Michelle Dawson posts

Post published on Twitter/X on 29 May 2023 11:01

Twitter/X DOI work crossref Extract quoted in the post Question asked by Dawson in the post External link integrated into the post Autism terms

Structured cited links

1 cited resource

DOI work Fetched Post crossref

Zoe Schmilovich, Vincent-Raphaël Bourque, Guillaume Huguet, Qin He, Jay P. Ross, Martineau Jean-Louis, Zohra Saci, Boris Chaumette, et al. (2023). Exploring the common genetic architecture of autism spectrum disorder using a novel multi-polygenic risk score approach. openRxiv.

Publication date
28 May 2023
Identifier
10.1101/2023.05.23.23290405
Authors
Zoe Schmilovich, Vincent-Raphaël Bourque, Guillaume Huguet, Qin He, Jay P. Ross, Martineau Jean-Louis, Zohra Saci, Boris Chaumette, Patrick A. Dion, Sébastien Jacquemont, Guy A. Rouleau
Reference type
preprint
Publisher
openRxiv
Metadata source
crossref

Abstract

ABSTRACT Compared to disorders of similar heritability and contribution of common variants, few genome-wide significant loci have been implicated in autism spectrum disorder (ASD). This undermines the use of polygenic risk scores (PRSs) to investigate the common genetic architecture of ASD. Deconstructing PRS-ASD into its related traits via “developmental deconstruction” could reveal the underlying genetic liabilities of ASD. Using the data of >24k individuals with ASD and >28k of their unaffected family members from the SSC, SPARK, and MSSNG cohorts, we computed the PRSs for ASD and 11 genetically-related traits. We applied an unsupervised learning approach to the ASD-related PRSs to derive “multi-PRSs” that captured their variability in orthogonal dimensions. We found that multi-PRSs captured a similar proportion of genetic risk for ASD in cases versus intrafamilial controls (OR multi-PRS =1.10, R 2 =0.501%), compared to PRS-ASD itself (OR PRS-ASD =1.16, R 2 =0.619%). While multi-PRS dimensions conferred risk for ASD, they had “mirroring” effects on developmental phenotypes among cases with ASD. We posit that this phenomenon may partially account for the paucity of genome-wide significant loci and the clinical heterogeneity of ASD. This approach can serve as a proxy for PRS-ASD in cases where non-overlapping and well-powered GWAS summary statistics are difficult to obtain, or accounting for heterogeneity in a single dimension is preferable. This approach may also capture the overall liability for a condition (i.e.: genetic “P-factor”). Altogether, we present a novel approach to studying the role of inherited, additive, and non-specific genetic risk factors in ASD.

Study authors in this cited reference