The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index; 2 of the 50 sources its claims cite were checked for support.
Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions
Open accessPublished version of a preprint
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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51 Own result · 36 Not checked yet · 19 Own interpretation · 4 Own method · 1 Partly supported · accuracy not yet measured
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genetic correlations with depression
What the sources say
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results section (“Implications based on relationships with other traits”; page not supplied) reports a genetic relationship between major depression and decreased age at menarche. The passages in our copy do not give the claimed rG of −0.12 or its standard error of 0.02, so the numerical part is not verified by this source. (LLM, conf=0.84)
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
The Results section, “Genetic correlations with depression” (page unavailable), reports correlations between depression phenotypes and 235 other traits. The Discussion says some correlations were generally aligned with earlier reports. Our copy does not identify age at menarche or give the claimed rG of −0.12 (s.e. 0.02); searches for menarche and that value found no matching passage. (LLM, conf=0.77)
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Causal relationships between depression and other traits
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genes and gene-sets putatively associated with depression
What the sources say
- Not checked yetcites [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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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Partitioning of the heritability component of depression
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genes and gene-sets putatively associated with depression
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Abstract
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- Also stated in: § 102 independent genetic variants associated with depression · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > 102 independent genetic variants associated with depression
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In the paper
“The MR analysis also identified a putative bi-directional casual effect between depression and neuroticism.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Causal relationships between depression and other traits
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genetic correlations with depression
What the sources say
- Not checked yetcites [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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [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 yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Drug - gene interactions
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > 102 independent genetic variants associated with depression
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Partitioning of the heritability component of depression
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
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In the paper
“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”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > 102 independent genetic variants associated with depression
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Causal relationships between depression and other traits
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Causal relationships between depression and other traits
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In the paper
“There was significant enrichment (Pcorrected < 0.05) of the anterior cingulate cortex, frontal cortex and cortex brain regions and neuron brain cells.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Partitioning of the heritability component of depression
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genetic correlations with depression
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Causal relationships between depression and other traits
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > 102 independent genetic variants associated with depression
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Causal relationships between depression and other traits
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“Both the meta-analysis PRS and the Wray PRS were significantly associated with MDD in each of the three cohorts”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > 102 independent genetic variants associated with depression
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“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genetic correlations with depression
