Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions
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Abstract
Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches. The authors conducted a genetic meta-analysis of depression and found 269 associated genes. These genes highlight several potential drug repositioning opportunities, and relationships with depression were found for neuroticism and smoking.
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- Findingown data
The neuroticism-to-depression effect remained consistent in leave-one-variant-out IVW (P = 1.58 × 10−21).
“This putative causal effect remained consistent (P = 1.58 × 10-21) in the ‘leave one variant out’, IVW analysis indicating that the observed effect was not driven by a single variant.”
Quote in the paperNot a substantial claim - Findingown data
A gene-set for somatosensory pyramidal neurons was significant (P = 3.87 × 10−5) using Skene et al. brain cell-type data.
“We also identified a significant gene-set (P = 3.87 × 10-5) containing somatosensory pyramidal neurons using brain cell-type data from Skene, et al. 15 (Table 3).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018). source
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- Findingown data
Overlap with neuroticism studies highlighted putative depression associations for DRD2, CELF4, and ELAVL2.
“Examining significant genes that overlap between the current meta-analysis of depression and the neuroticism studies of both Luciano, et al. 18 and Okbay, et al. 19 revealed putative associations with DRD2, CUGBP Elav-like family member 4 (CELF4) and ELAV like RNA binding protein 2 (ELAVL2).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [18] Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018). source
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- cites [19] Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016). source
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- Findingown data
Cell-type heritability partitioning showed enrichment for central nervous system and skeletal muscle tissues.
“Partitioning the heritability estimate by cell type enrichment (Figure 2A, and Supplementary Table 6) revealed that central nervous system (CNS) and skeletal muscle tissues were enriched (Pcorrected < 0.05) for genetic variants contributing to the heritability of depression.”
Quote in the paperNot a substantial claim - Findingown data
The remaining six gene-sets were biological processes: GO_BEHAVIOR, CO_COGNITION, GO_MODULATION_OF_SYNAPTIC_TRANSMISSION, GO_REGULATION_OF_SYNAPSE_STRUCTURE_OR_ACTIVITY, GO_MODULATION_OF_SYNAPTIC_PLASTICITY, and GO_SINGLE_ORGANISM_BEHAVIOR.
“The other six associated gene-sets were biological processes: GO_BEHAVIOR, CO_COGNITION, GO_MODULATION_OF_SYNAPTIC_TRANSMISSION, GO_REGULATION_OF_SYNAPSE_STRUCTURE_OR_ACTIVITY, GO_MODULATION_OF_SYNAPTIC_PLASTICITY, and GO_SINGLE_ORGANISM_BEHAVIOR.”
Quote in the paperNot a substantial claim
- Findingown data
The meta-analysis identified 102 independently segregating variants associated with depression.
“We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission.”
- Also stated in: § 102 independent genetic variants associated with depression · plain
“of which 102 variants in 101 loci were independently segregating (Supplementary Table 1)”
- Also stated in: § Discussion · plain
“The meta-analysis examined 8,098,588 variants and identified 102 independently segregating variants associated (P < 5 × 10-8) with depression.”
Quote in the paperSubstantial claim - Also stated in: § 102 independent genetic variants associated with depression · plain
- Findingown data
All 102 variants showed the same direction of effect across the three contributing studies and in the independent replication sample.
“All of the 102 associated variants had the same direction of effect on depression across the three contributing studies and also within an independent replication sample of 1,306,354 individuals”
Quote in the paperNot a substantial claim - Findingown data
MR analysis indicated a putative bidirectional causal relationship between depression and neuroticism.
“The MR analysis also identified a putative bi-directional casual effect between depression and neuroticism.”
Quote in the paperNot a substantial claim - Findingown data
Sensitivity analyses for depression on neuroticism were directionally consistent with IVW (weighted median beta = 0.119; MR Egger beta = 0.050).
“The additional sensitivity tests both had effects in the same direction as the IVW test (weighted median beta = 0.119, s.e. = 0.047; MR Egger beta = 0.050, s.e. = 0.235).”
Quote in the paperNot a substantial claim - Findingown data
The strongest gene association was SORCS3 on chromosome 10 (P = 2.27 × 10−19).
“The most significant gene (P = 2.27 × 10-19) was Sortilin related VPS10 domain containing receptor 3 (SORCS3) on chromosome 10”
Quote in the paperNot a substantial claim - Findingown data
The current meta-analysis PRS accounted for more phenotypic variance than the Wray PRS in every target cohort.
“and the current meta-analysis PRS explained a greater proportion of the phenotypic variance in each target cohort compared to Wray PRS”
Quote in the paperSubstantial claim - Findingown data
The study identified 15 gene-sets associated with depression.
“We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission.”
Quote in the paperNot a substantial claim - Findingown data
The drug-gene analysis found 560 interactions involving 57 genes and 514 drugs.
“A total of 560 interactions were identified between 57 genes and 514 drugs (see Supplementary Table 11).”
Quote in the paperNot a substantial claim - Findingown data
Neuroticism was the only trait showing a putative causal effect on depression in IVW MR (beta = 0.366, s.e. = 0.037; PFDR = 2.63 × 10−21).
“Neuroticism was the only trait with a putative causal effect on depression using the IVW regression analysis MR test (beta = 0.366, s.e = 0.037; PFDR = 2.63 × 10-21; Supplementary Figure 6).”
Quote in the paperSubstantial claim - Findingown data
A novel genetic correlation was found between depression and age at menopause (rG = −0.11).
“However, a novel genetic correlation was observed for age at menopause (rG = -0.11, s.e. 0.03), potentially indicating a shared genetic architecture between depression and earlier female reproductive life events.”
Quote in the paperNot a substantial claim - Findingown data
The MR Egger intercept test found no evidence of directional horizontal pleiotropy across the 33 tested causal effects.
“Using the MR Egger intercept test, there was no evidence (P ≥ 0.05) of directional horizontal pleiotropy for any of the 33 causal effects examined.”
Quote in the paperSubstantial claim - Findingown data
In an independent replication sample of 1,306,354 people, 87 of the 102 variants remained significant after multiple-testing correction.
“In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction.”
- Also stated in: § Discussion · plain
“and 87 variants were significant in that sample after multiple testing correction”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data
The gene-based analysis identified 269 genes putatively associated with depression.
“This technique identified a total of 269 putative genes that were associated (P < 2.80 × 10-6) with depression, Supplementary Table 9.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). source
Not checked in the free run.
- Findingown data
MAGMA identified 269 genes putatively associated with depression at P < 2.80 × 10−6.
“This technique identified a total of 269 putative genes that were associated (P < 2.80 × 10-6) with depression, Supplementary Table 9.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). source
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- Findingown data
Heritability partitioning found significant enrichment in conserved, intronic, and H3K4me1 genomic regions.
“This partitioning showed significant enrichment within conserved, intronic and H3K4me1 regions of the genome for the heritable component of depression (Pcorrected < 0.05)”
Quote in the paperNot a substantial claim - Findingown data
Gene-set analysis found 14 significant Gene Ontology gene-sets for depression.
“We identified 14 significant putative gene-sets (Pcorrected < 0.05) for depression (Table 3) using data from the Gene Ontology Consortium.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). source
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- Findingown data
After FDR correction, 41 of 234 behavioural and disease traits were genetically correlated with depression.
“Of these behavioural and disease traits, 41 were significantly genetically correlated (PFDR < 0.01) with our meta-analysis results after applying false discovery rate correction”
Quote in the paperNot a substantial claim - Findingown data
LDSC estimated the SNP-based heritability of depression at 0.089 (s.e. 0.003) on the liability scale.
“The estimate of the SNP-based heritability of depression within our meta-analysis was 0.089 (0.003) on the liability scale using LDSC regression 10.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10] Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). source
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- Findingown data
A 1.2 Mb region at 18q21.2 held three independent variants across two loci near TCF4 and RAB27B, both putatively associated with depression.
“A further short (1.2 Mb) region along 18q.21.2 contained three independently-associated variants across two loci (rs62091461, rs12966052 and rs12967143; Supplementary Figure 12 A-C). These variants were close to the Transcription Factor 4 (TCF4) and the RAB27B genes which were both putatively associated with depression (P = 4.55 × 10-16 and P = 1.39 × 10-9, respectively).”
Quote in the paperNot a substantial claim - Findingown data
After removing regions used in both directions, only the neuroticism-to-depression effect remained.
“Removing regions which were used to test the effect in both directions indicated that a putative effect remained only for neuroticism on depression.”
Quote in the paperSubstantial claim - Findingown data
The largest drug-gene interaction count was 47, between psycholeptics (N05) and DRD2.
“The greatest number of drug - gene interactions (ninteractions = 47) were observed between psycholeptics (N05, which includes antipsychotics and anxiolytics) and dopamine receptor D2 (DRD2).”
Quote in the paperNot a substantial claim - Findingcitation
Several genetic correlations matched earlier reports by Wray and Howard, including age at menarche (rG = −0.12).
“Many of these genetic correlations are similar to those reported by Wray, et al. 9 and Howard, et al. 5, including earlier age at menarche (rG = -0.12, s.e = 0.02).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [9] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
Not checked in the free run.
- cites [5] Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018). source
Not checked in the free run.
- Findingown data
IVW MR indicated a putative causal effect of depression on neuroticism (beta = 0.146, s.e. = 0.039; PFDR = 2.29 × 10−3).
“A putative causal effect of depression on neuroticism was detected using the IVW regression analysis MR test at the 1% significance threshold after false discovery rate (FDR) correction (beta = 0.146, s.e. = 0.039; PFDR = 2.29 × 10-3; Supplementary Figure 4).”
Quote in the paperSubstantial claim - Findingown data
The direction of effect for depression variants in earlier studies agreed with the current meta-analysis.
“In summary, the direction of effect of depression variants in the previous studies was consistent with the current meta-analysis.”
Quote in the paperNot a substantial claim - Findingown data
Eight significant gene-sets were nervous-system cellular components: GO_POSTSYNAPSE, GO_SYNAPSE, GO_NEURON_SPINE, GO_EXCITATORY_SYNAPSE, GO_SYNAPSE_PART, GO_NEURON_PROJECTION, GO_NEURON_PART, and GO_DENDRITE.
“Eight of these gene-sets were cellular components (areas where the genes were active) and were located in the nervous system: GO_POSTSYNAPSE, GO_SYNAPSE, GO_NEURON_SPINE, GO_EXCITATORY_SYNAPSE, GO_SYNAPSE_PART, GO_NEURON_PROJECTION, GO_NEURON_PART, and GO_DENDRITE.”
Quote in the paperSubstantial claim - Findingown data
VRK2 was identified as putatively associated with depression.
“The current gene-based analysis identified the vaccinia-related kinase 2 (VRK2) gene as putatively associated with depression.”
Quote in the paperNot a substantial claim - Findingown data
The meta-analysis found 102 independent genetic variants linked to depression across 101 loci.
“The meta-analysis identified 102 independently-segregating genetic variants associated with depression in 101 loci”
Quote in the paperSubstantial claim - Findingown data
A significant genetic correlation was found between ADHD and depression.
“The current study demonstrated that there was a significant genetic correlation between ADHD and depression.”
Quote in the paperNot a substantial claim - Findingown data
MEF2C appeared in 10 of the 15 putative depression gene-sets.
“MEF2C was also included in ten of the 15 putative gene-sets identified for depression.”
Quote in the paperNot a substantial claim - Findingown data
The DRD2 gene has a large number of interactions with the N05 drug classification, which mainly comprises typical and atypical antipsychotics.
“Most notable were the large number of interactions between the DRD2 gene and the N05 drug classification, which primarily comprises typical and atypical antipsychotics.”
Quote in the paperNot a substantial claim - Findingown data
The paper reports a meta-analysis of the three largest available depression cohorts, totaling 807,553 individuals (246,363 cases and 561,190 controls).
“This study describes a meta-analysis of the three largest depression cohorts (total n = 807,553, with 246,363 cases and 561,190 controls) currently available.”
