Deciphering genetic causes for sex differences in human health through drug metabolism and transporter genes

Sex differences in genetic architecture and genetic effects of DMET genes

Sex differences in human phenotypes can be driven by sex-differentiated genetic effects4, 18. However, such effects can be masked in GWASs because individuals of both sexes have typically been analyzed together7. In this study, we evaluated the sex differences in the genetic architecture of human complex traits in each sex separately using sex-stratified GWAS summary statistics from the UKBB (, Fig. 1a). We analyzed 564 traits (421 binary/categorical traits, 143 continuous traits, Supplementary Data 2), for which at least one DMET region SNP shows significant trait association based on the GWAS catalog. DMET genes, which encode 222 metabolism enzymes and 150 transporters, were retrieved from a previous publication (Supplementary Data 1)10.We first estimated the narrow-sense heritability of each trait separately in each sex to quantify the proportion of phenotypic variance explained by the common genetic variation19. Sex differences in trait heritability suggest a different molecular mechanism and/or a different degree of environmental effect between sexes on the trait. Of 564 traits, 83 traits (14.72%) showed significant sex differences in heritability (FDR < 0.05, Fig. 1b and Supplementary Data 3). Among them, 56 traits (67.4%) had higher heritability in males, including neuropsychiatric traits, such as ever attempted suicide and anxiety, and other diseases, such as gout, cardiovascular complications (coronary atherosclerosis) and diabetes. In comparison, traits such as hypothyroidism, osteoporosis and gallbladder disorders have higher heritability in females. These findings are robust across different methods employed to estimate heritability (Supplementary Fig. 1). When expanding the genome-wide heritability analysis to additional 1222 traits that have sufficient samples for both sexes in UKBB and are not known to related to DMET genetic regions, we found 13.7% (167/1222) of them showing sex differences in global heritability (Supplementary Fig. 2, Supplementary Data 4). Interestingly, similar 13.40% (71/530) traits showing significant differences in their heritability between two sexes have been reported by an independent study7.

Fig. 1: Sex-differentiated genetic effect exists in genome-wide and in near-DMET gene regions for complex traits.
figure 1

a Overview of trait selection and analytical pipeline. We defined each cis-DMET gene region as ±1 Mb from the DMET gene transcription start site. We first selected available human complex traits for which sex-stratified GWAS summary statistics are available17. The trait list was further narrowed down by the presence of significant SNP-trait associated with the DMET gene regions. In total, we evaluated 564 traits (421 categorical/binary traits and 143 non-binary/non-categorical traits). We then performed several sex-aware analyses (sex-stratified heritability, male-female genetic correlation, sex-differentiated genetic effects) to characterize sex differences both a genome-wide and in DMET gene regions. Lastly, we performed sex-stratified colocalization analysis between sexes to identify DMET variants’ unique SNP-traits association in each sex for the traits that demonstrated sex differences in genetic basis. b Male and female heritability estimates for 564 traits. Each point represents the estimated heritability for a given trait. Blue indicates the 83 traits with a significant (FDR < 0.05) sex difference in heritability. c Number of sex-differentiated effects (SDEs) mapping to DMET genes regions. d 15 overlapped traits with SDEs in cis-DMET gene regions and a significant sex difference in heritability. e Sex-stratified colocalization of GWAS signal for 8 traits. SDEs are labeled on the x-axis, and the traits are labeled on the y-axis. The posterior probability of hypothesis (PPH) 1 (blue, representing SNP putatively causal only in Male) and 2 (red, representing SNP putatively causal only in Female) are color-labeled and the value of PPH is represented by the size of the circle; only PPH > 0.25 is shown. The variant effect and Combined Annotation Dependent Depletion (CADD) score are shown for each SNP. The asterisk indicates self-reported traits. f LocusZoom plots of SLC22A12 (left) and SLC66A1 (right) from sex-stratified GWAS for self-reported gout and self-reported hypothyroidism, respectively. Linkage disequilibrium (LD) between variants is quantified by the squared Pearson coefficient of correlation (r2).