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Causal relationships between depression and other traits
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In the paper
“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”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genes and gene-sets putatively associated with depression
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“The lin-28 homolog b (LIN28B) gene was significant in our meta-analysis and has been associated with age of menarche 37.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [37] Ong, K. K. et al. Genetic variation in LIN28B is associated with the timing of puberty. Nat. Genet. 41, 729–733 (2009). source
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In the paper
“The heritability enrichments and gene-set analysis both provided evidence for the role of prefrontal brain regions in depression”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Conclusion
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The current meta-analysis identified a significant genetic correlation between age of menarche and depression, and nominal evidence of a bi-directional causal effect.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
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In the paper
“One such candidate is NRG1, identified here in depression, and also in a recent meta-analysis of antidepressant response 50.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [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
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In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [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 yetcites [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 yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Conclusion
boosteddirectn = 414,055- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [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 yetcites [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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, no tests were excluded due to directional horizontal pleiotropy.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Online Methods > Mendelian randomization
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Twin studies have provided heritability estimates of the disease of approximately 30-40% 3”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Abstract
What the sources say
- Not checked yetcites [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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Abstract
What the sources say
- Not checked yetcites [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
plaincorrelationaln = 113,769- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Results > Genes and gene-sets putatively associated with depression
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“however depression is a polygenic trait influenced by many genetic variants each of small effect 4”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Abstract
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“However, both of these studies failed to find any replicated variants associated with depression.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Abstract
What the sources say
- Not checked yetcites [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 yetcites [6] Hek, K. et al. A genome-wide association study of depressive symptoms. Biol. Psychiatry 73, 667–678 (2013). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, analysing larger samples, which have used different approaches to diagnosis, may provide advances in our understanding of the genetics of depression.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Estrogen Receptor 2 (ESR2) has previously been implicated in antidepressant action through up-regulation by 3β-hydroxysteroid dehydrogenase 47.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [49] Dunn, E. C. et al. Genetic determinants of depression: recent findings and future directions. Harv. Rev. Psychiatry 23, 1–18 (2015). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Online Methods
What the sources say
- Not checked yetcites [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 yetcites [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 yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Linkage disequilibrium score (LDSC) regression intercepts 10 were used for genomic inflation control of the three contributing cohorts and the final meta-analysis results.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Online Methods > Meta-analysis of genome-wide association study summary statistics
