Essay: How PCR Testing is Used to Study Human Genotype Variations



Introduction

Polymerase Chain Reaction, otherwise known as PCR, has been used for decades to help scientists around the world comprehend DNA and the role that segments of DNA have on organisms. The process of PCR consists of taking a sample of DNA and denaturing it at high temperatures, followed by allowing the denatured segment to cool in order to allow primers to anneal, or “choose”,  the DNA segment that is being targeted. After the primers have targeted the desired segment, a thermostable polymerase is added to the DNA in order to form complimentary strands of the “chosen” segment from the original DNA (Leicht 2018). However before PCR is begun, the DNA must be thoroughly examined in order to decipher which primers will anneal with the chosen sequence within the DNA. Moreover, choosing a primer is not a simple process, and Premier Biosoft breaks down the factors taken into consideration when choosing a primer. Some factors, but not all, are primer secondary structures, repeats of two bases within the primer, and runs of the same base (PCR 2017). The CYP gene studied in this lab corresponds to the metabolism of caffeine in humans. At this locus, there are three genotypes, each corresponding to quick, moderate, or slow metabolism of caffeine. The study of this metabolism has been thoroughly looked at in recent years as it is believed that caffeine may have pharmaceutical effects, including a potential treatment for Parkinson’s disease (Thorn 2012). Using PCR in order to study how caffeine is metabolized in different individuals can have unlimited benefits, which should make it easy to understand why PCR is used on numerous genes, including the TAS2R38 gene also studied in this lab.

In the 1930’s when a colleague of Arthur Fox got a bit of PTC dust in his mouth and reported that it was severely bitter, Fox was stunned. Rightfully so because shortly before this, Fox tried PTC for himself and declared it tasteless (Leicht 2018). Since then, the ability to taste PTC has become one of the most widely studied human genetic traits (Wooding 2006). Essentially, the TAS2R38 locus is the ability for individuals to taste glucosinolates (TAS2R38 2018). The study of genetic variation among humans has become a hot topic in recent years, and for good reason. Genetic variation is essential to the survival of any species, not just humans. The measurement of genetic variation can be done in many ways, however the most widely accepted form of measurement is the Hardy-Weinberg equilibrium. In our Foundations of Biology class, we will be conducting a lab to study the two loci CYP and TAS2R38, and using these loci we will calculate genotypic and allele frequencies. The genotypes of individuals will be observed through the use of Restriction Fragment Length Polymorphism (RFLP). RFLP’s are regulated by the primers that bind to enzymes and cut DNA sequences, which will result in different gel patterns, allowing the genotype to be determined. Using the calculated frequencies, we will test to see if the class data does fit the Hardy-Weinberg equilibrium, with an initial hypothesis that our data does indeed fit the Hardy-Weinberg.

Materials & Methods

For the CYP locus, a total of 365 students were observed, and for the TAS2R38 locus 471 students were observed. Both groups of students came from the Fall 2018 Foundations of Biology class, and Polymerase Chain Reaction was used for both groups. To obtain the DNA needed for the study, the students rubbed cotton swabs on the inside of the cheek, in essence collecting epithelial cells. After swabbing the cheek, the samples were placed into tubes containing a DNA extraction solution (Leicht 2018) and then followed a strict incubation cycle. The sample underwent a vortex for 10 seconds, and then was placed in an incubator at 65C for 1 minute. After being taken out, the sample underwent another vortex for 15 seconds, and then was placed in a different incubator at 98C for 2 minutes. After the two minutes was completed, the sample underwent a vortex for 15 seconds and then was allowed to cool. The result of this cycle was our extracted DNA, which was then split into 2 tubes. Both tubes contained .2 ml of PCR Master Mix, which ensured that the polymerase would function. After the tubes were obtained with the PCR Master Mix, 20 l of primer was added to the correct corresponding tubes. Then, the tubes were put into a thermocycler for 5 minutes at 95C. After the 5 minutes, the tubes underwent 40 cycles with the following conditions: 30 seconds at 94C, 30 seconds at 58C, 30 seconds at 68C. After the last cycle was complete, the tubes were placed in the thermocycler for 5 minutes at 68C to complete whatever synthesis had begun (Leicht 2018).

In order to visibly see the genotypes of each individual, the DNA samples of each student were put through electrophoresis. In lab, 30 ml of 1.6% agarose gel was formed by mixing .65 grams of agarose with 40 ml of 1XTBE buffer, and then heating in the microwave for 45 seconds on high power. Once the mixture was allowed to cool, the lab instructor added ethidium bromide in order to stain the DNA later. Then, the mixture was taken and poured into the gel tray so that it could solidify. Once the gel solidified, the comb was removed and the chamber for electrophoresis was filled with 1XTBE buffer. The samples of DNA that had been digested with the restriction enzyme received 3 l of 10X Blue Juice, whereas the DNA that was not digested received 6 l of Blue Juice. The DNA samples that resulted from these processes were loaded into the gel to undergo electrophoresis. The uncut DNA was loaded into wells 2 and 6, with a DNA size marker filling well 1 and the remaining group members digested DNA filling wells 3-5. The electrophoresis was run for 45 minutes, and the resulting gel was photographed and recorded.

