You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction
============

Several low-molecular-weight compounds, such as N-nitrosamines, vinyl chloride, benzene, and ethanol are metabolized by the cytochrome P4502E1 (CYP2E1). This enzyme is a member of the cytochrome P450 superfamily ([@ref-2]; [@ref-9]). CYP2E1 produces more reactive oxygen species compared to many other CYP enzymes because of high redox potential ([@ref-6]; [@ref-12]; [@ref-20]). Therefore, in alcohol-induced liver injury, CYP2E1 is thought to be an important source of free radicals ([@ref-6]). Among the many drugs metabolized partially or completely by CYP2E1 include halothane, enflurane, isoflurane, paracetamol, ethanol, theophylline, chlorzoxazone, zopiclone, eszopiclone and verapamil ([@ref-1]; [@ref-8]). In addition to metabolizing many pharmaceutical drugs, *CYP2E1* gene itself is regulated by many exogenous and endogenous substances and may substantially enhance the risk posed by many industrial and environmental chemicals ([@ref-4]).

Selected polymorphisms in this gene are associated with a higher risk of certain diseases such as cancer ([@ref-3]; [@ref-18]). For example, single nucleotide polymorphisms, *CYP2E1\*7C*, NC_000010.10:g.135340548A\>G (rs2070672) and *CYP2E1*, NC_000010.10:g.135339244G\>C (rs3813865), are significantly associated with high altitude polycythemia risk ([@ref-17]), with nasopharyngeal carcinoma risk in Cantonese ([@ref-13]), with poorer cancer-specific survival in head & neck cancer ([@ref-10]). These polymorphisms are also associated with inter-individual differences in drug response and adverse effects, especially the liver injury with ethanol, and the frequency of these polymorphisms varies in different ethnic groups around the world ([@ref-5]; [@ref-11]; [@ref-14]). As the frequencies of genetic polymorphisms are associated with interethnic differences in drug response and add significant risk for certain diseases, it is an extremely important subject to investigate. That is why frequencies of these polymorphisms in *CYP2E1* have been described for various populations.

Pakistan is a culturally diverse country, but little is known about the distribution of *CYP2E1* genetic polymorphism in this country of over 200 million people. Various parts of the country possess a unique lifestyle, diverse genetic background, dietary habits, culture, and geographical environment. There are more than 100 single nucleotide polymorphisms found in *CYP2E1* in addition to some copy number variants. Among them, rs2031920, rs3813867, rs6413432, rs6413420, rs72559710, rs55897648, rs2070673, rs3813865 and rs2070672 are well known. However, only a few might alter the enzyme activity or associated with certain diseases. Therefore, we specifically investigated samples drawn from six of Pakistan's most populous ethnic groups located in distinct geographical locations and found out frequencies of two important polymorphisms (rs3813865 and rs2070672) and then compared them with previous findings in other populations.

Materials and Methods
=====================

Sample collection and DNA extraction
------------------------------------

This study was approved by the Institutional Review Board and Ethics Committee of Shifa Tameer-e-Millat University, Islamabad, Pakistan (ref: IRB\# 990-265-2018). Written informed consent forms were obtained from all participating individuals. The study cohort comprised of 636 healthy human individuals from six major ethnicities of Pakistan including Punjabis, Pathan, Sindhi, Balochi, Seraiki, and Urdu Speaking. Ethnicity was self-reported. Five ml of venous blood drawn into sterile tubes containing EDTA as an anti-coagulant was stored at 4 °C. The whole-genome DNA was isolated using Gene Jet Genomic DNA extraction Kit (Thermo Fischer, Waltham, MA, USA) and was quantified using 1% agarose gel electrophoresis. Isolated genomic DNA was stored at −20 °C until further processing.

