2020
DOI: 10.1101/2020.08.20.258491
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study

Abstract: Along with the development of high-throughout sequencing technologies, both sample size and number of SNPs are increasing rapidly in Genome-Wide Association Studies (GWAS) and the associated computation is more challenging than ever. Here we present a Memory-efficient, Visualization-enhanced, and Parallel-accelerated R package called "rMVP" to address the need for improved GWAS computation. rMVP can: (1) effectively process large GWAS data; (2) rapidly evaluate population structure; (3) efficiently estimate va… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
32
0

Year Published

2020
2020
2022
2022

Publication Types

Select...
4
3
3

Relationship

0
10

Authors

Journals

citations
Cited by 67 publications
(34 citation statements)
references
References 28 publications
(31 reference statements)
0
32
0
Order By: Relevance
“…Candidate genes located within an LD block near a peak SNP were identified and analyzed. Manhattan plots of the GWAS were drawn using the cmplot package (Yin, 2019) and qqman (Turner, 2017) in r 3.6.0 (R Core Team, 2019).…”
Section: Methodsmentioning
confidence: 99%
“…Candidate genes located within an LD block near a peak SNP were identified and analyzed. Manhattan plots of the GWAS were drawn using the cmplot package (Yin, 2019) and qqman (Turner, 2017) in r 3.6.0 (R Core Team, 2019).…”
Section: Methodsmentioning
confidence: 99%
“…We used likelihood ratio P values from the GEMMA output files because likelihood ratio test makes fewer approximations compared to the Wald’s test. Manhattan and QQ-plots were drawn using rMVP package 66 in R version 3.6.1 in RStudio version 1.2.1335.…”
Section: Methodsmentioning
confidence: 99%
“…Association analysis for each trait on each SNP with an MAF larger than 0.05 was performed using a single-locus mixed linear model (MLM) implemented in GEMMA[40] (which corrects confounding by population structure and the relatedness matrix). The GWAS results were displayed using a Manhattan plot and a QQ-plot created with the R package CMplot[41]. A clump based method implemented in PLINK[39] was used to reduce a false peak and to detect real SALs.…”
Section: Methodsmentioning
confidence: 99%