Abdulqader Jighly1, Haifa Benhajali2, Zengting Liu3, Mike E Goddard4,5. 1. Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, 3083, Australia. abdulqader.jighly@agriculture.vic.gov.au. 2. Department of Animal Breeding and Genetics, Interbull Centre, Swedish University of Agricultural Sciences, Box 7023, 750 07, Uppsala, Sweden. 3. IT Solutions for Animal Production (vit), Heinrich-Schroeder-Weg 1, 27283, Verden, Germany. 4. Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, 3083, Australia. 5. Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia.
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.
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.
Authors: Sayantan Das; Lukas Forer; Sebastian Schönherr; Carlo Sidore; Adam E Locke; Alan Kwong; Scott I Vrieze; Emily Y Chew; Shawn Levy; Matt McGue; David Schlessinger; Dwight Stambolian; Po-Ru Loh; William G Iacono; Anand Swaroop; Laura J Scott; Francesco Cucca; Florian Kronenberg; Michael Boehnke; Gonçalo R Abecasis; Christian Fuchsberger Journal: Nat Genet Date: 2016-08-29 Impact factor: 38.330
Authors: Naomi R Wray; Sang Hong Lee; Divya Mehta; Anna A E Vinkhuyzen; Frank Dudbridge; Christel M Middeldorp Journal: J Child Psychol Psychiatry Date: 2014-08-01 Impact factor: 8.982
Authors: J Crossa; P Pérez; J Hickey; J Burgueño; L Ornella; J Cerón-Rojas; X Zhang; S Dreisigacker; R Babu; Y Li; D Bonnett; K Mathews Journal: Heredity (Edinb) Date: 2013-04-10 Impact factor: 3.821