The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index; none of the 50 sources its claims cite is checked for support yet.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
51 Own result · 37 Not checked yet · 19 Own interpretation · 4 Own method
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
“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
hedgeddirect- 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.
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
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.
None of the sources cited for this claim have been checked yet.
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
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.
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
“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
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.
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
“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.
- 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.
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 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
plaincorrelational- 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.
- 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
plainn = 1,306,354- 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.
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
“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.
- 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.
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
“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
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.
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
“The most significant gene (P = 2.27 × 10-19) was Sortilin related VPS10 domain containing receptor 3 (SORCS3) on chromosome 10”
§ 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
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. 38
- Probably not a substantial claim. 18
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.
- 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.
None of the sources cited for this claim have been checked yet.
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.
- 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
“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
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. 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 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
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.
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, 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.
- 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
“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
plaincorrelational- 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.
It may state two separate claims as one.
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.
- 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.
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
“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
hedged- 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.
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
“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
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.
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
“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
plaincorrelational- 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.
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
“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
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
“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
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.
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 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
plaincorrelational- 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.
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
“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
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.
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
“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
plaincorrelational- 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.
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
“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.
- 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
“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
plaindirect- 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.
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
“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
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.
In the paper
“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
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
“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
plaincorrelational- 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.
It may state two separate claims as one.
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.
- 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
