CrowdVariant: a crowdsourcing approach to classify copy number variants

Copy number variants (CNVs) are an important type of genetic variation that play a causal role in many diseases. The ability to identify high quality CNVs is of substantial clinical relevance. However, CNVs are notoriously difficult to identify accurately from array-based methods and next-generation sequencing (NGS) data, particularly for small (< 10kbp) CNVs. Manual curation by experts widely remains the gold standard but cannot scale with the pace of sequencing, particularly in fast-growing clinical applications. We present the first proof-of-principle study demonstrating high throughput manual curation of putative CNVs by non-experts. We developed a crowdsourcing framework, called CrowdVariant, that leverages Google's high-throughput crowdsourcing platform to create a high confidence set of deletions for NA24385 (NIST HG002/RM 8391), an Ashkenazim reference sample developed in partnership with the Genome In A Bottle (GIAB) Consortium. We show that non-experts tend to agree both with each other and with experts on putative CNVs. We show that crowdsourced non-expert classifications can be used to accurately assign copy number status to putative CNV calls and identify 1,781 high confidence deletions in a reference sample. Multiple lines of evidence suggest these calls are a substantial improvement over existing CNV callsets and can also be useful in benchmarking and improving CNV calling algorithms. Our crowdsourcing methodology takes the first step toward showing the clinical potential for manual curation of CNVs at scale and can further guide other crowdsourcing genomics applications.

Deep learning of genomic variation and regulatory network data

The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (e.g. deleterious variants and disease). This review summarizes lessons learned from the large-scale analyses of genome and exome data sets, modeling of population data and machine-learning strategies to solve complex genomic sequence regions. The review also portrays the rapid adoption of artificial intelligence/deep neural networks in genomics; in particular, deep learning approaches are well suited to model the complex dependencies in the regulatory landscape of the genome, and to provide predictors for genetic variant calling and interpretation.

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

This corrects the article DOI: 10.1038/sdata.2017.179.

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

A Low-Frequency Inactivating AKT2 Variant Enriched in the Finnish Population Is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.

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

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

Loss-of-function mutations in APOC3, triglycerides, and coronary disease

BACKGROUND

Plasma triglyceride levels are heritable and are correlated with the risk of coronary heart disease. Sequencing of the protein-coding regions of the human genome (the exome) has the potential to identify rare mutations that have a large effect on phenotype.

METHODS

We sequenced the protein-coding regions of 18,666 genes in each of 3734 participants of European or African ancestry in the Exome Sequencing Project. We conducted tests to determine whether rare mutations in coding sequence, individually or in aggregate within a gene, were associated with plasma triglyceride levels. For mutations associated with triglyceride levels, we subsequently evaluated their association with the risk of coronary heart disease in 110,970 persons.

RESULTS

An aggregate of rare mutations in the gene encoding apolipoprotein C3 (APOC3) was associated with lower plasma triglyceride levels. Among the four mutations that drove this result, three were loss-of-function mutations: a nonsense mutation (R19X) and two splice-site mutations (IVS2+1G→A and IVS3+1G→T). The fourth was a missense mutation (A43T). Approximately 1 in 150 persons in the study was a heterozygous carrier of at least one of these four mutations. Triglyceride levels in the carriers were 39% lower than levels in noncarriers (P<1×10(-20)), and circulating levels of APOC3 in carriers were 46% lower than levels in noncarriers (P=8×10(-10)). The risk of coronary heart disease among 498 carriers of any rare APOC3 mutation was 40% lower than the risk among 110,472 noncarriers (odds ratio, 0.60; 95% confidence interval, 0.47 to 0.75; P=4×10(-6)).

CONCLUSIONS

Rare mutations that disrupt APOC3 function were associated with lower levels of plasma triglycerides and APOC3. Carriers of these mutations were found to have a reduced risk of coronary heart disease. (Funded by the National Heart, Lung, and Blood Institute and others.).

A polygenic burden of rare disruptive mutations in schizophrenia

Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease.

The functional spectrum of low-frequency coding variation

Background

Rare coding variants constitute an important class of human genetic variation, but are underrepresented in current databases that are based on small population samples. Recent studies show that variants altering amino acid sequence and protein function are enriched at low variant allele frequency, 2 to 5%, but because of insufficient sample size it is not clear if the same trend holds for rare variants below 1% allele frequency.

Results

The 1000 Genomes Exon Pilot Project has collected deep-coverage exon-capture data in roughly 1,000 human genes, for nearly 700 samples. Although medical whole-exome projects are currently afoot, this is still the deepest reported sampling of a large number of human genes with next-generation technologies. According to the goals of the 1000 Genomes Project, we created effective informatics pipelines to process and analyze the data, and discovered 12,758 exonic SNPs, 70% of them novel, and 74% below 1% allele frequency in the seven population samples we examined. Our analysis confirms that coding variants below 1% allele frequency show increased population-specificity and are enriched for functional variants.

Conclusions

This study represents a large step toward detecting and interpreting low frequency coding variation, clearly lays out technical steps for effective analysis of DNA capture data, and articulates functional and population properties of this important class of genetic variation.

ContEst: estimating cross-contamination of human samples in next-generation sequencing data

SUMMARY

Here, we present ContEst, a tool for estimating the level of cross-individual contamination in next-generation sequencing data. We demonstrate the accuracy of ContEst across a range of contamination levels, sources and read depths using sequencing data mixed in silico at known concentrations. We applied our tool to published cancer sequencing datasets and report their estimated contamination levels.

AVAILABILITY AND IMPLEMENTATION

ContEst is a GATK module, and distributed under a BSD style license at http://www.broadinstitute.org/cancer/cga/contest

CONTACT

kcibul@broadinstitute.org; gadgetz@broadinstitute.org

SUPPLEMENTARY INFORMATION

Supplementary data is available at Bioinformatics online.