↓ Skip to main content

Evaluation of methods and marker Systems in Genomic Selection of oil palm (Elaeis guineensis Jacq.)

Overview of attention for article published in BMC Genetics, December 2017
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
24 Mendeley
Title
Evaluation of methods and marker Systems in Genomic Selection of oil palm (Elaeis guineensis Jacq.)
Published in
BMC Genetics, December 2017
DOI 10.1186/s12863-017-0576-5
Pubmed ID
Authors

Qi Bin Kwong, Chee Keng Teh, Ai Ling Ong, Fook Tim Chew, Sean Mayes, Harikrishna Kulaveerasingam, Martti Tammi, Suat Hui Yeoh, David Ross Appleton, Jennifer Ann Harikrishna

Abstract

Genomic selection (GS) uses genome-wide markers as an attempt to accelerate genetic gain in breeding programs of both animals and plants. This approach is particularly useful for perennial crops such as oil palm, which have long breeding cycles, and for which the optimal method for GS is still under debate. In this study, we evaluated the effect of different marker systems and modeling methods for implementing GS in an introgressed dura family derived from a Deli dura x Nigerian dura (Deli x Nigerian) with 112 individuals. This family is an important breeding source for developing new mother palms for superior oil yield and bunch characters. The traits of interest selected for this study were fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per palm (O/P) and oil-to-dry mesocarp (O/DM). The marker systems evaluated were simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits. The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods. Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 38%
Student > Ph. D. Student 4 17%
Student > Bachelor 3 13%
Student > Master 2 8%
Unspecified 2 8%
Other 4 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 71%
Unspecified 3 13%
Computer Science 2 8%
Biochemistry, Genetics and Molecular Biology 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 December 2017.
All research outputs
#7,149,110
of 12,889,878 outputs
Outputs from BMC Genetics
#337
of 858 outputs
Outputs of similar age
#170,320
of 385,766 outputs
Outputs of similar age from BMC Genetics
#28
of 87 outputs
Altmetric has tracked 12,889,878 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 858 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 59% 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 385,766 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 87 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.