Detecting identity by descent and homozygosity mapping in whole-exome sequencing data.
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.
Department of Computer Science, Columbia University, New York, NY, USA. zz2167@columbia.edu
Zhong
Z
Zhuang
Alexander
A
Gusev
Judy
J
Cho
Itsik
I
Pe'er
D000465:Algorithms; D003237:Connecticut; D003424:Crohn Disease / Q000235:genetics*; D059472:Exome / Q000235:genetics*; D005821:Genetic Techniques; D055106:Genome-Wide Association Study / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006720:Homozygote; D006801:Humans; D007585:Jews / Q000235:genetics; D009518:New York; D010375:Pedigree; D012680:Sensitivity and Specificity