Introduction

Cardiovascular disease (CVD) is one of the top reasons of mortality worldwide. A positive correlation between the rate of CVD and the concentration of low-density lipoprotein cholesterol (LDL-C) is well documented1. The family of lipid-dropping drugs known as statins acts by blocking the liver’s ability to produce cholesterol. Statins decrease cholesterol, minimizing the health risks associated with atherosclerosis2. Cardiovascular disease atherosclerosis influences the blood flow to essential organs by causing fatty, cholesterol-filled plaques to build up inside the artery walls. Statins are the most successful drugs in both preventing and treating cardiovascular disease2. Statins are also one of the best-studied drugs in cardiovascular disease3. Randomized clinical studies have revealed that statin can improve lifespan and lower heart attacks and stroke incidence, even in those with normal cholesterol levels4. For people with known Atherosclerotic Cardiovascular Disease (ASCVD), statins are advised5. Statins are also advised for both sexes who have an elevated risk of cardiovascular disease but have no history of heart disease or stroke6. Surely, everyone should follow a heart-healthy diet and exercise regularly7. However, statins can enhance cardiovascular health and prevent heart disease for those at higher risk even in those who have healthy lives or who change their lifestyles8.

According to the meta-analysis of cholesterol treatment studies, data that were gained from patients in whom statins reduced cardiovascular events were employed to determine the ratio of LDL-C level reduction achieved by administration of specific statins9. Therefore, US and UK national guidelines recommend intended LDL-C reduction targets for statin therapy to reduce cardiovascular disease. American College of Cardiology/American Heart Association (ACC/AHA) guidelines (2013) were released for identifying the dose of statins to decrease LDL-C by 30–49%, moderate and ≥ 50%10.

The differences between any two human genomes are millions upon millions11. Both numerous bigger variants, such as deletions, insertions, and copy number variations (CNVs), and minor differences in the individual nucleotides of the genomes (Single Nucleotide Polymorphisms or SNPs) exist11. Any of these variations might lead to changes in a person's phenotype, or traits, which can comprise anything from physical characteristics like height to illness risk. Thousands of genetic variations are tested across many genomes in genome-wide association studies (GWAS) to detect those that are statistically related to a particular characteristic or illness11. Several dominant correlations have been represented by this method for a variety of characteristics and illness symptoms. Noticeably, as GWAS sample sizes increase, it is anticipated that the number of target variants will increase gradually12. Other applications of GWAS data analysis include that the molecular biology behind a phenotype can be understood, its heritability can be estimated, genetic correlations can be computed, clinical risk predictions can be calculated, drug development initiatives can be learned, and possible causal relationships between risk factors and health outcomes can be inferred12. Genomewide association studies (GWAS) that recognize the genetic determinants of drug response have been made possible by the growing genotype data associated with phenotypic information of drug responses. Both therapeutic effectiveness and unfavorable drug responses to genetic variations have been linked by GWAS12.

In limited case studies, biological/adherent differences, as well as genetic elements involved in LDL response to statin administration have been addressed13. However, there is no evidence for observed variations in LDL-C response in the common population of patients started on statins for primary prevention of cardiovascular disease. So, such a large, futuristic, open-label, population cohort study which is aimed to evaluate variations in LDL-C responses in early detected patients is necessary in the future of cardiovascular topics14. In the present study, we have established a pipeline to clarify genetic hotspots as markers of statin response in cardiovascular patients. Through the analysis of the molecular activity in each cardiovascular patient and their association with response to statin (Training cohort, n = 3221), we recognized eight single-nucleotide polymorphisms (SNPs) and also candidate some luci as well as several key proteins as important areas in statin response types.

Material and methods

Study subjects

The Diabetes Audit and Research Tayside Study (DARTS), which dates back to 1992, provided the biochemical and clinical data used in this investigation. For each DARTS participant, this information may be obtained from central databases15. Since October 1997, all DARTS diabetes samples have been asked to participate in the Wellcome Trust United Kingdom Type 2 Diabetes (WTCCC2) case–control collection by taking their DNA collected. As of June 2009, the Genetics of DARTS (GoDARTS) research included 7000 controls and 8,000 patients of European ancestry. In the present study, the discovery cohort containing 4134 case samples, derived from the GoDARTS dataset which was part of the WTCCC2. Among this collection, only 3221 samples had enough information for both statin_LDL and statin_TC responses as phenotypes besides genotype information that was chosen for downstream analysis. Table 1 displays descriptive data for the qualities under investigation. Patients with CVDs and high-level cholesterol (Total Cholesterol: 240 mg/dL or higher, LDL: 100 mg/dL or higher) were included in the study. Individuals with other lipid disorders (such as Familial Hypercholesterolemia, dyslipidemia syndrome, etc.) were excluded from the study based on their treatment history and medical records. Most of the patients recieved 20 mg/day of statin but in some cases, the dose increased to 40 mg/day due to high cholesterol levels based on American Heart Association documents. Considering that the present study aims to find genetic variations that cause statin drug resistance, individuals who may have statin drug resistance were not excluded. Medication adherence rates were estimated at ~ 86% for four weeks.

