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Identifying rare variants in large families (RareIBD)

Our paper on identifying rare variants involved in diseases has been published in the American Journal of Human Genetics! Here’s a link to the paper.

Recently, there has been a tremendous amount of interest in identifying effect of rare variants in human complex traits and diseases. Because it is statistically difficult to identify effect of a single rare variant, a burden or “collapsing” approach that combines effects of multiple rare variants has been proposed. I also developed three methods (RWAS, LRT, VST) for the rare variant association analysis. However, it has been shown that we still need thousands of individuals or more even if we apply these techniques in case-controls study designs. On the other hand, family-based studies may be more powerful because a rare variant can be enriched and has high allele frequency in large families.

In this paper, we developed a new method called RareIBD, which is a general and powerful approach to identify rare variants in large extended pedigrees. Our method can be applied to large families with arbitrary structures including families with only affected individuals. Our method also accounts for individuals in top generations who are not usually genotyped or sequenced. We showed that our method is more powerful than other approaches, and our method is the only method that properly controls type I error in real family datasets. This paper is a joint work with Shamil Sunyaev, Susan Redline, and Edwin Silverman in Harvard Medical School and Brigham and Women’s Hospital.

Here is a full citation to the paper.

Jae Hoon Sul, Brian E. Cade, Michael H. Cho, Dandi Qiao, Edwin K. Silverman, Susan Redline, Shamil Sunyaev. “Increasing generality and power of rare variant tests utilizing extended pedigrees.” The American Journal of Human Genetics, 99(4), 846-859, 2016.

Software implementing RareIBD is available in Software.

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