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Study of the optimum haplotype length to build genomic relationship matrices

Overview of attention for article published in Genetics Selection Evolution, September 2016
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Title
Study of the optimum haplotype length to build genomic relationship matrices
Published in
Genetics Selection Evolution, September 2016
DOI 10.1186/s12711-016-0253-6
Pubmed ID
Authors

Mohammad H. Ferdosi, John Henshall, Bruce Tier

Abstract

As genomic data becomes more abundant, genomic prediction is more routinely used to estimate breeding values. In genomic prediction, the relationship matrix ([Formula: see text]), which is traditionally used in genetic evaluations is replaced by the genomic relationship matrix ([Formula: see text]). This paper considers alternative ways of building relationship matrices either using single markers or haplotypes of different lengths. We compared the prediction accuracies and log-likelihoods when using these alternative relationship matrices and the traditional [Formula: see text] matrix, for real and simulated data. For real data, we built relationship matrices using 50k genotype data for a population of Brahman cattle to analyze three traits: scrotal circumference (SC), age at puberty (AGECL) and weight at first corpus luteum (WTCL). Haplotypes were phased with hsphase and imputed with BEAGLE. The relationship matrices were built using three methods based on haplotypes of different lengths. The log-likelihood was considered to define the optimum haplotype lengths for each trait and each haplotype-based relationship matrix. Based on simulated data, we showed that the inverse of [Formula: see text] matrix and the inverse of the haplotype relationship matrices for methods using one-single nucleotide polymorphism (SNP) phased haplotypes provided coefficients of determination (R(2)) close to 1, although the estimated genetic variances differed across methods. Using real data and multiple SNPs in the haplotype segments to build the relationship matrices provided better results than the [Formula: see text] matrix based on one-SNP haplotypes. However, the optimal haplotype length to achieve the highest log-likelihood depended on the method used and the trait. The optimal haplotype length (7 to 8 SNPs) was similar for SC and AGECL. One of the haplotype-based methods achieved the largest increase in log-likelihood for SC, i.e. from -1330 when using [Formula: see text] to -1325 when using haplotypes with eight SNPs. Building the relationship matrix by using haplotypes that comprise multiple SNPs will increase the accuracy of estimated breeding values. However, the optimum haplotype length that shows the correct relationship among individuals for each trait can be derived from the data.

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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 %
Denmark 1 2%
France 1 2%
Unknown 43 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 27%
Researcher 12 27%
Other 4 9%
Student > Doctoral Student 4 9%
Student > Master 4 9%
Other 3 7%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 60%
Biochemistry, Genetics and Molecular Biology 4 9%
Medicine and Dentistry 2 4%
Mathematics 1 2%
Computer Science 1 2%
Other 3 7%
Unknown 7 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 30 September 2016.
All research outputs
#19,945,185
of 25,374,917 outputs
Outputs from Genetics Selection Evolution
#641
of 822 outputs
Outputs of similar age
#242,617
of 330,432 outputs
Outputs of similar age from Genetics Selection Evolution
#8
of 15 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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