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It may also be that the pathways of depression and serotonergic antidepressant effect are separate but entwine through common intermediary genes.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The Neuregulin 1 (NRG1) receptor ErbB4, found on GABAergic neurones, has been identified as a potentially druggable target for depression, anxiety and schizophrenia 45.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [45] Bi, L.-L. et al. Amygdala NRG1-ErbB4 is critical for the modulation of anxiety-like behaviors. Neuropsychopharmacology 40, 974–986 (2015). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“There is interest in developing agents, such as basimglurant and fenobam, as antidepressants and anxiolytics that target glutamatergic receptors 46.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion
What the sources say
- Not checked yetcites [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
- Not checked yetcites [21] Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The dopaminergic system has been previously implicated in depression, particularly the symptoms of anhedonia and decreased motivation”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [44] Bonci, A. & Hopf, F. W. The dopamine D2 receptor: new surprises from an old friend. Neuron 47, 335–338 (2005). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Data collection was not randomized and there was no randomization in the organization of the experimental conditions or stimulus presentations.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Online Methods > A Life Sciences Reporting Summary accompanies this manuscript
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Data collection and analysis were not performed blind to the conditions of the experiments.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Online Methods > A Life Sciences Reporting Summary accompanies this manuscript
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Estrogenic compounds have also been found to have antidepressant effects in rodent models of depression 48.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“antidepressant action has been reported for dopamine reuptake inhibitors and D2-receptor agonists in animal models of depression 44.”
§ Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions > Discussion > Drug - gene interactions
What the sources say
- Not checked yetcites [44] Bonci, A. & Hopf, F. W. The dopamine D2 receptor: new surprises from an old friend. Neuron 47, 335–338 (2005). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
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
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
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
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
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
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
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
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
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
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
Self-citation
Research works printed but not linked: 1
- 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
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Items 701–800 of 1,004
Shao, N., Gong, Y., Wang, X., Wei, J., Shi, J., Ding, H., Zhang, M., Kang, C., Wang, S., Chen, L., et al. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.12.114 · Cited by 21
Cognitive Function in People With Familial Risk of Depression
Cullen, B., Gameroff, M. J., Ward, J., Bailey, M. E. S., Lyall, D. M., Lyall, L. M., MacSweeney, N., Murphy, E. J., Sangha, N., Shen, X., et al. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.0716 · Cited by 19
Self-citationZhao, P., Ying, Z., Yuan, C., Zhang, H., Dong, A., Tao, J., Yi, X., Yang, M., Jin, W., Tian, W., et al. (2024) · General Psychiatry · doi:10.1136/gpsych-2023-101418 · Cited by 19
Choi, M., Alıev, F., Barr, P. B., Cooke, M. E., Kuo, S. I., Salvatore, J. E., Dick, D. M., & Brislin, S. J. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03454-9 · Cited by 20
Mokhtari, A., Porte, B., Belzeaux-R, R., Étain, B., Ibrahim, E. C., Cynthia Marie-claire, A., Lutz, P.-É., & Delahaye‐Duriez, A. (2022) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2022.110520 · Cited by 19
Segura, À. G., Mezquida, G., Martínez-Pinteño, A., Gassó, P., Rodríguez, N., Moreno-Izco, L., Amoretti, S., Bioque, M., Lobo, A., González-Pinto, A. M., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722001544 · Cited by 19
Fang, Y., Fritsche, L. G., Mukherjee, B., Sen, S., & Richmond‐Rakerd, L. S. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.06.004 · Cited by 19
Li, M., O’Donnell, K. J., Caron, J., Meaney, M. J., Kobor, M. S., D’Arcy, C., Su, Y., Liu, A., & Meng, X. (2022) · Development and Psychopathology · doi:10.1017/s0954579422000918 · Cited by 21
The neuroanatomy of social trust predicts depression vulnerability
Fermin, A. S. R., Kiyonari, T., Matsumoto, Y., Takagishi, H., Li, Y., Kanai, R., Sakagami, M., Akaishi, R., Ichikawa, N., Takamura, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-20443-w · Cited by 21
