TOMM40 and TOMM22 of the Translocase Outer Mitochondrial Membrane Complex rescue statin-impaired mitochondrial dynamics, morphology, and mitophagy in skeletal myotubes.

Background: Statins are the drugs most commonly used for lowering plasma low-density lipoprotein (LDL) cholesterol levels and reducing cardiovascular disease risk. Although generally well tolerated, statins can induce myopathy, a major cause of non-adherence to treatment. Impaired mitochondrial function has been implicated as a cause of statin-induced myopathy, but the underlying mechanism remains unclear. We have shown that simvastatin downregulates transcription of TOMM40 and TOMM22 , genes that encode major subunits of the translocase of outer mitochondrial membrane (TOM) complex which is responsible for importing nuclear-encoded proteins and maintaining mitochondrial function. We therefore investigated the role of TOMM40 and TOMM22 in mediating statin effects on mitochondrial function, dynamics, and mitophagy. Methods: Cellular and biochemical assays and transmission electron microscopy were used to investigate effects of simvastatin and TOMM40 and TOMM22 expression on measures of mitochondrial function and dynamics in C2C12 and primary human skeletal cell myotubes. Results: Knockdown of TOMM40 and TOMM22 in skeletal cell myotubes impaired mitochondrial oxidative function, increased production of mitochondrial superoxide, reduced mitochondrial cholesterol and CoQ levels, disrupted mitochondrial dynamics and morphology, and increased mitophagy, with similar effects resulting from simvastatin treatment. Overexpression of TOMM40 and TOMM22 in simvastatin-treated muscle cells rescued statin effects on mitochondrial dynamics, but not on mitochondrial function or cholesterol and CoQ levels. Moreover, overexpression of these genes resulted in an increase in number and density of cellular mitochondria. Conclusion: These results confirm that TOMM40 and TOMM22 are central in regulating mitochondrial homeostasis and demonstrate that downregulation of these genes by statin treatment mediates disruption of mitochondrial dynamics, morphology, and mitophagy, effects that may contribute to statin-induced myopathy.

Participant-derived cell line transcriptomic analyses and mouse studies reveal a role for ZNF335 in plasma cholesterol statin response.

Background: Statins lower circulating low-density lipoprotein cholesterol (LDLC) levels and reduce cardiovascular disease risk. Though highly efficacious in general, there is considerable inter-individual variation in statin efficacy that remains largely unexplained. Methods: To identify novel genes that may modulate statin-induced LDLC lowering, we used RNA-sequencing data from 426 control- and 2 µM simvastatin-treated lymphoblastoid cell lines (LCLs) derived from European and African American ancestry participants of the Cholesterol and Pharmacogenetics (CAP) 40 mg/day 6-week simvastatin clinical trial (ClinicalTrials.gov Identifier: NCT00451828). We correlated statin-induced changes in LCL gene expression with plasma LDLC statin response in the corresponding CAP participants. For the most correlated gene identified (ZNF335), we followed up in vivo by comparing plasma cholesterol levels, lipoprotein profiles, and lipid statin response between wild-type mice and carriers of a hypomorphic (partial loss of function) missense mutation in Zfp335 (the mouse homolog of ZNF335). Results: The statin-induced expression changes of 147 human LCL genes were significantly correlated to the plasma LDLC statin responses of the corresponding CAP participants in vivo (FDR=5%). The two genes with the strongest correlations were zinc finger protein 335 (ZNF335 aka NIF-1, rho=0.237, FDR-adj p=0.0085) and CCR4-NOT transcription complex subunit 3 (CNOT3, rho=0.233, FDR-adj p=0.0085). Chow-fed mice carrying a hypomorphic missense (R1092W; aka bloto) mutation in Zfp335 had significantly lower non-HDL cholesterol levels than wild type C57BL/6J mice in a sex combined model (p=0.04). Furthermore, male (but not female) mice carrying the Zfp335R1092W allele had significantly lower total and HDL cholesterol levels than wild-type mice. In a separate experiment, wild-type mice fed a control diet for 4 weeks and a matched simvastatin diet for an additional 4 weeks had significant statin-induced reductions in non-HDLC (-43±18% and -23±19% for males and females, respectively). Wild-type male (but not female) mice experienced significant reductions in plasma LDL particle concentrations, while male mice carrying Zfp335R1092W allele(s) exhibited a significantly blunted LDL statin response. Conclusions: Our in vitro and in vivo studies identified ZNF335 as a novel modulator of plasma cholesterol levels and statin response, suggesting that variation in ZNF335 activity could contribute to inter-individual differences in statin clinical efficacy.

