Unbiased Functional Clustering of Gene Variants with a Phenotypic-Linkage Network

Frantisek Honti, Stephen Meader and Caleb Webber

PLOS Computational Biology, 2014, vol. 10, issue 8, 1-7

Abstract: Groupwise functional analysis of gene variants is becoming standard in next-generation sequencing studies. As the function of many genes is unknown and their classification to pathways is scant, functional associations between genes are often inferred from large-scale omics data. Such data types—including protein–protein interactions and gene co-expression networks—are used to examine the interrelations of the implicated genes. Statistical significance is assessed by comparing the interconnectedness of the mutated genes with that of random gene sets. However, interconnectedness can be affected by confounding bias, potentially resulting in false positive findings. We show that genes implicated through de novo sequence variants are biased in their coding-sequence length and longer genes tend to cluster together, which leads to exaggerated p-values in functional studies; we present here an integrative method that addresses these bias. To discern molecular pathways relevant to complex disease, we have inferred functional associations between human genes from diverse data types and assessed them with a novel phenotype-based method. Examining the functional association between de novo gene variants, we control for the heretofore unexplored confounding bias in coding-sequence length. We test different data types and networks and find that the disease-associated genes cluster more significantly in an integrated phenotypic-linkage network than in other gene networks. We present a tool of superior power to identify functional associations among genes mutated in the same disease even after accounting for significant sequencing study bias and demonstrate the suitability of this method to functionally cluster variant genes underlying polygenic disorders.Author Summary: Plenty of gene variants have been associated with a disease, yet most of the heritability, along with the molecular basis, of common diseases remains unexplained. However, it is widely thought that the products of genes whose mutations are implicated in the same disease function together in the same biological pathways and it is the disruption of these pathways that underlies the disease. Such pathways are not well defined and their identification could help elucidate disease mechanisms. Consequently, groupwise functional analyses of gene variants to identify common disease-relevant pathways are becoming standard in next-generation sequencing studies, but we find that these analyses are confounded by coding-sequence length bias. We control for these bias and describe a phenotype-based approach which outperforms other methods in discerning functional associations among the disease-associated genes. We also demonstrate the suitability of this method to functionally dissect the gene variants underlying a complex disorder, the identified functional clusters offering insight into disease mechanisms.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003815

DOI: 10.1371/journal.pcbi.1003815

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