Open Access

ABCB1 polymorphism is associated with atorvastatin-induced liver injury in Japanese population

  • Koya Fukunaga1,
  • Hiroshi Nakagawa2,
  • Toshihisa Ishikawa3,
  • Michiaki Kubo4 and
  • Taisei Mushiroda1Email author
BMC GeneticsBMC series – open, inclusive and trusted201617:79

https://doi.org/10.1186/s12863-016-0390-5

Received: 8 October 2015

Accepted: 7 June 2016

Published: 13 June 2016

Abstract

Background

To investigate the associations between atorvastatin-induced liver injury (AILI) and polymorphisms in eight genes possibly involved in the hepatic metabolism (CYP2C9, CYP2C19, CYP3A4, CYP3A5 and UGT1A1) and membrane transport (ABCB1, ABCG2 and SLCO1B1) of atorvastatin, we genotyped 30 AILI and 414 non-AILI patients recruited at BioBank Japan for 15 single nucleotide polymorphisms (SNPs).

Results

An SNP in ABCB1 (rs2032582: 2677G > T/A) was significantly associated with AILI (P = 0.00068, odds ratio (OR) = 2.59 with 95 % confidence interval (CI) of 1.49-4.50, G allele versus T and A alleles), indicating that the G allele might be a risk factor for AILI. The cytotoxicity test demonstrated that IC50 value of atorvastatin to inhibit the growth and/or viability of Flp-In-293/ABCB1 (2677G) cells was 5.44 ± 0.10 mM, which was significantly lower than those in Flp-In-293/ABCB1 (2677 T) (6.02 ± 0.07 mM) and Flp-In-293/ABCB1 (2677A) cells (5.95 ± 0.08 mM).

Conclusions

These results indicate that ABCB1 rs2032582 may predict the risk of AILI in Japanese population.

Keywords

Atorvastatin-induced adverse reaction Genetic association Hepatotoxicity MDR1 Ala893Ser/Thr/

Background

Atorvastatin (atorvastatin calcium; Lipitor®) is widely used in the treatment of dyslipidemia of low- and high-density lipoproteins in patients with or without heart disease [1]. However, atorvastatin-induced liver injury (AILI) can be caused after atorvastatin treatment [1, 2]. In Japanese post-marketing surveillance of atorvastatin, 1.42 % of patients who received atorvastatin treatment suffered from liver injury. In general, drug-induced liver injury (DILI) can be divided into 3 types (hepatocellular injury, cholestatic liver injury and mixed liver injury) based on potential liver toxicity symptoms (e.g., anorexia, nausea, vomiting or jaundice), the presence or absence of risk factors (e.g., viral infection and alcohol consumption) and serum levels of alanine aminotransferase (ALT) and alkaline phosphatase (ALP) as well as the ALT/ALP ratio [3]. AILI falls within the hepatocellular injury category because ALT level of two patients treated with atorvastatin reportedly raised three-fold higher than that of the upper limit of normal but ALP and bilirubin levels did not change [4].

Atorvastatin is orally administered in the active acid form and undergoes marked first-pass metabolism by uptake into hepatocytes via passive diffusion and SLCO1B1 (encoding OATP1B1 [57]. Atorvastatin is metabolized mainly by CYP3A4, with minor contributions from CYP2C9, CYP2C19, CYP3A5, and UGT1A1 [812]. Subsequently, atorvastatin and the metabolites are predominantly eliminated by ABCB1 (encoding P-glycoprotein or MDR1)- and ABCG2 (encoding BCRP)-mediated transport from liver into bile [7, 1315]. Single nucleotide polymorphisms (SNPs) identified in ABCB1 rs1128503 (1236C > T), rs2032582 (2677G > T/A), and rs1045642 (3435C > T) markedly affected area under the plasma concentration versus time curve (AUC) of atorvastatin and the lipid-lowering effects of atorvastatin therapy [1618]. Therefore, we hypothesized that the genetic variability of eight candidate genes associated with the hepatic metabolism and membrane transport of atorvastatin may affect the risk of AILI because higher concentrations of atorvastatin can cause hepatocellular injury, even at appropriate atorvastatin dosages. However, to our knowledge, there are no reports on an association of the functional SNPs of the candidate genes with AILI.

