Incorporating False Discovery Rates Into Genetic Association in Autism

Autism spectrum disorder (ASD) is a highly heritable condition. It’s characterized by deficits in social communication, the presence of repetitive behaviors, and/or stereotyped interests.

It has already been proven by previous studies that genetic factors contribute strongly to the onset of Autism, yet the search for specific risk genes for ASD has only recently begun to gain ground, and there is still so much scientists are yet to discover.

Finding the specific genes that are linked to Autism would not only provide potential diagnoses but would also allow researchers to gain insight into the pathological processes that underpin the condition, which in turn could help further therapeutic approaches and assist individuals with Autism in their daily lives. 

In a paper published in Nature in 2014, researchers at the Autism Sequencing Consortium (ASC) used Whole Exome Sequencing (WES) to selectively sequence the coding regions of the genome to find rare genetic variants and genes associated with risk for ASD.

WES data was analyzed from nearly 4,000 individuals with autism and nearly 10,000 controls, and researchers were able to identify and analyze a set of 107 autosomal genes with a false discovery rate (FDR) of <30%.

This basically means that within those 107 genes, approximately 30% were not actually associated with Autism, meaning they could be considered ‘false positives’ as they’re simply present by chance. 

False discovery rate (FDR) is a research method used to conceptualize the rate of type I errors in null hypothesis testing when conducting multiple comparisons, and this method was considered a good fit for the study because WES captures discrete independent events within a gene, almost all de novo or very recent mutations, each of which has a complete affiliation to only one gene.

With over 1,000 genes relevant to ASD, FDR then becomes a useful construct for exploring patterns and processes but with a rational and principled approach.

Groups interested in specific disorders often use a genetics-first rationale to choose genes for follow up. Strong genetic support for a specific gene enhances the validity of the subsequent in vitro or in vivo analyses.

However, there remains significant investment in genes that do not have robust genetic support for a given genetic disorder, even when the rationale of the study begins with genetic relevance.

A major value of the FDR approach is to highlight pathways implicated in disease, rather than to implicate specific genes.

The FDR framework works well with WES analyses of rare variation in complex disease, though it is not nearly as obviously or readily applicable to Genome-wide Association Studies (GWAS).  

Even in the case of GWAS, the emerging evidence of the considerable polygenicity of disease and continued efforts to overcome the many analytic complexities unique to the GWAS data may yield similarly important uses if treated with caution.

Looking to the future, the identification of ASD genes will lead to better cell and animal models, which in turn will enhance our understanding of ASD as well as improve how our healthcare and education systems cater for individuals on the Autism spectrum.