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Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers

Overview of attention for article published in BMC Genomic Data, December 2015
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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3 X users
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1 Wikipedia page

Citations

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27 Dimensions

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45 Mendeley
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Title
Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers
Published in
BMC Genomic Data, December 2015
DOI 10.1186/s12863-015-0301-1
Pubmed ID
Authors

Yang Da

Abstract

The amount of functional genomic information has been growing rapidly but remains largely unused in genomic selection. Genomic prediction and estimation using haplotypes in genome regions with functional elements such as all genes of the genome can be an approach to integrate functional and structural genomic information for genomic selection. Towards this goal, this article develops a new haplotype approach for genomic prediction and estimation. A multi-allelic haplotype model treating each haplotype as an 'allele' was developed for genomic prediction and estimation based on the partition of a multi-allelic genotypic value into additive and dominance values. Each additive value is expressed as a function of h - 1 additive effects, where h = number of alleles or haplotypes, and each dominance value is expressed as a function of h(h - 1)/2 dominance effects. For a sample of q individuals, the limit number of effects is 2q - 1 for additive effects and is the number of heterozygous genotypes for dominance effects. Additive values are factorized as a product between the additive model matrix and the h - 1 additive effects, and dominance values are factorized as a product between the dominance model matrix and the h(h - 1)/2 dominance effects. Genomic additive relationship matrix is defined as a function of the haplotype model matrix for additive effects, and genomic dominance relationship matrix is defined as a function of the haplotype model matrix for dominance effects. Based on these results, a mixed model implementation for genomic prediction and variance component estimation that jointly use haplotypes and single markers is established, including two computing strategies for genomic prediction and variance component estimation with identical results. The multi-allelic genetic partition fills a theoretical gap in genetic partition by providing general formulations for partitioning multi-allelic genotypic values and provides a haplotype method based on the quantitative genetics model towards the utilization of functional and structural genomic information for genomic prediction and estimation.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 44 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Master 7 16%
Student > Ph. D. Student 7 16%
Student > Doctoral Student 4 9%
Student > Bachelor 4 9%
Other 7 16%
Unknown 7 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 56%
Biochemistry, Genetics and Molecular Biology 6 13%
Computer Science 2 4%
Engineering 2 4%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 8 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 18 August 2020.
All research outputs
#7,204,326
of 25,371,288 outputs
Outputs from BMC Genomic Data
#242
of 1,204 outputs
Outputs of similar age
#103,347
of 394,029 outputs
Outputs of similar age from BMC Genomic Data
#4
of 39 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 79% 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 394,029 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 73% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.