Meta-GWAS of PCSK9 levels detects two novel loci at APOB and TM6SF2.

BACKGROUND: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player in lipid metabolism, as it degrades low-density lipoprotein (LDL) receptors from hepatic cell membranes. So far, only variants of the PCSK9 gene locus were found to be associated with PCSK9 levels. Here we aimed to identify novel genetic loci that regulate PCSK9 levels and how they relate to other lipid traits. Additionally, we investigated to what extend the causal effect of PCSK9 on coronary artery disease (CAD) is mediated by low-density lipoprotein-cholesterol (LDL-C). METHODS AND RESULTS: We performed a genome-wide association study meta-analysis of PCSK9 levels in up to 12 721 samples of European ancestry. The estimated heritability was 10.3%, which increased to 12.6% using only samples from patients without statin treatment. We successfully replicated the known PCSK9 hit consisting of three independent signals. Interestingly, in a study of 300 African Americans, we confirmed the locus with a different PCSK9 variant. Beyond PCSK9, our meta-analysis detected three novel loci with genome-wide significance. Co-localization analysis with cis-eQTLs and lipid traits revealed biologically plausible candidate genes at two of them: APOB and TM6SF2. In a bivariate Mendelian Randomization analysis, we detected a strong effect of PCSK9 on LDL-C, but not vice versa. LDL-C mediated 63% of the total causal effect of PCSK9 on CAD. CONCLUSION: Our study identified novel genetic loci with plausible candidate genes affecting PCSK9 levels. Ethnic heterogeneity was observed at the PCSK9 locus itself. Although the causal effect of PCSK9 on CAD is mainly mediated by LDL-C, an independent direct effect also occurs.

A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts.

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

Identifying genetic modulators of statin response using subject-derived lymphoblastoid cell lines.

Although statins (3-hydroxy-3-methylglutaryl-CoA reductase inhibitors) have proven effective in reducing plasma low-density lipoprotein levels and risk of cardiovascular disease, their lipid lowering efficacy is highly variable among individuals. Furthermore, statin treatment carries a small but significant risk of adverse effects, most notably myopathy and new onset diabetes. Hence, identification of biomarkers for predicting patients who would most likely benefit from statin treatment without incurring increased risk of adverse effects can have a significant public health impact. In this review, we discuss the rationale for the use of subject-derived lymphoblastoid cell lines in studies of statin pharmacogenomics and describe a variety of approaches we have employed to identify novel genetic markers associated with interindividual variation in statin response.

The Pharmacogenetics of Statin Therapy on Clinical Events: No Evidence that Genetic Variation Affects Statin Response on Myocardial Infarction.