Quote in the paperSubstantial claim - Findingown data
Among the depression-associated genes found, serotonergic genes such as SLC6A4, GNB3, HTR2A, and TPH2 were conspicuously absent.
“An intriguing omission among the depression-associated genes identified in our study are genes linked with the serotonergic system, such as the serotonin transporter SLC6A4; the G protein subunit GNB3; the serotonin receptor HTR2A and tryptophan hydroxylase (TPH2).”
Quote in the paperSubstantial claim - Interpretationreasoning
Large population-based samples can help clarify the genetic architecture of depression.
“Therefore, larger population-based samples, where the timescales and costs of obtaining a clinically diagnosis would be prohibitive, can contribute to our understanding of the genetic architecture of the disease.”
Quote in the paperSubstantial claim - Interpretationcitation
The study reaffirmed the genetic correlation between schizophrenia and depression reported by Wray and colleagues.
“The current study further reaffirms the genetic correlation between schizophrenia and depression observed by Wray, et al. 9.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
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- Interpretationown data
The genetic correlations suggest the three studies captured a similar underlying genetic architecture despite using different diagnostic instruments.
“PGC_139k used a predominately clinically ascertained diagnosis of major depression and the observed genetic correlations indicate that each study was likely to be capturing a similar underlying genetic architecture, despite the use of different diagnostic instruments.”
Quote in the paperSubstantial claim - Premisecitation
Wray and colleagues' PGC meta-analysis of 35 cohorts (n = 461,134) found 44 loci associated with a spectrum of depression phenotypes.
“Finally, a meta-analysis of 35 cohorts (total n = 461,134; 130,664 cases and 330,470 controls) conducted by Wray, et al. 9 (PGC) found 44 loci that were significantly associated with a spectrum of depression phenotypes, some obtained from structured clinical interview and others based on broader criteria.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
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- Interpretationown data
The consistent direction of all 102 variants across contributing studies and replication suggests they are robust depression associations.
“All of the 102 variants associated with depression had an equivalent direction of allelic effect across the three studies5, 8, 9 that contributed to the meta-analysis and the replication sample (Supplementary Table 1), suggesting that these variants represent robust associations with depression.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5] Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018). source
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- cites [8] Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016). source
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- cites [9] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
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- Premisecitation
Hyde and colleagues identified 15 loci for depression using a self-reported clinical diagnosis in 23andMe participants (n = 459,481).
“A later study conducted by Hyde, et al. 8, examining research participants from the personal genetics company 23andMe, Inc., used a self-reported clinical diagnosis of depression as the phenotype and identified 15 associated loci (total n = 459,481; 121,380 cases and 338,101 controls).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [8] Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016). source
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- Interpretationown data
Enrichment analysis supported the importance of prefrontal brain regions in depression.
“An enrichment analysis provided further evidence of the importance of prefrontal brain regions.”
Quote in the paperSubstantial claim - Premisecitation
Howard and colleagues reported a strong genetic correlation (rG = 0.86, s.e. = 0.05) between broader self-declared depression and hospital-diagnosed MDD.
“The results of Howard, et al. 5 showed that there is a strong genetic correlation (rG = 0.86, s.e. = 0.05) between broader self-declared definitions of depression and clinically diagnosed major depressive disorder (MDD) within a hospital setting.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018). source
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- Interpretationreasoning
To detect causal genetic variants for depression, very large samples are needed.
“Therefore, to enable the detection of causal genetic variants associated with depression there is a need to study very large numbers of individuals.”
Quote in the paperSubstantial claim - Interpretationown data
The observed inflation likely reflected polygenic signal rather than population stratification.
“indicating that the inflation was due to polygenic signal and unlikely to be confounded by population structure.”
Quote in the paperSubstantial claim - Interpretationown data
The study showed that examining interactions between depression-associated genes and pharmacological treatments is useful.
“We detected 269 genes putatively associated with depression and demonstrated the utility of investigating their interaction with pharmacological treatments.”
Quote in the paperSubstantial claim - Interpretationown data
Studies with more than 100,000 participants show good directional agreement for depression variant effects.
“The examination of previous findings between studies and the current meta-analysis suggests that there is good degree of directionality agreement of causal effects of depression when using studies with over 100,000 participants.”
Quote in the paperNot a substantial claim - Premisecitation
The first study reporting replicable depression variants found two loci for severe, recurrent MDD enriched for melancholia in Han Chinese women (n = 10,640).
“The first study to report replicable genetic variants for depression found two significant loci associated with severe, recurrent MDD (85% enriched for melancholia) in a sample of Han Chinese women (total n = 10,640; 5,303 cases and 5,337 controls) 7.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] CONVERGE consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015). source
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- Interpretationown data
A novel genetic correlation between age at menopause and depression may involve a mechanism similar to that for age at menarche.
“The current analysis also identified a novel genetic correlation between age of menopause and depression which may share a similar biological mechanism to that of age of menarche.”
Quote in the paperNot a substantial claim - Interpretationown data
The authors point to additional depression-associated genes that might be druggable and are not yet linked to antidepressant treatment or mood-related side effects, such as CACNA1E and KMT2A.
“Further, our work highlights other potential druggable genes associated with depression which are not, to our knowledge, currently associated with antidepressant treatment or mood-associated adverse effects. These include the R-type calcium channel gene, Calcium Voltage-Gated Channel Subunit Alpha1 E (CACNA1E), and the nucleosome associated gene, Lysine Methyltransferase 2A (KMT2A).”
Quote in the paperNot a substantial claim - Interpretationown data
The authors found genes linked to medications that are not intended for depression but might yield unexpected benefits or side effects in people with the disorder.
“We also identified genes with associated medications which, although not aimed at treating depression, may provide unpredicted drug benefits or adverse effects for those with the disorder.”
Quote in the paperNot a substantial claim - Study design and conductown data
A main strength of the study is the greater power gained by analyzing three large, independent cohorts.
“The principal strength of this study is the increased power obtained from the analysis of three large independent cohorts.”
Quote in the paperSubstantial claim - Interpretationreasoning
This result may point to a functional split between the genetic pathways underlying depressive illness and those underlying antidepressant response.
“Our finding could indicate a functional separation between genetic pathways of depressive disease and pathways of antidepressant treatment.”
Quote in the paperSubstantial claim- Rests on an assumption: Most antidepressant treatments work by interacting with the serotonergic system.
- Study design and conductown data
Genome-wide significant variants from the meta-analysis were tested in an independent 23andMe sample of 1,306,354 unrelated people (414,055 cases, 892,299 controls).
“The variants that were significant (P < 5 × 10-8) in the meta-analysis, described above, were replicated in an independent sample of 1,306,354 unrelated individuals (414,055 cases and 892,299 controls) from 23andMe, Inc.”
Quote in the paperSubstantial claim - Study design and conductcitation
Conditional analysis, with UK Biobank linkage disequilibrium as reference, identified independent variants within each genome-wide significant merged locus.
“A conditional analysis 59 was used to identify independently-segregating variants in each of the merged loci that was genome-wide significant, using the linkage disequilibrium structure of UK Biobank as a reference panel.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [59] Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012). S1–S3. source
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- Interpretationreasoning
The identified genes add to knowledge of biological mechanisms and possible drug targets.
“the genes identified contribute to our understanding of biological mechanisms and potential drug targeting opportunities.”
Quote in the paperNot a substantial claim - Interpretationown data
The work identifies several genes that could be targets for drug repositioning.
“Our study highlights a number of potential gene targets for drug repositioning”
Quote in the paperSubstantial claim - Interpretationown data
Serotonergic genes might not be directly involved in the causation of depression, at least the component captured by common genetic variants in GWAS.
“Thus, serotonin-associated genes, while potentially relevant for predicting efficacy and adverse effects of serotonergic antidepressants, may not be directly associated with the aetiology of depression itself (or at least that which is determined by common genetic variation identified in GWAS).”
Quote in the paperSubstantial claim- Rests on an assumption: Most antidepressant treatments work by interacting with the serotonergic system.
- Interpretationown data
Some existing medications could act on the genetic contributors to depression.
“Examining the number of interactions between the genes associated with depression and the second level ATC drug classifications demonstrates that there are currently available pharmaceutical treatments that may target the genetic component of depression.”
Quote in the paperSubstantial claim
- May combine two claims. 38
- Classified as probably not a substantial claim. 18
May combine two claims.
Findingown dataThe three contributing cohorts showed strong pairwise genetic correlations: PGC_139k-23andMe_307k rG = 0.85, UK Biobank-PGC_139k rG = 0.87, and UK Biobank-23andMe_307k rG = 0.85.
“There was a strong genetic correlation between each of these cohorts. The rG between PGC_139k and 23andMe_307k was 0.85 (s.e = 0.03). The UK Biobank had a rG of 0.87 (s.e = 0.04) with PGC_139k. A similar rG was also found between UK Biobank and 23andMe_307k (0.85, s.e = 0.03).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10] Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). source
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May combine two claims.
FindingcitationDRD2 appeared in all 14 GO gene-sets and in prior depression studies.
“DRD2 was included in all 14 of the putative Gene Ontology Consortium gene-sets identified (but not the somatosensory pyramidal neurons gene-set) and has been identified in previous studies of depression5, 9, 22.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [5] Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018). source
Not checked in the free run.
- cites [9] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
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- cites [22] Whitmer, A. J. & Gotlib, I. H. Depressive rumination and the C957T polymorphism of the DRD2 gene. Cogn. Affect. Behav. Neurosci. 12, 741–747 (2012). source
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Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataATC classifications were available for 220 drugs, spanning 54 second-level classes and interacting with 37 genes.
“Anatomical Therapeutic Chemical (ATC) classifications were available for 220 of these drugs which belonged to 54 different second level ATC classes and which interacted with 37 genes, Figure 3.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the replication sample, 97 of 102 variants were nominally significant and 87 survived Bonferroni correction.
“In the replication sample, 97 out of the 102 associated variants were nominally significant (P < 0.05) and 87 were significant after Bonferroni correction (α = 0.05 / 102; P < 4.90 × 10-4).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataEnrichment was 1.16× for intronic, 1.41× for H3K4me1, and 17.49× for conserved regions.
“However, the estimates of enrichment for intronic (1.16×, s.e. = 0.05) and H3K4me1 (1.41×, s.e. = 0.13) regions were much smaller compared to the conserved regions (17.49×, s.e. = 1.68) of the genome.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe meta-analysis found significant genetic correlations between depression and neuroticism, anorexia nervosa, ADHD, schizophrenia, and bipolar disorder.
“Using the meta-analysed results in the current study this was evidenced by significant genetic correlations for depression with neuroticism, anorexia nervosa, attention deficit hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataLDSC regression estimated a genomic inflation factor of 1.63 and an intercept of 1.015 (s.e. 0.011) before inflation correction.
“Linkage Disequilibrium Score (LDSC) regression 10 produced a genomic inflation factor (λGC) estimate of 1.63 with an intercept of 1.015 (0.011) prior to inflation correction”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). source
Not checked in the free run.
May combine two claims.
Findingown dataLeave-one-variant-out IVW analyses showed the depression-to-neuroticism and depression-to-smoking effects were not driven by a single variant.
“Both the putative causal effects of depression on neuroticism and depression on ever vs. never smoked remained consistent (P < 6.46 × 10-4) in the ‘leave one variant out’ IVW analysis indicating that the observed effect was not driven by a single outlying variant.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIVW MR found a putative causal effect of depression on ever versus never smoking (beta = 0.285, s.e. = 0.077; PFDR = 2.29 × 10−3), without variant heterogeneity.
“There was also evidence of a putative causal effect of depression on ever vs. never smoked (beta = 0.285, s.e. = 0.077; PFDR = 2.29 × 10-3; Supplementary Figure 5), with no evidence of variant heterogeneity (P = 0.14).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataBrain-region and cell-type analyses found significant enrichment for anterior cingulate cortex, frontal cortex, cortex, and neurons.
“There was significant enrichment (Pcorrected < 0.05) of the anterior cingulate cortex, frontal cortex and cortex brain regions and neuron brain cells.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataDepression was genetically correlated with schizophrenia (rG = 0.32), bipolar disorder (0.33), college completion (−0.19), coronary artery disease (0.13), triglycerides (0.14), body fat (0.16), and waist-to-hip ratio (0.12).
