@article {588, title = {Atlas-CNV: a validated approach to call single-exon CNVs in the eMERGESeq gene panel.}, journal = {Genet Med}, volume = {21}, year = {2019}, month = {2019 09}, pages = {2135-2144}, abstract = {

PURPOSE: To provide a validated method to confidently identify exon-containing copy-number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs.

METHODS: DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples, when the target log ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap).

RESULTS: Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation, and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multiexon and 29 single-exon CNVs with high C-scores were assessed by~Multiplex Ligation-dependent Probe Amplification (MLPA).

CONCLUSION: Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We propose guidelines and criteria to identify high confidence single-exon CNVs.

}, issn = {1530-0366}, doi = {10.1038/s41436-019-0475-4}, author = {Chiang, Theodore and Liu, Xiuping and Wu, Tsung-Jung and Hu, Jianhong and Sedlazeck, Fritz J and White, Simon and Schaid, Daniel and Andrade, Mariza de and Jarvik, Gail P and Crosslin, David and Stanaway, Ian and Carrell, David S and Connolly, John J and Hakonarson, Hakon and Groopman, Emily E and Gharavi, Ali G and Fedotov, Alexander and Bi, Weimin and Leduc, Magalie S and Murdock, David R and Jiang, Yunyun and Meng, Linyan and Eng, Christine M and Wen, Shu and Yang, Yaping and Muzny, Donna M and Boerwinkle, Eric and Salerno, William and Venner, Eric and Gibbs, Richard A} } @article {25, title = {Loss-of-function variants influence the human serum metabolome.}, journal = {Sci Adv}, volume = {2}, year = {2016}, month = {2016 Aug}, pages = {e1600800}, abstract = {

The metabolome is a collection of small molecules resulting from multiple cellular and biological processes that can act as biomarkers of disease, and African-Americans exhibit high levels of genetic diversity. Exome sequencing of a sample of deeply phenotyped African-Americans allowed us to analyze the effects of annotated loss-of-function (LoF) mutations on 308 serum metabolites measured by untargeted liquid and gas chromatography coupled with mass spectrometry. In an independent sample, we identified and replicated four genes harboring six LoF mutations that significantly affected five metabolites. These sites were related to a 19 to 45\% difference in geometric mean metabolite levels, with an average effect size of 25\%. We show that some of the affected metabolites are risk predictors or diagnostic biomarkers of disease and, using the principle of Mendelian randomization, are in the causal pathway of disease. For example, LoF mutations in SLCO1B1 elevate the levels of hexadecanedioate, a fatty acid significantly associated with increased blood pressure levels and risk of incident heart failure in both African-Americans and an independent sample of European-Americans. We show that SLCO1B1 LoF mutations significantly increase the risk of incident heart failure, thus implicating the metabolite in the causal pathway of disease. These results reveal new avenues into gene function and the understanding of disease etiology by integrating -omic technologies into a deeply phenotyped population study.

}, keywords = {Aged, Biomarkers, Black or African American, Exome, Female, Genetic Variation, Heart Failure, High-Throughput Nucleotide Sequencing, Humans, Hypertension, Liver-Specific Organic Anion Transporter 1, Male, Metabolome, Middle Aged, Mutation, Risk Factors, White People}, issn = {2375-2548}, doi = {10.1126/sciadv.1600800}, author = {Yu, Bing and Li, Alexander H and Metcalf, Ginger A and Muzny, Donna M and Morrison, Alanna C and White, Simon and Mosley, Thomas H and Gibbs, Richard A and Boerwinkle, Eric} } @article {177, title = {Assessing structural variation in a personal genome-towards a human reference diploid genome.}, journal = {BMC Genomics}, volume = {16}, year = {2015}, month = {2015 Apr 11}, pages = {286}, abstract = {

BACKGROUND: Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods.

RESULTS: We demonstrate Parliament{\textquoteright}s efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8\%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus.

CONCLUSIONS: HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

}, keywords = {Computational Biology, Databases, Genetic, Diploidy, Genome, Human, Genomic Structural Variation, Humans, Sequence Analysis, DNA, Software}, issn = {1471-2164}, doi = {10.1186/s12864-015-1479-3}, author = {English, Adam C and Salerno, William J and Hampton, Oliver A and Gonzaga-Jauregui, Claudia and Ambreth, Shruthi and Ritter, Deborah I and Beck, Christine R and Davis, Caleb F and Dahdouli, Mahmoud and Ma, Singer and Carroll, Andrew and Veeraraghavan, Narayanan and Bruestle, Jeremy and Drees, Becky and Hastie, Alex and Lam, Ernest T and White, Simon and Mishra, Pamela and Wang, Min and Han, Yi and Zhang, Feng and Stankiewicz, Pawel and Wheeler, David A and Reid, Jeffrey G and Muzny, Donna M and Rogers, Jeffrey and Sabo, Aniko and Worley, Kim C and Lupski, James R and Boerwinkle, Eric and Gibbs, Richard A} } @article {270, title = {Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline.}, journal = {BMC Bioinformatics}, volume = {15}, year = {2014}, month = {2014 Jan 29}, pages = {30}, abstract = {

BACKGROUND: Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results.

RESULTS: To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts.

CONCLUSIONS: By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.

}, keywords = {Genome, Genomics, High-Throughput Nucleotide Sequencing, Humans, Internet, Software}, issn = {1471-2105}, doi = {10.1186/1471-2105-15-30}, author = {Reid, Jeffrey G and Carroll, Andrew and Veeraraghavan, Narayanan and Dahdouli, Mahmoud and Sundquist, Andreas and English, Adam and Bainbridge, Matthew and White, Simon and Salerno, William and Buhay, Christian and Yu, Fuli and Muzny, Donna and Daly, Richard and Duyk, Geoff and Gibbs, Richard A and Boerwinkle, Eric} }