Genotyping
----------

*CYP2E1* (rs3813865, G\>C; rs2070672, A\>G) were genotyped using Allele Refractory Mutation System-Polymerase Chain Reaction (ARMS-PCR) using a pair of outer primers and a pair of inner primers as shown in [Table 1](#table-1){ref-type="table"}. PCR for both the SNPs was performed separately in a total reaction volume of 25 µl containing 12.5 µl of 2X Dream Taq Mastermix (Thermo Fischer, Waltham, MA, USA), 0.18 mM of both OF and OR primers, 0.36 mM of both IF and IR primers, 7.7 µl of sterile PCR water and three µl of template DNA (20--50 ng/μl). Thermal profile was as follows: initial denaturation at 95 °C for 2 min followed by 35 cycles with denaturation at 95 °C for 30 s, 30 s of primer annealing at 54 °C for rs3813865 and 58 °C for rs2070672, initial extension at 72 °C for 1 min and a final extension at 72 °C for 2 min. For visualization 12 µl of PCR product was directly loaded onto 3% agarose gel. For rs3813865, homozygous wild type GG genotype had 499 bp and 303 bp fragments, homozygous mutant CC genotype had 499 bp and 236 bp fragments and heterozygous GC genotype had three fragments; 499 bp, 303 bp, and 236 bp. Whereas for rs2070672 homozygous wild type GG genotype had 455 bp and 277 bp fragments, homozygous mutant AA genotype had 455 bp and 218 bp fragments and heterozygous GA genotype had three fragments; 455 bp, 277 bp, and 218 bp. Selected samples were sent for Sanger sequencing, and the results obtained were found to conform with results from our laboratory.

10.7717/peerj.9721/table-1

###### Primer sequences for CYP2E1 rs3813865 and rs2070672 along with their product sizes.

![](peerj-08-9721-g002)

                  Primer ID                                 Primer sequence                     Product size
  --------------- ----------------------------------------- ----------------------------------- --------------
  rs3813865       OF                                        5′ TGA TGT TGG TTG GGC ATC TA 3′    499 bp
  OR              5′ CCT CGA GGT GAG AAC TGA CA 3′                                              
  IF (G allele)   5′ CTC ACC CCA CCA AAG CCT AC 3′          303 bp                              
  IR (C allele)   5′ CCA CAG ACT GAA ATT GAA CCC 3′         236 bp                              
  rs3813865       OF                                        5′ CCA TTC ATG TGG CAG GTG GTG 3′   455 bp
  OR              5′ CCA ATG CCC TCT TGC TAC TCG TCT A 3′                                       
  IF (G allele)   5′ TGG AGT TCC CCG TTG TCG AG 3′          277 bp                              
  IR (A allele)   5′ GTC CTG CCC TTT GGC ACT CGT 3′         218 bp                              

Statistical analysis
--------------------

Data were compiled according to the genotype and allele frequencies estimated from the observed numbers of each specific allele. The frequency of each allele and genotype in our samples is given together with the 95% confidence interval. The confidence interval for proportions was calculated using the formula (CI = *p* ± (1.96 × SE), SE = qrt \[*p*(1 − *p*)/*n*\], *p* = proportion, *n* = sample size). Chi-squared test and *p* values were calculated using observed and expected frequencies as per the Hardy--Weinberg equation.

Results
=======

Frequencies of *CYP2E1* (rs2070672) alleles in the Pakistani population are shown in [Table 2](#table-2){ref-type="table"} while representative agarose gel image of the experiments is shown in [Fig. S1](#supp-1){ref-type="supplementary-material"}. The frequency of the major allele was 89.62% and of minor allele was 10.37%. Major allele was found slightly less prevalent in Punjabi and Urdu populations at 87.18% and 84.14% respectively compared to Pathan, Sindhi, Seraiki and Baloch populations, where the prevalence of major allele was slightly higher ([Fig. 1](#fig-1){ref-type="fig"}; [Table 2](#table-2){ref-type="table"}). The frequency of AA genotype was 82.70%, AG was 13.83% and GG was 3.45% in the Pakistan population. Punjabi and Urdu populations showed a slightly lower frequency of wild type genotype at 79.83% and 78.04% respectively while Pathan, Sindhi, and Seraiki populations had a slightly higher prevalence of wild type genotype. Baloch population showed the highest frequency of wild type genotype at 92.68%. No homozygous GG genotype was found in Pathan, Seraiki and Baloch populations ([Table 3](#table-3){ref-type="table"}).