“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
plainconditional- 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
“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
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.
None of the sources cited for this claim have been checked yet.
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.
- 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.
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.
- 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
“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.
- 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.
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.
- 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
“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.
- 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
“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.
- 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
“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.
- 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.
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.
- 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.
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.
- 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
“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.
- 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
“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
Sorted by citations
Items 101–200 of 1,004
Assessing the Causal Effects of Human Serum Metabolites on 5 Major Psychiatric Disorders
Yang, J., Yan, B., Zhao, B., Fan, Y., He, X., Yang, L., Ma, Q., Zheng, J., Wang, W., Bai, L., et al. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz138 · Cited by 143
Cell-type-specific 3D epigenomes in the developing human cortex
Song, M., Pebworth, M.-P., Yang, X., Abnousi, A., Fan, C., Wen, J., Rosen, J. D., Choudhary, M. N. K., Cui, X., Jones, I. R., et al. (2020) · Nature · doi:10.1038/s41586-020-2825-4 · Cited by 145
The genetic basis of major depressive disorder
Flint, J. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01957-9 · Cited by 156
Gender Differences in Depression: Evidence From Genetics
Zhao, L., Han, G., Zhao, Y., Jin, Y., Ge, T., Yang, W., Cui, R., Xu, S., & Li, B. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.562316 · Cited by 155
Deng, Y., Ou, Y., Wu, B., Yang, Y., Jiang, Y., Huang, Y., Liu, Y., Tan, L., Dong, Q., Suckling, J., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01507-9 · Cited by 140
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. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0857-7 · Cited by 139
Li, G. H., Cheung, C., Chung, A. K.-K., Cheung, B. M. Y., Wong, I. C. K., Fok, M. L.-Y., Au, P. C.-M., & Sham, P. C. (2020) · Psychological Medicine · doi:10.1017/s0033291720003566 · Cited by 139
Peripheral and central kynurenine pathway abnormalities in major depression
Paul, E. R., Schwieler, L., Erhardt, S., Boda, S., Trepci, A., Kämpe, R., Asratian, A., Holm, L., Yngve, A., Dantzer, R., et al. (2022) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2022.01.002 · Cited by 134
Wen, J., Fu, C. H. Y., Tosun, D., Veturi, Y., Yang, Z., Abdulkadir, A., Mamourian, E., Srinivasan, D., Skampardoni, I., Singh, A., et al. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.0020 · Cited by 142
A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder
Winter, N. R., Blanke, J., Leenings, R., Ernsting, J., Fisch, L., Sarink, K., Barkhau, C., Emden, D., Thiel, K., Flinkenflügel, K., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.5083 · Cited by 133
Self-citationNeuroimmune, metabolic and oxidative stress pathways in major depressive disorder
Maes, M. H. J., Almulla, A. F., You, Z., & Zhang, Y. (2025) · Nature Reviews Neurology · doi:10.1038/s41582-025-01116-4 · Cited by 140
Arnau‐Soler, A., Macdonald-Dunlop, E., Adams, M. J., Clarke, T., MacIntyre, D. J., Milburn, K., Navrady, L. B., Scotland, G., Hayward, C., McIntosh, A. M., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-018-0360-y · Cited by 139
Self-citationGenetic heterogeneity and subtypes of major depression
Nguyen, T.-D., Harder, A., Xiong, Y., Kowalec, K., Hägg, S., Cai, N., Kuja-Halkola, R., Dalman, C., Sullivan, P. F., & Lu, Y. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01413-6 · Cited by 129
Self-citationTorvik, F. A., Eilertsen, E. M., McAdams, T. A., Gustavson, K., Zachrisson, H. D., Brandlistuen, R. E., Gjerde, L. C., Havdahl, A., Stoltenberg, C., Ask, H., et al. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13197 · Cited by 143
Major depression and the biological hallmarks of aging
Lorenzo, E. C., Kuchel, G. A., Kuo, C., Moffitt, T. E., & Diniz, B. S. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101805 · Cited by 132
Wang, J., Chen, H., Li, H., Wang, H., Zou, R.-S., Lu, X., Wang, J., Nie, B., Wu, J., Li, S., et al. (2022) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2022.12.019 · Cited by 129
Genetic Associations between Voltage-Gated Calcium Channels and Psychiatric Disorders
Andrade, A., Brennecke, A., Mallat, S., Brown, J., Gomez-Rivadeneira, J., Czepiel, N., & Londrigan, L. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20143537 · Cited by 131
