Genotype Analysis: Sub-Saharan Africa vs. Dublin Populations

Abstract

This lab report presents an analysis of genotype frequencies in two distinct population groups, Sub-Saharan Africa and Dublin, focusing on the C677T MTHFR gene. The study aims to explore geographical and ethnic variations in genotype distribution and assess the equilibrium status of these populations. The Hardy-Weinberg equilibrium and Chi-square tests were employed for statistical analysis.

Introduction

The C677T MTHFR gene mutation leads to the presence of T alleles, and its prevalence can vary among populations. Geographical and ethnic factors can impact the distribution of genotypes.

This study investigates genotype frequencies in Sub-Saharan Africa and Dublin populations.

Methods

Genotype data was collected from 301 individuals in Sub-Saharan Africa and 318 individuals in Dublin. Genotype frequencies were determined for CC, CT, and TT genotypes, along with the allele frequencies for C and T alleles. The Hardy-Weinberg equilibrium equation (p² + 2pq + q² = 1) was used to calculate expected genotype frequencies. Chi-square tests were performed to assess statistical significance.

Sub-Saharan Africa Population

Allele Frequencies:
C allele: 564 (0.937)
T allele: 38 (0.063)

Dublin Population

Allele Frequencies:
C allele: 425 (0.67)
T allele: 211 (0.33)

Results

In the Sub-Saharan Africa population, the CC genotype is the most prevalent (87.4%), while in Dublin, the CT genotype is more common (40.6%). The T mutation (TT and CT genotypes) is more prevalent in the Dublin population. The Chi-square results for both populations were not statistically significant, indicating that the populations are in equilibrium.

However, when considering the combined population, the Chi-square result was highly statistically significant (p = 0.

0003), suggesting that the null hypothesis of equilibrium should be rejected. This suggests that the equilibrium status can vary when analyzing larger populations.

It is important to note that the sample sizes in the Sub-Saharan Africa and Dublin studies were different, potentially leading to unequal variances and affecting statistical power.

Gene flow can occur when breeding within populations, leading to the transfer of new alleles. Genetic drift may impact smaller populations more significantly, altering allele frequencies.

Discussion

The geographical and ethnic variations in genotype distribution observed in the Sub-Saharan Africa and Dublin populations may be influenced by factors such as cytokine gene presence and historical attributes. The presence of the T mutation was more common in the Dublin group.

The Chi-square results for individual populations were not statistically significant, indicating equilibrium, but the combined population showed significant deviation from equilibrium. This highlights the importance of considering population size when analyzing genetic data.

Overall, understanding allele frequency differences between populations is essential for genomic research and can help identify regions with unique genetic attributes influenced by various factors, including historical and environmental pressures.

It's important to note that the Hardy-Weinberg equilibrium is not a constant state, as populations can deviate from equilibrium over time. Therefore, its application should be considered in the context of genetic variation within populations.

References

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2. Van Dyke, et al. (2010). Ethnic differences in cytokine gene polymorphisms: Potential implications for cancer development. Cancer, 116(16), 3559-3568.
3. Wiberg, et al. (2017). Genetic variation in the MTHFR gene and neural tube defects in Northern China. Pediatric Research, 82(6), 936-941.
4. Nature Education. (n.d.). Genetic Drift and Gene Flow. Retrieved from https://www.nature.com/scitable/knowledge/library/genetic-drift-and-gene-flow-16579995/