![Major allele prevalence in different ethnic populations.\
Every color indicates an ethnic population. The red and purple quadrants inside every ethnic region represents the frequency of *CYP2E1* (rs2070672) and *CYP2E1* (rs3813865) alleles, respectively.](peerj-08-9721-g001){#fig-1}

10.7717/peerj.9721/table-2

###### Allelic frequencies of CYP2E1 (rs2070672) invarious Pakistani ethnicities.

![](peerj-08-9721-g003)

  S. No.   Ethnicity   *N*   A% (95% CI)              G% (95% CI)
  -------- ----------- ----- ------------------------ ------------------------
  1        Pakistan    636   89.62 \[87.94--91.3\]    10.37 \[8.69--12.05\]
  2        Punjabi     238   87.18 \[84.18--90.18\]   12.81 \[9.81--15.81\]
  3        Pathan      107   90.65 \[86.75--94.55\]   9.21 \[5.34--13.08\]
  4        Sindhi      59    92.37 \[87.58--97.16\]   7.62 \[2.83--12.41\]
  5        Seraiki     68    92.64 \[88.25--97.03\]   7.93 \[3.39--12.47\]
  6        Baloch      82    96.34 \[93.47--99.21\]   3.63 \[0.77--6.49\]
  7        Urdu        82    84.14 \[78.55--89.73\]   15.85 \[10.26--21.44\]

10.7717/peerj.9721/table-3

###### Observed and expected frequencies of CYP2E1 (rs2070672) genotypes.

![](peerj-08-9721-g004)

  Population   Geno   *N*                      Observed frequency percentage (CI)   Expected frequency percentage by HW law   Chi squared   *P* value
  ------------ ------ ------------------------ ------------------------------------ ----------------------------------------- ------------- -----------
  Pakistani    AA     526                      82.70 \[79.76--85.64\]               80.32                                     35.10         \<0.05
  AG           88     13.83 \[11.15--16.51\]   18.58                                                                                        
  GG           22     3.45 \[2.03--4.87\]      1.07                                                                                         
  Punjabi      AA     190                      79.83 \[74.73--84.93\]               76.01                                     27.82         \<0.05
  AG           35     14.70 \[10.2--19.2\]     22.33                                                                                        
  GG           13     5.46 \[2.57--8.35\]      1.64                                                                                         
  Pathan       AA     87                       81.30 \[73.91--88.69\]               82.17                                     1.14          \>0.05
  AG           20     18.69 \[11.3--26.08\]    16.94                                                                                        
  GG           0      0                        0.87                                                                                         
  Urdu         AA     64                       78.04 \[67.08--88\]                  70.80                                     24.17         \<0.05
  AG           10     12.19 \[5.11--19.27\]    26.66                                                                                        
  GG           8      9.75 \[3.33--16.17\]     2.51                                                                                         
  Seraiki      AA     58                       85.29 \[76.87--93.71\]               78.20                                     0.43          \>0.05
  AG           10     14.70 \[6.28--23.12\]    28.87                                                                                        
  GG           0      0                        2.66                                                                                         
  Seraiki      AA     76                       92.68 \[87.04--98.32\]               92.81                                     0.11          \>0.05
  AG           6      7.31 \[1.68--1294\]      7.04                                                                                         
  GG           0      0                        0.13                                                                                         
  Sindhi       AA     51                       86.44 \[77.7--95.18\]                85.33                                     1.47          \>0.05
  AG           7      11.86 \[3.61--20.11\]    14.07                                                                                        
  GG           1      1.69 \[0--4.98\]         0.58                                                                                         

