(2023) 21:170
Wu et al. BMC Medicine
https://doi.org/10.1186/s12916-023-02876-w

BMC Medicine

RESEARCH ARTICLE

Open Access

Investigating the relationshipbetween depression and breast cancer:
observational and genetic analyses
Xueyao Wu1†, Wenqiang Zhang1†, Xunying Zhao1, Li Zhang1, Minghan Xu1, Yu Hao1, Jinyu Xiao1, Ben Zhang1,
Jiayuan Li1, Peter Kraft2,3†, Jordan W. Smoller4,5† and Xia Jiang1,6,7*†   Abstract
Background Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity andmortality among women, and previous studies have observed a potential depression-BC link. We aimed to comprehensively characterize the phenotypic and genetic relationships between depression and BC.
Methods We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank(N = 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest
genome-wide association study of European individuals conducted for depression (N = 500,199), BC (N = 247,173),and its subtypes based on the status of estrogen receptor (ER + : N = 175,475; ER − : N = 127,442).
Results Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10,
95%CIs = 0.95–1.26). A positive genetic correlation between depression and overall BC was observed (rg = 0.08,
P = 3.00 × ­10–4), consistent across ER + (rg = 0.06, P = 6.30 × ­10–3) and ER − subtypes (rg = 0.08, P = 7.20 × ­10–3). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and fourloci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC.
TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression wascausally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04–1.19), but risk of BC was not causally associatedwith risk of depression.
Conclusions Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationshipbetween depression and BC, highlighting a biological link underlying the observed phenotypic relationship; thesefindings may provide important implications for future studies aimed reducing BC risk.
Keywords Depression, Breast cancer, Longitudinal association, Genetic correlation, Pleiotropic loci, Causal inference†

Xueyao Wu, Wenqiang Zhang, Peter Kraft, Jordan W. Smoller and Xia Jiang
contributed equally to this work.
*Correspondence:
Xia Jiangxiajiang@scu.edu.cn
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
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Wu et al. BMC Medicine(2023) 21:170

Background
Individuals with mental health disorders are often at anincreased risk for an array of subsequent medical conditions including cancer [1]. One example of this phenotypic association is depression and breast cancer (BC),both of which disproportionately or primarily affectwomen and contribute to a substantial global burden of
morbidity and mortality [2, 3]. Hypothesized underlying
biological mechanisms include enhanced inflammationand oxidative stress, inhibited immune surveillance, anddysfunctional activation of the autonomic nervous system and the hypothalamic–pituitary–adrenal axis [1].
Population-based evidence regarding the depression-BC
relationship, however, remains inconsistent. While one
meta-analysis aggregating data from 11 cohort studies involving 182,241 individuals identified an increased(though non-significant) risk of BC among individuals
with depression (pooled relative risk (RR) = 1.13, 95%
confidence intervals (95%CIs) = 0.94–1.36) [4], anothermeta-analysis restricted to cohort studies with followup periods longer than ten years reported a significant
association (pooled RR = 2.50, 95%CIs = 1.06–5.91) [5].
Nevertheless, phenotypic correlations derived fromobservational studies can be subject to bias, confounding,and reverse causality [6].
One way to disentangle these conflicting findings is
to investigate the potential genetic underpinnings ofcomorbid disorders. Twin studies have established thatboth depression and BC are under genetic influence,with heritability estimates of 37% and 31%, respectively[7, 8]. More recently, large-scale genome-wide analysis ofcommon variants have found significant genetic correlations between depression and multiple female reproductive phenotypes [9–11] including age at menarche andage at natural menopause (well-established risk factors
for BC [12]). Multiple loci have further been identified(assessed in GWAS catalog [13] on Jan 15, 2023) as influencing both traits (i.e., TENM2, BTN2A1, ESR1, ASTN2,SLC6A15). These results suggest that depression and BC
may be linked by shared biology, though the extent andnature of such links remains unclear.
Recent advances in statistical genetics have yieldeda range of methods to enable comprehensive genomewide cross-trait analyses to characterize the shared anddistinct genetic influences across traits, driving forward
epidemiologic associations with novel insights into theunderlying biological mechanisms [6]. Here we apply
these methods to dissect the genetic and phenotypic relationships between depression and BC. Specifically, leveraging the hitherto largest observational and genetic data,
we quantified phenotypic association, global and local
genetic correlations, pleiotropic loci, and potential causal
relationships. Given the role estrogen receptor (ER) plays

Page 2 of 14

in the pathogenesis of depression [14], we further investigated the shared genetic architecture with depression
across BC subtypes characterized by distinct ER status.
Figure 1 illustrates the overall design of the study, withAdditional file 1: Figure S1 providing an accompanying
graphical abstract.

Methods
We first used data from UK Biobank (UKBB) to evaluate
the phenotypic association. We then leveraged summary
statistics from genome-wide association study (GWAS)
conducted for each trait to characterize the shared
genetic architecture through a genome-wide cross-trait
analysis. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklists forboth observational and genetic studies are detailed in
Additional file 2.
Data sources
UK Biobank data

UKBB is a large-scale prospective cohort study withmore than 500,000 UK participants (55% women) aged
40–69 years when recruited in 2006–2010 [15]. At
recruitment, all participants gave informed consent forparticipation and follow-up. Overall, 503,317 participants
consented to join the study cohort and visited an assessment center, among which we only considered women ofEuropean descent (N = 257,150). We defined a diagnosis of depression as the ICD-10 (international classification of diseases, 10th revision) codes F32, F33, F34, F38,
and F39, and a diagnosis of BC as the ICD-10 code C50
and the ICD-9 code 174, using linked records of hospital
admission in UKBB (Data-Field: 41,202 and Data-Field:
41,204). After excluding 6,856 participants with a history of BC at baseline, 250,294 participants were finally
included.
GWAS datasets

