猪全基因组低密度SNP芯片的设计与效果评价

doi:10.11843/j.issn.0366-6964.2025.06.018

Abstract

Abstract:

This study aimed to explore the application effect of low-density SNP chips in pig breeding, especially in terms of genotype imputation and breeding value estimation accuracy. This study designed a low-density SNP chip with a density of 5K based on the medium to high density SNP chip "KPS Porcine breeding chip v2", which can be used for detecting genetic marker typing in breeding pigs and imputed into the quality control panel of "KPS Porcine breeding chip v2)", ultimately applied to genome selection. Subsequently, this study used data from 3 239 purebred Large White pigs from a certain pig breeding farm to investigate the genotype imputation accuracy and genome breeding value estimation accuracy of the low-density chip through five fold cross validation method. The results showed that the allele accuracy of genotype imputation reached 99.46%, and the average accuracy of genetic evaluation for age adjusted to 100 kg weight and back-fat thickness adjusted to 100 kg weight were 0.374 2 and 0.402 1, respectively. Compared with the original genotype data, the accuracy loss was only 0.001 5 and 0.001 2. The results indicate that low-density SNP chips retain the vast majority of original information while reducing detection costs. This study provides a basis and reference for the design of low-density chips for the whole genome of livestock and poultry. This strategy significantly reduces the cost of genotype testing and promotes the popularization of genomic selection of pigs in China.

Key words: pigs, genomic selection, low density SNP chip, chip design, genotype imputation

CLC Number:

S828.2

WU Jianliang, SU Yang, MAO Ruihan, ZHOU Lei, YAN Tiantian, LI Zhi, LIU Jianfeng. Design and Effect Evaluation of A Whole-Genome Low-Density SNP Chip in Pigs[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(6): 2733-2740.

