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In any given population, individuals exhibit variations in physical characteristics and behaviors, primarily due to heritable traits passed down through generations. Examples of such heritable traits include blood type and the ability to taste specific chemicals. These variations are a consequence of molecular differences in the sequence and arrangement of nucleotides in DNA (Leicht, 2018). Charles Darwin proposed that these heritable traits vary due to the process of evolution by natural selection. According to Darwin's hypothesis of natural selection, individuals within a population possess different alleles, with one allele often being predominant, increasing the likelihood of survival and reproduction.
This discrepancy in survival and reproduction rates is termed "fitness," and the fitness of a genotype depends on the prevailing environmental conditions. Consequently, individuals possessing the predominant allele are more likely to see it propagate in subsequent generations (Futuyma, 2019). While natural selection plays a significant role in genetic variations, other factors also influence the genetic makeup of a population.
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Mutations are the most crucial factor affecting the genetic composition of a population. The mutational process is the ultimate source of all genetic variation and occurs at varying rates according to the organism's needs (Wright, 2003). Genetic drift, gene flow, mutations, natural selection, and nonrandom mating are distinct factors that can influence the genetic makeup of a population. When these conditions are at play, evolution occurs. Conversely, when these evolutionary conditions are not met, the allele and genotype frequencies in a population remain stable (Leicht, 2018). This concept is known as the Hardy-Weinberg Equilibrium, named after mathematician G.
H. Hardy and physician Wilhelm Weinberg. To maintain a non-evolving population, several restrictive conditions must be satisfied, including infinite population size, no mutations, no selection, no gene flow, and random mating. However, it is important to note that the Hardy-Weinberg model is often unrealistic because these conditions are rarely met, as virtually all populations violate at least one of these expectations. The objective of this laboratory experiment was to assess whether the Foundations of Biology class adheres to the conditions of the Hardy-Weinberg Equilibrium for either one or both of the target loci. The data collected in this study will be used to analyze the genetic variation among students in the Foundations of Biology class.
The two target loci investigated in this experiment are the CYP1A2 gene and the LCT gene. The CYP1A2 gene, situated on chromosome 15, encodes the Cytochrome P450 1A2 enzyme, which plays a significant role in the metabolism of various commonly used drugs, such as clozapine and caffeine (Sachse, 1999). This gene comprises two alleles, A and C, resulting in three possible genotypes for individuals: homozygous recessive AA (fast caffeine metabolizers), heterozygous AC (slow caffeine metabolizers), and homozygous dominant CC (slow caffeine metabolizers). These two alleles represent a single nucleotide polymorphism (SNP) located within intron 1. A PCR-based approach is used to amplify this SNP, and it also produces a Restriction Fragment Length Polymorphism (RFLP). RFLPs are alleles with detectable length differences. The C allele possesses a recognition site for the restriction enzyme Apa (5'GGGCCC3'), allowing it to be cut by the enzyme. In contrast, the A allele lacks this recognition site and remains uncut by the enzyme. In gel electrophoresis, the undigested allele is expected to produce a 743 base pair product, while the digested/cut allele yields products of 494 and 249 base pairs.
The LCT gene, located on Chromosome 2, encodes the lactase enzyme responsible for catalyzing the digestion of lactose, the sugar found in milk (Leitch, 2018). Individuals lacking the lactase enzyme in their small intestine are considered lactase non-persistent and are thus lactose intolerant. Lactase persistence refers to the ability of adults to digest dairy products. The inability to digest lactase in adulthood results from changes in LCT gene expression. The ability to digest lactase in adulthood may arise due to shifts in food production practices leading to an increased frequency of genetic variations that sustain lactase expression in adulthood (Segurel, 2017). The LCT gene consists of two alleles, C and T, resulting in three possible genotypes: homozygous dominant TT (lactose tolerant), heterozygotes TC, and homozygous recessive CC (lactose intolerant). During PCR amplification, the enzyme BsmF1 cleaves the T allele into 238 and 148 base pair fragments, while the C allele remains uncut due to the absence of the BsmF1 recognition site. The target base pair length for the LCT gene is 386.
The hypothesis for this laboratory experiment posits that if the genotype frequencies for the LCT and CYP1A2 genes are not constant, the Hardy-Weinberg Equilibrium is not met. This hypothesis is formulated based on the understanding that virtually all populations deviate from at least one of the restrictive conditions of the Hardy-Weinberg model.
