Work

Towards Increasing the Clinical Relevance of In Silico Methods to Predict Pathogenic Missense Variants

Year: 2016

Type: article

Abstract: As genetic sequencing throughput continues to accelerate, so does the accumulation of variants of unknown clinical significance.The great majority of these variants cause amino acid substitutions (cSNVs) in protein sequence.The need to interpret these variants continues to motivate development of better in silico bioinformatic methods.Despite the development of dozens of such methods over the past 15 years, clinically relevant prediction accuracy remains elusive.Here, we present some recent progress and shortcomings in the development of bioinformatics missense variant classifiers, and we argue for the increased use of endophenotypes.Endophenotypes are quantitative measurements that are correlated with phenotypes via shared genetic causes (e.g., enzyme catalytic activity, serum cholesterol or glucose level, volumetric lung capacity).In many cases, endophenotypes are more directly influenced by genetic variation, increasing their power to detect genotype-endophenotype associations relative to genotypephenotype associations.The data required to train and benchmark bioinformatic methods to predict endophenotype from cSNVs and other variant types is increasingly available and could be made widely available by concerted community effort to enhance locus-specific and disease variant databases.We highlight some currently available data and present results from published bioinformatics studies that use endophenotypes.

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1004725

Authors: David L. Masica, Rachel Karchin

Institutions: Johns Hopkins University, Johns Hopkins Medicine

Language: English

Topic: Genomics and Rare Diseases

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,043