We also estimated genetic correlation between sexes for each trait to evaluate potential sex differences in genetic architecture. Genetic correlation estimates the proportion of variance that two groups share due to genetic causes20. Low genetic correlation between males and females in the same trait suggests sex differences in the genetic architecture. We focused on only those heritable traits (defined by greater than median estimated heritability across all traits through sex-combined GWAS analysis) to generate robust estimates of the male-female genetic correlation. We identified 253 traits for which male-female genetic correlation differed significantly from 1 (Supplementary Fig. 3, Supplementary Data 3, FDR < 0.05), suggesting a global difference in genetic architecture between the sexes. These traits with sex-differentiated architecture include gout (rg = 0.459; FDR = 1.26 × 10−9), heart attack (rg = 0.573; FDR = 6.39 × 10−10), and cholelithiasis (rg = 0.634; FDR = 1.18 × 10−5).

For variants located in DMET gene regions, to quantify sex differences in SNP-trait associations, we calculated the z-score and P values for each SNP that mapped into the cis-DMET regions to each trait. We defined SNPs with sex-differentiated effects (SDEs) as those trait-associated SNPs with a significant sex difference in genetic effects (based on z-score FDR < 0.05). In total, we identified 25 traits harboring at least one SDEs (Fig. 1c, Table 1 and Supplementary Data 5) and mapped 954 SDEs onto 109 tagging loci (r2 < 0.2) and 125 genes. The two disease traits with the largest numbers of SDEs are self-reported gout and hypothyroidism/myxedema (Table 1). We indeed found SDEs mapped in genes previously implicated in disease, such as ABCG2 in gout15.

Table 1 Summary of SDEs found in different traits

15 traits were found to have sex differences both in heritability (global) and SDEs in DMET gene regions (Fig. 1d), affirming the presence of different genetic effects between males and females. However, whether these differential genetic effects are functionally related to human health is unknown. To test this, we employed colocalization analysis, which assess the probability of two GWAS traits sharing a common causal variant in a genomic region21. In another word, if for the same trait, the sex-stratified GWAS results were not colocalized, the probability is high that males and females have a different causal mechanism for that trait. Among the aforementioned 15 traits, we excluded three of them (Treatment code: levothyroxine sodium, treatment code: thyroxine product, major coronary heart disease event excludes revascularization) as they represented information redundant to other traits in the list. All 12 traits exhibit genetic correlation coefficients differing from 1 (Supplementary Fig. 4). We detected 22 male-specific causal loci in traits such as gout and major coronary heart disease events, and 13 female-specific causal loci in traits such as hypothyroidism (Fig. 1e, Supplementary Data 6) after colocalization analysis. All support the presence of sex-differentiated genetic effects of traits in cis-DMET gene regions.

Gout and hypothyroidism were found to have higher number of sex-differentiated causal SNPs in the DMET gene regions. Previous findings indicate that accumulation of crystal form of urate at the joint causes gout22. We identified male-specific SNP-trait associations of gout in the SLC22A12 region (Fig. 1f), specifically an upstream variant rs2360872. SLC22A12, also known as urate transporter 1, is involved in regulating urate levels in the blood and its variants are associated with serum uric acid level and gout development23. Our results suggest that rs2360872 affects gout uniquely in males. Additionally, we detected female-specific SNP-trait associations of hypothyroidism at the 5’UTR of SLC66A1 (Fig. 1f). SLC66A1 is a lysosomal amino acid transporter that mediates the export of cationic amino acids from lysosomes24. The relationship between SLC66A1 and thyroid function remains unknown.

Sex differences in the genetic regulation of gene expression may lead to observed sex differences in human complex traits

Sex-differentiated genetic regulation of gene expression can lead to different manifestations in downstream biological pathways. Therefore, we characterized sex differences of genetic regulation on gene expression in human liver samples using sex-stratified cis-eQTLs produced by the GTEx Consortium25.

