UPDhmm: detecting uniparental disomy from NGS trio data

TL;DR

UPDhmm is a new tool that detects chromosomal abnormalities called uniparental disomies in genomic data from families, improving detection accuracy and clinical relevance.

Contribution

UPDhmm introduces a Hidden Markov Model-based method for precise detection of UPD subtypes using trio NGS data, outperforming existing tools.

Findings

UPDhmm outperformed existing methods in detecting UPD events in exome and genome data simulations.

UPDhmm identified two UPD events in autism patients, including one previously unreported.

UPDhmm is implemented in R and available as a Bioconductor package.

Abstract

Uniparental disomies (UPDs) are copy-neutral chromosomal alterations that occur when both copies of a chromosome pair (entire or segmental) come from one parent. UPDs, including isodisomies (identical parental chromosome) and heterodisomies (two different homologs from the same parent), reflect meiotic and/or mitotic aberrations of chromosomal segregation that can be associated with congenital or acquired disease. Despite their relevance, current methods to detect UPDs using sequence data (exomes or genomes) have limited sensitivity for small events, cannot precisely determine the UPD sub-type or coordinates, and perform poorly when including individuals or populations with consanguinity. We present UPDhmm, a novel tool that uses trio-based sequence data (proband and parents) and models inheritance patterns. UPDhmm predicts the most likely inheritance scenario, normal Mendelian inheritance versus UPD event, based on genotype combinations using a Hidden Markov Model (HMM). We validated the method using simulations on exome and genome data from 1000-Genomes projects. UPDhmm overperformed currently available methods in detecting simulated UPD events in both data types. We applied UPDhmm to a collection of nearly 2400 families with a proband with autism spectrum disorder (Simons Simplex Collection Project) and identified UPD events in two affected individuals, one of them previously unreported. These two events, a paternal isodisomy of chr8 and a maternal heterodisomy of chr22, can be genetic causes of the disease, demonstrating the clinical utility of UPDhmm. Thus, UPDhmm can facilitate the incorporation of UPD detection into clinical pipelines of genomic analysis.

UPDhmm is implemented in R and is available in the Bioconductor package (version 1.5.0): https://www.bioconductor.org/packages/release/bioc/html/UPDhmm.html. The source code can be found at https://github.com/martasevilla/UPDhmm under the MIT license.

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