GWAS summary data for depression was obtained froma meta-analysis of 807,553 individuals (246,363 casesand 561,190 controls, all of European ancestry) combining the three largest existing genetic studies of depression (UKBB, 23andMe and the Psychiatric Genomics
Consortium (PGC)) [9]. The phenotypes ranged fromself-reported help-seeking for problems with “nerves,anxiety, tension or depression” (termed “broad depression”) (51.8%), self-reported clinical diagnosis of major
depression (MD) (30.7%), and clinically ascertained
diagnosis of MD (17.5%). Independent trait-associated genome-wide significant single nucleotide polymorphisms (SNPs) were identified at a P-threshold of
5 × ­10–8, after clumping SNPs in linkage disequilibriumWu et al. BMC Medicine(2023) 21:170

Page 3 of 14

Fig. 1 Flowchart of the overall study design. We first used longitudinal follow-up data from the UK Biobank to evaluate the phenotypic association
between depression and breast cancer. We then leveraged summary statistics from the hitherto largest genome-wide association study conducted
for each trait to characterize the shared genetic architecture through a genome-wide cross-trait analysis. BC: breast cancer; ER: estrogen receptor;
GWAS: genome-wide association study; GTEx: Genotype-Tissue Expression project; MR: Mendelian randomization(LD) (r 2 > 0.10 across a 3.0 Mb window) with the topassociated SNPs.
We extracted the effect size and relevant information of
the 102 GWAS-identified depression-associated significant SNPs and used those SNPs as instrumental variables(IVs) in Mendelian randomization (MR) analyses [16](Additional file 3: Table S1). We also retrieved the full setof summary statistics for depression, in which data from
23andMe were excluded (due to limited data availability).
The largest available GWAS summary data for overall
BC was obtained from a meta-analysis of 247,173 individuals (133,384 clinically ascertained BC cases and 113,789
controls, all of European ancestry), combining data from82 participating studies of the Breast Cancer Association
Consortium (BCAC) and 11 other BC genetic studies

Wu et al. BMC Medicine(2023) 21:170[17]. Top-associated SNPs in the combined meta-analysis
reaching a P-threshold of 5 × ­10–8 were reported. ThisGWAS identified 32 novel susceptibility loci in addition
to 153 previously detected loci (for a total of 185 genomewide significant loci). For subtype-specific BC, the hitherto largest GWAS summary data was obtained from ameta-analysis of 122,977 BC cases (69,501 ER + BC and21,468 ER − BC) and 105,974 controls (all of European
ancestry) combining data from BCAC and DRIVE (Discovery, Biology and Risk of Inherited Variants in Breast
Cancer Consortium) [18]. No overlapping participating
studies were shared between the BCs and depression
GWASs.
We extracted the effect size and relevant informationof the 185 GWAS-identified BC-associated significant
SNPs for reverse-direction MR analysis. As trait-associated SNPs were not reported for subtype-specific BC, we
determined IVs as the lead SNPs reaching genome-wide
significance (P < 5 × ­10–8) after removing SNPs in LD (r 2
> 0.001 across a 10.0 Mb window). We also downloaded
full summary statistics for all BCs (overall and subtypes).
Details on the characteristics of each included data set
are presented in Additional file 3: Table S2.
Statistical analysis
Observational analysis

We constructed a Cox proportional hazards regression
model with exposure to depression modeled as a timedependent variable. We used three sets of adjustments tominimize the role of confounding. Estimates in model 1
were adjusted only for age, assessment center, and the top
40 genetic principal components. Estimates in model 2
were adjusted additionally for income, Townsend deprivation index, body mass index (BMI), smoking, drinking,physical activity, sleep duration, and education. Estimates in model 3 were adjusted, on top of model 2, forfamily history of BC, reproductive factors (parity, age at
menarche, menopausal status, use of oral contraceptives,and hormone replacement therapy), diagnosis of othermental health disorders, and treatment/medicine for
antidepressants or antipsychotics. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC).
Global genetic correlation analysis

To evaluate an overall shared genetic basis between
depression and BC, we estimated their global genetic
correlation using cross-trait LD Score Regression (LDSC)[19]. LDSC requires only GWAS summary statistics
as input and quantifies the average sharing of genetic
effect between pairs of traits. The genetic correlation (rg )ranges from –1 to 1, with –1 indicating a perfect negative correlation and 1 indicating a perfect positive correlation. A Bonferroni corrected P-value of 0.017 was used

Page 4 of 14

as significant threshold (P < 0.05/3, number of overall BC
and subtypes).
Local genetic correlation analysis

To identify genomic regions that contribute disproportionately to the global genetic correlation between
depression and BC, we further estimated local genetic
correlation in 1,703 pre-defined LD-independent regions
using both Heritability Estimator from Summary Statistics (ρ-HESS) and Pairwise-GWAS (GWAS-PW)[20, 21]. While ρ-HESS provides a precise quantification of each genomic region, GWAS-PW uses a Bayesian framework to calculate the posterior probabilities ofassociation (PPA) of a genomic region under four models [21]. Significant local signal was determined if the
P-value from ρ-HESS survived multiple corrections (PρHESS < 0.05/1703), or if PPA of model 3 (PPA _3, the probability that a genomic region associated with both traits)
from GWAS-PW was larger than 0.5.
Cross‑trait meta‑analysis

