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. 2012 Nov 21;3:248. doi: 10.3389/fgene.2012.00248

Impact of the Interaction between 3′-UTR SNPs and microRNA on the Expression of Human Xenobiotic Metabolism Enzyme and Transporter Genes

Rongrong Wei 1, Fan Yang 1, Thomas J Urban 2, Lang Li 3, Naga Chalasani 4, David A Flockhart 5, Wanqing Liu 1,4,*
PMCID: PMC3502871  PMID: 23181071

Abstract

Genetic variation in the expression of human xenobiotic metabolism enzymes and transporters (XMETs) leads to inter-individual variability in metabolism of therapeutic agents as well as differed susceptibility to various diseases. Recent expression quantitative traits loci (eQTL) mapping in a few human cells/tissues have identified a number of single nucleotide polymorphisms (SNPs) significantly associated with mRNA expression of many XMET genes. These eQTLs are therefore important candidate markers for pharmacogenetic studies. However, questions remain about whether these SNPs are causative and in what mechanism these SNPs may function. Given the important role of microRNAs (miRs) in gene transcription regulation, we hypothesize that those eQTLs or their proxies in strong linkage disequilibrium (LD) altering miR targeting are likely causative SNPs affecting gene expression. The aim of this study is to identify eQTLs potentially regulating major XMETs via interference with miR targeting. To this end, we performed a genome-wide screening for eQTLs for 409 genes encoding major drug metabolism enzymes, transporters and transcription factors, in publically available eQTL datasets generated from the HapMap lymphoblastoid cell lines and human liver and brain tissue. As a result, 308 eQTLs significantly (p < 10−5) associated with mRNA expression of 101 genes were identified. We further identified 7,869 SNPs in strong LD (r2 ≥ 0.8) with these eQTLs using the 1,000 Genome SNP data. Among these 8,177 SNPs, 27 are located in the 3′-UTR of 14 genes. Using two algorithms predicting miR-SNP interaction, we found that almost all these SNPs (26 out of 27) were predicted to create, abolish, or change the target site for miRs in both algorithms. Many of these miRs were also expressed in the same tissue that the eQTL were identified. Our study provides a strong rationale for continued investigation for the functions of these eQTLs in pharmacogenetic settings.

Keywords: eQTL, xenobiotic metabolism enzyme and transporter, microRNA, pharmacogenetics, 3′-UTR

Introduction

Xenobiotic metabolizing enzymes and transporters (XMETs) are involved in biotransformation and detoxification of carcinogens, environmental toxins, and therapeutic drugs (Carlsten et al., 2008; Korkina et al., 2009). In humans, the process of biotransformation and detoxification of xenobiotics by XMETs can be divided into three phases: modification (phase I) primarily by enzymes of the cytochromes P450 superfamily; conjugation (phase II), e.g., glucuronidation by UDP-glucuronosyl transferase; and excretion (phase III) mainly by membrane transporters. XMETs are expressed in almost all tissue types, centrally and locally protecting the entire body against the damages caused by various natural and synthetic compounds. XMETs are highly expressed in digestive tract and especially in the liver, the most important organ for central metabolism (Conde-Vancells et al., 2010). Variations in the expression and activity of these XMETs lead to significant inter-individual difference in the disposition of exogenous chemicals including absorption, distribution, metabolism, and excretion (ADME) of pharmaceutical drugs. On the other hand, many XMETs are also found to be very abundant in non-digestive tract tissues/cells, e.g., brain, lung, bladder, and blood (Pavek and Dvorak, 2008). These XMETs could affect the local response to certain drugs at the site of action. Meanwhile, due to the crucial role of XMETs in detoxification of carcinogens and toxins, genetic variation in XMETs function in specific tissues/organs is also an important mechanism underlying genetic susceptibility to certain diseases, e.g., those XMETs expressed in lung and bladder may modify cancer risk. Recent genome-wide association studies have identified polymorphisms at the UGT1A locus strongly associated with urinary bladder cancer risk (Selinski et al., 2012). XMETs are sensitively regulated by various nuclear receptors (NRs) and transcription factors (TFs). These trans-acting regulators play a pivotal role in mediating cellular response to exposure to xenobiotics by modulating the transcription of XMETs, thus significantly contributing to the variability in the function of XMETs (Bourgine et al., 2012).

