Increasing imputation and prediction accuracy for <scp>C</scp>hinese <scp>H</scp>olsteins using joint <scp>C</scp>hinese–<scp>N</scp>ordic reference population

Ma, Peipei; Lund, Mogens Sandø; Ding, Xiangdong; Zhang, Q.; Su, Guosheng

doi:10.1111/jbg.12111

Cited by 9 publications

(13 citation statements)

References 24 publications

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“…The LD phase between Chinese and Nordic Holsteins was in high consistency, and reliabilities of genomic prediction for production traits in Chinese Holsteins increased using the joint Chinese-Nordic reference population 17 . With the help of Nordic Holsteins, the accuracy of imputation from 54 K to 777 K was also increased in Chinese Holsteins 18 . In 2015, a joint GWAS for milk fatty acid traits was performed using Illumina BovineHD BeadChip markers from 784 Chinese and 371 Danish Holstein cows, a large number of additional significant SNPs were detected 19 .…”

Section: Introductionmentioning

confidence: 99%

Genome-wide Association Studies for Female Fertility Traits in Chinese and Nordic Holsteins

Wang

Sahana

et al. 2017

Sci Rep

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Reduced female fertility could cause considerable economic loss and has become a worldwide problem in the modern dairy industry. The objective of this study was to detect quantitative trait loci (QTL) for female fertility traits in Chinese and Nordic Holsteins using various strategies. First, single-trait association analyses were performed for female fertility traits in Chinese and Nordic Holsteins. Second, the SNPs with P-value < 0.005 discovered in Chinese Holsteins were validated in Nordic Holsteins. Third, the summary statistics from single-trait association analyses were combined into meta-analyses to: (1) identify common QTL for multiple fertility traits within each Holstein population; (2) detect SNPs which were associated with a female fertility trait across two Holstein populations. A large numbers of QTL were discovered or confirmed for female fertility traits. The QTL segregating at 31.4~34.1 Mb on BTA13, 48.3~51.9 Mb on BTA23 and 34.0~37.6 Mb on BTA28 shared between Chinese and Nordic Holsteins were further ascertained using a validation approach and meta-analyses. Furthermore, multiple novel variants identified in Chinese Holsteins were validated with Nordic data as well as meta-analyses. The genes IL6R, SLC39A12, CACNB2, ZEB1, ZMIZ1 and FAM213A were concluded to be strong candidate genes for female fertility in Holsteins.

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Section: Introductionmentioning

confidence: 99%

Genome-wide Association Studies for Female Fertility Traits in Chinese and Nordic Holsteins

Wang

Sahana

et al. 2017

Sci Rep

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“…Ding et al [18] have reported prediction accuracy using such a reference data are greatly increased, compared with using pedigree information. The genomic prediction accuracy for Chinese Holsteins has been improved by using Chinese and Nordic Holsteins as reference animals and MT-GBLUP [4, 5]. Similar to the single trait model, MT-Bayesian methods with different SNP/regions having different variances may produce higher reliability of genomic prediction than MT-GBLUP assuming that all SNP have equal variances across genome.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is assumed that the Chinese Holstein population is genetically close to the other Holstein populations in the world. To date, Chinese and Nordic Holstein populations have been jointly studied widely [3–5]. It has been reported that around 30% of Chinese cows has a relationship coefficient above 0.20 with one or more Nordic Holstein bulls, calculated by using genomic data [5].…”

Section: Introductionmentioning

confidence: 99%

“…To date, Chinese and Nordic Holstein populations have been jointly studied widely [3–5]. It has been reported that around 30% of Chinese cows has a relationship coefficient above 0.20 with one or more Nordic Holstein bulls, calculated by using genomic data [5]. Zhou et al [4] reported that the extent of linkage disequilibrium (LD) was similar in the Chinese and Nordic Holstein populations, and the consistency of LD phase between two populations was very high with a correlation of 0.97.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it was successful in improving the prediction accuracy for Chinese Holstein population using a joint reference population including Nordic genotyped progeny-test bulls. In addition, the accuracy of imputation from the 54 K to the HD marker data for Chinese Holsteins was improved by adding the Nordic HD-genotyped bulls into the reference data [5]. …”

