Complex genetic traits in an era of high throughput genome technologies


Mon, May 04, 2015 12:00 pmuntilMon, May 04, 2015 1:00 pm


Life Sciences Building Auditorium
145 Bevier Road


(732) 445-3386

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Dr. Ellen Wijsman
Div. Genetics and Dept. Biostatistics
University of Washington, Seattle, WA

Identification of genes underlying variation in human traits provides information that can lead to interventions that modify disease risk. Success in identifying such genes, especially in the context of complex traits, depends on adequacy of our assumed genetic models coupled with use of statistical methods that make effective use of available data. In general, these methods make use of correlation in data resulting from well-understood biological processes. These include correlation between trait values because of inheritance in pedigrees, and correlation in the allelic states at different positions in the genome because of shared population histories. Challenges that arise in analysis include large computational demands, the need to make efficient use of the data, and complications introduced by the enormous variability present in genomic data. I will discuss approaches for integrating information from both population and pedigree data with data from high-throughput sequencing technologies under genetic models that include both rare and common underlying risk variants. These include (1) choice of samples to use and to sequence, (2) imputation of sequence data in pedigrees for efficient data use, and (3) restriction and grouping for reducing the multiple testing problem.