Identifying the DNA polymorphisms leading to the variations in XMET function is a major area of interest in pharmacogenetic and genomic research. To date, numerous studies focused on individual XMET genes have discovered a large number of sequence variations, many of which alter protein coding sequence and consequently affecting the activity of XMETs (Adjei et al., 2003; Hildebrandt et al., 2004; Ji et al., 2005; Moyer et al., 2007; Mrozikiewicz et al., 2011). Meanwhile, even more variants were suggested to quantitatively modulate gene transcription (Pavek and Dvorak, 2008). Recently, genome-wide mapping for gene expression quantitative trait loci (eQTLs) in a few human tissues/cells offered unprecedented opportunities to identify the most influential single nucleotide polymorphisms (SNPs) determining gene expression level of XMETs (Gamazon et al., 2010). However, unlike the variants located in the protein coding sequences for which the causality for altered enzyme activity can be more easily understood, how eQTLs affect gene transcription is largely unknown. Understanding the underlying mechanisms will lead to identification of novel causative DNA variants for XMET function as well as reliable pharmacogenetic markers.

MicroRNAs (miRs) are single stranded, about 22-nucleotides (nt) long, evolutionarily conserved, and function as important posttranscriptional regulators of mRNA expression by binding to the 3′-UTR of target mRNAs (Ambros, 2004; Bartel, 2004). MiRs are involved in various developmental and physiological processes by negatively regulating gene expression (Zhang et al., 2007). Over 30% of all protein-coding genes were estimated to be regulated by miRs (Brennecke et al., 2005; Krek et al., 2005; Lewis et al., 2005; Lim et al., 2005). Due to the conservation of the miR target site, SNPs located in 3′-UTR sequences may abolish or create a miR target, thus significantly affecting the mRNA expression (Saunders et al., 2007). Previous studies have suggested that many XMETs are regulated by miRs (Tsuchiya et al., 2006; Takagi et al., 2010; Patron et al., 2012). Several studies also demonstrated that SNPs in XMET gene 3′-UTRs led to different levels of enzyme activity (Saunders et al., 2007; Chin et al., 2008). Hence, we hypothesized that it may be an important mechanism that common SNPs or their linkage disequilibrium (LD) proxies located in the XMET gene 3′-UTR sequences alter mRNA expression via interference with miR targeting. In order to identify these candidate SNPs that may significantly modulate XMET expression, in this study we used multiple published human eQTL datasets to perform an in silico screening for SNPs that highly correlated with mRNA level of 409 major XMET genes. The significant SNPs and/or their LD proxies located in the gene 3′-UTRs were selected to predict a potential interference with miRs. We found that 27 SNPs located in the 3′-UTR of 14 XMET genes are likely associated with gene expression via altering miR binding.

Materials and Methods

Selection of eQTLs

The general strategy for the data analysis was presented in Figure 1. We used the published eQTLs datasets generated from the HapMap lymphoblastoid cell lines (LCLs; Montgomery et al., 2010), human liver (Schadt et al., 2008), and human brain (Gibbs et al., 2010). Although additional eQTL datasets in human LCLs are also available, we chose to use the one by Montgomery et al. (2010) which utilized high-throughput sequencing for the quantification of gene expression, as this technology has been suggested to produce more accurate gene expression data. To our knowledge, all datasets were collected from tissue/cells derived from individuals of Caucasian in origin. We used the online tool1 to search statistically significant eQTLs. As our study was focused on cis-acting eQTLs, we used a cut-off of p = 10−5 for significance, considering the window for genomic region (500 kb) of each gene and the potential number of SNPs (1 in every 100–1,000 bp).

Figure 1.