Sex-differentiated eQTLs were defined as expression associated variants who have significantly different effect size between males and females (quantified through a z-score testing). We identified 31 sex-differentiated eQTLs (FDR < 0.1) in and near 2 DMET genes (Supplementary Fig. 5, Supplementary Data 7). Among them, rs34109652, is associated with UGT2B17 gene expression only in males (βmale = 0.84; Pmale = 5.44 × 10−7; βfemale = 0.27; Pfemale = 0.078; Z-score = −2.95; Fig. 2a). This gene encodes an enzyme that transforms steroid hormones such as testosterone26. A previous study reported higher UGT2B17 enzyme activity in males27. This male-specific genetic regulation could in part explain the observation of higher UGT2B17 activity in male, though functional work is required for validation.

Fig. 2: Sex differences in the genetic regulation of gene expression in human liver in part mediate human complex traits.
figure 2

a Example of sex-differentiated/specific cis-DMET eQTLs in both sexes for UGT2B17 (left panels) and PON1 (right panels) in liver. b Manhattan plot of sex-stratified cis-eQTLs in DMET regions in human liver. Sex-differentiated/specific cis-eQTLs and their corresponding genes are labeled (blue: Male, red: Female) and parentheses indicates the number of cis-eQTLs. Highlighted genes are statistically significant after multiple testing correction (FDR < 0.1).  c Colocalization of GWAS traits with sex-differentiated/specific cis-eQTLs for DMET genes. PPH4 values are represented by the size of circles. Only colocalizations with PPH4 > 0.5 in one sex are shown (females: red; males: blue). d LocusZoom plots for alcohol intake frequency at the ADH1C locus. The top panels illustrate the results from the sex-combined GWAS, while female-only and male-only cis-eQTLs are shown in the middle and the bottom panels. rs34076947 is a male-specific ADH1C cis-eQTL in liver. In (a) and (d), linkage disequilibrium between loci is quantified by the squared Pearson coefficient of correlation (r2). In (a, b, d), association testing was performed using a linear regression model by FastQTL.

In addition to sex-differentiated eQTLs, we also identified 40 potential sex-specific eQTLs in the DMET gene regions (FDR < 0.1 in one sex and FDR > 0.1 in the other sex of eQTL, Fig. 2b, Supplementary Data 7). For example, variant rs854572 on chromosome 7, is associated with PON1 gene expression in males only (Pmale = 4.13 × 10−8; Pfemale = 0.01; Z-score = 0.33 Fig. 2a). This gene encodes serum paraoxonase and arylesterase 1 enzyme, an enzyme exerting protective effects in Major Adverse Cardiovascular Events (MACE = death, myocardial infarction, stroke). In a sex-combined GWAS, this variant is associated with the activity of paraoxonase and arylesterase 128. Moreover, higher arylesterase activity has been reported in females29. Although sex-specific association of this variant with serum enzyme activity has not been tested, this sex-specific eQTL provides a putative explanation for the observed higher PON1 serum activity in females. Together, our results support the presence of sex-differentiated genetic regulation of DMET gene expression.

