GENETIC RISK ANALYSIS OF METABOLIC SYNDROME IN THE EGYPTIAN POPULATION: A COMPREHENSIVE APPROACH USING ALLELE FREQUENCY INVESTIGATION AND MISSENSE3D PREDICTION /Mahmoud Bassyouni Elsayed Attia Abdallah

Supervisor:
Walaa Medhat

Thesis (M.A.)—Nile University, Egypt, 2024 .

"Includes bibliographical references"

Contents:
Contents
Page
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
Chapters:
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Thesis Outline and Summary of Contributions . . . . . . . . . . . . . . . . . . . . 2
2. Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1 Historical Perspective and Contemporary Challenges of Diabetes Mellitus . . . . . 3
2.2 Pathogenic Pathways and Types of Diabetes Mellitus . . . . . . . . . . . . . . . . . 4
2.3 Impact of Obesity on Diabetes Risk in Egypt . . . . . . . . . . . . . . . . . . . . . 5
2.3.1 Rising Obesity Trends and Their Consequences on Diabetes Prevalence in
Egypt Among Adults and Younger Population . . . . . . . . . . . . . . . . 5
2.3.2 Preventive Measures and Public Health Policies: . . . . . . . . . . . . . . . 5
2.4 Epidemiology and Genetic Landscape of Metabolic Syndrome in Egypt . . . . . . . 7
2.5 Precision Medicine: Tailoring Healthcare to Genetic, Environmental, and Lifestyle
Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1 Data Collection and Pre-processing: . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Development of the Genomic Analysis Pipeline: GenoVis (Genomic Visualization
and Insight System) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2.1 Computational Models in Variant Impact Analysis: . . . . . . . . . . . . . . 12
3.3 Genetic Analysis and Visualization of Populations: . . . . . . . . . . . . . . . . . . 12
3.4 Unique Egyptians’ Single Nucleotide Variants (SNVs) and Prediction of Protein
Structural and functional Damage: . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
vii
4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.1 Intersection Analysis of Diabetes Type II with Obesity, Hypertension and Hyperlipidemia: Annotation and Characterization of Associated Genes: . . . . . . . . . . 19
4.2 Genotypes PCA and Heatmap Analysis of Allele Frequencies Matrix Reveal Population Clustering: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.3 Analysis of Two Protein-Coding SNPs with Structural Damage Potential in CDKAL1 and PPARG Genes: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6. Conclusions and Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.2 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
7. Data Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
8. Supplementary Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Abstract:
Diabetes mellitus (DM) is increasingly recognized as a significant global health challenge, particularly in Egypt, where the incidence of prediabetes is rising annually. This complex disease is
influenced by a multitude of factors, including obesity, hyperlipidemia, and hypertension, which
have emerged as major health concerns. The intricate pathophysiology of diabetes is closely associated with genetic variations, which may predispose certain populations, such as those in Egypt,
to a higher risk of developing DM. In this context, my study undertakes a detailed analysis to discern the genetic commonalities and distinctions between the Egyptian genome reference and various
subpopulations from the 1000-genome project, including Europeans, Ad-Mixed Americans, South
Asians, East Asians, and Africans. This endeavor aims to identify potential genetic predispositions
to metabolic syndromes. The methodology encompasses an examination of allele frequency variations across these populations, augmented by a principal component analysis of genotypes. Findings
reveal that the metabolic genes in the Egyptian reference group exhibit close clustering with those
of Europeans, Ad-Mixed Americans, and South Asians. Furthermore, the analysis identified 8,563
unique variants within the Egyptian cohort. Of particular interest are two variants predicted to
cause structural damage: CDKAL1: 6_21065070 (A>T) and PPARG: 3_12351660 (C>T), as determined by the Missense3D database. The CDKAL1 variant is a protein-coding gene linked to type
2 diabetes mellitus, while PPARG is crucial for adipocyte differentiation and glucose homeostasis.
These variants were found in a heterozygous state in two separate individuals within a sample size of
110 Egyptians. This study illuminates distinctive genetic characteristics of the Egyptian population
that contribute to the high prevalence of DM in the region. The analytical pipeline developed for
this research, which is accessible via GitHub, offers a valuable tool for conducting similar genetic
analyses across different diseases and populations.
Keywords: Metabolic Syndrome, Principal Component Analysis (PCA), Genetic Analysis, Global
Health Challenge, Diabetes Mellitus (DM), Population Genetics, Allele Frequency Variations.

Text in English, abstracts in English and Arabic