LAUSR

Background Genomic studies in Autism Spectrum Disorder (ASD) have largely focused on high impact de novo mutations that disrupt protein-coding sequence. Given the size of the noncoding genome and its regulatory role in gene function, it is likely that rare, noncoding variation represents an important component of the genetic architecture of ASD. Characterization of noncoding mutations may also provide insights into the cell types and critical developmental stages involved in ASD. However, there is no obvious noncoding equivalent to protein-truncating variants, thus necessitating an unbiased and statistically rigorous association strategy for noncoding variation that parallels standards set by common variant analyses. Methods Using whole genome sequencing (WGS) of 2,076 individuals (519 cases, unaffected sibling controls, and both parents), we evaluated genomic variation across the spectrum of variant size and frequency. The results of 12 variant discovery algorithms were integrated to identify de novo SNVs, indels, and structural variants (SVs); cross-site validation exceeded 93% for all variant classes. Variants were annotated using an extensive series of noncoding functional annotations and gene sets, including gene-defined regions (e.g. promoters) and regulatory regions (e.g. histone marks), conservation, proximity to gene sets of interest (e.g. FMRP targets), and variant type (e.g. SNVs, indels), resulting in 51,801 combinations of annotation categories. ASD association within each category was assessed using a binomial test to compare variant counts in cases and controls in a Category-Wide Association Study (CWAS). To account for multiple testing, correlations of p-values were assessed between the 51,801 categories from 20,000 sets of simulated variants. Eigenvalue decomposition estimated that 4,211 effective tests explained 99% of the variation. Results A small excess of de novo noncoding variants was observed in cases (1.02-fold), which was not significant after correcting for paternal age. While we identified several variants that were likely causal for an individual, no annotation category was significant after correcting for 4,211 tests in the CWAS. The lowest p-values were observed in coding regions, including missense variation and SVs not detected by previous technologies. Similarly, no category of rare inherited variants demonstrated parental transmission bias or ASD association. Discussion Our results suggest that there is no clear category of rare noncoding variation with equivalent impact on ASD risk as large SVs or protein-disrupting mutations. Furthermore, given the multiple categories of noncoding mutations that may potentially confer ASD risk, identifying noncoding disease associations and quantifying this risk will require a statistically rigorous approach that includes stringent multiple testing correction for this multitude of plausible hypotheses. Analogous to genome-wide association studies of common variation, this approach is likely to identify sound and replicable noncoding associations, but will require substantially larger sample sizes, likely in excess of 5,000 cases.