In this study, we investigated whether 15 functional SNPs in eight candidate genes that are possibly involved in the pharmacokinetics of atorvastatin were associated with AILI in Japanese population. We found that ABCB1 rs2032582 was significantly associated with AILI. In addition, the cytotoxicity of atorvastatin was investigated using the Flp-In-293 cells stably expressing ABCB1 proteins encoded by ABCB1 rs2032582 [19]. We clarified that the ABCB1 rs2032582 G allele was a significant AILI risk factor in vivo and in vitro.

Methods

Subjects

The BioBank Japan project (https://biobankjp.org/) started in 2003 for the collection of genomic DNA, serum and clinical information from about 300,000 Japanese patients diagnosed with either of 47 diseases by a collaborative network of 66 hospitals in Japan. We diagnosed AILI based on symptoms, such as nausea, vomiting, loss of appetite, and jaundice, and results of a physical examination and blood tests after atorvastatin administration. From the registered samples in the BioBank Japan, we selected individuals that were clinically diagnosed as having AILI (AILI group, N = 30) and individuals that showed no liver injury during atorvastatin therapy (non-AILI group, N = 414).

Selection of SNPs and genotyping

A total of 15 functional SNPs in eight candidate genes (ABCB1, ABCG2, CYP2C9, CYP2C19, CYP3A4, CYP3A5, SLCO1B1 and UGT1A1) reportedly-altering pharmacokinetics of atorvastatin were genotyped by a multiplex polymerase chain reaction (PCR)–based invader assay as described previously [20] and direct sequencing using ABI 3730xl DNA analyzer (Applied Biosystems, Foster City, CA) for rs8175347 and rs2032582, according to the manufacturer’s protocol of the Big Dye Terminator v3.1 cycle sequencing kit (Applied Biosystems). HLA-A, -B and -C genotyping was carried out using a WAKFlow HLA Typing kit (Wakunaga, Osaka, Japan), which is based on PCR-sequence-specific oligonucleotide probes coupled with multiple analyte profiling (xMAP) technology (Luminex System; Luminex Corporation, Austin, TX). The data analysis was performed using the WAKFlow Typing software (Wakunaga).

Cell culture

HepaRG cells (KAC, Kyoto, Japan) were maintained in HepaRG Thawing and Seeding Medium 670 (KAC) and HepaRG Maintenance and Metabolism medium 620 (KAC) at 37 °C under 5 % CO2 and 95 % air according to the manufacturer’s instructions. Flp-In-293 cells (Life Technologies, Foster City, CA) were maintained in Dulbecco’s modified Eagle’s medium (Life Technologies) supplemented with 10 % heat-inactivated fetal bovine serum (Life Technologies) and Antibiotic-Antimycotic (100×) liquid (Life Technologies) at 37 °C under 5 % CO2 and 95 % air, where 100 μg/ml Zeocin (Life Technologies) and 100 μg/ml hygromycin B (Life Technologies) were also supplemented for the maintenance for parental and ABCB1-expressed Flp-In-293 cells, respectively.

Generation of ABCB1 2677G/T/A variant forms

The pcDNA5/FRT/ABCB1 (2677G) vector was generated by inserting ABCB1 (2677G) in pFastBac1/ABCB1 (2677G) into the pcDNA5/FRT vector (Life Technologies) between the restriction enzyme sites of BamH I and Xho I. ABCB1 2677 T/A variant forms were generated by using the QuikChange Site-directed Mutagenesis Kit (Agilent Technologies, Santa Clara, CA) according to the manufacturer’s protocol, where pcDNA5/FRT/ABCB1 (2677G) vector was used as the template [21]. The PCR reaction consisted of 94 °C for 2 min and then followed by 12 cycles of reactions at 94 °C for 30 sec, 55 °C for 30 sec and at 68 °C for 18 min, where Pfu Turbo DNA polymerase (Agilent Technologies) and the following PCR primers were used: 5’-GAAAGAACTAGAAGGTTCTGGGAAGATCGCTAC-3’ and 5’-GTAGCGATCTTCCCAGAACCTTCTAGTTCTTTC-3’ for ABCB1 2677 T, and 5’-GAAAGAACTAGAAGGTACTGGGAAGATCGCTAC-3’ and 5’-GTAGCGATCTTCCCAGTACCTTCTAGTTCTTTC-3’ for ABCB1 2677A. After the PCR, the reaction mixture was incubated with DpnI endonuclease at 37 °C for 1 h to digest the original template pcDNA5/FRT/ABCB1 (2677G) vector. The resulting sequence was examined to confirm the generation of the pcDNA5/FRT/ABCB1 (2677G/T/A) vectors.