Figure 1

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Schematic of the search for miRNAs and the associated SNPs from XMET genes.

Search for SNPs in LD with eQTLs

To search SNPs in LD with significant eQTLs, we used the SNAP2 program to screen the 1,000 Genome SNP data within 500 kb range of the eQTLs of interest in the CEU population with a LD level cut-off of R2 = 0.8. Annotation for the location of eQTLs and their proxies relative to the gene structure was also collected with the program. Only SNPs and/or their proxies located within the 3′-UTR of the studied genes of interest were retained for further analyses.

Prediction of SNP-miR interaction

In order to predict the potential SNP-miR interaction, two programs, MicroSNiPer3 and PolymiRTS4 were used. The major difference between the two programs is the algorithm used to predict the target site of miRs. The PolymiRTS program used the TargetScan5; Lewis et al., 2005; Friedman et al., 2009) algorithm (Bao et al., 2007). In contrast, the MicroSNiPer program used the FASTA (Pearson and Lipman, 1988) alignment program to determine if a change in a nucleotide in 3′-UTR sequence would change the miR binding capability, based on the requirement of perfect Watson–Crick match to the seed 2–7 nt of miRs (Lewis et al., 2005). To be conservative, we used 7-mers match as the cut-off value for a positive prediction.

Results

Genome-wide eQTL analysis of XMETs

Expression quantitative traits loci were screened for all 409 major XMET genes, including 144 phase I, 85 phase II and 111 phase III genes, 48 NRs, and transcription factor genes as well as another 21 genes related to drug ADME (Table A1 in Appendix). As a result, a total of 308 significant (p < 10−5) eQTLs were identified from 101 XMET genes. These include nine in LCL, 83 in liver, and 221 in brain tissues. Five SNPs were found as eQTLs shared in two tissue types: rs1023252 in both LCL and brain tissues, rs11101992, rs156697, rs2071474, and rs241440 in both liver and brain tissues (Figure 2). Among the total of 308 eQTLs, 20 SNPs were found to be located in the 3′-UTR region; 3 SNPs were in the 5′-UTRs; 171 SNPs were intronic; 8 and 6 SNPs were synonymous and non-synonymous coding variants, respectively; and 12 and 15 SNPs were located in the upstream and downstream flanking region of the genes, respectively. The remaining 73 SNPs were located in intergenic regions.

Figure 2.

Figure 2

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Significant eQTLs in different tissues. A total of 308 significant eQTLs were identified, including 9 eQTLs in LCL, 83 in liver, and 221 in brain tissues. Five eQTLs were shared in two tissue types.

eQTLs and their LD proxies

We chose to screen the 1,000 Genome SNP dataset as this would produce the most comprehensive coverage for the SNPs that may be in LD with a given eQTL. A total of 7,869 SNPs with significant LD with 260 eQTLs were identified. Combined with the remaining 48 eQTLs which had no reliable proxies in the 1,000 Genome dataset, a total of 8,177 SNPs (308 eQTLs and 7,869 proxy SNPs) were included in the subsequent analyses.

Prediction of miR-SNPs interaction

Of the 112 eQTLs and proxies located in the 3′-UTR sequences, 27 SNPs were found in the 3′-UTR of 14 genes of interest. The remaining SNPs were located in nearby genes thus were excluded from the subsequent analysis. These SNPs were all common SNPs with their minor allele frequency (MAF) ≥0.067. Among the 27 SNPs, 12 were found in liver, and 15 were identified in brain tissue. More detailed information for these SNPs was listed in Table A2 in Appendix.

We focused our study on the association between miRs and these 27 SNPs in the 14 genes. After screened with the two algorithms, MicroSNiPer (Barenboim et al., 2010) and PolymiRTs (Gong et al., 2012), all the 27 SNPs apart from rs11807 (which is not predicted to be in a target site in PolymiRTs database) were found to potentially create, abolish, or alter the target site for miRs in both algorithms. Notably, 34 miRs were predicted by both algorithms to interact with 19 of these SNPs (Table A2 in Appendix). Of these 34 overlap miRs, except for rs2480256 of CYP2E1 which is not located in the seed sequence of hsa-miR-570-3p, all the remaining SNPs were found to be located in the seed sequence of miR targets.

