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Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

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Abstract

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data.

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Data availability

Due to the subject confidentiality agreement, the data used during the current study are not publicly accessible but are available upon reasonable request from the first author.

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Acknowledgements

The authors would like to acknowledge the Genetic Analysis Workshop Grant [R01 GM031575] for providing the NARAC dataset. This work was made possible by funds from the National Institutes of Health [NO1-AR-2-2263 and RO1-AR-44422] and the National Arthritis Foundation (NAF).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Biomedical Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt

    Mohamed N. Saad, Fatma S. Ibrahim & Ashraf M. Said

  2. Biomedical Engineering Department, Misr University for Science and Technology, 6th of October City, Egypt

    Mohamed N. Saad

  3. Bioinformatics Department, Faculty of Computer and Information, Minia University, Minia, Egypt

    Galena W. Zareef

  4. Computer and Software Engineering Department, Misr University for Science and Technology, 6th of October City, Egypt

    Ashraf M. Said

  5. Department of Electrical Engineering, Faculty of Engineering, Egyptian-Russian University, Cairo, Egypt

    Hisham F. A. Hamed

  6. Electrical and Electronics Engineering Department, Faculty of Engineering, Minia University, Minia, Egypt

    Hisham F. A. Hamed

Authors
  1. Mohamed N. Saad
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  2. Galena W. Zareef
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  3. Fatma S. Ibrahim
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  4. Ashraf M. Said
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  5. Hisham F. A. Hamed
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Contributions

Conceptualization: MNS, AMS and HFAH. Data curation: FSI and MNS. Formal analysis: FSI and MNS. Investigation: FSI and MNS. Methodology: MNS, AMS and HFAH. Resources: MNS Software: FSI and MNS. Supervision: MNS, AMS and HFAH. Validation: GWZ and MNS. Visualization: FSI. Writing and original draft: GWZ, FSI and MNS Writing, review, and editing: GWZ, MNS, AMS and HFAH. All authors reviewed the manuscript.

Corresponding author

Correspondence to Galena W. Zareef.

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Saad, M.N., Zareef, G.W., Ibrahim, F.S. et al. Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint. Iran J Comput Sci 6, 387–396 (2023). https://doi.org/10.1007/s42044-023-00147-8

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  • DOI: https://doi.org/10.1007/s42044-023-00147-8

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