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.

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.

Computational solutions to large-scale data management and analysis.

Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist - such as cloud and heterogeneous computing - to successfully tackle our big data problems.

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.

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.

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.

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.

Costs of telaprevir-based triple therapy for hepatitis C: $189,000 per sustained virological response.

UNLABELLED: In registration trials, triple therapy with telaprevir (TVR), pegylated interferon (Peg-IFN), and ribavirin (RBV) achieved sustained virological response (SVR) rates between 64% and 75%, but the clinical effectiveness and economic burdens of this treatment in real-world practice remain to be determined. Records of 147 patients who initiated TVR-based triple therapy at the Mount Sinai Medical Center (May-December 2011) were reviewed. Direct medical costs for pretreatment, on-treatment, and posttreatment care were calculated using data from Medicare reimbursement databases, RED Book, and the Healthcare Cost and Utilization Project database. Costs are presented in 2012 U.S. dollars. SVR (undetectable hepatitis C virus [HCV] RNA 24 weeks after the end of treatment) was determined on an intention-to-treat basis. Cost per SVR was calculated by dividing the median cost by the SVR rate. Median age of the 147 patients was 56 years (interquartile range [IQR] = 51-61), 68% were male, 19% were black, 11% had human immunodeficiency virus/HCV coinfection, 36% had advanced fibrosis/cirrhosis (FIB-4 scores ≥3.25), and 44% achieved an SVR. The total cost of care was $11.56 million. Median cost of care was $83,721 per patient (IQR = $66,652-$98,102). The median cost per SVR was $189,338 (IQR = $150,735-$221,860). Total costs were TVR (61%), IFN (24%), RBV (4%), adverse event management (8%), professional fees (2%), and laboratory tests (1%). CONCLUSIONS: TVR and Peg-IFN accounted for 85% of costs. Pharmaceutical prices and the low (44%) SVR rate, in this real-world study, were major contributors to the high cost per SVR.

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.

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.

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.

HermesB: a continuous neural recording system for freely behaving primates.

Chronically implanted electrode arrays have enabled a broad range of advances in basic electrophysiology and neural prosthetics. Those successes motivate new experiments, particularly, the development of prototype implantable prosthetic processors for continuous use in freely behaving subjects, both monkeys and humans. However, traditional experimental techniques require the subject to be restrained, limiting both the types and duration of experiments. In this paper, we present a dual-channel, battery-powered neural recording system with an integrated three-axis accelerometer for use with chronically implanted electrode arrays in freely behaving primates. The recording system called HermesB, is self-contained, autonomous, programmable, and capable of recording broadband neural (sampled at 30 kS/s) and acceleration data to a removable compact flash card for up to 48 h. We have collected long-duration data sets with HermesB from an adult macaque monkey which provide insight into time scales and free behaviors inaccessible under traditional experiments. Variations in action potential shape and root-mean square (RMS) noise are observed across a range of time scales. The peak-to-peak voltage of action potentials varied by up to 30% over a 24-h period including step changes in waveform amplitude (up to 25%) coincident with high acceleration movements of the head. These initial results suggest that spike-sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance. During physically active periods (defined by head-mounted accelerometer), significantly reduced 5-25-Hz local field potential (LFP) power and increased firing rate variability were observed. Using a threshold fit to LFP power, 93% of 403 5-min recording blocks were correctly classified as active or inactive, potentially providing an efficient tool for identifying different behavioral contexts in prosthetic applications. These results demonstrate the utility of the HermesB system and motivate using this type of system to advance neural prosthetics and electrophysiological experiments.

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.

Factors associated with success of telaprevir- and boceprevir-based triple therapy for hepatitis C virus infection.

AIM: To evaluate new therapies for hepatitis C virus (HCV), data about real-world outcomes are needed. METHODS: Outcomes of 223 patients with genotype 1 HCV who started telaprevir- or boceprevir-based triple therapy (May 2011-March 2012) at the Mount Sinai Medical Center were analyzed. Human immunodeficiency virus-positive patients and patients who received a liver transplant were excluded. Factors associated with sustained virological response (SVR24) and relapse were analyzed by univariable and multivariable logistic regression as well as classification and regression trees. Fast virological response (FVR) was defined as undetectable HCV RNA at week-4 (telaprevir) or week-8 (boceprevir). RESULTS: The median age was 57 years, 18% were black, 44% had advanced fibrosis/cirrhosis (FIB-4 ≥ 3.25). Only 42% (94/223) of patients achieved SVR24 on an intention-to-treat basis. In a model that included platelets, SVR24 was associated with white race [odds ratio (OR) = 5.92, 95% confidence interval (CI): 2.34-14.96], HCV sub-genotype 1b (OR = 2.81, 95%CI: 1.45-5.44), platelet count (OR = 1.10, per x 104 cells/µL, 95%CI: 1.05-1.16), and IL28B CC genotype (OR = 3.54, 95%CI: 1.19-10.53). Platelet counts > 135 x 103/µL were the strongest predictor of SVR by classification and regression tree. Relapse occurred in 25% (27/104) of patients with an end-of-treatment response and was associated with non-FVR (OR = 4.77, 95%CI: 1.68-13.56), HCV sub-genotype 1a (OR = 5.20; 95%CI: 1.40-18.97), and FIB-4 ≥ 3.25 (OR = 2.77; 95%CI: 1.07-7.22). CONCLUSION: The SVR rate was 42% with telaprevir- or boceprevir-based triple therapy in real-world practice. Low platelets and advanced fibrosis were associated with treatment failure and relapse.

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.

Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system.

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic BioMe biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, COL27A1, with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.