Establishment of ABCB1 2677G/T/A variant forms-expressing cells

Flp-In-293 cells having the Flp Recombination Target (FRT) site at the telomeric region of only one of the pair of chromosomes 12 were transfected with the pcDNA5/FRT/ABCB1 (2677G/T/A) and the Flp recombinase expression plasmid pOG44 vectors as previously reported [19]. Single colonies resistant to hygromycin B (Life Technologies) were picked and sub-cultured as Flp-In-293/ABCB1 (2677G), Flp-In-293/ABCB1 (2677 T) and Flp-In-293/ABCB1 (2677A) cells. Protein expression levels of ABCB1 in these cells (2 × 107 cells) were determined using Membrane Protein Extraction Kit (BioVision, Milpitas, CA) and Human permeability glycoprotein (P-gp/ABCB1) ELISA kit (Cusabio Biotech, Wuhan, China) by a microplate reader (ARVOmx, PerkinElmer, Waltham, MA).

Cytotoxicity studies

In atorvastatin (Sigma-Aldrich, St Louis, Mo) cytotoxicity experiment, HepaRG (5 × 105 cells/well), Flp-In-293/ABCB1 (2677G), Flp-In-293/ABCB1 (2677 T) and Flp-In-293/ABCB1 (2677A) cells were cultured in monolayers at 37 °C for 24 h in 24-well collagen type I-coated plates (Iwaki Glass, Chiba, Japan). After the preculture, cells were cultured in the presence of different concentrations of atorvastatin 0, 0.3, 1, 3, 10, 30, 100 and 300 μM for HepaRG cells, 0, 0.6, 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 mM for Flp-In-293/ABCB1 (2677G/T/A) cells for 24 h. After the culture, 50 μl of WST-8 working solution (Cell Counting Kit-8, Dojindo Laboratories, Kumamoto, Japan) was added to each well and the plates were incubated at 37 °C for 1 h under 5 % CO2 and 95 % air. Optical density at 450 nm was measured by a microplate reader (ARVOmx, PerkinElmer, Waltham, MA). Lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and ALT releases from HepaRG cells into the medium were determined according to the manufacturers’ protocol of LDH, AST and ALT activity assay kits (Biovision, Milpitas, CA).

Statistical analysis

Association studies were conducted by using Fisher’s exact test under an allelic model. P values were corrected according to Bonferroni correction. A significance level was set at 0.0029 (0.05/17) in Table 1. In case of ABCB1 rs2032582, the patients were divided into two groups (T/A versus G, G/A versus T or G/T versus A) to evaluate the association of the three alleles by using the Fisher’s exact test (Table 1). The haplotype analysis was performed using SNPAlyze software (version. 8.0.1, Dynacom, Chiba, Japan). Statistical analysis of cytotoxicity test of HepaRG and ABCB1 protein expression levels in Flp-In-293 cells was performed by using one-way analysis of variance with Dunnett’s and Tukey’s post-hoc test using GraphPad Prism software (version 6, San Diego, CA). Cell viability was analyzed based on four independent experiments performed in duplicate to accurately estimate IC50 and statistical analysis of IC50 among three groups (2677G wild-type, 2677 T and 2677A alleles) was performed by using one-way analysis of variance with Dunnett’s post-hoc test using GraphPad Prism software.
Table 1

Association of 15 functional SNPs in eight candidate genes with atorvastatin-induced liver injury

Gene

SNP

Allele (1/2)

Amino acid change

Other name

AILIa

Non-AILIa

RAF

P valueb

HWE

     

11

12

22

11

12

22

AILI

Non-AILI

ALLELIC

DOM

REC

AILI

Non-AILI

ABCB1

rs1045642

C/T

I1145I

C3435T

12

15

3

151

196

67

0.65

0.60

0.50

0.70

0.60

0.59

0.80

 

rs2032582

T, A/G

S,T893A

G2677 T/A

2

16

12

138

191

85

0.67

0.44

0.00068

0.0017

0.020

0.27

0.21

  

G, A/T

A,T893S

 

15

14

1

158

193

63

0.73

0.61

0.073

0.24

0.10

0.29

0.75

  

G, T/A

A,S893T

 

