recipe bioconductor-genesis

The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.






package bioconductor-genesis

(downloads) docker_bioconductor-genesis



Depends bioconductor-biobase


Depends bioconductor-biocgenerics


Depends bioconductor-gdsfmt


Depends bioconductor-genomicranges


Depends bioconductor-gwastools


Depends bioconductor-iranges


Depends bioconductor-s4vectors


Depends bioconductor-seqarray


Depends bioconductor-seqvartools


Depends bioconductor-snprelate


Depends r-base


Depends r-data.table

Depends r-dplyr

Depends r-foreach

Depends r-igraph

Depends r-matrix

Depends r-reshape2



With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-genesis

and update with:

conda update bioconductor-genesis

or use the docker container:

docker pull<tag>

(see bioconductor-genesis/tags for valid values for <tag>)