Abstract
Recent breakthroughs in exome-sequencing technology have made possible the identification of many causal variants of monogenic disorders. Although extremely powerful when closely related individuals (eg, child and parents) are simultaneously sequenced, sequencing of a single case is often unsuccessful due to the large number of variants that need to be followed up for functional validation. Many approaches filter out common variants above a given frequency threshold (eg, 1%), and then prioritize the remaining variants according to their functional, structural and conservation properties. Here we present methods that leverage the genetic structure across different populations to improve filtering performance while accounting for the finite sample size of the reference panels. We show that leveraging genetic structure reduces the number of variants that need to be followed up by 16% in simulations and by up to 38% in empirical data of 20 exomes from individuals with monogenic disorders for which the causal variants are known.
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Acknowledgements
This work is supported in part by the National Institutes of Health (R03-CA162200, R01-GM053275 to BP and T32-HG002536 to RB). AE and SFN are supported by the Genomics/Informatics Core of the UCLA MUSCULAR DYSTROPHY CORE CENTER from NIAMS (P30AR057230). BR is a fellow of the Branco Weiss Foundation and an A∗STAR and EMBO Young Investigator. This work was funded by a Strategic Positioning Fund for Genetic Orphan Diseases and an inaugural A∗STAR Investigatorship from the Agency for Science, Technology and Research in Singapore. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Web resources: We provide publicly available software implementing our approach: http://bogdan.bioinformatics.ucla.edu/software/. Figure 1 was made on this website with Human Genome Diversity Panel data: http://hgdp.uchicago.edu/cgi-bin/gbrowse/HGDP/. 1000 Genomes local ancestry calls are available at: http://www.1000genomes.org/phase1-analysis-results-directory.
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Brown, R., Lee, H., Eskin, A. et al. Leveraging ancestry to improve causal variant identification in exome sequencing for monogenic disorders. Eur J Hum Genet 24, 113–119 (2016). https://doi.org/10.1038/ejhg.2015.68
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DOI: https://doi.org/10.1038/ejhg.2015.68
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