Frequencies of *CYP2E1* (rs3813865) alleles in the Pakistani population are shown in [Table 4](#table-4){ref-type="table"} while representative agarose gel image of the experiments is shown in [Fig. S2](#supp-2){ref-type="supplementary-material"}. The frequency of minor alleles for this polymorphism was higher in the Pakistani population, compared to rs2070672, and found to be 14.8% ([Table 4](#table-4){ref-type="table"}). In Punjabi and Urdu populations, the minor allele was found even more prevalent at 18.49% and 18.83% respectively. In Sindhi population, the frequency of minor allele was found to be the lowest at 1.81%. The frequency of the GG genotype was 72.47%, GC was 23.58% and CC was 3.93% in the Pakistan population. In Punjabi and Urdu populations, wild type genotype (GG) was slightly less prevalent at 63.47% and 67.53% respectively. Baloch population showed a higher frequency of wild type genotype at 83.60%. The highest prevalence of wild type genotype was found in the Sindhi population at 96.36%. Sindhi was also the only population in which no homozygous CC genotype was observed. All other ethnic groups showed CC genotype albeit at varying frequencies ([Table 5](#table-5){ref-type="table"}).

10.7717/peerj.9721/table-4

###### Allelic frequencies of CYP2E1 (rs3813865) in various Pakistani ethnicities.

![](peerj-08-9721-g005)

  S.No   Population   *N* (rs 38)   G% (95% CI)              C% (95% CI)
  ------ ------------ ------------- ------------------------ ------------------------
  1      Pakistan     585           85.12 \[82.34--88\]      14.8 \[11.92--17.68\]
  2      Punjabi      219           81.50 \[76.36--86.64\]   18.49 \[13.35--23.63\]
  3      Pathan       109           86.69 \[80.31--93.07\]   13.30 \[6.93--19.63\]
  4      Sindhi       55            98.18 \[94.65--100\]     1.81 \[0--5.33\]
  5      Seraiki      64            85.15 \[76.44--93.86\]   14.84 \[6.13--23.55\]
  6      Baloch       61            88.52 \[80.52--96.52\]   11.47 \[3.47--19.47\]
  7      Urdu         77            81.16 \[72.43--89.89\]   18.83 \[10.1--27.56\]

10.7717/peerj.9721/table-5

###### Observed and expected frequencies of CYP2E1 (rs3813865) genotypes.

![](peerj-08-9721-g006)

  Population   Geno   *N*                      Observed frequency percentage (CI)   Expected frequency percentage by HW law   Chi Squared
  ------------ ------ ------------------------ ------------------------------------ ----------------------------------------- -------------
  Pakistani    585                                                                                                            
  GG           424    72.47 \[68.85--76.09\]   71.01                                7.08                                      \<0.05
  GC           138    23.58 \[20.14--27.02\]   26.49                                                                          
  CC           23     3.93 \[2.36--5.50\]      2.47                                                                           
  Punjabi      219                                                                                                            
  GG           139    63.47 \[57.09--69.85\]   62.76                                0.40                                      \>0.05
  GC           69     31.50 \[25.35--37.65\]   32.91                                                                          
  CC           11     5.02 \[2.13--7.91\]      4.31                                                                           
  Pathan       109                                                                                                            
  GG           83     76.14 \[68.14--84.14\]   75.15                                0.79                                      \>0.05
  GC           23     21.10 \[13.44--28.76\]   23.06                                                                          
  CC           3      2.75 \[0--5.82\]         1.77                                                                           
  Urdu         77                                                                                                             
  GG           52     67.53 \[54.8--80.26\]    65.88                                0.89                                      \>0.05
  GC           21     27.27 \[15.17--39.37\]   30.56                                                                          
  CC           4      5.19 \[0--11.22\]        3.54                                                                           
  Seraiki      64                                                                                                             
  GG           46     71.87 \[60.85--82.89\]   72.51                                0.16                                      \>0.05
  GC           17     26.56 \[15.74--37.38\]   25.27                                                                          
  CC           1      1.56 (0-4.6)             2.20                                                                           
  Balochi      61                                                                                                             
  GG           51     83.60 \[74.31--92.89\]   78.36                                16.23                                     \<0.05
  GC           6      9.83 \[2.36--17.3\]      20.30                                                                          
  CC           4      6.55 \[0.34--12.76\]     1.31                                                                           
  Sindhi       55                                                                                                             
  GG           53     96.36 \[91.41--100\]     96.39                                0.02                                      \>0.05
  GC           2      3.63 \[0--8.57\]         3.56                                                                           
  CC           0      0                        0.03                                                                           

