Software & Resources2019-02-27T17:29:05+00:00

SOFTWARE

VikNGS logo

VikNGS is a freely available, user friendly C++ package that uses the RVS methodology to remove the bias in genetic association  due to differences in sequencing parameters  when combining external NGS data. VikNGS now enables RVS analysis that adjusts for covariates, conducts flexible power analyses and enables combination of NGS from multiple groups for both quantitative and binary trait analysis.

Documentation can be found at https://vikngsdocs.readthedocs.io/en/latest/
The source code and package can be found at https://github.com/ScottMastro/vikNGS

Reference:
Zeynep Baskurt, Scott Mastromatteo, Jiafen Gong and Lisa Strug
VikNGS: A C++ Variant Integrating Kit for Next Generation Sequencing across Research studies for robust rare and common variant association analysis
Presenting at 11:30-1:30pm on Oct 20, 2017, ASHG, Orlando, USA

Derkach, A et al.
Association Analysis Using Next Generation Sequence Data from Publicly Available Control Groups: The Robust Variance Score Statistic.
Bioinformatics 30 (15): 2179-2188 first published online April 14, 2014. PMID:24733292

Access the package and get updates | ViKNGS

The robust variance score (RVS) is a novel likelihood-based method for genetic association with NGS data from external control groups. RVS substitutes genotype calls by their expected values given observed sequence data and implements a robust variance estimate for the score statistic.

Reference:
Derkach, A et al.
Association Analysis Using Next Generation Sequence Data from Publicly Available Control Groups: The Robust Variance Score Statistic.
Bioinformatics 30 (15): 2179-2188 first published online April 14, 2014. PMID:24733292

Download R package | Github
To install in R, simply run the following commands:
install.packages(“devtools”)
library(devtools)
install_github(‘Struglab/RVS’)

Evidential Analysis of Genetic Association Data.

The evidential approach employs the Law of Likelihood, and uses the LR (likelihood ratio)
rather than p-values to plan/design, analyze, interpret and replicate genetic association studies.
Here is a R package that implements the evidential approach to analyze genetic association SNP data.

Reference:
Strug, L.J et al. A pure likelihood approach to the analysis of genetic association data: an alternative to Bayesian and frequentist analysis.
EJHG. Epub Apr 28, 2010. PMID: 20424645

R package

version 1.0.0 (Mar 19, 2010): evian_1.0.tar.gz.
version 1.0.1 (Apr 13, 2010): evian_1.0.1.tar.gz. (w/robust, mle inversion fix, 2df plot title fix)
version 1.0.2 (Apr 15, 2010): evian_1.0.2.tar.gz. (robust example in vignette/demo, listterms length fix, as.vector() for char covariates fix)
version 1.1.0 (May 6, 2010): evian_1.1.0.tar.gz. (with linear regression functions). Documentation: online or PDF
version 1.1.0 (May 17, 2010): evian_1.1.0.zip. (For windows). Documentation: online or PDF
version 2.0.0 (Mar. 30, 2018): evian_2.0.0.tar.gz available on CRAN. (NEW with parallel processing ability with support for Mac, Windows and Linux).

Reference:
Weili Li, Sara Dobbins, Ian Tomlinson, Richard Houlston, Deb Pal, Lisa Strug.
Prioritizing Rare Variants with Conditional Likelihood Ratios. Human Heredity 2015. PMID: 25659987

R code

  1. R script
  2. Example dataset

R-codes and Sample data  for ‘The analysis of correlated binary data in genetic association studies: direct inference using the composite likelihood ratio’
can be downloaded below.

Sample data used by R code
R functions called by R code
main R code

The R script used for the Simple Sum method used in PLoS Genetics 2018 can be downloaded here.

Resources

Summary statistics from the GWAS of Meconium Ileus from the International CF Gene Modifier Consortium in Nature Genetics, 2012 can be downloaded below.

README
GWAS1 Summary Statistics (indexed with tabix)

Summary statistics from the meta-GWAS of the International CF Gene Modifier Consortium in Nature Communication, 2015 can be downloaded below.

README
GWAS_Summary_Statistics

The web tool for Canadian FEV1 CF-Specific Percentiles  can be found Here

Summary statistics from the GWAS of Meconium Ileus (2019) from the International CF Gene Modifier Consortium can be downloaded below.

GWAS Summary Statistics (indexed with tabix)
README

Recent Publications

  • Gong, J., Wang, F., Xiao, B. et al. (2019) “Genetic association and transcriptome integration identify contributing genes and tissues at cystic fibrosis modifier loci.” PLoS Genetics doi.org/10.1371/journal.pgen.1008007.
  • Strug L.J. (2018). “The evidential statistical paradigm in genetics.” Genetic Epidemiology doi:10.1002/gepi.22151.
  • Strug L.J., Stephenson A.L., Panjwani N., Harris A. (2018). “Recent advances in developing therapeutics for cystic fibrosis.” Human Molecular Genetics 27(R2):R173-R186.
  • Soave, D. M. and L. J. Strug (2018). “Testing Calibration of Cox Survival Models at Extremes of Event Risk.” Frontiers in Genetics 9: 177.
  • Panjwani, N., Xiao B., et al. (2018). “Improving imputation in disease-relevant regions: lessons from cystic fibrosis.” Nature Genomic Medicine 3: 8.
  • Strug, L.J., et al. (2016). “Cystic fibrosis gene modifier SLC26A9 modulates airway response to CFTR-directed therapeutics.” Human Molecular Genetics 25(20): 4590-4600. Access the recommendation on F1000Prime