Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species.
Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species.
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DOI：
10.1186/s12711-016-0261-6
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发表时间：
2016-10-31
期刊：
影响因子：
--
通讯作者：
Misztal I
中科院分区：
文献类型：
--
作者：
Pocrnic I;Lourenco DA;Masuda Y;Misztal I
通过对少量核心动物进行递归，可以使用 Proven and Young 算法 (APY) 有效地反转基因组关系矩阵 (GRM)。理论上，核心动物的数量与有效种群规模（N e）相关。在模拟研究中，核心动物的最佳数量等于 GRM 最大特征值的数量，该特征值解释了 98% 的变异。本研究的目的是找到核心动物的最佳数量并估计不同物种的 N e 。数据集包括荷斯坦牛、泽西牛和安格斯牛、猪和肉鸡群体的表型、谱系和基因型。基因分型动物的数量从肉鸡的 15,000 只到荷斯坦牛的 77,000 只不等，用于基因组预测的单核苷酸多态性数量从 37,000 到 61,000 不等。每个群体的 GRM 特征值分解确定了对应于 90%、95%、98% 和 99% 变异的最大特征值的数量。对应于 90% (98%) 变异的特征值数量为荷斯坦猪 4527 (14,026)、泽西猪 3325 (11,500)、安格斯猪 3654 (10,605)、猪 1239 (4103) 和肉鸡 1655 (4171)。使用随机选择的核心动物的GRM的APY逆来分析每个物种的每个性状，其数量等于最大特征值的数量。核心动物数量实现的准确度达到峰值，对应于荷斯坦和泽西岛变异的 98%，其他品种/物种的变异接近 99%。 Ne 是根据模拟研究中特征值分解的比较来估计的。假设基因组长度为 30 Morgan，则荷斯坦猪的 N e 等于 149，泽西猪的 N e 等于 101，安格斯猪的 N e 等于 113，猪的 N e 等于猪的 32，肉鸡的 N e 等于 44。常见物种的 GRM 特征值概况与模拟研究中的特征值类似，尽管它们受到基因分型动物数量和基因分型质量的影响。对于所有调查的物种，APY 需要的核心动物少于 15,000 只。 APY 反演所实现的精度等于或高于常规反演的精度。 GRM 的特征值分析可以提供 N e 的实际估计。
A genomic relationship matrix (GRM) can be inverted efficiently with the Algorithm for Proven and Young (APY) through recursion on a small number of core animals. The number of core animals is theoretically linked to effective population size (N e). In a simulation study, the optimal number of core animals was equal to the number of largest eigenvalues of GRM that explained 98% of its variation. The purpose of this study was to find the optimal number of core animals and estimate N e for different species. Datasets included phenotypes, pedigrees, and genotypes for populations of Holstein, Jersey, and Angus cattle, pigs, and broiler chickens. The number of genotyped animals varied from 15,000 for broiler chickens to 77,000 for Holsteins, and the number of single-nucleotide polymorphisms used for genomic prediction varied from 37,000 to 61,000. Eigenvalue decomposition of the GRM for each population determined numbers of largest eigenvalues corresponding to 90, 95, 98, and 99% of variation. The number of eigenvalues corresponding to 90% (98%) of variation was 4527 (14,026) for Holstein, 3325 (11,500) for Jersey, 3654 (10,605) for Angus, 1239 (4103) for pig, and 1655 (4171) for broiler chicken. Each trait in each species was analyzed using the APY inverse of the GRM with randomly selected core animals, and their number was equal to the number of largest eigenvalues. Realized accuracies peaked with the number of core animals corresponding to 98% of variation for Holstein and Jersey and closer to 99% for other breed/species. N e was estimated based on comparisons of eigenvalue decomposition in a simulation study. Assuming a genome length of 30 Morgan, N e was equal to 149 for Holsteins, 101 for Jerseys, 113 for Angus, 32 for pigs, and 44 for broilers. Eigenvalue profiles of GRM for common species are similar to those in simulation studies although they are affected by number of genotyped animals and genotyping quality. For all investigated species, the APY required less than 15,000 core animals. Realized accuracies were equal or greater with the APY inverse than with regular inversion. Eigenvalue analysis of GRM can provide a realistic estimate of N e.
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DOI：
10.1098/rstb.2005.1668
发表时间：
2005-07-29
影响因子：
6.3
作者：
Brotherstone, S;Goddard, M
通讯作者：
Goddard, M
影响因子：
3.5
作者：
Masuda, Y.;Misztal, I.;Lawlor, T. J.
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影响因子：
3.7
作者：
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影响因子：
3.3
作者：
de Roos, A. P. W.;Hayes, B. J.;Goddard, M. E.
通讯作者：
Goddard, M. E.
影响因子：
3.5
作者：
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通讯作者：
Aguilar, I.