DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders

Abstract

In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a 400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that are the properties of genes, rather than of variants. It uses a machine-learning approach to extract discriminant information from a broad array of features (N ¼ 432), including: genomic data, intra-, and interspecies conservation, gene expression, protein-protein interactions, protein structure, etc. DOMINO's iterative architecture includes a training process on 985 genes with well-established inheritance patterns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power. When validated on 99 newly-discovered genes with pathogenic mutations, the algorithm displays an excellent final performance, with an area under the curve (AUC) of 0.92. Furthermore, unsupervised analysis by DOMINO of real sets of NGS data from individuals with intellectual disability or epilepsy correctly recognizes known genes and predicts 9 new candidates, with very high confidence. In summary, DOMINO is a robust and reliable tool that can infer dominance of candidate genes with high sensitivity and specificity, making it a useful complement to any NGS pipeline dealing with the analysis of the morbid human genome.

Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort

“… The inheritance pattern of phenotypes associated with variants in candidate epilepsy genes was determined with the DOMINO tool …”

Mapping of breakpoints in balanced chromosomal translocations by shallow whole-genome sequencing points toEFNA5,BAHD1andPPP2R5Eas novel candidates for genes causing human Mendelian disorders

“…Detected translocations (figure 1) were called using a newly proposed genomic nomenclature for structural chromosomal rearrangements detected by NGS 18. The likelihood that a gene was sensitive to mutation/deregulation of a single allele was estimated using pLI score from Exome Aggregation Consortium (ExAC)19 and the recently proposed DOMINO score 20…”

Variant filtering, digenic variants, and other challenges in clinical sequencing: a lesson from fibrillinopathies

“…Only genes exclusively associated with autosomal-dominant disorders (status "confirmed" in OMIM version 2018.5, omim.org) and with a DOMINO score ≥0.9 were selected. 13 To restrict our analyses to genes likely intolerant to loss-of-function (LOF) variants, only genes with a pLi score ≥0.9 and an "observed/expected" (o/e) metric 90% confidence interval upper bound <0.35 in gnomAD were further considered ( Figure 1). 2 Genes associated with disorders inherited in an autosomalrecessive or X-linked manner were not analyzed, because for the gnomAD dataset (r2.0.1) information on haplotype and sex is not (yet) available.…”