Significant genetic correlation either suggest genetic variants having independent effect on both traits (pleiotropy)
or genetic variants influencing one trait via its effecton the other (causality). We next performed a crosstrait meta-analysis using Cross-Phenotype Association(CPASSOC) to identify pleiotropic loci [22]. Based on the
fixed-effect model, CPASSOC provides two estimates,­SHom and ­SHet, to combine summary statistics acrosstraits while controlling population structure and cryptic relatedness. S­ Hom is the most powerful when genetic
effect sizes are homogenous, which is unlikely to be true
when meta-analyzing multiple traits. As an extensionof ­SHom, ­SHet maintains statistical power even under thepresence of heterogeneity by assigning more weights tothe larger trait-specific effect sizes, and was thus adopted
for the analysis herein.
We applied PLINK’s “clumping” function to obtain
independent loci (parameters: –clump-p1 5e-8 –clumpp2 1e-5 –clump-r2 0.2 –clump-kb 500) [23]. Within eachlocus, the variant with the lowest P-value was kept as
index SNP. Significant pleiotropic SNPs were defined asindex variants satisfying PCPASSOC < 5 × ­10–8 and Psingle–3(for both traits). Novel pleiotropic SNPs
trait < 1 × ­10
were defined as significant pleiotropic SNPs that did notreach genome-wide significance in single-trait GWASs(5 × ­10–8 < Psingle-trait < 1 × ­10–3), were independent (r 2
< 0.20) of previously reported genome-wide significant
SNPs (of depression and BC), and none of their neighboring SNPs (± 500 kb) reached P < 5 × ­10–8 in singletrait GWASs.
We used Ensembl Variant Effect Predictor (VEP) [24]and 3DSNP [25] for detailed functional annotation of theWu et al. BMC Medicine(2023) 21:170

identified pleiotropic SNPs. While VEP selects candidate
genes based on simple physical proximity [24], 3DSNP
annotates the regulatory function of SNPs by exploring
their 3D interactions with genes mediated by chromatinloops [25].
Fine mapping credible set analysis

Because an index SNP is not likely to be the causal SNP,
we further identified a credible set of variants that were
99% likely to contain causal variants at each of the pleiotropic loci through FM-summary (https://​github.​com/​haili​anghu​ang/​FM-​summa​r y) [26]. Briefly, FM-summaryis a Bayesian fine-mapping algorithm that maps only the
primary signal and uses a flat prior with steepest descentapproximation, assuming at least one causal variant
exists within a given region.
Colocalization analysis

To investigate whether the same variants are responsible for two GWAS signals as opposed to distinct genetic
variants in proximity, we next performed a colocalization
analysis using Coloc [27]. This method provides posterior probabilities for five mutually exclusive hypotheses
regarding the sharing of causal variants in a genomic
region. We extracted summary statistics for variants
within 500 kb of each shared index SNP, and calculated
the posterior probability for H4 (PPH4, the probability
that both traits associated through sharing a single causal
variant). A locus was considered colocalized if PPH4 wasgreater than 0.5.
Transcriptome‑wide association studies

Many genetic variants influence complex traits by modulating gene expression; thus, investigating overlapped
genes may help clarify causal mechanisms. To identify
associations between depression and BC with regard
to gene expression in specific tissues, we conducted atranscriptome-wide association study (TWAS) using
FUSION [28] by integrating GWAS summary data withexpression weights across 49 tissues from GTEx (Genotype-Tissue Expression, version 8). We first performed 49
TWASs for each trait, one tissue-trait pair at a time, toidentify an independent set of gene-tissue pairs. We thenintersected single-trait TWAS results to examine if there
were shared gene-tissue pairs across traits. The Bonferroni correction was used within each tissue to accountfor multiple comparisons.
Mendelian randomization analysis

We finally performed a comprehensive two-sample MR
analysis to detect potential causal relationships. We used
the random-effect inverse-variance weighted (IVW)
approach as the primary approach [29]. This method

Page 5 of 14

pools the Wald ratio estimate of each SNP and obtains
the average casual effect estimate between two traits.
To reduce biased estimate due to pleiotropic effects ofgenetic instruments, we adopted two complementary
methods: MR-Egger regression (detects and corrects forbias due to directional pleiotropy) [30], and weighted
median approach (provides a consistent causal estimation with even ≥ 50% invalid IVs) [31]. As these two
approaches were less powerful than IVW in detectingtrue causal effects, we defined a significant causal estimate as significant in IVW (P-value < 0.05/3, number ofoverall BC and subtypes) and showing directional consistency in MR-Egger regression and weighted medianapproach.
Additional sensitivity analyses were conducted tovalidate MR model assumptions (i.e., relevance, independence, and exclusion restriction) [32], including: (i)exclusion of palindromic IVs with strand ambiguity; (ii)exclusion of pleiotropic IVs associated with potential
confounding traits (confirmed by GWAS Catalog [13]);(iii) leave-one-out analysis where one SNP was removed
at a time and IVW was conducted based on the remaining SNPs; and (iv) MR-Pleiotropy Residual Sum andOutlier (MR-PRESSO) to evaluate the presence of horizontal pleiotropy and to re-calculate causal effects after
removing the detected outliers [33]. A multi-variable MR(MVMR) approach [34] was further employed to accountfor the effect from major confounders, including BMI[35], smoking initiation [36], alcohol consumption [36],physical activity [37], sleep duration [38], and educational
attainment [39], where confounders were incorporated
together with depression, one at a time as well as simultaneously. To evaluate whether genetically predicted BC
exerts a causal effect on depression, a reverse-direction
MR was performed where BC-associated independent
SNPs were used as IVs.
MR analyses were conducted using packages “TwoSampleMR” (version 0.5.4), “MRPRESSO” (version 1.0), and“MendelianRandomization” (version 0.7.0) in software R(version 4.1.0).

Results
Phenotypic association

Baseline characteristics of UKBB participants includedin the observational analysis are presented in Additional
file 3: Table S3. In total, participants were followed for
3,014,168 person-years (11.41 ± 2.95 years), during which
529 depression patients and 9,516 depression-free individuals developed BC (Table 1). After adjusting for age,assessment center, and the top 40 genetic principal components, we observed a positive association between
depression and risk of BC (hazard ratio (HR) = 1.075,
95%CIs = 0.963–1.199). The effect was strengthenedWu et al. BMC Medicine(2023) 21:170

Page 6 of 14

Table 1 Observational association between depression and breast cancer
Depression status duringfollowing up

Breast cancer cases/person Model 1
years

Model 2Model 3

No

9,516/2,912,715

1.000 (ref )1.000 (ref )1.000 (ref )Yes

529/101,453

1.075 (0.963–1.199)

1.090 (0.951–1.250)

1.096 (0.951–1.264)