Table 1 Descriptive statistics of the statin response.
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Genotyping and quality control

The genotypes of the sample were ascertained as previously mentioned16. To put it briefly, all patients’ genomic DNA had been transferred to the Sanger Institute in Cambridge. If the concentration of DNA in a sample was 50 ng/µL or higher, the DNA did not undergo degradation, the gender assignment from the Sequenom iPLEX assay matched the patient's data manifest, and genotypes were found for at least two-thirds of the iPLEX SNPs, the sample was deemed to have passed quality control. The Genome-Wide Human SNP Array 6.0 had been genotyped utilizing Affymetrix’s service facility. Using CelQuantileNorm (http://www.wtccc.org.uk/info/software.shtml), raw intensities were renormalized within collections for all samples that passed Affymetrix’s laboratory quality control17. We applied the GEN2VCF package to convert genotype probabilities (Chiamo software output) to a standard VCF file18.

In total, 893,634 autosomal SNPs and 3221 samples (with data on statin response) were extracted for quality control filtering before further analyses. Individual samples were filtered out by a genotyping call rate of less than 99%. Moreover, the dataset was cleaned of SNPs with unclear genomic locations, monomorphic or minor allele frequencies < 0.05, genotyping call rates < 99%, and SNPs deviating from the Hardy–Weinberg equilibrium at a P-value cut-off of 1 × 10−6. After filtering, 685,000 markers were retained. Therefore, on average at least one individual marker per 4500 bp existed, So the variations by these markers could be found in LD (linkage Disequilibrium). In 10,000 bp the minimum LD per chromosome was 0.3, so in 4500 bp of course the LD will be higher (Supplmentary File 1, Fig. 2). So no missing genotype was observed, and the imputation step was not carried out.

A principal component analysis (PCA) employing a group of linkage disequilibrium (LD) pruned (independent) SNPs was operated to evaluate the within-population frame and identify outlier samples. The first two principal components were joined into the model as covariates. A sample was considered an outlier if its value (a sample observation’s projection on PC1 and PC2) in the first and second PC was away from the mean of the corresponding PC by more than 3.5 standard deviations. LD patterns of each chromosome have also been considered in 10, 25, and 50 kb windows to understand the average amount of LD in these distances. Plink 1.9 and R software (R Core Team, 2021) were utilized for these analyses19.

Genome-wide association mapping

Using Mixed Linear Models, we conducted GWAS and found significant SNPs tied to statin response. To measure for covariance between related individuals and stratification of the population, a genomic connection matrix20 was incorporated into the model to calculate the additive genetic influence of SNPs. The GCTA software version 11 was employed to develop the subsequent univariate linear model:

$${\varvec{y}} = {\varvec{\mu}} + \user2{X\beta } + {\varvec{X}}_{{{\varvec{SNP}}}} {\varvec{\beta}}_{{{\varvec{SNP}}}} + {\varvec{PC}}_{1:2} + {\varvec{Zu}} + {\varvec{e}}$$
(1)

y = vector of individual observations (response variable) of statin response. µ = population mean for fitted observations. β = vector fixed environmental effects included in the model (i.e., sex, age, and weight). u = vector of random direct additive genetic effects gained from \(MVN\left( {u\sim 0, \sigma_{u}^{2} } \right)\). e = vector of random residue errors obtained from \(MVN\left( {u\sim 0, \sigma_{e}^{2} } \right)\).

The X and Z are designed matrices that relate individuals’ records to their fixed effects (β) and additive genetic effects (u), respectively. \(X_{SNP}\) = incidence matrix for the SNP markers. \(\beta_{SNP}\) = regression coefficient for each SNP (SNP effects). \(PC_{1:2}\) = first two PC levels which were joined in the model as covariates to account for population structure.

