Detection of identity by descent using next-generation whole genome sequencing data
1 Ernest Gallo Clinic and Research Center, University of California San Francisco, 5858 Horton St. Suite 200, Emeryville, CA 94608, USA
2 Department of Neurology, University of California San Francisco, 5858 Horton St. Suite 200, Emeryville, CA 94608, USA
3 Department of Psychiatry, University of California San Francisco, 5858 Horton St. Suite 200, Emeryville, CA 94608, USA
4 Department of Dermatology, University of California San Francisco, 5858 Horton St. Suite 200, Emeryville, CA 94608, USA
Citation and License
BMC Bioinformatics 2012, 13:121 doi:10.1186/1471-2105-13-121Published: 6 June 2012
Identity by descent (IBD) has played a fundamental role in the discovery of genetic loci underlying human diseases. Both pedigree-based and population-based linkage analyses rely on estimating recent IBD, and evidence of ancient IBD can be used to detect population structure in genetic association studies. Various methods for detecting IBD, including those implemented in the software programs fastIBD and GERMLINE, have been developed in the past several years using population genotype data from microarray platforms. Now, next-generation DNA sequencing data is becoming increasingly available, enabling the comprehensive analysis of genomes, including identifying rare variants. These sequencing data may provide an opportunity to detect IBD with higher resolution than previously possible, potentially enabling the detection of disease causing loci that were previously undetectable with sparser genetic data.
Here, we investigate how different levels of variant coverage in sequencing and microarray genotype data influences the resolution at which IBD can be detected. This includes microarray genotype data from the WTCCC study, denser genotype data from the HapMap Project, low coverage sequencing data from the 1000 Genomes Project, and deep coverage complete genome data from our own projects. With high power (78%), we can detect segments of length 0.4 cM or larger using fastIBD and GERMLINE in sequencing data. This compares to similar power to detect segments of length 1.0 cM or higher with microarray genotype data. We find that GERMLINE has slightly higher power than fastIBD for detecting IBD segments using sequencing data, but also has a much higher false positive rate.
We further quantify the effect of variant density, conditional on genetic map length, on the power to resolve IBD segments. These investigations into IBD resolution may help guide the design of future next generation sequencing studies that utilize IBD, including family-based association studies, association studies in admixed populations, and homozygosity mapping studies.