Discovery results for 800 genes selected for targeted sequencing in additional samples. For each gene, the fraction of controls and cases harboring at least one rare (MAF<5%) deleterious (nonsense, splice site, frameshift, or damaging missense) variant are shown, along with the p-values from Fisher's Exact Test (FET) and Sequence Kernel Association Test (SKAT).

Legend for Supplemental Tables and Figures.

Analysis of the discovery dataset yielded numerous genes with an excess of rare coding deleterious variants in (A) African-American and (B) European-American samples. Each point represents one gene. Shown are the relative proportions of cases or controls harboring at least one rare deleterious variant in the gene.

Multi-dimensional scaling (MDS) analysis of the discovery set. Shown are the first two MDS components for 572 individuals (coded by self-reported race) from PLINK MDS analysis of high-density SNP array data.

Deleterious variants in 800 genes selected for targeted sequencing. Using the combined dataset (discovery + extension), for each gene, the fraction of controls and cases harboring at least one rare (MAF<5%) deleterious (nonsense, splice site, frameshift, or damaging missense) variant are shown, along with the p-values from Fisher's Exact Test (FET) and a newer implementation of the Sequence Kernel Association Test (SKAT-O).

Bam-readcount is a utility for generating low-level information about sequencing data at specific nucleotide positions. Originally designed to help filter genomic mutation calls, the metrics it outputs are useful as input for variant detection tools and for resolving ambiguity between variant callers1,2. In addition, it has found broad applicability in diverse fields including tumor evolution, single-cell genomics, climate change ecology, and tracking community spread of SARS-CoV-2.3-6.

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low frequency SVs for association with 116 quantitative traits, and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including two novel loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB gene promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p=1.47×10−54), and is also associated with increased levels of total cholesterol (p=1.22×10−28) and 14 additional cholesterol-related traits, and (2) a multiallelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p=4.81×10−21) and alanine (p=6.14×10−12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs), and one linking recurrent HP gene deletion and cholesterol levels (p=6.24×10−10), which was also found to be strongly associated with increased glycoprotein level (p=3.53×10−35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0–11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing. Structural variants in more than 17,000 human genomes are mapped and characterized using whole-genome sequencing, showing how this type of variation contributes to rare deleterious coding and noncoding alleles.

Tumor heterogeneity and evolution drive treatment resistance in metastatic colorectal cancer (mCRC). Patient-derived xenografts (PDXs) can model mCRC biology; however, their ability to accurately mimic human tumor heterogeneity is unclear. Current genomic studies in mCRC have limited scope and lack matched PDXs. Therefore, the landscape of tumor heterogeneity and its impact on the evolution of metastasis and PDXs remain undefined. We performed whole-genome, deep exome, and targeted validation sequencing of multiple primary regions, matched distant metastases, and PDXs from 11 patients with mCRC. We observed intricate clonal heterogeneity and evolution affecting metastasis dissemination and PDX clonal selection. Metastasis formation followed both monoclonal and polyclonal seeding models. In four cases, metastasis-seeding clones were not identified in any primary region, consistent with a metastasis-seeding-metastasis model. PDXs underrepresented the subclonal heterogeneity of parental tumors. These suggest that single sample tumor sequencing and current PDX models may be insufficient to guide precision medicine.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

ABSTRACTSummaryLarge-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps – including deletions, duplications, mobile element insertions, inversions, and other rearrangements – in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g., via LUMPY), while providing fast and affordable joint analysis at the scale of ≥100,000 genomes. These tools will help enable the next generation of human genetics studies.Availability and Implementationsvtools is implemented in Python and freely available (MIT) fromhttps://github.com/hall-lab/svtools.Contactihall@wustl.edu

The original version of this Article contained errors in the depiction of confidence intervals in the NF1 BCSS data illustrated in Figure 3b. These have now been corrected in both the PDF and HTML versions of the Article. The incorrect version of Figure 3b is presented in the associated Author Correction.

ABSTRACT As yet undiscovered rare variants are hypothesized to substantially influence an individual’s risk for common diseases and traits, but sequencing studies aiming to identify such variants have generally been underpowered. In isolated populations that have expanded rapidly after a population bottleneck, deleterious alleles that passed through the bottleneck may be maintained at much higher frequencies than in other populations. In an exome sequencing study of nearly 20,000 cohort participants from northern and eastern Finnish populations that exemplify this phenomenon, most novel trait-associated deleterious variants are seen only in Finland or display frequencies more than 20 times higher than in other European populations. These enriched alleles underlie 34 novel associations with 21 disease-related quantitative traits and demonstrate a geographical clustering equivalent to that of Mendelian disease mutations characteristic of the Finnish population. Sequencing studies in populations without this unique history would require hundreds of thousands to millions of participants for comparable power for these variants.

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

To detect diverse and novel RNA species comprehensively, we compared deep small RNA and RNA sequencing (RNA-seq) methods applied to a primary acute myeloid leukemia (AML) sample. We were able to discover previously unannotated small RNAs using deep sequencing of a library method using broader insert size selection. We analyzed the long noncoding RNA (lncRNA) landscape in AML by comparing deep sequencing from multiple RNA-seq library construction methods for the sample that we studied and then integrating RNA-seq data from 179 AML cases. This identified lncRNAs that are completely novel, differentially expressed, and associated with specific AML subtypes. Our study revealed the complexity of the noncoding RNA transcriptome through a combined strategy of strand-specific small RNA and total RNA-seq. This dataset will serve as an invaluable resource for future RNA-based analyses.