Discussion
==========

According to its Statistics Bureau, Pakistan with an estimated population of over 210 million, is the sixth most populous country in the world ([@ref-15]). The country has a young, multi-ethnic and multi-cultural society and despite being home to a huge population, pharmacogenetic studies on how its population responds to various pharmaceutical drugs are rare. The largest ethnic group in Pakistan is Punjabi, which makes up about 38.78% of the population, followed by Pashtuns (18.24%), Sindhis (14.57%), Seraikis (10.53%), Urdu speaking (7.57%) and Baloch (3.57%) ([@ref-15]). These ethnic groups represent about 94% of the Pakistani population. Genetic variations in CYP genes affecting the metabolism of xenobiotics and drug response have not been investigated in these ethnic groups. Our study partly addresses this issue by reporting frequencies of two of the most important single nucleotide polymorphisms in the *CYP2E1* gene.

The frequency of rs2070672 minor allele (G) in the Pakistani population was similar to the one found in the American population ([Table 6](#table-6){ref-type="table"}). The lowest frequency of minor allele has previously been reported from Europe (0.027). Literature search shows that in the African population, minor allele is slightly more prevalent than in the Pakistani population while East and South Asian populations have the highest frequencies of the minor allele ([@ref-7]). Similar results were found for genotype frequency where the wild type genotype observed in the Pakistani population was comparable to the American population. The highest frequency of wild type genotype is reported from Europe, in which no homozygous GG genotype was found. The highest frequencies of heterozygous and homozygous GG are reported from East and South Asian populations ([@ref-7]). The difference in allele and genotype frequencies between earlier reports for South Asian populations and this study may be because our study estimated frequencies in six different ethnicities while in 1,000 Genome project, the Pakistani population is represented by one ethnicity only. The difference in sample size may be another reason for discrepancy.

10.7717/peerj.9721/table-6

###### CYP2E1 (rs2070672) allele frequencies in 1,000 Genome population.

![](peerj-08-9721-g007)

  Population   G       C       GG      GC      CC
  ------------ ------- ------- ------- ------- -------
  AFR          0.852   0.148   0.734   0.236   0.030
  AMR          0.889   0.102   0.813   0.170   0.017
  EAS          0.733   0.267   0.530   0.407   0.063
  EUR          0.974   0.26    0.948   0.52    0
  SAS          0.713   0.287   0.513   0.399   0.088
  BEB          0.686   0.315   0.488   0.395   0.116
  GIH          0.728   0.272   0.524   0.408   0.068
  ITU          0.721   0.279   0.520   0.402   0.078
  PJL          0.755   0.245   0.594   0.323   0.083
  STU          0.672   0.328   0.441   0.461   0.098

**Note:**

AFR, African; AMR, American; EAS, East Asian; EUR, European; SA, South Asian; BEB, Bengali in Bangladesh; GIH, Gujrati Indian in Houston, TX; ITU, Indian Telugu in UK; PJL, Punjabi in Lahore, Pakistan; STU, Sri Lankan Tamil in UK.

Among various ethnicities, Urdu speaking showed the highest frequency of rs2070672 minor allele. Punjabi ethnicity displayed the highest prevalence of the minor allele after Urdu speaking while Baloch people exhibited the lowest frequency of this allele. While comparing genotypes frequencies, Urdu speaking ethnicity showed the lowest frequency of wild type genotype (AA) followed by Punjabi ethnicity. The highest frequency of wild type genotype was exhibited by Baloch ethnicity. Pathan, Baloch and Seraiki ethnicities did not show any homozygous mutant genotype (GG) while Urdu speaking ethnicity displayed the highest frequency of this genotype among the study participants. Studies investigating this polymorphism in the regional populations reveal that the Chinese Uygur population has a low prevalence of this genetic variant at 0.25% ([@ref-19]). Other studies carried out at various geographical locations in China showed much higher frequencies. For example, this variant was found at a frequency of 18.8% in Shantou, 14.1% in Shanghai, 18.8% in Shenyang and 21.9% in Xian ([@ref-16]).

