Prioritizing Copy Number Variants using Phenotype and Gene Functional Similarity

Abstract

Abstract

Background: There are many types of genetic variation in the human genome, ranging from large chromosome anomalies to Single Nucleotide Variant (SNV). It is becoming necessary to develop methods for distinguishing disease-causing variants from a large number of neutral genetic variation in an individual. This problem is also relevant to Copy Number Variants (CNVs), which is a class of genetic variation where large segments of the genome differ in copy number amongst various individuals.

Results:. We have built a method that incorporates biological background knowledge about the relation between phenotypes resulting from a loss of function in mouse genes, gene functions as described using the Gene Ontology (GO), as well as the anatomical site of gene expression along with a score that predicts the pathogenicity of CNV SVScore. We use this information to build a machine learning model that ranks CNVs based on their predicted pathogenicity and the relation between genes affected by the CNV and the phenotype we observe in affected individuals. Our method achieves an F-score of 99.23%, with 99.18% precision in our evaluation set.

Introduction

Over the past several years, much progress has been made in the area of CNVs detection and understanding their role in human diseases 1,2,3. We now understand that CNVs account for much of human variability. Correspondingly, there have been several methods introduced to find disease-associated genes and SNVs 4,5,6. Constructing similar methods for CNV is challenging due to the heterogeneity in variant size, type and the possibility of multiple genes being affected by large CNVs. CNV impact prediction methods should consider these factors in order to robustly prioritize pathogenic variants.

Results

The performance of our methods is based on a dataset of CNVs detected in structure variants with known phenotypes. These CNVs were evaluated as harmful or benign. Our results show that incorporating this information leads to improvement over a baseline model (Fig 2) which uses only similarity scores between gene phenotype associations and disease associated phenotypes, as well as improvement over using only pathogenicity prediction methods for CNVs. Our method achieves an F-score of 99.23%, with 99.18% precision.

Future work

Future work is required to evaluate and improve our model using patient-derived WGS data. Moreover, establishing a workflow that incorporating existing tools for CNV calling from BAM/Fastq file to SV. Then we can test the method using real samples with known CNV disease.