The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index; 4 of the 59 sources its claims cite were checked for support.
A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles
Open accessPublished version of a preprint
Abstract
Most risk variants for brain disorders identified by genome-wide association studies reside in the noncoding genome, which makes deciphering biological mechanisms difficult. A commonly used tool, multimarker analysis of genomic annotation (MAGMA), addresses this issue by aggregating single nucleotide polymorphism associations to nearest genes. Here we developed a platform, Hi-C-coupled MAGMA (H-MAGMA), that advances MAGMA by incorporating chromatin interaction profiles from human brain tissue across two developmental epochs and two brain cell types. By analyzing gene regulatory relationships in the disease-relevant tissue, H-MAGMA identified neurobiologically relevant target genes. We applied H-MAGMA to five psychiatric disorders and four neurodegenerative disorders to interrogate biological pathways, developmental windows and cell types implicated for each disorder. Psychiatric-disorder risk genes tended to be expressed during mid-gestation and in excitatory neurons, whereas neurodegenerative-disorder risk genes showed increasing expression over time and more diverse cell-type specificities. H-MAGMA adds to existing analytic frameworks to help identify the neurobiological principles of brain disorders. Sey et al. report a computational tool, H-MAGMA, that extracts neurobiological insights from brain-disorder GWAS by linking risk variants to their cognate genes using chromatin interaction profiles from human brain tissue.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
68 Own result · 43 Not checked yet · 16 Own interpretation · 7 Own method · 2 Partly supported · 2 No basis given · 1 Couldn’t confirm · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Notably, genes involved in vocalization were associated with ASD, diagnostic criteria of which include impairment in vocalization29.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
What the sources say
- Could not verifycites [29] Edmunds, S. R., Kover, S. T. & Stone, W. L. The relation between parent verbal responsiveness and child communication in young children with or at risk for autism spectrum disorder: a systematic review and meta‐analysis. Autism Rev. 12, 715–731 (2019). sourceExperimental — accuracy not yet measured
The Introduction section on PVR and child communication in ASD describes reduced vocalization and communication difficulties in children with ASD. It does not address whether genes involved in vocalization are associated with ASD; searches for that association and for genetic terms yielded no supporting passage. (LLM, conf=0.76)
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.
- Could not verify
None of the sources cited for this claim have been checked yet.
In the paper
“We also found a significant overlap between H-MAGMA and two widely used expression quantitative trait loci (eQTL)-based gene mapping tools, coloc9 and TWAS10.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
What the sources say
- Not checked yetcites [9] Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014). source
- Not checked yetcites [10] Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016). 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.
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
“72.6% of TWAS genes, Fisher’s exact test, OR=12.14, 95% CI=10.20–14.49, P=3.94x10–206”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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.
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 only exception was MS, which displayed prenatal enrichment.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
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
“BD- and ADHD-associated genes were gradually increased during the prenatal stage with a peak at the developmental stage 6 (19–22 PCW).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
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.
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 total, cMAGMA and H-MAGMA linked 7.4M and 3.9M SNPs to genes, respectively (Extended Data Fig. 1d).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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
“Gene-level association statistics from H-MAGMA closely resembled genetic relationships among brain disorders, which enabled subsequent analyses to identify biological pathways, developmental windows, and cell types critical for each brain disorder.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
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.
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
“ASD-, SCZ-, and MDD-associated genes showed remarkedly similar expression patterns with a peak at the developmental stage 5 (16–19 PCW).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
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.
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
“Up to 60% of disorder risk genes were H-MAGMA selective (genes identified by H-MAGMA but not by cMAGMA), suggesting that functional genomics guided gene annotation can help identify novel genes and pathways (Extended Data Fig. 1b).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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.
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
“H-MAGMA selective genes were significantly enriched for heritability in all nine brain disorders, demonstrating the increase in power of H-MAGMA (Fig. 1c, Extended Data Fig. 1e, Methods).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
boostedcorrelationaln = 9- 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. 40
- Probably not a substantial claim. 21
- The quote only partly states this claim. 1
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Second, MAGMA does not take into account tissue-specific regulatory relationships, whereas disease risk SNPs are enriched in regulatory elements of the disease-relevant tissue5,6.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
What the sources say
- Supportedcites [5] Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015). source
The Discussion reports that variants associated with complex traits are highly enriched in epigenomic annotations of trait-relevant tissues. Section 8 gives examples of disease-associated variants enriched in enhancers of relevant tissues. This supports the tissue-enrichment point to which the citation is attached; the paper is not cited here as evidence about MAGMA. (LLM, conf=0.93)
- Partly supportedcites [6] Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015). sourceExperimental — accuracy not yet measured
The Results (Cell-type-specific analysis of 17 traits; page unavailable) report trait-specific enrichment in relevant cell types, including immune cells for three immunological disorders. This supports the tissue-relevance point, although the analysis measures SNP-heritability enrichment in functional annotations rather than directly showing that disease-risk SNPs are enriched in regulatory elements. The paper discusses stratified LD score regression, not whether MAGMA accounts for tissue-specific regulatory relationships. (LLM, conf=0.84)
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.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“It aggregates SNP associations to gene-level associations while correcting for confounding factors such as gene length, minor allele frequency, and gene density2.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
What the sources say
- Partly supportedcites [2] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). sourceExperimental — accuracy not yet measured
The Materials and Methods sections on summary SNP statistics and gene-set analysis say that MAGMA combines SNP associations into gene statistics and, in competitive gene-set analysis, corrects for gene size and gene density. The correction is described at the gene-set stage, not as part of generating gene-level associations, and our copy does not identify minor allele frequency as a correction factor. (LLM, conf=0.88)
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.