The Future of Neuroimplantable Devices: A Materials Science and Regulatory Perspective
Obidin, N., Tasnim, F., & Dagdeviren, C. (2020) · Advanced Materials · doi:10.1002/adma.201901482 · Cited by 126
Immuno-metabolic depression: from concept to implementation
Penninx, B. W. J. H., Lamers, F., Jansen, R., Berk, M., Khandaker, G. M., Picker, L. D., & Milaneschi, Y. (2024) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2024.101166 · Cited by 131
Gialluisi, A., Andlauer, T. F. M., Mirza‐Schreiber, N., Moll, K., Becker, J., Hoffmann, P., Ludwig, K. U., Czamara, D., Pourcain, B. S., Honbolygó, F., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-00898-x · Cited by 143
An overview on neurobiology and therapeutics of attention-deficit/hyperactivity disorder
da Silva, B. S., Grevet, E. H., Silva, L. C. F., Ramos, J. K. N., Rovaris, D. L., & Bau, C. H. D. (2023) · Discover Mental Health · doi:10.1007/s44192-022-00030-1 · Cited by 146
Polimanti, R. A., Walters, R. K., Johnson, E. C., McClintick, J. N., Adkins, A. E., Adkins, D. E., Bacanu, S., Bierut, L. J., Bigdeli, T. B., Brown, S. A., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0677-9 · Cited by 125
The molecular pathophysiology of depression and the new therapeutics
Tian, H., Hu, Z., Xu, J., & Wang, C. (2022) · MedComm · doi:10.1002/mco2.156 · Cited by 122
Becker, L. J., Fillinger, C., Waegaert, R., Journée, S. H., Hener, P., Ayazgök, B., Humo, M., Karatas, M., Thouaye, M., Gaikwad, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37878-y · Cited by 122
Miyoshi, E., Morabito, S., Henningfield, C. M., Das, S., Rahimzadeh, N., Shabestari, S. K., Michael, N., Emerson, N., Reese, F., Shi, Z., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01961-x · Cited by 115
Genetic contributions to bipolar disorder: current status and future directions
O’Connell, K. S., & Coombes, B. J. (2021) · Psychological Medicine · doi:10.1017/s0033291721001252 · Cited by 122
Circadian depression: A mood disorder phenotype
Carpenter, J. S., Crouse, J. J., Scott, E. M., Naismith, S. L., Wilson, C., Scott, J., Merikangas, K. R., & Hickie, I. B. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.045 · Cited by 114
Implementation and implications for polygenic risk scores in healthcare
Slunecka, J. L., van der Zee, M. D., Beck, J. J., Johnson, B. N., Finnicum, C. T., Pool, R., Hottenga, J., de Geus, E. J. C., & Ehli, E. A. (2021) · Human Genomics · doi:10.1186/s40246-021-00339-y · Cited by 120
Quach, B. C., Bray, M. A., Gaddis, N., Liu, M., Palviainen, T., Minică, C. C., Zellers, S. M., Sherva, R., Alıev, F., Nothnagel, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19265-z · Cited by 113
Hatoum, A. S., Johnson, E. C., Colbert, S. M. C., Polimanti, R. A., Zhou, H., Walters, R. K., Gelernter, J. E., Edenberg, H. J., Bogdan, R., & Arpana Agrawal, A. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01209-w · Cited by 122
Xue, K., Liang, S., Yang, B., Zhu, D., Xie, Y., Qin, W., Liu, F., Zhang, Y., & Yu, C. (2020) · Psychological Medicine · doi:10.1017/s0033291720003876 · Cited by 112
Lu, Y., Wang, Z., Georgakis, M. K., Lin, H., & Zheng, L. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.017986 · Cited by 116
Shen, X., Howard, D. M., Adams, M. J., Hill, W. D., Clarke, T.-K., Adams, M. J., Clarke, T., McIntosh, A. M., Deary, I. J., Wray, N. R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16022-0 · Cited by 112
Self-citationGagnon, É., Mitchell, P. L., Manikpurage, H. D., Abner, E., Taba, N., Esko, T., Ghodsian, N., Thériault, S., Mathieu, P., & Arsenault, B. J. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-022-03799-5 · Cited by 108
Symptom-level modelling unravels the shared genetic architecture of anxiety and depression
Thorp, J. G., Campos, A. I., Grotzinger, A. D., Gerring, Z. F., An, J., Ong, J., Wang, W., Shringarpure, S., Byrne, E. M., MacGregor, S., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01094-9 · Cited by 110
Self-citationThe genetics of depression: successful genome-wide association studies introduce new challenges
Ormel, J., Hartman, C. A., & Snieder, H. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0450-5 · Cited by 120
Ma, Y., Deng, C., Zhou, Y., Zhang, Y., Qiu, F., Jiang, D., Zheng, G., Li, J., Shuai, J., Zhang, Y., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100383 · Cited by 113
Peyrot, W. J., & Price, A. L. (2021) · Nature Genetics · doi:10.1038/s41588-021-00787-1 · Cited by 107
Mossink, B., van Rhijn, J.-R., Wang, S., Linda, K., Vitale, M. R., Zöller, J. E. M., van Hugte, E., Bak, J., Verboven, A. H. A., Selten, M. M., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01117-x · Cited by 109
Larivière, S., Royer, J., Rodríguez‐Cruces, R., Paquola, C., Caligiuri, M. E., Gambardella, A., Concha, L., Keller, S. S., Cendes, F., Yasuda, C. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31730-5 · Cited by 109
Delineating the Genetic Component of Gene Expression in Major Depression
Dall’Aglio, L., Lewis, C. M., & Pain, O. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.09.010 · Cited by 104
Self-citationIs subthreshold depression in adolescence clinically relevant?