Section: Introductionmentioning

confidence: 99%

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The patterns of genomic variances and covariances across genome for milk production traits between Chinese and Nordic Holstein populations

Lund

Janss

et al. 2017

BMC Genet

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BackgroundWith the development of SNP chips, SNP information provides an efficient approach to further disentangle different patterns of genomic variances and covariances across the genome for traits of interest. Due to the interaction between genotype and environment as well as possible differences in genetic background, it is reasonable to treat the performances of a biological trait in different populations as different but genetic correlated traits. In the present study, we performed an investigation on the patterns of region-specific genomic variances, covariances and correlations between Chinese and Nordic Holstein populations for three milk production traits.ResultsVariances and covariances between Chinese and Nordic Holstein populations were estimated for genomic regions at three different levels of genome region (all SNP as one region, each chromosome as one region and every 100 SNP as one region) using a novel multi-trait random regression model which uses latent variables to model heterogeneous variance and covariance. In the scenario of the whole genome as one region, the genomic variances, covariances and correlations obtained from the new multi-trait Bayesian method were comparable to those obtained from a multi-trait GBLUP for all the three milk production traits. In the scenario of each chromosome as one region, BTA 14 and BTA 5 accounted for very large genomic variance, covariance and correlation for milk yield and fat yield, whereas no specific chromosome showed very large genomic variance, covariance and correlation for protein yield. In the scenario of every 100 SNP as one region, most regions explained <0.50% of genomic variance and covariance for milk yield and fat yield, and explained <0.30% for protein yield, while some regions could present large variance and covariance. Although overall correlations between two populations for the three traits were positive and high, a few regions still showed weakly positive or highly negative genomic correlations for milk yield and fat yield.ConclusionsThe new multi-trait Bayesian method using latent variables to model heterogeneous variance and covariance could work well for estimating the genomic variances and covariances for all genome regions simultaneously. Those estimated genomic parameters could be useful to improve the genomic prediction accuracy for Chinese and Nordic Holstein populations using a joint reference data in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-017-0491-9) contains supplementary material, which is available to authorized users.

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Accuracy and bias of genomic prediction with different de-regression methods

Song

Zhang

et al. 2018

Animal

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Genomic selection has become increasingly important in the breeding of animals and plants. The response variable is an important factor, influencing the accuracy of genomic selection. The de-regressed proof (DRP) based on traditional estimated breeding value (EBV) is commonly used as response variable. In the current study, simulated data from 16th QTL-MAS Workshop and real data from Chinese Holstein cattle were used to compare accuracy and bias of genomic prediction with two methods of calculating DRP. Our results with simulated data showed that the correlation between genomic EBV and true breeding value achieved using the Jairath method (DRP_J) was superior to that achieved using the Garrick method (DRP_G) for simulated trait 1 but the reverse was true for simulated trait 3, and these two methods performed comparably for simulated trait 2. For all three simulated traits, DRP_J yielded larger bias of genomic prediction. However, DRP_J outperformed DRP_G in both accuracy and unbiasedness for four milk production traits in Chinese Holstein. In the estimation of genomic breeding value using genomic BLUP model, two methods for weighting diagonal elements of incidence matrix associated with residual error were also compared. With increasing the proportion of genetic variance unexplained by markers, the accuracy of genomic prediction was decreased and the bias was increased. Weighting by the reliability of DRP produced accuracy comparable to the evaluation where the proportion of genetic variance unexplained by markers was considered, but with smaller bias in general.

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Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese–Nordic reference population

Cited by 9 publications

References 24 publications

Genome-wide Association Studies for Female Fertility Traits in Chinese and Nordic Holsteins

Genome-wide Association Studies for Female Fertility Traits in Chinese and Nordic Holsteins

The patterns of genomic variances and covariances across genome for milk production traits between Chinese and Nordic Holstein populations

Accuracy and bias of genomic prediction with different de-regression methods

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