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Journal of Integrative Agriculture  2026, Vol. 25 Issue (10): 4383-4386    DOI: 10.1016/j.jia.2024.12.009
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Benchmarking 24 combinations of genotype pre-phasing and imputation tools for SNP arrays in pigs
Haonan Zeng, Kaixuan Guo, Zhanming Zhong, Jinyan Teng, Zhiting Xu, Chen Wei, Shaolei Shi, Zhe Zhang, Yahui Gao#
State Key Laboratory of Swine and Poultry Breeding Industry/National Engineering Research Center for Breeding Swine Industry/Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China

 Highlights 

● A total of 24 combinations of imputation tools for pigs were evaluated.

● Beagle-Minimac combination provided the best imputation accuracy.

● Beagle-Beagle combination standed out for convenience.

● Eagle-pbwt combination showed excellent performance in resource efficiency.

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摘要  

基因型填充可在不增加基因型检测成本的前提下提高标记密度,有利于最大化使用现有SNP芯片数据开展复杂性状的机制解析与遗传评估。在动物育种领域,准确的基因组数据对于基因组选择、关联研究以及育种预测至关重要。尽管有多种基因型填充软件可供选择,但在猪基因组研究中仍缺乏全面的基准测试。本研究基于PigGTEx项目的1602头多品种猪参考面板(PGRP)的全基因组测序数据,选用六种预定相软件(fastPHASE、MaCH、BIMBAM、Eagle、SHAPEIT和Beagle)以及四种填充软件(pbwt、Minimac、IMPUTE和Beagle)进行两两组合,对比了24种基因型填充软件组合在猪SNP芯片的应用效果。结果表明,使用Beagle进行预定相、Minimac进行填充的组合能够达到最高的填充准确性,其平均基因型一致性为0.983,特别是在处理低频SNP(MAF<0.05)时表现尤为出色。在资源利用效率方面,pbwt在四款填充软件中表现优异,体现在运行时间最短和内存占用量最少。基于评估结果,本研究提出三种基因型填充组合策略:1)Beagle与 Minimac组合。该组合能够获得最高的填充准确性;2)Beagle与Beagle 组合。虽然使用Beagle进行预相位和填充需要较大的内存,但它因操作简便且仍能保持较高的填充准确性而被广泛认可;3)Eagle与pbwt组合。该组合以计算成本最低且准确性相对较高为特点,适合计算资源有限的场景。综上所述,本研究为基因型填充技术在猪SNP芯片向全基因组水平填充中的应用提供了重要依据,并为畜禽的精准育种提供了理论支持。



Received: 29 June 2024   Accepted: 18 October 2024 Online: 10 December 2024  
Fund: This study was supported by the National Key R&D Program of China (2022YFF1000900), the earmarked fund for China Agriculture Research System (CARS-35), the Guangxi Science and Technology Program Project, China (GuikeJB23023003), the Local Innovative and Research Teams Project of Guangdong Province, China (2019BT02N630), the Dedicated Funds for the Construction of Key Disciplines in Targeted Universities, China (2023B10564001), and the Young Scientists Fund of the National Natural Science Foundation of China (32402714).
About author:  Haonan Zeng, E-mail: hnzeric@hotmail.com; #Correspondence Yahui Gao, E-mail: yahui.gao@scau.edu.cn

Cite this article: 

Haonan Zeng, Kaixuan Guo, Zhanming Zhong, Jinyan Teng, Zhiting Xu, Chen Wei, Shaolei Shi, Zhe Zhang, Yahui Gao. 2026. Benchmarking 24 combinations of genotype pre-phasing and imputation tools for SNP arrays in pigs. Journal of Integrative Agriculture, 25(10): 4383-4386.

Abdellaoui A, Yengo L, Verweij K J H, Visscher P M. 2023. 15 years of GWAS discovery: Realizing the promise. The American Journal of Human Genetics, 110, 179–194.

Browning B L, Tian X, Zhou Y, Browning S R. 2021. Fast two-stage phasing of large-scale sequence data. The American Journal of Human Genetics, 108, 1880–1890.

Browning B L, Zhou Y, Browning S R. 2018. A one-penny imputed genome from next-generation reference panels. The American Journal of Human Genetics, 103, 338–348.

Das S, Forer L, Schönherr S, Sidore C, Locke A E, Kwong A, Vrieze S I, Chew E Y, Levy S, McGue M, Schlessinger D, Stambolian D, Loh P R, Iacono W G, Swaroop A, Scott L J, Cucca F, Kronenberg F, Boehnke M, Abecasis G R, et al. 2016. Next-generation genotype imputation service and methods. Nature Genetics, 48, 1284–1287.

Delaneau O, Zagury J F, Robinson M R, Marchini J L, Dermitzakis E T. 2019. Accurate, scalable and integrative haplotype estimation. Nature Communications, 10, 5436.

Desta Z A, Ortiz R. 2014. Genomic selection: Genome-wide prediction in plant improvement. Trends in Plant Science, 19, 592–601.

Ding R, Savegnago R, Liu J, Long N, Tan C, Cai G, Zhuang Z, Wu J, Yang M, Qiu Y, Ruan D, Quan J, Zheng E, Yang H, Li Z, Tan S, Bedhane M, Schnabel R, Steibel J, Gondro C, et al. 2023. The SWine IMputation (SWIM) haplotype reference panel enables nucleotide resolution genetic mapping in pigs. Communications Biology, 6, 577.