Probably not a substantial claim.
- Partly supported
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 majority of non-coding SNPs associated with nearest genes showed additional association with distal genes, as 80% of intronic SNPs and 87% of intergenic SNPs showed associations with distal genes (Fig. 1b).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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
“we additionally used an eQTL resource from the human DLPFC3, from which we found 56% of intronic SNPs and 76% of intergenic SNPs did not show any association with nearest genes (Extended Data Fig. 1a, Methods).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
What the sources say
- Not checked yetcites [3] Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018). 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
“Genes associated with degenerative disorders except MS constantly and gradually increased during both prenatal and postnatal stages, suggesting that these genes may become more susceptible upon aging.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
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.
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
“Genes associated with psychiatric disorders were highly expressed during prenatal stages, while genes associated with degenerative disorders were highly expressed postnatally (Fig. 2b–c, see Supplementary Data 3 for statistics).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
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
“We found that only 20% of intronic SNPs and 5% of intergenic SNPs interact with nearest genes based on Hi-C (Fig. 1b, Extended Data Fig. 1a, Methods).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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
“This distinction between psychiatric and degenerative disorders was less clear in cMAGMA: ASD- and BD-associated genes were postnatally enriched, AD-associated genes did not display postnatal enrichment, and ALS-associated genes were prenatally enriched (Extended Data Fig. 3, Supplementary Data 3).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
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
“Consistent with the previous findings6, psychiatric disorders showed strong enrichment in brain tissues, while degenerative disorders lacked brain-specific enrichment (Extended Data Fig. 2).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
What the sources say
- Not checked yetcites [6] Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015). source
boostedcorrelational- 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
“H-MAGMA detected a much larger number of genes to be associated with SCZ, which explained a significant proportion of heritability (4.7% of SNPs explained 38.93% of heritability, Enrichment = 8.23, Enrichment P= 7.17x10–53, Methods).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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
“Further, while the majority of brain disorders displayed enrichment in glutamatergic signaling, ASD, SCZ, and ALS displayed enrichment in GABAergic signaling.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
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
“In support of this finding, multiple aspects of glial development were also associated with brain disorders, with stronger enrichment in degenerative disorders (Table 1).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
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
“Not surprisingly, neurotransmitter and synaptic pathways were implicated in multiple brain disorders, supporting decades of studies highlighting the importance of synaptic function in psychiatric disorders19.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
What the sources say
- Not checked yetcites [19] Zoghbi, H. Y. & Bear, M. F. Synaptic dysfunction in neurodevelopmental disorders associated with autism and intellectual disabilities. Cold Spring Harb. Perspect. Biol. 4, a009886 (2012). 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 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
“For example, all brain disorders showed postsynaptic associations, while a selected set of disorders (ADHD, SCZ, MDD, and MS) also exhibited presynaptic associations.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
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
“Neuronal differentiation and neuronal apoptotic pathways were also enriched in all brain disorders.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
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
“Additional interesting findings include amyloid beta enrichment for AD and PD, and tau enrichment for MS and PD (Table 1), supporting amyloid beta and tau pathology in degenerative disorders26,27.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
What the sources say
- Not checked yetcites [26] Iqbal, K., Liu, F. & Gong, C.-X. Tau and neurodegenerative disease: the story so far. Nat. Rev. Neurol. 12, 15–27 (2016). source
- Not checked yetcites [27] Haass, C. & Selkoe, D. J. Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer’s amyloid β-peptide. Nat. Rev. Mol. Cell Biol. 8, 101–112 (2007). 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
“While cMAGMA gave a similar result to H-MAGMA, there were important discrepancies, which include astrocytic expression of ASD-associated genes, lack of astrocytic expression of PD- and ALS-associated genes, and lack of endothelial expression of MS-associated genes (Extended Data Fig. 4).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specificity
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
“This selective enrichment in excitatory neurons prevailed across development, as adult neuronal expression profiles for psychiatric disorder-associated genes also indicated excitatory neuronal enrichment (Extended Data Fig. 4b).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specificity
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
“Cell-type specific H-MAGMA recapitulated biological processes, cell-type specificities, and developmental trajectories of brain homogenate H-MAGMA.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specific gene mapping
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
“ASD- and BD-associated genes were neither differentially regulated in psychiatric disorders nor enriched for any disease-associated co-expression modules (Extended Data Fig. 6).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Functional impact of genetic risk factors in transcriptomic signatures
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
“For example, we observed excitatory neuronal expression of psychiatric disorder risk genes, microglial expression of AD- and MS-associated genes, and astrocytic expression of PD- and ALS-associated genes (Fig. 3a).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specific gene mapping
