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Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits

Overview of attention for article published in BMC Genetics, April 2018
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Title
Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits
Published in
BMC Genetics, April 2018
DOI 10.1186/s12863-018-0609-8
Pubmed ID
Authors

Julia Piaskowski, Craig Hardner, Lichun Cai, Yunyang Zhao, Amy Iezzoni, Cameron Peace

Abstract

Sweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years. High broad-sense heritabilities of 0.83, 0.77, and 0.76 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic model resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance. Predicted breeding values and genetic values revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.

Twitter Demographics

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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 %
Student > Ph. D. Student 7 29%
Student > Master 5 21%
Researcher 3 13%
Student > Bachelor 2 8%
Professor 1 4%
Other 2 8%
Unknown 4 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 71%
Business, Management and Accounting 1 4%
Engineering 1 4%
Unknown 5 21%

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 14 February 2019.
All research outputs
#8,060,096
of 14,322,164 outputs
Outputs from BMC Genetics
#368
of 928 outputs
Outputs of similar age
#136,581
of 276,853 outputs
Outputs of similar age from BMC Genetics
#1
of 1 outputs
Altmetric has tracked 14,322,164 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 928 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 57% 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 276,853 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them