Ma, K., Song, P., Liu, Z., Yang, L., Wang, L., Zhou, J., Chen, J., & Dong, Q. G. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1026227 · Cited by 20
Wesseldijk, L. W., Lu, Y., Karlsson, R., Ullén, F., & Mosing, M. A. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02308-6 · Cited by 21
Kosciuszko, M., Steptoe, A., & Ajnakina, O. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02367-9 · Cited by 23
Bidirectional Mendelian randomization study of psychiatric disorders and Parkinson’s disease
Wu, Q., Liu, S., Huang, X., Liu, J., Wang, Y., Xiang, Y., Tang, X., Xu, Q., Yan, X., Tang, B., et al. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1120615 · Cited by 20
A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity
Baltramonaityte, V., Pingault, J., Cecil, C. A. M., Choudhary, P., Järvelin, M., Penninx, B. W. J. H., Felix, J. F., Sebert, S. P., Milaneschi, Y., & Walton, E. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010508 · Cited by 22
The United Kingdom Eating Disorders Genetics Initiative
Monssen, D., Davies, H. L., Kakar, S. A., Bristow, S., Curzons, S. C. B., Davies, M. R., Kelly, E. J., Ahmad, Z., Bradley, J. R., Bright, S., et al. (2023) · International Journal of Eating Disorders · doi:10.1002/eat.24037 · Cited by 19
Self-citationHalim, H. A., Hamzah, M. I., & Zulkifli, H. (2024) · International Journal of Evaluation and Research in Education (IJERE) · doi:10.11591/ijere.v13i2.26187 · Cited by 19
Song, J., Jönsson, L., Lu, Y., Bergen, S. E., Karlsson, R., Smedler, E., Gordon‐Smith, K., Jones, I. R., Jones, L. A., Craddock, N. J., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02448-1 · Cited by 19
Self-citationBenjamin, K. J., Arora, R., Feltrin, A. S., Pertea, G. M., Giles, H. H., Stolz, J. M., D’Ignazio, L., Collado‐Torres, L., Shin, J. H., Ulrich, W. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48048-z · Cited by 19
Cheng, Y., Zhu, Z., Yang, Z., Liu, X., Qian, X., Zhu, J., Hu, X., Jiang, P., Cui, T., Wang, Y., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-97369-6 · Cited by 21
Shohat, S., Amelan, A., & Shifman, S. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.05.019 · Cited by 19
Methyl-CpG-binding protein 2 mediates overlapping mechanisms across brain disorders
Bach, S., Ryan, N., Guasoni, P., Corvin, A. P., El-Nemr, R. A., Khan, D., Sanfeliu, A., & Tropea, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-79268-0 · Cited by 20
Silberstein, M. D., Nesbit, N., Cai, J., & Lee, P. H. (2021) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2021.01.007 · Cited by 21
van Calker, D., & Serchov, T. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.03.019 · Cited by 19
Gonda, X., Eszlári, N., Török, D., Gál, Z., Bokor, J., Millinghoffer, A., Baksa, D., Petschner, P., Antal, P., Breen, G. D., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01436-1 · Cited by 19
Self-citationJiang, X., Deng, Q., & Stener‐Victorin, E. (2021) · Human Reproduction · doi:10.1093/humrep/deab119 · Cited by 19
Lin, Y.-S., Wang, C., & Chen, C.-Y. (2021) · Genes · doi:10.3390/genes12101506 · Cited by 19
Major Depressive Disorder and Lifestyle: Correlated Genetic Effects in Extended Twin Pedigrees
Huider, F., Milaneschi, Y., van der Zee, M. D., de Geus, E. J. C., Helmer, Q., Penninx, B. W. J. H., & Boomsma, D. I. (2021) · Genes · doi:10.3390/genes12101509 · Cited by 19
Fabbri, C., Mutz, J., Lewis, C. M., & Serretti, A. (2021) · Psychological Medicine · doi:10.1017/s003329172100502x · Cited by 20
Self-citationXu, J., Johnson, J., Signer, R. H., Birgegård, A., Jordan, J., Kennedy, M. A., Landén, M., Maguire, S., Martin, N. G., Mortensen, P. B., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00099-1 · Cited by 19
Docherty, A. R., Shabalin, A. A., Adkins, D. E., Mann, F. D., Krueger, R. F., Bacanu, S., Campbell, A., Hayward, C., Porteous, D. J., McIntosh, A. M., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa042 · Cited by 21
Self-citationSun, J., Wang, W., Zhang, R., Duan, H., Tian, X., Xu, C., Li, X., & Zhang, D. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02074-x · Cited by 18
Cai, N., Verhulst, B., Andreassen, O. A., Buitelaar, J. K., Edenberg, H. J., Hettema, J. M., Gandal, M. J., Grotzinger, A. D., Jonas, K. G., Lee, P., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02878-x · Cited by 18
Gui, A., Hollowell, A., Wigdor, E. M., MORGAN, M., Hannigan, L. J., Corfield, E. C., Odintsova, V. V., Hottenga, J., Wong, A., Pool, R., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02145-1 · Cited by 20
Fiani, D., Chahine, S., Zaboube, M., Solmi, M., Powers, J. M., & Calarge, C. A. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106372 · Cited by 18
Ziegler, G. C., Ehlis, A., Weber, H., Vitale, M. R., Zöller, J. E. M., Ku, H.-P., Schiele, M. A., Kürbitz, L. I., Romanos, M., Pauli, P., et al. (2021) · Genes · doi:10.3390/genes12091356 · Cited by 19
