ArticleGenetics, selection, evolution : GSE2015
A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species.
Article in Genetics, selection, evolution : GSE, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Bayesian Genome-Wide Association Study of Feed Efficiency Traits in Pigs.Animals : an open access journal from MDPI · 2026Article
- From markers to mechanisms: a comprehensive review of major genes and quantitative trait loci shaping the modern sheep (Frontiers in genetics · 2026Review
- Construction of an ultrahigh-density genetic linkage map for Manihot esculenta Crantz and identification of QTL for root quantity traits.BMC plant biology · 2025Article
- Genomic selection for resistance to one pathogenic strain ofFrontiers in genetics · 2024Article
- Identification and Functional Prediction of Circular RNAs Related to Growth Traits and Skeletal Muscle Development in Duroc pigs.Frontiers in genetics · 2022Article
- Genetic × environment variation in sheep lines bred for divergent resistance to strongyle infection.Evolutionary applications · 2021Article
- Efficient Estimation of Marker Effects in Plant Breeding.G3 (Bethesda, Md.) · 2019Article
- Five genomic regions have a major impact on fat composition in Iberian pigs.Scientific reports · 2019Article
- Article
- GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait.G3 (Bethesda, Md.) · 2018Article
- Helitrons and Retrotransposons Are Co-localized inCurrent genomics · 2017Article
- Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens.Genetics, selection, evolution : GSE · 2017Article
- Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis.Genetics, selection, evolution : GSE · 2017Article
- Whole genome structural analysis of Caribbean hair sheep reveals quantitative link to West African ancestry.PloS one · 2017Article
- Unravelling the genomic architecture of bull fertility in Holstein cattle.BMC genetics · 2016Article
- Walking through the statistical black boxes of plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016Review
- Insights into the genetic variation of maternal behavior and suckling performance of continental beef cows.Genetics, selection, evolution : GSE · 2016Article
- Detection of quantitative trait loci for maternal traits using high-density genotypes of Blonde d'Aquitaine beef cattle.BMC genetics · 2016Article
- SNP- and haplotype-based genome-wide association studies for growth, carcass, and meat quality traits in a Duroc multigenerational population.BMC genetics · 2016Article
- Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundWith dense genotyping, many choices exist for methods to detect quantitative trait loci (QTL) in livestock populations. However, no across-species study has been conducted on the performance of different methods using real data. We compared three methods that correct for relatedness either implicitly or explicitly: linkage and linkage disequilibrium haplotype-based analysis (LDLA), efficient mixed-model association (EMMA) analysis, and Bayesian whole-genome regression (BayesC). We analyzed one chromosome in each of five datasets (dairy cattle, beef cattle, sheep, horses, and pigs) using real genotypes based on dense single nucleotide polymorphisms and phenotypes. The P values corrected for multiple testing or Bayes factors greater than 150 were considered to be significant. To complete the real data study, we also simulated quantitative trait loci (QTL) for the same datasets based on the real genotypes. Several scenarios were chosen, with different QTL effects and linkage disequilibrium patterns. A pseudo-null statistical distribution was chosen to make the significance thresholds comparable across methods.
resultsFor the real data, the three methods generally agreed within 1 or 2 cM for the locations of QTL regions and disagreed when no signals were significant (e.g. in pigs). For certain datasets, LDLA had more significant signals than EMMA or BayesC, but they were concentrated around the same peaks. Therefore, the three methods detected approximately the same number of QTL regions. For the simulated data, LDLA was slightly less powerful and accurate than either EMMA or BayesC but this depended strongly on how thresholds were set in the simulations.
conclusionsAll three methods performed similarly for real and simulated data. No method was clearly superior across all datasets or for any particular dataset. For computational efficiency and ease of interpretation, EMMA is recommended, but using more than one method is suggested.
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