Background: The pharmacogenetic effect on cardiovascular disease reduction in response to statin treatment has only been assessed in small studies. In a pharmacogenetic genome wide association study (GWAS) analysis within the Genomic Investigation of Statin Therapy (GIST) consortium, we investigated whether genetic variation was associated with the response of statins on cardiovascular disease risk reduction. Methods: The investigated endpoint was incident myocardial infarction (MI) defined as coronary heart disease death and definite and suspect non-fatal MI. For imputed single nucleotide polymorphisms (SNPs), regression analysis was performed on expected allelic dosage and meta-analysed with a fixed-effects model, inverse variance weighted meta-analysis. All SNPs with p-values <5.0 × 10-4 in stage 1 GWAS meta-analysis were selected for further investigation in stage-2. As a secondary analysis, we extracted SNPs from the Stage-1 GWAS meta-analysis results based on predefined hypotheses to possibly modifying the effect of statin therapy on MI. Results: In stage-1 meta-analysis (eight studies, n = 10,769, 4,212 cases), we observed no genome-wide significant results (p < 5.0 × 10-8). A total of 144 genetic variants were followed-up in the second stage (three studies, n = 1,525, 180 cases). In the combined meta-analysis, no genome-wide significant hits were identified. Moreover, none of the look-ups of SNPs known to be associated with either CHD or with statin response to cholesterol levels reached Bonferroni level of significance within our stage-1 meta-analysis. Conclusion: This GWAS analysis did not provide evidence that genetic variation affects statin response on cardiovascular risk reduction. It does not appear likely that genetic testing for predicting effects of statins on clinical events will become a useful tool in clinical practice.

Genetic variants modulate gene expression statin response in human lymphoblastoid cell lines.

BACKGROUND: Statins are widely prescribed to lower plasma low-density lipoprotein cholesterol levels. Though statins reduce cardiovascular disease risk overall, statin efficacy varies, and some people experience adverse side effects while on statin treatment. Statins also have pleiotropic effects not directly related to their cholesterol-lowering properties, but the mechanisms are not well understood. To identify potential genetic modulators of clinical statin response, we looked for genetic variants associated with statin-induced changes in gene expression (differential eQTLs or deQTLs) in lymphoblastoid cell lines (LCLs) derived from participants of the Cholesterol and Pharmacogenetics (CAP) 40 mg/day 6-week simvastatin clinical trial. We exposed CAP LCLs to 2 µM simvastatin or control buffer for 24 h and performed polyA-selected, strand-specific RNA-seq. Statin-induced changes in gene expression from 259 European ancestry or 153 African American ancestry LCLs were adjusted for potential confounders prior to association with genotyped and imputed genetic variants within 1 Mb of each gene's transcription start site. RESULTS: From the deQTL meta-analysis of the two ancestral populations, we identified significant cis-deQTLs for 15 genes (TBC1D4, MDGA1, CHI3L2, OAS1, GATM, ASNSD1, GLUL, TDRD12, PPIP5K2, OAS3, SERPINB1, ANKDD1A, DTD1, CYFIP2, and GSDME), eight of which were significant in at least one of the ancestry subsets alone. We also conducted eQTL analyses of the endogenous (control-treated), statin-treated, and average of endogenous and statin-treated LCL gene expression levels. We identified eQTLs for approximately 6000 genes in each of the three (endogenous, statin-treated, and average) eQTL meta-analyses, with smaller numbers identified in the ancestral subsets alone. CONCLUSIONS: Several of the genes in which we identified deQTLs have functions in human health and disease, such as defense from viruses, glucose regulation, and response to chemotherapy drugs. This suggests that DNA variation may play a role in statin effects on various health outcomes. These findings could prove useful to future studies aiming to assess benefit versus risk of statin treatment using individual genetic profiles.

Statin-induced LDL cholesterol response and type 2 diabetes: a bidirectional two-sample Mendelian randomization study.

It remains unclear whether the increased risk of new-onset type 2 diabetes (T2D) seen in statin users is due to low LDL-C concentrations, or due to the statin-induced proportional change in LDL-C. In addition, genetic instruments have not been proposed before to examine whether liability to T2D might cause greater proportional statin-induced LDL-C lowering. Using summary-level statistics from the Genomic Investigation of Statin Therapy (GIST, nmax = 40,914) and DIAGRAM (nmax = 159,208) consortia, we found a positive genetic correlation between LDL-C statin response and T2D using LD score regression (rgenetic = 0.36, s.e. = 0.13). However, mendelian randomization analyses did not provide support for statin response having a causal effect on T2D risk (OR 1.00 (95% CI: 0.97, 1.03) per 10% increase in statin response), nor that liability to T2D has a causal effect on statin-induced LDL-C response (0.20% increase in response (95% CI: -0.40, 0.80) per doubling of odds of liability to T2D). Although we found no evidence to suggest that proportional statin response influences T2D risk, a definitive assessment should be made in populations comprised exclusively of statin users, as the presence of nonstatin users in the DIAGRAM dataset may have substantially diluted our effect estimate.

