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Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle

Overview of attention for article published in BMC Genomics, May 2017
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Title
Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle
Published in
BMC Genomics, May 2017
DOI 10.1186/s12864-017-3821-4
Pubmed ID
Authors

Jicai Jiang, Botong Shen, Jeffrey R. O’Connell, Paul M. VanRaden, John B. Cole, Li Ma

Abstract

Although genome-wide association and genomic selection studies have primarily focused on additive effects, dominance and imprinting effects play an important role in mammalian biology and development. The degree to which these non-additive genetic effects contribute to phenotypic variation and whether QTL acting in a non-additive manner can be detected in genetic association studies remain controversial. To empirically answer these questions, we analyzed a large cattle dataset that consisted of 42,701 genotyped Holstein cows with genotyped parents and phenotypic records for eight production and reproduction traits. SNP genotypes were phased in pedigree to determine the parent-of-origin of alleles, and a three-component GREML was applied to obtain variance decomposition for additive, dominance, and imprinting effects. The results showed a significant non-zero contribution from dominance to production traits but not to reproduction traits. Imprinting effects significantly contributed to both production and reproduction traits. Interestingly, imprinting effects contributed more to reproduction traits than to production traits. Using GWAS and imputation-based fine-mapping analyses, we identified and validated a dominance association signal with milk yield near RUNX2, a candidate gene that has been associated with milk production in mice. When adding non-additive effects into the prediction models, however, we observed little or no increase in prediction accuracy for the eight traits analyzed. Collectively, our results suggested that non-additive effects contributed a non-negligible amount (more for reproduction traits) to the total genetic variance of complex traits in cattle, and detection of QTLs with non-additive effect is possible in GWAS using a large dataset.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 20%
Student > Ph. D. Student 8 15%
Student > Master 8 15%
Student > Doctoral Student 5 9%
Student > Postgraduate 2 4%
Other 4 7%
Unknown 16 30%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 54%
Biochemistry, Genetics and Molecular Biology 3 6%
Social Sciences 2 4%
Veterinary Science and Veterinary Medicine 1 2%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 17 31%
Attention Score in Context

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 02 June 2017.
All research outputs
#15,907,007
of 24,226,848 outputs
Outputs from BMC Genomics
#6,404
of 10,925 outputs
Outputs of similar age
#193,054
of 319,908 outputs
Outputs of similar age from BMC Genomics
#137
of 216 outputs
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So far Altmetric has tracked 10,925 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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