Hypothesis-free phenotype prediction within a genetics-first framework

Abstract

Cohort-wide sequencing studies have revealed that the largest category of variants is those deemed rare, even for the subset located in coding regions (99% of known coding variants are seen in less than 1% of the population1-3). Our understanding of how rare genetic variants influence disease and organism-level phenotypes has achieved limited progress, partly explained by the intrinsic difficulty in statistically evaluating the biological significance of rare events. Here we show that discoveries can instead be made through a knowledge-based approach using protein domains and ontologies (function and phenotype) that considers all coding variants regardless of allele frequency. We describe an ab initio, genetics-first method making molecular knowledge-based interpretations for exome-wide non-synonymous variants for phenotypes at the organism and cellular level. By using this reverse approach, we identify plausible novel genetic causes for developmental disorders that have eluded other established methods and present novel molecular hypotheses for the causal genetics of 40 phenotypes generated from a direct-to-consumer genotype cohort. This system offers a chance to extract further discovery from genetic data after standard tools have been applied.