GeneFishing to reconstruct context specific portraits of biological processes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.

ZNF542P is a pseudogene associated with LDL response to simvastatin treatment.

Statins are the most commonly prescribed cardiovascular disease drug, but their inter-individual efficacy varies considerably. Genetic factors uncovered to date have only explained a small proportion of variation in low-density lipoprotein cholesterol (LDLC) lowering. To identify novel markers and determinants of statin response, we used whole transcriptome sequence data collected from simvastatin and control incubated lymphoblastoid cell lines (LCLs) established from participants of the Cholesterol and Pharmacogenetics (CAP) simvastatin clinical trial. We looked for genes whose statin-induced expression changes were most different between LCLs derived from individuals with high versus low plasma LDLC statin response during the CAP trial. We created a classification model of 82 "signature" gene expression changes that distinguished high versus low LDLC statin response. One of the most differentially changing genes was zinc finger protein 542 pseudogene (ZNF542P), the signature gene with changes most correlated with statin-induced change in cellular cholesterol ester, an in vitro marker of statin response. ZNF542P knock-down in a human hepatoma cell line increased intracellular cholesterol ester levels upon simvastatin treatment. Together, these findings imply a role for ZNF542P in LDLC response to simvastatin and, importantly, highlight the potential significance of noncoding RNAs as a contributing factor to variation in drug response.

A large electronic-health-record-based genome-wide study of serum lipids.

A genome-wide association study (GWAS) of 94,674 ancestrally diverse Kaiser Permanente members using 478,866 longitudinal electronic health record (EHR)-derived measurements for untreated serum lipid levels empowered multiple new findings: 121 new SNP associations (46 primary, 15 conditional, and 60 in meta-analysis with Global Lipids Genetic Consortium data); an increase of 33-42% in variance explained with multiple measurements; sex differences in genetic impact (greater impact in females for LDL, HDL, and total cholesterol and the opposite for triglycerides); differences in variance explained among non-Hispanic whites, Latinos, African Americans, and East Asians; genetic dominance and epistatic interaction, with strong evidence for both at the ABO and FUT2 genes for LDL; and tissue-specific enrichment of GWAS-associated SNPs among liver, adipose, and pancreas eQTLs. Using EHR pharmacy data, both LDL and triglyceride genetic risk scores (477 SNPs) were strongly predictive of age at initiation of lipid-lowering treatment. These findings highlight the value of longitudinal EHRs for identifying new genetic features of cholesterol and lipoprotein metabolism with implications for lipid treatment and risk of coronary heart disease.

Generalized correlation measure using count statistics for gene expression data with ordered samples.

Motivation: Capturing association patterns in gene expression levels under different conditions or time points is important for inferring gene regulatory interactions. In practice, temporal changes in gene expression may result in complex association patterns that require more sophisticated detection methods than simple correlation measures. For instance, the effect of regulation may lead to time-lagged associations and interactions local to a subset of samples. Furthermore, expression profiles of interest may not be aligned or directly comparable (e.g. gene expression profiles from two species). Results: We propose a count statistic for measuring association between pairs of gene expression profiles consisting of ordered samples (e.g. time-course), where correlation may only exist locally in subsequences separated by a position shift. The statistic is simple and fast to compute, and we illustrate its use in two applications. In a cross-species comparison of developmental gene expression levels, we show our method not only measures association of gene expressions between the two species, but also provides alignment between different developmental stages. In the second application, we applied our statistic to expression profiles from two distinct phenotypic conditions, where the samples in each profile are ordered by the associated phenotypic values. The detected associations can be useful in building correspondence between gene association networks under different phenotypes. On the theoretical side, we provide asymptotic distributions of the statistic for different regions of the parameter space and test its power on simulated data. Availability and implementation: The code used to perform the analysis is available as part of the Supplementary Material. Contact: msw@usc.edu or hhuang@stat.berkeley.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Human genetic variation in VAC14 regulates Salmonella invasion and typhoid fever through modulation of cholesterol.

