit their full genomic context and gives rise to multiple comparison problems. Thus it is desirable to perform an overall test of whether any or all single-nucleotide polymorphisms (SNPs) in a gene are associated with a phenotype. Due to severe multiple comparison burden, some SNPs with weak or moderately strong effect might go undetected thus failing to explain a substantial amount of heritability. However, combining effects of multiple SNPs within a gene seems to be more promising in capturing relatively weak associations as well as reducing multiple comparison burden. We have developed a genetic association test based on multiple SNPs at a time that seems to be very promising. Although initially developed for case-control data, this test can be extended for quantitative data as well as for family data. In each case we have developed asymptotic distribution of the tests to calculate the p-value very fast. Extensive simulations show that our proposed method in this new paradigm might throw more light in deciphering the disease etiology.