NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation. RESULTS: NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping accuracy across different SV call sets, samples and variant types, including a 25% reduction in genotyping errors for the Genome-in-a-Bottle (GIAB) high-confidence SVs. NPSV-deep is not limited to the SVs as described; it improves deletion genotyping concordance a further 1.5 percentage points for GIAB SVs (92%) by automatically correcting imprecise/incorrectly described SVs. AVAILABILITY AND IMPLEMENTATION: Python/C++ source code and pre-trained models freely available at https://github.com/mlinderm/npsv2.

NPSV: A simulation-driven approach to genotyping structural variants in whole-genome sequencing data.

BACKGROUND: Structural variants (SVs) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole-genome next-generation sequencing data. SV genotypers that assume that the aligned sequencing data uniformly reflect the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases. RESULTS: We introduce NPSV, a machine learning-based approach for genotyping previously discovered SVs that uses next-generation sequencing simulation to model the combined effects of the genomic region, sequencer, and alignment pipeline on the observed SV evidence. We evaluate NPSV alongside existing SV genotypers on multiple benchmark call sets. We show that NPSV consistently achieves or exceeds state-of-the-art genotyping accuracy across SV call sets, samples, and variant types. NPSV can specifically identify putative de novo SVs in a trio context and is robust to offset SV breakpoints. CONCLUSIONS: Growing SV databases and the increasing availability of SV calls from long-read sequencing make stand-alone genotyping of previously identified SVs an increasingly important component of genome analyses. By treating potential biases as a "black box" that can be simulated, NPSV provides a framework for accurately genotyping a broad range of SVs in both targeted and genome-scale applications.

Development and Validation of a Comprehensive Genomics Knowledge Scale.

BACKGROUND: Genomic testing is increasingly employed in clinical, research, educational, and commercial contexts. Genomic literacy is a prerequisite for the effective application of genomic testing, creating a corresponding need for validated tools to assess genomics knowledge. We sought to develop a reliable measure of genomics knowledge that incorporates modern genomic technologies and is informative for individuals with diverse backgrounds, including those with clinical/life sciences training. METHODS: We developed the GKnowM Genomics Knowledge Scale to assess the knowledge needed to make an informed decision for genomic testing, appropriately apply genomic technologies and participate in civic decision-making. We administered the 30-item draft measure to a calibration cohort (n = 1,234) and subsequent participants to create a combined validation cohort (n = 2,405). We performed a multistage psychometric calibration and validation using classical test theory and item response theory (IRT) and conducted a post-hoc simulation study to evaluate the suitability of a computerized adaptive testing (CAT) implementation. RESULTS: Based on exploratory factor analysis, we removed 4 of the 30 draft items. The resulting 26-item GKnowM measure has a single dominant factor. The scale internal consistency is α = 0.85, and the IRT 3-PL model demonstrated good overall and item fit. Validity is demonstrated with significant correlation (r = 0.61) with an existing genomics knowledge measure and significantly higher scores for individuals with adequate health literacy and healthcare providers (HCPs), including HCPs who work with genomic testing. The item bank is well suited to CAT, achieving high accuracy (r = 0.97 with the full measure) while administering a mean of 13.5 items. CONCLUSION: GKnowM is an updated, broadly relevant, rigorously validated 26-item measure for assessing genomics knowledge that we anticipate will be useful for assessing population genomic literacy and evaluating the effectiveness of genomics educational interventions.

MySeq: privacy-protecting browser-based personal Genome analysis for genomics education and exploration.

BACKGROUND: The complexity of genome informatics is a recurring challenge for genome exploration and analysis by students and other non-experts. This complexity creates a barrier to wider implementation of experiential genomics education, even in settings with substantial computational resources and expertise. Reducing the need for specialized software tools will increase access to hands-on genomics pedagogy. RESULTS: MySeq is a React.js single-page web application for privacy-protecting interactive personal genome analysis. All analyses are performed entirely in the user's web browser eliminating the need to install and use specialized software tools or to upload sensitive data to an external web service. MySeq leverages Tabix-indexing to efficiently query whole genome-scale variant call format (VCF) files stored locally or available remotely via HTTP(s) without loading the entire file. MySeq currently implements variant querying and annotation, physical trait prediction, pharmacogenomic, polygenic disease risk and ancestry analyses to provide representative pedagogical examples; and can be readily extended with new analysis or visualization components. CONCLUSIONS: MySeq supports multiple pedagogical approaches including independent exploration and interactive online tutorials. MySeq has been successfully employed in an undergraduate human genome analysis course where it reduced the barriers-to-entry for hands-on human genome analysis.

DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark.

BACKGROUND: XHMM is a widely used tool for copy-number variant (CNV) discovery from whole exome sequencing data but can require hours to days to run for large cohorts. A more scalable implementation would reduce the need for specialized computational resources and enable increased exploration of the configuration parameter space to obtain the best possible results. RESULTS: DECA is a horizontally scalable implementation of the XHMM algorithm using the ADAM framework and Apache Spark that incorporates novel algorithmic optimizations to eliminate unneeded computation. DECA parallelizes XHMM on both multi-core shared memory computers and large shared-nothing Spark clusters. We performed CNV discovery from the read-depth matrix in 2535 exomes in 9.3 min on a 16-core workstation (35.3× speedup vs. XHMM), 12.7 min using 10 executor cores on a Spark cluster (18.8× speedup vs. XHMM), and 9.8 min using 32 executor cores on Amazon AWS' Elastic MapReduce. We performed CNV discovery from the original BAM files in 292 min using 640 executor cores on a Spark cluster. CONCLUSIONS: We describe DECA's performance, our algorithmic and implementation enhancements to XHMM to obtain that performance, and our lessons learned porting a complex genome analysis application to ADAM and Spark. ADAM and Apache Spark are a performant and productive platform for implementing large-scale genome analyses, but efficiently utilizing large clusters can require algorithmic optimizations and careful attention to Spark's configuration parameters.

Predispositional genome sequencing in healthy adults: design, participant characteristics, and early outcomes of the PeopleSeq Consortium.

BACKGROUND: Increasing numbers of healthy individuals are undergoing predispositional personal genome sequencing. Here we describe the design and early outcomes of the PeopleSeq Consortium, a multi-cohort collaboration of predispositional genome sequencing projects, which is examining the medical, behavioral, and economic outcomes of returning genomic sequencing information to healthy individuals. METHODS: Apparently healthy adults who participated in four of the sequencing projects in the Consortium were included. Web-based surveys were administered before and after genomic results disclosure, or in some cases only after results disclosure. Surveys inquired about sociodemographic characteristics, motivations and concerns, behavioral and medical responses to sequencing results, and perceived utility. RESULTS: Among 1395 eligible individuals, 658 enrolled in the Consortium when contacted and 543 have completed a survey after receiving their genomic results thus far (mean age 53.0 years, 61.4% male, 91.7% white, 95.5% college graduates). Most participants (98.1%) were motivated to undergo sequencing because of curiosity about their genetic make-up. The most commonly reported concerns prior to pursuing sequencing included how well the results would predict future risk (59.2%) and the complexity of genetic variant interpretation (56.8%), while 47.8% of participants were concerned about the privacy of their genetic information. Half of participants reported discussing their genomic results with a healthcare provider during a median of 8.0 months after receiving the results; 13.5% reported making an additional appointment with a healthcare provider specifically because of their results. Few participants (< 10%) reported making changes to their diet, exercise habits, or insurance coverage because of their results. Many participants (39.5%) reported learning something new to improve their health that they did not know before. Reporting regret or harm from the decision to undergo sequencing was rare (< 3.0%). CONCLUSIONS: Healthy individuals who underwent predispositional sequencing expressed some concern around privacy prior to pursuing sequencing, but were enthusiastic about their experience and not distressed by their results. While reporting value in their health-related results, few participants reported making medical or lifestyle changes.

Impacts of incorporating personal genome sequencing into graduate genomics education: a longitudinal study over three course years.

