Reverse Pathway Genetic Approach Identifies Epistasis in Autism Spectrum Disorders

Abstract

Although gene-gene interaction, or epistasis, plays a large role in complex traits in model organisms, genome-wide by genome-wide searches for two-way interaction have limited power in human studies. We thus used knowledge of a biological pathway in order to identify a contribution of epistasis to autism spectrum disorders (ASDs) in humans, a reverse-pathway genetic approach. Based on previous observation of increased ASD symptoms in Mendelian disorders of the Ras/MAPK pathway (RASopathies), we showed that common SNPs in RASopathy genes show enrichment for association signal in GWAS (P = 0.02). We then screened genome-wide for interactors with RASopathy gene SNPs and showed strong enrichment in ASD-affected individuals (P < 2.2 x 10−16), with a number of pairwise interactions meeting genome-wide criteria for significance. Finally, we utilized quantitative measures of ASD symptoms in RASopathy-affected individuals to perform modifier mapping via GWAS. One top region overlapped between these independent approaches, and we showed dysregulation of a gene in this region, GPR141, in a RASopathy neural cell line. We thus used orthogonal approaches to provide strong evidence for a contribution of epistasis to ASDs, confirm a role for the Ras/MAPK pathway in idiopathic ASDs, and to identify a convergent candidate gene that may interact with the Ras/MAPK pathway.

Discovering genetic interactions bridging pathways in genome-wide association studies

“…These methods typically employ a filtering step, either data driven 43-45 or knowledge driven 46,114 , before applying statistical analysis of interactions. Other illustrative examples of this class of approaches are from a recent autism spectrum disorder study where all possible SNPs were tested for interactions with the Ras/MAPK pathway 39 , and a melanoma risk study where SNP-SNP interactions were studied within the five pathways that are significant based on the traditional individual SNP based-GSEA analysis 40 . Most studies implementing this approach investigate interactions among a small set of genetic variants (genes or SNPs) that either statistically demonstrate evidence for individual association with the disease phenotype or are known to be relevant to the disease based on prior knowledge.…”

cis-Regulatory Chromatin Contacts in Neural Cells Reveal Contributions of Genetic Variants to Complex Neurological Disorders

“…For each cell type, we tested for epistatic effects between all combinations of SNP pairs occurring at both ends of our significant promoter-PIR interactions. Specifically, we evaluated enrichment in our interactions for independent ASD case-specific SNP pairs over non-overlapping matched pseudo-control-specific SNP pairs (see methods) 56 . Of all the cell types, only the hippocampal DG-like neurons showed enrichment at PLINK’s default significance threshold of 10 −7 (chi-squared test, p =2.7×10 −16 ) ( Fig.…”

“…In order to determine whether genetic variation in physically interacting regions might contribute to neurodevelopmental disorders via genetic interactions, we utilized GWAS data for ASD. For epistasis testing, we needed individual-level genotype data, so we used a dataset of 4,109 trios and 4,471,807 imputed and genotyped single nucleotide polymorphisms (SNPs), as previously reported 56 . This dataset includes publicly available ASD GWAS data [Autism Genetic Resource Exchange (AGRE), Autism Genome Project (AGP), Simons Simplex Collection (SSC)] in addition to in-house generated data [University of California, San Francisco (UCSF)], harmonized, imputed, and quality controlled (QC+) by us, as previously described 56 .…”

“…For epistasis testing, we needed individual-level genotype data, so we used a dataset of 4,109 trios and 4,471,807 imputed and genotyped single nucleotide polymorphisms (SNPs), as previously reported 56 . This dataset includes publicly available ASD GWAS data [Autism Genetic Resource Exchange (AGRE), Autism Genome Project (AGP), Simons Simplex Collection (SSC)] in addition to in-house generated data [University of California, San Francisco (UCSF)], harmonized, imputed, and quality controlled (QC+) by us, as previously described 56 . These data comprised trios with one ASD-affected offspring and both parents selected for homogeneous genetic ancestry by multidimensional scaling with PLINK 65 .…”

“…For each significant promoter-PIR interaction for each cell type, we extracted all independent SNPs in each region from our QC+ imputed GWAS data. We performed a case-only test for pairwise epistasis using the - fast-epistasis, case-only , and set-by-set options in PLINK with p ≤ 10 −7 as a default threshold in ASD cases and in matched pseudo-controls 56 . Across the interacting loci in the four neural cell types, we performed approximately 19.7 million epistasis tests and 12,637,825 SNP pairs showed p ≤ 10 −7 (65% tests performed).…”