SFARI announces Genomic Analysis for Autism Risk Variants in SPARK awardees

Zita / shutterstock

The Simons Foundation Autism Research Initiative (SFARI) is pleased to announce that it has awarded six grants in response to the Genomic Analysis for Autism Risk Variants in SPARK request for applications (RFA).

These grants will help to advance the understanding of the genetic basis of autism spectrum disorder (ASD) by analyzing different types of genetic variants and their roles in ASD risk. The data that will be analyzed in these projects includes whole-exome and whole-genome sequencing data, as well as genome-wide genotyping data, from SPARK (Simons Foundation Powering Autism Research for Knowledge) — a SFARI initiative that aims to recruit, engage and retain a cohort of 50,000 individuals with autism and their family members.

“The field has made remarkable advances, but there is still so much we have to learn about the genetic causes of autism,” says Wendy Chung, Director of Clinical Research at SFARI and the Kennedy Family Professor of Pediatrics in Medicine at Columbia University. “The analysis of a large cohort of individuals, including participants from SPARK, will be crucial to uncover unknown genetic risk factors for autism and to pave the way for therapeutic targets.”

Pamela Feliciano, SPARK Scientific Director and SFARI Senior Scientist, adds, “Data from the thousands of SPARK participants are an invaluable resource for the autism research community. We are grateful to the families and individuals who decided to contribute to autism genetic research, and we look forward to the novel findings that will come from these analyses.”

The following projects were selected for funding:

Mark Daly, Ph.D. (Massachusetts General Hospital, Broad Institute of MIT and Harvard, Institute for Molecular Medicine Finland)
SPARKing a global gene discovery effort in autism: Analysis of single nucleotide variants and indels

Mark Daly intends to combine SPARK exome-sequencing data with an existing data set of more than 35,000 exomes to create the largest autism discovery resource, with results distributed prepublication on a public website. The 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 and variants.

Evan Eichler, Ph.D. (University of Washington)
Integrated copy number variant analysis of SPARK exomes

In this project, 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 ASD) 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 ASD.

Jonathan Sebat, Ph.D. (University of California, San Diego)
Mechanisms of complex genetic inheritance in autism

Jonathan Sebat will investigate 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.

Yufeng Shen, Ph.D. (Columbia University)
Maximizing autism gene discovery by combining machine learning and single-cell expression data analyses

Yufeng Shen aims to maximize discovery of ASD risk genes by using new computational methods to analyze exome-sequencing data from SPARK. These methods include combining SPARK data with data from other cohorts to improve statistical power and applying machine learning to analyze missense and likely gene-disrupting variants. By using single-cell RNA-Seq data, Shen also plans to identify cell types and developmental processes associated with ASD genetic risk.

Michael Talkowski, Ph.D. (Massachusetts General Hospital, Broad Institute of MIT and Harvard)
SPARKing a global gene discovery effort in ASD: Analysis of structural variation

Michael Talkowski and colleagues from the Simons Simplex Collection — Autism Sequencing Consortium 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. To do so, they will apply 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.

Hyejung Won, Ph.D. and Jason Stein, Ph.D. (The University of North Carolina at Chapel Hill)
Integrative analysis of common variation associated with autism

Hyejung Won and Jason Stein intend to develop a comprehensive framework that will 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? Findings from this project are expected to provide mechanistic insights that connect common variants in ASD with cellular, regional and temporal substrates that can be used to prioritize further functional validation experiments.

Recent News