Madhusudan Gujral

An integrated map of structural variation in 2,504 human genomes

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

Whole-Genome Sequencing in Autism Identifies Hot Spots for De Novo Germline Mutation

De novo mutation plays an important role in autism spectrum disorders (ASDs). Notably, pathogenic copy number variants (CNVs) are characterized by high mutation rates. We hypothesize that hypermutability is a property of ASD genes and may also include nucleotide-substitution hot spots. We investigated global patterns of germline mutation by whole-genome sequencing of monozygotic twins concordant for ASD and their parents. Mutation rates varied widely throughout the genome (by 100-fold) and could be explained by intrinsic characteristics of DNA sequence and chromatin structure. Dense clusters of mutations within individual genomes were attributable to compound mutation or gene conversion. Hypermutability was a characteristic of genes involved in ASD and other diseases. In addition, genes impacted by mutations in this study were associated with ASD in independent exome-sequencing data sets. Our findings suggest that regional hypermutation is a significant factor shaping patterns of genetic variation and disease risk in humans.

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.

CAMERA 2.0: A Data-centric Metagenomics Community Infrastructure Driven by Scientific Workflows

Over the last decade, workflows have been established as a mechanism for scientific developers to create simplified views of complex scientific processes. However, there is a need for a comprehensive system architecture to link scientific developers creating workflows with researchers launching workflows in large scale computing environments. We present the architecture for the CAMERA 2.0 Cyber infrastructure platform that provides a scaffold where workflows can be uploaded into the system, and user interface components for launching and viewing results are automatically generated. In CAMERA 2.0, scientific developers and metagenomics researchers seamlessly collaborate to (i) wrap data-analysis software applications and heterogeneous tools as Resource Oriented Architecture (ROA) components integrating them using scientific workflows; (ii) publish and run scientific workflows via dynamically generated uniform portal interfaces; (iii) map heterogeneous workflow products to provenance and CAMERA semantic database through a transformation component, to save output data resulting from workflow runs based on this mapping; (iv) record and visualize the provenance of all workflow run-related data and processes; and (v) conduct queries across multiple workflow executions and link these workflow executions to each other through data and provenance related to these runs. Furthermore, workflows added to CAMERA also have access to a variety of physical resources for computation and data management. Here, we demonstrate the usability of this framework with some of the developed metagenomics workflows.

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.

Case Study on the Use of REST Architectural Principles for Scientific Analysis: CAMERA – Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis

The advent of Grid (and by extension Cloud) Computing along with Service Orientated Architecture (SOA) principles have lead to a fundamental shift in the development of end-user application environments. In the scientific domain, this loosely coupled, multi-tiered software architecture has been quickly adopted as raw data sizes have rapidly grown to a point where typical user workstations can no longer perform the necessary computational and data-intensive analyses. A current challenge facing the design and development of SOA involves the management and maintenance of many loosely coupled service components. As with many large applications, “integration” is equally important as “coding”. A resource orientated architecture style serves well in addressing these challenges. Here we present the CAMERA (Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis) project as a case study for a SOA in scientific research environments.