Figures/Tables 5

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Table 2

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References 40

1	MEUWISSEN T H , HAYES B J , GODDARD M E . Prediction of total genetic value using genome-wide dense marker maps[J]. Genetics, 2001, 157 (4): 1819- 1829. doi: 10.1093/genetics/157.4.1819
2	VAN EENENNAAM A L , WEIGEL K A , YOUNG A E , et al. Applied animal genomics: results from the field[J]. Annu Rev Anim Biosci, 2014, 2, 105- 139. doi: 10.1146/annurev-animal-022513-114119
3	严晓春, 习海娇, 李金泉, 等. 内蒙古绒山羊绒毛性状基因组育种值估计准确性研究[J]. 畜牧兽医学报, 2024, 55 (1): 120- 128. doi: 10.11843/j.issn.0366-6964.2024.01.013
	YAN X C , XI H J , LI J Q , et al. Study on estimates of genomic breeding value of fleece traits in Inner Mongolia cashmere goats[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55 (1): 120- 128. doi: 10.11843/j.issn.0366-6964.2024.01.013
4	张瑞锋, 黄珍, 谢水华, 等. 猪全基因组选择技术发展现状和应用前景[J]. 中国畜牧杂志, 2023, 59 (10): 21- 29.
	ZHANG R F , HUANG Z , XIE S H , et al. Genomic selection in swine: current advances and future applications[J]. Chinese Journal of Animal Science, 2023, 59 (10): 21- 29.
5	孙东晓, 张胜利, 张勤, 等. 我国奶牛基因组选择技术应用进展[J]. 畜牧兽医学报, 2023, 54 (10): 4028- 4039. doi: 10.11843/j.issn.0366-6964.2023.10.003
	SUN D X , ZHANG S L , ZHANG Q , et al. Application progress on genomic selection technology for dairy cattle in China[J]. Acta Veterinaria et Zootechnica Sinica, 2023, 54 (10): 4028- 4039. doi: 10.11843/j.issn.0366-6964.2023.10.003
6	洪渊, 阳文攀, 陈梦会, 等. 基于SNP芯片猪早期选留的基因型填充效果研究[J]. 中国畜牧杂志, 2024, 60 (8): 300- 303.
	HONG Y , YANG W P , CHEN M H , et al. genotype imputation effectiveness evaluation for early-stage swine selection using a snp chip[J]. Chinese Journal of Animal Science, 2024, 60 (8): 300- 303.
7	QU S , LU S , LIU Y , et al. Accurate genomic selection using low-density SNP panels preselected by maximum likelihood estimation[J]. Aquaculture, 2024, 579, 9. doi: 10.3969/j.issn.1004-2091.2024.11.002
8	CLOUARD C , NETTELBLAD C . Genotyping of SNPs in bread wheat at reduced cost from pooled experiments and imputation[J]. Theor Appl Genet, 2024, 137 (1): 26. doi: 10.1007/s00122-023-04533-5
9	AHMAD S F , SINGH A , DEB C K , et al. Evaluation of imputation possibility from low-density SNP panel in composite Vrindavani cattle[J]. Anim Genet, 2023, 54 (5): 647- 648. doi: 10.1111/age.13339
10	ZHAO J , KE Q , MIAO L , et al. Revealing the interaction effect on the accuracy of genomic selection between marker density and genetic architecture[J]. Aquaculture, 2023, 563, 8.
11	GROSSI D A , BRITO L F , JAFARIKIA M , et al. Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs[J]. Animal, 2018, 12 (11): 2235- 2245. doi: 10.1017/S175173111800085X
12	WELLMANN R , PREUSS S , THOLEN E , et al. Genomic selection using low density marker panels with application to a sire line in pigs[J]. Genet Sel Evol, 2013, 45 (1): 28. doi: 10.1186/1297-9686-45-28
13	SUN J , WEI J , PAN Y , et al. Improving genomic prediction accuracy of pig reproductive traits based on genotype imputation using preselected markers with different imputation platforms[J]. Animal, 2025, 19 (1): 101387. doi: 10.1016/j.animal.2024.101387
14	吴俊锋, 闫奕源, 杨宁, 等. 蛋鸡SNP芯片10K到50K基因型填充的准确性研究[J]. 畜牧兽医学报, 2024, 55 (10): 4325- 4333. doi: 10.11843/j.issn.0366-6964.2024.10.008
	WU J F , YAN Y Y , YANG N , et al. Accuracy analysis of genotype imputation from 10K to 50K SNP loci in layers[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55 (10): 4325- 4333. doi: 10.11843/j.issn.0366-6964.2024.10.008
15	程明, 孙军昌, 曹欣欣, 等. 奶山羊低密度液相SNP芯片开发及有效性验证[J]. 中国畜牧杂志, 2024, 60 (1): 229- 234.
	CHENG M , SUN J C , CAO X X , et al. Development and validation of a low-density liquid-phase snp chip for dairy goats[J]. Chinese Journal of Animal Science, 2024, 60 (1): 229- 234.
16	WANG H , WU H , ZHANG W , et al. Development and validation of a 5K low-density SNP chip for Hainan cattle[J]. BMC Genomics, 2024, 25 (1): 873. doi: 10.1186/s12864-024-10753-w
17	GARCIA-RUIZ A , COLE J B , VANRADEN P M , et al. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection[J]. Proc Natl Acad Sci U S A, 2016, 113 (28): E3995- E4004.
18	JUDGE M M , KEARNEY J F , MCCLURE M C , et al. Evaluation of developed low-density genotype panels for imputation to higher density in independent dairy and beef cattle populations[J]. J Anim Sci, 2016, 94 (3): 949- 962. doi: 10.2527/jas.2015-0044