Model 1: adjusted for age, assessment center, and the top 40 genetic principal components
Model 2: adjusted for age, assessment center, the top 40 genetic principal components, income, Townsend deprivation index, body mass index, smoking, drinking,physical activity (IPAQ), sleep duration, and education
Model 3: adjusted for age, assessment center, the top 40 genetic principal components, income, Townsend deprivation index, body mass index, smoking, drinking,physical activity (IPAQ), sleep duration, education, family history of breast cancer, parity, age at menarche, menopausal status, use of oral contraceptives, hormone
replacement therapy, diagnosis of other mental health disorders (i.e., bipolar disorder, multiple personality disorder, or schizophrenia/psychosis), and treatment/
medication for antidepressants or antipsychotics

when additionally adjusted for income, Townsend deprivation index, and lifestyle-related factors (HR = 1.090,
95%CIs = 0.951–1.250). In the fully adjusted model, theeffect stabilized to 1.096 (95%CIs = 0.951–1.264).
Global and local genetic correlation

A limited but significant positive genetic correlation
was found for depression with overall BC (rg = 0.08,
P = 3.00 × ­10–4), which was also observed in both ER + (rg= 0.06, P = 6.30 × ­10–3) and ER − subtypes (rg = 0.08,
P = 7.20 × ­10–3), all withstanding Bonferroni correction(Table 2).
When the whole genome was partitioned, one genomic
region at 6p22.1 (harboring a previous-reported depression locus ZSCAN12) was identified as contributing asignificant local genetic correlation to depression andoverall BC (Pρ-HESS = 1.83 × ­10–5). When examining
cancer subtypes, a region at 6p22.2 (harboring a previous-reported depression locus ABT1) was identified
as contributing to both depression and ER + BC (Pρ–6
HESS = 7.30 × ­10 ), which was further replicated by
GWAS-PW (PPA_3 = 0.73). Additionally, GWAS-PW
identified three regions showing high probability ofshared associations between depression and ER + BC,
including two regions near 6p22.2 (6p22.1, PPA_3 = 0.71;
6p22.3–22.2, PPA_3 = 0.89), and one region at 9q31.2(PPA_3 = 0.52, harboring a previous-reported BC locus
KLF4) (Fig. 2). No significant shared region was found for
depression with ER − BC.

Cross‑trait meta‑analysis and pleiotropic loci

Motivated by the significant genetic overlap observed
for depression and BC, we continued to explore at theindividual variant level. In total, 17 independent locireached genome-wide significance in CPASSOC (PCPAS–8
and Pdepression < 1 × ­10–3 and PBC < 1 × ­10–3),
SOC < 5 × ­10
including 12 loci shared between depression and overall BC, and nine loci shared between depression and
ER + BC (Table 3). No significant shared locus was foundfor depression with ER– BC.
Of these 17 pleiotropic SNPs, interestingly, SNP
rs2403907 represented the strongest shared signal acrossboth overall (PCPASSOC = 1.55 × ­10–33) and ER + BC
(PCPASSOC = 1.91 × ­10–32), located at an intergenic
region at 21q21.1. SNPs rs67981811 (overall BC: PCPAS–24; ER + BC: PCPASSOC = 3.14 × ­10–23)
SOC = 6.25 × ­10
and rs17693963 (overall BC: PCPASSOC = 8.96 × ­10–21;
ER + BC: PCPASSOC = 8.85 × ­10–21) were the second andthe third strongest shared signals, both located at the
6p22.1 region that harbors a depression-associated risk
locus [9].
In addition to “known” pleiotropic SNPs (SNPs significantly associated with both traits in previous GWASs,
N = 6) or “single-trait-driven” pleiotropic SNPs (SNPssignificantly associated with one of the two traits in previous GWASs, N = 10), we identified one novel shared
SNP (re56101042) for depression and overall BC (PCPAS–9
SOC = 6.53 × ­10 ). This SNP is located at 14q32.32 nearsnoU13 and TRAF3. The former gene represents a small
nucleolar RNA recently implicated as a biomarker inTable 2 Global genetic correlation between depression and breast cancer
Trait 1

Trait 2rg

r g _ser g _P

gcov

gcov_seDepression

Overall breast cancer

0.08

0.02130.01020.0028

ER + breast cancer

0.06

0.02313.00 × ­10–4

0.00930.00340.08

0.0311

7.20 × ­10–3

0.01070.0039ER– breast cancer

rg Genetic correlation, se Standard error, gcov Genetic covariance6.30 × ­10–3

Wu et al. BMC Medicine(2023) 21:170

Page 7 of 14

Fig. 2 Local genetic correlation between depression and breast cancer. Red bars in Manhattan plots and red dots in QQ plots represent locishowing significant local genetic correlation with ρ-HESS after multiple testing adjustments (P < 0.05/1,703). Blue bars in Manhattan plots andblue dots in QQ plots represent additional loci showing a high probability of shared associations using GWAS-PW (PPA _3 > 0.5). a Manhattan plot
showing the local genetic correlation, genetic covariance, and SNP-heritability estimated from ρ-HESS between depression and overall breast
cancer. b QQ plot presenting region-specific P-values for ρ-HESS-estimated local genetic correlation between depression and overall breast cancer.
c Manhattan plot showing the local genetic correlation, genetic covariance, and SNP-heritability estimated from ρ-HESS between depression
and ER + breast cancer. d QQ plot presenting region-specific P-values for ρ-HESS-estimated local genetic correlation between depression and
ER + breast cancer. DEP: depression; BC: breast cancer; ER: estrogen receptor

vulnerability to brain injury [40], while the latter genefunctions in immune modulation and inflammation via
its regulation of nuclear factor-κB signaling [41], which
has been implicated in the pathogenesis of both psychiatric disorders and tumorigenesis [1]. Four genes (AMN,ANKRD9, MIR4309, and RCOR1) were additionally identified to interact with rs56101042 through three-dimensional chromatin looping [25]. Detailed annotations foreach SNP discovered by CPASSOC are shown in Additional file 3: Tables S4-S5.
Identification of causal variants and colocalization

For each of the 17 pleiotropic SNPs, we further determined a 99% credible set of causal SNPs, providing targets for downstream experimental analysis (Additional
file 3: Tables S6-S7). We found 413 candidate causal
SNPs across all loci shared by depression and overall BC,
and 282 candidate causal SNPs across all loci shared bydepression and ER + BC. In particular, SNPs rs5757946(22q13.1) and rs11049366 (12p11.22) were identified ashaving a posterior probability of 1.00 in the 99% credibleset.