Risk, severity, and outcome of infection depend on the interplay of pathogen virulence and host susceptibility. Systematic identification of genetic susceptibility to infection is being undertaken through genome-wide association studies, but how to expeditiously move from genetic differences to functional mechanisms is unclear. Here, we use genetic association of molecular, cellular, and human disease traits and experimental validation to demonstrate that genetic variation affects expression of VAC14, a phosphoinositide-regulating protein, to influence susceptibility to Salmonella enterica serovar Typhi (S Typhi) infection. Decreased VAC14 expression increased plasma membrane cholesterol, facilitating Salmonella docking and invasion. This increased susceptibility at the cellular level manifests as increased susceptibility to typhoid fever in a Vietnamese population. Furthermore, treating zebrafish with a cholesterol-lowering agent, ezetimibe, reduced susceptibility to S Typhi. Thus, coupling multiple genetic association studies with mechanistic dissection revealed how VAC14 regulates Salmonella invasion and typhoid fever susceptibility and may open doors to new prophylactic/therapeutic approaches.

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals.

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

SUGP1 is a novel regulator of cholesterol metabolism.

A large haplotype on chromosome 19p13.11 tagged by rs10401969 in intron 8 of SURP and G patch domain containing 1 (SUGP1) is associated with coronary artery disease (CAD), plasma LDL cholesterol levels, and other energy metabolism phenotypes. Recent studies have suggested that TM6SF2 is the causal gene within the locus, but we postulated that this locus could harbor additional CAD risk genes, including the putative splicing factor SUGP1 Indeed, we found that rs10401969 regulates SUGP1 exon 8 skipping, causing non-sense-mediated mRNA decay. Hepatic Sugp1 overexpression in CD1 male mice increased plasma cholesterol levels 20-50%. In human hepatoma cell lines, SUGP1 knockdown stimulated 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) alternative splicing and decreased HMGCR transcript stability, thus reducing cholesterol synthesis and increasing LDL uptake. Our results strongly support a role for SUGP1 as a novel regulator of cholesterol metabolism and suggest that it contributes to the relationship between rs10401969 and plasma cholesterol.

RP1-13D10.2 Is a Novel Modulator of Statin-Induced Changes in Cholesterol.

BACKGROUND: Numerous genetic contributors to cardiovascular disease risk have been identified through genome-wide association studies; however, identifying the molecular mechanism underlying these associations is not straightforward. The Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) trial of rosuvastatin users identified a sub-genome-wide association of rs6924995, a single-nucleotide polymorphism ≈10 kb downstream of myosin regulatory light chain interacting protein (MYLIP, aka IDOL and inducible degrader of low-density lipoprotein receptor [LDLR]), with LDL cholesterol statin response. Interestingly, although this signal was initially attributed to MYLIP, rs6924995 lies within RP1-13D10.2, an uncharacterized long noncoding RNA. METHODS AND RESULTS: Using simvastatin and sham incubated lymphoblastoid cell lines from participants of the Cholesterol and Pharmacogenetics (CAP) simvastatin clinical trial, we found that statin-induced change in RP1-13D10.2 levels differed between cell lines from the tails of the white and black low-density lipoprotein cholesterol response distributions, whereas no difference in MYLIP was observed. RP1-13D10.2 overexpression in Huh7 and HepG2 increased LDLR transcript levels, increased LDL uptake, and decreased media levels of apolipoprotein B. In addition, we found a trend of slight differences in the effects of RP1-13D10.2 overexpression on LDLR transcript levels between hepatoma cells transfected with the rs6924995 A versus G allele and a suggestion of an association between rs6924995 and RP1-10D13.2 expression levels in the CAP lymphoblastoid cell lines. Finally, RP1-13D10.2 expression levels seem to be sterol regulated, consistent with its potential role as a novel lipid regulator. CONCLUSIONS: RP1-13D10.2 is a long noncoding RNA that regulates LDLR and may contribute to low-density lipoprotein cholesterol response to statin treatment. These findings highlight the potential role of noncoding RNAs as determinants of interindividual variation in drug response.