Druet T, Macleod I M, Hayes B J. 2014. Toward genomic prediction from whole-genome sequence data: Impact of sequencing design on genotype imputation and accuracy of predictions. Heredity, 112, 39–47.

Durbin R. 2014. Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT). Bioinformatics, 30, 1266–1272.

Han J, van Hylckama Vlieg A, Rosendaal F R. 2023. Genomic science of risk prediction for venous thromboembolic disease: Convenient clarification or compounding complexity. Journal of Thrombosis and Haemostasis, 21, 3292–3303.

Li Y, Bai X, Liu X, Wang W, Li Z, Wang N, Xiao F, Gao H, Guo H, Li H, Wang S. 2022. Integration of genome-wide association study and selection signatures reveals genetic determinants for skeletal muscle production traits in an F2 chicken population. Journal of Integrative Agriculture, 21, 2065–2075.

Li Y, Willer C, Sanna S, Abecasis G. 2009. Genotype imputation. Annual Review of Genomics and Human Genetics, 10, 387–406.

Liu P, Ma L, Jian S, He Y, Yuan G, Ge F, Chen Z, Zou C, Pan G, Lübberstedt T, Shen Y. 2024. Population genomic analysis reveals key genetic variations and the driving force for embryonic callus induction capability in maize. Journal of Integrative Agriculture, 23, 2178–2195.

Loh P R, Danecek P, Palamara P F, Fuchsberger C, A Reshef Y, K Finucane H, Schoenherr S, Forer L, McCarthy S, Abecasis G R, Durbin R, L Price A. 2016. Reference-based phasing using the Haplotype Reference Consortium panel. Nature Genetics, 48, 1443–1448.

De Marino A, Mahmoud A A, Bose M, Bircan K O, Terpolovsky A, Bamunusinghe V, Bohn S, Khan U, Novkovic B, Yazdi P G. 2022. A comparative analysis of current phasing and imputation software. PLoS ONE, 17, e0260177.

McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood A R, Teumer A, Kang H M, Fuchsberger C, Danecek P, Sharp K, Luo Y, Sidore C, Kwong A, Timpson N, Koskinen S, Vrieze S, Scott L J, Zhang H, Mahajan A, Veldink J, et al. 2016. A reference panel of 64,976 haplotypes for genotype imputation. Nature Genetics, 48, 1279–1283.

Rubinacci S, Delaneau O, Marchini J. 2020. Genotype imputation using the Positional Burrows Wheeler Transform. PLoS Genetics, 16, e1009049.

Scheet P, Stephens M. 2006. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. The American Journal of Human Genetics, 78, 629–644.

Servin B, Stephens M. 2007. Imputation-based analysis of association studies: Candidate regions and quantitative traits. PLoS Genetics, 3, e114.

Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H, Bai L, Cai Z, Zhao B, Li X, Xu Z, Lin Q, Pan Z, Yang W, Yu X, Guan D, Hou Y, Keel B N, Rohrer G A, Lindholm-Perry A K, et al. 2024. A compendium of genetic regulatory effects across pig tissues. Nature Genetics, 56, 112–123.

Teng J, Ye S, Gao N, Chen Z, Diao S, Li X, Yuan X, Zhang H, Li J, Zhang X, Zhang Z. 2022a. Incorporating genomic annotation into single-step genomic prediction with imputed whole-genome sequence data. Journal of Integrative Agriculture, 21, 1126–1136.

Teng J, Zhao C H, Wang D, Chen Z, Tang H, Li J B, Mei C, Yang Z P, Ning C, Zhang Q. 2022b. Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle. Journal of Dairy Science, 105, 3355–3366.

Wang Z, Li W, Tang Z. 2024. Enhancing the genomic prediction accuracy of swine agricultural economic traits using an expanded one-hot encoding in CNN models. Journal of Integrative Agriculture, doi: 10.1016/j.jia.2024.03.071.

Yang J A, Lee S H, Goddard M E, Visscher P M. 2011. GCTA: A tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88, 76–82.

Ye S, Yuan X, Huang S, Zhang H, Chen Z, Li J, Zhang X, Zhang Z. 2019. Comparison of genotype imputation strategies using a combined reference panel for chicken population. Animal, 13, 1119–1126.

Ye S, Zhou X, Lai Z, Ikhwanuddin M, Ma H. 2024. Systematic comparison of genotype imputation strategies in aquaculture: A case study in Nile tilapia (Oreochromis niloticus) populations. Aquaculture, 592, 741175.

Zhang K, Liang J, Fu Y, Chu J, Fu L, Wang Y, Li W, Zhou Y, Li J, Yin X, Wang H, Liu X, Mou C, Wang C, Wang H, Dong X, Yan D, Yu M, Zhao S, Li X, et al. 2024. AGIDB: A versatile database for genotype imputation and variant decoding across species. Nucleic Acids Research, 52, D835–D849.

Zhang Z, Xing S, Qiu A, Zhang N, Wang W, Qian C, Zhang J, Wang C, Zhang Q, Ding X. 2023. The development of a porcine 50K SNP panel using genotyping by target sequencing and its application. Journal of Integrative Agriculture, doi: 10.1016/j.jia.2023.07.033.

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