To test our hypothesis, genomic DNA was extracted from cheek cells collected from all participants in the Foundations of Biology class. The DNA strands were separated by heating the cheek cells. Subsequently, the two target loci, LCT and CYP1A2, were specifically amplified using polymerase chain reaction (PCR) and primers. After amplification, restriction enzyme digestion and gel electrophoresis were performed to identify allele differences. The data collected from these experiments will be analyzed to determine the extent of genetic variation within the Foundations of Biology class.
The following materials and methods were employed in this experiment as described in the Foundations of Biology lab manual. The primary objective was to investigate genetic variations within the Foundations of Biology class.
Genomic DNA was extracted from cheek cells by swabbing the inside of the cheek and placing the swab into a tube containing DNA extraction solution. Subsequently, the tube was incubated in a water bath at 65°C for 1 minute, followed by transfer to a 98°C heating block for 2 minutes to denature the DNA strands. The tube was then placed on ice until polymerase chain reaction (PCR) amplification.
Two PCR reactions were set up to amplify the desired regions of the CYP1A2 and LCT loci. Taq DNA polymerase, deoxyribonucleotides, buffers, and salts were mixed to create the PCR reaction mixture. To each tube, 20 microliters of corresponding primers were added to facilitate DNA synthesis during PCR. A 5-microliter DNA sample was then added to this mixture. The PCR process involved a three-step cycle of denaturation at 95°C for 30 seconds, annealing of DNA and primers at 55°C for 30 seconds, and polymerization at 72°C for 30 seconds. Following the cycle, the tube was maintained at 72°C for 5 minutes to complete any unfinished synthesis. The choice of 72°C was based on the optimal temperature for Taq polymerase activity. For the PCR process, the primer sequences used were as follows:
Following PCR amplification, a 5-microliter sample of PCR DNA was mixed with purified water and a 10-microliter sample of the corresponding restriction enzyme. The LCT gene was paired with the BsmF1 enzyme and incubated at 65°C for a minimum of 60 minutes, while the CYP1A2 gene was paired with the Apa enzyme and incubated at 25°C for a minimum of 60 minutes. After incubation, 3 microliters of 10X dye were added to the digested DNA to enhance visibility during DNA fragment analysis.
A 40 ml sample of 1.6% agarose solution was prepared by mixing 40 ml of agarose with 1.6% agarose, resulting in 0.64 grams of agarose. This mixture was added to a 125-ml Erlenmeyer flask with 40 ml of 1X TBE buffer and microwaved on high power for 1 minute to completely melt the solution. Once cooled, ethidium bromide was added to the flask to stain the DNA fragments. The gel tray was placed in the electrophoresis chamber and submerged in 1X TBE buffer. During gel electrophoresis, DNA fragments migrated from the negative to the positive electrode.
Following the acquisition and analysis of gel images, the Hardy-Weinberg Equilibrium principle was applied to determine genotype and allele frequencies. The following equations were used:
Given that the sum is equal to 1, if the frequency of q is known, the frequency of p can be calculated using the equation p = 1 - q. To assess whether the Hardy-Weinberg Equilibrium was met, the Chi-Square test with 1 degree of freedom was employed. The Chi-Square test equation used was:
X² = (observed – expected)² / expected
Table 1 presents the pooled data for the Foundations of Biology class for the CYP1A2 and LCT genotypes. It also includes the calculations for allele frequencies, which are subsequently used to determine the genotype frequencies shown in Table 2.
In the CYP1A2 locus the majority of alleles align with the 766 base pair product and between the 500 and 250 base pairs. This alignment is as expected since the undigested CYP1A2 should yield a 743 bp product, while the digested alleles should result in 494 and 249 base pair fragments. The labeled wells include:
Wells 1 and 5 show a single line at 743 bp, indicating homozygous recessive AA genotypes as the A allele lacks the recognition site for the Apa enzyme. This implies that wells 3 and 4 are heterozygous, containing one uncut gene and two smaller fragments from the dominant allele.