“Significant genetic correlations with depression included schizophrenia (rG = 0.32, s.e = 0.02), bipolar disorder (rG = 0.33, s.e = 0.03), college completion (rG = -0.19, s.e = 0.03), coronary artery disease (rG = 0.13, s.e = 0.02), triglycerides (rG = 0.14, s.e = 0.02), body fat (rG = 0.16, s.e = 0.03), and waist-to-hip ratio (rG = 0.12, s.e = 0.02).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFor neuroticism on depression, weighted median was similar to IVW (beta = 0.337; P = 1.94 × 10−18), while MR Egger was opposite and imprecise (beta = −0.128; P = 0.64).
“The weighted median test produced similar effect size and P-value to the IVW test (beta = 0.337, s.e. = 0.038; P = 1.94 × 10-18), but the MR Egger test had a large standard error and was in the opposite direction (beta = -0.128, s.e. = 0.271; P = 0.64).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe meta-analysis tested 8,098,588 variants and found 9,744 associated at P < 5 × 10−8, with 102 independently segregating across 101 loci.
“We tested the effects of 8,098,588 genetic variants on depression and identified 9,744 associated variants (P < 5 × 10-8) of which 102 variants in 101 loci were independently segregating (Supplementary Table 1).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataRemoving 15 LD-overlapping instruments attenuated the depression-to-neuroticism effect from 0.146 to 0.112, and it lost significance after FDR correction (PFDR = 0.037).
“The effect size for the IVW regression analysis MR test for depression on neuroticism was attenuated from 0.146 (s.e. = 0.039) to 0.112 (s.e. = 0.042) and the P-value was no longer significant after false discovery rate correction (PFDR = 0.037).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataBoth the current meta-analysis PRS and the Wray PRS were significantly associated with MDD in all three target cohorts.
“Both the meta-analysis PRS and the Wray PRS were significantly associated with MDD in each of the three cohorts”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataNovel genetic correlations were observed for depression with Crohn's disease (rG = 0.09) and earlier smoking initiation (rG = −0.21).
“Additionally, novel genetic correlations were observed between depression and Crohn’s disease (rG = 0.09, s.e. 0.03) and depression and an earlier age of smoking initiation (rG = -0.21, s.e. 0.06).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAfter removing 15 LD-overlapping instruments, the neuroticism-to-depression effect fell from 0.366 to 0.289 but stayed significant after FDR correction (PFDR = 1.34 × 10−10).
“The effect size for the IVW regression analysis MR test for neuroticism on depression was attenuated from 0.366 (s.e. = 0.037) to 0.289 (s.e. = 0.042) and the P-value remained significant after false discovery rate correction (PFDR = 1.34 × 10-10).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataNEGR1 was associated with depression (P = 3.55 × 10−15), with nearby independent variants rs2568958 and rs10890020 in separate loci.
“The Neuronal Growth Regulator 1 (NEGR1) gene was associated with depression (P = 3.55 × 10-15) and there were two nearby independently-associated variants (rs2568958 and rs10890020) located in separate loci”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataLIN28B was significant in the meta-analysis and has been linked to age at menarche.
“The lin-28 homolog b (LIN28B) gene was significant in our meta-analysis and has been associated with age of menarche 37.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [37] Ong, K. K. et al. Genetic variation in LIN28B is associated with the timing of puberty. Nat. Genet. 41, 729–733 (2009). source
Not checked in the free run.
May combine two claims.
Findingown dataBoth heritability enrichment and gene-set analyses supported a role for prefrontal brain regions in depression.
“The heritability enrichments and gene-set analysis both provided evidence for the role of prefrontal brain regions in depression”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationCDH13 was putatively associated with depression and has been implicated in ADHD.
“The cadherin 13 (CDH13) gene, which codes for cell adhesion molecules, was found to be putatively associated with depression and has also been implicated in ADHD 32.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] Lesch, K.-P. et al. Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J. Neural Transm. (Vienna) 115, 1573–1585 (2008). source
Not checked in the free run.
May combine two claims.
Findingown dataThe meta-analysis found a significant genetic correlation between age at menarche and depression, plus nominal evidence of a bidirectional causal effect.
“The current meta-analysis identified a significant genetic correlation between age of menarche and depression, and nominal evidence of a bi-directional causal effect.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationNRG1 is one such candidate, found here in depression and in a recent meta-analysis of antidepressant response.
“One such candidate is NRG1, identified here in depression, and also in a recent meta-analysis of antidepressant response 50.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [50] Biernacka, J. M. et al. The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Transl. Psychiatry 5, e553 (2015). source
Not checked in the free run.
May combine two claims.
FindingcitationThe TCF4 coding region contained two independent depression-associated variants and has been reported in earlier depression studies.
“The TCF4 gene coding region (Supplementary Figure 12) is also noteworthy as it contained two independently-associated variants for depression and has been identified in previous studies of depression 5, 9, 29.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [5] Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018). source
Not checked in the free run.
- cites [9] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
Not checked in the free run.
- cites [29] Mossakowska-Wójcik, J., Orzechowska, A., Talarowska, M., Szemraj, J. & Gałecki, P. The importance of TCF4 gene in the etiology of recurrent depressive disorders. Prog. Neuropsychopharmacol. Biol. Psychiatry 80, 304–308 (2018). source
Not checked in the free run.
May combine two claims.
Findingown dataEffect directions were consistent in a large replication sample (1,306,354 individuals; 414,055 cases and 892,299 controls) and across the three contributing studies, increasing confidence in the results.
“demonstrated a consistency of effect directionality in a large replication sample (total n = 1,306,354, with 414,055 cases and 892,299 controls) and across the three contributing studies allowing greater confidence in the findings.”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationASTN2 was putatively associated with depression and is involved in ADHD aetiology and neuronal development.
“A further gene involved in cell adhesion, astrotactin 2 (ASTN2), which we found to be putatively associated with depression is also involved in ADHD aetiology 32 and plays a role in neuronal development in the brain 33.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [32] Lesch, K.-P. et al. Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J. Neural Transm. (Vienna) 115, 1573–1585 (2008). source
Not checked in the free run.
- cites [33] Wilson, P. M., Fryer, R. H., Fang, Y. & Hatten, M. E. Astn2, a novel member of the astrotactin gene family, regulates the trafficking of ASTN1 during glial-guided neuronal migration. J. Neurosci. 30, 8529–8540 (2010). source
Not checked in the free run.
May combine two claims.
Findingown dataVariant-based heritability enrichment across brain regions and a complementary gene-set analysis both pointed to the importance of prefrontal brain regions.
“We have used variant-based analyses to calculate heritability enrichment across different brain regions and then also applied a complimentary approach using the genes assigned to functional gene-sets, with both methods highlighting the importance of the prefrontal brain regions.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe analysis could not test smoking's effect on depression due to too few instruments, but found a genetic correlation and a causal effect of depression on smoking.
“In the current analysis there was an insufficient number of instrumental variants to test the effect of smoking on depression, but there was evidence of a genetic correlation between depression and cigarette smoking as well as a causal effect of depression on smoking.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataNo analyses were dropped because of directional horizontal pleiotropy.
“However, no tests were excluded due to directional horizontal pleiotropy.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTwin studies estimate that depression is about 30-40% heritable.
“Twin studies have provided heritability estimates of the disease of approximately 30-40% 3”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Sullivan, P. F., Neale, M. C. & Kendler, K. S. Genetic epidemiology of major depression: review and meta-analysis. Am. J. Psychiatry 157, 1552–1562 (2000). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationHoward and colleagues' UK Biobank GWAS found 17 variants associated across three depression phenotypes (maximum n = 322,580).
“More recently, a genome-wide association analysis of UK Biobank by Howard, et al. 5 identified 17 variants associated across three depression phenotypes (maximum total n = 322,580; 113,769 cases and 208,811 controls).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [5] Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018). source
Not checked in the free run.
May combine two claims.
Interpretationown dataOverlap among the gene-sets suggests two clusters: one related to synaptic structure and activity, the other to response or behaviour to stimuli.
“The gene overlap between these gene-sets (Supplementary Table 10) suggests that the gene-sets generally fall within two clusters. One gene-set cluster typically relates to synaptic structure and activity while the other gene-set cluster relates to the response or behaviour to stimuli.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationDepression is a polygenic condition shaped by numerous genetic variants, each with a small effect.
“however depression is a polygenic trait influenced by many genetic variants each of small effect 4”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Ripke, S. et al. Major depressive disorder working group of the psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013). source
Not checked in the free run.
May combine two claims.
PremisecitationTwo earlier efforts, a mega-analysis of 9 cohorts and a meta-analysis of 17 cohorts, did not find replicated depression-associated variants.
“However, both of these studies failed to find any replicated variants associated with depression.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [4] Ripke, S. et al. Major depressive disorder working group of the psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder. Mol. Psychiatry 18, 497–511 (2013). source
Not checked in the free run.
- cites [6] Hek, K. et al. A genome-wide association study of depressive symptoms. Biol. Psychiatry 73, 667–678 (2013). source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationcitationUsing larger samples with varied diagnostic methods may improve knowledge of depression genetics.
“Therefore, analysing larger samples, which have used different approaches to diagnosis, may provide advances in our understanding of the genetics of depression.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrior work links ESR2 to antidepressant effects via up-regulation by 3β-hydroxysteroid dehydrogenase.
“Estrogen Receptor 2 (ESR2) has previously been implicated in antidepressant action through up-regulation by 3β-hydroxysteroid dehydrogenase 47.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [47] Espallergues, J., Temsamani, J., Laruelle, C. & Urani, A. & Maurice, T. The antidepressant-like effect of the 3β-hydroxysteroid dehydrogenase inhibitor trilostane involves a regulation of β-type estrogen receptors. Psychopharmacology (Berl.) 214, 455–463 (2011). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA recent review noted that serotonergic and other frequently studied candidate genes have not shown association with depression.
“Indeed, a recent review of the genetics studies of depression has remarked upon the failure to demonstrate association with depression for serotonergic and other popular candidate genes 49.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [49] Dunn, E. C. et al. Genetic determinants of depression: recent findings and future directions. Harv. Rev. Psychiatry 23, 1–18 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataSample sizes were not pre-determined statistically, though they resemble those in earlier reports.
“No statistical methods were used to pre-determine sample sizes but our sample sizes are similar to those reported in previous publications 5, 8, 9.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [5] Howard, D. M. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018). source
Not checked in the free run.
- cites [8] Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016). source
Not checked in the free run.
- cites [9] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
Not checked in the free run.
May combine two claims.
InterpretationcitationDopamine transmission may contribute to both ADHD and depression, with DRD2 and ANKK1 implicated.
“Dopamine transmission may also underlie ADHD and depression with both the DRD2 and ankyrin repeat and kinase domain containing 1 (ANKK1) genes implicated in our analysis of depression and in ADHD 34.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34] Mota, N. R. et al. NCAM1-TTC12-ANKK1-DRD2 gene cluster and the clinical and genetic heterogeneity of adults with ADHD.Am. J. Med. Genet. B Neuropsychiatr. Genet. 168, 433–444 (2015). source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductcitationGenomic inflation was controlled using LDSC regression intercepts for each contributing cohort and the final meta-analysis.
“Linkage disequilibrium score (LDSC) regression intercepts 10 were used for genomic inflation control of the three contributing cohorts and the final meta-analysis results.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningAlternatively, the pathways for depression and for serotonergic antidepressant effects could be distinct yet connected via shared intermediary genes.
“It may also be that the pathways of depression and serotonergic antidepressant effect are separate but entwine through common intermediary genes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationErbB4, the receptor for NRG1 on GABAergic neurons, has been proposed as a possible drug target for depression, anxiety, and schizophrenia.
