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An Improved Expectation–Maximization Bayesian Algorithm for GWAS

Author

Listed:
  • Ganwen Zhang

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China
    These authors contributed equally to this work.)

  • Jianini Zhao

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China
    These authors contributed equally to this work.)

  • Jieru Wang

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China)

  • Guo Lin

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China)

  • Lin Li

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China)

  • Fengfei Ban

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China)

  • Meiting Zhu

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China)

  • Yangjun Wen

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China)

  • Jin Zhang

    (College of Science, Nanjing Agricultural University, Nanjing 210095, China)

Abstract

Genome-wide association studies (GWASs) are flexible and comprehensive tools for identifying single nucleotide polymorphisms (SNPs) associated with complex traits or diseases. The whole-genome Bayesian models are an effective way of incorporating important prior information into modeling. Bayesian methods have been widely used in association analysis. However, Bayesian analysis is often not feasible due to the high-throughput genotype and large sample sizes involved. In this study, we propose a new Bayesian algorithm under the mixed linear model framework: the expectation and maximization BayesB Improved algorithm (emBBI). The emBBI algorithm corrects polygenic and environmental noise and reduces dimensions; then, it estimates and tests marker effects using emBayesB and the LOD test, respectively. We conducted two simulation experiments and analyzed a real dataset related to flowering time in Arabidopsis to demonstrate the validation of the new algorithm. The results show that the emBBI algorithm is more flexible and accurate in simulation studies compared to established methods, and it performs well under complex genetic backgrounds. The analysis of the Arabidopsis real dataset further illustrates the advantages of the emBBI algorithm for GWAS by detecting known genes. Furthermore, 12 candidate genes are identified in the neighborhood of the significant quantitative trait nucleotides (QTNs) of flowering-related QTNs in Arabidopsis . In addition, we also performed enrichment analysis and tissue expression analysis of candidate genes, which will help us better understand the genetic basis of flowering-related traits in Arabidopsis .

Suggested Citation

  • Ganwen Zhang & Jianini Zhao & Jieru Wang & Guo Lin & Lin Li & Fengfei Ban & Meiting Zhu & Yangjun Wen & Jin Zhang, 2024. "An Improved Expectation–Maximization Bayesian Algorithm for GWAS," Mathematics, MDPI, vol. 12(13), pages 1-14, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:1944-:d:1420475
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    References listed on IDEAS

    as
    1. Gerhard Moser & Sang Hong Lee & Ben J Hayes & Michael E Goddard & Naomi R Wray & Peter M Visscher, 2015. "Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model," PLOS Genetics, Public Library of Science, vol. 11(4), pages 1-22, April.
    2. Susanna Atwell & Yu S. Huang & Bjarni J. Vilhjálmsson & Glenda Willems & Matthew Horton & Yan Li & Dazhe Meng & Alexander Platt & Aaron M. Tarone & Tina T. Hu & Rong Jiang & N. Wayan Muliyati & Xu Zha, 2010. "Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines," Nature, Nature, vol. 465(7298), pages 627-631, June.
    3. Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
    4. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
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