Comparison with other regional populations reveal relatively lower frequencies of the minor allele (rs207067) in Pakistani ethnicities. Sri Lankan Tamils in the UK (STU) and Bengali from Bangladesh (BEB) have shown a much higher frequency of minor allele at 0.328 and 0.315 respectively whereas, in Pakistani population, the highest frequency of this allele is reported by Punjabi and Urdu ethnicities at 0.128 and 0.158, respectively. Even Gujrati Indian in Houston (GIH), Punjabi in Lahore (PJL) and Indian Telugu in the UK (ITU) have reported minor allele frequencies that are higher than most Pakistan ethnicities. Although, largely in agreement with regional ethnicities, this relatively low frequency of minor alleles in Pakistani ethnicities might be due to broad geographical locations from which our samples were obtained. For example, PJL data in the 1,000 genome project, was obtained from one center in Lahore while our samples were collected from various centers in Rawalpindi, Islamabad, and Lahore.

The frequency of rs3813865 minor allele (C) in the Pakistani population was similar to the one found in African population. The lowest frequency of the minor allele is reported from Europe (0.026) ([Table 7](#table-7){ref-type="table"}). The highest frequencies of this polymorphism are reported from South and East Asia at 0.287 and 0.267 respectively ([@ref-7]). The frequency of this genetic variant in the American population is reported at 0.102, which is the second-lowest frequency reported in the 1,000 genome database for this variant. Looking at the genotype frequencies, wild type genotype observed in the Pakistani population was closest to the one reported for the African population. South Asian and East Asian populations are reported to have the lowest frequencies of wild type genotype at 0.513 and 0.530 respectively ([@ref-7]). The highest frequency of wild type genotype (GG) is reported from European populations at 0.948 with a 0.52 heterozygous genotype (GC). However, European populations are the only ones reported in the 1,000 genome to have no homozygous CC genotype.

10.7717/peerj.9721/table-7

###### CYP2E1 (rs2070672) allele frequencies in 1,000 Genome population.

![](peerj-08-9721-g008)

  Population   A       G       AA        AG      GG
  ------------ ------- ------- --------- ------- -------
  AFR          0.850   0.150   0.735     0.230   0.035
  AMR          0.899   0.101   0.816     0.167   0.017
  EAS          0.736   0.264   0.534     0.405   0.062
  EUR          0.973   0.027   0.946     0.054   0
  SAS          0.713   0.287   0.513     0.399   0.088
  BEB          0.686   0.314   0.488     0.395   0.116
  GIH          0.728   0.272   0\. 524   0.408   0.068
  ITU          0.721   0.279   0.520     0.402   0.078
  PJL          0.755   0.245   0.594     0.323   0.083
  STU          0.672   0.328   0.441     0.461   0.098

**Note:**

AFR, African; AMR, American; EAS, East Asian; EUR, European; SA, South Asian; BEB, Bengali in Bangladesh; GIH, Gujrati Indian in Houston, TX; ITU, Indian Telugu in UK; PJL, Punjabi in Lahore, Pakistan; STU, Sri Lankan Tamil in UK.

