William M. Brandler

Frequency and Complexity of De Novo Structural Mutation in Autism

Genetic studies of autism spectrum disorder (ASD) have established that de novo duplications and deletions contribute to risk. However, ascertainment of structural variants (SVs) has been restricted by the coarse resolution of current approaches. By applying a custom pipeline for SV discovery, genotyping, and de novo assembly to genome sequencing of 235 subjects (71 affected individuals, 26 healthy siblings, and their parents), we compiled an atlas of 29,719 SV loci (5,213/genome), comprising 11 different classes. We found a high diversity of de novo mutations, the majority of which were undetectable by previous methods. In addition, we observed complex mutation clusters where combinations of de novo SVs, nucleotide substitutions, and indels occurred as a single event. We estimate a high rate of structural mutation in humans (20%) and propose that genetic risk for ASD is attributable to an elevated frequency of gene-disrupting de novo SVs, but not an elevated rate of genome rearrangement.

From De Novo Mutations to Personalized Therapeutic Interventions in Autism

The high heritability, early age at onset, and reproductive disadvantages of autism spectrum disorders (ASDs) are consistent with an etiology composed of dominant-acting de novo (spontaneous) mutations. Mutation detection by microarray analysis and DNA sequencing has confirmed that de novo copy-number variants or point mutations in protein-coding regions of genes contribute to risk, and some of the underlying causal variants and genes have been identified. As our understanding of autism genes develops, the spectrum of autism is breaking up into quanta of many different genetic disorders. Given the diversity of etiologies and underlying biochemical pathways, personalized therapy for ASDs is logical, and clinical genetic testing is a prerequisite.

Paternally inherited cis-regulatory structural variants are associated with autism

Inherited variation contributes to autism About one-quarter of genetic variants that are associated with autism spectrum disorder (ASD) are due to de novo mutations in protein-coding genes. Brandler et al. wanted to determine whether changes in noncoding regions of the genome are associated with autism. They applied whole-genome sequencing to ∼2600 families with at least one affected child. Children with ASD had inherited structural variants in noncoding regions from their father. Regulatory regions of some specific genes were disrupted among multiple families, supporting the idea that a component of autism risk involves inherited noncoding variation. Science, this issue p. 327 Whole-genome sequencing identifies inherited noncoding variants in families affected by autism spectrum disorder. The genetic basis of autism spectrum disorder (ASD) is known to consist of contributions from de novo mutations in variant-intolerant genes. We hypothesize that rare inherited structural variants in cis-regulatory elements (CRE-SVs) of these genes also contribute to ASD. We investigated this by assessing the evidence for natural selection and transmission distortion of CRE-SVs in whole genomes of 9274 subjects from 2600 families affected by ASD. In a discovery cohort of 829 families, structural variants were depleted within promoters and untranslated regions, and paternally inherited CRE-SVs were preferentially transmitted to affected offspring and not to their unaffected siblings. The association of paternal CRE-SVs was replicated in an independent sample of 1771 families. Our results suggest that rare inherited noncoding variants predispose children to ASD, with differing contributions from each parent.

Paternally inherited noncoding structural variants contribute to autism

The genetic architecture of autism spectrum disorder (ASD) is known to consist of contributions from gene-disrupting de novo mutations and common variants of modest effect. We hypothesize that the unexplained heritability of ASD also includes rare inherited variants with intermediate effects. We investigated the genome-wide distribution and functional impact of structural variants (SVs) through whole genome analysis (≥30X coverage) of 3,169 subjects from 829 families affected by ASD. Genes that are intolerant to inactivating variants in the exome aggregation consortium (ExAC) were depleted for SVs in parents, specifically within fetal-brain promoters, UTRs and exons. Rare paternally-inherited SVs that disrupt promoters or UTRs were over-transmitted to probands (P = 0.0013) and not to their typically-developing siblings. Recurrent functional noncoding deletions implicate the gene LEO1 in ASD. Protein-coding SVs were also associated with ASD (P = 0.0025). Our results establish that rare inherited SVs predispose children to ASD, with differing contributions from each parent.

Genome Wide Association Scan identifies new variants associated with a cognitive predictor of dyslexia.

Developmental dyslexia (DD) is one of the most prevalent learning disorders among children and is characterized by deficits in different cognitive skills, including reading, spelling, short term memory and others. To help unravel the genetic basis of these skills, we conducted a Genome Wide Association Study (GWAS), including nine cohorts of reading-impaired and typically developing children of European ancestry, recruited across different countries (N=2,562-3,468). We observed a genome-wide significant effect (p<1×10−8) on rapid automatized naming of letters (RANlet) for variants on 18q12.2 within MIR924HG (micro-RNA 924 host gene; p = 4.73×10−9), and a suggestive association on 8q12.3 within NKAIN3 (encoding a cation transporter; p = 2.25 ×10−8). RAN represents one of the best universal predictors of reading fluency across orthographies and linkage to RAN has been previously reported within CELF4 (18q12.2), a gene highly expressed in the fetal brain which is co-expressed with NKAIN3 and predicted to be a target of MIR924. These findings suggest new candidate DD susceptibility genes and provide insights into the genetics and neurobiology of dyslexia.

SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes

Motivation: Structural variation (SV) detection from short‐read whole genome sequencing is error prone, presenting significant challenges for population or family‐based studies of disease. Results: Here, we describe SV2, a machine‐learning algorithm for genotyping deletions and duplications from paired‐end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. Availability and implementation: SV2 is freely available on GitHub (https://github.com/dantaki/SV2). Contact: jsebat@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

SV2: Accurate Structural Variation Genotyping and De Novo Mutation Detection

Structural Variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for analysis, de novo mutations in particular.Here we describe SV2, a machine-learning algorithm for genotyping deletions and tandem duplications from paired-end whole genome sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified callset with low rates of false discoveries and Mendelian errors with accurate de novo detection.