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使用多性状阈值模型和吉布斯抽样对双胎率和排卵率进行贝叶斯分析。

Bayesian analysis of twinning and ovulation rates using a multiple-trait threshold model and Gibbs sampling.

作者信息

Van Tassell C P, Van Vleck L D, Gregory K E

机构信息

Roman L. Hruska U.S. Meat Animal Research Center, USDA, ARS, Lincoln, NE 68583-0908, USA.

出版信息

J Anim Sci. 1998 Aug;76(8):2048-61.

PMID:9734854
Abstract

The Multiple-Trait Gibbs Sampler for Animal Models programs were extended to allow analysis of ordered categorical data using a Bayesian threshold model. The algorithm is based on data augmentation, where a value on the unobserved underlying normally distributed variable (liability) is generated in each round of iteration for each categorical observation. The programs allow analysis of several continuous and ordered categorical traits. Categorical traits can have any number of response levels. Models can be different for each trait. The programs were used to analyze twinning and ovulation rates from a herd of cattle selected for twinning rate at the U.S. Meat Animal Research Center. Data included number of calves born at each parturition for the lifetime of a cow and number of eggs ovulated for several estrous cycles before first breeding as heifers. A total of 6,411 calvings was recorded for 2,087 cows with 83.2% single and 16.8% multiple births. A total of 19,849 ovulations was recorded for 2,332 heifers with 85.2% single and 14.8% multiple ovulations. Mean posterior estimates of heritability and fraction of variance accounted for by permanent environmental effects (PE) were .128 and .103 for twinning rate and .168 and .079 for ovulation rate. Mean posterior estimate of genetic correlation was .808, and correlation of PE effects was .517. Use of a threshold model could allow for more rapid genetic improvement of the twinning herd through improved identification and selection of genetically superior animals because of higher heritability on the underlying scale.

摘要

用于动物模型的多性状吉布斯采样器程序得到了扩展,以允许使用贝叶斯阈值模型分析有序分类数据。该算法基于数据增强,即在每次迭代中为每个分类观测生成未观测到的潜在正态分布变量(易感性)上的值。这些程序允许分析多个连续和有序分类性状。分类性状可以有任意数量的响应水平。每个性状的模型可以不同。这些程序被用于分析美国肉类动物研究中心为双胎率选择的一群牛的双胎率和排卵率。数据包括一头母牛一生中每次分娩的犊牛数量,以及作为小母牛首次配种前几个发情周期的排卵数量。共记录了2087头母牛的6411次产犊,其中单胎率为83.2%,多胎率为16.8%。共记录了2332头小母牛的19849次排卵,其中单次排卵率为85.2%,多次排卵率为14.8%。双胎率的遗传力和由永久环境效应(PE)解释的方差比例的平均后验估计分别为0.128和0.103,排卵率分别为0.168和0.079。遗传相关的平均后验估计为0.808,PE效应的相关为0.517。使用阈值模型可以通过改进对遗传上优良动物的识别和选择,实现双胎牛群更快速的遗传改良,因为在潜在尺度上具有更高的遗传力。

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