26

4

0

283

114

17

0.93

0.82

0.031

0.039

0.62

0.70

0.21

 

rs1128503

T/C

G412G

C1236T

15

12

3

146

197

71

0.70

0.59

0.10

0.12

0.45

0.79

0.74

ABCG2

rs2231142

C/A

Q141K

C421A

12

15

3

195

180

39

0.35

0.31

0.57

0.57

0.76

0.59

0.78

CYP2C9

rs1799853

C/T

R144C

CYP2C9*2

30

0

0

412

0

0

1.00

1.00

1.00

1.00

1.00

1.00

1.00

 

rs1057910

A/C

I359L

CYP2C9*3

28

2

0

393

20

0

0.03

0.02

0.66

0.65

1.00

0.85

0.61

CYP2C19

rs4244285

G/A

P227P

CYP2C19*2

16

9

5

197

180

36

0.32

0.31

0.88

0.58

0.18

0.09

0.57

 

rs4986893

G/A

W212X

CYP2C19*3

27

3

0

321

86

6

0.95

0.88

0.14

0.16

1.00

0.77

0.93

CYP3A4

rs12721627

C/G

T185S

CYP3A4*16

29

1

0

405

8

0

0.02

0.01

0.47

0.47

1.00

0.93

0.84

 

rs28371759

T/C

L292P

CYP3A4*18

29

1

0

399

14

0

0.98

0.98

1.00

1.00

1.00

0.93

0.73

CYP3A5

rs776746

G/A

CYP3A5*3

18

11

1

223

165

26

0.78

0.74

0.54

0.57

1.00

0.66

0.54

SLCO1B1

rs2306283

G/A

N130D

SLCO1B1*1B

14

12

4

185

179

50

0.67

0.66

1.00

0.85

0.77

0.58

0.51

 

rs4149056

T/C

V174A

SLCO1B1*5

18

11

1

306

95

13

0.22

0.15

0.14

0.13

1.00

0.66

0.10

UGT1A1

rs4148323

G/A

G71R

UGT1A1*6

25

5

0

281

118

15

0.92

0.82

0.075

0.10

0.61

0.62

0.55

 

rs8175347

(TA)6/(TA)7

UGT1A1*28

23

7

0

332

76

6

0.12

0.11

0.83

0.64

1.00

0.47

0.49

Abbreviation: AILI atorvastatin-induced liver injury, RAF risk allele frequency, ALLELIC Allelic model, Dom Dominant model, REC Recessive model, HWE Hardy–Weinberg equilibrium

aAILI, N = 30; Non-AILI, N = 414

bThe lowest significant P value after Bonferroni correction among three models is shown in bold (P < 0.0011)

Results

No significant association of disease background was observed between AILI and non-AILI patients (Additional file 1: Table S1). The median age values were 61 years (range 27–82) and 66 years (32–89) in AILI and non-AILI groups, respectively. The 60.0 and 53.9 % were male in AILI and non-AILI groups, respectively. All SNPs met quality control criteria (call rate > 95 %, Hardy–Weinberg equilibrium P value > 10-3 and minor allele frequency > 1 %). ABCB1 rs2032582 was found to be associated with an increased risk of AILI (P = 0.00068, odds ratio (OR) = 2.59 with 95 % confidence interval (CI) of 1.49–4.50, G allele versus T and A alleles) by genotyping 444 Japanese subjects for 15 functional SNPs in eight candidate genes that reportedly affect the pharmacokinetics of atorvastatin (Table 1). No other polymorphisms showed a significant association with AILI. The frequency for ABCB1 rs2032582 G allele in AILI patients was significantly higher than that in non-AILI patients whereas the frequencies of ABCB1 rs2032582 T and A alleles were not significantly different between AILI and non-AILI groups, indicating that the G allele might be a risk factor for AILI (Table 1 and Additional file 1: Table S2). Although we performed haplotype analysis using three SNPs of ABCB1 (rs1128503, rs2032582 and rs1045642), no haplotype constructed from the SNPs showed an extremely smaller P value than a single marker association of the ABCB1 rs2032582 (Table 2). No association of HLA-A, -B and -C genotypes with AILI was shown (Additional file 1: Table S3, Additional file 1: Table S4 and Additional file 1: Table S5).
Table 2

Association of haplotypes consisting of three SNPs of ABCB1 with atorvastatin-induced liver injury

rs1128503

rs2032582

rs1045642

Number of carriers

P valueb

   

AILIa (%)

Non-AILIa (%)

 

T

T

T

15 (50.0)