To examine the potential biological consequences of these sex-differentiated/specific eQTLs, we performed colocalization21 between them and the 564 complex traits described above. We identified 11 traits with a sex-differentiated/specific eQTL colocalization (PPH4 > 0.5 in only a single sex, Fig. 2c, Supplementary Data 8 Binary Traits, Supplementary Data 9 continuous traits). Among them, multiple traits related to alcohol intake are colocalized with a male-specific eQTL for ADH1C (PPH4male = 0.73, PPH4female = 0.073, Fig. 2d). This gene encodes enzymes that metabolize a wide variety of substrates, including alcohol30. Alcohol metabolism determines blood alcohol level over time, and the extent of organ exposure to alcohol. Therefore, ADH1C has been implicated in alcohol dependency. Sex differences in alcohol consumption have been widely reported, where males are more likely to drink alcohol and to consume more than females31. Genetic variants in ADH1C are associated with alcohol metabolism capacity32. In males, genetic variants of ADH1C showed significant association and a larger effective size with heavy/excessive alcohol drinking habits than females33. Our results provided a plausible explanation for this phenomenon where sex-differentiated genetic regulation of ADH1C may be the culprit. Importantly, all traits that showed significant sex-dependent colocalization with ADH1C eQTLs were related to alcohol drinking frequency, but not alcoholism. This is concordant with previous reports that ADH1C genetic variants were not associated with alcoholism33. Of note, the observed sex dimorphism in alcohol consumption can be affected by factors, such as body size, sociocultural behavior. The identification of genetic contributor to this trait should not be interpreted without these other factors. Our results provided a plausible explanation for this phenomenon where sex-differentiated genetic regulation of ADH1C may play a part in this complex issue. Overall, our colocalization results linked the genetic basis of a small number of complex traits to sex-dependent genetic effects on expression levels of specific DMET genes.

Sex differences in the DMET region genetic regulation of human serum biomarkers and their impact on human health outcomes

Clinical laboratory tests are frequently used to diagnose diseases and monitor human health status. Many of these serum biomarkers are transformed in the liver by DMET enzymes10. Importantly, sex differences have been known for the level of many serum biomarkers34. While the genetic basis of serum biomarkers has been studied in large cohorts in a sex-combined fashion35, sex differences in genetic basis of those biomarkers and putative causal relationships with human diseases have not been extensively studied. Given the relevance of serum biomarkers in human health and the potential masking effect introduced by the sex-combined model, we hypothesized that sex-differentiated causal relationships exist, and can be revealed by sex-aware characterization of the genetic regulation of serum biomarkers and human disease risks. To test this hypothesis, we performed Mendelian Randomization (MR) in both sex-combined and sex-stratified models. We pre-selected traits that harbor at least 1 significant SNP (P < 5 × 10−8) that are mapped to a DMET gene region. In total, we have 29 serum biomarker traits as exposures and 186 outcomes selected from the 564 human complex traits described above. A sex-specific likelihood of causal relationship is defined if it is significant in only a single sex but not in the sex-combined population (P < 0.05/(186*29) threshold, Fig. 3a).

Fig. 3: Sex-specific relationships between serum biomarkers and human health outcomes.
figure 3

a Analytical pipeline for the Mendelian Randomization (MR) analysis to establish the causal relationship between serum biomarkers and human health outcomes. We evaluated SNP-trait associations for 30 serum biomarkers and 564 human complex traits from UKBB separately, focusing on those SNPs in DMET gene regions. MR tests were conducted using both sex-combined and sex-stratified GWAS summary statistics, limiting to those traits with significant DMET region genetic associations (P < 5 × 10−8), which include 29 exposures (serum biomarkers) and 186 outcomes (traits). b SNPs in DMET gene regions are important in the genetic regulation of serum biomarkers from UKBB. The barplot shows the total count of significant variants in the sex-combined serum biomarker analysis. The proportion of variants in DMET gene regions is shown in blue. c Volcano plot of sex-combined and sex-stratified MR results. The x-axis represents the −log10 (P value) from the MR-Egger test. The y-axis represents the effect size of MR-Egger tests. Each point represents a MR test from sex-combined (diamond) or sex-stratified (square if females; triangle if males) GWAS summary statistics. The color of points indicates sex-specificity of the MR relationship; blue indicates males (blue) or females (red) or not sex specific (gray). The plot does not display those cases with extremely large −log10(p) values without sex-specific MR relationships. The vertical dashed line represents the significance threshold (P < 0.05/(186*29)). d Sex-stratified MR results between 24 diseases and serum biomarkers. Rows represent 24 diseases as outcome and columns represent 28 serum biomarker traits as exposure. The color represents the effect size. Significant causal effects are indicated with a solid black dot. Colored outlines highlight sex-specific MR relationships, with female-specific in red, and male-specific in blue. e Manhattan plot for GWAS of testosterone (top) and doctor-diagnosed high blood pressure (bottom) in females. SNPs associated with both traits (P < 5 × 10−8) are highlighted in red. SNPs in DMET gene regions and included in the MR test are labeled with DMET gene names. f Colocalization between testosterone level and doctor-diagnosed high blood pressure in the CYP11B1 locus, shown separately for males and females. The posterior probability of shared causal effects (PPH4) is calculated separately for each sex. Linkage disequilibrium between variants is quantified by the squared Pearson coefficient of correlation (r2).

