Format

Send to

Choose Destination
Genet Sel Evol. 2015 Feb 12;47:6. doi: 10.1186/s12711-015-0087-7.

A comparison of methods for whole-genome QTL mapping using dense markers in four livestock species.

Author information

1
INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. andres.legarra@toulouse.inra.fr.
2
INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. pascal.croiseau@jouy.inra.fr.
3
INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. marie-pierre.sanchez@jouy.inra.fr.
4
INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. steyssedre@ragt.fr.
5
Current address: RAGT-R2n, Le bourg, 12510, Druelle, France. steyssedre@ragt.fr.
6
INRA, UMR1282 Infectiologie et Santé Publique, F-37380, Nouzilly, France. guillaume.salle@tours.inra.fr.
7
Université François Rabelais de Tours, UMR1282 Infectiologie et Santé Publique, 37000, Tours, France. guillaume.salle@tours.inra.fr.
8
Agrocampus Ouest, UMR1348 Pegase, F-35000, Rennes, France. sophie.allais@agrocampus-ouest.fr.
9
INRA, UMR1348 Pegase, F-35590, Saint-Gilles, France. sophie.allais@agrocampus-ouest.fr.
10
Université Européenne de Bretagne, Rennes, France. sophie.allais@agrocampus-ouest.fr.
11
UNCEIA, Genetics Team, 75595, Paris, France. sebastien.fritz@jouy.inra.fr.
12
INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. carole.moreno@toulouse.inra.fr.
13
INRA, UMR 1313 GABI, Domaine de Vilvert, 78352, Jouy-en-Josas, France. anne.ricard@toulouse.inra.fr.
14
Recherche et Innovation, IFCE, 61310 Exmes, Paris, France. anne.ricard@toulouse.inra.fr.
15
INRA, UMR 1388 GenPhySE, BP52627, 31326, Castanet Tolosan, France. jean-michel.elsen@toulouse.inra.fr.

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.

PMID:
25885597
PMCID:
PMC4324410
DOI:
10.1186/s12711-015-0087-7
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center