We also performed colocalization analysis to determine whether pleiotropic SNPs driving the associations
in two traits were the same (Additional file 3: Table S8).
A substantial proportion of shared loci (five out of the 12loci shared by depression and overall BC; and five out of
the nine loci shared by depression and ER + BC) colocalized at the same candidate SNPs (eight of which showed a
PPH4 > 0.90), reinforcing shared causal associations.
Transcriptome‑wide association studies and shared genes

Results from tissue-specific TWAS revealed gene-level
genetic overlap (single-trait results shown in Additional
file 3: Table S9 and Additional file 1: Figure S2). After
multiple corrections, a total of five genes were sharedby depression and overall BC of which two were also
shared by depression and ER + BC, enriched in six tissues
including brain, blood, uterus, colon, artery and heart(Table 4). Four genes were located at pleiotropic lociidentified in cross-trait meta-analysis, including FLOT1
and HLA-S at 6p21.33, ENSG00000247934.4 at 12p11.22,
and GRAP2 at 22q13.1.
While data on ENSG00000247934.4 was limited, it represents an antisense to CCDC91 previously implicated inCHR:Position

chr6:25219582-26090025

chr6:26095643-27091661

chr6:27103654-27626631

chr6:27219491-28173219

chr6:27854963-28411941

chr6:31078804-31321919

chr14:103229696-103387971

chr14:103833385-104174123

chr21:16488615-16578800

chr22:28540492-29090363

chr22:40809034-40809034

rs68006638

rs35400317

rs35768595

rs17693963

rs67981811

rs9263717

rs56101042

rs4906340

rs2403907

rs5762586

rs5757946

chr6:26111271-27091661

chr6:27103654-27626631

chr6:27219491-28173219

chr6:27854963-28411941

chr9:110297639-110333432

chr12:28288716-2857324

chr13:53617781-54049489

chr21:16488615-16582933

rs45527431

rs35768595

rs17693963

rs67981811

rs7847927

rs10843109

rs1343607

rs2403907

3D interacting genes of index SNPs were mapped by using 3DSNP

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0.04
3.87×10-14
8.21×10-14
2.31×10-16
1.14×10-18
2.32×10-05
5.06×10-07-09

6.19×10-04
5.40×10-04
4.05×10-04

5.35×10-08
1.45×10-07
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9.58×10-04-04

1.38×10-33
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3.62×10-20
1.60×10-09-04

2.95×10-33

8.33×10

1.07×10

3.83×10-14

6.74×10

2.93×10-08-08

1.06×10

2.68×10-07

3.37×10

2.13×10-08

4.98×10

4.72×10-04-07

Depression

3.57×10-10

BCBC

Depression

Psingle-trait

BETA

SNP Single nucleotide polymorphism, CHR Chromosome, BC Breast cancer, ER Estrogen receptor

Linear closest genes of index SNPs were mapped by using VEP

C

G

C

G

G

C

C

G

G

G

T

C

G

G

G

G

C

C

C

G

T

A2

b

A

A

T

C

C

A

T

A

A

A

A

A

A

A

A

C

A

T

T

A

A

A1

a

Position is under build 37 (hg19)

chr6:25419094-26090025

rs13198474

Depression and ER+ breast cancer

chr5:45072777-45968819

rs78955704

Depression and overall breast cancer

SNP

1.91×10-32

3.31×10-09

8.32×10-09

3.12×10-19

3.14×10-23

8.85×10-21

1.38×10-17

3.92×10-18

2.30×10-11

1.28×10-11

1.43×10-08

1.55×10-33

4.22×10-12

6.53×10-09

1.92×10-08

6.52×10-24

8.96×10-21

8.42×10-19

1.41×10-19

1.44×10-12

1.29×10-09

PCPASSOC..

..

CCDC91

RP11-438P9.2

ZSCAN12

RP1-97D16.1

..

ABT1

SLC17A3

SGSM3, MKL1, RP5-1042K10.13

TTC28

..

RP11-73ƒM18.2, APOPT1, KLC1

TRAF3, snoU13

PSORS1C1, CR753819.4,
BX927139.2, CR938714.1

ZSCAN12

RP1-97D16.1

..

ABT1

..

HCN1

Linear closest genea

Table 3 Genome-wide significant loci shared between depression and breast cancer in cross-trait meta-analysis (PCPASSOC < 5 × ­10–8 and Psingle-trait < 1 × ­10–3)

..

OLFM4

..

..

..

PRSS16 and other 44..

BTN3A2 and other 11..

MKL1 and other 1..

..

KLC1 and other 2

TRAF3 and other 4

DDR1 and other 19..

PRSS16 and other 44

ZNF184 and other 30

BTN3A2 and other 11..

..

Interacting geneb

Wu et al. BMC Medicine(2023) 21:170
Page 8 of 14

Wu et al. BMC Medicine(2023) 21:170

Page 9 of 14

Table 4 Significant shared transcriptome-wide association analysis results between depression and breast cancer
TissueEnsembl gene ID

Gene symbol CHR BC

Depression

Best.GWAS.ID TWAS.Z PBonferroni

Best.GWAS.ID TWAS.Z PBonferroni

Depression and overall breast cancer
Whole Blood
UterusArtery Coronary
Brain Cerebellum

ENSG00000137312.14 FLOT1
ENSG00000225851.1
ENSG00000247934.4
ENSG00000168612.4

HLA-S.
ZSWIM1

Heart Left Ventricle ENSG00000100351.16 GRAP2
Depression and ER + breast cancer
Colon Transverse
Artery Coronary

ENSG00000137312.14 FLOT1
ENSG00000247934.4

.