We first examined the proportion of significant serum biomarker level-associated SNPs (P < 5 × 10−8) that are located in the DMET regions relative to the whole genome (Fig. 3b, Supplementary Data 10); and found the DMET region variants were associated with levels of 29 serum biomarkers. The DMET region serum biomarker associations account for 20% (in the case of c-reactive protein) to 100% (in case of oestradiol) of overall genomic association with these serum biomarkers. The proportion of serum biomarker-associated variants that mapped to cis-DMET gene regions is greater than random selection of variants from genome sequence of the same length (Supplementary Fig. 6), which suggests the important impact of DMET genes on serum biomarker levels.

Using the same analytical pipeline described earlier on human complex traits (not including any of the serum biomarker traits), we characterized sex differences in the genetic basis of serum biomarker traits. As expected, we observed that hormone-related traits, such as testosterone, harbor the largest sex differences in estimated heritability (Supplementary Data 11), genetic correlations (Supplementary Fig. 8, Supplementary Data 11), and SDEs (Supplementary Fig. 9, Supplementary Data 12)34.

By conducting MR analysis using sex-combined and sex-stratified models, we identified a total of 141 female-specific and 167 male-specific putative causal relationships (Fig. 3c, Supplementary Data 13), which were not seen in the sex-combined model. Serum biomarkers, such as bilirubin and aspartate aminotransferase, have a large number of female-significant causal relationships, whereas testosterone, cholesterol and glucose have a large number of male significant causal relationships (Supplementary Fig. 10). We found sex-specific causal relationships between blood cell counts (e.g., monocyte count, platelet count) and several anthropometric traits (e.g., BMI, waist-to-hip ratio, hip circumference). While sex differences have been known for either the blood cell counts or the anthropometric traits36, 37, these putative sex-specific causal relationships have not been reported. Among diseases, we identified 24 traits that exhibit at least 1 sex-specific causal relationship (Fig. 3d). We observed that testosterone increases the likelihood of high blood pressure only in females; and apolipoprotein B increases the likelihood of coronary atherosclerosis and major coronary heart disease events only in males. Given that DMET genes are largely involved in the transformation of these endogenous compounds, we hypothesized that genetic variations in DMET gene regions affect levels of these serum biomarkers and result in sex-specific human health outcomes. We further conducted MR analysis only considering SNPs in DMET gene regions in traits with sex-specific causal effect; and identified 61/141 (43.2%) female-specific and 66/167 (39.5%) male-specific putative causal relationships that remained valid (Bonferroni adjusted P < 0.05, Supplementary Fig. 11, Supplementary Data 14). Overall, our results highlight the importance of examination of causal relationships in each sex separately, by which sex-specific causal effect can be identified.

