Abstract
BackgroundComplex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. Coalitional game theory (CGT) is an exciting method that can consider the combinatorial effect of groups of variants working in concert to produce a phenotype. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; however, this previous approach cannot take into account for prior biological knowledge. Here we extend CGT to incorporate a priori knowledge from biological networks through a game theoretic centrality measure based on Shapley value to rank genes by their relevance–the individual gene’s synergistic influence in a gene-to-gene interaction network. Game theoretic centrality extends the notion of Shapley value to the evaluation of a gene’s contribution to the overall connectivity of its corresponding node in a biological network.ResultsWe implemented and applied game theoretic centrality to rank genes on whole genomes from 756 multiplex autism families. Top ranking genes with the highest game theoretic centrality in both the weighted and unweighted approaches were enriched for pathways previously associated with autism, including pathways of the immune system. Four of the selected genes HLA-A, HLA-B, HLA-G, and HLA-DRB1–have also been implicated in ASD and further support the link between ASD and the human leukocyte antigen complex.ConclusionsGame theoretic centrality can prioritize influential, disease-associated genes within biological networks, and assist in the decoding of polygenic associations to complex disorders like autism.
Highlights
Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model
Coalitional game theory (CGT) has been applied to fully sequenced genomes to assess the impact of groups of variants on phenotype and has previously been used to implicate likely gene disrupting (LGD) variants in Autism Spectrum Disorder (ASD) [6, 7]
We extend the CGT method implemented in Gupta et al (2017) by combining it with the neighborhood-based game theoretic centrality measure introduced in Cesari et al (2017), allowing for the incorporation of a priori network knowledge [6, 11]
Summary
Complex human health conditions with etiological heterogeneity like Autism Spectrum Disorder (ASD) often pose a challenge for traditional genome-wide association study approaches in defining a clear genotype to phenotype model. CGT has been applied to associate likely-gene-disrupting variants encoded from whole genome sequence data to ASD; this previous approach cannot take into account for prior biological knowledge. CGT has been applied to fully sequenced genomes to assess the impact of groups of variants on phenotype and has previously been used to implicate likely gene disrupting (LGD) variants in Autism Spectrum Disorder (ASD) [6, 7]. These previous applications are unable to combine a priori biological knowledge like pathway information and autism genes of interest
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