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
“Further, psychiatric disorder-associated genes showed prenatal enrichment with a peak during mid-gestation, while degenerative disorder-associated genes were postnatally enriched with a gradual increase in expression across a lifespan (Fig. 3b–c).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specific gene mapping
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
“We found that AD- and PD-associated genes were expressed in mature astrocytes, while ALS-associated genes were highly expressed in fetal astrocytes.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specific gene mapping
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
“One striking difference between psychiatric and degenerative disorders was that psychiatric disorder-associated genes coalesced in neurons, while degenerative disorder-associated genes were highly expressed in glia (microglia for AD and MS, astrocytes for ALS and PD, Extended Data Fig. 4a).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specificity
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
“Gene-level overlaps recapitulated the previously reported genetic architecture of brain disorders25: psychiatric disorders exhibited strong overlaps in their ranked gene lists, whereas degenerative disorders did not display significant overlaps (Extended Data Fig. 7a).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Shared genetic architecture among brain disorders
What the sources say
- Not checked yetcites [25] The Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018). source
boostedcorrelational- 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 found that AD-associated genes were significantly enriched for DEGs in microglia and oligodendrocytes, but not in neurons (Fig. 4a).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Functional impact of genetic risk factors in transcriptomic signatures
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
“Pleiotropic genes were involved in gene regulation, synaptic function, and neuronal and dendritic development (Fig. 5b).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Biological pathways underlying pleiotropy
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
“Consistent with the previous finding3,11, we detected a substantial overlap.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
What the sources say
- Not checked yetcites [3] Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018). source
- Not checked yetcites [11] Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (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.
The quote only partly states this claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“SNPs mapped to proximal genes may also have distal associations, while SNPs mapped to distal genes do not have any association with proximal genes.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
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: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since psychiatric disorder-associated genes showed neurodevelopmental and neuronal enrichment, we used fetal brain and neuronal H-MAGMA results.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Identification of pleiotropic genes
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no 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
“In the end, we obtained 1,841 genes that are shared in more than four disorders, and defined them as pleiotropic genes.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Identification of pleiotropic genes
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
“Psychiatric disorders show strong genetic relationships both at the level of genetic correlations (bottom left, rg) and gene-level overlaps (top right, RRHO).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
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.
In the paper
“Multi-marker Analysis of GenoMic Annotation (MAGMA) was initially developed to extract biological insights from GWAS by linking risk variants to their cognate genes2.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
What the sources say
- Not checked yetcites [2] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). source
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.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“MAGMA assigns SNPs to the nearest genes, which has two major pitfalls. First, it is becoming increasingly recognized that non-coding SNPs can regulate distal genes via long range (>10 kb) regulatory interactions, whereby distal enhancers are brought into contact with the gene promoter3,4.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
What the sources say
- Not checked yetcites [3] Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018). source
- Not checked yetcites [4] Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016). 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.
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
“By comparing H-MAGMA with cMAGMA, we found that non-coding SNPs often interact with distal genes, necessitating the use of functional genomic evidence in assigning SNPs to cognate genes.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
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
“Most risk variants for brain disorders identified by genome-wide association studies (GWAS) reside in non-coding genome, which makes deciphering biological mechanisms difficult.”
- Also stated in: § Introduction · plain
“the majority of common risk variants reside in non-coding regions of the genome1.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Abstract
What the sources say
- Not checked yetcites [1] Edwards, S. L., Beesley, J., French, J. D. & Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function. Am. J. Hum. Genet. 93, 779–797 (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.
- Also stated in: § Introduction · plain
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
“H-MAGMA advances conventional MAGMA (hereafter referred to as cMAGMA) by incorporating relevant functional genomics evidence and allowing developmental stage and cell-type specific gene mapping.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
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
“While traditional Hi-C guided gene mapping restricts its analysis to genome-wide significant (GWS) loci7, H-MAGMA can leverage signals from sub-threshold loci that explain a significant proportion of heritability8.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Introduction
What the sources say
- Not checked yetcites [7] Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017). source
- Not checked yetcites [8] Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016). 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.
