结合GWAS先验标记信息的肉鸡RFI性状全基因组选择研究

doi:10.11843/j.issn.0366-6964.2022.10.013

摘要/Abstract

摘要： 旨在比较结合全基因组关联分析（genome-wide association study，GWAS）先验标记信息的基因组育种值（genomic estimated breeding value，GEBV）估计与基因组最佳线性无偏预测（genomic best linear unbiased prediction，GBLUP）方法对鸡剩余采食量性状育种值估计的准确性，为提高基因组选择准确性提供理论与技术支持。本研究选用广西金陵花鸡3个世代共2 510个个体作为素材，其中公鸡1 648只，母鸡862只，以42～56日龄期间的剩余采食量（residual feed intake，RFI）为目标性状，将试验群体随机分为两组，其中一组作为先验标记信息发现群体，用于GWAS分析并筛选最显著的top5%、top10%、top15%和top20%的位点作为先验标记信息；另外一组分别结合不同的先验标记信息进行遗传参数估计并比较基因组育种值的预测准确性，使用重复10次的五倍交叉验证法获取准确性，随后两组群体再进行交叉验证。研究结果表明，GBLUP计算RFI的遗传力为0.153，预测准确性为0.387～0.429，结合GWAS先验标记信息的基因组选择方法计算RFI的遗传力为0.139～0.157，预测准确性为0.401～0.448。将GWAS结果中P值最显著的top10%～top15%的SNPs作为先验信息整合至基因组选择模型中可以将RFI的预测准确性提升2.10%～5.17%。

关键词: 基因组选择, 全基因组关联分析, 先验标记信息, 预测准确性

Abstract: This study aimed to compare the prediction accuracy for chicken residual feed intake (RFI) trait estimated by genomic estimated breeding value (GEBV) estimates combined with prior marker information from genome-wide association study (GWAS) and genomic best linear unbiased prediction (GBLUP) method, and provide theoretical and technical support for improving the accuracy of genomic selection. In this study, 2 510 individuals from 3 generations of Guangxi Jinling flower chicken were selected, including 1 648 roosters and 862 hens, using residual feed intake from 42 to 56 days of age as target trait. The experimental population was randomly divided into two groups, one of which was used as a priori marker information discovery group for GWAS analysis, and the most significant top 5%, top 10%, top 15% and top 20% SNPs were selected as prior marker; in another group, the genetic parameters and prediction accuracy were estimated using different prior marker information. The accuracy was obtained by 10 repetitions of the five-fold cross-validation method, and then the two groups were cross-validated. The results showed that the heritability of RFI calculated by GBLUP was 0.153, and the prediction accuracy was 0.387-0.429. The heritability of RFI calculated by the genomic selection method combined with GWAS prior marker information was 0.139-0.157, and the prediction accuracy was 0.401-0.448. Incorporating the top 10%-top 15% SNPs with the most significant P values in the GWAS results as priors into the genomic selection model can improve the prediction accuracy of RFI by 2.10%-5.17%.

Key words: genomic selection, GWAS, prior marker information, prediction accuracy

中图分类号:

S831.2

杜永旺, 黄超, 王一东, 李森, 文杰, 陈智武, 赵桂苹, 郑麦青. 结合GWAS先验标记信息的肉鸡RFI性状全基因组选择研究[J]. 畜牧兽医学报, 2022, 53(10): 3403-3411.

DU Yongwang, HUANG Chao, WANG Yidong, LI Sen, WEN Jie, CHEN Zhiwu, ZHAO Guiping, ZHENG Maiqing. Genomic Selection for RFI in Broiler Combining GWAS Prior Marker Information[J]. Acta Veterinaria et Zootechnica Sinica, 2022, 53(10): 3403-3411.

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