Evidence map›Paper›PMID 28702191›Full record

ArticleJournal of animal science and biotechnology2017

Regularized quantile regression for SNP marker estimation of pig growth curves.

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 and 1 more

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In one paragraph

Article in Journal of animal science and biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

L M A BarrosoDepartment of Statistics, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
M NascimentoDepartment of Statistics, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
A C C NascimentoDepartment of Statistics, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
F F SilvaDepartment of Animal Science, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
N V L SerãoDepartment of Animal Science, Iowa State University, Kildee Hall 50011 Ames, Iowa, USA.
C D CruzDepartment of General Biology, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
M D V ResendeDepartment of Statistics, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
F L SilvaDepartment of Plant Science, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
C F AzevedoDepartment of Statistics, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
P S LopesDepartment of Animal Science, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.
S E F GuimarãesDepartment of Animal Science, Federal University of Viçosa, Av. P H Rolfs, s/n, University Campus, Viçosa, MG 36570-000 Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenomic 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).

resultsThe 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.

conclusionsRQR 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.

Indexed as

Genome associationGrowth curvePigQTLRegularized quantile regression

Identifiers

PMID28702191
PMCPMC5504997

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.