Noyes, B. K., Munoz, D. P., Khalid‐Khan, S., Brietzke, E. M., & Booij, L. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.04.067 · Cited by 110
Unraveling the epigenetic landscape of depression: focus on early life stress
Torres‐Berrío, A., Issler, O., Parise, E. M., & Nestler, E. J. (2019) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2019.21.4/enestler · Cited by 107
Fang, Y., Forger, D. B., Frank, E., Sen, S., & Goldstein, C. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00400-z · Cited by 107
Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder
Strom, N. I., Gerring, Z. F., Galimberti, M., Yu, D., Halvorsen, M., Abdellaoui, A., Rodriguez‐Fontenla, C., Sealock, J., Bigdeli, T. B., Coleman, J. R. I., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02189-z · Cited by 123
Self-citationSynaptic Mechanisms Regulating Mood State Transitions in Depression
Parekh, P. K., Johnson, S. B., & Liston, C. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-110920-040422 · Cited by 101
Jokela, M., & Laakasuo, M. (2023) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2023.05.034 · Cited by 101
Tang, B., Yuan, S., Xiong, Y., He, Q., & Larsson, S. C. (2020) · Diabetologia · doi:10.1007/s00125-020-05131-6 · Cited by 105
Neurobiological functions of transcriptional enhancers
Nord, A. S., & West, A. E. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0538-5 · Cited by 106
Green, C., Shen, X., Stevenson, A. J., Conole, E. L. S., Harris, M. A., Barbu, M. C., Hawkins, E. L., Adams, M. J., Hillary, R. F., Lawrie, S. M., et al. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.11.024 · Cited by 103
Self-citationSystems biology dissection of PTSD and MDD across brain regions, cell types, and blood
Daskalakis, N. P., Iatrou, A., Chatzinakos, C., Jajoo, A., Snijders, C., Wylie, D., DiPietro, C. P., Tsatsani, I., Chen, C.-Y., Pernia, C. D., et al. (2024) · Science · doi:10.1126/science.adh3707 · Cited by 109
Self-citationTreatment-Resistant Depression Revisited: A Glimmer of Hope
Halaris, A. E., Sohl, E., & Whitham, E. A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11020155 · Cited by 101
Xue, K., Guo, L., Zhu, W., Liang, S., Xu, Q., Ma, L., Liu, M., Zhang, Y., & Liu, F. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01474-3 · Cited by 98
Depression and 24 gastrointestinal diseases: a Mendelian randomization study
Ruan, X., Chen, J., Sun, Y., Zhang, Y., Zhao, J., Wang, X., Li, X., Yuan, S., & Larsson, S. C. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02459-6 · Cited by 99
Kember, R. L., Vickers‐Smith, R., Xu, H., Toikumo, S., Niarchou, M., Zhou, H., Hartwell, E. E., Crist, R. C., Rentsch, C. T., Program, M. V., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01160-z · Cited by 104
Matoba, N., Liang, D., Sun, H., Aygün, N., McAfee, J. C., Davis, J. E., Raffield, L. M., Qian, H., Piven, J., Li, Y., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00953-9 · Cited by 97
Le-Niculescu, H., Roseberry, K., Gill, S. S., Levey, D. F., Phalen, P. L., Mullen, J., Williams, A., Bhairo, S., Voegtline, T., Davis, H., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01061-w · Cited by 94
Yuan, S., Yao, H., & Larsson, S. C. (2020) · Scientific Reports · doi:10.1038/s41598-020-70458-4 · Cited by 95
Treur, J. L., Munafo, M. R., Logtenberg, E., Wiers, R. W. H. J., & Verweij, K. J. H. (2021) · Psychological Medicine · doi:10.1017/s003329172100180x · Cited by 101
Mallard, T. T., Linnér, R. K., Grotzinger, A. D., Sanchez‐Roige, S., Seidlitz, J., Okbay, A., de Vlaming, R., Meddens, S. F. W., Palmer, A. A., Davis, L. K., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100140 · Cited by 110
Gerring, Z. F., Gamazon, E. R., & Derks, E. M. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008245 · Cited by 94
Hur, J., Stockbridge, M. D., Fox, A. S., & Shackman, A. J. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.03.012 · Cited by 95
Davies, M. R., Kalsi, G., Armour, C., Jones, I. R., McIntosh, A. M., Smıth, D. J., Walters, J., Bradley, J. R., Kingston, N., Ashford, S. E., et al. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.103503 · Cited by 99
