Regularized quantile regression for SNP marker estimation of pig growth curves

L.M.A.Barroso; P.S.Lopes; S.E.F.Guimar(a)es; M.Nascimento; A.C.C.Nascimento; F.F.Silva; N.V.L.Ser(a)o; C.D.Cruz; M.D.V.Resende; F.L.Silva; C.F.Azevedo

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Regularized quantile regression for SNP marker estimation of pig growth curves

机译：用于猪生长曲线SNP标记估计的正则分位数回归

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Background: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).Results: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.Conclusions;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.

机译：背景：基因组生长曲线通常仅以总体均值定义;在生长曲线的基因组分析中尚未开发的另一种方法是分位数回归（Quanttile Regression，QR）。我们的目的是为猪生长曲线的SNP标记效应估计提出正则的分位数回归并进行评估，并确定最相关标记的染色体区域并估计随时间变化的遗传个体体重轨迹（结果：正则化分位数回归（RQR）能够在不同的关注水平（分位数）发现最相关的标记，从而鉴定QTL区域。我们发现了相同的结果同时影响不同生长曲线参数（成熟体重和成熟率）的相关标记：两个（ALGA0096701和ALGA0029483）用于RQR（0.2），RQR（0.5）的一个（ALGA0096701）和RQR（0.8）的一个（ALGA0003761）。获得了三条平均基因组生长曲线，并用分位数0.2的曲线解释了行为，这与其他分位数不同结论; RQR允许构建基因组生长曲线，这是确定和选择最理想的动物进行育种的关键。此外，该模型使我们能够找到不同兴趣水平（分位数）的最大动物每个性状的相关标记（生长曲线参数估计）及其各自的染色体位置（识别猪生长曲线的新QTL区域）这些标记可以在标记辅助选择的背景下加以利用，同时旨在改变猪的生长形状曲线。

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《畜牧与生物技术杂志(英文版)》 |2017年第4期|824-832|共9页
作者
L.M.A.Barroso; P.S.Lopes; S.E.F.Guimar(a)es; M.Nascimento; A.C.C.Nascimento; F.F.Silva; N.V.L.Ser(a)o; C.D.Cruz; M.D.V.Resende; F.L.Silva; C.F.Azevedo;
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Department of Statistics, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Animal Science, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Animal Science, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Statistics, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Statistics, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Animal Science, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Animal Science, Iowa State University, Kildee Hall 50011 Ames, Iowa, USA;

Department of General Biology, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Statistics, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Embrapa Forestry, Estrada da Ribeira, km 111,Colombo, PR, Brazil;

Department of Plant Science, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

Department of Statistics, Federal University of Vi(c)osa, Av.P H Rolfs, s/n, University Campus, Vi(c)osa, MG 36570-000, Brazil;

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Regularized quantile regression for SNP marker estimation of pig growth curves

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