To further validate the interaction between miRs and SNPs, we investigated whether the identified miRs were expressed in the same tissue as the identified eQTL. We used the GEO datasets (GSE21279 and GSE26545) to screen miR expression in liver and brain tissues, respectively (Hou et al., 2011; Hu et al., 2011). Since many predicted miRs were new and not probed by the published platforms, we thus only concentrate on the list of miRs probed in the platforms. Overall, over 74% (20 out of 27) of the identified miR-SNPs were found to have at least one predicted miR co-expressed with the gene of interest in the same tissue.

We further aimed to investigate whether these 27 SNPs are more likely to be targeted by miRs especially by the co-expressed miR in liver and brain tissues, compared to random-selected 3′-UTR SNPs with similar MAF. No statistical significance were found, possibly due to the limited power caused by the small number (n = 27) of SNPs involved (data not shown).

Discussion

Although a large number of DNA variants affecting the function of XMETs have been identified, and many of them have been well linked with clinical response to pharmacotherapy or disease susceptibility (Motsinger-Reif et al., 2010), genetic variations in the activity of most XMETs remain incompletely explained. Recent studies continue to discover novel functional variants in XMET genes (Ramsey et al., 2012). Meanwhile, genome-wide association studies have found a number of XMET SNPs without previously known function significantly associated with different phenotypes in humans (Teichert et al., 2009; Estrada et al., 2012). These studies consistently suggested that additional sequence variants with fundamental role in XMET function have not been identified. Recent eQTL mapping in human tissues provided an opportunity to discover functional XMET polymorphisms at the genome-wide level. However, questions remain whether the identified eQTLs are causal for the altered gene expression and via what mechanism. Our study provides a comprehensive evaluation for this question in major human XMET genes, and generated a list of candidate SNPs that may modulate XMET genes via interference with miR targeting in multiple human tissue types.

Single nucleotide polymorphisms located in the gene 3′-UTRs could have great impact on miR targeting. It has been demonstrated that the entire 3′-UTR sequence could play important roles in miR function in addition to miR target sites (Hu and Bruno, 2011). In particular, negative selection in humans is stronger on computationally predicted conserved miR binding sites than on other conserved sequence motifs in 3′-UTRs, and polymorphisms in predicted miR binding sites are highly likely to be deleterious (Chen and Rajewsky, 2006). Gong et al. (2012) mapped SNPs to the 3′-UTRs of all human protein coding genes. Their results showed that among the 225,759 SNPs identified in 3′-UTRs, over 25% of SNPs potentially abolished 90,784 original miR target sites, while another 25% created a similar number of putative miRNA target sites. Besides these in silico studies, a number of SNPs altering miR targeting have been experimentally demonstrated to be associated with multiple diseases as well as drug metabolism and environmental procarcinogen detoxification (Abelson et al., 2005; Tan et al., 2007; Yu et al., 2007; Yokoi and Nakajima, 2011). Although the seed sequences for miR binding are critical and highly conserved, recent studies have also suggested that 3′-UTR sequences outside of the seed sequences, e.g., flanking sequences may be equally important for miR targeting by controlling the accessibility of the miR or local RNA structure (Grimson et al., 2007). For example, a SNP (829C > T) located 14 bp downstream of a miR-24 binding site in the 3′-UTR of human dihydrofolate reductase gene (DHFR) was demonstrated to affect DHFR expression by interfering with miR-24 function, resulting in DHFR over expression and methotrexate resistance (Mishra et al., 2007). By using two algorithms predicting potential SNP-miR interaction, we suggested that 27 eQTLs or their proxies in high LD for 14XMET genes may function through interference with one or more miRs, with most of the SNPs located in the seed sequences. Meanwhile, the majority (20 out of 27) of the identified miR-SNPs were found to have predicted miR co-expressed with the gene of interest in the same tissue. Although no statistically significant enrichment of miR targeting for these SNPs, the strong trends observed here warrants further experimental validations.

