Calculating the statistical significance of rare variants causal for Mendelian and complex disorders

Abstract

With the expanding use of next-gen sequencing (NGS) to diagnose the thousands of rare Mendelian genetic diseases, it is critical to be able to interpret individual DNA variation. We developed a general method to better interpret the likelihood that a rare variant is disease causing if observed in a given gene or genic region mapping to a described protein domain, using genome-wide information from a large control sample. We implemented these methods as a web tool and demonstrate application to 19 relevant but diverse next-gen sequencing studies. Additionally, we calculate the statistical significance of findings involving multi-family studies with rare Mendelian disease and studies of large-scale complex disorders such as autism spectrum disorder.

Confirmation that variants in TTI2 are responsible for autosomal recessive intellectual disability

“…All four variants were classified as probably pathogenic (class 4) according to American College of Medical Genetics recommendations: (a) All variants have a low frequency in gnomAD (PM2), (b) there are multiple lines of computational evidence supporting their deleterious effect on the gene (PP3), (c) all four variants were detected in trans with another variant (PM3), (d) these missense variants occurred in TTI2 , a gene that has a low rate of benign missense variation and for which missense variants are a common mechanism of disease (PP2). This latter criterion was supported by the fact that all TTI2 variants currently reported are missense and by the absence of homozygous or compound heterozygous variants of TTI2 among the 2500 controls referenced in the SORVA database …”

A phenotype-specific framework for identifying the eye abnormalities causative nonsynonymous-variants

“…30 human tissue expression were obtained from TiGER. 4 scores of gene predictors, RVIS [19], GDI[20], LoFtool[21], and SORVA[22], and 23 scores of 12 popular variant predictors, SIFT[23], PolyPhen-2[24], LRT[25], MutationTaster[26], MutationAssessor[27], FATHMM[28], PROVEAN[29], fitCons[30], PhyloP[31], GERP++[32], SiPhy[33] and phastCons[34] were obtained from the dbNSFP database. We selected the most serious one across all isoforms for any predictors with multiple protein isoforms in a given variant.…”