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Title: Unbiased Functional Clustering of Gene Variants with a Phenotypic-Linkage Network}
Article:
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.
Introduction:
It is widely postulated that the products of genes whose variants are implicated in the same disease participate in the same biological function or process whose disruption leads to the disease[1],[2]. This concept is supported by examples of complex disease in which the proteins encoded by the implicated genes interact, form a molecular complex or function at different steps of the same biochemical pathway[3],[4]. As there is limited power to associate rare variants with disease by case–control studies, the use of functional-enrichment approaches that identify a shared function in a set of mutated genes is becoming standard in the interpretation of variants[5]–[8]. Since the function of many genes is not known and their classification to pathways is scant, functional associations between genes are often inferred from large-scale omics data[4],[6]–[9]. However, the suitability of such data types, including protein–protein interactions and gene co-expression networks, for functional-enrichment analysis remains unclear. Moreover, the inferred functional associations can be affected by confounding factors, potentially resulting in false positive findings. Thus, it is important to identify any bias affecting the implicated genes and control for them. Multiple exome-sequencing studies currently test variants for functional enrichment and yet there is no consensus concerning what to control for[6]–[9]. In this study, we have inferred functional associations between human genes from diverse data types and assessed the phenotypic agreement of the inferred gene–gene associations. We have examined different data types and networks and found that genes mutated in the same disease cluster more significantly in an integrated phenotypic-linkage network than in other gene networks. Examining the functional association between de novo gene variants, we have identified a confounding bias in coding-sequence length that we control for. We present a tool that identifies functional associations among genes mutated in the same disease even after accounting for significant sequencing study bias and demonstrate the power of this tool to functionally subcluster the gene variants underlying a polygenic disorder.
Discussion:
We have inferred functional-association networks of human genes from diverse data types and assessed the phenotypic agreement of the inferred links. Having examined different data types and networks, we have found that genes mutated in the same disease cluster more significantly in an integrated phenotypic-linkage network than in other gene networks( Figure 3C). We note that another gene network, called NETBAG, has been developed by Gilman and colleagues[22]. We could not access NETBAG for the performance comparison. Nevertheless, Gilman and colleagues state the use of shared disease associations among 478 human genes as the gold standard in their network construction[22] and the used disease associations originate from a study published in 2001[23]. By comparison, our method takes advantage of over 100, 000 mammalian genotype–phenotype relations and fully exploits bio-ontologies by means of semantic similarity, with both advances expected to enhance greatly the phenotypic-linkage network that we explicitly present here. Examining the functional association between de novo gene variants, we have identified a confounding bias in coding-sequence length that we control for to avoid false positive findings. Numerous implicated variants are in fact expected to be neutral mutations but they are more likely to appear in genes with longer CDS, leading to a tendency of the implicated genes to be interconnected in gene networks( see Figure 2). These bias have confounded functional analyses and likely led to an overestimation of functional clustering in former studies. We have found that the CDS-length bias were highly significant in all the studied gene sets, including the unaffected siblings, while the node degrees were not. The higher node degrees in some of the candidate gene sets may indicate a functional signal, as the same genes are significantly more conserved( Figure S8). We conclude that controlling for CDS length in functional analyses of gene variants is appropriate. One way of controlling for CDS length is to compare the interconnectedness of the implicated genes with that of genes mutated in unaffected controls[9]. However, we observe that the control genes tend to be less interconnected than random genes( Figure S9), which suggests that our way of controlling for CDS length( see Methods) is more conservative. The nature of the phenotypic-linkage network suggests that the clustered genes function together in the same disease-relevant cellular pathways( Figure S10). The functional convergence that we identify among the three sets of genes from independent exome studies of autism spectrum disorder demonstrates that the method is able to detect biological coherence among variant genes( Figures 3 and S10). Throughout, we have considered the larger class of non-synonymous variants which is likely to possess a more diluted signal than nonsense variants. As with all clustering methods, our method is sensitive to the number of variants identified and the likelihood of their causal relation. Half of our study sets included only 5–10 genes with nonsense variants, between which we either did not find any functional links or the sum of link weights was not significantly higher than expected after controlling for CDS length. For studies of rare or de novo variants derived from a single or small number of genomes, gene prioritizing methods based upon phenotypic similarity may be more appropriate[24]. Continuing efforts to systematically phenotype model organisms and to enrich the phenotype ontologies could further improve the resulting phenotypic-linkage networks that are constructed[25]. The integrated network toolkit is made available at http://groups. mrcfgu. ox. ac. uk/webber-group/resources.
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 .