- Probabilistic whitening model with parameter estimates and efficient algorithms removes LD induced correlation, improving statistical and computational performance.
- In schizophrenia GWAS imputation, the method attained lowest mean square error and was up to an order of magnitude faster than alternatives.
- Approach reveals tandem repeats explaining regulatory signals for disease relevant genes and is available in R packages decorrelate and imputez.
PLoS Genet. 2026 Oct 28;22(10):e1012320. doi: 10.1371/journal.pgen.1012320. Online ahead of print.
ABSTRACT
Statistical methods often make assumptions about independence between the samples or features of a dataset, yet correlation structure is ubiquitous in real data. In statistical genetics, correlation between regression summary statistics due to linkage disequilibrium (LD) between nearby variants complicates downstream analysis. Many methods use LD reference panels to account for this correlation. Whitening transformations comprise a family of methods to remove correlation structure, and are used implicitly by many common models. Here we propose a probabilistic model for data whitening, and propose parameter estimates and efficient algorithms to improve statistical and computational performance. In an application to impute z-statistics from unobserved genetic variants from a genome-wide association study of schizophrenia, the probabilistic whitening transformation, had the lowest mean square error while being up to an order of magnitude faster than other methods. Using this approach, we also identify tandem repeats that explain genetic regulatory signals for disease-relevant genes. Analyses are implemented in our novel open source R packages decorrelate and imputez.
PMID:42949994 | DOI:10.1371/journal.pgen.1012320
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