基于荷斯坦牛群体基因组数据填充软件的准确性比较(Minimac 3与Beagle 5.1)

doi:10.16431/j.cnki.1671-7236.2021.05.017

中国畜牧兽医 ›› 2021, Vol. 48 ›› Issue (5): 1664-1671.doi: 10.16431/j.cnki.1671-7236.2021.05.017

基于荷斯坦牛群体基因组数据填充软件的准确性比较(Minimac 3与Beagle 5.1)

罗汉鹏¹, 窦金焕¹, 安涛¹, 陈少侃², 王雅春¹

1. 中国农业大学动物科学技术学院, 北京 100193;
2. 北京首农畜牧发展有限公司, 北京 100029

收稿日期:2020-11-06 出版日期:2021-05-20 发布日期:2021-05-20
通讯作者: 王雅春 E-mail:wangyachun@cau.edu.cn
作者简介:罗汉鹏(1993-),男,江西南昌人,博士生,研究方向:分子数量遗传学,E-mail:hanpengluo@qq.com
基金资助:
现代农业（奶牛）产业技术体系建设专项资金（CARS-36）；长江学者和创新团队发展计划（IRT_15R62）；农业品种改良提升专项（2130135）

Comparison of Software (Minimac 3 and Beagle 5.1) for Genomic Imputation Using Holstein Cow Population

LUO Hanpeng¹, DOU Jinhuan¹, AN Tao¹, CHEN Shaokan², WANG Yachun¹

1. College of Animal Science and Technology, China Agricultural University, Beijing 100193, China;
2. Beijing Sunlon Livestock Development Co., Ltd., Beijing 100029, China

Received:2020-11-06 Online:2021-05-20 Published:2021-05-20

摘要/Abstract

摘要： 为探究基因组数据填充软件准确性的影响因素和展示填充具体过程，本研究使用两款主要填充软件Beagle 5.1和Minimac 3对奶牛基因组50K芯片数据进行填充至150K，使用个体的填充结果和真实数据进行填充一致性计算，比较两软件的填充准确性和一致性的差异及其主要影响因素。研究结果表明，Minimac 3软件需要使用其他软件进行基因定向后再进行填充，而Beagle 5.1软件可同时进行基因定向和基因组填充。Beagle 5.1与Minimac 3软件填充一致性的相关系数为0.98；Beagle 5.1软件平均填充的准确性（r²）为0.9841，一致性为0.9914，填充准确性与一致性的相关系数为0.39；Minimac 3软件平均填充的准确性为0.9782，一致性为0.9911，填充准确性（r²）和一致性的相关系数为0.36。由于软件计算填充准确性原理问题，填充的准确性（r²）受最小等位基因影响较大。填充的一致性在最小等位基因频率和位点杂合度上升时均呈下降趋势，当位点杂合度>0.6时显著下降（填充一致性低于0.8），但Beagle 5.1软件的填充效果在相同的最小等位基因频率和杂合度下均优于Minimac 3软件。本研究发现填充准确性（r²）受填充位点的杂合度影响较大，而Beagle 5.1软件进行基因组数据填充的准确性更高，基因组数据填充后使用填充一致性作为填充准确性的判断标准可避免删除过多有效填充位点。

关键词: 荷斯坦牛; 群体; 基因组填充; 准确性

Abstract: The aims of current study were to show the process of genomic imputation and investigate the factors affecting accuracy of genomic imputation.The data of 50K panel imputed to 150K for dairy cattle was used to compare accuracy and concordance of imputation for two software (Beagle 5.1 and Minimac 3).Concordance was calculated by cross validation of individuals with imputed data and real data.The target population for imputation should be phased by Minimac 3 and the function of Beagle 5.1 including pre-phasing and imputation.The correlation of concordance between Minimac 3 and Beagle 5.1 were 0.98.For Beagle 5.1,the average of imputation accuracy (r²) and concordance were 0.9841 and 0.9914,respectively,and the correlation between imputation accuracy and concordance was 0.39.For Minimac 3,the average of imputation accuracy (r²) and concordance were 0.9782 and 0.9911,respectively,and the correlation between imputation accuracy (r²) and concordance was 0.36.Imputation accuracy (r²) was associated with minor allele frequency due to the formula for calculating accuracy from the software.With the increasing of minor allele frequency and heterozygosity for makers,the concordance of imputation was decreased.There was a steep decline when heterozygosity was higher than 0.6 (concordance of imputation was lower than 0.8).However, the accuracy of Beagle 5.1 software was better than that of Minimac 3 software under the same minor allele frequency and heterozygosity of imputed site. The accuracy of imputation (r²) was mainly affected by heterozygosity of SNPs and Beagle 5.1 had better performance on imputation accuracy than that of Minimac 3. Using concordance as the accuracy of imputation to select SNPs could avoid losing useful makers for further study.

