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.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

SUMMARY: The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/GENESIS; vignettes included. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Whole-genome sequencing (WGS) data are being generated at an unprecedented rate. Analysis of WGS data requires a flexible data format to store the different types of DNA variation. Variant call format (VCF) is a general text-based format developed to store variant genotypes and their annotations. However, VCF files are large and data retrieval is relatively slow. Here we introduce a new WGS variant data format implemented in the R/Bioconductor package 'SeqArray' for storing variant calls in an array-oriented manner which provides the same capabilities as VCF, but with multiple high compression options and data access using high-performance parallel computing. RESULTS: Benchmarks using 1000 Genomes Phase 3 data show file sizes are 14.0 Gb (VCF), 12.3 Gb (BCF, binary VCF), 3.5 Gb (BGT) and 2.6 Gb (SeqArray) respectively. Reading genotypes in the SeqArray package are two to three times faster compared with the htslib C library using BCF files. For the allele frequency calculation, the implementation in the SeqArray package is over 5 times faster than PLINK v1.9 with VCF and BCF files, and over 16 times faster than vcftools. When used in conjunction with R/Bioconductor packages, the SeqArray package provides users a flexible, feature-rich, high-performance programming environment for analysis of WGS variant data. AVAILABILITY AND IMPLEMENTATION: http://www.bioconductor.org/packages/SeqArray. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: VariantAnnotation is an R / Bioconductor package for the exploration and annotation of genetic variants. Capabilities exist for reading, writing and filtering variant call format (VCF) files. VariantAnnotation allows ready access to additional R / Bioconductor facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources. AVAILABILITY AND IMPLEMENTATION: This package is implemented in R and available for download at the Bioconductor Web site (http://bioconductor.org/packages/2.13/bioc/html/VariantAnnotation.html). The package contains extensive help pages for individual functions and a 'vignette' outlining typical work flows; it is made available under the open source 'Artistic-2.0' license. Version 1.9.38 was used in this article.

Abstract Summary: GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R’s memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation. Availability and implementation: GWASTools is implemented in R and is available from Bioconductor (http://www.bioconductor.org). An extensive vignette detailing a recommended work flow is included. Contact: [email protected]

Abstract Summary: Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed gdsfmt and SNPRelate (R packages for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized. Benchmarks show the uniprocessor implementations of PCA and identity-by-descent are ∼8–50 times faster than the implementations provided in the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs, respectively, and can be sped up to 30–300-fold by using eight cores. SNPRelate can analyse tens of thousands of samples with millions of SNPs. For example, our package was used to perform PCA on 55 324 subjects from the ‘Gene-Environment Association Studies’ consortium studies. Availability and implementation: gdsfmt and SNPRelate are available from R CRAN (http://cran.r-project.org), including a vignette. A tutorial can be found at https://www.genevastudy.org/Accomplishments/software. Contact: [email protected]