Improving genetic variant identification for quantitative traits using ensemble learning-based approaches.

Journal: BMC genomics
PMID: 40075256

Abstract

BACKGROUND: Genome-wide association studies (GWAS) are rapidly advancing due to the improved resolution and completeness provided by Telomere-to-Telomere (T2T) and pangenome assemblies. While recent advancements in GWAS methods have primarily focused on identifying genetic variants associated with discrete phenotypes, approaches for quantitative traits (QTs) remain underdeveloped. This has often led to significant variants being overlooked due to biases from genotype multicollinearity and strict p-value thresholds.

Authors

  • Jyoti Sharma
    Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India.
  • Vaishnavi Jangale
    Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, 342030, Rajasthan, India.
  • Rajveer Singh Shekhawat
    Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, 342030, Rajasthan, India.
  • Pankaj Yadav
    1Institute of Medical Bioinformatics and Systems Medicine Medical Center, Faculty of Medicine, Albert-Ludwigs University of Freiburg, 79110Freiburg, Germany; 2Department of Computer Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India; 3Department of Bioscience and Bio- engineering, Indian Institute of Technology, Jodhpur, India; 4Institute of Bioinformatics, International Technology Park, Bangalore, 560066, India; 5Manipal Academy of Higher Education (MAHE), Manipal576104, Karnataka, India.

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