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The majority of publications in computational biology and biocomputing develop or apply software approaches to relevant biological problems to some degree. While journals and conferences often prompt authors to make their source code available, these are often only basic requirements. Investigators often wish their software and tools were widely usable to the scientific community, but there are limited resources available to maximize the distribution and provide easy use of developed software. Even when authors adhere to standards of source code availability, the growing problem of system configuration issues, language and library version conflicts, and other implementation issues often impede the broad distribution, availability of software tools, and reproducibility of research. There are a variety of solutions to these implementation issues, but the learning curve for applying these solutions can be steep. This tutorial demonstrates tools and approaches for packaging and distribution of published code, and provides methodological practices for the broad and open sharing of new biocomputing software.

Modern genomic studies are rapidly growing in scale, and the analytical approaches used to analyze genomic data are increasing in complexity. Genomic data management poses logistic and computational challenges, and analyses are increasingly reliant on genomic annotation resources that create their own data management and versioning issues. As a result, genomic datasets are increasingly handled in ways that limit the rigor and reproducibility of many analyses. In this work, we examine the use of the Spark infrastructure for the management, access, and analysis of genomic data in comparison to traditional genomic workflows on typical cluster environments. We validate the framework by reproducing previously published results from the Alzheimer's Disease Sequencing Project. Using the framework and analyses designed using Jupyter notebooks, Spark provides improved workflows, reduces user-driven data partitioning, and enhances the portability and reproducibility of distributed analyses required for large-scale genomic studies.

Return of results is not common in research settings as standards are not yet in place for what to return, how to return, and to whom. As a pioneer of large-scale of return of research results, the Precision Medicine Initiative Cohort now known of All of Us plans to return pharmacogenomic results and variants of clinical significance to its participants starting late 2019. To better understand the local landscape of possibilities regarding return of research results, we assessed the frequency of pathogenic variants and APOL1 renal risk variants in a small diverse cohort of chronic kidney disease patients (CKD) ascertained from a public hospital in Cleveland, Ohio genotyped on the Illumina Infinium MegaEX. Of the 23,720 ClinVar-designated variants directly assayed by the MegaEX, 8,355 (35%) had at least one alternate allele in the 130 participants genotyped. Of these, 18 ClinVar variants deemed pathogenic by multiple submitters with no conflicts in interpretation were distributed across 27 participants. The majority of these pathogenic ClinVar variants (14/18) were associated with autosomal recessive disorders. Of note were four African American carriers of TTR rs76992529 associated with amyloidogenic transthyretin amyloidosis, otherwise known as familial transthyretin amyloidosis (FTA). FTA, an autosomal dominant disorder with variable penetrance, is more common among African-descent populations compared with European-descent populations. Also common in this CKD population were APOL1 renal risk alleles G1 (rs73885319) and G2 (rs71785313) with 60% of the study population carrying at least one renal risk allele. Both pathogenic ClinVar variants and APOL1 renal risk alleles were distributed among participants who wanted actionable genetic results returned, wanted genetic results returned regardless of actionability, and wanted no results returned. Results from this local genetic study highlight challenges in which variants to report, how to interpret them, and the participant's potential for follow-up, only some of the challenges in return of research results likely facing larger studies such as All of Us.

The majority of accepted papers in computational biology and biocomputing describe new software approaches to relevant biological problems. While journals and conferences often require the availability of software and source code, there are limited resources available to maximize the distribution and use of developed software within the scientific community. The accepted standard is to make source code available for new approaches in published work, the growing problem of system configuration issues, language, library version conflicts, and other implementation issues often impede the broad distribution, availability of software tools, and reproducibility of research. There are a variety of solutions to these implementation issues, but the learning curve for applying these solutions is steep. This tutorial demonstrates tools and approaches for packaging and distribution of published code, and provides methodological practices for the broad and open sharing of new biocomputing software.

Genomic maps of local ancestry identify ancestry transitions - points on a chromosome where recent recombination events in admixed individuals have joined two different ancestral haplotypes. These events bring together alleles that evolved within separate continential populations, providing a unique opportunity to evaluate the joint effect of these alleles on health outcomes. In this work, we evaluate the impact of genetic variants in the context of nearby local ancestry transitions within a sample of nearly 10,000 adults of African ancestry with traits derived from electronic health records. Genetic data was located using the Metabochip, and used to derive local ancestry. We develop a model that captures the effect of both single variants and local ancestry, and use it to identify examples where local ancestry transitions significantly interact with nearby variants to influence metabolic traits. In our most compelling example, we find that the minor allele of rs16890640 occuring on a European background with a downstream local ancestry transition to African ancestry results in significantly lower mean corpuscular hemoglobin and volume. This finding represents a new way of discovering genetic interactions, and is supported by molecular data that suggest changes to local ancestry may impact local chromatin looping.

