Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency
Abstract Background Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensi...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
BMC
2022-09-01
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| Series: | Genetics Selection Evolution |
| Online Access: | https://doi.org/10.1186/s12711-022-00749-z |
| _version_ | 1828735239038435328 |
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| author | Sunduimijid Bolormaa Iona M. MacLeod Majid Khansefid Leah C. Marett William J. Wales Filippo Miglior Christine F. Baes Flavio S. Schenkel Erin E. Connor Coralia I. V. Manzanilla-Pech Paul Stothard Emily Herman Gert J. Nieuwhof Michael E. Goddard Jennie E. Pryce |
| author_facet | Sunduimijid Bolormaa Iona M. MacLeod Majid Khansefid Leah C. Marett William J. Wales Filippo Miglior Christine F. Baes Flavio S. Schenkel Erin E. Connor Coralia I. V. Manzanilla-Pech Paul Stothard Emily Herman Gert J. Nieuwhof Michael E. Goddard Jennie E. Pryce |
| author_sort | Sunduimijid Bolormaa |
| collection | DOAJ |
| description | Abstract Background Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. Results GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. Conclusions The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended. |
| first_indexed | 2024-04-12T23:02:04Z |
| format | Article |
| id | doaj.art-1fd8f15c792044cc9755c1186e0c3c46 |
| institution | Directory Open Access Journal |
| issn | 1297-9686 |
| language | deu |
| last_indexed | 2024-04-12T23:02:04Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj.art-1fd8f15c792044cc9755c1186e0c3c462022-12-22T03:13:00ZdeuBMCGenetics Selection Evolution1297-96862022-09-0154111710.1186/s12711-022-00749-zSharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiencySunduimijid Bolormaa0Iona M. MacLeod1Majid Khansefid2Leah C. Marett3William J. Wales4Filippo Miglior5Christine F. Baes6Flavio S. Schenkel7Erin E. Connor8Coralia I. V. Manzanilla-Pech9Paul Stothard10Emily Herman11Gert J. Nieuwhof12Michael E. Goddard13Jennie E. Pryce14Agriculture Victoria Research, AgribioAgriculture Victoria Research, AgribioAgriculture Victoria Research, AgribioAgriculture Victoria Research, Ellinbank Centre, EllinbankAgriculture Victoria Research, Ellinbank Centre, EllinbankLACTANETCGIL, University of GuelphCGIL, University of GuelphAnimal Genomics and Improvement Laboratory, USDA, Agricultural Research Service, Beltsville Agricultural Research CenterCenter for Quantitative Genetics and Genomics, Aarhus UniversityFaculty of Agricultural, Life & Environmental Sciences, University of AlbertaFaculty of Agricultural, Life & Environmental Sciences, University of AlbertaAgriculture Victoria Research, AgribioAgriculture Victoria Research, AgribioAgriculture Victoria Research, AgribioAbstract Background Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. Results GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. Conclusions The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.https://doi.org/10.1186/s12711-022-00749-z |
| spellingShingle | Sunduimijid Bolormaa Iona M. MacLeod Majid Khansefid Leah C. Marett William J. Wales Filippo Miglior Christine F. Baes Flavio S. Schenkel Erin E. Connor Coralia I. V. Manzanilla-Pech Paul Stothard Emily Herman Gert J. Nieuwhof Michael E. Goddard Jennie E. Pryce Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency Genetics Selection Evolution |
| title | Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency |
| title_full | Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency |
| title_fullStr | Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency |
| title_full_unstemmed | Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency |
| title_short | Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency |
| title_sort | sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency |
| url | https://doi.org/10.1186/s12711-022-00749-z |
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