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
“This result is consistent with a strong neurodevelopmental predisposition for psychiatric disorders, in contrast with degenerative disorders which have a postnatal origin.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
boosteddirect- 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
“Developmental stages 5 and 6 represent mid-gestation, the period during which upper layer neurons are generated and neuronal differentiation including axonogenesis and dendritic arborization takes place12,13.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
What the sources say
- Not checked yetcites [12] Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011). source
- Not checked yetcites [13] De La Torre-Ubieta, L., Won, H., Stein, J. L. L. & Geschwind, D. H. H. Advancing the understanding of autism disease mechanisms through genetics. Nat. Med. 22, 345–361 (2016). 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
“These results collectively highlight synaptic dysfunction in brain disorders, albeit we could detect distinctions among disorders based on neurotransmitters and pre/post synaptic associations.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Pathways implicated for brain disorders.
hedgedcorrelational- 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
“For example, ASD postmortem brains exhibit cell-type specific gene expression signatures such as upregulation of glial genes and downregulation of neuronal genes30.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specificity
What the sources say
- Not checked yetcites [30] Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016). 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
“Microglia are increasingly recognized as a central cell type contributing to the etiology of AD32.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specificity
What the sources say
- Not checked yetcites [32] Hansen, D. V., Hanson, J. E. & Sheng, M. Microglia in Alzheimer’s disease. J. Cell Biol. 217, 459–472 (2018). 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
“Common variation in SCZ maps onto specific groups of cells including pyramidal neurons and medium spiny neurons31.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specificity
What the sources say
- Not checked yetcites [31] 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: 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
“Cross-disorder GWAS of eight psychiatric disorders recently identified more than a hundred GWS loci increasing risk for multiple disorders, further providing evidence of widespread pleiotropy among psychiatric disorders14.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Biological pathways underlying pleiotropy
What the sources say
- Not checked yetcites [14] Cross-Disorder Group of the Psychiatric Genomics Consortium. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell 179, 1469–1482.e11 (2019). 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.
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
“Compared with cMAGMA, H-MAGMA (1) links non-coding SNPs to their target genes based on functional genomic evidence, and (2) adds relevant cellular context to gene mapping by using chromatin interaction data from disease-relevant tissue and cell types.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Second, it has been recently shown that the variants associated with chromatin accessibility capture stimulus-sensitive signals and explain a significant proportion of heritability, even more so than eQTLs47,48.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
What the sources say
- Not checked yetcites [47] Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018). source
- Not checked yetcites [48] Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet. 50, 1140–1150 (2018). source
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.
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
“(3) data collection and analysis were not performed blind to the conditions of the experiments.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > GWAS
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.
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
“Since we used publicly available GWAS summary statistics, (1) no data points were excluded from analysis”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > GWAS
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.
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
“(2) no statistical methods were used to pre-determine the sample size”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > GWAS
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
“Similar cell-type specificity was reported by the interactome study, demonstrating the robustness of the result using an orthogonal approach49.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
What the sources say
- Not checked yetcites [49] Mohammadi, S., Davila-Velderrain, J. & Kellis, M. Reconstruction of cell-type-specific interactomes at single-cell resolution. Cell Syst. 9, 559–568.e4 (2019). 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.
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
“Pleiotropic genes were associated with neuronal development and synaptic plasticity, suggesting that inappropriate neuronal activity and regulation may act as key components in the pathogenesis of psychiatric disorders.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
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.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“We generated an input file for cMAGMA that is comparable to H-MAGMA. We used the same gene model (Gencode v26) and a SNP list used for H-MAGMA, and allowed a window of 35kb upstream and 10kb downstream of each gene as previously described16,52.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Gene annotation for conventional MAGMA (cMAGMA)
What the sources say
- Not checked yetcites [16] Network Pathway Analysis Subgroup of the Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015). source
- Not checked yetcites [52] Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018). 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.
Probably not a substantial claim.
In the paper
“Differentially expressed genes (DEG) in postmortem brains with psychiatric disorders were obtained from Gandal et al., 201840 (FDR<0.05).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Gene-set analysis
What the sources say
- Not checked yetcites [40] Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018). 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.
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
“To ensure that developmental expression trajectories are not dictated by the developmental stage from which Hi-C data was obtained, we used union disorder risk genes.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Developmental and Cellular Expression Profiles
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.
Probably not a substantial claim.
In the paper
“Cell-type specific DEG in AD postmortem brains was obtained from Mathys et al., 201924.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Gene-set analysis
What the sources say
- Not checked yetcites [24] Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). 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.
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
“We restricted our H-MAGMA results to those derived from the adult brain so that we can match the developmental period (adult) and brain region (DLPFC) with the eQTL database.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Comparison between H-MAGMA and eQTL-based gene mapping algorithms
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.
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
“In comparing H-MAGMA outputs (common variation) with the gene lists that harbor protein disrupting variation (rare variation), we used a generalized linear model controlling for the exome length (controlling for rare variation) and the number of SNPs mapped to each gene (controlling for common variation).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Gene-set analysis
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.
- Rests on an assumption: Protein-disrupting variation in brain disorders is dependent on the exon but not on gene length.
In the paper
“In comparing H-MAGMA outputs with DEG, we used the gene-set analysis embedded in MAGMA, which utilizes the whole gene-level association statistics while controlling for covariates such as gene size and LD2.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Gene-set analysis
What the sources say
- Not checked yetcites [2] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). source
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.