Self-citationSnapHiC: a computational pipeline to identify chromatin loops from single-cell Hi-C data
Yu, M., Abnousi, A., Zhang, Y., Li, G., Lee, L., Chen, Z., Fang, R., Lagler, T. M., Yang, Y., Wen, J., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01231-2 · Cited by 92
Genetic evidence suggests posttraumatic stress disorder as a subtype of major depressive disorder
Zhang, F., Rao, S., Cao, H., Zhang, X., Wang, Q., Xu, Y., Sun, J., Wang, C., Chen, J., Xu, X., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci145942 · Cited by 93
Ji, G., Sun, J., Hua, Q., Zhang, L., Zhang, T., Bai, T., Wei, L., Wang, X., Qiu, B., Wang, K., et al. (2023) · Nature Mental Health · doi:10.1038/s44220-023-00111-2 · Cited by 91
Epigenetic prediction of major depressive disorder
Barbu, M. C., Shen, X., Walker, R. M., Howard, D. M., Evans, K. L., Whalley, H. C., Porteous, D. J., Morris, S. W., Deary, I. J., Zeng, Y., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0808-3 · Cited by 92
Self-citationBigdeli, T. B., Fanous, A. H., Li, Y., Rajeevan, N., Sayward, F. G., Genovese, G., Gupta, R., Radhakrishnan, K., Malhotra, A. K., Sun, N., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa133 · Cited by 87
Kwong, A. S. F., Morris, T., Pearson, R. M., Timpson, N. J., Rice, F. J., Stergiakouli, E., & Tilling, K. M. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13422 · Cited by 98
Fanelli, G., Franke, B., Witte, W. D., Ruisch, I. H., Haavik, J., van Gils, V., Jansen, W. J., Vos, S. J. B., Lind, L., Buitelaar, J. K., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01817-0 · Cited by 91
Legge, S. E., Cardno, A. G., Allardyce, J., Dennison, C. A., Hubbard, L., Pardiñas, A. F., Richards, A. L., Rees, E., Florio, A. D., Escott‐Price, V., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.1961 · Cited by 89
Polygenic risk scores in psychiatry: Will they be useful for clinicians?
Fullerton, J. M., & Nürnberger, J. I. (2019) · F1000Research · doi:10.12688/f1000research.18491.1 · Cited by 87
The brain-gut-microbiota interplay in depression: A key to design innovative therapeutic approaches
Varesi, A., Campagnoli, L. I. M., Chirumbolo, S., Candiano, B., Carrara, A., Ricevuti, G., Esposito, C., & Pascale, A. (2023) · Pharmacological Research · doi:10.1016/j.phrs.2023.106799 · Cited by 88
Ward, J., Lyall, L. M., Bethlehem, R. A. I., Ferguson, A., Strawbridge, R. J., Lyall, D. M., Cullen, B., Graham, N., Johnston, K. J. A., Bailey, M. E. S., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0635-y · Cited by 83
Self-citationCabana‐Domínguez, J., Torrico, B., Reif, A., Fernàndez‐Castillo, N., & Cormand, B. (2022) · Translational Psychiatry · doi:10.1038/s41398-021-01771-3 · Cited by 87
Dear, R., Wagstyl, K., Seidlitz, J., Markello, R. D., Arnatkevičiūtė, A., Anderson, K. M., Bethlehem, R. A. I., Raznahan, A., Bullmore, E. T., & Vértes, P. E. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01624-4 · Cited by 84
A cross-species proteomic map reveals neoteny of human synapse development
Wang, L., Pang, K., Zhou, L., Cebrián-Silla, A., González-Granero, S., Wang, S., Bi, Q., White, M. L., Ho, B., Li, J., et al. (2023) · Nature · doi:10.1038/s41586-023-06542-2 · Cited by 82
Using the tools of genetic epidemiology to understand sex differences in neuropsychiatric disorders
Merikangas, A. K., & Almasy, L. (2020) · Genes, Brain and Behavior · doi:10.1111/gbb.12660 · Cited by 82
A cross-species proteomic map reveals neoteny of human synapse development
Wang, L., Pang, K., Zhou, L., Cebrián-Silla, A., González-Granero, S., Wang, S., Bi, Q., White, M. L., Ho, B., Li, J., et al. (2022) · doi:10.1101/2022.10.24.513541 · Cited by 82
Lu, Z., Pu, C., Zhang, Y., Sun, Y., Liao, Y., Kang, Z., Feng, X., & Yue, W. (2022) · Antioxidants · doi:10.3390/antiox11071386 · Cited by 82
Yang, Z., Zhou, D., Li, H., Cai, X. M., Liu, W., Wang, L., Chang, H., Li, M., & Xiao, X. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0592-0 · Cited by 82
