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Regularized quantile regression for SNP marker estimation of pig growth curves

Overview of attention for article published in Journal of Animal Science and Biotechnology, July 2017
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Title
Regularized quantile regression for SNP marker estimation of pig growth curves
Published in
Journal of Animal Science and Biotechnology, July 2017
DOI 10.1186/s40104-017-0187-z
Pubmed ID
Authors

L. M. A. Barroso, M. Nascimento, A. C. C. Nascimento, F. F. Silva, N. V. L. Serão, C. D. Cruz, M. D. V. Resende, F. L. Silva, C. F. Azevedo, P. S. Lopes, S. E. F. Guimarães

Abstract

Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 21%
Student > Master 3 21%
Student > Doctoral Student 2 14%
Professor 1 7%
Professor > Associate Professor 1 7%
Other 0 0%
Unknown 4 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 36%
Biochemistry, Genetics and Molecular Biology 2 14%
Engineering 1 7%
Unknown 6 43%
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 08 August 2017.
All research outputs
#20,660,571
of 25,382,440 outputs
Outputs from Journal of Animal Science and Biotechnology
#657
of 904 outputs
Outputs of similar age
#250,883
of 324,855 outputs
Outputs of similar age from Journal of Animal Science and Biotechnology
#16
of 20 outputs
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