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
“To further verify the developmental trajectories in a cell-type specific fashion, we used neuronal Hi-C for psychiatric disorders and astrocytic Hi-C for degenerative disorders.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Developmental and Cellular Expression Profiles
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
“Schizophrenia risk genes with elevated burden of rare variation were obtained from Singh et al., 201666 (110 genes with FDR<0.3).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Gene-set analysis
What the sources say
- Not checked yetcites [66] Singh, T. et al. Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat. Neurosci. 19, 571–577 (2016). 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.
The paper relies on this without support.
Protein-disrupting variation in brain disorders is dependent on the exon but not on gene length.
“We took this alternative approach than using MAGMA gene-set analysis because protein disrupting variation in brain disorders was detected by exome-sequencing and is dependent on the exon, but not gene length.”
The paper relies on this without support.
Non-coding genes have much lower expression values than protein-coding genes, which can dilute signals.
“We restricted our analysis only on protein-coding genes, because (1) the majority of genes detected in the spatiotemporal transcriptomic atlas12, single cell expression datasets60–62, and gene ontologies were protein-coding genes, and (2) non-coding genes have much lower expression values compared with protein-coding genes, which can dilute the signals.”
The paper relies on this without support.
The majority of genes detected in the spatiotemporal transcriptomic atlas, single-cell expression datasets, and gene ontologies are protein-coding genes.
“We restricted our analysis only on protein-coding genes, because (1) the majority of genes detected in the spatiotemporal transcriptomic atlas12, single cell expression datasets60–62, and gene ontologies were protein-coding genes, and (2) non-coding genes have much lower expression values compared with protein-coding genes, which can dilute the signals.”
The paper relies on this without support.
The complexity of linkage disequilibrium in the MHC region can override the overall pattern.
“We also excluded genes within the MHC region due to the complexity of LD, which can override the overall pattern.”
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
“Given that genetic correlation between SCZ and BD is remarkably high (rg = 0.67)46,53,54, these findings suggest cellular substrates for molecular convergence (Ex7 and In6 are shared between two disorders) and divergence (Ex1 is BD-specific, while Ex2 is SCZ-specific) among two highly genetically correlated disorders.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [46]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In contrast to AD in which multiple glial cells are implicated, previous work has indicated neurons as the central cell-type harboring genetic risk for the majority of psychiatric disorders3,22,46,58–61.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [46]
In the citing paper
“TADA genes were defined as a set of 113 ASD genes that were associated with ASD at an FDR < 0.05.”
From A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex (2022) · cites this paper as [65]
In the citing paper
“This analysis identified 181 AD risk genes (FDR < 0.05) based upon their potential regulation by associated non-coding variants (“Methods”, Supplementary Data 6), many of which included well-known AD risk genes47.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [46]
In the citing paper
“In our previous study, we found that genes associated with psychiatric disorders display substantial molecular convergence within neurons, which was in stark contrast to neurodegenerative disorders like AD46.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [46]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Biological pathways underlying pleiotropy
“we next examined genes shared in multiple psychiatric disorders (n≥4) to identify common molecular mechanisms of psychiatric disorders”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Biological pathways underlying pleiotropy
“In total, we found 1,841 genes (hereby referred to as pleiotropic genes) that are shared in more than four psychiatric disorders”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Psychiatric disorders exhibit neurodevelopmental origin, while degenerative disorders exhibit adult origin.
“BD- and ADHD-associated genes were gradually increased during the prenatal stage with a peak at the developmental stage 6 (19–22 PCW).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Developmental and Cellular Expression Profiles
“stage 6, 19 PCW ≤ Age < 24 PCW”
Citations
Cites
Biological 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
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid β-peptide
Haass, C., & Selkoe, D. J. (2007) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2101 · Cited by 4,744