Individual and Combined Associations of Genetic Variants in CYP3A4, CYP3A5, and SLCO1B1 With Simvastatin and Simvastatin Acid Plasma Concentrations.

Our objective was to evaluate the associations of genetic variants affecting simvastatin (SV) and simvastatin acid (SVA) metabolism [the gene encoding cytochrome P450, family 3, subfamily A, polypeptide 4 (CYP3A4)*22 and the gene encoding cytochrome P450, family 3, subfamily A, polypeptide 5 (CYP3A5)*3] and transport [the gene encoding solute carrier organic anion transporter family member 1B1 (SLCO1B1) T521C] with 12-hour plasma SV and SVA concentrations. The variants were genotyped, and the concentrations were quantified by high performance liquid chromatography-tandem mass spectrometry in 646 participants of the Cholesterol and Pharmacogenetics clinical trial of 40 mg/d SV for 6 weeks. The genetic variants were tested for association with 12-hour plasma SV, SVA, or the SVA/SV ratio using general linear models. CYP3A5*3 was not significantly associated with 12-hour plasma SV or SVA concentration. CYP3A4*1/*22 participants had 58% higher 12-hour plasma SV concentration compared with CYP3A4*1/*1 participants (P = 0.006). SLCO1B1 521T/C and 521C/C participants had 71% (P < 0.001) and 248% (P < 0.001) higher 12-hour plasma SVA compared with SLCO1B1 521T/T participants, respectively. CYP3A4 and SLCO1B1 genotypes combined categorized participants into low (<1), intermediate (≈1), and high (>1) SVA/SV ratio groups (P = 0.001). In conclusion, CYP3A4*22 and SLCO1B1 521C were significantly associated with increased 12-hour plasma SV and SVA concentrations, respectively. CYP3A5*3 was not significantly associated with 12-hour plasma SV or SVA concentrations. The combination of CYP3A4*22 and SLCO1B1 521C was significantly associated with SVA/SV ratio, which may translate into different clinical SV risk/benefit profiles.

Prediction of LDL cholesterol response to statin using transcriptomic and genetic variation.

BACKGROUND: Statins are widely prescribed for lowering LDL-cholesterol (LDLC) levels and risk of cardiovascular disease. There is, however, substantial inter-individual variation in the magnitude of statin-induced LDLC reduction. To date, analysis of individual DNA sequence variants has explained only a small proportion of this variability. The present study was aimed at assessing whether transcriptomic analyses could be used to identify additional genetic contributions to inter-individual differences in statin efficacy. RESULTS: Using expression array data from immortalized lymphoblastoid cell lines derived from 372 participants of the Cholesterol and Pharmacogenetics clinical trial, we identify 100 signature genes differentiating high versus low statin responders. A radial-basis support vector machine prediction model of these signature genes explains 12.3% of the variance in statin-mediated LDLC change. Addition of SNPs either associated with expression levels of the signature genes (eQTLs) or previously reported to be associated with statin response in genome-wide association studies results in a combined model that predicts 15.0% of the variance. Notably, a model of the signature gene associated eQTLs alone explains up to 17.2% of the variance in the tails of a separate subset of the Cholesterol and Pharmacogenetics population. Furthermore, using a support vector machine classification model, we classify the most extreme 15% of high and low responders with high accuracy. CONCLUSIONS: These results demonstrate that transcriptomic information can explain a substantial proportion of the variance in LDLC response to statin treatment, and suggest that this may provide a framework for identifying novel pathways that influence cholesterol metabolism.