6
6
12
20

rs3130975
rs3130975
rs7297051
rs3746506

-4.66
4.30
7.41
-4.60

22

rs17001868

-4.87

6

rs3130975

-4.49

12

rs7297051

5.50

2.56 × ­10–02 rs3130557

-6.15

–02

rs3130557

4.84

–10

rs11049301

-4.44

–02

rs90744.83–03

rs139915-4.50

4.68 × ­10–02 rs3130557-7.49

4.07 × ­10
4.87 × ­10
3.15 × ­10
6.69 × ­10–04

1.49 × ­10

rs11049301-4.44

6.10 × ­10–06

3.07 × ­10–03
3.48 × ­10–02
9.79 × ­10–03
3.93 × ­10–02
4.31 × ­10–10
3.48 × ­10–02

All these loci were TWAS significant (PBonferroni < 0.05) for both depression and breast cancer in at least one GTEx tissues
TWAS Transcriptome-wide association studies, CHR Chromosome, Best.GWAS.ID rsID of the most significant GWAS SNP in locus, TWAS.Z TWAS Z-score, BC Breast cancer,
ER Estrogen receptor

BC and brain morphology estimates (compared with datafrom GWAS Catalog [13]). Among other TWAS-significant shared genes, FLOT1 was previously implicated indepression and aerodigestive squamous cell carcinoma.
We also identified three novel genes, HLA-S, ZSWIM1,
and GRAP2. As a pseudogene located at the major histocompatibility complex (MHC) region, HLA-S wasreported to be associated with a broad range of conditions including schizophrenia, gastric, and hepatocellular cancers. By regulating T helper cells development,
ZSWIM1 plays a critical role in immune system [42], andsignals a poor prognosis of ovarian cancer [43]. Similarly, GRAP2 was implicated in T cell-mediated immune
responses [44], as well as in medullary thyroid cancer andlung adenocarcinoma prognosis [45, 46].
Mendelian randomization

Finally, we conducted a two-sample MR analysis combining 102 GWAS-identified depression-associated
SNPs (F-statistic ranging from 91.5 to 329.6 (Additional
file 3: Table S1)) as an IV to examine a potential causal
effect of depression on BC. Using IVW, genetic liability to depression was significantly associated with anincreased risk of overall BC (odds ratio (OR) = 1.12,
95%CIs = 1.04–1.19, P = 1.40 × ­10–3). The estimatesremained directionally consistent in MR-Egger regression and the weighted median approach, despite larger
statistical uncertainties (Fig. 3). No indication of horizontal pleiotropy was observed (PMR-Egger intercept = 0.86).
Sensitivity analyses excluding palindromic SNPs or pleiotropic SNPs supported the robustness of these results.
Leave-one-out analysis demonstrated the absence of
outlying variants (Additional file 1: Figure S3) which wasvalidated by MR-PRESSO (OR = 1.13, 95%CIs = 1.06–
1.21, P = 5.00 × ­10–4). MVMR accounting for potential

confounders generated similar results with even more
pronounced magnitude and significance, suggesting a
depression-BC causal relationship independent of common confounders (Additional file 1: Figure S4).
In subgroup analysis defined by ER status, we found a
non-significant association of depression with ER + BC
(IVW OR = 1.08, 95%CIs = 0.99–1.18, P = 6.98 × ­10–2;
PMR-Egger intercept = 0.86), and a suggestive significant association of depression with ER– BC (IVW OR = 1.12,
95%CIs = 1.01–1.24, P = 3.77 × ­10–2; PMR-Egger intercept = 0.82), though neither withstood multiple testingcorrections (Fig. 3). In MVMR, while a putative causal
effect of depression on ER + BC was observed, the causal
effect of depression with ER– BC attenuated to null after
adjustment for each confounder, and none survived multiple corrections (Additional file 1: Figure S4).
No evidence of reverse causality was found suchthat genetically predicted BC did not seem to influence depression risk (IVW OR = 1.01, P = 0.49; MREgger OR = 0.98, P = 0.20; weighted median OR = 1.01,
P = 0.34). Similar null effects were identified for ER + and
ER– subtypes (Fig. 3).

Discussion
To our knowledge, this is the largest observational andgenetic analysis to systematically investigate the phenotypic association as well as the shared genetic architecture underlying depression and BC. Our observational
study demonstrated a positive association of depression
with BC risk, which was corroborated by findings fromgenetic analyses. We found evidence supporting a significant shared genetic basis, both globally and regionally, indicating shared biology between the two traits.
This genetic overlap was further decomposed into bothpleiotropy and causality, reflected by the pleiotropic

Wu et al. BMC Medicine(2023) 21:170

Page 10 of 14

Fig. 3 Bi-directional causal relationship underlying depression and breast cancer. a Estimates of causal effects for genetically predicted depression
with overall breast cancer, ER + breast cancer, and ER– breast cancer, respectively. b Estimates of causal effects for genetically predicted overall
breast cancer, genetically predicted ER + breast cancer, and genetically predicted ER– breast cancer with depression. Boxes represent the point
estimates of causal effects, and error bars represent 95% confidence intervals. Inverse-variance weighted approach was adopted as the primary
analysis. MR-Egger regression, weighted median, and MR-PRESSO approaches were adopted as sensitivity analysis. BC: breast cancer; ER: estrogen
receptorloci identified in CPASSOC, the shared genes revealed
by TWAS, and the putative causal relationship demonstrated by MR.
Overall, the genetic correlation between depression
and BC was limited ( rg = 0.08) but statistically significant. In addition, significant local genetic correlations
were found for several specific genomic regions including a locus at 9q31.2, and 4 loci at, or close to 6p22.1.
Chr6q22.1 represents an important locus at MHC, aregion well-known for containing multiple genes essential for the adaptive immune system as well as the antitumoral immune response [47]. The MHC region has
been associated with depression in some prior studies[9–11, 48], with the strongest association located in theclassical or extended class I region; moreover, a study
has also identified a 66% down-regulation of MHC
class I expression in BC tissues [49], collectively highlighting its role in the etiological connection underlying these two traits. Leveraging genotype imputation, a
recent (also the largest) study on depression and classical MHC variants indicated an overall non-significant
association [50], calling for additional efforts to dissect
signals within the extended MHC.