To understand the molecular basis of sex differences in this causal relationship, we examined shared loci that associated with testosterone and high blood pressure in each sex. In females, six loci (P < 5 × 10−8) were shared between testosterone and high blood pressure, and 3 (CYP11B1, SLC16A1, SLC22A7) of them were in DMET gene regions (Fig. 3e). In males, only 4 loci were shared between testosterone and high blood pressure, none of these loci were located in the DMET gene regions (Supplementary Fig. 12). Variant rs7003319 in CYP11B1 3′UTR is associated with both testosterone level and high blood pressure in females, but not males. Colocalization analysis of these two traits revealed a shared genetic basis only in females (PPH4females = 0.73, PPH4males = 0.00034; Fig. 3f). CYP11B1-encoded enzymes catalyze glucocorticoid production and lead to the production of 11-ketotestosterone (11-KT)38. A previous study reported that 11β-hydroxylase deficiency (11βOHD), a rare autosomal recessive disorder caused by mutations in the CYP11B1 gene, is associated with hypertension39. Further investigation of the interplay between CYP11B1, testosterone, and high blood pressure in the context of biological sex is warranted.

Sex differences in DMET gene expression are linked to sex differences in drug response

Elimination of drugs and exogenous toxins is the major function of DMET gene products. Such processes mainly take place in the liver. Differences in the abundance and activity of DMET have been shown to play a major role in drug responses and side effects10. Although sex differences in the gene expression of DMET genes have been studied in different contexts40, the impact of these differentially expressed genes on responses to relevant medications is sparsely reported. Here, we hypothesized that sex differences in DMET gene expression would provide insights into observed sex differences in drug responses and side effects.

Using RNA-seq data from GTEx liver tissue, we identified 20 DMET genes with significant sex differences in expression; 10 expressed higher in males and 10 others expressed higher in females (FDR < 0.05, Fig. 4a, Supplementary Data 15). Among them, several well-characterized pharmacogenes, such as CYP1A2, CYP3A4 and CYP2C19, were found to exhibit sex-differential expression. When assessing the reproducibility of our discovery in an independent dataset, we recapitulated the differential expression for 14 of 19 genes (expression of CYP1A2 was not quantified in the validation dataset) (Supplementary Fig. 13)41. Two additional smaller datasets42, 43 were also evaluated and our top differential expressed genes, such as UGT2B17, UGT2A3, CYP3A4, SLC3A1, SLC16A14 are concordant with previous finding (Supplementary Data 16). Protein abundance of two pharmacogenes, CYP1A2 and CYP3A4, was quantified in pooled human liver microsomes (HLMs) that were collected separately from male and female donors. We confirmed higher protein abundance of CYP1A2 in the male-pooled HLMs and the opposite trend for CYP3A4 (Fig. 4b), which is correlated with a previous report11.

Fig. 4: Sex differential DMET gene expression is linked to sex difference of clinical drug response.
figure 4

a Volcano plot of differential gene expression of DMET genes in human liver. Each point represents a DMET gene. There are 10 DMET genes more highly expressed in males (blue), and 10 more highly expressed in females (red). Wald test was performed using DEseq2. The horizontal dashed line represents FDR < 0.05. b Western blot analysis of sex-stratified human liver microsomes (HLMs; female HLMs were pooled from 21 donors, male HLMs were pooled from 12 donors) showing expression of CYP1A2 and CYP3A4. Top: Representative Western blot. Bottom: Quantification of CYP1A2 and CYP3A4 protein normalized over GAPDH from six replicates. Data are presented as means ± SEM. PCYP1A2 = 0.0126, PCYP3A4 = 0.0209. c Workflow for web-scraping analysis. d Web-scraping identified 49 CYP1A2-associated drugs with reported sex differences in clinical drug response. Sanky plot shows the number of articles reporting sex differences in drug response for those drugs annotated with CYP1A2 in DrugBank and/or PharmGKB. e Schematic representation of sex difference in clozapine metabolism and toxicity. The active metabolites (N-desmethylclozapine) were primarily produced by CYP1A2, which has a higher abundance in the male liver. More N-desmethylclozapine could contribute to the reported higher frequency of side effects in males receiving clozapine. Font size of the gene name represents the relative contribution of each gene in generating the corresponding metabolites. f Impact of sex on clozapine metabolites generated in male and female pools of HLMs. N-desmethylclozapine, an active metabolite of clozapine and clozapine N-oxide, an inactive metabolite of clozapine, were shown in (f). Metabolite concentrations were measured by liquid chromatography-mass spectrometry (LC-MS). Each point represents an independent microsomal incubation experiment. The horizontal bar indicates the mean concentration. The color of points indicates sex (Blue: male; Red: female). P values (*P < 0.05) were determined by two-tailed t test.

