The Million Veteran Program (MVP), initiated by the Department of Veterans Affairs (VA), aims to collect biosamples with consent from at least one million veterans. Presently, blood samples have been collected from over 800,000 enrolled participants. The size and diversity of the MVP cohort, as well as the availability of extensive VA electronic health records, make it a promising resource for precision medicine. MVP is conducting array-based genotyping to provide a genome-wide scan of the entire cohort, in parallel with wholegenome sequencing, methylation, and other 'omics assays. Here, we present the design and performance of the MVP 1.0 custom Axiom array, which was designed and developed as a single assay to be used across the multi-ethnic MVP cohort. A unified genetic quality-control analysis was developed and conducted on an initial tranche of 485,856 individuals, leading to a high-quality dataset of 459,777 unique individuals. 668,418 genetic markers passed quality control and showed high-quality genotypes not only on common variants but also on rare variants. We confirmed that, with non-European individuals making up nearly 30%, MVP's substantial ancestral diversity surpasses that of other large biobanks. We also demonstrated the quality of the MVP dataset by replicating established genetic associations with height in European Americans and African Americans ancestries. This current dataset has been made available to approved MVP researchers for genome-wide association studies and other downstream analyses. Further data releases will be available for analysis as recruitment at the VA continues and the cohort expands both in size and diversity.

People of the Qatar peninsula represent a relatively recent founding by a small number of families from three tribes of the Arabian Peninsula, Persia, and Oman, with indications of African admixture. To assess the roles of both this founding effect and the customary first-cousin marriages among the ancestral Islamic populations in Qatar's population genetic structure, we obtained and genotyped with Affymetrix 500k SNP arrays DNA samples from 168 self-reported Qatari nationals sampled from Doha, Qatar. Principal components analysis was performed along with samples from the Human Genetic Diversity Project data set, revealing three clear clusters of genotypes whose proximity to other human population samples is consistent with Arabian origin, a more eastern or Persian origin, and individuals with African admixture. The extent of linkage disequilibrium (LD) is greater than that of African populations, and runs of homozygosity in some individuals reflect substantial consanguinity. However, the variance in runs of homozygosity is exceptionally high, and the degree of identity-by-descent sharing generally appears to be lower than expected for a population in which nearly half of marriages are between first cousins. Despite the fact that the SNPs of the Affymetrix 500k chip were ascertained with a bias toward SNPs common in Europeans, the data strongly support the notion that the Qatari population could provide a valuable resource for the mapping of genes associated with complex disorders and that tests of pairwise interactions are particularly empowered by populations with elevated LD like the Qatari.

Dr. Stein owns founders shares and stock options in Resilience Therapeutics and has stock options in Oxeia Biopharmaceuticals. Data Availability The GWAS summary statistics generated during and/or analyzed during the current study are available via dbGAP; the dbGaP accession assigned to the Million Veteran Program is phs001672.v1.p. The website is: https://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs001672.v1.p1 Additionally, the data that support the findings of this study are available from the corresponding authors upon request.

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The American Association for Cancer Research (AACR) Project GENIE is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from USA, Canada, the United Kingdom, France, Netherlands, and Spain. Here, we demonstrate use of these real-world data, harmonized through a centralized data resource to accurately predict enrollment on genome-guided trials, discover driver alterations in rare tumors, and identify cancer types without actionable mutations that could benefit from comprehensive genomic analysis. The extensible data infrastructure and governance framework support additional deep patient phenotyping through biopharma collaborations, and expansion to include new data types such as cell-free DNA sequencing. AACR Project GENIE continues to serve a global precision medicine knowledgebase of increasing impact to inform clinical decision making and bring together cancer researchers internationally.

Natural selection inference methods often target one mode of selection of a particular age and strength. However, detecting multiple modes simultaneously, or with atypical representations, would be advantageous for understanding a population's evolutionary history. We have developed an anomaly detection algorithm using distributions of pairwise time to most recent common ancestor (TMRCA) to simultaneously detect multiple modes of natural selection in whole-genome sequences. As natural selection distorts local genealogies in distinct ways, the method uses pairwise TMRCA distributions, which approximate genealogies at a nonrecombining locus, to detect distortions without targeting a specific mode of selection. We evaluate the performance of our method, TSel, for both positive and balancing selection over different time-scales and selection strengths and compare TSel's performance with that of other methods. We then apply TSel to the Complete Genomics diversity panel, a set of human whole-genome sequences, and recover loci previously inferred to be under positive or balancing selection.

Functional, usable, and maintainable open-source software is increasingly essential to scientific research, but there is a large variation in formal training for software development and maintainability. Here, we propose 10 “rules” centered on 2 best practice components: clean code and testing. These 2 areas are relatively straightforward and provide substantial utility relative to the learning investment. Adopting clean code practices helps to standardize and organize software code in order to enhance readability and reduce cognitive load for both the initial developer and subsequent contributors; this allows developers to concentrate on core functionality and reduce errors. Clean coding styles make software code more amenable to testing, including unit tests that work best with modular and consistent software code. Unit tests interrogate specific and isolated coding behavior to reduce coding errors and ensure intended functionality, especially as code increases in complexity; unit tests also implicitly provide example usages of code. Other forms of testing are geared to discover erroneous behavior arising from unexpected inputs or emerging from the interaction of complex codebases. Although conforming to coding styles and designing tests can add time to the software development project in the short term, these foundational tools can help to improve the correctness, quality, usability, and maintainability of open-source scientific software code. They also advance the principal point of scientific research: producing accurate results in a reproducible way. In addition to suggesting several tips for getting started with clean code and testing practices, we recommend numerous tools for the popular open-source scientific software languages Python, R, and Julia.

The Million Veteran Program (MVP), initiated by the Department of Veterans Affairs (VA), aims to collect consented biosamples from at least one million Veterans. Presently, blood samples have been collected from over 800,000 enrolled participants. The size and diversity of the MVP cohort, as well as the availability of extensive VA electronic health records make it a promising resource for precision medicine. MVP is conducting array-based genotyping to provide genome-wide scan of the entire cohort, in parallel with whole genome sequencing, methylation, and other omics assays. Here, we present the design and performance of MVP 1.0 custom Axiom® array, which was designed and developed as a single assay to be used across the multi-ethnic MVP cohort. A unified genetic quality control analysis was developed and conducted on an initial tranche of 485,856 individuals leading to a high-quality dataset of 459,777 unique individuals. 668,418 genetic markers passed quality control and showed high quality genotypes not only on common variants but also on rare variants. We confirmed the substantial ancestral diversity of MVP with nearly 30% non-European individuals, surpassing other large biobanks. We also demonstrated the quality of the MVP dataset by replicating established genetic associations with height in European Americans and African Americans ancestries. This current data set has been made available to approved MVP researchers for genome-wide association studies and other downstream analyses. Further data releases will be available for analysis as recruitment at the VA continues and the cohort expands both in size and diversity.