A framework for the interpretation of de novo mutation in human disease

By Kaitlin E. Samocha, E. Robinson, Stephan J Sanders, C. Stevens, A. Sabo, L. McGrath, J. Kosmicki, K. Rehnström, Swapan Mallick, Andrew W. Kirby and 16 more. 2014

1,035 citations recorded by the source corpus.

Spontaneously arising (de novo) mutations have an important role in medical genetics. For diseases with extensive locus heterogeneity, such as autism spectrum disorders (ASDs), the signal from de novo mutations is distributed across many genes, making it difficult to distinguish disease-relevant mutations from background variation. Here we provide a statistical framework for the analysis of excesses in de novo mutation per gene and gene set by calibrating a model of de novo mutation. We applied this framework to de novo mutations collected from 1,078 ASD family trios, and, whereas we affirmed a significant role for loss-of-function mutations, we found no excess of de novo loss-of-function mutations in cases with IQ above 100, suggesting that the role of de novo mutations in ASDs might reside in fundamental neurodevelopmental processes. We also used our model to identify ∼1,000 genes that are significantly lacking in functional coding variation in non-ASD samples and are enriched for de novo loss-of-function mutations identified in ASD cases.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

De novo mutations in Congenital Heart Disease with Neurodevelopmental and Other Birth Defects (2015, neutral): “De novo mutation rates per variant class were accurately predicted in controls, replicating previous model validations (10).”
Settling the score: variant prioritization and Mendelian disease (2017, methodology): “Building on previous work 56 , modelling the expected number of de novo mutations per gene, pLI compares the observed and expected numbers of LOF variants to derive a probability that each gene is intol-erant of LOF mutations 4 .”
The ExAC browser: displaying reference data information from over 60 000 exomes (2016, background): “The page begins with gene metadata and external references, along with constraint information, which summarizes the gene’s intolerance to variation for multiple functional classes (3,8) (Figure 1A).”
The human gene damage index as a gene-level approach to prioritizing exome variants (2015, background): “Leading gene-level strategies are based on selective pressure (12) and de novo mutation rate estimates (13).”
Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder (2017, neutral): “Regarding genotypic heterogeneity, using established criteria 17,47 , in this study we were able to resolve a molecular basis in 11.2% of ASD cases; this tally should rise as we acquire more rare variant data to compare against 22 .”
Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci (2015, background): “…specifically: two datasets of genes that show evolutionary constraint (p = 0.04, p = 1 3 10 5, logistic regression) (Petrovski et al., 2013; Samocha et al., 2014), one dataset of genes that are targeted by the Fragile X protein FMRP in mouse brain (p = 4 3 10 8, logistic (A) 2,080 unique…”
Prevalence and architecture of de novo mutations in developmental disorders (2017, positive): “We identified genes that were significantly enriched for damaging DNMs by comparing the observed gene-wise DNM count to the expected count under a null-mutation model 23 , as described ARTICLE RESEARCH previously 15 .”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, background): “3 Given the role of essential genes in human disease, considerable efforts have gone into developing methods for haploinsufficiency prediction [5, 27-30].”