Thorough understanding the genetic causes of autism spectrum disorder is critical to improving clinical care and advancing biomedical sciences. Yufeng Shen aims to maximize genetic discovery from the exome sequencing data generated by the SPARK project by combining it with machine learning and single cell RNA-seq data analyses.

Jonathan Sebat is investigating the nature of complex genetic inheritance in autism by assembling a large combined data set from SPARK, the Simons Simplex Collection and other ongoing genome-sequencing efforts at the University of California, San Diego. The goal of this study is to identify direct evidence of a multifactorial etiology in families affected by ASD and to elucidate specific mechanisms of complex genetic inheritance.

Hyejung Won and Jason Stein will develop a comprehensive framework to link common genetic variation associated with autism risk to biological mechanisms. They will utilize this framework to ask: (1) When do these risk variants alter brain development? (2) What cell types do they impact? (3) Which brain regions are impacted by these variants?

Mark Daly aims to combine SPARK exome sequencing data with all earlier exome data to create the largest existing autism discovery resource, with results distributed prepublication on a public website. Exome meta-analysis of this data set is expected to greatly increase the number of autism risk genes and will help elucidate the phenotypic impact of identified genes/variants.

Michael Talkowski and colleagues from the SSC-ASC Genomics Consortium (SSC-GC) plan to integrate SPARK data with complementary SSC-GC resources to perform copy number variation (CNV) detection, jointly analyze SPARK CNVs against population-reference data sets and greatly expand the scope of gene discovery in SPARK by applying systematic statistical analyses to aggregated ASD data sets that incorporate single nucleotide variants, indels and structural variants in a singular association framework. Findings from these studies are expected to estimate the contribution of coding and noncoding regulatory variation in ASD and provide foundational tools and data sets for future studies by the community.

Evan Eichler aims to significantly increase the yield of high-impact autism mutations by focusing on the discovery of both copy number and single nucleotide variants in approximately 15,000 individuals (4,500 families with autism) from SPARK. Using established and novel computational pipelines, his laboratory will work with the SPARK consortium to generate a high-confidence set of potential pathogenic variants and then integrate these data into larger genetic variant databases to pinpoint pathogenic variants and novel genes associated with autism.