Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs

Azevedo, Camila Ferreira; Nascimento, Moysés; Silva, F F; Resende, Marcos Deon Vilela de; Lopes, Paulo Sávio; Guimarães, Simone Eliza Facioni; Glória, Leonardo Siqueira

doi:10.4238/2015.october.9.10

Cited by 11 publications

(16 citation statements)

References 20 publications

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“…Figure 1 shows that the best methodology was BLASSO, and that the second factor, "fat", showed the most accurate value (0.56) of all four factors. Given the higher accuracy, and its practical relevance for the pig industry (Silva et al, 2011;Paixão et al, 2012;Azevedo et al, 2013aAzevedo et al, , 2015, only the second factor ("fat") will be discussed. The results for the other factors showed the same pattern, and are available at http:// www.det.ufv.br/~moyses/links.php.…”

Section: Resultsmentioning

confidence: 99%

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

Teixeira

Nascimento

et al. 2016

Genet. Mol. Res.

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ABSTRACT. The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genomewide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: "weight", "fat", "loin", and "performance". These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

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Section: Resultsmentioning

confidence: 99%

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

Teixeira

Nascimento

et al. 2016

Genet. Mol. Res.

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“…These weights were measured in 345 animals from a F2 outbred population (Brazilian Piau X commercial). More details about this population are found by Azevedo et al [3] and Band et al [4]. …”

Section: Methodsmentioning

confidence: 99%

Regularized quantile regression for SNP marker estimation of pig growth curves

Barroso

Nascimento

et al. 2017

J Animal Sci Biotechnol

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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.

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“…These methodologies guarantee the absence of multicollinearity between the components and a solution for the problems of high dimensionality. In this module, only the inclusion of additive effects is possible, as described by Azevedo, Resende, Silva, Lopes, and Guimarães (2013), Azevedo et al (2014) and Azevedo et al (2015a). The statistical methods and procedures available in this module are described below:…”

Section: Based On Dimensionality Reductionmentioning

confidence: 99%

GenomicLand: Software for genome-wide association studies and genomic prediction

et al. 2019

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GenomicLand is free software intended for prediction and genomic association studies based on the R software. This computational tool has an intuitive interface and supports large genomic databases, without requiring the user to use the command line. GenomicLand is available in English, can be downloaded from the Internet (https://licaeufv.wordpress.com/), and requires the Windows or Linux operating system. The software includes statistical procedures based on mixed models, Bayesian inference, dimensionality reduction and artificial intelligence. Examples of data files that can be processed by GenomicLand are available. The examples are useful to learn about the operation of the modules and statistical procedures.

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Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs

Cited by 11 publications

References 20 publications

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs

Regularized quantile regression for SNP marker estimation of pig growth curves

GenomicLand: Software for genome-wide association studies and genomic prediction

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