In the LCT locus the majority of alleles fall between the 400 and 150 base pair product. This alignment is expected since the undigested DNA yields a 386 base pair PCR product.
| Locus | Genotype | Count | Allele Frequencies |
|---|---|---|---|
| CYP1A2 | AA | 15 | p(A) = 0.30, q(C) = 0.70 |
| AC | 25 | ||
| CC | 10 | ||
| LCT | TT | 18 | p(T) = 0.36, q(C) = 0.64 |
| TC | 20 | ||
| CC | 12 |
| Locus | Genotype | Observed | Expected |
|---|---|---|---|
| CYP1A2 | AA | 15 | 18.9 |
| AC | 25 | 32.2 | |
| CC | 10 | 12.9 | |
| LCT | TT | 18 | 16.2 |
| TC | 20 | 21.6 | |
| CC | 12 | 11.2 |
The Chi-Square value, used to test for adherence to the Hardy-Weinberg Equilibrium, is also included in Table 2. The p-value for LCT is less than 5%, indicating a rejection of the null hypothesis for Hardy-Weinberg Equilibrium. Conversely, the p-value for CYP1A2 is greater than 5%, indicating that CYP1A2 does not reject the null hypothesis for Hardy-Weinberg Equilibrium.
The results obtained from this genetic variation lab experiment provide valuable insights into the genetic makeup of the Foundations of Biology class. Despite our group's inability to generate interpretable gel results, the data from a representative gel taken from the class PowerPoint presentation has allowed us to draw meaningful conclusions regarding the CYP1A2 and LCT loci.
In the case of the CYP1A2 locus, the observed patterns align with expectations. The predominant 766 base pair product suggests that most individuals possess the homozygous recessive AA genotype, characterized by the absence of the Apa enzyme recognition site. Wells 3 and 4 indicate heterozygous individuals with one uncut gene and two smaller fragment DNA from the dominant allele, which is consistent with the 494 and 249 base pair fragments produced upon digestion.
Similarly, at the LCT locus, Figure 2 illustrates that alleles predominantly fall between the 400 and 150 base pair range. This conforms to expectations, as undigested DNA should result in a 386 base pair PCR product. Wells S1 and S4 display three lines, indicating heterozygous genotypes.
Table 1 summarizes the pooled data for the CYP1A2 and LCT genotypes, as well as allele frequencies. Notably, a higher frequency of the dominant C allele is observed for the LCT locus, whereas a higher frequency of the recessive A allele is evident for the CYP1A2 locus.
Table 2 presents the observed and expected genotypes for the two target loci, along with the Chi-Square values used to assess adherence to the Hardy-Weinberg Equilibrium. Importantly, the Chi-Square test results reveal that the p-value for LCT is less than 5%, leading to the rejection of the null hypothesis for Hardy-Weinberg Equilibrium. In contrast, the p-value for CYP1A2 exceeds 5%, indicating that the null hypothesis for Hardy-Weinberg Equilibrium is not rejected for this locus.
These findings suggest that the genetic variation within the Foundations of Biology class does not conform to Hardy-Weinberg Equilibrium for the LCT locus but maintains equilibrium for the CYP1A2 locus. The deviation from equilibrium at the LCT locus may be attributed to various factors, including evolutionary influences, gene flow, genetic drift, and natural selection. Additionally, demographic factors and the relatively small sample size of the class could contribute to these deviations.
In conclusion, this genetic variation lab experiment has provided valuable insights into the genetic composition of the Foundations of Biology class. While challenges in obtaining interpretable gel results were encountered, the representative gel and data analysis yielded meaningful information.
Our findings indicate that genetic variation within the class does not adhere to Hardy-Weinberg Equilibrium for the LCT locus, suggesting the presence of evolutionary forces and genetic influences affecting allele frequencies. In contrast, the CYP1A2 locus maintains equilibrium, indicating that this specific genetic variation conforms to Hardy-Weinberg expectations.
It is essential to acknowledge the limitations of this study, including the small sample size and the complexity of genetic dynamics within populations. Further research and a larger dataset would be valuable to validate and extend these findings.
This experiment underscores the significance of genetic variation studies in understanding population genetics and evolutionary processes. It also highlights the relevance of the Hardy-Weinberg Equilibrium as a theoretical framework for assessing genetic equilibrium within populations.
Genetic Variation Analysis in Foundations of Biology. (2024, Jan 23). Retrieved from https://studymoose.com/document/genetic-variation-analysis-in-foundations-of-biology
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