SPARKing a global gene discovery effort in autism: Analysis of single nucleotide variants and indels

  • Awarded: 2018
  • Award Type: Targeted: Genomic Analysis for Autism Risk Variants in SPARK
  • Award #: 608540

Autism spectrum disorder (ASD) is among the most highly heritable neuropsychiatric disorders, with a genetic architecture including significant contributions from both common and rare genetic variation. While most of the overall liability to ASD traces to common variation, single rare mutations exert significant impact in a subset of cases. Studies have identified the critical role of ultra-rare primarily de novo single nucleotide variants (SNVs), as well as insertions/deletions (indels) and larger copy number variations (CNVs). Although these sequencing studies have combined to identify over 65 likely ASD genes, multiple estimates suggest there are hundreds or even thousands of such genes. Similarly, common variant studies in ASD to date indicate an extremely polygenic architecture underlying ASD risk, with the most recent genome-wide association study (GWAS) meta-analysis discovering the first five robustly associated loci1. While this decade has seen great progress in gene discovery, it is clear that only a fraction of all ASD genes have been conclusively identified, and there is a compelling need to increase sample sizes and enhance analytical approaches to continue to identify associated genes.

SPARK offers the best opportunity to advance gene discovery by dramatically increasing the number of families participating in genetic research, creating a more definitively sized genotype-phenotype resource. In order for this data to have its maximum impact on gene discovery, it must be combined with other data sets. Therefore, Mark Daly and colleagues propose to jointly analyze the largest current set of existing data (>35,000 exomes) with the SPARK data, along with newly generated sequencing data. This significant influx of data, which doubles the number of individuals with ASD who have undergone full exome sequencing, promises to identify substantially more risk genes than ever before. Daly’s team will release variant-level data through a public web portal and will leverage other data sets to conduct bioinformatic follow-up and elucidate the role these newly identified genes play in ASD.


1.Grove J. et al. Nat. Genet. 51, 431–444 (2019) PubMed
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