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, outperforms existing methods in false discovery rate control, power, and fine-mapping resolution.
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.
The standard analysis pipeline for genome-wide association studies (GWAS) is based on marginal tests of association. These are computationally convenient and portable, but the discoveries are not immediately interpretable, and require post-processing such as “clumping” and “fine mapping.” An interesting alternative i...
Benjamin B. Chu, Zi-Huai He, C. Sabatti· Bioinformatics Advances· 0 citations
Genome-wide association studies (GWAS) have greatly advanced the discovery of genetic variants underlying complex traits and diseases. Yet in heterogeneous populations, existing GWAS strategies typically either pool all individuals under an assumption of population homogeneity or perform meta-analysis across predefined...
Xiong Shen, Zhen-Shuang Tang, Yong Liao et al.· 0 citations
This tutorial reviews several widely used methods for pleiotropy detection from GWAS summary statistics, including ASSET, PLACO, GPA, CPBayes, and GCPBayes, and demonstrates their application using breast and thyroid cancer datasets.
Christina Y. Feng, P. Sugier, Nan Zou et al.· Statistics in Medicine· 0 citations
This work systematically compared predicted effect sizes for fine-mapped eQTLs to nearby putatively non-causal SNVs to demonstrate that causal variant underestimation is a core issue facing S2F expression predictors, with future improvements dependent on better local activity detection and enhancer-gene linking.
Shiron Drusinsky, Katherine S. Pollard· bioRxiv· 1 citation
While deep learning has recently been used for identifying risk variants in genome-wide association studies, its stochastic nature and the complex correlation within genetic data have posed significant challenges for deep-learning-based methods to pinpoint risk variants. We introduce KNOT, a knockoff-augmented neural...
To support reproducible best practice, a simple command set is provided for selecting and documenting study-appropriate backgrounds and for assessing sensitivity of GO Biological Process results to the chosen universe.
Brian Timoney, P. Guasoni, Komal Zade et al.· bioRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