“The Neuregulin 1 (NRG1) receptor ErbB4, found on GABAergic neurones, has been identified as a potentially druggable target for depression, anxiety and schizophrenia 45.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [45] Bi, L.-L. et al. Amygdala NRG1-ErbB4 is critical for the modulation of anxiety-like behaviors. Neuropsychopharmacology 40, 974–986 (2015). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationBasimglurant and fenobam are being explored as antidepressants and anxiolytics acting on glutamatergic receptors.
“There is interest in developing agents, such as basimglurant and fenobam, as antidepressants and anxiolytics that target glutamatergic receptors 46.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [46] Fuxe, K. & Borroto-Escuela, D. O. Basimglurant for treatment of major depressive disorder: a novel negative allosteric modulator of metabotropic glutamate receptor 5. Expert. Opin. Investig. Drugs. 24, 1247–1260 (2015). source
Not checked in the free run.
May combine two claims.
Premiseown dataRSRC1 and MEF2C have been linked to schizophrenia and were both putatively associated with depression here.
“Additionally, associations have been found between schizophrenia and the arginine and serine rich coiled-coil 1 (RSRC1) 28 and myocyte enhancer factor 2C (MEF2C) 21 genes, both of which we found to be putatively associated with depression.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [28] Potkin, S. G. et al. Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia. Mol. Psychiatry 14, 416–428 (2009). source
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- cites [21] Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPrior work links the dopaminergic system to depression, especially anhedonia and reduced motivation.
“The dopaminergic system has been previously implicated in depression, particularly the symptoms of anhedonia and decreased motivation”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [44] Bonci, A. & Hopf, F. W. The dopamine D2 receptor: new surprises from an old friend. Neuron 47, 335–338 (2005). source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningBecause depression is causally heterogeneous, dividing it into subtypes will probably clarify distinctions among subtypes and treatments.
“however, due to the causal heterogeneity of depression, it is likely that stratifying depression will lead to clearer distinction between depression subtypes and potential treatments.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenData collection was not randomized, and experimental conditions and stimulus presentations were not randomized.
“Data collection was not randomized and there was no randomization in the organization of the experimental conditions or stimulus presentations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenNeither data collection nor analysis was blinded to experimental conditions.
“Data collection and analysis were not performed blind to the conditions of the experiments.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn rodent models of depression, estrogenic compounds show antidepressant effects.
“Estrogenic compounds have also been found to have antidepressant effects in rodent models of depression 48.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [48] Estrada-Camarena, E., Fernández-Guasti, A. & López-Rubalcava, C. Antidepressant-like effect of different estrogenic compounds in the forced swimming test. Neuropsychopharmacology 28, 830–838 (2003). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn animal models of depression, dopamine reuptake inhibitors and D2-receptor agonists have shown antidepressant effects.
“antidepressant action has been reported for dopamine reuptake inhibitors and D2-receptor agonists in animal models of depression 44.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [44] Bonci, A. & Hopf, F. W. The dopamine D2 receptor: new surprises from an old friend. Neuron 47, 335–338 (2005). source
Not checked in the free run.
The authors state this as an assumption.
The analysis assumed normal distributions for all data without formally testing this.
“All data distributions were assumed to be normal but this was not formally tested.”
The authors state this as an assumption.
For Mendelian randomization, the instruments must be associated with the exposure.
“the genetic variants are associated with the exposure”
The authors state this as an assumption.
For Mendelian randomization, the instruments must not be associated with confounders of the exposure-outcome relationship.
“the genetic variants are independent of confounders in the exposure-outcome association”
The authors state this as an assumption.
For Mendelian randomization, the instruments must affect the outcome only through the exposure, without horizontal pleiotropy.
“the genetic variants are associated with the outcome only via their effect on the exposure, i.e. there is no horizontal pleiotropy whereby the variants affect both exposure and outcome independently.”
The paper relies on this without support.
Most antidepressant treatments work by interacting with the serotonergic system.
“This is surprising, as interaction with the serotonergic system forms the basis of most antidepressant treatments.”
The authors state this as an assumption.
The principal Mendelian randomization test (inverse-variance weighted regression) assumes a zero intercept.
“This method is based on a regression of the exposure and the outcome which assumes the intercept is constrained to zero”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The latest GWAS of MDD meta-analyzed data from 807,553 individuals and identified 102 loci associated with MDD at genome-wide significance.
“The latest GWAS of MDD meta-analyzed data from 807 553 individuals and identified 102 loci that were associated with the disorder at genome-wide significance.”
From Epigenetics and depression (2019) · cites this paper as [5]
The Results section, “102 independent genetic variants associated with depression,” confirms a meta-analysis of 807,553 individuals and genome-wide significant associations. It reports 102 independently segregating variants in 101 loci, not 102 loci. The study also combined several definitions of depression rather than exclusively clinically diagnosed MDD. (LLM, conf=0.91)
Experimental — accuracy not yet measuredMDD is a polygenic disorder with multiple loci identified, each with a small effect.
“What has become clear from these studies is that MDD is a polygenic disorder, with multiple loci being identified, but each with a small effect.”
From Epigenetics and depression (2019) · cites this paper as [5]
GWAS have discovered and successfully replicated genome-wide associations of common gene variants for schizophrenia and major depression.
“large consortia that have discovered and successfully replicated genome-wide associations of common gene variants (single nucleotide polymorphisms, SNPs) for several disorders including schizophrenia [2] and major depression [3–6].”
From Neandertal introgression partitions the genetic landscape of neuropsychiatric disorders and associated behavioral phenotypes (2022) · cites this paper as [6]
Meta-analysis of GWAS studies identified 102 genetic risk variants in MDD patients, many of which display high expression in frontal cortex and are enriched in biological pathways related to synaptic transmission.
“A meta-analysis of GWAS studies identified 102 genetic risk variants in MDD patients, many of which display high expression in frontal cortex and are enriched in biological pathways related to synaptic transmission 208.”
From Mechanisms of synaptic transmission dysregulation in the prefrontal cortex: pathophysiological implications (2022) · cites this paper as [208]
Several prospective studies suggest that depression prior to completing education predicts substantially reduced income-earnings in adulthood after adjusting for level of educational attainment.
“Several prospective studies suggest that depression prior to completing education predicts substantially reduced income-earnings in adulthood after adjusting for level of educational attainment. 230,231”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [231]
The primary meta-analysis finding is an increase of 77 loci over the largest previous study of a comparable phenotype.
“This finding is an increase of 77 loci over the largest previous study that investigated a comparable phenotype.10”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [10]
No statistical methods were used to pre-determine sample sizes, but the sample sizes were similar to those in previous publications.
“No statistical methods were used to pre-determine sample sizes but our sample sizes are similar to those reported in previous publications. 8,10”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [10]
Stress, depression and anxiety have been suggested as triggers of flares and clinical deterioration in IBD.
“Stress, depression and anxiety have been suggested as triggers of flares and clinical deterioration in IBD. 10,11”
From Depressive symptoms predict clinical recurrence of inflammatory bowel disease (2022) · cites this paper as [10]
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.6.593 · Cited by 20,391 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
Induced Pluripotent Stem Cell Lines Derived from Human Somatic Cells
Yu, J., Vodyanik, M. A., Smuga-Otto, K., Antosiewicz-Bourget, J., Frane, J. L., Tian, S., Nie, J., Jonsdottir, G. A., Ruotti, V., Stewart, R., et al. (2007) · Science · doi:10.1126/science.1151526 · Cited by 10,310 · see citing works
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · see citing works
Self-citationBiological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577 · see citing works
Self-citationIntegrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
Self-citationMETAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · see citing works
circlize implements and enhances circular visualization in R
Gu, Z., Gu, L., Eils, R., Schlesner, M., & Brors, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu393 · Cited by 4,290 · see citing works
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255 · see citing works
Super-enhancers in the control of cell identity and disease
Hnisz, D., Abraham, B. J., Lee, T. I., Lau, A., Saint-André, V., Sigova, A. A., Hoke, H. A., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.09.053 · Cited by 3,662 · see citing works
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653 · see citing works
Robust relationship inference in genome-wide association studies
Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq559 · Cited by 3,541 · see citing works
Genetic Epidemiology of Major Depression: Review and Meta-Analysis
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000) · American Journal of Psychiatry · doi:10.1176/appi.ajp.157.10.1552 · Cited by 3,431 · see citing works
Self-citationCahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129 · see citing works
(2013) · The Lancet · doi:10.1016/s0140-6736(12)62129-1 · Cited by 3,010 · see citing works
eQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0090-3 · Cited by 3,005 · see citing works
Self-citationA reference panel of 64,976 haplotypes for genotype imputation
the Haplotype Reference Consortium (2016) · Nature Genetics · doi:10.1038/ng.3643 · Cited by 2,836 · see citing works
Self-citationLocusZoom: regional visualization of genome-wide association scan results
Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., & Willer, C. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq419 · Cited by 2,707 · see citing works
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604 · see citing works
Self-citationAn atlas of active enhancers across human cell types and tissues
