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MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics

Jighly, Abdulqader and Benhajali, Haifa and Liu, Zengting and Goddard, Mike and Goddard, Mike E. (2022). MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics. Genetics Selection Evolution. 54 , 37
[Research article]

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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.

Authors/Creators:Jighly, Abdulqader and Benhajali, Haifa and Liu, Zengting and Goddard, Mike and Goddard, Mike E.
Title:MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics
Series Name/Journal:Genetics Selection Evolution
Year of publishing :2022
Volume:54
Article number:37
Number of Pages:11
Publisher:BMC
ISSN:0999-193X
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Genetics and Breeding
URN:NBN:urn:nbn:se:slu:epsilon-p-117562
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-117562
Additional ID:
Type of IDID
DOI10.1186/s12711-022-00725-7
Web of Science (WoS)000805173700002
ID Code:28571
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Animal Breeding and Genetics
Deposited By: SLUpub Connector
Deposited On:26 Aug 2022 08:10
Metadata Last Modified:26 Aug 2022 08:11

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