BACKGROUND: To address the need for more effective genomics training, beginning in 2012 the Icahn School of Medicine at Mount Sinai has offered a unique laboratory-style graduate genomics course, "Practical Analysis of Your Personal Genome" (PAPG), in which students optionally sequence and analyze their own whole genome. We hypothesized that incorporating personal genome sequencing (PGS) into the course pedagogy could improve educational outcomes by increasing student motivation and engagement. Here we extend our initial study of the pilot PAPG cohort with a report on student attitudes towards genome sequencing, decision-making, psychological wellbeing, genomics knowledge and pedagogical engagement across three course years. METHODS: Students enrolled in the 2013, 2014 and 2015 course years completed questionnaires before (T1) and after (T2) a prerequisite workshop (n = 110) and before (T3) and after (T4) PAPG (n = 66). RESULTS: Students' interest in PGS was high; 56 of 59 eligible students chose to sequence their own genome. Decisional conflict significantly decreased after the prerequisite workshop (T2 vs. T1 p < 0.001). Most, but not all students, reported low levels of decision regret and test-related distress post-course (T4). Each year baseline decisional conflict decreased (p < 0.001) suggesting, that as the course became more established, students increasingly made their decision prior to enrolling in the prerequisite workshop. Students perceived that analyzing their own genome enhanced the genomics pedagogy, with students self-reporting being more persistent and engaged as a result of analyzing their own genome. More than 90% of respondents reported spending additional time outside of course assignments analyzing their genome. CONCLUSIONS: Incorporating personal genome sequencing in graduate medical education may improve student motivation and engagement. However, more data will be needed to quantitatively evaluate whether incorporating PGS is more effective than other educational approaches.

Concordance between Research Sequencing and Clinical Pharmacogenetic Genotyping in the eMERGE-PGx Study.

There has been extensive debate about both the necessity of orthogonal confirmation of next-generation sequencing (NGS) results in Clinical Laboratory Improvement Amendments-approved laboratories and return of research NGS results to participants enrolled in research studies. In eMERGE-PGx, subjects underwent research NGS using PGRNseq and orthogonal targeted genotyping in clinical laboratories, which prompted a comparison of genotyping results between platforms. Concordance (percentage agreement) was reported for 4077 samples tested across nine combinations of research and clinical laboratories. Retesting was possible on a subset of 1792 samples, and local laboratory directors determined sources of genotype discrepancy. Research NGS and orthogonal clinical genotyping had an overall per sample concordance rate of 0.972 and per variant concordance rate of 0.997. Genotype discrepancies attributed to research NGS were because of sample switching (preanalytical errors), whereas the majority of genotype discrepancies (92.3%) attributed to clinical genotyping were because of allele dropout as a result of rare variants interfering with primer hybridization (analytical errors). These results highlight the analytical quality of clinically significant pharmacogenetic variants derived from NGS and reveal important areas for research and clinical laboratories to address with quality management programs.

Psychological and behavioural impact of returning personal results from whole-genome sequencing: the HealthSeq project.

Providing ostensibly healthy individuals with personal results from whole-genome sequencing could lead to improved health and well-being via enhanced disease risk prediction, prevention, and diagnosis, but also poses practical and ethical challenges. Understanding how individuals react psychologically and behaviourally will be key in assessing the potential utility of personal whole-genome sequencing. We conducted an exploratory longitudinal cohort study in which quantitative surveys and in-depth qualitative interviews were conducted before and after personal results were returned to individuals who underwent whole-genome sequencing. The participants were offered a range of interpreted results, including Alzheimer's disease, type 2 diabetes, pharmacogenomics, rare disease-associated variants, and ancestry. They were also offered their raw data. Of the 35 participants at baseline, 29 (82.9%) completed the 6-month follow-up. In the quantitative surveys, test-related distress was low, although it was higher at 1-week than 6-month follow-up (Z=2.68, P=0.007). In the 6-month qualitative interviews, most participants felt happy or relieved about their results. A few were concerned, particularly about rare disease-associated variants and Alzheimer's disease results. Two of the 29 participants had sought clinical follow-up as a direct or indirect consequence of rare disease-associated variants results. Several had mentioned their results to their doctors. Some participants felt having their raw data might be medically useful to them in the future. The majority reported positive reactions to having their genomes sequenced, but there were notable exceptions to this. The impact and value of returning personal results from whole-genome sequencing when implemented on a larger scale remains to be seen.