The FANTOM Consortium, Andersson, R., Gebhard, C., Miguel-Escalada, I., Hoof, I., Bornholdt, J., Boyd, M., Chen, Y., Zhao, X., Schmidl, C., et al. (2014) · Nature · doi:10.1038/nature12787 · Cited by 2,550 · see citing works
BEDTools: The Swiss‐Army Tool for Genome Feature Analysis
Quinlan, A. R. (2014) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1112s47 · Cited by 2,286 · see citing works
Analysis of shared heritability in common disorders of the brain
The Brainstorm Consortium, Anttila, V., Bulik-Sullivan, B., Finucane, H. K., Walters, R. K., Bras, J., Duncan, L., Escott-Price, V., Falcone, G. J., Gormley, P., et al. (2018) · Science · doi:10.1126/science.aap8757 · Cited by 1,924 · see citing works
Self-citationExpansion of the Gene Ontology knowledgebase and resources
The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850 · see citing works
Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works
Bias due to participant overlap in two‐sample Mendelian randomization
Burgess, S., Davies, N. M., & Thompson, S. G. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21998 · Cited by 1,624 · see citing works
A mega-analysis of genome-wide association studies for major depressive disorder
Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., Breen, G., Byrne, E. M., Blackwood, D. H. R., Boomsma, D. I., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.21 · Cited by 1,120 · see citing works
Self-citationA high-resolution map of human evolutionary constraint using 29 mammals
Broad Institute Sequencing Platform and Whole Genome Assembly Team, Lindblad-Toh, K., Baylor College of Medicine Human Genome Sequencing Center Sequencing Team, Genome Institute at Washington University, Garber, M., Zuk, O., Lin, M. F., Parker, B. J., Washietl, S., Kheradpour, P., et al. (2011) · Nature · doi:10.1038/nature10530 · Cited by 1,108 · see citing works
Genome-wide meta-analyses identify multiple loci associated with smoking behavior
The Tobacco and Genetics Consortium (2010) · Nature Genetics · doi:10.1038/ng.571 · Cited by 1,098 · see citing works
Self-citationFinucane, H. K., Reshef, Y. A., Anttila, V., Slowikowski, K., Gusev, A., Byrnes, A., Gazal, S., Loh, P.-R., Lareau, C., Shoresh, N., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0081-4 · Cited by 1,097 · see citing works
Study, L. C., Okbay, A., Baselmans, B. M. L., De Neve, J.-E., Turley, P., Nivard, M. G., Fontana, M. A., Meddens, S. F. W., Linnér, R. K., Rietveld, C. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3552 · Cited by 1,013 · see citing works
Self-citationZheng, J., Erzurumluoglu, A. M., Elsworth, B. L., Kemp, J. P., Howe, L., Haycock, P. C., Hemani, G., Tansey, K., Laurin, C., Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium, et al. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw613 · Cited by 869 · see citing works
Self-citationSparse whole-genome sequencing identifies two loci for major depressive disorder
CONVERGE consortium, Cai, N., Bigdeli, T. B., Kretzschmar, W., Li, Y., Liang, J., Song, L., Hu, J., Li, Q., Jin, W., et al. (2015) · Nature · doi:10.1038/nature14659 · Cited by 864 · see citing works
Hyde, C. L., Nagle, M. W., Tian, C., Chen, X., Paciga, S. A., Wendland, J. R., Tung, J. Y., Hinds, D. A., Perlis, R. H., & Winslow, A. R. (2016) · Nature Genetics · doi:10.1038/ng.3623 · Cited by 784 · see citing works
Self-citationAgudelo, L. Z., Femenía, T., Orhan, F., Porsmyr-Palmertz, M., Goiny, M., Martinez-Redondo, V., Correia, J. C., Izadi, M., Bhat, M., Schuppe-Koistinen, I., et al. (2014) · Cell · doi:10.1016/j.cell.2014.07.051 · Cited by 739 · see citing works
Moss, E. G., Lee, R. C., & Ambros, V. (1997) · Cell · doi:10.1016/s0092-8674(00)81906-6 · Cited by 723 · see citing works
Genetic identification of brain cell types underlying schizophrenia
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Skene, N. G., Bryois, J., Bakken, T. E., Breen, G., Crowley, J. J., Gaspar, H. A., Giusti-Rodriguez, P., Hodge, R. D., Miller, J. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0129-5 · Cited by 651 · see citing works
Self-citationHoward, D. M., Adams, M. J., Shirali, M., Clarke, T.-K., Marioni, R. E., Davies, G., Coleman, J. R. I., Alloza, C., Shen, X., Barbu, M. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03819-3 · Cited by 612 · see citing works
Self-citationGenome-wide genetic data on ~500,000 UK Biobank participantsPreprint
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166298 · Cited by 606 · see citing works
Wootton, R. E., Richmond, R. C., Stuijfzand, B. G., Lawn, R. B., Sallis, H. M., Taylor, G. M. J., Hemani, G., Jones, H. J., Zammit, S., Davey Smith, G., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291719002678 · Cited by 587 · see citing works
Self-citationPartitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases
Gusev, A., Lee, S. H., Trynka, G., Finucane, H., Vilhjálmsson, B. J., Xu, H., Zang, C., Ripke, S., Bulik-Sullivan, B., Stahl, E., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.10.004 · Cited by 544 · see citing works
Self-citationDGIdb: mining the druggable genome
Griffith, M., Griffith, O. L., Coffman, A. C., Weible, J. V., McMichael, J. F., Spies, N. C., Koval, J., Das, I., Callaway, M. B., Eldred, J. M., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2689 · Cited by 527 · see citing works
Cigarette smoking and depression: tests of causal linkages using a longitudinal birth cohort
Boden, J. M., Fergusson, D. M., & Horwood, L. J. (2010) · British Journal of Psychiatry · doi:10.1192/bjp.bp.109.065912 · Cited by 477 · see citing works
Ladd, A. N., Charlet‐Berguerand, N., & Cooper, T. A. (2001) · Molecular and Cellular Biology · doi:10.1128/mcb.21.4.1285-1296.2001 · Cited by 449 · see citing works
Smith, B. H., Campbell, A., Linksted, P., Fitzpatrick, B., Jackson, C., Kerr, S. M., Deary, I. J., MacIntyre, D. J., Campbell, H., McGilchrist, M., et al. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dys084 · Cited by 401 · see citing works
Self-citationUK biobank data: come and get it
Allen, N. E., Sudlow, C., Peakman, T., Collins, R., & Biobank, O. B. O. U. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3008601 · Cited by 399 · see citing works
Chronis-Tuscano, A., Molina, B., Pelham, W., Applegate, B., Dahlke, A., Overmyer, M., & Lahey, B. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.127 · Cited by 393 · see citing works
Luciano, M., Hagenaars, S. P., Davies, G., Hill, W. D., Clarke, T.-K., Shirali, M., Harris, S. E., Marioni, R. E., Liewald, D. C., Fawns-Ritchie, C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0013-8 · Cited by 391 · see citing works
Self-citationLesch, K.-P., Timmesfeld, N., Renner, T. J., Halperin, R., Röser, C., Nguyen, T. T., Craig, D. W., Romanos, J., Heine, M., Meyer, J., et al. (2008) · Journal of Neural Transmission · doi:10.1007/s00702-008-0119-3 · Cited by 374 · see citing works
Genetic variation in LIN28B is associated with the timing of puberty
Ong, K. K., Elks, C. E., Li, S., Zhao, J. H., Luan, J., Andersen, L. B., Bingham, S. A., Brage, S., Smith, G. D., Ekelund, U., et al. (2009) · Nature Genetics · doi:10.1038/ng.382 · Cited by 366 · see citing works
Smıth, D. J., Nicholl, B. I., Cullen, B., Martin, D. J., Haq, Z. U., Evans, J. J., Gill, J. M. R., Roberts, B. A., Gallacher, J., Mackay, D., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075362 · Cited by 361 · see citing works
Self-citationDopamine, depression and antidepressants
Dailly, É., Chenu, F., Renard, C. E., & Bourin, M. (2004) · Fundamental and Clinical Pharmacology · doi:10.1111/j.1472-8206.2004.00287.x · Cited by 302 · see citing works
Exercise-induced histone modifications in human skeletal muscle
McGee, S. L., Fairlie, E., Garnham, A. P., & Hargreaves, M. (2009) · The Journal of Physiology · doi:10.1113/jphysiol.2009.181065 · Cited by 296 · see citing works
Pubertal timing, sexual behaviour and self‐reported depression in middle adolescence
Kaltiala‐Heino, R., Kosunen, E., & Rimpelä, M. (2003) · Journal of Adolescence · doi:10.1016/s0140-1971(03)00053-8 · Cited by 294 · see citing works
Meta-analysis of genome-wide association data identifies two loci influencing age at menarche
Perry, J. R. B., Stolk, L., Franceschini, N., Lunetta, K. L., Zhai, G., McArdle, P. F., Smith, A. V., Aspelund, T., Bandinelli, S., Boerwinkle, E., et al. (2009) · Nature Genetics · doi:10.1038/ng.386 · Cited by 294 · see citing works
Genome-wide association study identifies sequence variants on 6q21 associated with age at menarche
Sulem, P., Gudbjartsson, D. F., Rafnar, T., Holm, H., Olafsdottir, E. J., Olafsdottir, G. H., Jonsson, T., Alexandersen, P., Feenstra, B., Boyd, H. A., et al. (2009) · Nature Genetics · doi:10.1038/ng.383 · Cited by 219 · see citing works
RBFOX1 regulates both splicing and transcriptional networks in human neuronal development
Fogel, B. L., Wexler, E., Wahnich, A., Friedrich, T., Vijayendran, C., Gao, F., Parikshak, N., Konopka, G., & Geschwind, D. H. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds240 · Cited by 216 · see citing works
Antidepressant-Like Effect of Different Estrogenic Compounds in the Forced Swimming Test
Estrada-Camarena, E., Fernández-Guasti, A., & López-Rubalcava, C. (2003) · Neuropsychopharmacology · doi:10.1038/sj.npp.1300097 · Cited by 211 · see citing works
Common variants at VRK2 and TCF4 conferring risk of schizophrenia
Steinberg, S., de Jong, S., Andreassen, O. A., Werge, T. M., Børglum, A. D., Mors, O. N., Mortensen, P. B., Gústafsson, Ó., Costas, J., Pietiläinen, O. P. H., et al. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddr325 · Cited by 210 · see citing works
Polygenic dissection of major depression clinical heterogeneity
Milaneschi, Y., Lamers, F., Peyrot, W. J., Abdellaoui, A., Willemsen, G. A. H. M., Hottenga, J., Jansen, R., Mbarek, H., Dehghan, A., Lu, C., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2015.86 · Cited by 183 · see citing works
Case-Control Genome-Wide Association of Attention-Deficit / Hyperactivity Disorder
Neale, B., Medland, S., Ripke, S., Anney, R., Asherson, P., Buitelaar, J., Franke, B., Gill, M., Kent, L., Holmans, P., et al. (2010) · Journal of the American Academy of Child and Adolescent Psychiatry · doi:10.1016/j.jaac.2010.06.007 · Cited by 175 · see citing works
Self-citationA Genome-Wide Association Study of Depressive Symptoms
Hek, K., Demirkan, A., Lahti, J., Terracciano, A., Teumer, A., Cornelis, M. C., Amin, N., Bakshis, E., Baumert, J., Ding, J., et al. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.09.033 · Cited by 172 · see citing works
Self-citationWilson, P. M., Fryer, R. H., Fang, Y., & Hatten, M. E. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0032-10.2010 · Cited by 169 · see citing works
Lionel, A. C., Tammimies, K., Vaags, A. K., Rosenfeld, J. A., Ahn, J. W., Merico, D., Noor, A., Runke, C. K., Pillalamarri, V. K., Carter, M. T., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddt669 · Cited by 163 · see citing works
Genetic Determinants of Depression: Recent Findings and Future Directions
Dunn, E. C., Brown, R. C., Dai, Y., Rosand, J., Nugent, N. R., Amstadter, A. B., & Smoller, J. W. (2015) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000054 · Cited by 158 · see citing works
Tcf4 Regulates Synaptic Plasticity, DNA Methylation, and Memory Function
Kennedy, A. J., Rahn, E. J., Paulukaitis, B. S., Savell, K. E., Kordasiewicz, H. B., Wang, J., Lewis, J. W., Posey, J. L., Strange, S. K., Karlsson, M. G., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.08.004 · Cited by 153 · see citing works
Biernacka, J. M., Sangkuhl, K., Jenkins, G., Whaley, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V, Brockmöller, J., Chen, C. H., et al. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.47 · Cited by 140 · see citing works
Self-citationWagnon, J. L., Briese, M., Sun, W., Mahaffey, C. L., Curk, T., Rot, G., Ule, J., & Frankel, W. N. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1003067 · Cited by 125 · see citing works