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
Functional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653
eQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0090-3 · Cited by 3,005
Partitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604
Single-cell transcriptomic analysis of Alzheimer’s disease
Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568
Cited by
Sorted by citations
Stroke genetics informs drug discovery and risk prediction across ancestries
Mishra, A., Malik, R., Hachiya, T., Jürgenson, T., Namba, S., Posner, D. C., Kamanu, F., Koido, M., Grand, Q. L., Shi, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05165-3 · Cited by 623
Garvin, M. R., Alvarez, C., Miller, J. I., Prates, E. T., Walker, A. M., Amos, B. K., Mast, A. E., Justice, A. C., Aronow, B. J., & Jacobson, D. (2020) · eLife · doi:10.7554/elife.59177 · Cited by 372
Corces, M. R., Shcherbina, A., Kundu, S., Gloudemans, M. J., Frésard, L., Granja, J. M., Louie, B. H., Eulalio, T., Shams, S., Bagdatli, S. T., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00721-x · Cited by 358
A large-scale genome-wide association study meta-analysis of cannabis use disorder
Johnson, E. C., Demontis, D., Thorgeirsson, T. E., Walters, R. K., Polimanti, R. A., Hatoum, A. S., Sanchez‐Roige, S., Paul, S. E., Wendt, F. R., Clarke, T.-K., et al. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30339-4 · Cited by 334
Karlsson Linnér, R., Mallard, T. T., Barr, P. B., Sanchez-Roige, S., Madole, J. W., Driver, M. N., Poore, H. E., de Vlaming, R., Grotzinger, A. D., Tielbeek, J. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00908-3 · Cited by 317
“Because of the strong polygenic signal identified in the GWAS of EXT, four different gene-based analyses identified an abundance of implicated genes ( > 3,000): (1) functional annotation of the 579 SNPs to their nearest gene with FUMA 26 , which suggested 587 genes; (2) MAGMA gene-based association analysis 27 , which…”
MethodInfluentialAdams, M. J., Streit, F., Meng, X., Awasthi, S., Adey, B. N., Choi, K. W., Chundru, V. K., Coleman, J. R. I., Ferwerda, B., Foo, J. C., et al. (2025) · Cell · doi:10.1016/j.cell.2024.12.002 · Cited by 372
Common genetic variation influencing human white matter microstructure
Zhao, B., Li, T., Yang, Y., Wang, X., Luo, T., Shan, Y., Zhu, Z., Xiong, D., Hauberg, M. E., Bendl, J. M., et al. (2021) · Science · doi:10.1126/science.abf3736 · Cited by 222
Gong, W., Guo, P., Li, Y., Liu, L., Yan, R., Liu, S., Wang, S., Xue, F., Zhou, X., & Yuan, Z. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.4974 · Cited by 225
Antaki, D., Guevara, J., Maihofer, A. X., Klein, M., Gujral, M., Grove, J., Carey, C. E., Hong, O., Arranz, M. J., Hervas, A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01064-5 · Cited by 206
Meng, X., Navoly, G., Giannakopoulou, O., Levey, D. F., Koller, D., Pathak, G. A., Koen, N., Lin, K., Adams, M. J., Rentería, M. E., et al. (2024) · Nature Genetics · doi:10.1038/s41588-023-01596-4 · Cited by 207
Natural Variation in Crops: Realized Understanding, Continuing Promise
Liang, Y., Liu, H., Yan, J., & Tian, F. (2021) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-080720-090632 · Cited by 186
Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals
Zhou, H., Kember, R. L., Deak, J. D., Xu, H., Toikumo, S., Yuan, K., Lind, P. A., Farajzadeh, L., Wang, L., Hatoum, A. S., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02653-5 · Cited by 179
Single-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka, W. B., Mohammadi, S., Fullard, J. F., Dávila-Velderrain, J., Subburaju, S., Tso, D. R., Hourihan, M., Jiang, S., Lee, H.-C., Bendl, J. M., et al. (2024) · Science · doi:10.1126/science.adg5136 · Cited by 163
Self-citationCell-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
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
Self-citationMallard, T. T., Savage, J. E., Johnson, E. C., Huang, Y., Edwards, A. C., Hottenga, J., Grotzinger, A. D., Gustavson, D. E., Jennings, M. V., Anokhin, A. P., et al. (2021) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.20091390 · Cited by 110
Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders
Hu, B., Won, H., Mah, W., Park, R. B., Kassim, B., Spiess, K., Kozlenkov, A., Crowley, C. A., Pochareddy, S., The PsychENCODE Consortium, et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24243-0 · Cited by 103
Self-citationMatoba, 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
Self-citationSanchez‐Roige, S., Fontanillas, P., Jennings, M. V., Bianchi, S. B., Huang, Y., Hatoum, A. S., Sealock, J., Davis, L. K., Elson, S. L., Agee, M., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01335-3 · Cited by 89
Xie, Y., Xu, Z., Xia, M., Liu, J., Shou, X., Cui, Z., Liao, X., & Yong He, A. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.12.004 · Cited by 81
Warrier, V., Stauffer, E.-M., Huang, Q. Q., Wigdor, E. M., Slob, E. A. W., Seidlitz, J., Ronan, L., Valk, S. L., Mallard, T. T., Grotzinger, A. D., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01475-y · Cited by 91
