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Prediction of Cacao (Theobroma cacao) Resistance to Moniliophthora spp. Diseases via Genome-Wide Association Analysis and Genomic Selection

Overview of attention for article published in Frontiers in Plant Science, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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Title
Prediction of Cacao (Theobroma cacao) Resistance to Moniliophthora spp. Diseases via Genome-Wide Association Analysis and Genomic Selection
Published in
Frontiers in Plant Science, March 2018
DOI 10.3389/fpls.2018.00343
Pubmed ID
Authors

Michel S. McElroy, Alberto J. R. Navarro, Guiliana Mustiga, Conrad Stack, Salvador Gezan, Geover Peña, Widem Sarabia, Diego Saquicela, Ignacio Sotomayor, Gavin M. Douglas, Zoë Migicovsky, Freddy Amores, Omar Tarqui, Sean Myles, Juan C. Motamayor

Abstract

Cacao (Theobroma cacao) is a globally important crop, and its yield is severely restricted by disease. Two of the most damaging diseases, witches' broom disease (WBD) and frosty pod rot disease (FPRD), are caused by a pair of related fungi: Moniliophthora perniciosa and Moniliophthora roreri, respectively. Resistant cultivars are the most effective long-term strategy to address Moniliophthora diseases, but efficiently generating resistant and productive new cultivars will require robust methods for screening germplasm before field testing. Marker-assisted selection (MAS) and genomic selection (GS) provide two potential avenues for predicting the performance of new genotypes, potentially increasing the selection gain per unit time. To test the effectiveness of these two approaches, we performed a genome-wide association study (GWAS) and GS on three related populations of cacao in Ecuador genotyped with a 15K single nucleotide polymorphism (SNP) microarray for three measures of WBD infection (vegetative broom, cushion broom, and chirimoya pod), one of FPRD (monilia pod) and two productivity traits (total fresh weight of pods and % healthy pods produced). GWAS yielded several SNPs associated with disease resistance in each population, but none were significantly correlated with the same trait in other populations. Genomic selection, using one population as a training set to estimate the phenotypes of the remaining two (composed of different families), varied among traits, from a mean prediction accuracy of 0.46 (vegetative broom) to 0.15 (monilia pod), and varied between training populations. Simulations demonstrated that selecting seedlings using GWAS markers alone generates no improvement over selecting at random, but that GS improves the selection process significantly. Our results suggest that the GWAS markers discovered here are not sufficiently predictive across diverse germplasm to be useful for MAS, but that using all markers in a GS framework holds substantial promise in accelerating disease-resistance in cacao.

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X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 113 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 19%
Researcher 15 13%
Student > Bachelor 14 12%
Student > Ph. D. Student 10 9%
Student > Doctoral Student 5 4%
Other 17 15%
Unknown 30 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 38%
Biochemistry, Genetics and Molecular Biology 15 13%
Computer Science 5 4%
Business, Management and Accounting 2 2%
Nursing and Health Professions 2 2%
Other 12 11%
Unknown 34 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 27 April 2018.
All research outputs
#4,107,010
of 24,701,594 outputs
Outputs from Frontiers in Plant Science
#2,098
of 23,532 outputs
Outputs of similar age
#76,710
of 337,172 outputs
Outputs of similar age from Frontiers in Plant Science
#64
of 466 outputs
Altmetric has tracked 24,701,594 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 23,532 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 337,172 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 466 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.