Our findings may also provide useful information in addition to the previous observations on the function of these SNPs. Previous studies demonstrated that SNP rs2480256 in the CYP2E1 gene was significantly associated with systemic lupus erythematosus (Liao et al., 2011). Another study showed that cyclosporine A concentration in serum was significantly correlated with the genotype of the CYP3A5 rs15524 polymorphism (Onizuka et al., 2011). In addition, a GSTM3 haplotype including rs1537236 was significantly associated with a decreased growth for maximum mid-expiratory flow rate (MMEF) in a large population-based lung function study (Breton et al., 2009). SNP rs11807 in the 3′ region of GSTM5 was found to be associated with hypertension (Delles et al., 2008). Our results thus may help further elucidate the mechanism(s) by which the SNPs are involved in the susceptibility to these specific phenotypes.

In conclusion, our study summarized the potentially interacting SNP-miRs that may affect the expression of major XMET gene, which may ultimately facilitate to elucidate the mechanism how these genes are regulated as well as how they are involved in the genetic variations in drug metabolism and disease pathogenesis. Further investigations are necessary to corroborate the hypotheses generated in this study.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work is partly supported by the 2012 Ralph W. and Grace M. Showalter Research Trust Award (Wanqing Liu) and the start-up fund (to Wanqing Liu) from the Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University.

Appendix

Table A1.

Major XMETs and related genes investigated in this study.

