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Multiple-trait QTL mapping and genomic prediction for wool traits in sheep

Overview of attention for article published in Genetics Selection Evolution, August 2017
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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 (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

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Title
Multiple-trait QTL mapping and genomic prediction for wool traits in sheep
Published in
Genetics Selection Evolution, August 2017
DOI 10.1186/s12711-017-0337-y
Pubmed ID
Authors

Sunduimijid Bolormaa, Andrew A. Swan, Daniel J. Brown, Sue Hatcher, Nasir Moghaddar, Julius H. van der Werf, Michael E. Goddard, Hans D. Daetwyler

Abstract

The application of genomic selection to sheep breeding could lead to substantial increases in profitability of wool production due to the availability of accurate breeding values from single nucleotide polymorphism (SNP) data. Several key traits determine the value of wool and influence a sheep's susceptibility to fleece rot and fly strike. Our aim was to predict genomic estimated breeding values (GEBV) and to compare three methods of combining information across traits to map polymorphisms that affect these traits. GEBV for 5726 Merino and Merino crossbred sheep were calculated using BayesR and genomic best linear unbiased prediction (GBLUP) with real and imputed 510,174 SNPs for 22 traits (at yearling and adult ages) including wool production and quality, and breech conformation traits that are associated with susceptibility to fly strike. Accuracies of these GEBV were assessed using fivefold cross-validation. We also devised and compared three approximate multi-trait analyses to map pleiotropic quantitative trait loci (QTL): a multi-trait genome-wide association study and two multi-trait methods that use the output from BayesR analyses. One BayesR method used local GEBV for each trait, while the other used the posterior probabilities that a SNP had an effect on each trait. BayesR and GBLUP resulted in similar average GEBV accuracies across traits (~0.22). BayesR accuracies were highest for wool yield and fibre diameter (>0.40) and lowest for skin quality and dag score (<0.10). Generally, accuracy was higher for traits with larger reference populations and higher heritability. In total, the three multi-trait analyses identified 206 putative QTL, of which 20 were common to the three analyses. The two BayesR multi-trait approaches mapped QTL in a more defined manner than the multi-trait GWAS. We identified genes with known effects on hair growth (i.e. FGF5, STAT3, KRT86, and ALX4) near SNPs with pleiotropic effects on wool traits. The mean accuracy of genomic prediction across wool traits was around 0.22. The three multi-trait analyses identified 206 putative QTL across the ovine genome. Detailed phenotypic information helped to identify likely candidate genes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 21%
Student > Ph. D. Student 10 19%
Student > Master 7 13%
Student > Bachelor 4 8%
Student > Doctoral Student 4 8%
Other 9 17%
Unknown 8 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 51%
Biochemistry, Genetics and Molecular Biology 6 11%
Veterinary Science and Veterinary Medicine 3 6%
Social Sciences 2 4%
Economics, Econometrics and Finance 1 2%
Other 3 6%
Unknown 11 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 26 January 2023.
All research outputs
#5,449,088
of 25,382,440 outputs
Outputs from Genetics Selection Evolution
#146
of 821 outputs
Outputs of similar age
#86,516
of 326,133 outputs
Outputs of similar age from Genetics Selection Evolution
#5
of 17 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 821 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 80% 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 326,133 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 72% of its contemporaries.
We're also able to compare this research output to 17 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 70% of its contemporaries.