基于高密度SNP标记估计群体间遗传关联

doi:10.16288/j.yczz.20-351

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Hereditas(Beijing) ›› 2021, Vol. 43 ›› Issue (4): 340-349.doi: 10.16288/j.yczz.20-351

• Research Article • Previous Articles Next Articles

Measuring genetic connectedness between herds based on high density SNP markers

Ziwen Zhou, Xue Wang, Xiangdong Ding()

National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University,Beijing 100193, China

Received:2020-10-19 Revised:2021-02-17 Online:2021-04-20 Published:2021-04-20
Contact: Ding Xiangdong E-mail:xding@cau.edu.cn
Supported by:
Supported by China Agriculture Research System No(CARS-35);the National Key Research and Development Project No(2019YFE0106800);Modern Agriculture Science and Technology Key Project of Hebei Province No(19226376D)

Abstract

Abstract:

The accuracy of genetic evaluations in different herds is affected by the degree of genetic connectedness among herds. In this study, we explored the application of high density SNP markers in the assessment of genetic connectedness by comparing the genetic connectedness based on pedigree data and genomic data. Six methods, including PEVD (prediction error variance of differences between estimated breeding values), PEVD (x), VED (variance of estimated difference between the herd effects), CD (generalized coefficient of determination), r (prediction error correlation) and CR (connectedness rating), were implemented to measure the genetic connectedness based on different relationship matrices (A, G, G_s, G_{0. 5} and H). Our results from both simulated data and SNP chip data indicated that, except for the PEVD (x) and VED methods, the genetic connectedness obtained by PEVD, CD, r and CR based on G. G_s and G_0.5 matrices (using genome information only) were superior to those based on A matrix (using pedigree information only). Generally, for most approaches, the genetic connectedness based on H matrix (using both pedigree and genome information) was somewhere between A matrix and G matrices. CD could overestimate the degree of genetic connectedness as it was still very high when CR and r were close to 0. The method r could not accurately reflect the true genetic connectedness of the populations. It generated 0.01 of genetic connectedness for all three pig breeding farms, which were actually genetically different with each other. With increasing of heritability, the degree of genetic connectedness obtained by all methods were increased as well. However, in the case of heritability 0.1, PEVD based on A matrix performed better than based on G matrix, suggesting that traits with medium and high heritability are more suitable for the assessment of genetic connectedness compared to traits with low heritability. Our findings indicated that high-density SNP markers have advantages over pedigree analysis for the measurement of genetic connectedness, and CR is a robust and reliable method to assess genetic connectedness. Further, CR is easily calculated and less affected by heritability of trait. PEVD is good supplement to quantify the prediction errors of estimated breeding values under the specific genetic connectedness. In comparison, G matrix can reflect genetic connectedness better than its extensions G_s and G_0.5 matrix.

Key words: swine, genetic connectedness, pedigree, genome, relationship matrix

Ziwen Zhou, Xue Wang, Xiangdong Ding. Measuring genetic connectedness between herds based on high density SNP markers[J]. Hereditas(Beijing), 2021, 43(4): 340-349.

Figures/Tables 6

Table 1

Table 2

Fig. 1

References 23

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方法	关系矩阵
方法	A	G	G_0.5	G_s	H
PEVD	1.6471	1.3218	0.9285	1.5287	1.3725
PEVD(x)	0.0003	0.3248	0.2662	0.4165	0.0016
VED	0.0019	0.3279	0.2690	0.4196	0.0037
CD	0.5882	0.6896	0.6790	0.7366	0.6550
r	0	0.0008	0.7478	NaN	0.0003
CR	0	0.0031	0.9109	0.0035	0.0200

群体	方法	关系矩阵
群体	方法	A	G	G_s	G_0.5
BJLM-BBSC	PEVD	15.5600	11.9300	12.3100	9.0500
	PEVD(x)	0.0200	1.0600	1.1100	0.8600
	VED	0.0500	1.0800	1.1300	0.8800
	CD	0.5500	0.6700	0.6800	0.6700
	r	0	0.0100	0.0200	0.5900
	CR	0	0.1500	0.1500	0.9400
BJLM-XJTC	PEVD	15.1500	14.1300	13.4300	11.3200
	PEVD(x)	0.0200	2.5200	2.3300	2.3000
	VED	0.0400	2.5400	2.3500	2.3300
	CD	0.5600	0.6200	0.6100	0.6200
	r	0	0.0100	0.0100	0.4900
	CR	0	0.0700	0.0700	0.8200
BBSC-XJTC	PEVD	13.2300	14.5100	13.9800	12.200
	PEVD(x)	0.0300	2.1600	2.0500	2.4800
	VED	0.0600	2.1900	2.0800	2.5100
	CD	0.6200	0.6000	0.5900	0.5900
	r	0	0.0100	0.0100	0.4800
	CR	0	0.0400	0.0400	0.8200

方法	世代
方法	5	6	7
PEVD	1.3218	1.4136	1.5150
PEVD(x)	0.3248	0.4380	0.5835
VED	0.3279	0.4412	0.5866
CD	0.6896	0.6733	0.6616
r	0.0008	0.0001	0
CR	0.0031	0.0002	0

方法	关系矩阵	遗传力(h²)
方法	关系矩阵	0.1	0.3	0.5	0.7
PEVD	A	1.8900	1.6500	1.3300	0.9200
	G	2.1900	1.3200	0.8900	0.3600
	H	1.9000	1.3700	1.0100	0.6500
PEVD(x)	A	0.0003	0.0003	0.0003	0.0003
	G	0.4000	0.3200	0.2700	0.2200
	H	0.0095	0.0016	0.0007	0.0003
VED	A	0.0063	0.0019	0.0010	0.0006
	G	0.4200	0.3300	0.2700	0.2200
	H	0.0120	0.0040	0.0020	0.0010
CD	A	0.5300	0.5900	0.6700	0.7700
	G	0.4900	0.6900	0.7900	0.9200
	H	0.5200	0.6600	0.7500	0.8400
r	A	0	0	0	0
	G	0.0003	0.0008	NaN	NaN
	H	0.0003	0.0003	NaN	NaN
CR	A	0	0	0	0
	G	0.0020	0.0030	0.0030	0.0100
	H	0.1500	0.0200	0.0090	0.0050

群体	关系矩阵	遗传力
群体	关系矩阵	0.1	0.3	0.5	0.7
1	A	1.8900	1.6500	1.3300	0.9200
	G	1.7400	0.9600	0.6300	0.0800
	H	1.8800	1.1000	0.6900	0.3700
2	A	1.8900	1.6500	1.3300	0.9200
	G	1.8300	1.0400	0.6000	0.2100
	H	1.8900	1.6500	1.3300	0.9200

Measuring genetic connectedness between herds based on high density SNP markers

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