Phase I
(n = 144)		 Phase II
(n = 85)		 Phase III
(n = 111)		 Nuclear receptors and
transcription factors (n = 48)		 Miscellaneous
genes (n = 21)
AADAC AANAT ABC1 AHR CRABP1
ABP1 ACSL1 ABCA1 AHRR CRABP2
ADH1A ACSL3 ABCA2 AIP CYB5A
ADH1B ACSL4 ABCA3 ARNT GZMA
ADH1C ACSM1 ABCA7 ARNT2 GZMB
ADH4 ACSM2B ABCA8 CREBBP MT1A
ADH5 ACSM3 ABCB1 EP300 MT1B
ADH6 AGXT ABCB10 ESR1 MT1F
ADH7 AS3MT ABCB11 ESR2 MT1H
ADHFE1 ASMT ABCB4 FOXA2 MT1M
AKR1A1 BAAT ABCB5 FOXO1 MT1X
AKR1B1 CCBL1 ABCB6 HIF1A MT2A
AKR1B10 CES5A ABCB7 HIF3A MT3
AKR1C1 COMT ABCB8 HNF4A MT4
AKR1C2 DDOST ABCB9 HSP90AA1 MTHFR
AKR1C3 GAMT ABCC1 KEAP1 POR
AKR1C4 GGT1 ABCC10 NCOA1 RBP1
AKR1CL1 GLYAT ABCC11 NCOA2 RBP2
AKR1D1 GNMT ABCC12 NCOA3 TP53
AKR1E2 GSTA1 ABCC12 NCOR1 TXN
AKR7A2 GSTA2 ABCC2 NCOR2 TXN2
AKR7A3 GSTA3 ABCC3 NFE2L2
AKR7L GSTA4 ABCC4 NR0B2
ALDH16A1 GSTA5 ABCC5 NR1H2
ALDH18A1 GSTK1 ABCC6 NR1H3
ALDH1A1 GSTM1 ABCC8 NR1H4
ALDH1A2 GSTM2 ABCC9 NR1I2
ALDH1A3 GSTM3 ABCD4 NR1I3
ALDH1B1 GSTM4 ABCG2 NR3C1
ALDH1L1 GSTM5 ABCG8 NR3C2
ALDH2 GSTO1 ALD NR5A2
ALDH3A1 GSTO2 AQP1 PPARA
ALDH3A2 GSTP1 AQP7 PPARD
ALDH3B1 GSTT1 AQP9 PPARG
ALDH3B2 GSTT2 ATP6V0C PPARGC1A
ALDH4A1 GSTT2B ATP7A PPARGC1B
ALDH5A1 GSTZ1 ATP7B PPRC1
ALDH6A1 HNMT KCNK9 PTGES3
ALDH7A1 INMT MARCKSL1 RARA
ALDH8A1 MGST1 MDR/TAP RARB
ALDH9A1 MGST2 MRP RARG
AOC2 MGST3 MVP RXRA
AOC3 MPST OABP RXRB
AOX1 NAA20 OATP2 RXRG
BCHE NAT1 SLC10A1 THRA
CBR1 NAT2 SLC10A2 THRB
CBR3 NNMT SLC15A1 TRIP11
CBR4 PNMT SLC15A2 VDR
CEL PTGES SLC16A1
CES1 SAT1 SLC18A2
CES2 SULT1A1 SLC19A1
CES3 SULT1A2 SLC19A2
CES4 SULT1A3 SLC19A3
CES7 SULT1A4 SLC1A1
CYP11A1 SULT1B1 SLC1A2
CYP11B1 SULT1C2 SLC1A3
CYP11B2 SULT1C3 SLC1A6
CYP17A1 SULT1C4 SLC1A7
CYP19A1 SULT1E1 SLC21A5
CYP1A1 SULT2A1 SLC22A1
CYP1A2 SULT2B1 SLC22A11
CYP1B1 SULT4A1 SLC22A12
CYP20A1 SULT6B1 SLC22A16
CYP21A2 TPMT SLC22A2
CYP24A1 TST SLC22A3
CYP26A1 UGT1A1 SLC22A4
CYP26B1 UGT1A10 SLC22A5
CYP26C1 UGT1A3 SLC22A6
CYP27A1 UGT1A4 SLC22A7
CYP27B1 UGT1A5 SLC22A8
CYP27C1 UGT1A6 SLC22A9
CYP2A13 UGT1A7 SLC25A13
CYP2A6 UGT1A8 SLC28A1
CYP2A7 UGT1A9 SLC28A2
CYP2B6 UGT2A1 SLC28A3
CYP2C18 UGT2A3 SLC29A1
CYP2C19 UGT2B10 SLC29A2
CYP2C8 UGT2B11 SLC29A3
CYP2C9 UGT2B15 SLC29A4
CYP2D6 UGT2B17 SLC2A1
CYP2E1 UGT2B28 SLC31A1
CYP2F1 UGT2B4 SLC38A1
CYP2J2 UGT2B7 SLC38A2
CYP2R1 UGT3A1 SLC38A5
CYP2S1 UGT3A2 SLC3A1
CYP2U1 SLC3A2
CYP2W1 SLC47A1
CYP39A1 SLC47A2
CYP3A4 SLC5A4
CYP3A43 SLC6A3
CYP3A5 SLC6A4
CYP3A7 SLC7A11
CYP46A1 SLC7A5
CYP4A11 SLC7A6
CYP4A22 SLC7A7
CYP4B1 SLC7A8
CYP4F11 SLCO1A2
CYP4F12 SLCO1B1
CYP4F2 SLCO1B3
CYP4F22 SLCO1C1
CYP4F3 SLCO2A1
CYP4F8 SLCO2B1
CYP4V2 SLCO3A1
CYP4X1 SLCO4A1
CYP4Z1 SLCO4C1
CYP51A1 SLCO5A1
CYP7A1 SLCO6A1
CYP7B1 TAP1
CYP8B1 TAP2
DHRS2 VDAC2
DHRS4 VDAC3
DHRS9
DPYD
EPHX1
EPHX2
ESD
FMO1
FMO2
FMO3
FMO4
FMO5
HSD17B10
KCNAB1
KCNAB2
KCNAB3
KDM1A
KDM1B
MAOA
MAOB
NQO1
NQO2
PAOX
PON1
PON2
PON3
PTGIS
PTGS1
PTGS2
SPR
SUOX
TBXAS1
UCHL1
UCHL3
XDH
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Table A2.

