低深度全基因组测序在大白猪繁殖性状基因组选择的应用

doi:10.3969/j.issn.1674-7968.2022.02.011

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摘要随着新一代测序技术的不断改进和测序成本的下降,低深度重测序因兼具经济性和超高密度多态位点,越来越多应用于全基因组关联分析,但低深度重测序用于猪基因组选择较少有报道。本研究对1 097头大白猪(Sus scrofa)的全基因组进行低深度重测序,平均测序深度2.4×。分析结果表明不同批次重测序数据在错配率、比对率方面稳定性较好。随后采用STITCH (Sequencing to Imputation Through Constructing Haplotypes)软件对1×、2.4×测序数据进行基因型检测与填充,分别获得15 506 511和15 994 848个SNP位点,基因型填充准确性分别为99.1%和99.8%。此外,研究发现1×的填充数据与60 K SNP芯片数据(PorcineSNP60 v2)重合或相邻位点达47 421个,显示低深度重测序与SNP芯片数据具有很好的兼容性。最后,采用BLUP (基于系谱)、GBLUP-WGS (基于抽样填充到全基因组水平的标记信息)、GBLUP-snp60 (基于模拟芯片的标记信息)分析方法对验证群体窝总产仔数(total number born, TNB)、窝产活仔数(number born alive, NBA)、窝产健仔数(healthy piglet, HP)、出生窝重(litter weight, LW) 4个繁殖相关性状的育种值进行预测。结果显示除TNB外,其余性状的预测准确性为GBLUP-WGS>GBLUP-snp60>BLUP,提高范围分别为33.5%~218%、16.7%~190%。本研究结果表明低深度全基因组重测序是一种经济可靠的基因分型方法,未来可用于大规模畜禽基因组研究和基因组选择育种实践。

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	李勇
	杨漫漫
	苗泽圃
	沈俊燃
	陈涛
	魏强

关键词 ：低深度全基因组测序, 繁殖性状, 基因组选择, 大白猪

Abstract：With the improvement of next-generation sequencing technology and the decline of sequencing costs, low-depth resequencing is applied to genome-wide association study analysis because of its cost-effective and ultra-high density of polymorphic loci. Nevertheless, research on low-depth resequencing for pig genome selection is still lack. In this study, 1 097 Large White pigs (Sus scrofa) were genotyped by low-coverage resequencing with an average sequencing depth of 2.4×. The results showed that the mismatch rate, mapping rate, actual depth and coverage between different batches were in stability. Then, the STITCH (Sequencing to Imputation Through Constructing Haplotypes) software was used for genotype calling and imputation from 1× and 2.4× re-sequencing data, and 15 506 511, 15 994 848 SNPs with 99.1% and 99.8% imputation accuracy were identified, respectively. In addition, overlapping or adjacent SNPs between 1× imputation data and 60K SNP chip (PorcineSNP60 v2) reached 47 421, indicating that low-depth resequencing had good compatibility with SNP chips, Further, BLUP (BLUP based on pedigree), GBLUP-WGS (GBLUP based on imputation from whole genome sequencing data), GBLUP-snp60 (GBLUP based on 60K SNP chip data) were used to predict the breeding values of 4 reproductive traits, including total number born (TNB), number born alive (NBA), healthy piglets (HP) and litter weight (LW) in the validation population. The results showed that except TNB, the prediction accuracy of other traits was GBLUP-WGS>GBLUP-snp60>BLUP, and the improvement range was 33.5%~218%, 16.7%~190%, corresponding to comparison between GBLUP-WGS and BLUP as well as between GBLUP-snp60 and BLUP. This results indicate that low-depth whole-genome sequencing offers a reliable and cost-effective method for large-scale livestock and poultry genome research and genome selection breeding.

Key words： Low-depth whole genome sequencing Reproductive traits Genomic selection Large White pig

收稿日期: 2021-04-30

ZTFLH:

S813.3

基金资助:广东省重点领域研发计划项目(2018B020203002); 深圳市科技计划项目(JCYJ20180307163440037)

通讯作者: *liyong3@genomics.cn

引用本文:

李勇, 杨漫漫, 苗泽圃, 沈俊燃, 陈涛, 魏强. 低深度全基因组测序在大白猪繁殖性状基因组选择的应用[J]. 农业生物技术学报, 2022, 30(2): 325-334.
LI Yong, YANG Man-Man, MIAO Ze-Pu, SHEN Jun-Ran, CHEN Tao, WEI Qiang. Application of Low-depth Whole Genome Sequencing in Genomic Selection of Reproductive Traits in Large White Pigs (Sus scrofa). 农业生物技术学报, 2022, 30(2): 325-334.

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http://journal05.magtech.org.cn/Jwk_ny/CN/10.3969/j.issn.1674-7968.2022.02.011 或 http://journal05.magtech.org.cn/Jwk_ny/CN/Y2022/V30/I2/325

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