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Guo G.,China Agricultural University | Guo G.,Chinese Academy of Agricultural Sciences | Guo G.,Beijing Sanyuan Lvhe Dairy Cattle Breeding Center | Guo X.,China Agricultural University | And 11 more authors.
Canadian Journal of Animal Science | Year: 2014

The objective of this study was to estimate genetic parameters for fertility traits in Chinese Holstein heifers and cows. Data of 20 169 animals with 42 106 records over a period of 10 yr (2001-2010) were collected from Sanyuan Lvhe Dairy Cattle Center in Beijing, China. Traits included age at first service (AFS), number of services (NS), days from calving to first service (CTFS), days open (DO), and calving interval (CI). Genetic parameters were estimated with multiple-trait animal model using the DMU software. Heritability estimates for AFS, NS, CTFS, DO and CI were 0.10090.012, 0.04090.017, 0.03490.011, 0.05390.019 and 0.05690.014, respectively. Genetic correlations between traits observed ranged from -0.13 to 0.99. Genetic correlations between AFS with NS, CTFS, DO and CI were -0.31, 0.15, -0.13 and -0.15, respectively. Calving interval was strongly correlated with NS, CTFS and DO (0.49-0.99), and DO showed strong correlation with NS and CTFS (0.49 and 0.58, respectively). The genetic correlation between CTFS and NS was negative moderate (-0.25). Results were in range with previous literature estimates and can be used in Chinese Holstein genetic evaluation for fertility traits.

Fan Y.,China Agricultural University | Wang P.,China Agricultural University | Fu W.,China Agricultural University | Dong T.,China Agricultural University | And 9 more authors.
Animal Genetics | Year: 2014

With the Illumina BovineSNP50K BeadChip, we performed a genome-wide association study (GWAS) for two pigmentation traits in a Chinese Holstein population: proportion of black (PB) and teat colour (TC). A case-control design was used. Cases were the cows with PB <0.30 (n = 129) and TC <2 points (n = 140); controls were those with PB >0.90 (n = 58) and TC >4 points (n = 281). The RM test of ROADTRIPS (version 1.2) was applied to detect SNPs for the two traits with 42 883 and 42 741 SNPs respectively. A total of nine and 12 genome-wide significant (P < 0.05) SNPs associated with PB and TC respectively were identified. Of these, two SNPs for PB were located within the KIT and IGFBP7 genes, and the other four SNPs were 23-212 kb away from the PDGFRA gene on BTA6; nine SNPs associated with TC were located within or 21-78.8 kb away from known genes on chromosomes 4, 11, 22, 23 and 24. By combing through our GWAS results and the biological functions of the genes, we suggest that the KIT, IGFBP7, PDGFRA, MITF, ING3 and WNT16 genes are promising candidates for PB and TC in Holstein cattle, providing a basis for further investigation on the genetic mechanism of pigmentation formation. © 2014 Stichting International Foundation for Animal Genetics.

Guo G.,Chinese Academy of Agricultural Sciences | Guo G.,Beijing Sanyuan Lvhe Dairy Cattle Breeding Center | Guo G.,China Agricultural University | Guo G.,University of Aarhus | And 5 more authors.
BMC Genetics | Year: 2014

Background: In this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero. Results: The results showed that when there were no missing records, MTGM and STGM gave the same reliability for the genomic predictions for trait I while, for trait II, MTGM performed better that STGM. When there were missing records for one of the two traits, MTGM performed much better than STGM. In general, the difference in reliability of genomic EBVs predicted using the EBV response variables estimated from either the multiple-trait or single-trait models was relatively small for the trait without missing data. However, for the trait with missing data, the EBV response variable obtained from the multiple-trait model gave a more reliable genomic prediction than the EBV response variable from the single-trait model. Conclusions: These results indicate that MTGM performed better than STGM for the trait with low heritability and for the trait with a limited number of records. Even when the EBV response variable was obtained using the multiple-trait model, the genomic prediction using MTGM was more reliable than the prediction using the STGM. © 2014 Guo et al.; licensee BioMed Central Ltd.

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