Putative miRNAs associated with SNPs in the 3′-UTR region.

Gene Classification SNP Tissue Putative miRNAs
microSNiPer PolymiRTs Overlap
ALDH16A1 Phase I rs1055637 Liver hsa-miR-4265 hsa-miR-3151 hsa-miR-4669
hsa-miR-1231 hsa-miR-4447
hsa-miR-3120-5p hsa-miR-4472
hsa-miR-4322 hsa-miR-491-5p
hsa-miR-4669 hsa-miR-132-5p
hsa-miR-4726-3p hsa-miR-4669
CYP2E1 Phase I rs2480256 Liver hsa-miR-570 hsa-miR-570-3p hsa-miR-570-3p
CYP2E1 Phase I rs2480257 Liver hsa-miR-4762-5p hsa-miR-5582-3p
hsa-miR-570-3p
CYP2U1 Phase I rs8727 Liver hsa-miR-549 hsa-miR-549 hsa-miR-549
hsa-miR-125b-2*
CYP3A5 Phase I rs15524 Liver hsa-miR-562 hsa-miR-500a-5p hsa-miR-500a-5p
hsa-miR-501-5p hsa-miR-5680
hsa-miR-500b
hsa-miR-500a
hsa-miR-4668-3p
hsa-miR-3973
hsa-miR-362-5p
CYP3A7 Phase I rs10211 Liver N/A hsa-miR-125a-5p
hsa-miR-125b-5p
hsa-miR-345-3p
hsa-miR-3920
hsa-miR-4319
hsa-miR-4732-3p
hsa-miR-670
EPHX2 Phase I rs1042032 Brain hsa-miR-4476 hsa-miR-183-5p hsa-miR-2392
hsa-miR-4533 hsa-miR-2392 hsa-miR-183-5p
hsa-miR-2392
hsa-miR-432*
hsa-miR-761
hsa-miR-183
hsa-miR-3665
hsa-miR-32390
EPHX2 Phase I rs1042064 Brain hsa-miR-31 hsa-miR-4696 hsa-miR-4696
hsa-miR-576-3p
hsa-miR-22
hsa-miR-4696
GSTM3 Phase II rs1109138 Brain hsa-miR-4766-3p N/A
hsa-miR-2964a-3p
hsa-let-7i*
GSTM3 Phase II rs1537236 Brain hsa-miR-4762-5p hsa-miR-182-5p hsa-miR-4470
hsa-miR-4470 hsa-miR-4470
GSTM3 Phase II rs1537235 Brain hsa-miR-4790-3p hsa-miR-409-5p
GSTM3 Phase II rs3814309 Brain hsa-miR-4421 hsa-miR-3130-3p
hsa-miR-3182 hsa-miR-4793-3p hsa-miR-4793-3p
hsa-miR-1237
hsa-miR-486-5p
hsa-miR-4793-3p
hsa-miR-3120-5p
hsa-miR-4527
hsa-miR-29b
GSTM5 Phase II rs11807 Liver hsa-miR-1202 N/A
hsa-miR-1227
hsa-miR-1973
MGST3 Phase II rs8133 Liver hsa-miR-875-3p hsa-miR-582-3p hsa-miR-582-3p
hsa-miR-582-3p hsa-miR-875-3p hsa-miR-875-3p
hsa-miR-4698 hsa-miR-224-3p hsa-miR-3688-3p
hsa-miR-4694-3p hsa-miR-3688-3p hsa-miR-4694-3p
hsa-miR-4495 hsa-miR-4694-3p
hsa-miR-411* hsa-miR-522-3p
hsa-miR-3688-3p
ATP7B Phase III rs928169 Liver hsa-miR-4734 hsa-miR-4447 hsa-miR-4472
hsa-miR-4430 hsa-miR-4472 hsa-miR-4481
hsa-miR-4481 hsa-miR-4481 hsa-miR-4745-5p
hsa-miR-4472 hsa-miR-4745-5p hsa-miR-4785
hsa-miR-3652 hsa-miR-4785
hsa-miR-3135b hsa-miR-4787-5p
