A Genome-wide Association Study of Autism Using the Simons Simplex Collection: Does Reducing Phenotypic Heterogeneity in Autism Increase Genetic Homogeneity?

Abstract

Background Phenotypic heterogeneity in autism has long been conjectured to be a major hindrance to the discovery of genetic risk factors, leading to numerous attempts to stratify children based on phenotype to increase power of discovery studies. This approach, however, is based on the hypothesis that phenotypic heterogeneity closely maps to genetic variation, which has not been tested. Our study examines the impact of sub-phenotyping of a well-characterized ASD sample on genetic homogeneity and the ability to discover common genetic variants conferring liability to ASD. Methods Genome-wide genotypic data of 2576 families from the Simons Simplex Collection (SSC) were analyzed in the overall sample and phenotypic subgroups defined on the basis of diagnosis, IQ, and symptom profiles. We conducted a family-based association study as well as estimating heritability and evaluating allele scores for each phenotypic subgroup. Results Association analyses revealed no genome-wide significant association signal. Sub-phenotyping did not increase power substantially. Moreover, allele scores built from the most associated SNPs, based on the odds ratio in the full sample, predicted case status in subsets of the sample equally well and heritability estimates were very similar for all subgroups. Conclusions In genome-wide association analysis of the SSC sample, reducing phenotypic heterogeneity had at most a modest impact on genetic homogeneity. Our results are based on a relatively small sample, one with greater homogeneity than the entire population; if they apply more broadly, they imply that analysis of sub-phenotypes is not a productive path forward for discovering genetic risk variants in ASD.

ASD Validity

“…Similarly, Chaste et al (2015) found that subgrouping ASD by distinct phenotypes did not result in finding sets of genetic variants that were unique to distinct clinical phenotypes. In fact, fewer and greater numbers of overlapping genetic variants were reported across the ASD phenotypic groups.…”

“…In fact, fewer and greater numbers of overlapping genetic variants were reported across the ASD phenotypic groups. Chaste et al (2015) nonetheless argued that clinical phenotypes must have some Bpower for discovering genetic associations^(p. 781).…”

“…There has been ASD gender subgrouping Ypma et al 2016), ASD brain feature subgrouping (Piras et al 2014;Hahamy et al 2015), ASD behavior subgrouping (Chaste et al 2015;Kim et al 2016;Veatch et al 2014), ASD developmental course subgrouping (Fountain et al 2012;Lord et al 2015), ASD behavior and brain feature subgrouping , ASD MPAs subgrouping , ASD MCAs subgrouping (Timonen-Soivio et al 2015), and ASD risk gene variant subgrouping (Hormozdiari et al 2015;Krumm et al 2014;Noh et al 2013).…”

Streptococcal infection-related autoimmunity and autism: crosstalk in protein functional networks

“…This data was used in a reconstruction of functional networks linking the antibodies targets to genes relevant to autism (Supplementary table 2). The genes were chosen via GWAS studies by the correlation of relevant SNPs to autistic phenotype (genes) [28][29][30][31].…”

Whole Genome Sequencing in Psychiatric Disorders: the WGSPD Consortium

“…However, such an approach is risky for common, non-Mendelian psychiatric disorders given: 1) current lack of insight into relevant subtypes; and 2) reduced sample size. A GWAS based on ~2,500 cases in the Simons Simplex Collection ASD cohort showed no improvement in the proportion of genetic heritability explained by the top SNPs accounting for changes in sample size for over 10 phenotypic characteristics 49 . In contrast, a GWAS of a nonpsychiatric phenotype, bone mineral density, showed benefits of subgrouping, leading to the identification of 16 new loci 50 .…”