Key words: Holstein cow; population; genomic imputation; accuracy

中图分类号:

S813.3

罗汉鹏, 窦金焕, 安涛, 陈少侃, 王雅春. 基于荷斯坦牛群体基因组数据填充软件的准确性比较(Minimac 3与Beagle 5.1)[J]. 中国畜牧兽医, 2021, 48(5): 1664-1671.

LUO Hanpeng, DOU Jinhuan, AN Tao, CHEN Shaokan, WANG Yachun. Comparison of Software (Minimac 3 and Beagle 5.1) for Genomic Imputation Using Holstein Cow Population[J]. China Animal Husbandry and Veterinary Medicine, 2021, 48(5): 1664-1671.

参考文献

[1] NAJ A C.Genotype imputation in genome-wide association studies[J].Current Protocols in Human Genetics,2019,102(1):e84.
[2] CLARK A,LI J.Conjuring SNPs to detect association[J].Nature Genetics,2007,39:815-816.
[3] VANRADEN P,WIGGANS G R,TASSELL C,et al.Benefits from cooperation in genomics[J].Interbull Bulletin,2009:67-72.
[4] LI N,STEPHENS M.Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data[J].Genetics,2003,165(4):2213-2233.
[5] SCHEET P,STEPHENS M.A fast and flexible statistical model for large-scale population genotype data:Applications to inferring missing genotypes and haplotypic phase[J].The American Journal of Human Genetics,2006,78(4):629-644.
[6] MARCHINI J,HOWIE B,MYERS S,et al.A new multipoint method for genome-wide association studies by imputation of genotypes[J].Nature Genetics,2007,39(7):906-913.
[7] HOWIE B,FUCHSBERGER C,STEPHENS M,et al.Fast and accurate genotype imputation in genome-wide association studies through pre-phasing[J].Nature Genetics,2012,44(8):955-959.
[8] LI Y,WILLER C J,DING J,et al.MaCH:Using sequence and genotype data to estimate haplotypes and unobserved genotypes[J].Genetic Epidemiology,2010,34(8):816-834.
[9] BROWNING B L,ZHOU Y,BROWNING S R.A one-penny imputed genome from next-generation reference panels[J].The American Journal of Human Genetics,2018,103(3):338-348.
[10] PURCELL S,NEALE B,TODD-BROWN K,et al.PLINK:A tool set for whole-genome association and population-based linkage analyses[J].American Journal of Human Genetics,2007,81(3):559-575.
[11] DANECEK P,AUTON A,ABECASIS G,et al.The variant call format and VCFtools[J].Bioinformatics,2011,27(15):2156-2158.
[12] LOH P,DANECEK P,PALAMARA P F,et al.Reference-based phasing using the haplotype reference consortium panel[J].Nature Genetics,2016,48(11):1443-1448.
[13] POOK T,MAYER M,GEIBEL J,et al.Improving imputation quality in BEAGLE for crop and livestock data[J].Genes Genomes Genetics,2020,10(1):177-188.
[14] WHALEN A,GORJANC G,ROS-FREIXEDES R,et al.Assessment of the performance of hidden Markov models for imputation in animal breeding[J].Genetics Selection Evolution,2018,50(1):44.
[15] WILLIAMS A L,PATTERSON N,GLESSNER J,et al.Phasing of many thousands of genotyped samples[J].The American Journal of Human Genetics,2012,91(2):238-251.
[16] HOWIE B N,DONNELLY P,MARCHINI J.A flexible and accurate genotype imputation method for the next generation of genome-wide association studies[J].PLoS Genetics,2009,5(6):e1000529.
[17] BROWNING S R,BROWNING B L.Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering[J].The American Journal of Human Genetics,2007,81(5):1084-1097.
[18] BOLORMAA S,CHAMBERLAIN A J,KHANSEFID M,et al.Accuracy of imputation to whole-genome sequence in sheep[J].Genetics,Selection,Evolution,2019,51(1):1.
[19] SCHURZ H,MVLLER S J,VAN HELDEN P D,et al.Evaluating the accuracy of imputation methods in a five-way admixed population[J].Front Genetics,2019,10:34.
[20] VENTURA R V,MILLER S P,DODDS K G,et al.Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population[J].Genetics,Selection,Evolution,2016,48(1):71.
[21] WANG X,SU G,HAO D,et al.Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations[J].Journal of Animal Science and Biotechnology,2020,11(1):3.