ObjectiveIn many rheumatoid arthritis (RA) patients, disease is controlled with anti–tumor necrosis factor (anti‐TNF) biologic therapies. However, in a significant number of patients, the disease fails to respond to anti‐TNF therapy. We undertook the present study to examine the hypothesis that rare and low‐frequency genetic variants might influence response to anti‐TNF treatment.MethodsWe sequenced the coding region of 750 genes in 1,094 RA patients of European ancestry who were treated with anti‐TNF. After quality control, 690 genes were included in the analysis. We applied single‐variant association and gene‐based association tests to identify variants associated with anti‐TNF treatment response. In addition, given the key mechanistic role of TNF, we performed gene set analyses of 27 TNF pathway genes.ResultsWe identified 14,420 functional variants, of which 6,934 were predicted as nonsynonymous 2,136 of which were further predicted to be “damaging.” Despite the fact that the study was well powered, no single variant or gene showed study‐wide significant association with change in the outcome measures disease activity or European League Against Rheumatism response. Intriguingly, we observed 3 genes, of 27 with nominal signals of association (P < 0.05), that were involved in the TNF signaling pathway. However, when we performed a rigorous gene set enrichment analysis based on association P value ranking, we observed no evidence of enrichment of association at genes involved in the TNF pathway (Penrichment = 0.15, based on phenotype permutations).ConclusionOur findings suggest that rare and low‐frequency protein‐coding variants in TNF signaling pathway genes or other genes do not contribute substantially to anti‐TNF treatment response in patients with RA.

Abstract Background: Common variants have been associated with prostate cancer risk. Unfortunately, few are reproducibly linked to aggressive disease, the phenotype of greatest clinical relevance. One possible explanation is that rare genetic variants underlie a significant proportion of the risk for aggressive disease. Method: To identify such variants, we performed a two-stage approach using whole-exome sequencing followed by targeted sequencing of 800 genes in 652 aggressive prostate cancer patients and 752 disease-free controls in both African and European Americans. In each population, we tested rare variants for association using two gene-based aggregation tests. We established a study-wide significance threshold of 3.125 × 10−5 to correct for multiple testing. Results: TET2 in African Americans was associated with aggressive disease, with 24.4% of cases harboring a rare deleterious variant compared with 9.6% of controls (FET P = 1.84 × 10−5, OR = 3.0; SKAT-O P = 2.74 × 10−5). We report 8 additional genes with suggestive evidence of association, including the DNA repair genes PARP2 and MSH6. Finally, we observed an excess of rare truncation variants in 5 genes, including the DNA repair genes MSH6, BRCA1, and BRCA2. This adds to the growing body of evidence that DNA repair pathway defects may influence susceptibility to aggressive prostate cancer. Conclusions: Our findings suggest that rare variants influence risk of clinically relevant prostate cancer and, if validated, could serve to identify men for screening, prophylaxis, and treatment. Impact: This study provides evidence that rare variants in TET2 may help identify African American men at increased risk for clinically relevant prostate cancer. Cancer Epidemiol Biomarkers Prev; 25(11); 1456–63. ©2016 AACR.

Estrogen receptor alpha-positive (ERα+) luminal tumors are the most frequent subtype of breast cancer. Stat1−/− mice develop mammary tumors that closely recapitulate the biological characteristics of this cancer subtype. To identify transforming events that contribute to tumorigenesis, we performed whole genome sequencing of Stat1−/− primary mammary tumors and matched normal tissues. This investigation identified somatic truncating mutations affecting the prolactin receptor (PRLR) in all tumor and no normal samples. Targeted sequencing confirmed the presence of these mutations in precancerous lesions, indicating that this is an early event in tumorigenesis. Functional evaluation of these heterozygous mutations in Stat1−/− mouse embryonic fibroblasts showed that co-expression of truncated and wild-type PRLR led to aberrant STAT3 and STAT5 activation downstream of the receptor, cellular transformation in vitro, and tumor formation in vivo. In conclusion, truncating mutations of PRLR promote tumor growth in a model of human ERα+ breast cancer and warrant further investigation.

The genomic events responsible for the pathogenesis of relapsed adult B-lymphoblastic leukemia (B-ALL) are not yet clear. We performed integrative analysis of whole-genome, whole-exome, custom capture, whole-transcriptome (RNA-seq), and locus-specific genomic assays across nine time points from a patient with primary de novo B-ALL. Comprehensive genome and transcriptome characterization revealed a dramatic tumor evolution during progression, yielding a tumor with complex clonal architecture at second relapse. We observed and validated point mutations in EP300 and NF1, a highly expressed EP300-ZNF384 gene fusion, a microdeletion in IKZF1, a focal deletion affecting SETD2, and large deletions affecting RB1, PAX5, NF1, and ETV6. Although the genome analysis revealed events of potential biological relevance, no clinically actionable treatment options were evident at the time of the second relapse. However, transcriptome analysis identified aberrant overexpression of the targetable protein kinase encoded by the FLT3 gene. Although the patient had refractory disease after salvage therapy for the second relapse, treatment with the FLT3 inhibitor sunitinib rapidly induced a near complete molecular response, permitting the patient to proceed to a matched-unrelated donor stem cell transplantation. The patient remains in complete remission more than 4 years later. Analysis of this patient's relapse genome revealed an unexpected, actionable therapeutic target that led to a specific therapy associated with a rapid clinical response. For some patients with relapsed or refractory cancers, this approach may indicate a novel therapeutic intervention that could alter outcome.