The impact of QTL sharing and properties on multi-breed GWAS in cattle: a simulation study
Irene van den Berg
A * and Iona M. MacLeod A B
+ Author Affiliations
- Author Affiliations
A Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Vic. 3083, Australia.
B School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, Vic. 3083, Australia.
Animal Production Science 63(11) 996-1007 https://doi.org/10.1071/AN22460
Submitted: 14 December 2022 Accepted: 13 March 2023 Published: 6 April 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Context: Genome-wide association studies (GWAS) and meta-analyses can be used to detect variants that affect quantitative traits. Multi-breed GWAS may lead to increased power and precision compared with within-breed GWAS. However, not all causal variants segregate in all breeds, and variants that segregate in multiple breeds may have different allele frequencies in different breeds. It is not known how differences in minor allele frequency (MAF) affect multi-breed GWAS and meta-analyses.
Aims: Our aim was to study the impact of differences in MAF at causal variants on mapping power and precision.
Methods: We used real imputed sequence data to simulate quantitative traits in three dairy cattle breeds. Causal variants (QTN) were simulated according to the following three scenarios: variants with a similar MAF in all breeds, variants with a lower MAF in one breed than the other, and variants that each only segregated in one of the breeds. We analysed the simulated quantitative traits with three methods to compare mapping power and precision: within-breed GWAS, multi-breed GWAS and meta-analysis.
Key results: Our results indicated that the multi-breed analyses (multi-breed GWAS or meta-analysis) detected similar or more QTN than did within-breed GWAS, with improved mapping precision in most scenarios. However, when MAF differed between breeds, or variants were breed specific, the advantage of the multi-breed analyses over within breed GWAS decreased. Regardless of the type of QTN (similar MAF in all breeds, different MAF in different breeds, or only segregating in one breed), multi-breed GWAS and meta-analyses performed similar or better than did within-breed GWAS, demonstrating the benefits of multi-breed GWAS. We did not find large differences between the results obtained with the meta-analysis and multi-breed GWAS, confirming that a meta-analysis can be a suitable approximation of a multi-breed GWAS.
Conclusions: Our results showed that multi-breed GWAS and meta-analysis generally detect more QTN with improved precision than does within-breed GWAS, and that even with differences in MAF, multi-breed analyses did not perform worse than within-breed GWAS.
Implications: Our study confirmed the benefits of multi-breed GWAS and meta-analysis.
Keywords: allele frequency, dairy cattle, GWAS, meta-analysis, multi-breed, QTL detection, quantitative traits, within breed.
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