基于荷斯坦牛群体基因组数据填充软件的准确性比较(Minimac 3与Beagle 5.1)

Comparison of Software (Minimac 3 and Beagle 5.1) for Genomic Imputation Using Holstein Cow Population

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	曾成容, 刘馨, 毕文文, 梅世慧, 何广霞, 张俊杰, 文明, 周碧君, 陈江凤, 姜海波. 嗜水气单胞菌群体感应系统及其抑制剂应用研究进展[J]. 中国畜牧兽医, 2023, 50(5): 1991-2000.
[2]	曾治钦, 娄文琦, 许静漪, 马烨桦, 张永福, 任小丽, 陈少侃, 闫磊, 郭刚, 张震, 王雅春. 泌乳期中国荷斯坦牛转化孕酮浓度的影响因素分析及其表型预测[J]. 中国畜牧兽医, 2023, 50(1): 186-197.
[3]	徐昊祺, 许静漪, 胡丽蓉, 张帆, 罗汉鹏, 张海亮, 师睿, 李想, 刘林, 刘巧香, 郭刚, 王雅春. 中国荷斯坦牛PIK3CB基因多态性及其与繁殖和产奶性状的关联分析[J]. 中国畜牧兽医, 2022, 49(9): 3438-3452.
[4]	鲍麒, 包鹏甲, 马晓明, 吴晓云, 孟光耀, 褚敏, 曹红梅, 郎建英, 安玉峰, 梁春年, 阎萍. 肃南牦牛群体遗传结构、选择信号分析和ROH检测[J]. 中国畜牧兽医, 2022, 49(8): 2992-3005.
[5]	张强, 洛桑顿珠, 巴桑旺堆, 彭阳洋, 俄广鑫, 尼玛次仁, 索朗多吉, 巴多, 旦巴, 鲜莉莉, 旦巴加参, 支张, 平措占堆. 利用微卫星遗传标记评估5个西藏牦牛群体遗传多样性及群体结构[J]. 中国畜牧兽医, 2022, 49(6): 2228-2238.
[6]	赵番番, 王丹, 马砚军, 马新兵, 葛建军, 张晓雪, 张梦华, 胥磊, 姜徽, 桑扎根, 黄锡霞. 非遗传因素对新疆地区荷斯坦奶牛在群寿命的影响[J]. 中国畜牧兽医, 2020, 47(3): 822-827.
[7]	卜李那, 彭业博, 张春媛, 刘洋秀, 赵毅强. 基于祖源分析的猪遗传成分鉴定[J]. 中国畜牧兽医, 2020, 47(2): 544-553.
[8]	赵番番, 葛建军, 李红燕, 张梦华, 魏趁, 王丹, 杜建文, 胥磊, 姜徽, 桑扎根, 黄锡霞, 谭世新. 新疆地区荷斯坦牛乳中体细胞评分对产奶量与乳成分的影响[J]. 中国畜牧兽医, 2019, 46(11): 3323-3331.
[9]	王梦琦, 倪炜, 唐程, 郭佳禾, 张慧敏, 李明勋, 杨章平, 毛永江. 中国荷斯坦牛金黄色葡萄球菌诱导型乳房炎CDH13基因甲基化分析[J]. 《中国畜牧兽医》, 2018, 45(7): 1926-1932.
[10]	史新平, 陈燕, 徐玲, 金生云, 宝金山, 牛红, 王泽昭, 李俊雅, 孙少华. 肉用西门塔尔牛与和牛杂交群体的肉品质分析[J]. 《中国畜牧兽医》, 2018, 45(4): 953-960.
[11]	张娟, 母童, 蔡正云, 顾亚玲, 刘丽元, 杨雪瑶, 温万, 孙东晓. 中国荷斯坦奶牛EEF1D基因的SNPs检测及其与产奶性状的关联分析[J]. 《中国畜牧兽医》, 2018, 45(4): 961-969.
[12]	王丹, 刘奎国, 马那提·哈山, 张梦华, 魏趁, 席艾力·依明, 张超, 姬鹏飞, 黄锡霞. 新疆褐牛与荷斯坦牛犊牛死亡率的影响因素分析[J]. 《中国畜牧兽医》, 2017, 44(5): 1363-1368.
[13]	张志飞, 梁春年, 郭宪, 王宏博, 丁学智, 吴晓云, 褚敏, 裴杰, 拉环, 张国模, 阎萍. 无角牦牛与有角牦牛血液生理、生化指标的比较研究[J]. 《中国畜牧兽医》, 2016, 43(12): 3257-3262.
[14]	孟菲, 黎书长, 杨武宁, 刘伟, 银慧慧, 韦晓洁, 姜源明, 赵武, 覃振华, 孙建华. 铜绿假单胞菌生物膜研究进展[J]. 《中国畜牧兽医》, 2016, 43(11): 2976-2981.
[15]	马力鹏, 黄涛, 卢国昌, 李秀英, 卢振宇, 翟腾蛟, 程悦聪, 冯书堂. 新疆褐牛近交群体的鉴定研究[J]. , 2015, 42(12): 3274-3281.