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.
Zihuai He, Benjamin B. Chu, James Yang et al.· Genome Biology· 0 citations
Perturb-seq measures transcriptomic responses to genetic perturbations at scale, but conventional designs that enrich for one guide RNA per cell remain resource-intensive. Standard analyses discard cells carrying multiple guides, further limiting the usable yield from each experiment. Here, we characterize how incorporating these guide multiplets affects signal recovery, information loss, and cost reduction. At the highest guide burden, cells showed increased stress and suppressed cell-cycle progression. We develop PerturbMatch, a scalable statistical framework to analyze guide multiplets. Among different classes of guide multiplets, doublets and triplets recovered perturbation responses more accurately than higher-order multiplets. Across three 5000-gene Perturb-seq screens with increasing guide loading, per-cell costs decreased by up to 81% while information loss remained within 1.5-fold of the loss observed between technical replicates. In existing genome-wide Perturb-seq data, incorporating previously discarded guide multiplets increased usable cell numbers and improved statistical power. Compared with a singlet holdout set, adding guide multiplets moved signal recovery closer to the theoretical expected reproducibility. Overall, we recommend a design that intentionally includes single-guide cells, guide doublets, and guide triplets to improve cost efficiency while preserving signal recovery.
Jake Yeung, Jenille Tan, Liang Wang et al.· bioRxiv· 0 citations
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