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

Teixeira, Felipe Roberti; Nascimento, Moysés; Nascimento, Ana Carolina Campana; Silva, F. F e; Cruz, Cosme Damião; Azevedo, Camila Ferreira; Paixão, Débora Martins; Barroso, Laís Mayara Azevedo; Verardo, Lucas Lima; Resende, Marcos Deon Vilela de; Guimarães, Simone Eliza Facioni; Lopes, Paulo Sávio

doi:10.4238/gmr.15028231

Cited by 7 publications

(7 citation statements)

References 22 publications

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“…The estimation predictive ability and accuracy indicate that it is possible to use the “vigor factor” to predict the genetic merit of genotypes. PC values similar to or higher than those obtained by individual analysis, as obtained in our work, have also been observed in pigs and have been efficiently used in the improvement of this species (Teixeira et al 2016 ). On the other hand, the predictive capacity for the trait and the production factor was low.…”

Section: Discussionsupporting

confidence: 87%

“…Paiva et al ( 2019 ), used formed latent variables, identified as pseudo-phenotypes, in the genetic evaluation of broiler chickens under Bayesian structure. Latent variables also allowed to simultaneously study a set of important characters in pigs, for later use in GS Analyses (Teixeira et al 2016 ). For plant species, these studies are still incipient.…”

Section: Introductionmentioning

confidence: 99%

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Factor analysis applied in genomic selection studies in the breeding of Coffea canephora

Paixão

Nascimento

et al. 2022

Euphytica

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Brazil stands out worldwide in the production of coffee. The observed increases in its productivity and morpho agronomic traits are the results of the improvement of several methodologies applied in obtaining improved cultivars, among which the predictive methods of genetic value stand out. These contribute significantly to the selection of higher genotypes, increasing the genetic gain per unit time. In this context, genomic-wide selection (GWS) is a tool that stands out, since it allows predicting the future phenotype of an individual based only on molecular information. Performing joint selection of traits is the interest of most breeding programs, and factor analysis (FA) has been used to assist in this end. The aim of this study was to evaluate the use of FA in the context of GWS, in genotypes of Coffea canephora . It was found that FA was efficient to elucidate the relationships between the traits and generate new variables. The factors formed can assist in the selection, as in addition to allowing joint interpretations, they present good estimates of predictive capacity, heritability and accuracy. Furthermore, high agreement was observed between the individuals selected based on the factors and those selected considering the individual traits. Additionally, it was observed agreement between the top 10% individuals selected based on the “vigor factor” and each variable individually. However, the selection based on “vigor factor” presented individuals with more suitable size from the phytotechnical point of view.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Factor analysis applied in genomic selection studies in the breeding of Coffea canephora

Paixão

Nascimento

et al. 2022

Euphytica

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“…When using the Factor analysis methodology, commonality is a useful measure to determine the proportion of the variance of each characteristic explained by established common factors (Teixeira et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Factor analysis for plant and production variables in Coffea canephora in the Western Amazon

Silva

Barroso

Cruz

et al. 2022

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The evaluation of morphological characters related to the hulled coffee yield subsidizes the selection of Coffea canephora plants that combine a set of favorable traits. However, the greater the number of traits considered, the more difficult the selection process becomes. In this context, multivariate analyzes can be useful to overcome this problem. The aim of this study was to identify, in a set of agronomic traits of Coffea canephora, the determining factors of biological phenomena and use these factors to recognize patterns of diversity and similarity from biological complexes of interest to the breeder. To this, eleven morphological descriptors were evaluated of 130 clones of the botanical varieties Conilon and Robusta and intervarietal hybrids over two crop years in the experimental field of Embrapa, in the municipality of Ouro Preto do Oeste, state of Rondônia (RO). To group the traits, the multivariate technique of Factor Analysis was used. The effect of genotype x year interaction was significant for the eleven traits analyzed. Based on the scree plot, three factors were established. Factors were interpreted as architecture, vigor and grains with a satisfactory percentage of explained variability. The interpretation of the factors highlighted the importance of the Conilon variety to improve the architecture of the Robusta botanical variety. These results show that it is possible to use factor scores to identify varieties and traits that favor higher production of hulled coffee.

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“…The entire described process was performed with the use of cross-validation. As in Teixeira et al (2016), the pig population was divided into three distinct populations: two were used to estimate the marker effects and one to validate the estimates obtained from the prediction equations. The three possible combinations for this situation were used, and the final result refers to the mean values of accuracy.…”

Section: Methodsmentioning

confidence: 99%

Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits

Santos

Nascimento

et al. 2018

Pesq. agropec. bras.

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The objective of this work was to evaluate the use of regularized quantile regression (RQR) to predict the genetic merit of pigs for asymmetric carcass traits, compared with the Bayesian lasso (Blasso) method. The genetic data of the traits carcass yield, bacon thickness, and backfat thickness from a F2 population composed of 345 individuals, generated by crossing animals from the Piau breed with those of a commercial breed, were used. RQR was evaluated considering different quantiles (τ = 0.05 to 0.95). The RQR model used to estimate the genetic merit showed accuracies higher than or equal to those obtained by Blasso, for all studies traits. There was an increase of 6.7 and 20.0% in accuracy when the quantiles 0.15 and 0.45 were considered in the evaluation of carcass yield and bacon thickness, respectively. The obtained results are indicative that the regularized quantile regression presents higher accuracy than the Bayesian lasso method for the prediction of the genetic merit of pigs for asymmetric carcass variables.

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

Cited by 7 publications

References 22 publications

Factor analysis applied in genomic selection studies in the breeding of Coffea canephora

Factor analysis applied in genomic selection studies in the breeding of Coffea canephora

Factor analysis for plant and production variables in Coffea canephora in the Western Amazon

Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits

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