Significant genetic overlap can be the result of biological or horizontal pleiotropy (in which a genetic variant
affects multiple traits) and/or mediated or vertical pleiotropy (in which a genetic variant affects a trait via its effecton an intermediate trait) [51]. In our downstream analysis performed to explore these alternatives, 17 pleiotropic
loci between depression and BC were identified, harboring genes which were previously implicated in neural development and brain functions (i.e., HCN1, ABT1,ZSCAN12, ZNF184, KLC1, MKL1, SGSM3, CCDC91), or
biological processes related to tumor growth (i.e., ABT1,ZSCAN12, ZNF184, KLC1, TTC28, MKL1, CCDC91,
OLFM4, DDR1). Notably, several shared loci were locatedat genes that play essential roles in immune, inflammatory, and stress responses (i.e., PSORS1C1, TRAF3,
APOPT1, PRSS16, BTN3A2). Multiple genes showedstrong evidence of colocalization, especially among thoselocated near the MHC (i.e., ABT1, BTN3A2, ZNF184,
PRSS16, ZSCAN12, SLC17A3).
One advantage of meta-analyzing GWASs of different traits is that combining association evidence acrossmultiple studies can reveal signals which might nothave reached genome-wide significance in a single-trait

Wu et al. BMC Medicine(2023) 21:170

analysis. Indeed, we found a novel locus (lead SNP
rs56101042) shared between depression and BC, only
reported previously as associated with allergic disease[52]. While its direct involvement in the developmentof depression or BC is not known, this SNP interacts
with several genes that have already been reported (i.e.,
TRAF3 and RCOR1 [53, 54]), or that could play a role in
the pathogenesis of the two traits (i.e., AMN, ANKRD9,and MIR4309). Encoding a transmembrane protein
named amnionless, AMN is involved in the plasma membrane transport of Cubilin, and may influence cancer
progression through the Cubilin-FGF8 (fibroblast growth
factor 8) interaction [55]. AMN may also function in psychiatric illness given its role in the vitamin B­ 12 transportation into nervous system [56]. ANKRD9 encodes anankyrin repeat domain protein which was implicated in
drug-induced mental disorder [57], and recognized as atumor suppressor in gastric cancer among Asians [58].
MIR4309 has been found to be significantly elevatedamong patients with disordered sleep (a core symptomof depression) [59], as well as up-regulated in tissues ofgastric cancer [60]. Further experimental studies would
be needed to provide more detailed functional annotation of rs56101042, particularly in relation to the onset of
depression and BC.
By integrating data from GWAS and GTEx tissue expression, TWAS suggested shared mechanistic
hypotheses between depression and BC on a gene-tissue
pair level. The four loci identified in both CPASSOC andTWAS analysis implicate common biological mechanisms in depression and BC regulation, involving cellproliferation [61], brain structure [62, 63], and immune
response [44]. In addition to tissues of brain, uterus, andblood (well-recognized as relevant to depression and/or BC [64–66]), our TWAS reported shared regulatory
features in the digestive (i.e., colon transverse) and cardiovascular systems (i.e., artery coronary and hear left
ventricle), suggesting the possibility of shared pathways
extending to a wider range of organs. A growing bodyof literature has shown that functional disorders of thedigestive system may play a critical role in a variety ofpsychiatric disorders including depression. One of the
speculated mechanisms is the communication of gut
microbiome with the brain through the brain-gut axis[67]. Interestingly, gut microbiome may also affect breast
carcinogenesis by promoting anti-tumor immunity and/or modulating systemic estrogen levels [68]. As anothermajor cause of mortality and morbidity, cardiovasculardisease shares a number of common risk factors withboth conditions, and represents one of the most prevalent comorbidities for depression and BC in clinical settings [69, 70]. More studies are needed to clarify the roleof these hypothesized mechanisms.

Page 11 of 14

To date, epidemiological evidence regarding thedepression-BC relationship has been inconclusive, but
our observational and MR analyses showed consistent evidence for a putative causal role of depressionin BC among European populations. Our MR results
extended existing MR analyses [71, 72] with substantially improved accuracy and precision of causal estimates by applying: (i) the most updated GWAS of
overall BC; (ii) a broader range of complementary
analysis to validate the MR assumptions; and (iii) an
extensive MVMR design to evaluate the independent effect of depression. The estimated causal effects
remained directionally consistent across a variety ofsensitivity analyses, as well as after adjustment of multiple confounders, supporting the robustness of ourMR findings. When extended to BC subtypes, we foundconsistently positive associations with greater statistical uncertainties. Future studies with larger sample
sizes are warranted to establish or rule out a potential
causal link in a subtype-specific manner.
Several limitations need to be acknowledged. Using
data of European ancestry populations restricted the generalizability of our results, and future studies including
more diverse ancestries are needed. The broad depression phenotype used in the current study is regarded asa relatively non-specific index of vulnerability to psychological distress, rather than highly specific for MD [73].
However, we performed sensitivity analysis restricting toclinically ascertained MD cases and identified consistent
results (fully adjusted observational analysis using only
ICD-10 codes F32 and F33 for MD diagnosis: HR = 1.091,
95%CIs = 0.946–1.258; genetic correlation and MR analyses applying an older MD GWAS [10] involving only
clinically defined MD cases: rg = 0.07, P = 3.00 × ­10–4;­ORMR = 1.08, P = 4.30 × ­10–2). Additionally, due to limited data availability at the time of conducting the analysis, we were unable to utilize sex-specific GWAS data ofdepression to match with the female-specific outcome
BC. However, sex-heterogeneity did not seem to play asignificant role when using female-specific depression
GWAS summary statistics which now become available[74]. Although underpowered, female-specific genetic
correlation analysis and MR analysis yielded statistically significant and directionally consistent results (rg= 0.09, P = 1.60 × ­10−3; ­ORIVW = 1.11, P = 0.04). Using
sufficiently powered female-specific data would be valuable for future studies. Although depression is highly heterogeneous, we did not investigate depression subtypes(i.e., typical vs. atypical) or additional BC subtypes due
to limited well-powered GWAS available for these phenotypes. Finally, validation of the study findings in additional follow-up cohorts and GWAS is needed. Future
in-depth experimental works are also warranted to betterWu et al. BMC Medicine(2023) 21:170

