Regularized quantile regression for SNP marker estimation of pig growth curves

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.

Quantile Regression Applied to Genome-Enabled Prediction of Traits Related to Flowering Time in the Common Bean

“…Therefore, our results indicate that to improve predictive ability, an effective strategy is to evaluate all of the phenotypic distributions so as to choose the "best" quantile fit model. In addition, the heterogeneous variance that is frequently observed in high dimensional data sets suggests that a single slope is not able to characterize changes over the probability distribution, therefore indicating that RQR is a good tool to deal with those situations [29].…”

“…Unlike the traditional single-SNP GWAS model, the QR methodology was able to find SNP-trait associations considering one extreme quantile (τ = 0.1). Barroso et al [29] successfully used RQR for the 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 under different quantiles. However, to the best of our knowledge, reports in the literature about the use of QR for GS are limited.…”

Quantile regression in genomic selection for oligogenic traits in autogamous plants: A simulation study

“…To deal with dimensionality problems in GWS studies, which are common in the marker matrix, Li and Zhu [26] proposed the Regularized Quantile Regression (RQR). The use of RQR in a GWS study was proposed by Nascimento et al [27], in order to estimate GEBV for different quantiles of the phenotype of interest [28,29]. In their study, Nascimento et al [27] used RQR to estimate GEBV from simulated data with scenarios with different skewness levels in the phenotype distribution.…”

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

“…In a genomic association and prediction analysis, Barroso et al (2017) used RQR to estimate single nucleotide polymorphism (SNP) marker effects, considering as traits pig growth curve parameters. The authors identified the genomic regions that showed the most relevant marker effects and also estimated the genetic individual growth trajectory of the animals over time (genomic growth curves) under different quantiles.…”