@article{10.11648/j.cbb.20251302.13,
  author = {Tanmay Bandbe and Vineeta Johri and Uma Kumari},
  title = {Structure-guided Genome-wide Association Analysis of ALK Variants with GWAS Data Using R},
  journal = {Computational Biology and Bioinformatics},
  volume = {13},
  number = {2},
  pages = {72-85},
  doi = {10.11648/j.cbb.20251302.13},
  url = {https://doi.org/10.11648/j.cbb.20251302.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20251302.13},
  abstract = {Anaplastic lymphoma kinase (ALK) has been linked to several hematological malignancies; however, its comprehensive genetic variability and potential disease associations are not fully understood. In this study, a structure-guided genome-wide association analysis (GWAS) of ALK variants was performed using publicly available summary statistics and R-based analytical pipelines. The GWAS datasets were acquired, filtered, and ranked based on sample size to ensure sufficient statistical power. A focused analysis on two distinct datasets, which were selected based on sample size and phenotypic diversity: one representing lymphoma-related genetic traits from the UK Biobank, and another capturing ALK-associated proteomic variation. Rigorous quality control and comprehensive data visualization were performed using a set of diagnostic and analytical plots, including volcano plots, QQ plots, histograms, size effects, and a correlation matrix heatmap of numerical variables. Regional Manhattan plots highlighted distinct, highly significant associations at the ALK locus in both datasets, enabling the identification of independent lead variants. Interpretation of the QQ plots and histograms confirmed adequate control for population stratification and minimal inflation of test statistics. Integration of insights from the effect size distribution and SE versus Beta plots provided a clear assessment of the precision and reliability of estimated genetic effects. By mapping genetic variants onto the ALK protein structure, single-nucleotide polymorphisms (SNPs) with potential functional relevance and evaluating their associations with disease phenotypes across populations were prioritized. This strategy facilitates the identification of variants likely to influence protein structure and function, thereby enhancing the interpretability of GWAS findings in a protein-centric context. This approach demonstrates the power of integrating structural bioinformatics with statistical genetics to reveal novel genotype-phenotype relationships, offering valuable insights for precision medicine and targeted ALK-directed therapies. Overall, this integrative methodology establishes a reproducible framework for detailed regional GWAS analyses, successfully pinpointing strong ALK locus associations and identifying candidate variants for subsequent functional validation relevant to the phenotypes, and assessing their potential role in therapeutic investigation for hematological malignancies.},
 year = {2025}
}

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TY  - JOUR
T1  - Structure-guided Genome-wide Association Analysis of ALK Variants with GWAS Data Using R
AU  - Tanmay Bandbe
AU  - Vineeta Johri
AU  - Uma Kumari
Y1  - 2025/12/11
PY  - 2025
N1  - https://doi.org/10.11648/j.cbb.20251302.13
DO  - 10.11648/j.cbb.20251302.13
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 72
EP  - 85
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20251302.13
AB  - Anaplastic lymphoma kinase (ALK) has been linked to several hematological malignancies; however, its comprehensive genetic variability and potential disease associations are not fully understood. In this study, a structure-guided genome-wide association analysis (GWAS) of ALK variants was performed using publicly available summary statistics and R-based analytical pipelines. The GWAS datasets were acquired, filtered, and ranked based on sample size to ensure sufficient statistical power. A focused analysis on two distinct datasets, which were selected based on sample size and phenotypic diversity: one representing lymphoma-related genetic traits from the UK Biobank, and another capturing ALK-associated proteomic variation. Rigorous quality control and comprehensive data visualization were performed using a set of diagnostic and analytical plots, including volcano plots, QQ plots, histograms, size effects, and a correlation matrix heatmap of numerical variables. Regional Manhattan plots highlighted distinct, highly significant associations at the ALK locus in both datasets, enabling the identification of independent lead variants. Interpretation of the QQ plots and histograms confirmed adequate control for population stratification and minimal inflation of test statistics. Integration of insights from the effect size distribution and SE versus Beta plots provided a clear assessment of the precision and reliability of estimated genetic effects. By mapping genetic variants onto the ALK protein structure, single-nucleotide polymorphisms (SNPs) with potential functional relevance and evaluating their associations with disease phenotypes across populations were prioritized. This strategy facilitates the identification of variants likely to influence protein structure and function, thereby enhancing the interpretability of GWAS findings in a protein-centric context. This approach demonstrates the power of integrating structural bioinformatics with statistical genetics to reveal novel genotype-phenotype relationships, offering valuable insights for precision medicine and targeted ALK-directed therapies. Overall, this integrative methodology establishes a reproducible framework for detailed regional GWAS analyses, successfully pinpointing strong ALK locus associations and identifying candidate variants for subsequent functional validation relevant to the phenotypes, and assessing their potential role in therapeutic investigation for hematological malignancies.
VL  - 13
IS  - 2
ER  - 

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