MOTIVATION: The routine exclusion of admixed individuals from traditional genome-wide association studies (GWAS) due to concerns about spurious associations has limited multi-ancestry genetic discovery. Tractor addresses this issue by incorporating local ancestry into association testing, enabling the identification of ancestry-enriched signals and generating ancestry-specific summary statistics. However, adoption has been constrained by the complexity of prerequisite steps, including phasing and local ancestry inference, which require substantial bioinformatics expertise and introduce key analytical decision points. RESULTS: We developed a scalable, automated Nextflow workflow that integrates phasing, local ancestry inference, and Tractor association testing into a reproducible end-to-end pipeline. To demonstrate its utility, we applied the workflow to 32 blood biomarkers in 6245 two-way African-European admixed individuals from the UK Biobank. This pipeline performed efficiently at scale, replicating known associations and uncovering key ancestry-specific loci. These associations were largely driven by variants present on African ancestral tracts but absent from European tracts, underscoring the value of local ancestry-aware methods in uncovering previously masked genetic signals. AVAILABILITY AND IMPLEMENTATION: The workflow is modular, customizable, and compatible with commonly used phasing and local ancestry tools, minimizing manual intervention while preserving analytical flexibility. By lowering technical barriers to implementation, this framework facilitates broader adoption of local ancestry-aware GWAS, paving the way for expanded genetic discovery.

SUMMARY: Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic studies of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations. AVAILABILITY AND IMPLEMENTATION: Admix-kit package is open-source and available at https://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available at https://github.com/UW-GAC/admix-kit_workflow.

OBJECTIVE: The All of Us Evenings with Genetics (EwG) Research Program at Baylor College of Medicine (BCM), funded to engage research scholars to work with the All of Us data, developed a training curriculum for the Researcher Workbench, the platform to access and analyze All of Us data. All of Us EwG developed the curriculum so that it could teach scholars regardless of their skills and background in programming languages and cloud computing. All of Us EwG delivered this curriculum at the first annual All of Us EwG Faculty Summit in May 2022. The curriculum was evaluated both during and after the Faculty Summit so that it could be improved for future training. MATERIALS AND METHODS: Surveys were administered to assess scholars' familiarity with the programming languages and computational tools required to use the Researcher Workbench. The curriculum was developed using backward design and was informed by the survey results, a review of available resources for training users on the Researcher Workbench, and All of Us EwG members' collective experience training students. The curriculum was evaluated using feedback surveys during the Faculty Summit as well as virtual meetings and emails following the Faculty Summit. RESULTS: The evaluation results demonstrated the success of the curriculum and identified areas for improvement. DISCUSSION AND CONCLUSION: The curriculum has been adapted and improved in response to evaluations and in response to changes to the All of Us data and infrastructure to train more researchers through this program and other scholarly programs.

OBJECTIVE: Educational offerings to fill the bioinformatics knowledge gap are a key component to enhancing access and use of health data from the All of Us Research Program. We developed a Train the Trainer-based, innovative training series including project-based learning, modular on-demand demonstrations, and unstructured tutorial time as a model for educational engagement in the All of Us community. MATERIALS AND METHODS: We highlight our training modules and content, with training survey data informing cycles of development in the creation of a 6-module training series with modular demonstrations. RESULTS: We have conducted 2 public iterations of the Train the Trainer (Tx3) Series based on survey feedback while training over 300 registered researchers to access and analyze data on the All of Us Researcher Workbench. DISCUSSION AND CONCLUSION: Future directions of the Tx3 Series include enhanced focus on project-based learning and learner requests for modularity and asynchronous materials access.