Calculating the variance components and narrow sense heritability (h2) for each statin response was performed using fastGWA-REML21 through the run for GWAS, using the same model and with a restricted maximum likelihood (REML) method.

The quantile–quantile (QQ) plot was devoted to compare observed and expected statistics of each SNP under the null hypothesis of “no association” to detect any inflation. For visualizing the association results, the CMplot R package was utilized for preparing Manhattan plots22. For multiple testing correction, since the Bonferroni correction was extremely conservative for the correlation test among loci with significant LD, we applied the statistical simpleM approach to illustrate the effective number of individual correlation tests23. This approach performs a principal component evaluation on the SNP association matrix to determine the least “effective number” of tests accounting for variance ≥ 95%. Once the effective number of SNPs was assessed, a standard Bonferroni correction was used to control for the multiple tests. We applied this strategy on each chromosome to find the effective number of SNPs in a chromosome base (Supplementary file 2). The total number of standalone tests in this investigation was 153,977. Therefore, the Genome- and chromosome-wide significance level was adopted as 0.05/153,977 = 3.247238 × 10−7 and 0.05/(153,977/22) = 7.143924 × 10–06, respectively.

Functional annotation

Some well-known databases such as BioMart-Ensembl (www.ensembl.org/biomart), UCSC (http://genome.ucsc.edu) Genome Browser3, and National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov) were used beside the human reference genome assembly (GRCh38.p13) to detect important candidate genes within an area of about 1000 kb upstream/downstream of the confirmed associated SNPs. Also, genes including the target SNPs in them were recommended as candidate genes for more analysis. In cases where the target SNP was not within a gene, the closest genes were recommended as candidates. Moreover, the WebGestalt (WEB-based Gene SeT AnaLysis Toolkit) software was used to investigate the functional analysis of the candidate genes. WebGestalt is a functional enrichment analysis tool that provides three deep-rooted and complementary approaches for enrichment analysis, including Over-Representation Analysis (ORA), Gene Set Enrichment Analysis (GSEA), and Network Topology-based Analysis (NTA).

Results

Quality controls and data filtration

In this study, after quality controls of extracted data, we aimed to reach high genotyping quality, so no samples were filtered out and all 3221 ones (which had information on statin response) remained for further analysis. Out of 893,634 SNP markers, 208893 SNPs and 20719 SNPs were removed due to minor allele threshold (MAF < 0.05) and deviations from Hardy–Weinberg equilibrium test (p < 10−6), respectively. Finally, the 658,084 SNPs were kept for downstream analysis. Based on our outlier detection test through PCA, two samples have been recognized as outliers and excluded during the analysis procedure (Supplementary File 1, Fig. 1). LD pattern investigation in 10, 25, and 50 kb, revealed a smooth decline in LD (0.34, 0.29, and 0.27 on average) and almost remained constant for 100 kb distance which was not represented on the plot (Supplementary File1, Fig. 2).

Genome-wide association mapping

Calculations of variance components and heritability (h2) for the traits were done using the GEN2VCF programming software. The results illustrated that the heritability (h2) of Statin-LDL and Statin-TC reached 0.175 and 0.067, respectively. The results also revealed additive genetic, residual, and phenotypic variance for Statin-LDL samples, which was expressed as a scattering of data around the mean, estimated at 0.059, 0.277, and 0.336, respectively. Therefore, the additive genetic variance was lower variance and the phenotypic one was upper (Table 2). Our results also illustrated that additive genetic, residual, and phenotypic variance for Statin-TC samples were estimated at 0.025, 0.348, and 0.373, respectively, which as same as Statin-LDL.

Table 2 Variance components and genetic parameter estimates for the studied traits.
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The results represented eight SNPs associated with changing the levels of LDL/TC in response to a statin drug in cardiovascular patients. Also, the results represented the closest protein-coding gene that could be associated with statin response (Table 3). Among the candidate SNPs, three (rs17502794, rs10785232, rs4785621) are located in the intragenic (intronic) area and five (rs10820084, rs44744370, rs102179528, rs1966503, rs484071) are located in intergenic regions (Table3). The p-value for the correlation of all eight SNPs with the response of statin was in the range of 2.79E−07 to 8.14E−06. The best correlation was observed in rs17502794 with the lowest p-value (2.79E−07). The results suggest that the Minor Allele Frequency (MAF) of candidate SNPs varied from 0.093 to 0.245 in Statin-LDL and Statin-TC patient samples. The highest MAF was corresponding to rs484071 (Table 3).

