Smits, G. | Search bioRxiv

bioRxiv2019

Predicting disease-causing variant combinations

Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (bi-locus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bi-locus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bi-locus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important new step towards the genetic understanding of rare diseases, paving the way to new clinical knowledge and improved patient care.

bioinformatics↗

bioRxiv2017

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

BackgroundThe advent of next-generation sequencing technologies has opened new avenues for clinical genomics research. In particular, as sequencing costs continue to decrease, an ever-growing number of clinical genomics institutes now rely on DNA sequencing studies at varying scales - genome, exome, mendeliome - for uncovering disease-associated variants or genes, in both rare and non-rare diseases.\n\nA common methodology for identifying such variants or genes is to rely on genetic association studies (GAS), that test whether allele or genotype frequencies differ between two groups of individuals, usually diseased subjects and healthy controls. Current bioinformatics tools for performing GAS are designed to run on standalone machines, and do not scale well with the increasing size of study designs and the search for multi-locus genetic associations. More efficient distributed and scalable data analysis solutions are needed to address this challenge.\n\nResultsWe developed a Big Data solution stack for distributing computations in genetic association studies, that address both single and multi-locus associations. The proposed stack, called DiGeST (Distributed Gene/variant Scoring Tool) is divided in two main components: a Hadoop/Spark high-performance computing back-end for efficient data storage and distributed computing, and a Web front-end providing users with a rich set of options to filter, compare and explore exome data from different sample populations. Using exome data from the 1000 Genomes Project, we show that our distributed implementation smoothly scales with computing resources. We make the resulting software stack Open-Source, and provide virtualisation scripts to run the complete environment both on standalone machine or Hadoop-based cluster.\n\nConclusionsHadoop/Spark provides a powerful and well-suited distributed computing framework for genetic association studies. Our work illustrates the flexibility, ease of use and scalability of the framework, and more generally advocates for its wider adoption in bioinformatics pipelines.

bioinformatics↗