19	WU X , XU J , FENG G , et al. Optimal design of low-density snp arrays for genomic prediction: Algorithm and applications[J]. PLoS One, 2016, 11 (9): e0161719. doi: 10.1371/journal.pone.0161719
20	张镜宇, 王银娟, 滕军, 等. 荷斯坦牛胚胎基因分型策略和基因组选择性能评估[J]. 中国畜牧杂志, 2024, 61 (3): 91- 99.
	ZHANG J Y , WANG Y J , TENG J , et al. Genotyping strategies and genomic selection performance evaluation in holstein cattle embryos[J]. Chinese Journal of Animal Science, 2024, 61 (3): 91- 99.
21	SHASHKOVA T I , MARTYNOVA E U , AYUPOVA A F , et al. Development of a low-density panel for genomic selection of pigs in Russia[J]. Transl Anim Sci, 2020, 4 (1): 264- 274. doi: 10.1093/tas/txz182
22	ZHANG T , XU Q , ZHOU B , et al. Development and validation of a 5K liquid chip for identifying cashmere goat populations in Inner Mongolia Autonomous Region[J]. Animals, 2024, 14 (24): 11.
23	O'BRIEN A C , JUDGE M M , FAIR S , et al. High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1[J]. J Anim Sci, 2019, 97 (4): 1550- 1567. doi: 10.1093/jas/skz043
24	RAOUL J , SWAN A A , ELSEN J . Using a very low-density SNP panel for genomic selection in a breeding program for sheep[J]. Genet Sel Evol, 2017, 49 (1): 76. doi: 10.1186/s12711-017-0351-0
25	BOLORMAA S , GORE K , VAN DER WERF J H J , et al. Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy[J]. Anim Genet, 2015, 46 (5): 544- 556. doi: 10.1111/age.12340
26	WANG J , XING S , ZHOU Y , et al. Research note: A low-density SNP genotyping panel for Chinese native chickens[J]. Poult Sci, 2025, 104 (1): 104609. doi: 10.1016/j.psj.2024.104609
27	HERRY F , HERAULT F , PICARD DRUET D , et al. Design of low density SNP chips for genotype imputation in layer chicken[J]. BMC Genet, 2018, 19 (1): 108. doi: 10.1186/s12863-018-0695-7
28	WANG C , HABIER D , PEIRIS B L , et al. Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens[J]. Poult Sci, 2013, 92 (7): 1712- 1723. doi: 10.3382/ps.2012-02941
29	MAO R , ZHOU L , WANG Z , et al. A comprehensive strategy combining feature selection and local optimization algorithm to optimize the design of low-density chip for genomic selection[J]. Agriculture-Basel, 2023, 13 (3): 11.
30	WU X , LI H , FERRETTI R , et al. A unified local objective function for optimally selecting SNPs on arrays for agricultural genomics applications[J]. Anim Genet, 2020, 51 (2): 306- 310. doi: 10.1111/age.12916
31	PURCELL S , NEALE B , TODD-BROWN K , et al. PLINK: a tool set for whole-genome association and population-based linkage analyses[J]. Am J Hum Genet, 2007, 81 (3): 559- 575. doi: 10.1086/519795
32	DANECEK P , BONFIELD J K , LIDDLE J , et al. Twelve years of SAMtools and BCFtools[J]. Gigascience, 2021, 10 (2): giab008. doi: 10.1093/gigascience/giab008
33	DELANEAU O , ZAGURY J , ROBINSON M R , et al. Accurate, scalable and integrative haplotype estimation[J]. Nat Commun, 2019, 10 (1): 5436. doi: 10.1038/s41467-019-13225-y
34	RUBINACCI S , DELANEAU O , MARCHINI J . Genotype imputation using the Positional Burrows Wheeler Transform[J]. PLoS Genet, 2020, 16 (11): e1009049. doi: 10.1371/journal.pgen.1009049
35	DASSONNEVILLE R , BRONDUM R F , DRUET T , et al. Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations[J]. J Dairy Sci, 2011, 94 (7): 3679- 3686. doi: 10.3168/jds.2011-4299
36	ALI M , ZHANG Y , RASHEED A , et al. Genomic prediction for grain yield and yield-related traits in Chinese winter wheat[J]. Int J Mol Sci, 2020, 21 (4): 1342. doi: 10.3390/ijms21041342
37	MADSEN P , SU G , LABOURIAU R , et al. DMU-a package for analyzing multivariate mixed models[J]. J Anim Sci, 2014, 92 (12): 5406- 5416. doi: 10.2527/jas.2014-7781
38	FERNANDES JUNIOR G A , ROSA G J M , VALENTE B D , et al. Genomic prediction of breeding values for carcass traits in Nellore cattle[J]. Genet Sel Evol, 2016, 48, 7. doi: 10.1186/s12711-016-0188-y
39	GUO X , CHRISTENSEN O F , OSTERSEN T , et al. Improving genetic evaluation of litter size and piglet mortality for both genotyped and nongenotyped individuals using a single-step method[J]. J Anim Sci, 2015, 93 (2): 503- 512. doi: 10.2527/jas.2014-8331
40	ZHOU L , WANG Y , YU J , et al. Improving genomic prediction for two Yorkshire populations with a limited size using the single-step method[J]. Anim Genet, 2019, 50 (4): 391- 394. doi: 10.1111/age.12806