Self-citationXu, M., Zhang, W., Hochwalt, P., Yang, C., Liu, N., Qu, J., Sun, H. J., DelBello, M. P., Lui, S., & Nery, F. G. (2022) · Human Brain Mapping · doi:10.1002/hbm.25827 · Cited by 18
Pereira, S., Muñoz, K. A., Small, B. J., Soda, T., Torgerson, L. N., Sanchez, C. E., Austin, J., Storch, E. A., & Lázaro‐Muñoz, G. (2022) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32912 · Cited by 19
Thorp, J. G., Mitchell, B. L., Gerring, Z. F., Ong, J., Gharahkhani, P., Derks, E. M., & Lupton, M. K. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.07.011 · Cited by 19
SYT1-Associated Neurodevelopmental Disorder: A Narrative Review
Riggs, E., Shakkour, Z., Anderson, C. L., & Carney, P. R. (2022) · Children · doi:10.3390/children9101439 · Cited by 18
The P2X7 receptor in mood disorders: Emerging target in immunopsychiatry, from bench to bedside
von Mücke-Heim, I.-A., & Deussing, J. M. (2022) · Neuropharmacology · doi:10.1016/j.neuropharm.2022.109366 · Cited by 19
Zhang, W., Liu, W., Liu, S., Su, F., Kang, X., Ke, Y., & Ming, D. (2022) · Clinical Neurophysiology · doi:10.1016/j.clinph.2022.11.013 · Cited by 18
Genetic variants associated with syncope implicate neural and autonomic processes
Aegisdottir, H. M., Þórólfsdóttir, R. B., Sveinbjörnsson, G., Stefánsson, Ó. A., Gunnarsson, B., Tragante, V., Þorleifsson, G., Stefánsdóttir, L., Thorgeirsson, T. E., Ferkingstad, E., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad016 · Cited by 18
Ahern, J., Thompson, W. K., Fan, C. C., & Loughnan, R. J. (2023) · Behavior Genetics · doi:10.1007/s10519-023-10139-w · Cited by 19
Dang, X., Song, M., Lv, L., Yang, Y., & Luo, X. (2023) · Human Genetics · doi:10.1007/s00439-023-02562-0 · Cited by 18
Holen, B., Shadrin, A. A., Icick, R., Filiz, T. T., Hindley, G., Rødevand, L. N., O’Connell, K. S., Hagen, E., Frei, O., Bahrami, S., et al. (2023) · Addiction Biology · doi:10.1111/adb.13282 · Cited by 20
Sjaarda, J., Delacrétaz, A., Dubath, C., Laaboub, N., Piras, M., Grosu, C., Vandenberghe, F., Crettol, S., Ansermot, N., Gamma, F., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02082-3 · Cited by 18
Shakeshaft, A., Heron, J., Blakey, R., Riglin, L., Smith, G. D., Stergiakouli, E., Tilling, K. M., & Thapar, A. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13851 · Cited by 18
Wang, X., Walker, A., Revez, J. A., Ni, G., Adams, M. J., McIntosh, A. M., Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.06.006 · Cited by 19
Self-citationExome-wide association study of treatment-resistant depression suggests novel treatment targets
Shah, S., Peddada, T., Song, C., Mensah, M., Sung, H., Yavi, M., Yuan, P., Zarate, C. A., Mickey, B. J., Burmeister, M., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-38984-z · Cited by 20
Zuo, H., & Li, M. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1277959 · Cited by 18
Andreu-Bernabeu, Á., González‐Peñas, J., Arango, C., & Díaz‐Caneja, C. M. (2023) · BMJ Mental Health · doi:10.1136/bmjment-2023-300821 · Cited by 19
Liu, A., Hong, P.-Y., Su, B., Wu, J.-H., & Chen, Z. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.02.022 · Cited by 18
Susceptibility to Treatment-Resistant Depression Within Families
Cheng, C., Chen, M., Tsai, S., Chang, W.-H., Tsai, C.-F., Lin, W., Bai, Y., Su, T., Chen, T., & Li, C. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2024.0378 · Cited by 18
Qiu, R., Lin, H., Jiang, H., Shen, J., He, J., & Fu, J. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05682-7 · Cited by 18
Genetic correlation and causal associations between psychiatric disorders and lung cancer risk
Shi, J., Wen, W., Long, J., Gamazon, E. R., Tao, R., & Cai, Q. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.04.080 · Cited by 18
Zhang, X., Lu, Q., Luo, Y., Wang, L., Tian, Y., & Luo, X. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.05.097 · Cited by 19
Sex-Specific Association Between Genetic Risk of Psychiatric Disorders and Cardiovascular Diseases
Jiang, J.-C., Singh, K., Nitin, R., Davis, L. K., Wray, N. R., & Shah, S. H. (2024) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.124.004685 · Cited by 19
Self-citationCilleros‐Portet, A., Lesseur, C., Marí, S., Cosín‐Tomás, M., Lozano, M., Irizar, A., Burt, A., García‐Santisteban, I., Garrido-Martín, D., Escaramís, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57760-3 · Cited by 20
No association between FKBP5 gene methylation and acute and long-term cortisol output
Alexander, N., Kirschbaum, C., Stalder, T., Muehlhan, M., & Vogel, S. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0846-2 · Cited by 22
Lapsley, C. R., Irwin, R. E., McLafferty, M., Thursby, S. J., O’Neill, S. M., Bjourson, A. J., Walsh, C. P., & Murray, E. K. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00877-7 · Cited by 18
