Nature GeneticsGenomicsPeer-reviewed journal article
Inferring compound heterozygosity from large-scale exome sequencing data
Michael H. GuoiD, Laurent C. FrancioliiD, Sarah L. StentoniD, Julia K. GoodrichiD, Nicholas A. Watts, Moriel Singer‐BerkiD, Emily GroopmaniD, Philip W. Darnowsky, Matthew SolomonsoniD, Samantha M. BaxteriD, Maria Teresa AbreuiD, Carlos Alberto Aguilar-SalinasiD, Tariq AhmadiD, Christine M. AlbertiD, Jessica AlföldiiD, Diego ArdissinoiD, Irina M. ArmeaniD, Gil AtzmoniD, Eric BanksiD, John BarnardiD, Laurent BeaugerieiD, Emelia J. BenjaminiD, David I. Benjamin, Louis Bergelson, Michael BoehnkeiD, , Bong-Jo KimiD
Abstract
Recessive diseases arise when both the maternal and the paternal copies of a gene are impacted by a damaging genetic variant in the affected individual. When a patient carries two different potentially causal variants in a gene for a given disorder, accurate diagnosis requires determining that these two variants occur on different copies of the chromosome (i.e., are in trans) rather than on the same copy (i.e. in cis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. We developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in exome sequencing data from the Genome Aggregation Database (gnomAD v2, n=125,748). When applied to trio data where phase can be determined by transmission, our approach estimates phase with 95.7% accuracy and remains accurate even for very rare variants (allele frequency < 1x10-4). We also correctly phase 95.9% of variant pairs in a set of 293 patients with Mendelian conditions carrying presumed causal compound heterozygous variants. We provide a public resource of phasing estimates from gnomAD, including phasing estimates for coding variants across the genome and counts per gene of rare variants in trans, that can aid interpretation of rare co-occurring variants in the context of recessive disease.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 36
- FWCI
- 5.97
- Field percentile
- top 2%
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