R-universe search: plink2

Showing 6 of total 6 results (show query)

quantgen

BEDMatrix:Extract Genotypes from a PLINK .bed File

A matrix-like data structure that allows for efficient, convenient, and scalable subsetting of binary genotype/phenotype files generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole genome association analysis toolset, without loading the entire file into memory.

Maintained by Alexander Grueneberg. Last updated 7 months ago.

bed genetics genomics plink plink2 r-pkg

11.5 match 18 stars 7.13 score 196 scripts 6 dependents

dovinij

GxEprs:Genotype-by-Environment Interaction in Polygenic Score Models

A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).

Maintained by Dovini Jayasinghe. Last updated 10 months ago.

9.3 match 2 stars 3.30 score

ochoalab

genio:Genetics Input/Output Functions

Implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data.

Maintained by Alejandro Ochoa. Last updated 1 months ago.

cpp

3.4 match 18 stars 8.04 score 213 scripts 4 dependents

mcanouil

eggla:Early Growth Genetics Longitudinal Analysis

Tools for longitudinal analysis within the EGG (Early Growth Genetics) Consortium (<http://egg-consortium.org/>).

Maintained by Mickaël Canouil. Last updated 2 months ago.

genetics growth-curves infancy longitudinal-analysis mixed-effects-models spline-regression

1.8 match 3 stars 4.15 score 19 scripts

annaltyler

cape:Combined Analysis of Pleiotropy and Epistasis for Diversity Outbred Mice

Combined Analysis of Pleiotropy and Epistasis infers predictive networks between genetic variants and phenotypes. It can be used with standard two-parent populations as well as multi-parent populations, such as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the multi-parent advanced generation intercross (MAGIC) population of Arabidopsis thaliana. It uses complementary information of pleiotropic gene variants across different phenotypes to resolve models of epistatic interactions between alleles. To do this, cape reparametrizes main effect and interaction coefficients from pairwise variant regressions into directed influence parameters. These parameters describe how alleles influence each other, in terms of suppression and enhancement, as well as how gene variants influence phenotypes. All of the final interactions are reported as directed interactions between pairs of parental alleles. For detailed descriptions of the methods used in this package please see the following references. Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>. Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.

Maintained by Anna Tyler. Last updated 1 years ago.

1.8 match 3.27 score 37 scripts

uekim

stmgp:Rapid and Accurate Genetic Prediction Modeling for Genome-Wide Association or Whole-Genome Sequencing Study Data

Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.

Maintained by Masao Ueki. Last updated 2 months ago.

0.5 match 1.70 score 2 scripts