Fernandez-Pujals, A. M., Adams, M. J., Thomson, P. A., McKechanie, A. G., Blackwood, D., Smith, B. H., Dominiczak, A. F., Morris, A. D., Matthews, K., Campbell, A. I., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142197 · Cited by 125 · see citing works
Self-citationvan den Berg, S. M., de Moor, M. H. M., McGue, M. K., Pettersson, E., Terracciano, A., Verweij, K. J. H., Amin, N., Derringer, J. L., Esko, T., van Grootheest, G., et al. (2014) · Behavior Genetics · doi:10.1007/s10519-014-9654-x · Cited by 123 · see citing works
Self-citationMunafo, M. R., Hitsman, B. L., Rende, R., Metcalfe, C. D., & NIAURA, R. S. (2007) · Addiction · doi:10.1111/j.1360-0443.2007.02052.x · Cited by 123 · see citing works
Cigarette smoking and depression: a question of causation
Munafo, M. R., & Araya, R. (2010) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.109.074880 · Cited by 119 · see citing works
The Dopamine D2 Receptor: New Surprises from an Old Friend
Bonci, A., & Hopf, F. W. (2005) · Neuron · doi:10.1016/j.neuron.2005.07.015 · Cited by 115 · see citing works
Sequeira, M., Lewis, S. J., Bonilla, C., Smith, G. D., & Joinson, C. J. (2016) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.115.168617 · Cited by 111 · see citing works
Nagy, R., Boutin, T., Marten, J., Huffman, J. E., Kerr, S. M., Campbell, A. I., Evenden, L., Gibson, J., Amador, C., Howard, D. M., et al. (2017) · Genome Medicine · doi:10.1186/s13073-017-0414-4 · Cited by 111 · see citing works
Self-citationRannals, M. D., Hamersky, G. R., Page, S. C., Campbell, M. N., Briley, A., Gallo, R. A., Phan, B. N., Hyde, T. M., Kleinman, J. E., Shin, J. H., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.02.021 · Cited by 106 · see citing works
Forrest, M. P., Hill, M., Kavanagh, D. H., Tansey, K. E., Waite, A. J., & Blake, D. J. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx164 · Cited by 103 · see citing works
Anxiolytics for smoking cessation
Hughes, J. R., Stead, L. F., & Lancaster, T. (2000) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd002849 · Cited by 101 · see citing works
Potkin, S. G., Turner, J. A., Fallon, J. A., Lakatos, A., Keator, D. B., Guffanti, G., Macciardi, F., & FBIRN (2009) · Molecular Psychiatry · doi:10.1038/mp.2008.127 · Cited by 97 · see citing works
Mitchell, A., Javidfar, B., Pothula, V., Ibi, D., Shen, E., Peter, C. J., Bicks, L. K., Fehr, T., Jiang, Y. P., Brennand, K., et al. (2017) · Molecular Psychiatry · doi:10.1038/mp.2016.254 · Cited by 91 · see citing works
Berto, S., Usui, N., Konopka, G., & Fogel, B. L. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw110 · Cited by 80 · see citing works
Amygdala NRG1–ErbB4 Is Critical for the Modulation of Anxiety-Like Behaviors
Bi, L.-L., Sun, X.-D., Zhang, J., Lu, Y.-S., Chen, Y.-H., Wang, J., Geng, F., Liu, F., Zhang, M., Liu, J.-H., et al. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2014.274 · Cited by 76 · see citing works
Pergola, G., Carlo, P. D., D’Ambrosio, E., Gelao, B., Fazio, L., Papalino, M., Monda, A., Scozia, G., Pietrangelo, B., Attrotto, M. T., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2016.253 · Cited by 75 · see citing works
Teismann, H., Wersching, H., Nagel, M., Arolt, V., Heindel, W. L., Baune, B. T., Wellmann, J., Hense, H., & Berger, K. (2014) · BMC Psychiatry · doi:10.1186/1471-244x-14-174 · Cited by 74 · see citing works
Self-citationBjørngaard, J. H., Gunnell, D., Elvestad, M. B., Smith, G. D., Skorpen, F., Krokan, H., Vatten, L., & Romundstad, P. (2013) · Psychological Medicine · doi:10.1017/s0033291712001274 · Cited by 71 · see citing works
Early Menarche and Depressive Symptoms From Adolescence to Young Adulthood in a UK Cohort
Joinson, C. J., Heron, J., Araya, R., & Lewis, G. (2013) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2013.03.018 · Cited by 65 · see citing works
Barone, R. M. E., Fichera, M., Grandi, M. D., Battaglia, M., Faro, V. L., Mattina, T., & Rizzo, R. (2017) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.38205 · Cited by 63 · see citing works
Wium-Andersen, M. K., Orsted, D. D., & Nordestgaard, B. G. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv090 · Cited by 59 · see citing works
Wootton, R. E., Richmond, R. C., Stuijfzand, B. G., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/381301 · Cited by 59 · see citing works
Elevation of Il6 is associated with disturbed let-7 biogenesis in a genetic model of depression
Wei, Y. B., Liu, J. J., Villaescusa, J. C., Åberg, E., Brené, S., Wegener, G., Mathé, A. A., & Lavebratt, C. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.136 · Cited by 57 · see citing works
A Longitudinal Study on the Causal Association Between Smoking and Depression
Kang, E., & Lee, J. (2010) · Journal of Preventive Medicine and Public Health · doi:10.3961/jpmph.2010.43.3.193 · Cited by 53 · see citing works
Lewis, S. J., Araya, R., Davey Smith, G., Freathy, R., Gunnell, D., Palmer, T., & Munafò, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021689 · Cited by 53 · see citing works
Shen, E., Jiang, Y. P., Javidfar, B., Kassim, B. S., Loh, Y. E., Ma, Q., Mitchell, A., Pothula, V., Stewart, A. F., Ernst, P., et al. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.144 · Cited by 52 · see citing works
Li, M., Wang, Y., Zheng, X.-B., Ikeda, M., Iwata, N., Luo, X., Chong, S. A., Lee, J., Rietschel, M., Zhang, F., et al. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.10.008 · Cited by 47 · see citing works
Cognitive ability and physical health: a Mendelian randomization study
Hagenaars, S. P., Galé, C. R., Deary, I. J., & Harris, S. E. (2017) · Scientific Reports · doi:10.1038/s41598-017-02837-3 · Cited by 46 · see citing works
Self-citationSalatino-Oliveira, A., Genro, J., Polanczyk, G., Zeni, C., Schmitz, M., Kieling, C., Anselmi, L., Menezes, A., Barros, F. C., Polina, E. R., et al. (2015) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32293 · Cited by 44 · see citing works
Fuxe, K., & Borroto-Escuela, D. O. (2015) · Expert Opinion on Investigational Drugs · doi:10.1517/13543784.2015.1074175 · Cited by 43 · see citing works
Heinrich, H., Grunitz, J., Stonawski, V., Frey, S., Wahl, S., Albrecht, B., Goecke, T., Beckmann, M., Kornhuber, J., Fasching, P., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-03326-3 · Cited by 43 · see citing works
Halgren, C., Bache, I., Bak, M., Myatt, M. W., Anderson, C. M., Brøndum‐Nielsen, K., & Tommerup, N. (2012) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.92 · Cited by 42 · see citing works
The importance of TCF4 gene in the etiology of recurrent depressive disorders
Mossakowska-Wójcik, J., Orzechowska, A., Talarowska, M., Szemraj, J., & Gałecki, P. (2018) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.03.014 · Cited by 32 · see citing works
Depressive rumination and the C957T polymorphism of the DRD2 gene
Whitmer, A. J., & Gotlib, I. H. (2012) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-012-0112-z · Cited by 30 · see citing works
Mota, N. R., Rovaris, D. L., Kappel, D. B., Picon, F. A., Vitola, E. S., Salgado, C. A. I., Karam, R. G., Rohde, L. A., Grevet, E. H., & Bau, C. H. D. (2015) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32317 · Cited by 25 · see citing works
Markett, S. A., de Reus, M. A., Reuter, M., Montag, C., Weber, B., Schoene‐Bake, J., & van den Heuvel, M. P. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.005 · Cited by 24 · see citing works
Interaction between DRD2 variation and sound environment on mood and emotion-related brain activity.
Quarto, T., Fasano, M. C., Taurisano, P., Fazio, L., Antonucci, L., Gelao, B., Romano, R., Mancini, M., Porcelli, A., Masellis, R., et al. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2016.11.010 · Cited by 16 · see citing works
Further evidence ofVRK2rs2312147 associated with schizophrenia
Chang, H., Zhang, C., Xiao, X., Pu, X., Liu, Z., Wu, L., & Li, M. (2016) · The World Journal of Biological Psychiatry · doi:10.1080/15622975.2016.1200746 · Cited by 14 · see citing works
Taylor, A., Fluharty, M., & Munafo, M. R. (2014) · Oral Presentations · doi:10.1136/jech-2014-204726.94 · Cited by 9 · see citing works
Espallergues, J., Temsamani, J., Laruelle, C., Urani, A., & Maurice, T. (2011) · Psychopharmacology · doi:10.1007/s00213-010-2053-y · Cited by 8 · see citing works
(2018) · Science · doi:10.1126/science.360.6387.360 · Cited by 5 · see citing works
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- First, M. B., Spitzer, R. L., Gibbon, M. & Williams, J. B. W. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). (BiometricsResearch, New York State Psychiatric Institute, New York, NY, USA, 2002).
Cited by
Sorted by citations
The Bidirectional Relationship of Depression and Inflammation: Double Trouble
Beurel, E., Toups, M., & Nemeroff, C. B. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.002 · Cited by 2,136 · see citing works
Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00857-4 · Cited by 1,517 · see citing works
Self-citationPolygenic risk scores: from research tools to clinical instruments
Lewis, C. M., & Vassos, E. (2020) · Genome Medicine · doi:10.1186/s13073-020-00742-5 · Cited by 1,360 · see citing works
Self-citationHandbook of Religion and Health
Koenig, H. G., VanderWeele, T. J., & Peteet, J. R. (2023) · doi:10.1093/oso/9780190088859.001.0001 · Cited by 1,150 · see citing works
Time for united action on depression: a Lancet–World Psychiatric Association Commission
Herrman, H., Patel, V., Kieling, C., Berk, M., Buchweitz, C., Cuijpers, P., Furukawa, T. A., Kessler, R. C., Kohrt, B. A., Maj, M., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(21)02141-3 · Cited by 1,104 · see citing works
The genetic architecture of the human cerebral cortex
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., et al. (2020) · Science · doi:10.1126/science.aay6690 · Cited by 876 · see citing works
Thapar, A., Eyre, O., Patel, V., & Brent, D. (2022) · The Lancet · doi:10.1016/s0140-6736(22)01012-1 · Cited by 869 · see citing works
Marx, W., Penninx, B. W. J. H., Solmi, M., Furukawa, T. A., Firth, J., Carvalho, A. F., & Berk, M. (2023) · Nature Reviews Disease Primers · doi:10.1038/s41572-023-00454-1 · Cited by 850 · see citing works
Demontis, D., Walters, G. B., Athanasiadis, G., Walters, R., Therrien, K., Nielsen, T. T., Farajzadeh, L., Voloudakis, G., Bendl, J., Zeng, B., et al. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2022.07.018 · Cited by 823 · see citing works
Nievergelt, C. M., Maihofer, A. X., Klengel, T., Atkinson, E. G., Chen, C.-Y., Choi, K. W., Coleman, J. R. I., Dalvie, S., Duncan, L. E., Gelernter, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12576-w · Cited by 635 · see citing works
Self-citationWootton, R. E., Richmond, R. C., Stuijfzand, B. G., Lawn, R. B., Sallis, H. M., Taylor, G. M. J., Hemani, G., Jones, H. J., Zammit, S., Davey Smith, G., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291719002678 · Cited by 587 · see citing works
Self-citationMental health before and during the COVID-19 pandemic in two longitudinal UK population cohorts
Kwong, A. S. F., Pearson, R. M., Adams, M. J., Northstone, K., Tilling, K. M., Major‐Smith, D., Fawns‐Ritchie, C., Bould, H., Warne, N., Zammit, S., et al. (2020) · The British Journal of Psychiatry · doi:10.1192/bjp.2020.242 · Cited by 518 · see citing works
Self-citationAnimal models of major depression: drawbacks and challenges
Planchez, B., Surget, A., & Belzung, C. (2019) · Journal of Neural Transmission · doi:10.1007/s00702-019-02084-y · Cited by 480 · see citing works
Gut microbiome-wide association study of depressive symptoms
Radjabzadeh, D., Bosch, J. A., Uitterlinden, A. G., Zwinderman, A. H., Ikram, M. A., van Meurs, J. B. J., Luik, A. I., Nieuwdorp, M., Lok, A., van Duijn, C. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34502-3 · Cited by 446 · see citing works
