(Work in progress) Release v0.2 #21
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dcdehaas
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Jan 20, 2026
dcdehaas
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- Missing data support for non-standardized LinearOperators
- Standardized matrix (input) support for GWAS and GWAS with covariates
- Missing data support for GWAS (with and without covariates, with and without standardization)
- Other minor bug fixes related to CLI inputs/outputs
1. The standardized operators don't need any changes. Just pass in the missingness-adjusted allele frequencies when constructing the operators. 2. The non-standardized operators need an adjustmet before or after the multiplication, depending on the direction of the matmul(). The user can pass in a mean value (e.g., the allele frequencies) when constructing the operator, and the operator will use it to perform the adjustments for the user.
1. Add linear regression-based GWAS with covariates method, and compare it to the QR method. 2. Adjust the beta computation for the QR method: it was missing the subtraction of the means, as per the derivation. 3. Ensure that you can run `grapp pca` and pipe the results directly in as covariate input to `grapp assoc`, by using Pandas-style tsv format if the covariates files ends in ".tsv" and plink-style format if the file ends in ".txt"
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