Polygenic prediction of the risk of perinatal depressive symptoms
Rantalainen, V., Binder, E. B., Lahti‐Pulkkinen, M., Czamara, D., Laivuori, H. M., Villa, P., Girchenko, P. V., Kvist, T., Hämäläinen, E. K., Kajantie, E., et al. (2020) · Depression and Anxiety · doi:10.1002/da.23066 · Cited by 20
Rajkumar, A. P., Qvist, P., Donskov, J. G., Lazarus, R. M., Pallesen, J., Nava, N., Winther, G., Liebenberg, N., la Cour, S. H., Paternoster, V., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00914-2 · Cited by 19
Lira, S. S., & Ahammad, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-02715-z · Cited by 18
Shared genetic influences on depression and menopause symptoms
Meijsen, J. J., Shen, H., Vemuri, M., Rasgon, N., Koenen, K. C., & Duncan, L. E. (2021) · Psychological Medicine · doi:10.1017/s0033291721004037 · Cited by 19
Datta, S., Suryadevara, U., & Cheong, J. (2021) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/con.0000000000001051 · Cited by 21
A replication study separates polymorphisms behind migraine with and without depression
Petschner, P., Baksa, D., Hullám, G., Török, D., Millinghoffer, A., Deakin, J. F. W., Bagdy, G., & Juhász, G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0261477 · Cited by 18
Sex Differences in Depression Caused by Early Life Stress and Related Mechanisms
An, X., Guo, W., Wu, H., Fu, X., Li, M., Zhang, Y., Li, Y., Cui, R., Yang, W., Zhang, Z., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.797755 · Cited by 20
Barr, P. B., Mallard, T. T., Sanchez‐Roige, S., Poore, H. E., Linnér, R. K., Collaborators, C., Porjesz, B., HESSELBROCK, V. M., Foroud, T., Arpana Agrawal, A., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02171-x · Cited by 22
Advances in the pathophysiology of bipolar disorder
Wartchow, K. M., Cordeiro, R. C., & Scaini, G. (2022) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000836 · Cited by 18
Common variants contribute to intrinsic human brain functional networksPreprint
Zhao, B., Li, T., Smith, S. M., Xiong, D., Wang, X., Yang, Y., Luo, T., Zhu, Z., Shan, Y., Matoba, N., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.30.229914 · Cited by 18
Intracranial directed connectivity links subregions of the prefrontal cortex to major depression
Myers, J. C., Xiao, J., Mathura, R. K., Shofty, B., Gates, V., Adkinson, J., Allawala, A. B., Anand, A., Gadot, R., Najera, R. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61487-6 · Cited by 18
Shen, X., Barbu, M. C., Caramaschi, D., Arathimos, R., Czamara, D., David, F. S., Dearman, A., Dilkes, E., Herrera-Rivero, M., Huider, F., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-025-00486-4 · Cited by 19
Self-citationDepression and multiple sclerosis: A bidirectional Mendelian randomisation study
Binzer, S., Jiang, X., Hillert, J. A., & Manouchehrinia, A. (2021) · Multiple Sclerosis Journal · doi:10.1177/1352458521996601 · Cited by 17
Cheung, J. P., Tubbs, J. D., & Sham, P. C. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2022.06.023 · Cited by 18
Misganaw, B., Yang, R., Gautam, A., Muhie, S. Y., Mellon, S. H., Wolkowitz, O. M., Ressler, K. J., Doyle, F. J., Marmar, C. R., Jett, M., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232012504 · Cited by 17
Li, J., Keller, S. S., Seidlitz, J., Chen, H., Li, B., Weng, Y., Meng, Y., Yang, S., Xu, Q., Zhang, Q., et al. (2022) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12857 · Cited by 17
Mompeó, O., Freidin, M. B., Gibson, R., Hysi, P. G., Christofidou, P., Segal, E., Valdes, A. M., Spector, T. D., Menni, C., & Mangino, M. (2022) · Nutrients · doi:10.3390/nu14204431 · Cited by 17
Chitre, A. S., Hebda‐Bauer, E. K., Blandino, P., Bimschleger, H. V., Nguyen, K., Maras, P. M., Li, F., Ozel, A. B., Pan, Y., Polesskaya, O., et al. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1003074 · Cited by 17
Zięba, A., Matosiuk, D., & Kaczor, A. A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032946 · Cited by 20
Martin, E., Schoeler, T., Pingault, J., & Barkhuizen, W. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115218 · Cited by 17
Polygenicity of Comorbid Depression in Multiple Sclerosis
Kowalec, K., Fitzgerald, K. C., Salter, A. R., Dolovich, C., Harder, A., Bernstein, C. N., Bolton, J. M., Cutter, G., Graff, L. A., Hägg, S., et al. (2023) · Neurology · doi:10.1212/wnl.0000000000207457 · Cited by 17
Zhang, Y., Yang, M., & Li, M. (2023) · Medicine · doi:10.1097/md.0000000000035071 · Cited by 17
Yu, Y., Hou, L., Wu, Y., Yu, Y., Liu, X., Wu, S., He, Y., Ge, Y., Wei, Y., Qian, F., et al. (2023) · BMC Psychiatry · doi:10.1186/s12888-023-05203-y · Cited by 17
Turner, C. A., Khalil, H., Murphy-Weinberg, V., Hagenauer, M. H., Gates, L., Tang, Y., Weinberg, L. S., Grysko, R., Floran-Garduno, L., Dokas, T., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305779120 · Cited by 17
Drug targeting in psychiatric disorders — how to overcome the loss in translation?
