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Ajasa et al. Genetics Selection Evolution (2025) 57:9 https://doi.org/10.1186/s12711-025-00959-1SHORT COMMUNICATIONOpen Access© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom-mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Genetics Selection EvolutionGenome-wide association analysis using multiple Atlantic salmon populationsAfees A. Ajasa 1,2* , Hans M. Gjøen 2 , Solomon A. Boison 3 and Marie Lillehammer 1 Abstract Background In a previous study, we found low persistence of linkage disequilibrium (LD) phase across breeding populations of Atlantic salmon. Accordingly, we observed no increase in accuracy from combining these populations for genomic prediction. In this study, we aimed to examine if the same were true for detection power in genome-wide association studies (GWAS), in terms of reduction in p-values, and if the precision of mapping quantitative trait loci (QTL) would improve from such analysis. Since individual records may not always be available, e.g. due to pro-prietorship or conf i dentiality, we also compared mega-analysis and meta-analysis. Mega-analysis needs access to all individual records, whereas meta-analysis utilizes parameters, such as p-values or allele substitution ef f ects, from mul-tiple studies or populations. Furthermore, dif f erent methods for determining the presence or absence of independ-ent or secondary signals, such as conditional association analysis, approximate conditional and joint analysis (COJO), and the clumping approach, were assessed.Results Mega-analysis resulted in increased detection power, in terms of reduction in p-values, and increased preci-sion, compared to the within-population GWAS. Only one QTL was detected using conditional association analysis, both within populations and in mega-analysis, while the number of QTL detected with COJO and the clumping approach ranged from 1 to 19. The allele substitution ef f ect and -log 10 p-values obtained from mega-analysis were highly correlated with the corresponding values from various meta-analysis methods. Compared to mega-analysis, a higher detection power and reduced precision were obtained with the meta-analysis methods.Conclusions Our results show that combining multiple datasets or populations in a mega-analysis can increase detection power and mapping precision. With meta-analysis, a higher detection power was obtained compared to mega-analysis. However, care must be taken in the interpretation of the meta-analysis results from multiple popula-tions because their test statistics might be inf l ated due to population structure or cryptic relatedness.BackgroundGenome-wide association studies (GWAS) can help provide insight into the genetic architecture of complex traits. Th e success of GWAS depends on several factors such allele frequency, allele substitution ef f ect size, sample size, and degree of complexity of the trait [1]. Of these factors, it is only sample size that is within the control of the investigator. GWAS in aquaculture populations have so far often been based on small sample sizes, usually less than or around 1000 [2–4], probably due to the fairly recent adoption of genomic technologies *Correspondence:Afees A. Ajasaafees.ajasa@nof i ma.no1 Department of Breeding and Genetics, Nof i ma (Norwegian Institute of Food, Fisheries and Aquaculture Research), P. O. Box 210, N-1431 Ås, Norway2 Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 5003 NMBU, N-1432 Ås, Norway3 Mowi Genetics AS, Sandviksboder 77AB, Bergen, Norway