To evaluate the potential clinical consequences of these sex differentially expressed DMET genes with respect to medication use, we annotated these genes with FDA-approved drugs. Specifically, using data from PharmGKB44 and DrugBanks45, both of which have summarized gene and drug relationships from published literature, we found 1,166 drugs that have been linked to at least one of the sex differentially expressed genes identified in this study (Supplementary Fig. 14, Supplementary Data 17). Not surprisingly, CYP3A4, a cytochrome P450 family member which is responsible for metabolizing approximately 50% of drugs on the market, has been linked with the greatest number of drugs.

To further investigate whether these annotated drugs have been reported to have sex differences in response, we performed web scraping to comprehensively search literature in PubMed for evidence supporting sex differences in response to these annotated drugs. Using “sex difference” and drug names as our key search terms, we identified 2,340 journal articles that contained information for 306 drugs. Given the high intensity of inspecting all articles, we focused on those drugs that were annotated with CYP1A2, as a proof-of-concept. This resulted in 519 journal articles which were then manually inspected to determine whether sex differences in drug responses were reported. Among them, we identified 75 articles that reported sex differences in either efficacy, pharmacokinetics, or toxicity of 49 drugs that are metabolized by CYP1A2 (Fig. 4c, Supplementary Data 18). Fluoxetine, for example, was reported to have greater weight reduction in females than in males at the same dosage46. Furthermore, antipsychotic medications, such as clozapine and olanzapine, have been reported to have a slower elimination in females than males47, which is in agreement with our findings that males have a higher expression of CYP1A2 (Fig. 4d).

Our analysis identified a number of sex-differential drug responses that were supported by existing literature. For example, flunarizine, another CYP1A2 substrate, which is used in treating epilepsy48, was reported to not affect catalepsy in male mice, but attenuated catalepsy in females at the same doses49. Another study found that male rats formed two oxidative metabolites of flunarizine at higher rate than female rats50. These are in agreement with our findings that higher CYP1A2 expression in male can lead to faster metabolism/breakdown of this drug and therefore less response. Similarly, female mice have been reported to be less susceptible to acetaminophen overdose induced hepatotoxicity than male mice51. Acetaminophen is also metabolized by CYP1A2 to N-acetyl-p-benzoquinone imine (NAPQI), known to induced hepatotoxicity. In this case, the lower expression of CYP1A2 in female liver would lead to lower production of the toxic metabolite and therefore partially explain the lower toxic response in females.

Given the wide usage of clozapine and the higher rate of clozapine-induced toxicity observed in males52, we hypothesized that the higher abundance of CYP1A2 contributes to higher formation of an active metabolite, and subsequently leads to higher rate of clozapine-induced side effects in males (Fig. 4e). We quantified the sex differences in clozapine metabolism using separate pools of HLMs from men and women. Because CYP3A4 also contributes to the metabolism of clozapine, in our HLM experiments we employed ketoconazole, a CYP3A4 inhibitor, to specifically evaluate CYP1A2-mediated metabolite formation. In the presence of ketoconazole, the formation of N-demesthylclozapine, the active metabolite, was significantly higher in the male HLMs (Fig. 4f, P = 0.0309). We observed no sex differences in the formation of an inactive metabolite, clozapine N-oxide (Fig. 4f) and parent drug clozapine (Supplementary Fig. 15). Higher plasma concentration of N-desmethylclozapine has been associated with clozapine side effects, such as agranulocytosis53. Our observation supports the notion that the observed higher frequency of clozapine-induced toxicity in males can be due to elevated formation of N-desmethylclozapine resulting from higher CYP1A2 levels in males (Fig. 4e).