237 (57.2)

0.45

C

G

C

11 (36.7)

158 (38.2)

1.00

T

G

C

16 (53.3)

126 (30.4)

0.014

C

A

C

4 (13.3)

128 (30.9)

0.060

T

G

T

3 (10.0)

25 (6.0)

0.45

T

T

C

0 (0.0)

24 (5.8)

0.39

C

G

T

2 (6.7)

11 (2.7)

0.22

T

A

C

0 (0.0)

5 (1.2)

1.00

C

T

T

0 (0.0)

2 (0.5)

1.00

C

T

C

0 (0.0)

2 (0.5)

1.00

Abbreviation: AILI atorvastatin-induced liver injury

aAILI, N = 30; Non-AILI, N = 414

bThe significant P value after Bonferroni correction is less than 0.005

The cytotoxicity study using HepaRG cells demonstrated concentration-dependent effects of atorvastatin on cell viability as well as on LDH, AST and ALT release from the cells (Additional file 1: Figure S1). To estimate the effects of ABCB1 rs2032582 on cytotoxicity induced by atorvastatin, we conducted cytotoxicity study using Flp-In-293 cells stably expressing ABCB1 proteins encoded by 2677G wild-type [Flp-In-293/ABCB1 (2677G) cells], 2677A [Flp-In-293/ABCB1 (2677A) cells] and 2677 T [Flp-In-293/ABCB1 (2677 T) cells] alleles. No significant differences were observed in ABCB1 protein expression levels in Flp-In-293/ABCB1 (2677G/T/A) cells (Additional file 1: Figure S2). The IC50 value in Flp-In-293/Mock cells was about two-fold lower than those in Flp-In-293/ABCB1 (2677G/T/A) cells, indicating higher accumulation of atorvastatin in the Flp-In-293/Mock cells compared to that in Flp-In-293/ABCB1 (2677G/T/A) cells. The IC50 value in Flp-In-293/ABCB1 (2677G) cells was significantly lower than those in Flp-In-293/ABCB1 (2677 T) and Flp-In-293/ABCB1 (2677A) cells (Table 3, Additional file 1: Figure S3).
Table 3

Atorvastatin-dependent cytotoxicity in Flp-In-293 cells stably expressing different ABCB1 proteins

Cell name

ABCB1

 

IC50 (mM)

P value

 

Allele

Amino acid

Mean ± SE

 

Mock

2.74 ± 0.04

2677G wild-type

G

Alanine

5.44 ± 0.10

2677 T

T

Serine

6.02 ± 0.07

0.009

2677A

A

Threonine

5.95 ± 0.08

0.026

Abbreviation: SE standard error, CI confidence interval

Experiments were performed in duplicate wells and repeated four times. The significant P value is shown in bold (P < 0.05, versus 2677G wild-type, one-way analysis of variance with Dunnett’s post-hoc test)

Discussion

To identify the genetic markers associated with AILI, we genotyped 15 functional SNPs in eight genes that are possibly involved in the hepatic metabolism (CYP2C9, CYP2C19, CYP3A4, CYP3A5 and UGT1A1) and membrane transport (ABCB1, ABCG2 and SLCO1B1) of atorvastatin. ABCB1 rs2032582 was significantly associated with AILI. ABCB1 rs2032582 changes ABCB1 amino acid 893 from alanine to serine or threonine, respectively. These variants did not appear to affect ABCB1 protein expression levels in Flp-In-293/ABCB1 (2677G/T/A) cells, but gave a lower IC50 in Flp-In-293/ABCB1 (2677G) cells than those in Flp-In-293/ABCB1 (2677 T/A) cells. ATP-dependent uptake of [3H]-vincristine into membrane vesicles is also reportedly slower in cells expressing the ABCB1 rs2032582 G allele than those expressing the ABCB1 rs2032582 T/A alleles [22]. Therefore, we speculate that patients carrying the ABCB1 rs2032582 G allele experience lower atorvastatin efflux activity from the hepatocytes into bile and higher hepatocellular concentrations of atorvastatin than carriers of the ABCB1 rs2032582 T/A alleles. The higher hepatocellular concentration of atorvastatin can increase the risk of hepatotoxicity because atorvastatin induced concentration-dependent cytotoxicity in HepaRG cells (Additional file 1: Figure S1).

