CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics
Year: 2026
Type: article
Abstract: We present CIT-Lasso, a framework that uses only summary statistics to identify, genome-wide, sets of variants carrying non-redundant information on a phenotype, distinguishing likely causal variants from correlated variants that are merely associated. The open-source implementation completes genome-wide analysis in under 15 min on one CPU. In simulations, it outperforms existing methods in false discovery rate control, power, and fine-mapping resolution. Applied to an Alzheimer's disease meta-analysis, it identified 82 loci, 37 beyond conventional GWAS; prior MPRA and CRISPR-Cas9 studies corroborate prioritized variants. Results on other 67 large-scale GWAS reveal the method's generalizability to make discoveries beyond conventional GWAS pipeline.
Source: Genome biology
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Awards: The pursuit of genetic causal mechanisms, G3536477815, New Statistical Methods for Medical Signals and Images, Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics, Statistical and computational methods for integrative analysis of Alzheimer's Disease genetics, Interpretable machine learning methods for the analysis of Alzheimers disease genetics, Stanford Alzheimer's Disease Research Center, Elucidating sex-specific risk for Alzheimer's disease through state-of-the-art genetics and multi-omics
OpenAlex ID: W7168138063