Khodosevich, K., Dragičević, K., & Howes, O. D. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00847-7 · Cited by 17
Zong, L., Ge, M., Wang, J., Kuang, D., Wei, H., Wang, Z., Hu, Z., Zhao, C., Jin, Q., Chen, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52442-4 · Cited by 19
Zhou, H., Zhu, H., Wang, J., Gao, X., & Jiang, C. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.02.006 · Cited by 18
The bidirectional relationship of depression and disturbances in B cell homeostasis: Double trouble
Wu, S., Yin, Y., & Du, L. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.110993 · Cited by 17
Bigdeli, T. B., Barr, P. B., Rajeevan, N., Graham, D. P., Li, Y., Meyers, J. L., Gorman, B. R., Peterson, R. E., Sayward, F. G., Radhakrishnan, K., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02472-1 · Cited by 18
Moix, S., Sadler, M. C., Kutalik, Z., & Auwerx, C. (2024) · Genome biology · doi:10.1186/s13059-024-03269-9 · Cited by 18
Blood‐based multivariate methylation risk score for cognitive impairment and dementia
Koetsier, J., Cavill, R., Reijnders, R. A., Harvey, J., Homann, J., Kouhsar, M. P., Deckers, K., Köhler, S., Eijssen, L., van den Hove, D., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14061 · Cited by 17
Zhang, Z., Robinson, L., Whelan, R., Jollans, L., Wang, Z., Nees, F., Chu, C., Bobou, M., Du, D., Cristea, I., et al. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.12.053 · Cited by 19
Marchetti, L., Lauria, M., Caberlotto, L., Musazzi, L., Popoli, M., Mathé, A. A., Domenici, E., & Carboni, L. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2019.11.004 · Cited by 17
Burns, R. A., Andrews, S. J., Cherbuin, N., & Anstey, K. J. (2020) · BJPsych Open · doi:10.1192/bjo.2020.29 · Cited by 17
Using major depression polygenic risk scores to explore the depressive symptom continuum
Jermy, B. S., Hagenaars, S. P., Glanville, K. P., Coleman, J. R. I., Howard, D. M., Breen, G. D., Vassos, E., & Lewis, C. M. (2020) · Psychological Medicine · doi:10.1017/s0033291720001828 · Cited by 17
Self-citationde Jong, T. V., Kim, P., Guryev, V., Mulligan, M. K., Williams, R. W., Redei, E. E., & Chen, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-92993-4 · Cited by 17
Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes
Ma, S., Dalgleish, J. L. T., Lee, J., Wang, C., Liu, L., Gill, R. J., Buxbaum, J. D., Chung, W. K., Aschard, H., Silverman, E. K., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105191118 · Cited by 17
Corradin, O., Sallari, R. C., Hoang, A. T., Kassim, B. S., Hutta, G. B., Cuoto, L., Quach, B. C., Lovrenert, K., Hays, C., Gryder, B. E., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01477-y · Cited by 19
Social Determinants of Inter-Individual Variability and Vulnerability: The Role of Dopamine
Fauré, P., Fayad, S. L., Solié, C., & Reynolds, L. M. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.836343 · Cited by 17
Nakamura, T., Ueda, J., Mizuno, S., Honda, K., Kazuno, A., Yamamoto, H., Hara, T., & Takata, A. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100488 · Cited by 17
Metadata
- 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
- 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 (medical) › 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
About these checks
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.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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