Functional genomic dissection and prediction of body size traits in pigs.
Paper
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The authors' code
R · 37 lines · 1017 B · no license
- #' CumuPos: Determine the cumulative positions of scan.gwaa.object for plotting.
- #' @param scan.gwaa.object GenABEL scan.gwaa.object object containing GWAS results.
- #' @import GenABEL
- #' @export
- #'
- CumuPos<-function(scan.gwaa.object) {
- #~~ Extract results and calculate cumulative positions
- gwa_res <- results(scan.gwaa.object)
- gwa_res$Chromosome <- as.character(gwa_res$Chromosome)
- if("X" %in% gwa_res$Chromosome){
- chrvec <- unique(gwa_res$Chromosome)
- if(length(chrvec > 1)){
- chrvec <- as.numeric(chrvec[-which(chrvec == "X")])
- gwa_res$Chromosome[which(gwa_res$Chromosome == "X")] <- max(chrvec) + 1
- }
- }
- gwa_res$Chromosome <- as.numeric(gwa_res$Chromosome)
- gwa_res <- gwa_res[with(gwa_res, order(Chromosome,Position)), ]
- gwa_res$Diff <- c(0,diff(gwa_res$Position))
- gwa_res$Diff[gwa_res$Diff < 0] <- 1
- gwa_res$Cumu <- cumsum(gwa_res$Diff)
- gwa_res$Cumu2 <- gwa_res$Cumu + (25000000 * gwa_res$Chromosome)
- #~~ Plot the uncorrected p-values
- return(gwa_res)
- }
CumuPos.R at commit b4c09a3, no license · at the source
Overview
Abstract
Background: Body Size traits, particularly body weight (BW) and body mass index (BMI) at slaughter age, determine the meat yield and productivity of pigs. These phenotypes are shaped by numerous small-effect polygenes and regulated mostly by non-coding variants. Although genome-wide association studies (GWAS) have identified several loci and candidate functional variants, the regulatory mechanisms and causative genes of most traits remain uncharacterized, which limits the effectiveness of genomic prediction (GP). The purpose of this study was to bridge the gap between association studies and GP by integrating regulatory genomics into the GP framework to enhance prediction accuracy for body size traits.
Results: Using imputation-based GWAS in 1226 Shanxia Long Black pigs, multiple genome-wide significant loci were identified to be associated with BW and BMI. Linkage disequilibrium (LD) analysis, SuSiE fine-mapping, and regulatory modeling with Basenji deep-learning predictions refined these associations to 10 quantitative trait loci (QTLs) with 45 high-confidence candidate functional variants. Integration of chromatin-state annotations and high-throughput chromosome conformation capture (Hi-C) data revealed receptor tissue regulatory architectures; BW-associated variants on Sus scrofa chromosome 2 (SSC2) were enriched for brain regulatory regions, whereas BMI-associated loci showed enhancer activity across adipose, brain, and liver tissues. Multi-omics analyses converged on ZER1, KLHL29, and HAO1 as high-confidence candidate genes, while OR2T27 was a putative candidate on SSC2. In Basenji prediction, several specific candidate variants were identified as a liver enhancer. Incorporating these top-prioritized functional variants into genomic prediction models, GP yielded up to 21% gains in accuracy.
Conclusions: This study dissects the multi-tissue regulatory architecture, identifying functional variants, effector tissues, and target genes underlying porcine BW and BMI. By leveraging these biologically prioritized loci, we established a functionally informed GP framework that enhances prediction accuracy and biological interpretability simultaneously and also offered a scalable strategy for genetic improvement of complex traits in livestock.
Supplementary Information: The online version contains supplementary material available at 10.1186/
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above.
susjoh/wildsim
b4c09a3c2d5c554393cbd4eda31f38b9f32b8a06, 17 January 2018Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
23 files
- R/
CumuPos.R , R, 37 lines - R/
FullGwasPlot.R , R, 103 lines - R/
FullGwasSummary.R , R, 55 lines - R/
FullPpPlot.R , R, 77 lines - R/
RunGCTA.R , R, 26 lines - R/
RunPLINK.R , R, 26 lines - R/
RunQMSim.R , R, 27 lines - R/
RunSED.R , R, 10 lines - R/
createKinshipMatrix.R , R, 21 lines - R/
estimateGenomicHeritabil , R, 69 linesity.R - R/
estimateLD.R , R, 58 lines - R/
estimateQTLEffects.R , R, 226 lines - R/
hello.R , R, 18 lines - R/
multiplot.R , R, 49 lines - R/
parseParamFile.R , R, 39 lines - R/
parseQMSIM.R , R, 228 lines - scenarios/
Scenario_1_full.R , R, 147 lines - scenarios/
Scenario_1_full.sh , Shell, 15 lines - scenarios/
Scenario_3_full.sh , Shell, 14 lines - scenarios/
Scenario_3_test.R , R, 219 lines - scenarios/
Scenario_4_full.R , R, 147 lines - scenarios/
Scenario_4_full.sh , Shell, 14 lines - scenarios/
example.sh , Shell, 17 lines
Tracing map
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Data
Datasets cited
- figshare:33325903, at figshare; found in DataCite
- figshare:33325906, at figshare; found in DataCite
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 13 MeSH terms, 2 funders, 90 references.
Cite
This paper
Yu, N., Cui, D., Xie, L., Tang, X., Xiong, S., Zhang, Y., He, R., Li, L., Xiao, S., & Guo, Y. (2026). Functional genomic dissection and prediction of body size traits in pigs. Genetics, selection, evolution : GSE, 58(1), 55. https://
BibTeX
@article{yu2026functiona
author = {Yu, Naibiao and Cui, Dengshuai and Xie, Lei and Tang, Xi and Xiong, Sanya and Zhang, Yang and He, Ruiqiu and Li, Longyun and Xiao, Shijun and Guo, Yuanmei},
title = {{Functional genomic dissection and prediction of body size traits in pigs}},
journal = {Genetics, selection, evolution : GSE},
year = {2026},
month = aug,
volume = {58},
number = {1},
pages = {55},
publisher = {BMC},
issn = {0999-193X},
doi = {10.1186/
url = {https://
pmid = {42638083},
pmcid = {PMC13501698}
}
RIS
TY - JOUR
AU - Yu, Naibiao
AU - Cui, Dengshuai
AU - Xie, Lei
AU - Tang, Xi
AU - Xiong, Sanya
AU - Zhang, Yang
AU - He, Ruiqiu
AU - Li, Longyun
AU - Xiao, Shijun
AU - Guo, Yuanmei
TI - Functional genomic dissection and prediction of body size traits in pigs
T2 - Genetics, selection, evolution : GSE
J2 - Genet Sel Evol
PY - 2026
DA - 2026/
VL - 58
IS - 1
SP - 55
SN - 0999-193X
PB - BMC
DO - 10.1186/
UR - https://
LA - en
ER -
CSL-JSON
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