性别/性状 Gender/trait	日龄 Age	背膘厚 Back-fat thickness
公Male	50.775	－7.277
母Female	46.415	－9.440

染色体号 Chromosome number	位点数量 Number of loci		位点平均MAF Average MAF of loci		位点平均LD Average LD of loci
染色体号 Chromosome number	50K	5K	50K	5K	50K	5K
1	5 474	664	0.265 7	0.319 0	0.608 1	0.556 2
2	2 989	357	0.262 1	0.319 1	0.576 1	0.522 4
3	2 633	320	0.247 2	0.306 1	0.575 7	0.439 1
4	2 485	314	0.259 9	0.319 7	0.589 0	0.506 5
5	1 922	244	0.254 8	0.316 5	0.579 0	0.442 5
6	2 897	402	0.273 5	0.307 9	0.606 6	0.489 2
7	2 464	291	0.261 7	0.309 0	0.548 0	0.452 1
8	2 834	329	0.269 6	0.320 4	0.574 6	0.488 7
9	2 913	328	0.272 9	0.318 8	0.570 4	0.492 5
10	1 387	158	0.264 5	0.305 4	0.541 0	0.408 3
11	1 557	190	0.269 9	0.316 4	0.567 9	0.427 6
12	1 207	146	0.279 8	0.324 4	0.529 9	0.464 7
13	4 122	505	0.303 1	0.331 8	0.628 6	0.521 4
14	2 854	338	0.271 4	0.309 9	0.612 7	0.496 0
15	2 823	335	0.258 3	0.312 6	0.579 8	0.452 7
16	1 635	188	0.256 8	0.311 7	0.550 6	0.476 0
17	1 254	148	0.265 0	0.307 0	0.522 3	0.456 0
18	1 056	128	0.246 3	0.316 4	0.521 3	0.422 7
平均Mean	--	--	0.265 7	0.315 1	0.571 2	0.473 0

基因组信息来源 Genomic information source	准确性Accuracy		无偏性Unbiasedness
基因组信息来源 Genomic information source	达100 kg日龄 Age adjusted to 100 kg weight	达100 kg背膘厚 Back-fat thickness adjusted to 100 kg weight	达100 kg日龄 Age adjusted to 100 kg weight	达100 kg背膘厚 Back-fat thickness adjusted to 100 kg weight
原始基因型 Raw genotype	0.375 7±0.036 7	0.403 3±0.032 5	0.788 8±0.123 3	0.961 5±0.117 5
填充后基因型 Imputed genotype	0.374 2±0.036 7	0.402 1±0.033 5	0.786 3±0.124 7	0.961 5±0.120 9

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Design and Effect Evaluation of A Whole-Genome Low-Density SNP Chip in Pigs

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