This dataset contains the Linkage Disequilibrium (LD) matrices that were used in the analyses described in the manuscript: Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference
Shadi Zabad, Simon Gravel, Yue Li
McGill University LD matrices record the SNP-by-SNP correlations in a given sample of individuals from a general population. In this case, we threshold the matrices so that we only record the correlations between SNPs that are at most 3 centi Morgan apart. These matrices record the SNP correlations in a random sample of 50,000 individuals from the White British cohort in the UK Biobank dataset. There is one matrix per autosomal chromosome (chr_1, chr_2, ..., chr_22). The matrices are stored in Zarr format, a chunked on-disk array storage format that allows for multi-threaded read and write access. To access these matrices, consult the codebase of magenpy, our custom python package with special data structures for processing these LD matrices. UPDATE (03/09/2022): We updated the matrices to add the reference allele attribute (A2) and we also now have one tar archive per chromosome.

This dataset contains the Linkage Disequilibrium (LD) matrices that were used in the analyses described in the manuscript: Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference
Shadi Zabad, Simon Gravel, Yue Li
McGill University LD matrices record the SNP-by-SNP correlations in a given sample of individuals from a general population. In this case, we threshold the matrices so that we only record the correlations between SNPs that are at most 3 centi Morgan apart. These matrices record the SNP correlations in a random sample of 50,000 individuals from the White British cohort in the UK Biobank dataset. There is one matrix per autosomal chromosome (chr_1, chr_2, ..., chr_22). The matrices are stored in Zarr format, a chunked on-disk array storage format that allows for multi-threaded read and write access. To access these matrices, consult the codebase of magenpy, our custom python package with special data structures for processing these LD matrices. UPDATE (03/09/2022): We updated the matrices to add the reference allele attribute (A2) and we also now have one tar archive per chromosome.

This dataset contains the Linkage Disequilibrium (LD) matrices that were used in the analyses described in the manuscript: Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference
Shadi Zabad, Simon Gravel, Yue Li
McGill University LD matrices record the SNP-by-SNP correlations in a given sample of individuals from a general population. In this case, we threshold the matrices so that we only record the correlations between SNPs that are at most 3 centi Morgan apart. These matrices record the SNP correlations in a random sample of 50,000 individuals from the White British cohort in the UK Biobank dataset. There is one matrix per autosomal chromosome (chr_1, chr_2, ..., chr_22). The matrices are stored in Zarr format, a chunked on-disk array storage format that allows for multi-threaded read and write access. To access these matrices, consult the codebase of magenpy, our custom python package with special data structures for processing these LD matrices.