The ABCB1 rs2032582 allele frequencies in our 444 patients (45.2 %, 37.7 % and 17.1 % for G, T and A alleles, respectively) are consistent with the previous report of 154 Japanese subjects (42.9 %, 40.6 % and 16.6 % for G, T and A alleles, respectively) [23]. The above report revealed that the ABCB1 rs2032582 G and T/A allele frequencies in a Japanese population were comparable with those in a Caucasian population (42.9 % vs. 50.0 % and 57.2 % vs. 50.0 % for G and T/A alleles, respectively) [23]. Taking into account that no differences were reported in the systemic exposure to atorvastatin between Asian and Caucasian subjects [24], the ABCB1 rs2032582 allele might be also associated with the risk of AILI in the Caucasian population.

Of the atorvastatin-induced adverse reactions, myopathy is one of the most fatal adverse reactions [25, 26]. No statistically significant difference in AUC and the maximum plasma concentrations was observed between 14 patients with atorvastatin-induced myopathy and 15 healthy controls [27]. However, patients with atorvastatin-induced myopathy showed 2.4- and 3.1-fold higher AUC to atorvastatin lactone and p-hydroxy atorvastatin, respectively, compared to controls [27]. Atorvastatin is converted to its corresponding lactone form spontaneously or via glucuronidation mediated by UGT1A1, 1A3 and 1A4 and is metabolized to p-hydroxy atorvastatin by CYP3A4/5 [11, 28]. The present association studies showed that known functional SNPs of UGT1A1 and CYP3A4/5 were not associated with AILI. The higher accumulation of atorvastatin in the liver of patients carrying the ABCB1 rs2032582 G allele may cause hepatotoxicity, rather than those of atorvastatin lactone and p-hydroxy atorvastatin, the atorvastatin metabolites generated by UGT1A1 and CYP3A4/5. Therefore, the genetic markers might differ between liver injury and myopathy induced by atorvastatin.

In general, DILI can be divided into dose-dependent and idiosyncratic types [29]. The former is related to the pharmacokinetics and/or pharmacological actions of the drug and the latter is related to immune systems, such as human leukocyte antigen (HLA) in a dose-independent manner. In fact, several HLA alleles showed drug-specific associations with DILI, such as HLA-A*33:03 for ticlopidine and HLA-B*57:01 for flucloxacillin [30]. Therefore, we examined association of HLA alleles with AILI. However, no significant association was observed for HLA-A, -B and -C alleles with AILI (Additional file 1: Table S3, Additional file 1: Table S4 and Additional file 1: Table S5).

Conclusions

Our results showed that ABCB1 rs2032582 was associated with an increased risk of AILI in the Japanese population. A genetic test of ABCB1 rs2032582 may provide useful information for predicting individuals at higher risk of AILI. However, additional studies with larger sample size are needed before applying this genetic marker in clinical practice.

Abbreviations

AILI, Atorvastatin-induced liver injury; ALP, Alkaline phosphatase; ALT, Alanine aminotransferase; AUC, Area under the plasma concentration versus time curve; CI, Confidence interval; DILI, Drug-induced liver injury; FRT, Flp recombination target; HLA, human leukocyte antigen; LDH, Lactate dehydrogenase; OR, Odds ratio; PCR, Polymerase chain reaction; SNPs, Single nucleotide polymorphisms.

Declarations

Acknowledgments

We thank all the patients who participated in the BioBank Japan project. We thank all members of BioBank Japan, Institute of Medical Science, The University of Tokyo and RIKEN Center for Integrative Medical Sciences, for their contribution to the completion of our study. We also express our gratitude to Dr. Siew-Kee Low and Dr. Atsushi Takahashi of RIKEN Center for Integrative Medical Sciences for their helpful comments and discussion.

Funding

This study was supported by grants from the BioBank Japan project.

Availability of data and materials

The data sets supporting the results of this article are included within the article and its additional files.

Authors’ contributions

Conceived and designed the experiments: KF, MK, TM. Performed the experiments: KF, HN. Analyzed the data: KF, TM. Contributed reagents/materials/analysis tools: HN, TI. Wrote the paper: KF, HN, TI, MK, TM. All authors approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethical approval and consent to participate

All individuals who participated in this study provided written informed consent. This project was approved by the ethical committees at the RIKEN Yokohama Branch, Japan and The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences
(2)
Department of Biological Chemistry, College of Bioscience and Biotechnology, Chubu University
(3)
NPO Personalized Medicine & Healthcare
(4)
Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences

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