Self-citationDear, 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
The functional and evolutionary impacts of human-specific deletions in conserved elements
Xue, J. R., Mackay-Smith, A., Mouri, K., García, M. F., Dong, M. X., Akers, J. F., Noble, M., Li, X., Lindblad‐Toh, K., Karlsson, E. K., et al. (2023) · Science · doi:10.1126/science.abn2253 · Cited by 81
Self-citationThe genetic architecture of biological age in nine human organ systems
Wen, J., Tian, Y., Skampardoni, I., Yang, Z., Cui, Y., Anagnostakis, F., Mamourian, E., Zhao, B., Toga, A. W., Zalesky, A., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00662-8 · Cited by 77
Feleke, R., Reynolds, R. H., Smith, A. M., Tilley, B. S., Taliun, S. A. G., Hardy, J., Matthews, P. M., Gentleman, S., Owen, D. R., Johnson, M. R., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02343-x · Cited by 69
MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies
Bhattacharya, A., Li, Y., & Love, M. I. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009398 · Cited by 70
Gerring, Z. F., Mina-Vargas, A., Gamazon, E. R., & Derks, E. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab115 · Cited by 67
Mapping the cellular etiology of schizophrenia and complex brain phenotypes
Duncan, L. E., Li, T., Salem, M., Li, W., Mortazavi, L., Senturk, H., Shahverdizadeh, N., Vesuna, S. S., Shen, H., Yoon, J. H., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-024-01834-w · Cited by 69
The genetic relationships between brain structure and schizophrenia
Stauffer, E.-M., Bethlehem, R. A. I., Dorfschmidt, L., Won, H., Warrier, V., & Bullmore, E. T. (2023) · Nature Communications · doi:10.1038/s41467-023-43567-7 · Cited by 61
Self-citationToikumo, S., Jennings, M. V., Pham, B. K., Lee, H., Mallard, T. T., Bianchi, S. B., Meredith, J. J., Vilar‐Ribó, L., Xu, H., Hatoum, A. S., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01851-6 · Cited by 62
Wendt, F. R., Pathak, G. A., Levey, D. F., Nuñez, Y. Z., Overstreet, C. M., Tyrrell, C., Adhikari, K., Angelis, F. D., Tylee, D. S., Goswami, A., et al. (2021) · Neurobiology of Stress · doi:10.1016/j.ynstr.2021.100309 · Cited by 55
Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain
Wen, C. H., Margolis, M. P., Dai, R., Zhang, P., Przytycki, P. F., Vo, D., Bhattacharya, A., Matoba, N., Tang, M., Jiao, C., et al. (2024) · Science · doi:10.1126/science.adh0829 · Cited by 65
Self-citationMassively parallel techniques for cataloguing the regulome of the human brain
Townsley, K. G., Brennand, K., & Huckins, L. M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00740-1 · Cited by 52
Self-citationTen challenges for clinical translation in psychiatric genetics
Derks, E. M., Thorp, J. G., & Gerring, Z. F. (2022) · Nature Genetics · doi:10.1038/s41588-022-01174-0 · Cited by 58
Somatic mutations in neurodegeneration: An update
Proukakis, C. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.105021 · Cited by 58
Ruzicka, W. B., Mohammadi, S., Dávila-Velderrain, J., Subburaju, S., Tso, D. R., Hourihan, M., & Kellis, M. (2020) · medRxiv · doi:10.1101/2020.11.06.20225342 · Cited by 53
Bourque, V.-R., Poulain, C., Proulx, C., Moreau, C. A., Joober, R., Forgeot d’Arc, B., Huguet, G., & Jacquemont, S. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02866-3 · Cited by 48
Air pollution interacts with genetic risk to influence cortical networks implicated in depression
Li, Z., Yan, H., Zhang, X., Shah, S., Yang, G., Chen, Q., Han, S., Zhang, D., Weinberger, D. R., Yue, W., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109310118 · Cited by 51
Fröhlich, A. S., Gerstner, N., Gagliardi, M., Ködel, M., Yusupov, N., Matosin, N., Czamara, D., Sauer, S., Roeh, S., Murek, V., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01742-z · Cited by 54
Shared genetic liability and causal effects between major depressive disorder and insomnia
Baranova, A. V., Cao, H., & Zhang, F. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab328 · Cited by 50
Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants
McAfee, J. C., Lee, S., Lee, J., Bell, J., Krupa, O., Davis, J. E., Insigne, K. D., Bond, M. L., Zhao, N., Boyle, A. P., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100404 · Cited by 53
Self-citationThe iPSC perspective on schizophrenia
Räsänen, N., Tiihonen, J., Koskuvi, M., Lehtonen, Š., & Koıstınaho, J. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.11.002 · Cited by 56
A multi-ancestry genetic study of pain intensity in 598,339 veterans
Toikumo, S., Vickers‐Smith, R., Jinwala, Z., Xu, H., Saini, D., Hartwell, E. E., Pavicic, M., Sullivan, K. A., Xu, K., Jacobson, D., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02839-5 · Cited by 48
H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility
Giambartolomei, C., Seo, J.-H., Schwarz, T., Freund, M. K., Johnson, R., Spisák, S., Baca, S. C., Gusev, A., Mancuso, N., Paşaniuc, B., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.007 · Cited by 49
