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Effective variant filtering and expected candidate variant yield in studies of rare human disease

Authors :
Rong Mao
Joseph Brown
Brent S. Pedersen
Harriet Dashnow
Amelia D. Wallace
Joshua D. Schiffman
Pinar Bayrak-Toydemir
Aaron R. Quinlan
D. Hunter Best
Tatiana Tvrdik
Martin Tristani-Firouzi
Matt Velinder
Source :
npj Genomic Medicine, Vol 6, Iss 1, Pp 1-8 (2021), NPJ Genomic Medicine
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

In studies of families with rare disease, it is common to screen for de novo mutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we explore effective guidelines for variant (SNP and INDEL) filtering and report the expected number of candidates for de novo dominant, recessive, and autosomal dominant modes of inheritance. We derived these guidelines using two large family-based cohorts that underwent whole-genome sequencing, as well as two family cohorts with whole-exome sequencing. The filters are applied to common attributes, including genotype-quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield ~10 candidate SNP and INDEL variants per exome, and 18 per genome for recessive and de novo dominant modes of inheritance, with substantially more candidates for autosomal dominant inheritance. For family-based, whole-genome sequencing studies, this number includes an average of three de novo, ten compound heterozygous, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. The slivar software we developed to establish and rapidly apply these filters to VCF files is available at https://github.com/brentp/slivar under an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.

Details

Language :
English
ISSN :
20567944
Volume :
6
Issue :
1
Database :
OpenAIRE
Journal :
npj Genomic Medicine
Accession number :
edsair.doi.dedup.....33fed3fe84605d658ebfd7d672e6fdd3