RT Journal Article
SR Electronic
T1 Effective variant filtering and expected candidate variant yield in studies of rare human disease
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.08.13.249532
DO 10.1101/2020.08.13.249532
A1 Brent S. Pedersen
A1 Joe M. Brown
A1 Harriet Dashnow
A1 Amelia D. Wallace
A1 Matt Velinder
A1 Tatiana Tvrdik
A1 Rong Mao
A1 D. Hunter Best
A1 Pinar Bayrak-Toydemir
A1 Aaron R. Quinlan
YR 2020
UL http://biorxiv.org/content/early/2020/08/14/2020.08.13.249532.1.abstract
AB 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 derive effective guidelines for variant filtering and report the expected number of candidates for de novo dominant and recessive modes of inheritance. The filters are applied to common attributes, including genotype quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield approximately 10 candidate SNP and INDEL variants per exome, and 18 per genome. For whole genomes, this includes an average of three de novo, ten compound-heterozygotes, 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.Competing Interest StatementThe authors have declared no competing interest.