Table 3 Significant SNP associated with the statin response.
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The result for associating SNPs with the statin response showed three SNPs (rs10820084, rs4803750, rs10989887) significantly accompanied the response of decreased LDL levels in cardiovascular patients (Fig. 1A). Also, it revealed that one SNP (rs1966503) is close to significant in association with statin response. Based on the results, two significant SNPs were located in chromosome 9 and one in chromosome 19. In addition, a SNP close to the significant level change was located on chromosome 16.

Figure 1
figure 1

(A) Manhattan plot for associations of SNPs with the statin_LDL response. X-axis: SNPs positions on chromosomes, Y-axis: − Log10 p-value. The green and red dashed lines show the genome- and chromosome-wide significance level (3.247 × 10−7 and 7.144 × 10−06) based on the number of independent SNPs identified by the statistical simpleM approach. (B) QQ plot for statin_LDL. The genomic control “inflation factor” lambda = 1.004 for linear regression. The blue area shows a 95% confidence interval.

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In the same way, the result for the association of SNPs with the statin response showed that three SNPs (rs17502794, rs10785232, rs484071) significantly accompanied the response of decreased total cholesterol (TC) levels in cardiovascular patients (Fig. 2A). Also, it revealed that one SNP (rs4785621) was close to significant in association with statin response. Based on the results, individual significant SNPs were located in chromosomes 12, 18, and 20. In addition, a SNP close to the significant level change was located on chromosome 16.

Figure 2
figure 2

(A) Manhattan plot for associations of SNPs with the statin_TC response. X-axis: SNPs positions on chromosomes, Y-axis: − Log10 p-value. The green and red dashed lines show the genome- and chromosome-wide significance level (3.247 × 10−7 and 7.144 × 10–6) based on the number of independent SNPs identified by the statistical simpleM approach. (B) QQ plot for statin_TC. The genomic control “inflation factor” lambda = 1.011 for linear regression. The blue area shows a 95% confidence interval.

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The results indicated that in the statin-LDL group, two SNPs (rs10820084, rs10989887) caused resistance to statin drugs in cardiovascular patients (\(\beta_{SNP}\) = 0.140997, \(\beta_{SNP}\) = 0.137665), and two SNPs (rs4803750, rs1966503) caused response to statin drugs in cardiovascular patients (\(\beta_{SNP}\) = − 0.133404, \(\beta_{SNP}\) = − 0.121344). In a similar way, the results revealed that in the statin-TC group, two SNPs (rs484071, rs4785621) caused resistance to statin drugs in cardiovascular patients (\(\beta_{SNP}\) = 0.0785785, \(\beta_{SNP}\) = 0.111714), and two SNPs (rs17502794, rs10785232) caused response to statin drugs in cardiovascular patients (\(\beta_{SNP}\) = − 0.132693, \(\beta_{SNP}\) = − 0.0957455).

The deviation of the observed P-value from the null hypothesis was illustrated graphically in a QQ plot (Fig. 1B). Plotting the observed P-values versus the anticipated value from the theoretical χ distribution involves sorting each SNP's P-values from greatest to lowest. All points occur on or close to the midline between the x and y axes if the actual values agree with the predicted values (null hypothesis: blue lines in Figs. 1B and 2B). The data revealed that in an LDL response to statins, certain observed P-values were much less than predicted by the null hypothesis, and the points moved in the x-axis direction. Because of systematic changes in allele frequencies among subpopulations in the collection of individuals tested, a significant percentage of p-values were lower than anticipated.

The results illustrated how the total cholesterol (TC) response to the statin shows some observed P-values that were much higher than predicted under the null hypothesis and move the points in the direction of the y-axis (Fig. 2B). Similar to LDL response because of systematic changes in allele frequencies among subpopulations in the collection of individuals tested, a significant percentage of p-values are lower than anticipated.

Functional analysis

The results captured from the GWAS Catalogue revealed that just one SNP (rs4803750) among eight candidate SNPs, has 12 reported phenotype associations (Supplmentary File 2). Our results illustrated that the phenotypes were about lipid metabolism, total cholesterol levels, high-density lipoprotein cholesterol levels, low-density lipoprotein cholesterol levels, age-related disease endophenotypes, and Alzheimer’s disease or fasting insulin levels (pleiotropy). Other seven candidate SNPs were introduced in association with a phenotype of statin responses for the first time in this study without any previous reports. The results also showed that the nearest genes with candidate SNPs are involved in several drug responses and molecular pathways based on GLAD4U and KEGG databases (Table 4). Moreover, the results of the gene ontology indicated that all candidate genes were categorized into several groups based on ‘biological process’, ‘cellular components’, and ‘molecular functions’ (Fig. 3).

