Using Relatedness to Identify Disease Genes

ibd-figure

An example of IBD graph. IBD detection method provides IBD information (Table). Then we build a graph where vertices are individuals and edges are IBD relationships.

The standard approach for detecting genetic variants involved in disease is the association study where genetic information is collected from a set of individuals who have the disease and a set of healthy individuals. Any genetic variants which are more common in the set of individuals who have the disease, referred to as “associated variants”, may be involved in the disease.

Our group has just published a paper on a alternative and complementary approach for identifying regions involved in disease from the same genetic data. The basic idea is that we consider the patterns of how the individuals are related in different parts of their genomes and how this relates to their disease status. The idea is that if a region is involved in disease, individuals who have the disease will likely have more similar DNA sequences than individuals who do not have the disease. Identifying pairs of individuals with similar DNA sequences is called Identity By Descent (IBD) mapping and there are several methods which can identify IBD relations efficiently(18971310),(21310274),(24207118).

The way our approach works is that in each region of the genome, we build an IBD graph based on which pairs of individual are related where a vertex in the graph is an individual and an edge is a IBD relation which implies that the two individuals have similar DNA sequences at that point.  In our graph, individuals who have the disease are red squares (cases) and individuals who are healthy are green circles (controls).  Following our intuition, if the region is involved in the disease, we expect more edges between pairs of case individuals than between pairs of control individuals.  Our approach simply considers this difference and then apples permutation where the assignment of case and control status to the individuals are randomized in order to obtain a significance level.  Our approach was not the first method to apply this idea and follows the paper by Thompson and Browning(23733848).  The advantage of our paper is that we use a technique called importance sampling to speed up the computation of the significance levels by orders of magnitude. The hope is that this type of approach maybe more effective to identify regions of the genome that are involved in disease through rare variants which are difficult to detect in association studies.

The full citation for the paper is:

Han, Buhm; Kang, Eun Yong ; Raychaudhuri, Soumya ; de Bakker, Paul I W; Eskin, Eleazar

Fast Pairwise IBD Association Testing in Genome-wide Association Studies. Journal Article

In: Bioinformatics, 2013, ISSN: 1367-4811.

Abstract | Links | BibTeX

Bibliography

Thesis Defense: Dr. Jae Hoon Sul

Dr. Jae Hoon Sul with his committee.

Dr. Jae Hoon Sul with his committee.

Jae Hoon Sul successfully defended his thesis on Wednesday September 19th.  His talk is posted on our YouTube Channel ZarlabUCLA.  Jae Hoon’s talk discusses several projects including using mixed model to correct for population structure, rare variant association studies and a meta-analysis approach for detecting multi-tissue eQTLs.  Fortunately for the lab, Jae Hoon is staying at UCLA for another year as a post-doc.

More details about what he talks about in his talk are available in the papers he discusses:

Sul, Jae Hoon; Han, Buhm ; Ye, Chun ; Choi, Ted ; Eskin, Eleazar

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches Journal Article

In: PLoS Genet, 9 (6), pp. e1003491, 2013, ISSN: 1553-7404.

Abstract | Links | BibTeX

Sul, Jae Hoon; Han, Buhm ; He, Dan ; Eskin, Eleazar

An Optimal Weighted Aggregated Association Test for Identification of Rare Variants Involved in Common Diseases. Journal Article

In: Genetics, 188 (1), pp. 181-188, 2011, ISSN: 1943-2631.

Abstract | Links | BibTeX

Kang, Hyun Min; Sul, Jae Hoon ; Service, Susan K; Zaitlen, Noah A; Kong, Sit-Yee Y; Freimer, Nelson B; Sabatti, Chiara ; Eskin, Eleazar

Variance component model to account for sample structure in genome-wide association studies. Journal Article

In: Nat Genet, 42 (4), pp. 348-54, 2010, ISSN: 1546-1718.

Abstract | Links | BibTeX