MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics
Abstract Background Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | deu |
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BMC
2022-06-01
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| Series: | Genetics Selection Evolution |
| Online Access: | https://doi.org/10.1186/s12711-022-00725-7 |
| _version_ | 1828328656826531840 |
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| author | Abdulqader Jighly Haifa Benhajali Zengting Liu Mike E. Goddard |
| author_facet | Abdulqader Jighly Haifa Benhajali Zengting Liu Mike E. Goddard |
| author_sort | Abdulqader Jighly |
| collection | DOAJ |
| description | Abstract Background Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy restrictions, which can limit the size of their reference populations and creates a need for a practical meta-analysis method. Results We developed a meta-analysis, named MetaGS, that duplicates the results of multi-trait best linear unbiased prediction (mBLUP) analysis without accessing raw data. MetaGS exploits the correlations among different populations to produce more accurate population-specific single nucleotide polymorphism (SNP) effects. The method improves SNP effect estimations for a given population depending on its relations to other populations. MetaGS was tested on milk, fat and protein yield data of Australian Holstein and Jersey cattle and it generated very similar genomic estimated breeding values to those produced using the mBLUP method for all traits in both breeds. One of the major difficulties when combining SNP effects across populations is the use of different variants for the populations, which limits the applications of meta-analysis in practice. We solved this issue by developing a method to impute missing summary statistics without using raw data. Our results showed that imputing summary statistics can be done with high accuracy (r > 0.9) even when more than 70% of the SNPs were missing with a minimal effect on prediction accuracy. Conclusions We demonstrated that MetaGS can replace the mBLUP model when raw data cannot be shared, which can lead to more flexible collaborations compared to the single-trait BLUP model. |
| first_indexed | 2024-04-13T20:12:23Z |
| format | Article |
| id | doaj.art-0b223763f73947708a9512d17e0ab20f |
| institution | Directory Open Access Journal |
| issn | 1297-9686 |
| language | deu |
| last_indexed | 2024-04-13T20:12:23Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj.art-0b223763f73947708a9512d17e0ab20f2022-12-22T02:31:48ZdeuBMCGenetics Selection Evolution1297-96862022-06-0154111110.1186/s12711-022-00725-7MetaGS: an accurate method to impute and combine SNP effects across populations using summary statisticsAbdulqader Jighly0Haifa Benhajali1Zengting Liu2Mike E. Goddard3Agriculture Victoria, AgriBio, Centre for AgriBiosciencesDepartment of Animal Breeding and Genetics, Interbull Centre, Swedish University of Agricultural SciencesIT Solutions for Animal Production (vit)Agriculture Victoria, AgriBio, Centre for AgriBiosciencesAbstract Background Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy restrictions, which can limit the size of their reference populations and creates a need for a practical meta-analysis method. Results We developed a meta-analysis, named MetaGS, that duplicates the results of multi-trait best linear unbiased prediction (mBLUP) analysis without accessing raw data. MetaGS exploits the correlations among different populations to produce more accurate population-specific single nucleotide polymorphism (SNP) effects. The method improves SNP effect estimations for a given population depending on its relations to other populations. MetaGS was tested on milk, fat and protein yield data of Australian Holstein and Jersey cattle and it generated very similar genomic estimated breeding values to those produced using the mBLUP method for all traits in both breeds. One of the major difficulties when combining SNP effects across populations is the use of different variants for the populations, which limits the applications of meta-analysis in practice. We solved this issue by developing a method to impute missing summary statistics without using raw data. Our results showed that imputing summary statistics can be done with high accuracy (r > 0.9) even when more than 70% of the SNPs were missing with a minimal effect on prediction accuracy. Conclusions We demonstrated that MetaGS can replace the mBLUP model when raw data cannot be shared, which can lead to more flexible collaborations compared to the single-trait BLUP model.https://doi.org/10.1186/s12711-022-00725-7 |
| spellingShingle | Abdulqader Jighly Haifa Benhajali Zengting Liu Mike E. Goddard MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics Genetics Selection Evolution |
| title | MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics |
| title_full | MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics |
| title_fullStr | MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics |
| title_full_unstemmed | MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics |
| title_short | MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics |
| title_sort | metags an accurate method to impute and combine snp effects across populations using summary statistics |
| url | https://doi.org/10.1186/s12711-022-00725-7 |
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