蛋鸡SNP芯片10K到50K基因型填充的准确性研究

doi:10.11843/j.issn.0366-6964.2024.10.008

Abstract

Abstract:

The aim of this study was to analyze the accuracy of using low-density chip genotyping data to obtain high-density genotype data through genotype imputation. In this study, the genotypic consistency between the imputed genotypes based on 10K and the 50K real genotypes was analyzed. The 4 435 healthy brown laying hens from pure-line were selected, and 50K SNP array of laying hens was used for genotype determination. Some individuals were randomly selected as test and reference populations, respectively. The 10K genotype data was evenly selected from 50K typing data as known genotypes, and Beagle 4.0 software was used to impute genotypes of the rest 40K to obtain 50K data. The consistency of the imputed genotype and the real genotype was compared. The accuracy of genotype filling was evaluated by genotype filling consistency. We also analyzed the influence of following 3 aspects on the accuracy of genotype filling: 1) usage of pedigree or not (100 individuals in test population vs. 1 000 individuals in reference population); 2) kinship between reference and test population (100 individuals in test population vs. 1 000 individuals in reference population); 3) reference population size (100 individuals in test population vs. 500, 1 000, 2 000, 3 000 individuals in reference population). The results showed that the consistency of genotypic imputation was not affected by the use of genealogical information or not (0.973 vs. 0.973). The consistency of genotypic imputation was changed as the change of inter-population kinship. Using the individuals from 18th generation (1 000 individuals) as reference population to impute the genotypes of 19th generation population (100 individuals), the consistency of genotypic imputation was to 0.972. When using individuals from the 16th, 17th and 18th generations as reference population to impute the genotypes of 19th generation population, and the consistency of genotypic imputation was decreased to 0.968. The imputation consistency of genotype was increased with the increase of reference population size. The consistency of genotype imputation was 0.959, 0.973, 0.980, and 0.984 when the reference population size was 500, 1 000, 2 000, and 3 000, respectively. Collectively, this study shows that the imputation of layer SNP array from 10K to 50K is feasible, and it can be applied in genome selection breeding to reduce genotyping costs.

Key words: laying hens, SNP array, genotype imputation, molecular breeding

CLC Number:

S831.2

Junfeng WU, Yiyuan YAN, Ning YANG, Congjiao SUN, Guangqi LI, Bin WANG, Guiqin WU, Ling LIAN. Accuracy Analysis of Genotype Imputation from 10K to 50K SNP Loci in Layers[J]. Acta Veterinaria et Zootechnica Sinica, 2024, 55(10): 4325-4333.

Figures/Tables 7

Table 1

Descriptive statistics of 10K and 50K SNP loci on each chromosome"

染色体 Chromosome	10K				50K
染色体 Chromosome	SNPs数 Number of SNPs	平均间隔/bp Average distance	最小等位基因频率 MAF	连锁不平衡程度 R²	SNPs数 Number of SNPs	平均间隔/bp Average distance	最小等位基因频率 MAF	连锁不平衡程度 R²
Chr1	1 639	120 290	0.298	0.389	7 807	25 263	0.302	0.389
Chr2	1 167	126 163	0.313	0.401	5 556	26 624	0.306	0.401
Chr3	978	112 598	0.291	0.410	4 656	23 696	0.291	0.409
Chr4	746	120 931	0.296	0.377	3 552	25 606	0.293	0.377
Chr5	521	112 847	0.292	0.324	2 480	23 938	0.300	0.324
Chr6	356	98 085	0.337	0.369	1 697	20 635	0.332	0.366
Chr7	387	92 890	0.293	0.346	1 844	19 739	0.314	0.346
Chr8	290	101 059	0.296	0.292	1 383	21 511	0.299	0.294
Chr9	272	83 585	0.300	0.291	1 296	18 175	0.333	0.289
Chr10	238	82 340	0.291	0.289	1 134	17 391	0.295	0.286
Chr11	201	99 018	0.297	0.273	886	22 476	0.285	0.273
Chr12	307	59 453	0.328	0.358	1 230	16 078	0.323	0.358
Chr13	240	60 477	0.313	0.349	961	16 035	0.305	0.346
Chr14	181	69 687	0.275	0.267	724	19 543	0.298	0.267
Chr15	194	62 109	0.262	0.248	815	15 450	0.337	0.246
Chr16	3	555 532	0.336	0.235	14	119 643	0.336	0.237
Chr17	149	57 342	0.323	0.244	595	15 625	0.338	0.250
Chr18	161	64 390	0.299	0.191	685	15 755	0.307	0.191
Chr19	168	58 214	0.291	0.195	672	14 710	0.294	0.199
Chr20	205	64 342	0.305	0.251	859	15 882	0.277	0.252
Chr21	119	56 265	0.330	0.215	476	14 123	0.338	0.215
Chr22	54	84 299	0.279	0.167	218	21 123	0.276	0.167
Chr23	73	78 035	0.290	0.134	294	19 376	0.255	0.133
Chr24	100	61 991	0.285	0.167	401	15 459	0.279	0.165
Chr25	23	166 397	0.268	0.140	94	41 112	0.266	0.140
Chr26	133	38 166	0.291	0.182	532	9 666	0.294	0.182
Chr27	80	48 474	0.341	0.169	320	16 639	0.310	0.171
Chr28	93	50 277	0.271	0.159	374	13 079	0.276	0.158
Chr30	2	242 272	0.294	0.190	5	96 909	0.271	0.243
Chr31	2	483 221	0.348	0.241	9	166 190	0.355	0.249
Chr32	2	59 559	0.360	0.210	9	13 235	0.350	0.209
Chr33	21	326 054	0.320	0.271	86	79 839	0.307	0.271
合计Total	10 146	118 000	0.300	0.263	43 681	30 000	0.290	0.261

Table 1

Table 2

Fig. 1

Fig. 2

Table 3

Table 4

Fig. 3

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参考群体规模 Reference population size	方法 Method	基因型一致性 Genotype consistency	显著性检验P P-value
1 000	Beagle	0.973±0.000 65(0.000 67)	0.493
1 000	Beagle+Ped	0.973±0.000 64(0.000 66)

参考群体规模 Reference population size	方法 Method	基因型一致性 Genotype consistency	显著性检验P P-value
1 000(以18世代组成的禽类群体) 1 000(birds from 18th generation)	Beagle	0.972±0.000 82(0.000 84)	8.176×10^-5
1 000(以16、17、18世代组成的禽类群体) 1 000 (birds from 16th, 17th, 18th generation)	Beagle	0.968±0.001 32(0.001 36)

参考群体规模 Reference population	方法 Method	基因型一致性 Genotype consistency	显著性检验P P-value
500	Beagle	0.959±0.013 53(0.014 11)	2.693×10^-7
1 000	Beagle	0.973±0.009 58(0.009 85)	7.672×10^-9
2 000	Beagle	0.980±0.006 56(0.006 69)	1.148×10^-7
3 000	Beagle	0.984±0.005 45(0.005 54)

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Accuracy Analysis of Genotype Imputation from 10K to 50K SNP Loci in Layers

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