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