Table 4 Gene enrichment results.
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Figure 3
figure 3

Gene Ontology Graph. Each Biological Process, Cellular Component, and Molecular Function category is represented by a red, blue, and green bar, respectively. The height of the bar represents the number of gene IDs in the primary list and each category. The genes that were intended for lipid metabolism were located in each group.

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Discussion

Cardiovascular diseases are among the most common mortality and morbidity reasons worldwide24. Several studies have proved the direct correlation of increased levels of LDL and total cholesterol with cardiovascular diseases24. Noticeably drugs that block cholesterol synthesis can improve cardiovascular disease risk factors. Statins are a group of drugs that block cholesterol synthesis in the liver by inhibiting the last enzyme, HMG-CoA reductase in the signaling pathway25. However, individual patient's bodies react differently to statin drugs. In such a way that some patients show a lower level of cholesterol or LDL by taking statin drugs and some others do not respond to receiving medicine25. Therefore, in this study, we sought to discover the relationship between candidate SNPs and statin drug response using genome-wide association studies (GWAS). Our results represented candidate eight SNPs were significantly or near significant situations associated with statin response. By analyzing the close genes to candidate SNPs, protein coding and non-coding gene activities and statin response in each cardiovascular patient (exercise cohort, n = 3221) were evaluated. In this case, we identified several genes and functional proteins that can accompanied by the statin response (Tables 3 and 4). Identifying the SNPs, genes, and molecular mechanisms involved in drug response is of great importance in today`s personalized and precision medicine.

An increased sample size of cardiovascular diseases (CVDs) meta-analysis of genome-wide association studies (GWAS) illustrated that over one hundred and fifty single nucleotide polymorphisms (SNPs) are strongly tied by CVDs as well as coronary artery problems26. In 2013, the CARDIoGRAMplusC4D group represented 15 new loci correlated with CVDs from a collection of about sixty thousand coronary heart disease cases and one hundred thousand controls27. They expanded the number of important CVD SNPs to 46 and proposed an additional 104 CVD-related loci. Altogether, the mentioned SNPs described near heritability of 10.6% of cardiovascular disease. Most of the detected loci are involved in lipid metabolism. This indicates that 12 out of 46 CARDIOGRAMplusC4D SNPs show a significant association with lipid traits, as expected based on other data on the importance of dyslipidemia in the CVD incidence28.

Our results introduced eight candidate SNPs located in different chromosomes including numbers 9, 19, 16, 20, 12, and 18. Interestingly, two SNPs (rs10820084, rs10989887) are harbored in the region of 9q31.1 at a distance of 1 kb from each other. Therefore, it can be concluded that this region represented a hot spot of chromosome 9 that directly corresponds to the response for the statin drugs in CVD patients. This region is full of multiple genes that are involved in several biological processes, response to drugs (such as Acamprosate), and molecular pathways (such as pathways in cell adhesion molecules (CAMs) and biosynthesis of unsaturated fatty acids) (Table 4). To find the importance of this region, we investigated its microdeletion symptoms in the literature. 9q31.1 Microdeletion syndrome is a rare, genetic, syndromic intellectual disability characterized by mild intellectual disability, short stature with high body mass index, short neck with cervical gibbous, and dysmorphic facial features29. Noticeably, a metabolic syndrome, including type 2 diabetes, hypercholesterolemia, and hypertension has also been reported29. Hypercholesterolemia may correlate with the response of statin to decrease the level of LDL in CVD patients.

When we investigated the 9q31.1 region in focus, we found that one of the nearest genes to two candidate SNPs (rs10820084, rs10989887) is the BAAT gene. This gene produces a protein known as BAAT (Bile Acid-CoA: Amino Acid N-Acyltransferase), a liver enzyme that catalyzes the second step in the synthesis of bile acid-amino acid conjugates by transferring C24 bile acids from the acyl-CoA thioester to either glycine or taurine30. The bile acid conjugates then function as a digestive tract detergent, improving the absorption of lipids and fat-soluble vitamins. Familial hyperchloremia (FHCA) is caused by errors in this gene31. Individually knocked down of the BAAT gene led to massive cholesterol accumulation in lysosomes31. Hence, the expression and function of the BAAT gene are correlated with the cholesterol level and may affected by two candidate SNPs. Therefore, the impact of these two SNPs in response to statin can be justified.

