Next PQG Working Group - Jae-Hoon Sul - 5/12
Andelman, Shaina B
Tuesday, May 12, 2015
Building 2, Room 426 - Biostatistics Conference Room
12:30-2:00 PM
a pizza lunch will be served
Large-scale genetic studies of human complex traits
In the past decade, genotyping and next-generation sequencing (NGS) technologies have generated an enormous amount of data to discover genetic variants present in human genomes and to find the genetic basis of diseases. The technologies have shifted the paradigm of genetic studies from studies that analyzed fewer than a hundred individuals at hundreds of markers to studies that analyze more than tens of thousand individuals at millions of genetic variants. With rapid decrease in sequencing costs and emphasis on genomic medicine, studies will sequence hundreds of thousands of individuals in the near future. A major challenge in these large-scale genetic studies is developing computational methods that can utilize this big data efficiently.
In this talk, I will describe my work to address this challenge. As genome-wide association studies have discovered numerous non-coding genetic variants associated with traits, there has been increasing focus on interpreting these variants using functional genomics. Expression quantitative trait loci (eQTL) studies that attempt to detect genetic variants associated with gene expression may provide clues as to which variants are functional. I will discuss a method to perform multiple testing correction accurately and rapidly in eQTL studies for identification of genes whose expression is influenced by genetic variants. As eQTL studies have grown larger in sample size, multiple testing correction using the permutation test has become a major computational bottleneck. I developed a multivariate normal sampling approach (MVN), and MVN is more than 100 times faster than the permutation test for the sample size of 2,000 while generating almost the same results. Next, I will present a novel approach to detect rare variants associated with a disease in large families. NGS enables studies to evaluate effects of rare variants on complex traits, and family-based studies have attracted great attention recently because of their higher power for rare variant testing than case-control studies. I developed a method called RareIBD that can be applied to large pedigrees, both binary and quantitative traits, and affected-only pedigrees. Using simulations, I will show my method achieves higher power than previous approaches
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