title: A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species.
abstract: BACKGROUND
  With 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.
  
  
  RESULTS
  For 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.
  
  
  CONCLUSIONS
  All 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.
authors.affiliation: INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. andres.legarra@toulouse.inra.fr.
authors.forename: Andres
authors.initials: A
authors.lastname: Legarra
authors.affiliation: INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. pascal.croiseau@jouy.inra.fr.
authors.forename: Pascal
authors.initials: P
authors.lastname: Croiseau
authors.affiliation: INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. marie-pierre.sanchez@jouy.inra.fr.
authors.forename: Marie Pierre
authors.initials: MP
authors.lastname: Sanchez
authors.affiliation: INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. steyssedre@ragt.fr.|Current address: RAGT-R2n, Le bourg, 12510, Druelle, France. steyssedre@ragt.fr.
authors.forename: Simon
authors.initials: S
authors.lastname: Teyssèdre
authors.affiliation: INRA, UMR1282 Infectiologie et Santé Publique, F-37380, Nouzilly, France. guillaume.salle@tours.inra.fr.|Université François Rabelais de Tours, UMR1282 Infectiologie et Santé Publique, 37000, Tours, France. guillaume.salle@tours.inra.fr.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Sallé
authors.affiliation: Agrocampus Ouest, UMR1348 Pegase, F-35000, Rennes, France. sophie.allais@agrocampus-ouest.fr.|INRA, UMR1348 Pegase, F-35590, Saint-Gilles, France. sophie.allais@agrocampus-ouest.fr.|Université Européenne de Bretagne, Rennes, France. sophie.allais@agrocampus-ouest.fr.
authors.forename: Sophie
authors.initials: S
authors.lastname: Allais
authors.affiliation: UNCEIA, Genetics Team, 75595, Paris, France. sebastien.fritz@jouy.inra.fr.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Fritz
authors.affiliation: INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. carole.moreno@toulouse.inra.fr.
authors.forename: Carole Rénée
authors.initials: CR
authors.lastname: Moreno
authors.affiliation: INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. anne.ricard@toulouse.inra.fr.|Recherche et Innovation, IFCE, 61310 Exmes, Paris, France. anne.ricard@toulouse.inra.fr.
authors.forename: Anne
authors.initials: A
authors.lastname: Ricard
authors.affiliation: INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. jean-michel.elsen@toulouse.inra.fr.
authors.forename: Jean-Michel
authors.initials: JM
authors.lastname: Elsen
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002417:Cattle / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods*; D008040:Genetic Linkage; D005819:Genetic Markers; D016678:Genome; D005838:Genotype; D006239:Haplotypes / Q000235:genetics; D006736:Horses / Q000235:genetics; D015810:Linkage Disequilibrium; D058751:Livestock / Q000235:genetics*; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci / Q000235:genetics*; D012756:Sheep / Q000235:genetics; D034421:Sus scrofa / Q000235:genetics
chemical_list: D005819:Genetic Markers