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Mixed model approach for IBD-based QTL mapping in a complex oil palm pedigree

Overview of attention for article published in BMC Genomics, October 2015
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Title
Mixed model approach for IBD-based QTL mapping in a complex oil palm pedigree
Published in
BMC Genomics, October 2015
DOI 10.1186/s12864-015-1985-3
Pubmed ID
Authors

Sébastien Tisné, Marie Denis, David Cros, Virginie Pomiès, Virginie Riou, Indra Syahputra, Alphonse Omoré, Tristan Durand-Gasselin, Jean-Marc Bouvet, Benoît Cochard

Abstract

Elaeis guineensis is the world's leading source of vegetable oil, and the demand is still increasing. Oil palm breeding would benefit from marker-assisted selection but genetic studies are scarce and inconclusive. This study aims to identify genetic bases of oil palm production using a pedigree-based approach that is innovative in plant genetics. A quantitative trait locus (QTL) mapping approach involving two-step variance component analysis was employed using phenotypic data on 30852 palms from crosses between more than 300 genotyped parents of two heterotic groups. Genome scans were performed at parental level by modeling QTL effects as random terms in linear mixed models with identity-by-descent (IBD) kinship matrices. Eighteen QTL regions controlling production traits were identified among a large genetically diversified sample from breeding program. QTL patterns depended on the genetic origin, with only one region shared between heterotic groups. Contrasting effects of QTLs on bunch number and weights reflected the close negative correlation between the two traits. The pedigree-based approach using data from ongoing breeding programs is a powerful, relevant and economic approach to map QTLs. Genetic determinisms contributing to heterotic effects have been identified and provide valuable information for orienting oil palm breeding strategies.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 2%
United Kingdom 1 2%
France 1 2%
Unknown 49 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 37%
Student > Ph. D. Student 9 17%
Student > Master 6 12%
Student > Doctoral Student 5 10%
Other 4 8%
Other 6 12%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 73%
Biochemistry, Genetics and Molecular Biology 5 10%
Psychology 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Computer Science 1 2%
Other 2 4%
Unknown 3 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 October 2015.
All research outputs
#4,416,185
of 6,278,272 outputs
Outputs from BMC Genomics
#3,822
of 5,085 outputs
Outputs of similar age
#123,817
of 195,299 outputs
Outputs of similar age from BMC Genomics
#308
of 371 outputs
Altmetric has tracked 6,278,272 research outputs across all sources so far. This one is in the 26th percentile – i.e., 26% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,085 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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We're also able to compare this research output to 371 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.