Page 12 of 14

understand pathophysiological mechanisms that may
underlie genetic overlap between depression and BC.

J.W.S., P.K., J.L., and B.Z. contributed to the interpretations of the findings andthe critical revision of the article. X.J. supervised the study. All authors read andapproved the final manuscript.

Conclusions
In sum, the current study advances our understandingof previously reported associations between depression
and BC by providing evidence of genetic correlation,pleiotropic loci, and a possible causal effect of depression
on BC. Our findings further suggest potential biologicalmechanisms linking these disorders and may have implications for future studies aimed reducing BC risk.

Funding
Open access funding provided by Karolinska Institute. This study was
supported by funds from the National Key R&D Program of China(2022YFC3600600, 2022YFC3600604), the National Natural Science Foundation of China (82204170, U22A20359, 81874283, 81673255), the Recruitment
Program for Young Professionals of China, the Promotion Plan for Basic Medical Sciences and the Development Plan for Cutting-Edge Disciplines, Sichuan
University, and other Projects from West China School of Public Health andWest China Fourth Hospital, Sichuan University. The funders of the study had
no role in study design, data collection, data analysis, data interpretation, or
writing of the report.

Abbreviations
BCBreast cancer
BMIBody mass index
CIsConfidence intervals
CPASSOC
Cross-Phenotype Association
EREstrogen receptor
GTEx
Genotype-Tissue Expression
GWAS
Genome-wide association study
GWAS-PW
Pairwise-GWAS
HESS
Heritability Estimator from Summary Statistics
HRHazard ratioIVInstrumental variable
IVW
Inverse-variance weighted
LDLinkage disequilibrium
LDSC
Linkage disequilibrium Score RegressionMDMajor depression
MHC
Major histocompatibility complexMRMendelian randomization
MR-PRESSO MR-Pleiotropy Residual Sum and Outlier
MVMR
Multi-variable Mendelian randomization
OROdds ratio
PPAPosterior probabilities of associationrgGenetic correlation
RR
Relative risk
SNP
Single nucleotide polymorphism
TWAS
Transcriptome-wide association study
UKBB
UK Biobank
VEPEnsembl Variant Effect Predictor

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​023-​02876-w.
Additional file 1: Figure S1. Graphical abstract. Figure S2. Number of TWAS
significant genes for depression, overall BC, and subtype-specific BC.
Figure S3. Box plot of betas in leave-one-out analysis. Figure S4. Results ofmulti-variable Mendelian randomization analysis.
Additional file 2. STROBE and STROBE-MR checklists.
Additional file 3: Table S1. Depression IVs. Table S2. Summary of GWAS
data. Table S3. Baseline characteristics of UK Biobank participants. Tables
S4-S5. Annotation of CPASSOC-identified SNPs. Tables S6-S7. 99% credible
sets of CPASSOC-identified SNPs. Table S8. Results from colocalization
analysis for CPASSOC-identified SNPs. Table S9. TWAS significant genetissue pairs for depression, overall BC, and subtype-specific BC.
Acknowledgements
Not applicable.
Authors’ contributions
X.J. and X.W. conceived and designed the study. X.W., W.Z., X.Z., L.Z., M.X., Y.H.,
and J.X contributed to analysis of the data. X.W. and X.J. drafted the article.

Availability of data and materials
Data from UK Biobank is available for open access to scientific researcher(www.​bioba​nk.​ac.​uk). Depression GWAS summary statistics (without
23andMe) is publicly available from https://​datas​hare.​ed.​ac.​uk/​handle/​10283/​
3203. GWAS summary statistics for overall and subtype-specific BCs are publicly available from https://​bcac.​ccge.​medsc​hl.​cam.​ac.​uk/​bcacd​ata/​oncoa​rray/​oncoa​rray-​and-​combi​ned-​summa​r y-​result/.

Declarations
Ethics approval and consent to participate
UK Biobank had obtained ethics approval from the North West Multi-Centre
Research Ethics Committee which covers the UK (approval number: 11/
NW/0382) and had obtained informed consent from all participants. The
current study was approved by the UK Biobank access management board.
Participants who had withdrawn consent by the time of the analysis were
excluded from the analysis dataset. The GWAS summary statistics used in thepresent study are aggregated level of data which do not contain any personal
identifiers. The original GWAS have obtained ethical approval from relevant
ethics review committees.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 16 Ren Min
Nan Lu, Chengdu, Sichuan 610041, People’s Republic of China. 2 Departmentof Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
3
Department of Epidemiology, Harvard T.H. Chan School of Public Health,Boston, MA, USA. 4 Psychiatric and Neurodevelopmental Genetics Unit, Center
for Genomic Medicine, and Department of Psychiatry, Massachusetts General
Hospital, MA, Boston, USA. 5 Stanley Center for Psychiatric Research, Broad
Institute of MIT and Harvard, MA, Cambridge, USA. 6 Department of Clinical
Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Stockholm,
Solna, Sweden. 7 Department of Nutrition and Food Hygiene, West China
School of Public Health and West China Fourth Hospital, Sichuan University,
Chengdu, China.
Received: 16 November 2022 Accepted: 20 April 2023

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