Levey, D. F., Stein, M. B., Wendt, F. R., Pathak, G. A., Zhou, H., Aslan, M., Quaden, R., Harrington, K. M., Nuñez, Y. Z., Overstreet, C., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00860-2 · Cited by 422 · see citing works
Self-citationAn integrated framework for local genetic correlation analysis
Werme, J., van der Sluis, S., Posthuma, D., & de Leeuw, C. (2022) · Nature Genetics · doi:10.1038/s41588-022-01017-y · Cited by 420 · see citing works
Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration
Revez, J. A., Lin, T., Qiao, Z., Xue, A., Holtz, Y., Zhu, Z., Zeng, J., Wang, H., Sidorenko, J., Kemper, K. E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15421-7 · Cited by 408 · see citing works
Self-citationHautakangas, H. M., Winsvold, B. S., Ruotsalainen, S., Björnsdóttir, G., Harder, A. V. E., Kogelman, L. J. A., Thomas, L. F., Noordam, R., Benner, C., Gormley, P., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00990-0 · Cited by 402 · see citing works
Candidate biomarkers in psychiatric disorders: state of the field
Abi‐Dargham, A., Moeller, S. J., Ali, F., DeLorenzo, C., Domschke, K., Horga, G., Jutla, A., Kotov, R., Paulus, M. P., Rubio, J. M., et al. (2023) · World Psychiatry · doi:10.1002/wps.21078 · Cited by 390 · see citing works
Self-citationGender Differences in Depression: Biological, Affective, Cognitive, and Sociocultural Factors
Hyde, J. S., & Mezulis, A. H. (2020) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000230 · Cited by 385 · see citing works
Milaneschi, Y., Kappelmann, N., Ye, Z., Lamers, F., Moser, S., Jones, P. B., Burgess, S., Penninx, B. W. J. H., & Khandaker, G. M. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01188-w · Cited by 369 · see citing works
Major Depressive Disorder: Advances in Neuroscience Research and Translational Applications
Li, Z., Ruan, M., Chen, J., & Fang, Y. (2021) · Neuroscience Bulletin · doi:10.1007/s12264-021-00638-3 · Cited by 352 · see citing works
Zhou, H., Sealock, J., Sanchez‐Roige, S., Clarke, T., Levey, D. F., Cheng, Z., Li, B., Polimanti, R. A., Kember, R. L., Vickers‐Smith, R., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0643-5 · Cited by 343 · see citing works
Self-citationKundaković, M., & Rocks, D. (2022) · Frontiers in Neuroendocrinology · doi:10.1016/j.yfrne.2022.101010 · Cited by 333 · see citing works
Als, T. D., Kurki, M., Grove, J., Voloudakis, G., Therrien, K., Tasanko, E. M., Nielsen, T. T., Naamanka, J., Veerapen, K., Levey, D. F., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02352-1 · Cited by 321 · see citing works
Li, J., Seidlitz, J., John Suckling, A., Fan, F., Ji, G., Meng, Y., Yang, S., Wang, K., Qiu, J., Chen, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21943-5 · Cited by 316 · see citing works
Adams, M. J., Streit, F., Meng, X., Awasthi, S., Adey, B. N., Choi, K. W., Chundru, V. K., Coleman, J. R. I., Ferwerda, B., Foo, J. C., et al. (2025) · Cell · doi:10.1016/j.cell.2024.12.002 · Cited by 311 · see citing works
Self-citationGrotzinger, A. D., Mallard, T. T., Akingbuwa, W. A., Ip, H. F., Adams, M. J., Lewis, C. M., McIntosh, A. M., Grove, J., Dalsgaard, S., Lesch, K., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01057-4 · Cited by 309 · see citing works
Self-citationZhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., Taychameekiatchai, A., Rao, P., Pisco, A. O., Zou, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01167-z · Cited by 302 · see citing works
Yan, Z., & Rein, B. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01092-3 · Cited by 300 · see citing works
Sey, N. Y. A., Hu, B., Mah, W., Fauni, H., McAfee, J. C., Rajarajan, P., Brennand, K. J., Akbarian, S., & Won, H. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0603-0 · Cited by 297 · see citing works
Opel, N., Goltermann, J., Hermesdorf, M., Berger, K., Baune, B. T., & Dannlowski, U. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.04.027 · Cited by 287 · see citing works
Self-citationWatson, D., Levin‐Aspenson, H. F., Waszczuk, M. A., Conway, C. C., Dalgleish, T., Dretsch, M. N., Eaton, N. R., Forbes, M. K., Forbush, K. T., Hobbs, K. A., et al. (2022) · World Psychiatry · doi:10.1002/wps.20943 · Cited by 285 · see citing works
Khandaker, G. M., Zuber, V., Rees, J. M. B., Carvalho, L. A., Mason, A. M., Foley, C. N., Gkatzionis, A., Jones, P. B., & Burgess, S. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0395-3 · Cited by 279 · see citing works
Levey, D. F., Gelernter, J. E., Polimanti, R. A., Zhou, H., Cheng, Z., Aslan, M., Quaden, R. M., Concato, J., Radhakrishnan, K., Bryois, J., et al. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.19030256 · Cited by 275 · see citing works
Self-citationCould Polygenic Risk Scores Be Useful in Psychiatry?
Murray, G. K., Lin, T., Austin, J., McGrath, J. J., Hickie, I. B., & Wray, N. R. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3042 · Cited by 274 · see citing works
Self-citationRisk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture
Zhang, Q., Sidorenko, J., Couvy-Duchesne, B., Marioni, R. E., Wright, M. J., Goate, A. M., Marcora, E., Huang, K.-L., Porter, T., Laws, S. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18534-1 · Cited by 272 · see citing works
Self-citationDissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics
Buch, A. M., & Liston, C. M. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00789-3 · Cited by 271 · see citing works
The future of rodent models in depression research
Gururajan, A., Reif, A., Cryan, J. F., & Slattery, D. A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0221-6 · Cited by 269 · see citing works
Single-cell epigenomics reveals mechanisms of human cortical development
Ziffra, R. S., Kim, C. N., Ross, J. M., Wilfert, A. B., Turner, T. N., Haeussler, M., Casella, A. M., Przytycki, P. F., Keough, K. C., Shin, D. S., et al. (2021) · Nature · doi:10.1038/s41586-021-03209-8 · Cited by 267 · see citing works
Kappelmann, N., Arloth, J., Georgakis, M. K., Czamara, D., Rost, N., Ligthart, S., Khandaker, G. M., & Binder, E. B. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.3436 · Cited by 262 · see citing works
Andreassen, O. A., Hindley, G., Frei, O., & Smeland, O. B. (2023) · World Psychiatry · doi:10.1002/wps.21034 · Cited by 255 · see citing works
Penner-Goeke, S., & Binder, E. B. (2019) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2019.21.4/ebinder · Cited by 254 · see citing works
Yuan, M., Yang, B., Rothschild, G., Mann, J. J., Sanford, L. D., Tang, X., Huang, C., Wang, C., & Zhang, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01519-z · Cited by 246 · see citing works
“15,16 Moreover, the variance explained by major depression polygenic risk scores based on these genomic loci is still a very low fraction of the total heritable risk.”
BackgroundGenetic control of RNA splicing and its distinct role in complex trait variation
Qi, T., Wu, Y., Fang, H., Zhang, F., Liu, S., Zeng, J., & Yang, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01154-4 · Cited by 237 · see citing works
The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets
Larivière, S., Paquola, C., Park, B., Royer, J., Wang, Y., Benkarim, O. M., de Wael, R. V., Valk, S. L., Thomopoulos, S. I., Kirschner, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01186-4 · Cited by 234 · see citing works
How genome-wide association studies (GWAS) made traditional candidate gene studies obsolete
Duncan, L. E., Ostacher, M. J., & Ballon, J. S. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0389-5 · Cited by 230 · see citing works
Stein, M. B., Levey, D. F., Cheng, Z., Wendt, F. R., Harrington, K. M., Pathak, G. A., Cho, K., Quaden, R. M., Radhakrishnan, K., Girgenti, M. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00767-x · Cited by 230 · see citing works
Genetic variants associated with longitudinal changes in brain structure across the lifespan
Brouwer, R. M., Klein, M., Grasby, K. L., Schnack, H. G., Jahanshad, N., Teeuw, J., Thomopoulos, S. I., Sprooten, E., Franz, C. E., Gogtay, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01042-4 · Cited by 227 · see citing works
Self-citationGong, W., Guo, P., Li, Y., Liu, L., Yan, R., Liu, S., Wang, S., Xue, F., Zhou, X., & Yuan, Z. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.4974 · Cited by 225 · see citing works
Warrier, V., Kwong, A. S. F., Luo, M., Dalvie, S., Croft, J., Sallis, H. M., Baldwin, J., Munafò, M. R., Nievergelt, C. M., Grant, A. J., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30569-1 · Cited by 224 · see citing works
Self-citationNievergelt, C. M., Maihofer, A. X., Atkinson, E. G., Chen, C., Choi, K. W., Coleman, J. R. I., Daskalakis, N. P., Duncan, L. E., Polimanti, R. A., Aaronson, C. J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01707-9 · Cited by 221 · see citing works
Self-citationEarly life stress alters transcriptomic patterning across reward circuitry in male and female mice
Peña, C. J., Smith, M., Ramakrishnan, A., Cates, H. M., Bagot, R. C., Kronman, H. G., Patel, B., Chang, A. B., Purushothaman, I., Dudley, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13085-6 · Cited by 219 · see citing works
Tran, M. N., Maynard, K. R., Spangler, A., Huuki, L. A., Montgomery, K. D., Sadashivaiah, V., Tippani, M., Barry, B. K., Hancock, D. B., Hicks, S. C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.001 · Cited by 219 · see citing works
Pitharouli, M. C., Hagenaars, S. P., Glanville, K. P., Coleman, J. R. I., Hotopf, M., Lewis, C. M., & Pariante, C. M. (2021) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20060947 · Cited by 218 · see citing works
Self-citationBidirectional relationship between sleep and depression
Yasugaki, S., Okamura, H., Kaneko, A., & Hayashi, Y. (2023) · Neuroscience Research · doi:10.1016/j.neures.2023.04.006 · Cited by 216 · see citing works
The genetic basis of major depression
Kendall, K. M., Van Assche, E., Andlauer, T. F. M., Choi, K. W., Luykx, J. J., Schulte, E. C., & Lu, Y. (2021) · Psychological Medicine · doi:10.1017/s0033291721000441 · Cited by 213 · see citing works
Szymkowicz, S. M., Gerlach, A., Homiack, D., & Taylor, W. D. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02464-9 · Cited by 212 · see citing works
Pizzagalli, D. A. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20220423 · Cited by 204 · see citing works
Ni, G., Zeng, J., Revez, J. A., Wang, Y., Zheng, Z., Ge, T., Restuadi, R., Kiewa, J., Nyholt, D. R., Coleman, J. R. I., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.04.018 · Cited by 203 · see citing works
Self-citationThe polygenic architecture of schizophrenia — rethinking pathogenesis and nosology
Smeland, O. B., Frei, O., Dale, A. M., & Andreassen, O. A. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0364-0 · Cited by 202 · see citing works
Coleman, J. R. I., Gaspar, H. A., Bryois, J., Byrne, E. M., Forstner, A. J., Holmans, P. A., de Leeuw, C. A., Mattheisen, M., McQuillin, A., Whitehead Pavlides, J. M., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.10.015 · Cited by 199 · see citing works
Self-citationZhang, Y., Lu, Q., Ye, Y., Huang, K., Liu, W., Wu, Y., Zhong, X., Li, B., Yu, Z., Travers, B. G., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02478-w · Cited by 200 · see citing works
Meng, X., Navoly, G., Giannakopoulou, O., Levey, D. F., Koller, D., Pathak, G. A., Koen, N., Lin, K., Adams, M. J., Rentería, M. E., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01596-4 · Cited by 198 · see citing works
Self-citationGelernter, J. E., Sun, N., Polimanti, R. A., Pietrzak, R. H., Levey, D. F., Bryois, J., Lu, Q., Hu, Y., Li, B., Radhakrishnan, K., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0447-7 · Cited by 197 · see citing works
Self-citationGenetic contributions to autism spectrum disorder
Havdahl, A., Niarchou, M., Starnawska, A., Uddin, M., van der Merwe, C., & Warrier, V. (2021) · Psychological Medicine · doi:10.1017/s0033291721000192 · Cited by 193 · see citing works