hsa-miR-4745-5p
hsa-miR-3944-3p
hsa-miR-1275
hsa-miR-491-5p
hsa-miR-4446-3p
hsa-miR-4498
hsa-miR-194*
hsa-miR-122
hsa-miR-4734
hsa-miR-4430
hsa-miR-3652
hsa-miR-4309
hsa-miR-4785
hsa-miR-3198
hsa-miR-1298
SLC31A1 Phase III rs10759637 Liver hsa-miR-4448 hsa-miR-3672
hsa-miR-3119 hsa-miR-4524a-3p
hsa-miR-4461
TAP2 Phase III rs13501 Brain hsa-miR-3198 hsa-miR-1289 hsa-miR-1289
hsa-miR-1289 hsa-miR-3198 hsa-miR-3198
hsa-miR-4309 hsa-miR-4294 hsa-miR-4309
hsa-miR-3127-5p hsa-miR-4309
hsa-miR-5702
TAP2 Phase III rs17034 Brain hsa-miR-4772-3p hsa-miR-1271-3p
hsa-miR-4763-5p
hsa-miR-550a-3-5p
hsa-miR-550a-5p
hsa-miR-4327
hsa-miR-636
TAP2 Phase III rs241451 Brain hsa-miR-1260 hsa-miR-4684-5p hsa-miR-4684-5p
hsa-miR-4758-3p
hsa-miR-4684-5p
TAP2 Phase III rs241452 Brain hsa-miR-1206 hsa-miR-1206 hsa-miR-1206
hsa-miR-1
hsa-miR-4789-5p
TAP2 Phase III rs241453 Brain hsa-miR-4298 hsa-miR-1302 hsa-miR-1302
hsa-miR-1302 hsa-miR-4298 hsa-miR-4298
TAP2 Phase III rs241454 Brain hsa-miR-4476 hsa-miR-4476 hsa-miR-4476
hsa-miR-4779 hsa-miR-4533 hsa-miR-4779
hsa-miR-3173-3p
hsa-miR-4779
TAP2 Phase III rs241455 Brain hsa-miR-130a* hsa-miR-2116-3p hsa-miR-130a-5p
hsa-miR-323-3p hsa-miR-130a-5p
hsa-miR-23a-3p
hsa-miR-23b-3p
hsa-miR-23c
hsa-miR-3680-5p
hsa-miR-4798-3p
TAP2 Phase III rs241456 Brain hsa-miR-3940-5p hsa-miR-2110 hsa-miR-4450
hsa-miR-4507 hsa-miR-3150a-3p
hsa-miR-92a-1* hsa-miR-4450
hsa-miR-4450 hsa-miR-450a-3p
hsa-miR-1270
hsa-miR-3676-5p
hsa-miR-4531
hsa-miR-4683
hsa-miR-620
TAP2 Phase III rs2857101 Brain hsa-miR-944 hsa-miR-126-5p hsa-miR-944
hsa-miR-4795-3p hsa-miR-4795-3p hsa-miR-4795-3p
hsa-miR-183* hsa-miR-944
UGT2A1 Phase II rs4148312 Liver hsa-miR-548t hsa-miR-3662 hsa-miR-3662
hsa-miR-548ah hsa-miR-548c-3p hsa-miR-3609
hsa-miR-3662 hsa-miR-3609 hsa-miR-548ah-5p
hsa-miR-3646 hsa-miR-548ah-5p hsa-miR-548t-5p
hsa-miR-3609 hsa-miR-548n
hsa-miR-340 hsa-miR-548t-5p
hsa-miR-1245
hsa-miR-106a
ARNT Nuclear receptors rs11552229 Liver hsa-miR-4716-5p hsa-miR-4717-3p
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The miRs expressed in the tissue where the eQTL was identified are highlighted in bold.

Footnotes

References

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