Moreover, our results revealed that one SNPs (rs4803750) are located in 19q13.3 near the BCL3 gene. BCL3, or BCL3 Transcription Coactivator, is a potential proto-oncogene32. The transcription coactivator BCL3 has the potential to regulate lipid metabolism through its effects on inflammation, gene–gene and gene-environment interactions, and BCL3-PVRL2-TOMM40 SNPs. In 2018, Miao et al.33 could detect 12 BCL3-PVRL2-TOMM40 SNPs, as well as gene–gene and gene-environment interactions, which were shown to be associated with dyslipidemia in the Chinese Maonan population. This research included 832 patients with dyslipidemia and 1130 persons with normal cholesterol levels. They screened for the optimal interaction that was combined between SNPs and environmental exposures using enhanced multifactor dimensionality reduction. Their findings demonstrated that the two groups’ genotypes and allele frequencies for the SNPs were diverse. Finally, their research revealed a correlation between the 12 SNPs neighboring to BCL3 and the blood lipid levels33. Therefore, the association of the candidate SNP (rs4803750) presented in our study was confirmed by other investigations about the BCL3 nearest gene.

Another candidate SNP (rs1966503) is located in 16q21 near a group of genes including CMTM6. CMTM6 (CKLF Like MARVEL Transmembrane Domain Containing 6) is a chemokine which is part of the innate immune system34. In 2017, Gao et al. reported that among the new 62 loci, two SNPs showed a more significant association with coronary artery disease (CAD) near RBM5 (rs2013208, for HDL) and CMTM6 (rs7640978, for LDL) in two Chinese ethnic groups35. Furthermore, several investigations illustrated the relationship between CMTM genes and CADs36,37. Therefore, our results can suggest the correlation of new candidate SNP (rs1966503) with the response of statin by the effects of CMTM genes.

Interestingly, two candidate SNPs (rs1966503, rs484071) are located in the near region of the cadherin genes such as cadherin 2, 11, and 5 in different chromosomes based on our results (Table 4). CDH2 (Cadherin 2) is a calcium-dependent cell adhesion protein which, by dimerization with a CDH2 chain from another cell, primarily facilitates homotypic cell–cell attachment. So, cadherins could aid in the separation of diverse cell types. Cadherins act as a modulator of neural stem cell quiescence by mediating neural stem cell anchoring to ependymocytes in the adult sub-ependymal zone38. When CDH2-mediated anchorage is compromised by MMP24 cleavage, neural stem cell quiescence is impacted. Cadherins play a part in the development of neural crest stem (NCS) cells' processes by facilitating the creation of cell-to-cell junctions between pancreatic beta cells and NCS cells39. Cadherins are necessary for cell-to-cell junctions in other tissues but the correlation of these protein-coding genes with the response to statins is not clear yet although some investigations indicated they are involved in lipid biosynthesis40.

Since Type 2 Diabetes causes disturbances in fat metabolism and leads to increased blood cholesterol, it can be considered one of the major predispositions to cardiovascular diseases. In other words, nearly 30–40% of patients with coronary artery disease have diabetes, according to a large study published on World Health Day in the European Journal of Preventive Cardiology (A journal of the European Society of Cardiology (ESC))41. Therefore, a notable population of cardiovascular patients also have diabetes. In our investigation, all included subjects equivalently had type 2 diabetes and cardiovascular disease. So, this issue makes the pipeline, and the represented results of this study reliable because the patients who had the same condition were compared to each other for the response to statin drugs. Assuming that a diabetic situation changes the results in comparison to non-diabetic patients, we can still claim that the results can be valuable for a large population of cardiovascular patients.

Conclusion

In the present study, genotype data from 3221 cardiovascular patients who were administered statin drugs were analyzed as GWAS with two parameters including total cholesterol and LDL level. We were able to identify and introduce eight novel SNPs associated with statin response in different individuals with cardiovascular disease for the first time. In addition, several genes and their functions that are close to the SNPs were also represented and discussed. Based on the results, some of the candidate genes such as BAAT, BCL3, and CMTM6 have a direct functional impact on total cholesterol level or LDL biosynthesis which leads to an effect on response to statin drugs.