Abstract
The detection of genetic segments of Identical by Descent (IBD) in Genome-Wide Association Studies has proven successful in pinpointing genetic relatedness between reportedly unrelated individuals and leveraging such regions to shortlist candidate genes. These techniques depend on high-density genotyping arrays and their effectiveness in diverse sequence data is largely unknown. Due to decreasing costs and increasing effectiveness of high throughput techniques for whole-exome sequencing, an influx of exome sequencing data has become available. Studies using exomes and IBD-detection methods within known pedigrees have shown that IBD can be useful in finding hidden genetic candidates where known relatives are available. We set out to examine the viability of using IBD-detection in whole exome sequencing data in population-wide studies. In doing so, we extend GERMLINE, a method to detect IBD from exome sequencing data by finding small slices of matching alleles between pairs of individuals and extending them into full IBD segments. This algorithm allows for efficient population-wide detection in dense data. We apply this algorithm to a cohort of Crohn's Disease cases where whole-exome and GWAS array data is available. We confirm that GWAS-based detected segments are highly accurate and predictive of underlying shared variation. Where segments inferred from GWAS are expected to be of high accuracy, we compare exome-based detection accuracy of multiple detection strategies. We find detection accuracy to be prohibitively low in all assessments, both in terms of segment sensitivity and specificity. Even after isolating relatively long segments beyond 10cM, exome-based detection continued to offer poor specificity/sensitivity tradeoffs. We hypothesize that the variable coverage and platform biases of exome capture account for this decreased accuracy and look toward whole genome sequencing data as a higher quality source for detecting population-wide IBD.
Highlights
The identification of co-inherited genomic regions, referred to as being Identical by Descent (IBD) has been used to find hidden relatedness [1], make inferences regarding population genetics [2], detect association [3], and correct errors in genotyping and phasing [4]
Evaluating segment overlap We focused on measuring overlap in IBD segments from array data and whole-exome sequencing data using GERMLINE
We measured low signal within IBD generated from whole exome IBD detection
Summary
The identification of co-inherited genomic regions, referred to as being Identical by Descent (IBD) has been used to find hidden relatedness [1], make inferences regarding population genetics [2], detect association [3], and correct errors in genotyping and phasing [4]. IBD falls into three categories: IBD = 0, IBD = 1, and IBD = 2 Siblings that inherit both of the same chromosomes from both parents are IBD = 2. The frequency of IBD segments depends on the population, and scales quadratically with the cohort size [5]. Discovering such segments can be done efficiently by looking for short, exact matches between individuals and extending them to identify long, nearly identical segmental sharing that is indicative of such relatedness. Some array-based IBD discovery methods use haplotype sampling and to detect large segments of identity by state [4,5,6]. Given the numerous developments in array-based IBD detection, there is hope that advances can be made in detecting IBD directly from next-generation sequence data
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