The Genetic Architecture of Depression in Individuals of East Asian Ancestry
Giannakopoulou, O., Lin, K., Meng, X., Su, M., Kuo, P., Peterson, R. E., Awasthi, S., Moscati, A., Coleman, J. R. I., Bass, N. J., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.2099 · Cited by 189 · see citing works
Self-citationPerry, B. I., Upthegrove, R., Kappelmann, N., Jones, P. B., Burgess, S., & Khandaker, G. M. (2021) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2021.07.009 · Cited by 187 · see citing works
Peripheral Blood Cell–Stratified Subgroups of Inflamed Depression
Lynall, M.-E., Turner, L., Bhatti, J., Cavanagh, J., de Boer, P., Mondelli, V., Jones, D., Drevets, W. C., Cowen, P., Harrison, N. A., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.11.017 · Cited by 187 · see citing works
Shared mechanisms across the major psychiatric and neurodegenerative diseases
Wingo, T. S., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Wynne, M. E., Liu, J., Lori, A., Faundez, V., Bennett, D. A., Seyfried, N. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31873-5 · Cited by 199 · see citing works
Winter, N. R., Leenings, R., Ernsting, J., Sarink, K., Fisch, L., Emden, D., Blanke, J., Goltermann, J., Opel, N., Barkhau, C., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.1780 · Cited by 185 · see citing works
Self-citationGut Microbiota and Psychiatric Disorders: A Two-Sample Mendelian Randomization Study
Ni, J., Xu, Q., Yan, S., Han, B., Zhang, H., Wei, X., Feng, G.-J., Zhao, M., Pei, Y., & Zhang, L. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.737197 · Cited by 182 · see citing works
Lynch, C. J., Gunning, F. M., & Liston, C. M. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.01.012 · Cited by 181 · see citing works
Polygenic risk scores in the clinic: new perspectives needed on familiar ethical issues
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Association of Antihypertensive Drug Target Genes With Psychiatric Disorders
Chauquet, S., Zhu, Z., O’Donovan, M. C., Walters, J., Wray, N. R., & Shah, S. H. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.0005 · Cited by 176 · see citing works
Self-citationYang, G., Zhu, K., Guo, W., Wu, D., Quan, X., Huang, X., Liu, S., Li, Y. Y., Fang, H., Qiu, Y., et al. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202200457 · Cited by 176 · see citing works
Glutamate and GABA Homeostasis and Neurometabolism in Major Depressive Disorder
Sarawagi, A., Soni, N. D., & Patel, A. B. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.637863 · Cited by 172 · see citing works
Resource profile and user guide of the Polygenic Index Repository
Becker, J., Burik, C. A. P., Goldman, G., Wang, N., Jayashankar, H., Bennett, M., Belsky, D. W., Linnér, R. K., Ahlskog, R., Kleinman, A., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01119-3 · Cited by 171 · see citing works
Self-citationRole of stress in skin diseases: A neuroendocrine-immune interaction view
Zhang, H., Wang, M., Zhao, X., Wang, Y., Chen, X., & Su, J. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.12.005 · Cited by 169 · see citing works
Maitra, M., Mitsuhashi, H., Rahimian, R., Chawla, A., Yang, J., Fiori, L. M., Davoli, M. A., Perlman, K., Aouabed, Z., Mash, D. C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38530-5 · Cited by 164 · see citing works
ADHD and depression: investigating a causal explanation
Riglin, L., Leppert, B., Dardani, C., Thapar, A. K., Rice, F. J., O’Donovan, M. C., Smith, G. D., Stergiakouli, E., Tilling, K., & Thapar, A. (2020) · Psychological Medicine · doi:10.1017/s0033291720000665 · Cited by 161 · see citing works
Hamzah, H., Hamzah, M. I., & Zulkifli, H. (2022) · Sustainability · doi:10.3390/su14020813 · Cited by 161 · see citing works
Docherty, A. R., Mullins, N., Ashley-Koch, A. E., Qin, X., Coleman, J. R. I., Shabalin, A., Kang, J., Murnyak, B., Wendt, F., Adams, M., et al. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21121266 · Cited by 160 · see citing works
Self-citationUncovering the Genetic Architecture of Major Depression
McIntosh, A. M., Sullivan, P. F., & Lewis, C. M. (2019) · Neuron · doi:10.1016/j.neuron.2019.03.022 · Cited by 160 · see citing works
Self-citationBrain proteome-wide association study implicates novel proteins in depression pathogenesis
Wingo, T. S., Liu, Y., Gerasimov, E. S., Gockley, J., Logsdon, B. A., Duong, D. M., Dammer, E. B., Lori, A., Kim, P. J., Ressler, K. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00832-6 · Cited by 159 · see citing works
Causal associations between modifiable risk factors and the Alzheimer's phenome
Andrews, S. J., Fulton‐Howard, B., O'Reilly, P., Marcora, E., Goate, A. M., & collaborators of the Alzheimer's Disease Genetics Consortium (2021) · Annals of Neurology · doi:10.1002/ana.25918 · Cited by 156 · see citing works
Sex-Specific Role for the Long Non-coding RNA LINC00473 in Depression
Issler, O., van der Zee, Y. Y., Ramakrishnan, A., Wang, J., Tan, C., Loh, Y.-H. E., Purushothaman, I., Walker, D. M., Lorsch, Z. S., Hamilton, P. J., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.023 · Cited by 156 · see citing works
Fabbri, C., Hagenaars, S. P., John, C., Williams, A. T., Shrine, N. R. G., Moles, L., Hanscombe, K. B., Serretti, A., Shepherd, D. J., Free, R. C., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01062-9 · Cited by 156 · see citing works
Self-citationDepression and Inflammatory Bowel Disease: A Bidirectional Two-sample Mendelian Randomization Study
Luo, J. M., Xu, Z., Noordam, R., van Heemst, D., & Li‐Gao, R. (2021) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjab191 · Cited by 155 · see citing works
Birey, F., Li, M.-Y., Gordon, A., Thete, M. V., Valencia, A. M., Revah, O., Pașca, A. M., Geschwind, D. H., & Pașca, S. P. (2022) · Cell stem cell · doi:10.1016/j.stem.2021.11.011 · Cited by 155 · see citing works
Single-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka, W. B., Mohammadi, S., Fullard, J. F., Dávila-Velderrain, J., Subburaju, S., Tso, D. R., Hourihan, M., Jiang, S., Lee, H.-C., Bendl, J. M., et al. (2024) · Science · doi:10.1126/science.adg5136 · Cited by 155 · see citing works
Ye, J., Wen, Y. F., Sun, X., Chu, X., Li, P., Cheng, B., Cheng, S., Liu, L., Zhang, L., Ma, M., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.11.019 · Cited by 154 · see citing works
Cai, N., Choi, K. W., & Fried, E. I. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa115 · Cited by 153 · see citing works
Nemeroff, C. B. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20060845 · Cited by 153 · see citing works
Amin, N., Liu, J., Bonnechère, B., MahmoudianDehkordi, S., Arnold, M., Batra, R., Chiou, Y., Fernandes, M., Ikram, M. A., Kraaij, R., et al. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.0685 · Cited by 153 · see citing works
Zhang, M., Chen, J., Yin, Z., Wang, L., & Peng, L. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01759-z · Cited by 152 · see citing works
Maina, J. G., Balkhiyarova, Z., Nouwen, A., Pupko, I., Ulrich, A., Boissel, M., Bonnefond, A., Froguel, P., Khamis, A., Prokopenko, I., et al. (2023) · Diabetes Care · doi:10.2337/dc22-2373 · Cited by 151 · see citing works
Rare and common genetic determinants of metabolic individuality and their effects on human health
Surendran, P., Stewart, I. D., Auyeung, V. P. W., Pietzner, M., Raffler, J., Wörheide, M. A., Li, C., Smith, R. F., Wittemans, L. B. L., Bomba, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02046-0 · Cited by 150 · see citing works
Common variants contribute to intrinsic human brain functional networks
Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01039-6 · Cited by 149 · see citing works
Jami, E. S., Hammerschlag, A. R., Bartels, M., & Middeldorp, C. M. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01300-2 · Cited by 145 · see citing works
- Authors
- Howard, D. M., Adams, M. J., Clarke, T.-K., Hafferty, J. D., Gibson, J., Shirali, M., Coleman, J. R. I., Hagenaars, S. P., Ward, J., Wigmore, E. M., Alloza, C., Shen, X., Barbu, M. C., Xu, E. Y., Whalley, H. C., Marioni, R. E., Porteous, D. J., Davies, G., Deary, I. J., Hemani, G., Berger, K., Teismann, H., Rawal, R., Arolt, V., Baune, B. T., Dannlowski, U., Domschke, K., Tian, C., Hinds, D. A., 23andMe Research Team, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Trzaskowski, M., Byrne, E. M., Ripke, S., Smith, D. J., Sullivan, P. F., Wray, N. R., Breen, G., Lewis, C. M., & McIntosh, A. M.
- Affiliations
- Royal Edinburgh Hospital (GB)
- King's College London (GB)
- University of Glasgow (GB)
- University of Edinburgh (GB)
- University of Bristol (GB)
- University of Münster (DE)
- The University of Melbourne (AU)
- University of Freiburg (DE)
- 23andMe (United States) (US)
- The University of Queensland (AU)
- Broad Institute (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Howard, David M.
- ORCID
- Howard, David M.
- Adams, Mark J.
- Clarke, Toni-Kim
- Hafferty, Jonathan D.
- Gibson, Jude
- Shirali, Masoud
- Coleman, Jonathan R. I.
- Hagenaars, Saskia P.
- Ward, Joey
- Wigmore, Eleanor M.
- Alloza, Clara
- Shen, Xueyi
- Barbu, Miruna C.
- Xu, Eileen Y.
- Whalley, Heather C.
- Marioni, Riccardo E.
- Porteous, David J.
- Davies, Gail
- Deary, Ian J.
- Hemani, Gibran
- Berger, Klaus
- Teismann, Henning
- Rawal, Rajesh
- Arolt, Volker
- Baune, Bernhard T.
- Dannlowski, Udo
- Domschke, Katharina
- Hinds, David A.
- Trzaskowski, Maciej
- Byrne, Enda M.
- Ripke, Stephan
- Smith, Daniel J.
- Sullivan, Patrick F.
- Wray, Naomi R.
- Breen, Gerome
- Lewis, Cathryn M.
- McIntosh, Andrew M.
- Venue
- Nature Neuroscience
- Published
- 2019-02-04
- Volume
- 22
- Issue
- 3
- Pages
- 343-352
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC6522363
- OpenAlex
- W2911487850
- Semantic Scholar
- 4577583573d02a179e1e2779ffe112fe516f64f5
- Cited by
- 2,565
- References
- 114
- Citations by year
- 2026: 235 · 2025: 386 · 2024: 512 · 2023: 452 · 2022: 383 · 2021: 304 · 2020: 206 · 2019: 80 · 2018: 1 · 2017: 1 · 2014: 2 · 2012: 1
- FWCI
- 32.94
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Research Portal (Queen's University Belfast) (submittedVersion)
- Funders
- South London and Maudsley NHS Foundation Trust
- Wellcome Trust (208806, Z/17/Z, 104036/Z/14/Z, 208806/Z/17/Z, 104036, 2016-2021)
- National Institute for Health and Care Research
- King's College London
- Lister Institute of Preventive Medicine
- Deutsche Forschungsgemeinschaft (Dan3/012/17, grant FOR2107, FOR 2107/ DA1151/5-1, DA 1151/5-2 to UD), DA1151/5-2, TRR‐58, DA1151/5-1, FOR 2107, DA1151, SFB TRR 58)
- Bundesministerium für Bildung und Forschung (DA1151/5-2, FOR2107 DA1151/5-1, 01ER0816, 01ER1506, 01ER0816 and 01ER1506)
- Dr Mortimer and Theresa Sackler Foundation
- Westfälische Wilhelms-Universität Münster (DA1151/5-2, FOR2107 DA1151/5-1)
- Centre for Cognitive Ageing and Cognitive Epidemiology (MR/K026992/1)
- Directorate for Biological Sciences
- Medical Research Council (MC_PC_17209, MR/K026992/1, 104036/Z/14/Z, 11/NW/0382, MR/S015132/1, MR/J000914/1, 1078901, MC_PC_17228)
- Biotechnology and Biological Sciences Research Council (MR/K026992/1)
- National Institute of Mental Health (U01MH109528, SFB-TRR58, U01MH109536, DA 1151/5-1, U01 MH1095320, MH109528, MH1095320, FOR2107, DA 1151/5-2, U01MH109532)
- National Institute of Environmental Health Sciences (P01ES000040)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- major depressive disorder, genetic architecture, genome-wide association study, prefrontal cortex, synaptic structure, neurotransmission, gene pathways, neuroticism, smoking, drug repurposing, heritability
- MeSH terms
- Depression, Major Depressive Disorder, Female, Humans, Male, Genetic Variation, Cohort Studies, Prefrontal Cortex, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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