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Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics

Heredity volume 133, pages 11–20 (2024)Cite this article

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Abstract

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

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Fig. 1: Bivariate genome-wide association analysis of two developmental traits.
Fig. 2: Hexbin scatter plot comparing all Z-scores of the two traits across the genome, showing the bivariate statistical significance of the detected locus.
Fig. 3: The discovered locus is highly confounded with population structure.
Fig. 4: Comparisons of single-trait and dual-trait GWAS results of the target SNP under different simulated scenarios using 199 natural Arabidopsis thaliana inbred lines genotype data.
Fig. 5: Performance and computational efficiency comparisons between MultiABEL and GEMMA using simulations.
Fig. 6: Prioritized candidate genes at the detected locus for flowering time using SMR analysis.

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Data availability

All genotypes and phenotypes data we used in this study are publicly available from Atwell et al. (2010), Kawakatsu et al. (2016), Schmitz et al. (2013), The 1001 Genomes Consortium (2016). Atwell et al.’s dataset, which includes 199 natural Arabidopsis thaliana inbred lines containing 107 phenotypes and corresponding genotypes are publicly available at https://github.com/Gregor-Mendel-Institute/atpolydb/blob/master/miscellaneous_data/phenotype_published_raw.tsvand https://github.com/Gregor-Mendel-Institute/atpolydb/blob/master/250k_snp_data/call_method_75.tar.gz. The genotypes and phenotypes related to flowering time at 10 °C in A. thaliana within the 1001 Genomes project are publicly available at https://1001genomes.org and https://arapheno.1001genomes.org/phenotype/261. The expression data for A. thaliana is publicly available at the NCBI Gene Expression Omnibus (GEO) database under the accession numbers GSE80744 and GSE43858.

Code availability

The multivariate analysis method was implemented in the MultiABEL package. The source code is available on GitHub (https://github.com/xiashen/MultiABEL).

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Acknowledgements

XS was in receipt of a National Key Research and Development Program grant (No. 2022YFF1202100 & No. 2022YFF1202105), a National Natural Science Foundation of China (NSFC) grant (No. 12171495), a Natural Science Foundation of Guangdong Province grant (No. 2021A1515010866), and Swedish Research Council (Vetenskapsrådet) grants (No. 2014-00371, No. 2017-02543, No. 2022-01309). International collaboration within this work was partly supported by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) initiation grant to XS (No. IB2015-6000) and Karolinska Institutet travel grant (No. 2017-00534). The work from XF was supported by the China Postdoctoral Science Foundation (2023M740690). The work from TL was supported by the China Postdoctoral Science Foundation (2023M740696). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Xiao Feng, Yanjun Zan.

Authors and Affiliations

  1. Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Guangzhou, China

    Xiao Feng, Ting Li, Yue Yao, Jiabei Li, Hadi Charati & Xia Shen

  2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Xiao Feng, Zheng Ning, Weilin Xu, Qianhui Wan & Xia Shen

  3. Key Laboratory of Tobacco Improvement and Biotechnology, Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China

    Yanjun Zan

  4. Department of Computational Biology, Cornell University, Ithaca, NY, USA

    Weilin Xu

  5. Department of Mathematics, University of California, Davis, CA, USA

    Qianhui Wan

  6. State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Shenzhen, China

    Dongyu Zeng & Yang Liu

  7. School of Engineering, Sun Yat-sen University, Guangzhou, China

    Ziyi Zeng

  8. Center for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK

    Xia Shen

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Contributions

XS initiated and coordinated the study. XS and YL supervised the study. XF and YZ performed the data analysis. ZN and XS contributed to statistical modeling and interpretation. TL, YY, JL, HC, WX, QW, DZ and ZZ contributed to data processing. XF, YZ and XS wrote the manuscript. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Yang Liu or Xia Shen.

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Competing interests

XS is the founder of Quantix BioSciences. The other authors declare no competing interests.

Research Ethics statement

No approval from research ethics committees is required for this study because it utilizes publicly available data on the plant Arabidopsis thaliana.

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Associate editor: Yuan-Ming Zhang.

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Feng, X., Zan, Y., Li, T. et al. Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics. Heredity 133, 11–20 (2024). https://doi.org/10.1038/s41437-024-00688-z

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