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 subphenotyping of a well-characterized autism spectrum disorder (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 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. Subphenotyping did not increase power substantially. Moreover, allele scores built from the most associated single nucleotide polymorphisms, 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 Simons Simplex Collection 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 subphenotypes is not a productive path forward for discovering genetic risk variants in ASD.

Keywords: Autism; GWAS; Genetics; Heterogeneity; Phenotype; Power.

Fig. 1

A) Relationship between the number of informative families and the average difference in z statistics for selected SNPs. For SNPs with p-value < 0.01 for association in the full sample, the difference in average z-statistics is calculated as the absolute value of the z-statistic for the full sample minus the absolute value of the z-statistic achieved for the subsample and the average is taken over all qualifying SNPs. The straight line shows the expected relationship for the difference if the samples were drawn at random from the full sample. B) Box plot of difference of one sided z in each phenotypic subset of the whole sample from one sided z score in all ASD. The difference was standardized. The vertical line in the plot is drawn at the one-sided Z -score of 4.58, corresponding to p=0.05/21,351.

Fig. 2

Heritability estimates in each phenotypic subset of the whole sample. All ASD=Whole sample, Autism=Restricted autism diagnosis, High vIQ=Verbal IQ> 60, Severe ASD=Higher ADOS ASD symptoms, Severe RBB=Higher ADOS RRB symptoms, Severe SA=Higher ADOS social affect impairment, Sameness1=Higher ADI-R circumscribed interests, Sameness2=Higher ADI-R difficulty with change, Noise=Higher sensitivity to noise, RBB > SA=ADOS CSS RRB higher than SA, RBB ≫ SA=ADOS CSS RRB much higher than SA.

Fig. 3

Portion of the variance of each phenotypic subset from the whole sample explained by the allele score computed from the whole sample analysis odds ratios. All ASD=Whole sample, Autism=Restricted autism diagnosis, High vIQ=Verbal IQ> 60, Severe ASD=Higher ADOS ASD symptoms, Severe RBB=Higher ADOS RRB symptoms, Severe SA=Higher ADOS social affect impairment, Sameness1=Higher ADI-R circumscribed interests, Sameness2=Higher ADI-R difficulty with change, Noise=Higher sensitivity to noise, RBB > SA=ADOS CSS RRB higher than SA, RBB ≫ SA=ADOS CSS RRB much higher than SA.