Single‐step genomic evaluation of Russian dairy cattle using internal and external information

Kudinov, Andrei A.; Mäntysaari, Esa; Pitkänen, Timo; Сакса, Е.И.; Aamand, G.P.; Uimari, Pekka; Strandén, Ismo

doi:10.1111/jbg.12660

Cited by 6 publications

(2 citation statements)

References 75 publications

(94 reference statements)

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“…Animals with SNP missing rates greater than 10% were also excluded from the analysis. After the quality control tests, 497 animals and 12,906 SNPs were used for the genomic prediction [12,13].…”

Section: Quality Control and Data Preparationmentioning

confidence: 99%

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Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

Önder,

Sitskowska,

Kurnaz

et al. 2023

Animals

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The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.

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Section: Quality Control and Data Preparationmentioning

confidence: 99%

“…A great majority of the genomic selection studies [12][13][14][15] have focused on singletrait breeding value estimation. However, multi-trait methods are expected to yield more accurate predictions than single-trait methods as in traditional BLUP.…”

Section: Introductionmentioning

confidence: 99%

Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

Önder,

Sitskowska,

Kurnaz

et al. 2023

Animals

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Single‐step genomic evaluation of Russian dairy cattle using internal and external information

Kudinov

Mäntysaari

Pitkänen

et al. 2021

J Animal Breeding Genetics

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Genomic data are widely used in predicting the breeding values of dairy cattle. The accuracy of genomic prediction depends on the size of the reference population and how related the candidate animals are to it. For populations with limited numbers of progeny‐tested bulls, the reference populations must include cows and data from external populations. The aim of this study was to implement state‐of‐the‐art single‐step genomic evaluations for milk and fat yield in Holstein and Russian Black & White cattle in the Leningrad region (LR, Russia), using only a limited number of genotyped animals. We complemented internal information with external pseudo‐phenotypic and genotypic data of bulls from the neighbouring Danish, Finnish and Swedish Holstein (DFS) population. Three data scenarios were used to perform single‐step GBLUP predictions in the LR dairy cattle population. The first scenario was based on the original LR reference population, which constituted 1,080 genotyped cows and 427 genotyped bulls. In the second scenario, the genotypes of 414 bulls related to the LR from the DFS population were added to the reference population. In the third scenario, LR data were further augmented with pseudo‐phenotypic data from the DFS population. The inclusion of foreign information increased the validation reliability of the milk yield by up to 30%. Suboptimal data recording practices hindered the improvement of fat yield. We confirmed that the single‐step model is suitable for populations with a low number of genotyped animals, especially when external information is integrated into the evaluations. Genomic prediction in populations with a low number of progeny‐tested bulls can be based on data from genotyped cows and on the inclusion of genotypes and pseudo‐phenotypes from the external population. This approach increased the validation reliability of the implemented single‐step model in the milk yield, but shortcomings in the LR data recording scheme prevented improvements in fat yield.

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Identification of homozygosity-rich regions in the Holstein genome

Smaragdov

2023

Vestn. VOGiS

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In this study, 371 Holstein cows from six herds and 26 Holstein bulls, which were used in these herds, were genotyped by the Illumina BovineSNP50 array. For runs of homozygosity (ROH) identification, consecutive and sliding runs were performed by the detectRUNS and Plink software. The missing calls did not significantly affect the ROH data. The mean number of ROH identified by consecutive runs was 95.4 ± 2.7, and that by sliding runs was 86.0 ± 2.6 in cows, while this number for Holstein bulls was lower 58.9 ± 1.9. The length of the ROH segments varied from 1 Mb to over 16 Mb, with the largest number of ROH having a length of 1–2 Mb. Of the 29 chromosomes, BTA 14, BTA 16, and BTA 7 were the most covered by ROH. The mean coefficient of inbreeding across the herds was 0.111 ± 0.003 and 0.104 ± 0.004 based on consecutive and sliding runs, respectively, and 0.078 ± 0.005 for bulls based on consecutive runs. These values do not exceed those for Holstein cattle in North America. The results of this study confirmed the more accurate identification of ROH by consecutive runs, and also that the number of allowed heterozygous SNPs may have a significant effect on ROH data.

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Single‐step genomic evaluation of Russian dairy cattle using internal and external information

Cited by 6 publications

References 75 publications

Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

Single‐step genomic evaluation of Russian dairy cattle using internal and external information

Identification of homozygosity-rich regions in the Holstein genome

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