Personal Genome Sequencing in Ostensibly Healthy Individuals and the PeopleSeq Consortium.

Thousands of ostensibly healthy individuals have had their exome or genome sequenced, but a much smaller number of these individuals have received any personal genomic results from that sequencing. We term those projects in which ostensibly healthy participants can receive sequencing-derived genetic findings and may also have access to their genomic data as participatory predispositional personal genome sequencing (PPGS). Here we are focused on genome sequencing applied in a pre-symptomatic context and so define PPGS to exclude diagnostic genome sequencing intended to identify the molecular cause of suspected or diagnosed genetic disease. In this report we describe the design of completed and underway PPGS projects, briefly summarize the results reported to date and introduce the PeopleSeq Consortium, a newly formed collaboration of PPGS projects designed to collect much-needed longitudinal outcome data.

Impact of Genomic Counseling on Informed Decision-Making among ostensibly Healthy Individuals Seeking Personal Genome Sequencing: the HealthSeq Project.

Personal genome sequencing is increasingly utilized by healthy individuals for predispositional screening and other applications. However, little is known about the impact of 'genomic counseling' on informed decision-making in this context. Our primary aim was to compare measures of participants' informed decision-making before and after genomic counseling in the HealthSeq project, a longitudinal cohort study of individuals receiving personal results from whole genome sequencing (WGS). Our secondary aims were to assess the impact of the counseling on WGS knowledge and concerns, and to explore participants' satisfaction with the counseling. Questionnaires were administered to participants (n = 35) before and after their pre-test genomic counseling appointment. Informed decision-making was measured using the Decisional Conflict Scale (DCS) and the Satisfaction with Decision Scale (SDS). DCS scores decreased after genomic counseling (mean: 11.34 before vs. 5.94 after; z = -4.34, p < 0.001, r = 0.52), and SDS scores increased (mean: 27.91 vs. 29.06 respectively; z = 2.91, p = 0.004, r = 0.35). Satisfaction with counseling was high (mean (SD) = 26.91 (2.68), on a scale where 6 = low and 30 = high satisfaction). HealthSeq participants felt that their decision regarding receiving personal results from WGS was more informed after genomic counseling. Further research comparing the impact of different genomic counseling models is needed.

Motivations, concerns and preferences of personal genome sequencing research participants: Baseline findings from the HealthSeq project.

Whole exome/genome sequencing (WES/WGS) is increasingly offered to ostensibly healthy individuals. Understanding the motivations and concerns of research participants seeking out personal WGS and their preferences regarding return-of-results and data sharing will help optimize protocols for WES/WGS. Baseline interviews including both qualitative and quantitative components were conducted with research participants (n=35) in the HealthSeq project, a longitudinal cohort study of individuals receiving personal WGS results. Data sharing preferences were recorded during informed consent. In the qualitative interview component, the dominant motivations that emerged were obtaining personal disease risk information, satisfying curiosity, contributing to research, self-exploration and interest in ancestry, and the dominant concern was the potential psychological impact of the results. In the quantitative component, 57% endorsed concerns about privacy. Most wanted to receive all personal WGS results (94%) and their raw data (89%); a third (37%) consented to having their data shared to the Database of Genotypes and Phenotypes (dbGaP). Early adopters of personal WGS in the HealthSeq project express a variety of health- and non-health-related motivations. Almost all want all available findings, while also expressing concerns about the psychological impact and privacy of their results.

Preparing the next generation of genomicists: a laboratory-style course in medical genomics.

The growing gap between the demand for genome sequencing and the supply of trained genomics professionals is creating an acute need to develop more effective genomics education. In response we developed "Practical Analysis of Your Personal Genome", a novel laboratory-style medical genomics course in which students have the opportunity to obtain and analyze their own whole genome. This report describes our motivations for and the content of a "practical" genomics course that incorporates personal genome sequencing and the lessons we learned during the first three iterations of this course.

Novel, compound heterozygous, single-nucleotide variants in MARS2 associated with developmental delay, poor growth, and sensorineural hearing loss.

