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Polygenic architecture informs potential vulnerability to drug-induced liver injury


Drug-induced liver injury (DILI) is a leading cause of termination in drug development programs and removal of drugs from the market; this is partially due to the inability to identify patients who are at risk1. In this study, we developed a polygenic risk score (PRS) for DILI by aggregating effects of numerous genome-wide loci identified from previous large-scale genome-wide association studies2. The PRS predicted the susceptibility to DILI in patients treated with fasiglifam, amoxicillin–clavulanate or flucloxacillin and in primary hepatocytes and stem cell-derived organoids from multiple donors treated with over ten different drugs. Pathway analysis highlighted processes previously implicated in DILI, including unfolded protein responses and oxidative stress. In silico screening identified compounds that elicit transcriptomic signatures present in hepatocytes from individuals with elevated PRS, supporting mechanistic links and suggesting a novel screen for safety of new drug candidates. This genetic-, cellular-, organoid- and human-scale evidence underscored the polygenic architecture underlying DILI vulnerability at the level of hepatocytes, thus facilitating future mechanistic studies. Moreover, the proposed ‘polygenicity-in-a-dish’ strategy might potentially inform designs of safer, more efficient and robust clinical trials.

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Fig. 1: Polygenic architecture of DILI.
Fig. 2: Polygenic risk score based human stratification by in vitro multi-drug-induced CM-DILI assays.
Fig. 3: Mechanistic association studies for CM-DILI vulnerability.
Fig. 4: PRS associated transcriptomic signatures associate with known CM-DILI responses.

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Data availability

The AmpliSeq data used in this study are deposited in the Gene Expression Omnibus under accession number GSE152447. The genotype data for TAK-875 DILI GWAS, the related phenotype information and the summary statistics are stored at Takeda Pharmaceutical Company, owing to the ethical approval in this study. These data sets are available upon reasonable request to Takeda Pharmaceutical Company via the corresponding author and after being approved by the Ethics Committee of Takeda Pharmaceutical Company. Web links of publicly available data sets are as follows: transcriptome data of PHH under drug treatments (TG-GATEs),; transcriptome data of multi-donor liver tissues,!Synapse:syn4492; transcriptome data of PHH (SRR4000958); GENCODE (v26),; MSigDB (v6),; and 1KGP3 imputation reference panel, Source data are provided with this paper.

Code availability

We used publicly available software and parameters as described in the Methods section for the analysis. The software programs are available from the following URLs: PLINK,; Minimac3,; SNPTEST,; FaQCs,; PRSice,; STAR,; Cufflinks,; GARFIELD,; Pascal,; GSEA,; and R,


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We thank S. Yamanaka, S. Izumo and Y. Kajii for their critical comments. We thank T. Kono, K. Araki, K. Enya and H. Kawaguchi for technical and analytical supports and W. L. Thompson for critical reading of the manuscript. The authors thank the Drug Induced Liver Injury Network (DILIN) and the international Drug-Induced Liver Injury Consortium (iDILIC) for providing data included in this paper. The DILIN and iDILIC were not involved in data analyses, manuscript preparation or manuscript review. This study was supported by the T-CiRA Joint Program from Takeda Pharmaceutical Company to T.T. T.T. is a New York Stem Cell Foundation Robertson Investigator and also the recipient of a Cincinnati Children’s Research Foundation grant, National Institutes of Health grant UG3 DK119982, the Dr. Ralph and Marian Falk Medical Research Trust Awards Program, a Takeda Science Foundation award, a Mitsubishi Foundation award and AMED JP19fk0210037, JP19bm0704025, JP19fk0210060, JP19bm0404045 and JSPS JP18H02800, 19K22416. G.P.A. was supported by Medical Research Council: Confidence in Concept, reference number MC_PC_17173.

Author information

Authors and Affiliations



M.K., E.K. and T.T. conceived and designed the study, analyzed the data and wrote the manuscript. P.N., A.K.D., G.P.A. and P.B.W. conducted iDILIC/DILIN DILI GWAS and critically revised the manuscript. M.K., M.O., E.K. and T.T. conducted the other data analysis. E.K. J.F., Yu.N. performed cell culture experiments. Ya.N., T.S, H.A., Y.D. and T.T. provided critical discussion.

Corresponding author

Correspondence to Takanori Takebe.

Ethics declarations

Competing interests

T.T. received research funding related to this project from Takeda Pharmaceutical Company. M.O., E.K., Y. Nio, T.S., Y.D. and H.A. are employees of Takeda Pharmaceutical Company. P.N. is an employee of Sema4. The remaining authors declare no competing interests.

Additional information

Peer review information Kate Gao was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Overview of our polygenicity analysis.

Workflow of shared genetic aetiology analysis was shown. Left: Processing strategy of iDILIC/DILIN GWAS summary statistics (summary data); Right: TAK-875 GWAS procedures.

