README.md

In Iuliana-Ionita-Laza/GeneScan3DKnock: Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes

GeneScan3DKnock

This is an R package for performing improved gene-based testing by integrating long-range chromatin interactions and knockoff statistics.

Description

The package contain functions for the gene-based association tests (GeneScan3D) that integrate both common and rare genetic variation from promoter and enhancers for each gene, along with the knockoff-enhanced tests. The GeneScan3DKnock has two steps: Step 1. Knockoff generation using function GeneScan3D.KnockoffGeneration() and Step 2. Knockoff filter using function GeneScan3DKnock(), after obtaining the original and knockoff p-values for each gene.

To deal with the case-control imbalance issue for binary traits, we apply the Saddlepoint approximation (SPA) of the gene-based tests to avoid the inflation of Type I error rate.

Besides, we also optimize the knockoff generations for GeneScan3DKnock using Shrinkage leveraging (SL) algorithm. After the optimization, knockoffs generation of whole-genome UK biobank genotypes only take 11 CPU hours for 1,000 computational cores.

Workflow

Prerequisites

R (recommended version >= 3.6.0)

Dependencies

GeneScan3DKnock depends on R packages SKAT, Matrix, MASS, WGScan, SPAtest, CompQuadForm, abind and irlba. Make sure to install those packages before installing GeneScan3DKnock.

Installation

library(devtools)

devtools::install_github("Iuliana-Ionita-Laza/GeneScan3DKnock")

The current version is 0.3 (August 29, 2021).

Version history

Version 0.3 (Add an option of resampling in GeneScan1D and GeneScan3D functions. Don't need to conduct resampling-based moment-matching when the sample size is large, especially for UK biobank-scale data; Add W.threshold in the GeneScan3DKnock function; Apply SPA gene-based test for binary traits, to deal with imbalance case-control issues; The knockoff generations are optimized using shrinkage leveraging algorithm.)

Usage

Please see the GeneScan3DKnock user manual for detailed usage of GeneScan3DKnock package. Please see the tutorial of using the GeneScan3DKnock package.

Functional annotation scores

For functional annotation scores, we use the genome-wide functional annotations in 127 different cell types and tissues (GenoNet scores). The precomputed GenoNet scores for human genome assembly GRCh37 (hg19) can be downloaded here.

The functional annotation scores can help to increase the power of the gene-based test, but this is not mandatory. If one don't want to use any functional score, just set Z=NULL, and the package would automatically use the Beta(MAF;1,25) for rare variants and Beta(MAF;1,1) for common variants as the weights.

Contact

If you have any questions about GeneScan3DKnock please contact

• [e-mail]

If you want to submit a issue concerning the software please do so using the GeneScan3DKnock Github repository.

Citation

• The GeneScan3DKnock paper: Ma, S., Dalgleish, J., Lee, J., Wang, C., Liu, L., Gill, R., Buxbaum, J. D., Chung, W. K., Aschard, H., Silverman, E. K., Cho, M. H., He, Z. and Ionita-Laza, I. (2021). "Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes". Proceedings of the National Academy of Sciences of the United States of America, 118, e2105191118.

• Shrinkage leveraging algorithm for knockoff generation: He, Z., Guen, Y. L., Liu, L., Lee, J., Ma, S., Yang, A. C., Liu. X., Rutledge, J., Losada, P. M., Song, B., Belloy, M. E., Butler, R. R., Longo, F. M., Tang, H., Mormino, E. C., Wyss-Coray, T., Greicius, M. D. and Ionita-Laza, I. (2021) "Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics". American Journal of Human Genetics, 108, 2336-2353.

License

This software is licensed under GPL-3.

Iuliana-Ionita-Laza/GeneScan3DKnock documentation built on July 31, 2023, 4:32 a.m.