Novel, single-nucleotide mutations were identified in the mitochondrial methionyl amino-acyl tRNA synthetase gene (MARS2) via whole exome sequencing in two affected siblings with developmental delay, poor growth, and sensorineural hearing loss.We show that compound heterozygous mutations c.550C>T:p.Gln 184* and c.424C>T:p.Arg142Trp in MARS2 lead to decreased MARS2 protein levels in patient lymphoblasts. Analysis of respiratory complex enzyme activities in patient fibroblasts revealed decreased complex I and IV activities. Immunoblotting of patient fibroblast and lymphoblast samples revealed reduced protein levels of NDUFB8 and COXII, representing complex I and IV, respectively. Additionally, overexpression of wild-type MARS2 in patient fibroblasts increased NDUFB8 and COXII protein levels. These findings suggest that recessive single-nucleotide mutations in MARS2 are causative for a new mitochondrial translation deficiency disorder with a primary phenotype including developmental delay and hypotonia. Identification of additional patients with single-nucleotide mutations in MARS2 is necessary to determine if pectus carinatum is also a consistent feature of this syndrome.

How do students react to analyzing their own genomes in a whole-genome sequencing course?: outcomes of a longitudinal cohort study.

PURPOSE: Health-care professionals need to be trained to work with whole-genome sequencing (WGS) in their practice. Our aim was to explore how students responded to a novel genome analysis course that included the option to analyze their own genomes. METHODS: This was an observational cohort study. Questionnaires were administered before (T3) and after the genome analysis course (T4), as well as 6 months later (T5). In-depth interviews were conducted at T5. RESULTS: All students (n = 19) opted to analyze their own genomes. At T5, 12 of 15 students stated that analyzing their own genomes had been useful. Ten reported they had applied their knowledge in the workplace. Technical WGS knowledge increased (mean of 63.8% at T3, mean of 72.5% at T4; P = 0.005). In-depth interviews suggested that analyzing their own genomes may increase students' motivation to learn and their understanding of the patient experience. Most (but not all) of the students reported low levels of WGS results-related distress and low levels of regret about their decision to analyze their own genomes. CONCLUSION: Giving students the option of analyzing their own genomes may increase motivation to learn, but some students may experience personal WGS results-related distress and regret. Additional evidence is required before considering incorporating optional personal genome analysis into medical education on a large scale.

Analytical validation of whole exome and whole genome sequencing for clinical applications.

BACKGROUND: Whole exome and genome sequencing (WES/WGS) is now routinely offered as a clinical test by a growing number of laboratories. As part of the test design process each laboratory must determine the performance characteristics of the platform, test and informatics pipeline. This report documents one such characterization of WES/WGS. METHODS: Whole exome and whole genome sequencing was performed on multiple technical replicates of five reference samples using the Illumina HiSeq 2000/2500. The sequencing data was processed with a GATK-based genome analysis pipeline to evaluate: intra-run, inter-run, inter-mode, inter-machine and inter-library consistency, concordance with orthogonal technologies (microarray, Sanger) and sensitivity and accuracy relative to known variant sets. RESULTS: Concordance to high-density microarrays consistently exceeds 97% (and typically exceeds 99%) and concordance between sequencing replicates also exceeds 97%, with no observable differences between different flow cells, runs, machines or modes. Sensitivity relative to high-density microarray variants exceeds 95%. In a detailed study of a 129 kb region, sensitivity was lower with some validated single-base insertions and deletions "not called". Different variants are "not called" in each replicate: of all variants identified in WES data from the NA12878 reference sample 74% of indels and 89% of SNVs were called in all seven replicates, in NA12878 WGS 52% of indels and 88% of SNVs were called in all six replicates. Key sources of non-uniformity are variance in depth of coverage, artifactual variants resulting from repetitive regions and larger structural variants. CONCLUSION: We report a comprehensive performance characterization of WES/WGS that will be relevant to offering laboratories, consumers of genome sequencing and others interested in the analytical validity of this technology.

Informed decision-making among students analyzing their personal genomes on a whole genome sequencing course: a longitudinal cohort study.