Parker, N., Patel, Y., Jackowski, A. P., Pan, P. M., Salum, G. A., Pausová, Z., & Paus, T. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.1495 · Cited by 87
Yao, X., Glessner, J., Li, J., Qi, X., Hou, X., Zhu, C., Li, X., March, M., Yang, L., Mentch, F. D., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-020-01195-5 · Cited by 82
Cai, J., Wei, Z., Chen, M., He, L., Wang, H., Li, M., & Peng, Y. (2022) · European Psychiatry · doi:10.1192/j.eurpsy.2022.18 · Cited by 87
A sex-specific genome-wide association study of depression phenotypes in UK Biobank
Silveira, P. P., Pokhvisneva, I., Howard, D. M., & Meaney, M. J. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01960-0 · Cited by 86
Self-citationPage, C. E., Epperson, C. N., Novick, A. M., Duffy, K. A., & Thompson, S. M. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02625-2 · Cited by 80
Davyson, E., Shen, X., Gadd, D. A., Bernabeu, E., Hillary, R. F., McCartney, D. L., Adams, M. J., Marioni, R. E., & McIntosh, A. M. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.01.027 · Cited by 80
Self-citationHübel, C., Gaspar, H. A., Coleman, J. R. I., Hanscombe, K. B., Purves, K. L., Prokopenko, I., Graff, M., Ngwa, J. S., Workalemahu, T., O’Reilly, P. F., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13544-0 · Cited by 86
Self-citationMulti-PGS enhances polygenic prediction by combining 937 polygenic scores
Albiñana, C., Zhu, Z., Schork, A. J., Ingason, A., Aschard, H., Brikell, I., Bulik, C. M., Petersen, L. V., Agerbo, E., Grove, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40330-w · Cited by 84
A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank
Leppert, B., Millard, L. A. C., Riglin, L., Smith, G. D., Thapar, A., Tilling, K. M., Walton, E., & Stergiakouli, E. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008185 · Cited by 80
Cohort profile: the Australian genetics of depression study
Byrne, E. M., Kirk, K. M., Medland, S. E., McGrath, J. J., Colodro‐Conde, L., Parker, R., Cross, S., Sullivan, L., Statham, D. J., Levinson, D. F., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-032580 · Cited by 79
Self-citationDissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores
Coombes, B. J., Markota, M., Mann, J. J., Colby, C. L., Stahl, E. A., Talati, A., Pathak, J., Weissman, M. M., McElroy, S. L., Frye, M. A., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00996-y · Cited by 79
Tang, B., Meng, W., Hägg, S., Burgess, S., & Jiang, X. (2021) · Pain · doi:10.1097/j.pain.0000000000002305 · Cited by 77
Yu, X., & Ren, X. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-03742-4 · Cited by 84
Structural neural plasticity evoked by rapid-acting antidepressant interventions
Liao, C., Dua, A. N., Wojtasiewicz, C., Liston, C. M., & Kwan, A. C. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00876-0 · Cited by 81
Strom, N. I., Verhulst, B., Bacanu, S., Cheesman, R., Purves, K. L., Gedik, H., Mitchell, B. L., Kwong, A. S. F., Faucon, A. B., Singh, K., et al. (2026) · Nature Genetics · doi:10.1038/s41588-025-02485-8 · Cited by 82
Self-citationSchultebraucks, K., Choi, K. W., Galatzer‐Levy, I. R., & Bonanno, G. A. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.0228 · Cited by 78
Rajagopal, V. M., Duan, J., Vilar‐Ribó, L., Grove, J., Zayats, T., Ramos‐Quiroga, J. A., Satterstrom, F. K., Artigas, M. S., Bybjerg‐Grauholm, J., Bækvad‐Hansen, M., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01143-7 · Cited by 81
Meta-Analyses of Genome-Wide Association Studies for Postpartum Depression
Guintivano, J., Byrne, E. M., Kiewa, J., Yao, S., Bauer, A. E., Åberg, K. A., Adams, M. J., Campbell, A., Campbell, M. L., Choi, K. W., et al. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20230053 · Cited by 80
Self-citationBadini, I., Coleman, J. R. I., Hagenaars, S. P., Hotopf, M. H., Breen, G. D., Lewis, C. M., & Fabbri, C. (2020) · Psychological Medicine · doi:10.1017/s0033291720002342 · Cited by 74
Self-citation
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).
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 ›
Are you an author, or do you see a problem with this page? Request a review
