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.
Highlights
Accurate quantitative trait loci (QTL) mapping is a prerequisite in the search for causative mutations
The first step in the identification of causative mutations underlying quantitative traits is to search for genomic regions associated with these traits, called quantitative trait loci (QTL)
The performance of Bayes Cπ and Bayes C was highly affected by heritability, number of QTL and number of records
Summary
Bayesian genomic selection models that analyse many markers simultaneously should provide more accurate QTL detection results than single-marker models. A large variety of models for QTL detection exists, including single-marker models, interval mapping [7] and Bayesian models [8]. Since quantitative traits are likely to be influenced by a large number of QTL, models that van den Berg et al Genetics Selection Evolution 2013, 45:19 http://www.gsejournal.org/content/45/1/19 analyse all markers simultaneously should give more accurate results than models that analyse only one or a few markers at a time. With the Bayesian models developed for genomic selection, a large number of markers can be analysed simultaneously [9]. In a simulation study by Sahana et al [10], Bayesian QTL mapping resulted in higher power and more precise QTL locations than single-marker, haplotype-based or mixed-model approaches. Bayes C has been used to identify QTL for various traits in beef cattle [15,16], the horse [17] and pigs [18,19]
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