ArticleGenetics, selection, evolution : GSE2013
QTL fine mapping with Bayes C(π): a simulation study.
Article in Genetics, selection, evolution : GSE, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Enhancing Genomic Prediction Accuracy with a Single-Step Genomic Best Linear Unbiased Prediction Model Integrating Genome-Wide Association Study Results.Animals : an open access journal from MDPI · 2025Article
- Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study.Genetics, selection, evolution : GSE · 2023Article
- Accuracy of genomic prediction of maternal traits in pigs using Bayesian variable selection methods.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2022Article
- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- Article
- Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.Journal of animal science · 2020Article
- Genomics-assisted breeding for ear rot resistances and reduced mycotoxin contamination in maize: methods, advances and prospects.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Review
- SNP and Haplotype-Based Genomic Selection of Quantitative Traits inPlants (Basel, Switzerland) · 2019Article
- Genome Association Study for Visual Scores in Nellore Cattle Measured at Weaning.BMC genomics · 2019Article
- Article
- Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows.Scientific reports · 2018Article
- Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture.Genetics, selection, evolution : GSE · 2017Article
- Assessing the value of phenotypic information from non-genotyped animals for QTL mapping of complex traits in real and simulated populations.BMC genetics · 2016Article
- Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population.BMC genomics · 2015Article
- Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle.BMC genomics · 2014Article
- High-resolution association mapping of number of teats in pigs reveals regions controlling vertebral development.BMC genomics · 2014Article
- Concordance analysis for QTL detection in dairy cattle: a case study of leg morphology.Genetics, selection, evolution : GSE · 2014Article
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3 authors.
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Abstract
backgroundAccurate QTL mapping is a prerequisite in the search for causative mutations. Bayesian genomic selection models that analyse many markers simultaneously should provide more accurate QTL detection results than single-marker models. Our objectives were to (a) evaluate by simulation the influence of heritability, number of QTL and number of records on the accuracy of QTL mapping with Bayes Cπ and Bayes C; (b) estimate the QTL status (homozygous vs. heterozygous) of the individuals analysed. This study focussed on the ten largest detected QTL, assuming they are candidates for further characterization.
methodsOur simulations were based on a true dairy cattle population genotyped for 38,277 phased markers. Some of these markers were considered biallelic QTL and used to generate corresponding phenotypes. Different numbers of records (4387 and 1500), heritability values (0.1, 0.4 and 0.7) and numbers of QTL (10, 100 and 1000) were studied. QTL detection was based on the posterior inclusion probability for individual markers, or on the sum of the posterior inclusion probabilities for consecutive markers, estimated using Bayes C or Bayes Cπ. The QTL status of the individuals was derived from the contrast between the sums of the SNP allelic effects of their chromosomal segments.
resultsThe proportion of markers with null effect (π) frequently did not reach convergence, leading to poor results for Bayes Cπ in QTL detection. Fixing π led to better results. Detection of the largest QTL was most accurate for medium to high heritability, for low to moderate numbers of QTL, and with a large number of records. The QTL status was accurately inferred when the distribution of the contrast between chromosomal segment effects was bimodal.
conclusionsQTL detection is feasible with Bayes C. For QTL detection, it is recommended to use a large dataset and to focus on highly heritable traits and on the largest QTL. QTL statuses were inferred based on the distribution of the contrast between chromosomal segment effects.
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