BACKGROUND: Multiple laboratories now offer clinical whole genome sequencing (WGS). We anticipate WGS becoming routinely used in research and clinical practice. Many institutions are exploring how best to educate geneticists and other professionals about WGS. Providing students in WGS courses with the option to analyze their own genome sequence is one strategy that might enhance students' engagement and motivation to learn about personal genomics. However, if this option is presented to students, it is vital they make informed decisions, do not feel pressured into analyzing their own genomes by their course directors or peers, and feel free to analyze a third-party genome if they prefer. We therefore developed a 26-hour introductory genomics course in part to help students make informed decisions about whether to receive personal WGS data in a subsequent advanced genomics course. In the advanced course, they had the option to receive their own personal genome data, or an anonymous genome, at no financial cost to them. Our primary aims were to examine whether students made informed decisions regarding analyzing their personal genomes, and whether there was evidence that the introductory course enabled the students to make a more informed decision. METHODS: This was a longitudinal cohort study in which students (N = 19) completed questionnaires assessing their intentions, informed decision-making, attitudes and knowledge before (T1) and after (T2) the introductory course, and before the advanced course (T3). Informed decision-making was assessed using the Decisional Conflict Scale. RESULTS: At the start of the introductory course (T1), most (17/19) students intended to receive their personal WGS data in the subsequent course, but many expressed conflict around this decision. Decisional conflict decreased after the introductory course (T2) indicating there was an increase in informed decision-making, and did not change before the advanced course (T3). This suggests that it was the introductory course content rather than simply time passing that had the effect. In the advanced course, all (19/19) students opted to receive their personal WGS data. No changes in technical knowledge of genomics were observed. Overall attitudes towards WGS were broadly positive. CONCLUSIONS: Providing students with intensive introductory education about WGS may help them make informed decisions about whether or not to work with their personal WGS data in an educational setting.

Genome-wide association analysis of red blood cell traits in African Americans: the COGENT Network.

Laboratory red blood cell (RBC) measurements are clinically important, heritable and differ among ethnic groups. To identify genetic variants that contribute to RBC phenotypes in African Americans (AAs), we conducted a genome-wide association study in up to ~16 500 AAs. The alpha-globin locus on chromosome 16pter [lead SNP rs13335629 in ITFG3 gene; P < 1E-13 for hemoglobin (Hgb), RBC count, mean corpuscular volume (MCV), MCH and MCHC] and the G6PD locus on Xq28 [lead SNP rs1050828; P < 1E - 13 for Hgb, hematocrit (Hct), MCV, RBC count and red cell distribution width (RDW)] were each associated with multiple RBC traits. At the alpha-globin region, both the common African 3.7 kb deletion and common single nucleotide polymorphisms (SNPs) appear to contribute independently to RBC phenotypes among AAs. In the 2p21 region, we identified a novel variant of PRKCE distinctly associated with Hct in AAs. In a genome-wide admixture mapping scan, local European ancestry at the 6p22 region containing HFE and LRRC16A was associated with higher Hgb. LRRC16A has been previously associated with the platelet count and mean platelet volume in AAs, but not with Hgb. Finally, we extended to AAs the findings of association of erythrocyte traits with several loci previously reported in Europeans and/or Asians, including CD164 and HBS1L-MYB. In summary, this large-scale genome-wide analysis in AAs has extended the importance of several RBC-associated genetic loci to AAs and identified allelic heterogeneity and pleiotropy at several previously known genetic loci associated with blood cell traits in AAs.

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data.

MOTIVATION: Recent advances in flow cytometry enable simultaneous single-cell measurement of 30+ surface and intracellular proteins. CytoSPADE is a high-performance implementation of an interface for the Spanning-tree Progression Analysis of Density-normalized Events algorithm for tree-based analysis and visualization of this high-dimensional cytometry data. AVAILABILITY: Source code and binaries are freely available at http://cytospade.org and via Bioconductor version 2.10 onwards for Linux, OSX and Windows. CytoSPADE is implemented in R, C++ and Java. CONTACT: michael.linderman@mssm.edu SUPPLEMENTARY INFORMATION: Additional documentation available at http://cytospade.org.