Extended Data Fig. 2 TAK-875 DILI severity and GWAS analysis.

a, Ratio of ALT, AST, and BILT peak values to their basal values. b, Distribution of time from start of drug to DILI onset (days). c, Exclusion criteria of TAK-875 samples based on genetic ancestors. d, The outlier of TAK-875 samples was not observed in Northern Europeans from Utah (CEU), Tuscans from Italy (TSI) and Mexican (MEX) ancestry. e, Quantile-Quantile plot for the TAK-875 GWAS. f, g, Polygenic test using hepatocellular DILI-GWAS summary statistics in (a) and All DILI-GWAS ones in (b). X-axis, the total number of SNPs, ordered by iDILIC/DILIN GWAS association; y-axis, explained the variance of TAK-875 white GWAS; color scale, p-value for the shared genetic aetiology analysis.

Extended Data Fig. 3 Distribution and performance of each PRS.

a, Distribution of CM-DILI PRSlimited in TAK-875 DILI patients and the tolerances (see Fig. 1 and Supplementary Text). b, AUROC values and their 95% confidence interval for the indicated PRS. c, Histogram of the indicated PRS.

Extended Data Fig. 4 Correlation between CM-DILI PRSgw and biomarkers for DILI in TAK-875 treated subjects.

Scatter plot and the linear regression line of clinical laboratory test value in basal state and CM-DILI PRSgw in 172 TAK-875 treated patients (cases and controls). *, p < 0.05; **, p < 0.01, ***, p < 0.001 in coefficients of risk score. ALTB, Basal ALT; ASTB, Basal AST; BILTB, Basal total bilirubin.

Extended Data Fig. 5 Predictive accuracy of CM-DILI PRSgw for Flucloxacillin or Amoxicillin-clavulanate DILI.

Distribution of CM-DILI PRSgw and HC-DILI PRSgw in Flucloxacillin or Amoxicillin-clavulanate DILI patients with the indicated DILI type (cholestasis/mixed or hepatocellular). AUROC (95% CI) and P-value of two-tailed Wilcoxon–Mann–Whitney U test were shown.

Extended Data Fig. 6 GARFIELD plot of CM-DILI GWAS summary statistics in Cirulli et al., 2019.

a, Chromatin state, b, FAIR-seq, c, ENCODE DNase1 footprints, d, Genic annotation.

Extended Data Fig. 7 Multi-donor iPSC-HLO cholestatic DILI assays.

a, Bright field images of multi-donor iPSC and iPSC-HLO. b, Immunofluorescence staining of Albumin (ALB), BSEP, CD31 and HNF4a in multi-donor iPSC-HLO. c, ALB production during 24 hours in multi-donor iPSC-HLO. Data represent means ± SD (n = 3). d, CYP3A4 activity. Data represent means ± SD (n = 3). e, CLF accumulation and cell death signal in 1383D2 iPSCs derived iPSC-HLOs under CsA treatment for 24hr and 72hr. f, Viability after 24hr (dotted line) and 72hr (black line) CsA treatment in iPSC-HLO model. The ATP levels were normalized by the area of iPSC-HLO (see Materials and Methods).

Extended Data Fig. 8 Transcriptomic expression profiling of PHH cells at basal state.

a-g, Basal expression levels of mRNA were shown by the indicated signature of gene sets. Color scale and sample annotation colors were shown in the bottom right. (a) Liver signature genes1. (b) Signature of gene set characterizing liver sinusoidal endothelial cell (LSEC)2. (c) Signature of gene set characterizing hepatic stellate cell (HSC)2. (d) all drug transport proteins and proteins involved in bile transport and cholestasis (TCP, OATPs, OCT1, OAT2, CNT1, CNT2, ENT1, ENT2, MDR1, MDR3, MRP2, BSEP, BCRP, MATE1, MRP3, MRP4), mRNA coding proteins of N; (e-g) mRNA coding phase 1, 2, and 3 enzymes3. The Z-scores of log2(RPM+1) values were calculated within the indicated samples. Gene with RPM = 0 in all of the samples were excluded. PHH_1day_1/_2, PHH cells after 1 day of culturing (suffix means experimental batch), which was used for assessments of drug-induced transcriptomic change; PHH_2d, PHH cells after 2days of culturing; FLT, fetal liver tissue; ALT, adult liver tissue.

Extended Data Fig. 9 Reproducibility of drug toxicity assay of PHH cells under LCA pretreatment.

ac, Drug sensitivity comparison between different days’ experiments under LCA pretreatments. (a) HUM4133. (b) HEP187269. (c) HEP187277. d–f, Drug sensitivity comparison between different days’ experiments under none pretreatments. (d) HUM4133. (e) HEP187269. (f) HEP187277. (g, h) Viability comparison of multi-donor iPSC-HLO models under the indicated CsA treatment without LCA. Pearson’s r for correlation with PRSgw (g) or PRSgw+ h), and its P-value is described. *, p<0.05. We regress mean viability for each donor by PRSgw or PRSgw+ and calculated the P-values.

Extended Data Fig. 10 Decreased mitochondria activity by cholestatic DILI drug treatment.

Live staining for TMRM and mitochondria levels (mitotracker) in 1383 iPSC-HLO model under CsA treatment for 72hr.

Supplementary information

Supplementary Information

Supplementary Discussion, Methods and References.

Reporting Summary

Supplementary Tables 1-10.

Source data

Source Data Fig. 3

Unprocessed western blots.

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Koido, M., Kawakami, E., Fukumura, J. et al. Polygenic architecture informs potential vulnerability to drug-induced liver injury. Nat Med 26, 1541–1548 (2020).

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