projectglow/glow: v1.0.0

We are excited to announce the release of Glow 1.0.0. This release includes major scalability and usability improvements, particularly for GloWGR whole-genome regression and genome-wide association study regression tests. These improvements create a more performant GloWGR workflow with simpler APIs.

Major features and changes include:

• #302, #309: Pandas-based linear regression. Introduced the linear_regression Python function which can be used to perform GWAS linear regression tests for multiple phenotypes simultaneously. The function is optimized for performance through one-time calculation of intermediate matrices common across multiple phenotypes and genotypes. The function can also accept WGR terms as an offset parameter. This function is superior in performance compared to the existing SQL-based linear_regression_gwas function, which only works on a single phenotype.
• #316, #318, #319: Pandas-based logistic regression. Introduced the logistic_regression Python function with the same properties mentioned above for linear regression. This function implements a fast multi-phenotype multi-genotype score test with fallback logic for significant variants indicated by the score test. The currently supported fallback test is the Approximate Firth method presented in REGENIE.
• #323: Improved the WGR API so that the user can now provide all the input to a single class and run different functions without passing any arguments. An estimate_loco_offsets function was added to perform an end-to-end generation of loco predictors using a single command. In addition, GloWGR was revised to make its behavior regarding standardization of phenotypes and genotypes, and treatment of intercept match the REGENIE algorithm.
• #300: Conversion from Hail MatrixTables to Glow-compatible Spark DataFrames.
• #274: Faster default VCF reader.
• #294: Streamlined GloWGR between WGR and GWAS functions.
• #282: Improved scalability of GloWGR.
• #303: Added hard calling by default to the BGEN reader.