In order to calculate the genotypic frequencies we used the equation: p^2+2pq+q^2=1. For calculating the allele frequencies we used the equation: p + q = 1. In both equations, “p” is the frequency of the dominant allele, and “q” is the frequency of the recessive allele. From the observed values, the goal was to determine if the pooled class data fits the Hardy-Weinberg equilibrium. From the equations mentioned above, the solutions were used to determine the number of individuals that are expected to have specific genotypes. This was found by multiplying the total number of individuals tested by the genotypic frequencies. Following the computation of the expected number of individuals, a chi-squared test was run to determine the goodness of fit of the expected and observed data in comparison with the Hardy-Weinberg equilibrium. The chi-squared equation is ( (|observed-expected|^2)/expected ), and the value that this equations gives is then compared to a P-Table. The P-Table contains p-values and different degrees of freedom, and if the chi-squared test results in a value that gives a p-value less than .05, or 5%, it can be concluded that the results are significant and the null hypothesis should be rejected.

Results

Before the pooled data of observed genotypes was given, our lab group calculated the expected number of each genotype, and then compared the expected to the actual observed genotypes. By doing this, we can see how well the data fits into the Hardy-Weinberg principle. The results are shown in Table 1.1 and Table 1.2 below.

Genotype

Expected # of Individuals

Observed # of Individuals

Expected Genotypic Frequency

Observed Genotypic Frequency

AA 79 81 .44 .45

AC 82 78 .45 .43

CC 21 23 .12 .13

Total 182 182 1 1

CYP Locus Pooled Data

Table 1.1: In this table, the calculated estimated genotypes are displayed with the observed genotypes. The equation used to calculate the estimated genotypic frequency can be found above. The estimated number of individuals was calculated by multiplying the estimated genotypic frequency with the total population. The observed genotypic frequency was calculated by dividing the total number of observed individuals with the total population.

TAS2R38 Locus Pooled Data

Genotype Expected # of Individuals Observed # of Individuals Expected Genotypic Frequency Observed Genotypic Frequency

TT 38 52 .17 .23

Tt 109 82 .35 .36

tt 79 92 .48 .41

Total 226 226 1 1

Table 1.2: In this table, the calculated estimated genotypes are displayed with the observed genotypes. The equation used to calculate the estimated genotypic frequency can be found above. The estimated number of individuals was calculated by multiplying the estimated genotypic frequency with the total population. The observed genotypic frequency was calculated by dividing the total number of observed individuals with the total population.

When looking at both tables, both loci display a larger number of homozygotes than expected, and a smaller number of heterozygotes than expected according to the Hardy-Weinberg principle. When taking the values and calculating the chi-squared value for the CYP locus, the chi-squared test returns a value of .436 which corresponds to a p-value greater than .05. However, when the chi-squared test was run for the TAS2R38 locus, the chi-squared test returned a value of 13.99 which corresponds to a p-value of far less than .05. For both loci mentioned here, only 1 degree of freedom was used when looking at the p-value table. Given the p-values for the loci, we fail to reject the null hypothesis that the data fits the Hardy-Weinberg equilibrium for the CYP locus. However, we reject the null hypothesis that the data fits the Hardy-Weinberg equilibrium for the TAS2R38 locus.

In Figure 1.2 below, it can be seen that individuals 2 and 3 both show only 1 band, corresponding to the tt homozygote genotype, and individual 4 shows 2 bands, which corresponds to the TT homozygote genotype.

test produced a value of .436, which corresponds to a p-value greater than .05. This was not the case for the TAS2R38 locus as we had to reject the null hypothesis. The chi-squared test for the TAS2R38 locus produced a value of 13.99, which corresponds to a p-value of less than .05. Despite this, the population used to compare with the Hardy-Weinberg principle was not ideal in many aspects. The population was not infinite to begin, and there was not random mating because of the fact that all subject were from the same generation. Furthermore, majority of the individuals tested in this study were of North American descent, resulting in gene flow. In order to be in true Hardy-Weinberg conditions there must be no mutations, random mating, infinite population size, no gene flow, and no selection (Leicht 2018).

Works Cited

Leicht, Brenda G, and Bryant F McAllister (2017). Foundations of Biology 1411. Sixth edition.

Southlake, Texas: Fountainhead Press.

“PCR Primer Design Guidelines.” Primer Design Guide for PCR :: Learn Designing Primers for PCR, www.premierbiosoft.com/tech_notes/PCR_Primer_Design.html.

“TAS2R38 Taste 2 Receptor Member 38 [Homo Sapiens (Human)] – Gene – NCBI.” Current Neurology and Neuroscience Reports., U.S. National Library of Medicine (2018), www.ncbi.nlm.nih.gov/gene/5726.

Thorn, Caroline F et al. “PharmGKB summary: caffeine pathway” Pharmacogenetics and genomics vol. 22,5 (2012): 389-95.

Wooding, Stephen. “Phenylthiocarbamide: A 75-Year Adventure in Genetics and Natural Selection.” Genetics, Genetics, 1 APR. 2006, www.genetics.org/content/172/4/2015