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基因组信息的维度以及不同家畜物种的“已证实和年轻个体算法”的性能。

Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species.

作者信息

Pocrnic Ivan, Lourenco Daniela A L, Masuda Yutaka, Misztal Ignacy

机构信息

Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.

出版信息

Genet Sel Evol. 2016 Oct 31;48(1):82. doi: 10.1186/s12711-016-0261-6.

Abstract

BACKGROUND

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 ). 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 for different species.

METHODS

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.

RESULTS

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 was estimated based on comparisons of eigenvalue decomposition in a simulation study. Assuming a genome length of 30 Morgan, N was equal to 149 for Holsteins, 101 for Jerseys, 113 for Angus, 32 for pigs, and 44 for broilers.

CONCLUSIONS

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 .

摘要

背景

基因组关系矩阵(GRM)可通过对少量核心动物进行递归,利用已验证和年轻动物算法(APY)高效求逆。核心动物的数量在理论上与有效种群大小(N )相关。在一项模拟研究中,核心动物的最佳数量等于GRM中解释其98%变异的最大特征值数量。本研究的目的是找出不同物种的核心动物最佳数量并估计N 。

方法

数据集包括荷斯坦奶牛、泽西奶牛、安格斯牛、猪和肉鸡群体的表型、系谱和基因型。基因分型动物的数量从肉鸡的15,000头到荷斯坦奶牛的77,000头不等,用于基因组预测的单核苷酸多态性数量从37,000个到61,000个不等。对每个群体的GRM进行特征值分解,确定对应于90%、95%、98%和99%变异的最大特征值数量。

结果

对应于90%(98%)变异的特征值数量,荷斯坦奶牛为4527(14,026),泽西奶牛为3325(11,500),安格斯牛为3654(10,605),猪为1239(4103),肉鸡为1655(4171)。对每个物种的每个性状,使用GRM的APY逆矩阵并随机选择核心动物进行分析,核心动物数量等于最大特征值数量。实际准确性在核心动物数量对应于荷斯坦和泽西奶牛98%变异时达到峰值,其他品种/物种则更接近99%变异。基于模拟研究中特征值分解的比较估计N 。假设基因组长度为30摩根,荷斯坦奶牛的N 等于149,泽西奶牛为101,安格斯牛为113,猪为32,肉鸡为44。

结论

常见物种GRM的特征值分布与模拟研究中的相似,尽管受基因分型动物数量和基因分型质量影响。对于所有研究的物种,APY所需的核心动物数量少于15,000头。使用APY逆矩阵的实际准确性与常规求逆相等或更高。GRM的特征值分析可以对N 进行实际估计。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/692c/5088690/c4e39bf6327e/12711_2016_261_Fig1_HTML.jpg

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