| 1. |
- Berglund, Britt, et al.
(författare)
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Ny hedersdoktor vid SLU
- 2017
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Ingår i: Husdjur. - 0046-8339. ; , s. 30-30
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Tidskriftsartikel (populärvet., debatt m.m.)
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| 2. |
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| 3. |
- Jorjani, Hossein
(författare)
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Multiple-breed genomic evaluation by principal component analysis in small size populations
- 2015
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Ingår i: Animal. - 1751-7311 .- 1751-732X. ; 9, s. 738-749
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Tidskriftsartikel (refereegranskat)abstract
- In this study, the effects of breed composition and predictor dimensionality on the accuracy of direct genomic values (DGV) in a multiple breed (MB) cattle population were investigated. A total of 3559 bulls of three breeds were genotyped at 54 001 single nucleotide polymorphisms: 2093 Holstein (H), 749 Brown Swiss (B) and 717 Simmental (S). DGV were calculated using a principal component (PC) approach for either single (SB) or MB scenarios. Moreover, DGV were computed using all SNP genotypes simultaneously with SNPBLUP model as comparison. A total of seven data sets were used: three with a SB each, three with different pairs of breeds (HB, HS and BS), and one with all the three breeds together (HBS), respectively. Editing was performed separately for each scenario. Reference populations differed in breed composition, whereas the validation bulls were the same for all scenarios. The number of SNPs retained after data editing ranged from 36 521 to 41 360. PCs were extracted from actual genotypes. The total number of retained PCs ranged from 4029 to 7284 in Brown Swiss and HBS respectively, reducing the number of predictors by about 85% (from 82% to 89%). In all, three traits were considered: milk, fat and protein yield. Correlations between deregressed proofs and DGV were used to assess prediction accuracy in validation animals. In the SB scenarios, average DGV accuracy did not substantially change when either SNPBLUP or PC were used. Improvement of DGV accuracy were observed for some traits in Brown Swiss, only when MB reference populations and PC approach were used instead of SB-SNPBLUP (+10% HBS, +16%HB for milk yield and +3% HBS and +7% HB for protein yield, respectively). With the exclusion of the abovementioned cases, similar accuracies were observed using MB reference population, under the PC or SNPBLUP models. Random variation owing to sampling effect or size and composition of the reference population may explain the difficulty in finding a defined pattern in the results.
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| 4. |
- Jorjani, Hossein
(författare)
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SNPchiMp v.3: integrating and standardizing single nucleotide polymorphism data for livestock species
- 2015
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Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- Background: In recent years, the use of genomic information in livestock species for genetic improvement, association studies and many other fields has become routine. In order to accommodate different market requirements in terms of genotyping cost, manufacturers of single nucleotide polymorphism (SNP) arrays, private companies and international consortia have developed a large number of arrays with different content and different SNP density. The number of currently available SNP arrays differs among species: ranging from one for goats to more than ten for cattle, and the number of arrays available is increasing rapidly. However, there is limited or no effort to standardize and integrate array- specific (e.g. SNP IDs, allele coding) and species-specific (i.e. past and current assemblies) SNP information. Results: Here we present SNPchiMp v.3, a solution to these issues for the six major livestock species (cow, pig, horse, sheep, goat and chicken). Original data was collected directly from SNP array producers and specific international genome consortia, and stored in a MySQL database. The database was then linked to an open-access web tool and to public databases. SNPchiMp v.3 ensures fast access to the database (retrieving within/across SNP array data) and the possibility of annotating SNP array data in a user-friendly fashion. Conclusions: This platform allows easy integration and standardization, and it is aimed at both industry and research. It also enables users to easily link the information available from the array producer with data in public databases, without the need of additional bioinformatics tools or pipelines. In recognition of the open-access use of Ensembl resources, SNPchiMp v.3 was officially credited as an Ensembl E!mpowered tool. Availability at http://bioinformatics.tecnoparco.org/ SNPchimp.
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| 5. |
- Loberg, Anne, et al.
(författare)
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Estimates of genetic variance and variance of predicted genetic merits using pedigree or genomic relationship matrices in six Brown Swiss cattle populations for different traits
- 2015
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Ingår i: Journal of Animal Breeding and Genetics. - : Wiley. - 0931-2668 .- 1439-0388. ; 132, s. 376-385
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Tidskriftsartikel (refereegranskat)abstract
- The amount of variance captured in genetic estimations may depend on whether a pedigree-based or genomic relationship matrix is used. The purpose of this study was to investigate the genetic variance as well as the variance of predicted genetic merits (PGM) using pedigree-based or genomic relationship matrices in Brown Swiss cattle. We examined a range of traits in six populations amounting to 173 population-trait combinations. A main aim was to determine how using different relationship matrices affect variance estimation. We calculated ratios between different types of estimates and analysed the impact of trait heritability and population size. The genetic variances estimated by REML using a genomic relationship matrix were always smaller than the variances that were similarly estimated using a pedigree-based relationship matrix. The variances from the genomic relationship matrix became closer to estimates from a pedigree relationship matrix as heritability and population size increased. In contrast, variances of predicted genetic merits obtained using a genomic relationship matrix were mostly larger than variances of genetic merit predicted using pedigree-based relationship matrix. The ratio of the genomic to pedigree-based PGM variances decreased as heritability and population size rose. The increased variance among predicted genetic merits is important for animal breeding because this is one of the factors influencing genetic progress.
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| 7. |
- Splittorff, Haifa, et al.
(författare)
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SNPMace – A meta-analysis to estimate SNP effects by combining results from multiple countries
- 2018
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Ingår i: Interbull bulletin. - 2001-340X. ; , s. 1-6
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Selection of bulls and cows is increasingly made on genomic estimated breeding values (GEBVs) calculated from their SNP genotypes and the estimated effects of each SNP. To obtain the most accurate GEBVs a large training population of animals with phenotypes and genotypes is needed. For some traits, some breeds and some countries such a large training population is not available. In these cases it would increase the accuracy of GEBVs if information from multiple countries and breeds were combined. This paper describes a meta-analysis to combine SNP effects from multiple countries. A project to test this procedure is under way and, if successful, may result in a new Interbull service.
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