De novo mutations in protein-coding regions are strongly associated with autism, and family-based sequencing studies have identified numerous genes that harbor excess mutations in probands. However, the aggregate contribution of this class of variation to autism remains unclear. Here, we model the distribution of de novo autosomal coding variant effect sizes in 38,680 autism trios to estimate fundamental features of de novo genetic architecture. We find that damaging de novo single-nucleotide variants and frameshift indels explain 3.4% (95% CI: 2.1% - 4.7%) of autism variance on the observed scale. Approximately 7.0% (95% CI: 5.6% - 8.4%) of cases carry a large-effect mutation (rate ratio > 5), and most such mutations are incompletely penetrant. Although hundreds of genes make some nonzero contribution, 50% of mutational variance on the autosomes is explained by just 15 genes. De novo enrichments vary across cohorts with different ascertainment strategies; making projections for future trio studies, we show that many large-effect genes remain to be found.
A. Nadig, J. Fu, F. Satterstrom et al.· medRxiv· 0 citations
Long-read sequencing (LRS) and diploid genome assembly have enabled nearly complete structural variant (SV) discovery. Using 293 nearly complete genomes, we characterize the full spectrum of genetic variation and show that while 99% of the variants between any two genomes are single base-pair substitutions, 88% of the euchromatic variant base pairs are SVs, including insertions, deletions, duplications, and inversions. We identify 24 gene-rich regions subject to megabase-scale variation, 2,293 potentially unstable tandem repeats, and 890 novel expression quantitative trait loci associated with SVs in humans. Expanding to 1,218 LRS samples from the 1000 Genomes Project and applying a newly developed cross-platform breakpoint evaluation tool, BoostSV, we construct a nonredundant callset comprising 614,522 SVs. We demonstrate the utility of this population-level SV reference callset by filtering >99% of the common variation from 44 unsolved LRS probands from the Undiagnosed Diseases Network to discover likely disease-causing SVs. Second, we genotype 1,053 high-impact biallelic SVs from the pangenome callset in 232,090 samples from All of Us and discover 105 SVs with significant associations, including 26% where the SV is the lead variant. This publicly available pangenome SV resource will drive new disease associations and further our understanding of the missing heritability of human genetic disease.
J. Lin, J. Gustafson, J. Wertz et al.· medRxiv· 0 citations