Calcineurin-inhibitors CI are immunosuppressive agents prescribed to patients after solid organ transplant to prevent rejection. Although these drugs have been transformative for allograft survival, long-term use is complicated by side effects including nephrotoxicity. Given the narrow therapeutic index of CI, therapeutic drug monitoring is used to prevent acute rejection from underdosing and acute toxicity from overdosing, but drug monitoring does not alleviate long-term side effects. Patients on calcineurin-inhibitors for long periods almost universally experience declines in renal function, and a subpopulation of transplant recipients ultimately develop chronic kidney disease that may progress to end stage renal disease attributable to calcineurin inhibitor toxicity (CNIT). Pharmacogenomics has the potential to identify patients who are at high risk for developing advanced chronic kidney disease caused by CNIT and providing them with existing alternate immunosuppressive therapy. In this study we utilized BioVU, Vanderbilt University Medical Center's DNA biorepository linked to de-identified electronic medical records to identify a cohort of 115 heart transplant recipients prescribed calcineurin-inhibitors to identify genetic risk factors for CNIT We identified 37 cases of nephrotoxicity in our cohort, defining nephrotoxicity as a monthly median estimated glomerular filtration rate (eGFR)<30 mL/min/1.73 m2 at least six months post-transplant for at least three consecutive months. All heart transplant patients were genotyped on the Illumina ADME Core Panel, a pharmacogenomic genotyping platform that assays 184 variants across 34 genes. In Cox regression analysis adjusting for age at transplant, pre-transplant chronic kidney disease, pre-transplant diabetes, and the three most significant principal components (PCAs), we did not identify any markers that met our multiple-testing threshold. As a secondary analysis we also modeled post-transplant eGFR directly with linear mixed models adjusted for age at transplant, cyclosporine use, median BMI, and the three most significant principal components. While no SNPs met our threshold for significance, a SNP previously identified in genetic studies of the dosing of tacrolimus CYP34A rs776746, replicated in an adjusted analysis at an uncorrected p-value of 0.02 (coeff(S.E.)=14.60(6.41)). While larger independent studies will be required to further validate this finding, this study underscores the EMRs usefulness as a resource for longitudinal pharmacogenetic study designs.

Genome-wide association studies (GWAS) have identified hundreds of genomic regions associated with common human disease and quantitative traits. A major research avenue for mature genotype-phenotype associations is the identification of the true risk or functional variant for downstream molecular studies or personalized medicine applications. As part of the Population Architecture using Genomics and Epidemiology (PAGE) study, we as Epidemiologic Architecture for Genes Linked to Environment (EAGLE) are fine-mapping GWAS-identified genomic regions for common diseases and quantitative traits. We are currently genotyping the Metabochip, a custom content BeadChip designed for fine-mapping metabolic diseases and traits, in∼15,000 DNA samples from patients of African, Hispanic, and Asian ancestry linked to de-identified electronic medical records from the Vanderbilt University biorepository (BioVU). As an initial study of quality control, we report here the genotyping data for 360 samples of European, African, Asian, and Mexican descent from the International HapMap Project. In addition to quality control metrics, we report the overall allele frequency distribution, overall population differentiation (as measured by FST), and linkage disequilibrium patterns for a select GWAS-identified region associated with low-density lipoprotein cholesterol levels to illustrate the utility of the Metabochip for fine-mapping studies in the diverse populations expected in EAGLE, the PAGE study, and other efforts underway designed to characterize the complex genetic architecture underlying common human disease and quantitative traits.

Genetic association studies have rapidly become a major tool for identifying the genetic basis of common human diseases. The advent of cost-effective genotyping coupled with large collections of samples linked to clinical outcomes and quantitative traits now make it possible to systematically characterize genotype-phenotype relationships in diverse populations and extensive datasets. To capitalize on these advancements, the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) project, as part of the collaborative Population Architecture using Genomics and Epidemiology (PAGE) study, accesses two collections: the National Health and Nutrition Examination Surveys (NHANES) and BioVU, Vanderbilt University's biorepository linked to de-identified electronic medical records. We describe herein the workflows for accessing and using the epidemiologic (NHANES) and clinical (BioVU) collections, where each workflow has been customized to reflect the content and data access limitations of each respective source. We also describe the process by which these data are generated, standardized, and shared for meta-analysis among the PAGE study sites. As a specific example of the use of BioVU, we describe the data mining efforts to define cases and controls for genetic association studies of common cancers in PAGE. Collectively, the efforts described here are a generalized outline for many of the successful approaches that can be used in the era of high-throughput genotype-phenotype associations for moving biomedical discovery forward to new frontiers of data generation and analysis.

Understanding how genetic variants impact the regulation and expression of genes is important for forging mechanistic links between variants and phenotypes in personal genomics studies. In this work, we investigate statistical interactions among variants that alter gene expression and identify 79 genes showing highly significant interaction effects consistent with genetic heterogeneity. Of the 79 genes, 28 have been linked to phenotypes through previous genomic studies. We characterize the structural and statistical nature of these 79 cis-epistasis models, and show that interacting regulatory SNPs often lie far apart from each other and can be quite distant from the gene they regulate. By using cis-epistasis models that account for more variance in gene expression, investigators may improve the power and replicability of their genomics studies, and more accurately estimate an individual's gene expression level, improving phenotype prediction.

Genome-wide association studies provide an unprecedented opportunity to identify combinations of genetic variants that contribute to disease susceptibility. The combinatorial problem of jointly analyzing the millions of genetic variations accessible by high-throughput genotyping technologies is a difficult challenge. One approach to reducing the search space of this variable selection problem is to assess specific combinations of genetic variations based on prior statistical and biological knowledge. In this work, we provide a systematic approach to integrate multiple public databases of gene groupings and sets of disease-related genes to produce multi-SNP models that have an established biological foundation. This approach yields a collection of models which can be tested statistically in genome-wide data, along with an ordinal quantity describing the number of data sources that support any given model. Using this knowledge-driven approach reduces the computational and statistical burden of large-scale interaction analysis while simultaneously providing a biological foundation for the relevance of any significant statistical result that is found.