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Approximate Importance Sampling and Its Implications for Mapping Disease-Genes

Dr William C. L. Stewart Ohio State University

Date:15 November 2018, Thursday

Location:S16-06-118, Seminar Room

Time:03:00pm - 04:00pm


For co-segregation studies involving a large number of small affected families and commercially available SNP arrays, it is difficult to improve upon our near-optimal estimator of disease-gene location (denoted DSE), which averages location estimates from random subsamples of dense SNP data. However, for studies with a small number of large affected families, accurate estimation of the variance of the DSE is not trivial because variance estimators that rely on asymptotic theory are no longer appropriate. Here, I describe an importance sampling approach that accurately approximates the variance of the DSE. In principle, additional gains in precision are possible through the inclusion of publicly available data. I applied my approximate importance sampling approach to dense SNP data simulated under recessive and dominant models. In each setting, the variance of the DSE was accurately estimated, and relative to approximate 95% confidence intervals (CIs) constructed from existing methods, my CIs for disease-gene location were substantially narrower. As such, researchers with dense SNP data and a handful of large affected families should now be able to significantly reduce their targeted re-sequencing costs, and expedite the rate at which disease-genes are found.


My work is a hybrid of methodological and applied research that oscillates between estimation, hypothesis testing, and the genetics of common complex human diseases. Currently, we are: (1) conducting a whole-exome association analysis or preterm infants to identify rare variants influencing complications of preterm birth; (2) actively pursuing co-segregation analyses for genetic generalized epilepsy and specific language impairment; and, (3) developing software that simulates alternative ways of distributing donor organs to patients on waiting lists to improve the health outcomes of lung transplant recipients. The impact of our research is greatly enhanced by a constellation of complementary software that we continue to develop, document, and distribute. Researchers all over the world continue to use our software, which has fueled more than 30 peer-reviewed papers, a host of trainees, and a vibrant collection of academic collaborations. The March of Dimes and CHEST Foundations fund most of my recent work.