Comparative genomics approaches accurately predict deleterious variants in plants

Abstract
Recent advances in genome resequencing have led to increased interest in prediction of the functional consequences of genetic variants. Variants at phylogenetically conserved sites are of particular interest, because they are more likely than variants at phylogenetically variable sites to have deleterious effects on fitness and contribute to phenotypic variation. Numerous comparative genomic approaches have been developed to predict deleterious variants, but they are nearly always judged based on their ability to identify known disease-causing mutations in humans. Determining the accuracy of deleterious variant predictions in nonhuman species is important to understanding evolution, domestication, and potentially to improving crop quality and yield. To examine our ability to predict deleterious variants in plants we generated a curated database of 2,910 Arabidopsis thaliana mutants with known phenotypes. We evaluated seven approaches and found that while all performed well, the single best-performing approach was a likelihood ratio test applied to homologs identified in 42 plant genomes. Although the approaches did not always agree, we found only slight differences in performance when comparing mutations with gross versus biochemical phenotypes, duplicated versus single copy genes, and when using a single approach versus ensemble predictions. We conclude that deleterious mutations can be reliably predicted in A. thaliana and likely other plant species, but that the relative performance of various approaches can depend on the organism to which they are applied.

Description
The genes and mutations information in this table were downloaded from UniProt/Swiss-Prot database (http://www.uniprot.org/) and http://www.arabidopsis.org. Single nucleotide polymorphisms (SNPs) without any known phenotype were obtained from a set of 80 sequenced A. thaliana strains (Ensembl, version 81, “Cao_SNPs”, Cao, et al., 2011). We used six approaches: LRT, PolyPhen2, SIFT 4G, Provean, MAPP, Gerp++ to predict deleterious varaints. The details can be avaible in Kono, et al., 2017 (http://www.biorxiv.org/content/early/2017/02/27/112318)

Funding information
Sponsorship: US National Science Foundation Plant Genome Program grant (DBI-1339393 to JCF and PLM); US Department of Agriculture Biotechnology Risk Assessment Research Grants Program (BRAG) (USDA BRAG 2015-06504 to PLM); University of Minnesota Doctoral Dissertation Fellowship (to TJYK)