SNPMace - A meta-analysis to estimate SNP effects across countries
The accuracy of genomic prediction could be improved by combining datasets across countries, but it is not always possible to combine the individual animal data. This project has tested a meta-analysis, called SNPMace, that mimics the combined analysis but requires only summary statistics, such as estimated SNP effects, from participating countries. The method uses the genetic correlation between a trait measured in different countries to produce country specific estimated SNP effects. We tested this method on data from 6 countries on the protein yield of Brown Swiss cattle and on the milk, fat and protein yields of Australian Holstein and Jersey cattle. In both cases the meta-analysis generated estimated breeding values that had a correlation with those obtained by analyzing the raw data in the range 0.99 to 1. The method is implemented in software called MetaGS which also converts data on a subset of SNPs to a common SNP set for analysis.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).