基于非线性混合模型的吉兰多牛泌乳曲线基因组预测

IF 0.6 Q4 GENETICS & HEREDITY

Genetics and Molecular Research Pub Date : 2021-01-01 DOI:10.4238/GMR18691

F. R. F. Teixeira, M. Nascimento, P. Cecon, C. D. Cruz, F. F. Silva, A. C. Nascimento, Carolina Azevedo, D. B. D. Marques, M.V.G.B. da Silva, A. Carneiro, D. M. Paixão

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引用次数: 0

摘要

了解奶牛的泌乳曲线对于了解牛奶生产系统中的动物生产至关重要。哺乳曲线的基因组预测代表了畜群中动物产奶量的遗传模式。在此背景下，我们通过全基因组选择(GWS)对泌乳曲线进行基因组预测，以非线性混合效应(NLME)模型估计的参数为基础，表征吉兰多牛泌乳性状的遗传模式。对226只吉兰多动物1822份乳控记录的37673个单核苷酸多态性基因分型数据进行分析。比较了9个NLME模型，以确定最适合的方程。将最佳模型估计的泌乳性状进行GWS分析。

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Research Article Genomic prediction of lactation curves of Girolando cattle based on nonlinear mixed models

Knowledge of lactation curves in dairy cattle is essential for understanding the animal production in milk production systems. Genomic prediction of lactation curves represents the genetic pattern of milk production of the animals in the herd. In this context, we made genomic predictions of lactation curves through genome-wide selection (GWS) to characterize the genetic pattern of lactation traits in Girolando cattle based on parameters estimated by nonlinear mixed effects (NLME) models. Data of 1,822 milk control records from 226 Girolando animals genotyped for 37,673 single nucleotide polymorphisms were analyzed. Nine NLME models were compared to identify the equation with the best fit. The lactation traits estimated by the best model were submitted to GWS analysis,

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来源期刊

Genetics and Molecular Research 生物-生化与分子生物学

CiteScore

1.00

自引率

25.00%

发文量

审稿时长

3 months

期刊介绍： Genetics and Molecular Research (GMR), maintained by the Research Foundation of Ribeirão Preto (Fundação de Pesquisas Científicas de Ribeirão Preto), publishes high quality research in genetics and molecular biology. GMR reflects the full breadth and interdisciplinary nature of this research by publishing outstanding original contributions in all areas of biology. GMR publishes human studies, as well as research on model organisms—from mice and flies, to plants and bacteria. Our emphasis is on studies of broad interest that provide significant insight into a biological process or processes. Topics include, but are not limited to gene discovery and function, population genetics, evolution, genome projects, comparative and functional genomics, molecular analysis of simple and complex genetic traits, cancer genetics, medical genetics, disease biology, agricultural genomics, developmental genetics, regulatory variation in gene expression, pharmacological genomics, evolution, gene expression, chromosome biology, and epigenetics.