Comparing rs3813865 polymorphism among various ethnicities revealed that Urdu and Punjabi ethnicities have the highest prevalence of the minor allele (C). The highest major allele frequency was exhibited by Sindhi ethnicity. Pathan, Seraiki and Baloch ethnicity showed frequency of minor allele in the same range but was considerably higher than Sindhi ethnicity. Studying genotype frequencies of various ethnicities showed that Sindhi ethnicity possesses the highest wild type genotype (GG). The lowest wild type genotype was exhibited by Punjabi ethnicity. Pathan, Seraiki, Baloch, and Urdu speaking ethnicities showed an intermediate prevalence of wild type genotype compared to Sindhi and Punjabi ethnicities. Only Sindhi ethnicity did not report any homozygous mutant genotype (CC). Consistent with our findings with rs207067, the frequency of rs3813865 minor allele was found slightly lower in our ethnic populations in comparison with other regional ethnicities. Sri Lankan Tamils in the UK (STU) and Bengali from Bangladesh (BEB) have shown a higher frequency of minor allele at 0.328 and 0.315 respectively whereas, in Pakistani population, the highest frequency of this allele is reported by Punjabi and Urdu ethnicities at 0.185 and 0.188, respectively. Even Gujrati Indian in Houston (GIH), Punjabi in Lahore (PJL) and Indian Telugu in the UK (ITU) have reported minor allele frequencies that are higher than most Pakistan ethnicities. A literature search shows that the frequency of this genetic variant at various geographical locations in China displayed comparable and, in some cases, higher frequencies. For example, this variant was found at a frequency of 18.7% in Shantou, 14% in Shanghai, 23.4% in Shenyang and 22.4% in Xian ([@ref-16]).

Limitations of our study include finding out the prevalence of only two SNPs while there are more than 100 SNPs found in *CYP2E1*. However, only a few might alter the enzyme activity or associated with certain diseases. Our methods for the determination of these SNPs also prohibited us from finding novel SNPs in our population. Sequencing all samples could have helped find new SNPs in our population. This would have also helped us finding copy number variants in the *CYP2E1* gene in our population if any. Functional analysis of the CYP2E1 enzyme, containing these SNPs could have helped establish the functional relevance of observed SNPs.

Conclusions
===========

To our knowledge, this is the first study to report frequencies of *CYP2E1* gene polymorphisms in various ethnicities of the Pakistani population. Genetic information about patients' *CYP2E1* gene is likely to help physicians prescribe to patients the most suitable and safest drug based on their genetic make-up. We propose further studies with individual drugs metabolized by CYP2E1 to shed more light on genotype phenotype relations. Carrying out enzyme activities of CYP2E1 containing these SNPs would be helpful to establish functional relevance and importance of these SNPs in the Pakistani population.

Supplemental Information
========================

10.7717/peerj.9721/supp-1

###### A representative gel of CYP2E1 (rs3813865) genotyping assay 1.

###### 

Click here for additional data file.

10.7717/peerj.9721/supp-2

###### A representative gel of CYP2E1 (rs2070672) genotyping assay 1.

###### 

Click here for additional data file.

10.7717/peerj.9721/supp-3

###### Polymorphism of CYP2E1 (rs2070672) in the ethnic groups.

Shows all subjects examined according to their gender, ethnicity, age, genotype, addiction (eg. smoking), diet (red meat consumption), sedentary life, family history, medication, co-morbidities, and diseases.

###### 

Click here for additional data file.

10.7717/peerj.9721/supp-4

###### Polymorphism of CYP2E1 (rs3813865) in the ethnic groups.

Shows all subjects examined according to their gender, ethnicity, age, genotype, addiction (eg. smoking), diet (red meat consumption), sedentary life, family history, medication, co-morbidities, and diseases.

###### 

Click here for additional data file.

Additional Information and Declarations
=======================================

The authors declare that they have no competing interests.

[Sagheer Ahmed](#author-1){ref-type="contrib"} conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, and approved the final draft.

[Nadeem Altaf](#author-2){ref-type="contrib"} conceived and designed the experiments, performed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

[Mahnoor Ejaz](#author-3){ref-type="contrib"} conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft.

[Zaira Zulfiqar](#author-4){ref-type="contrib"} conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft.

[Kholood Janjua](#author-5){ref-type="contrib"} conceived and designed the experiments, performed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

[Dana Festila](#author-6){ref-type="contrib"} analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

[Nicula Cristina](#author-7){ref-type="contrib"} analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

The Institutional Review Board and Ethics Committee of Shifa Tameer-e-Millat University, Islamabad, Pakistan approved this research (990-265-2018).

The following information was supplied regarding data availability:

The raw measurements are available in the [Supplemental Files](#supplemental-information){ref-type="supplementary-material"}.