Zhang, Z., Wang, X., Park, S. S., Song, H., & Ming, G. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.12.015 · Cited by 49
The Genesis of Schizophrenia: An Origin Story
Birnbaum, R., & Weinberger, D. R. (2024) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20240305 · Cited by 50
Lee, S., McAfee, J. C., Lee, J., Gomez, A., Ledford, A. T., Clarke, D., Min, H., Gerstein, M., Boyle, A. P., Sullivan, P. F., et al. (2025) · Cell · doi:10.1016/j.cell.2024.12.022 · Cited by 45
Self-citationMulti-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms
Schilder, B. M., Navarro, E., & Raj, T. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105580 · Cited by 45
Patient-Derived Midbrain Organoids to Explore the Molecular Basis of Parkinson's Disease
Galet, B., Cheval, H., & Ravassard, P. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.01005 · Cited by 47
Assigning function to SNPs: Considerations when interpreting genetic variation
Fadason, T., Farrow, S. L., Gokuladhas, S., Golovina, E., Nyaga, D. M., O'Sullivan, J. M., & Schierding, W. S. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.08.008 · Cited by 44
Mulvey, B., Lagunas, T., & Dougherty, J. D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.011 · Cited by 41
Initiative, A. D. N., Ramanan, V. K., Lesnick, T. G., Przybelski, S. A., Heckman, M. G., Knopman, D. S., Graff‐Radford, J., Lowe, V. J., Machulda, M. M., Mielke, M. M., et al. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01154-1 · Cited by 40
Zhu, A., Matoba, N., Wilson, E., Tapia, A. L., Li, Y., Ibrahim, J. G., Stein, J. L., & Love, M. I. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009455 · Cited by 32
Jin, T., Rehani, P. R., Ying, M., Huang, J., Liu, S., Roussos, P., & Wang, D. (2021) · Genome Medicine · doi:10.1186/s13073-021-00908-9 · Cited by 42
Sexually divergent development of depression-related brain networks during healthy human adolescence
Dorfschmidt, L., Bethlehem, R. A. I., Seidlitz, J., Váša, F., White, S. R., Romero-García, R., Kitzbichler, M. G., Aruldass, A. R., Morgan, S. E., Goodyer, I. M., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm7825 · Cited by 42
Examining the shared etiology of psychopathology with genome-wide association studies
Mallard, T. T., Grotzinger, A. D., & Smoller, J. W. (2023) · Physiological Reviews · doi:10.1152/physrev.00016.2022 · Cited by 38
Focus on your locus with a massively parallel reporter assay
McAfee, J. C., Bell, J., Krupa, O., Matoba, N., Stein, J. L., & Won, H. (2022) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-022-09461-x · Cited by 40
Self-citationMa, Q., Shams, H., Didonna, A., Baranzini, S. E., Cree, B., Hauser, S. L., Henry, R. G., & Oksenberg, J. R. (2023) · Communications Biology · doi:10.1038/s42003-023-04713-5 · Cited by 45
Human genetics and epigenetics of alcohol use disorder
Zhou, H., & Gelernter, J. E. (2024) · Journal of Clinical Investigation · doi:10.1172/jci172885 · Cited by 48
Wendt, F. R., Pathak, G. A., Deak, J. D., Angelis, F. D., Koller, D., Cabrera‐Mendoza, B., Lebovitch, D. S., Levey, D. F., Stein, M. B., Kranzler, H. R., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01469-y · Cited by 35
3D spatial genome organization in the nervous system: From development and plasticity to disease
Fujita, Y., Pather, S. R., Ming, G., & Song, H. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.004 · Cited by 37
Chawla, A., Cakmakci, D., Fiori, L. M., Zang, W., Maitra, M., Yang, J., Żurawek, D., Frosi, G., Rahimian, R., Mitsuhashi, H., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02249-4 · Cited by 39
Self-citationPublicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia
Dobrindt, K., Zhang, H., Das, D., Abdollahi, S., Prorok, T., Ghosh, S. D., Weintraub, S. J., Genovese, G., Powell, S. K., Lund, A. N., et al. (2020) · Complex Psychiatry · doi:10.1159/000512716 · Cited by 32
Self-citationPCM1 is necessary for focal ciliary integrity and is a candidate for severe schizophrenia
Monroe, T. O., Garrett, M. E., Kousi, M., Rodriguiz, R. M., Moon, S., Bai, Y., Brodar, C., Soldano, K. L., Savage, J. H., Hansen, T. F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19637-5 · Cited by 35
Targeting synaptic plasticity in schizophrenia: insights from genomic studies
Mould, A. W., Hall, N., Milošević, I., & Tunbridge, E. M. (2021) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2021.07.014 · Cited by 34
Capturing large genomic contexts for accurately predicting enhancer-promoter interactions
Chen, K., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab577 · Cited by 33
Okhuijsen‐Pfeifer, C., van der Horst, M. Z., Bousman, C., Lin, B. D., van Eijk, K. R., Ripke, S., Ayhan, Y., Babaoglu, M. O., Bak, M., Alink, W., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01884-3 · Cited by 33
Reynolds, T., Johnson, E. C., Huggett, S. B., Bubier, J. A., Palmer, R. H. C., Arpana Agrawal, A., Baker, E. J., & Chesler, E. J. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00795-5 · Cited by 32
Non‐coding regulatory elements: Potential roles in disease and the case of epilepsy
Pagni, S., Mills, J. D., Frankish, A., Mudge, J. M., & Sisodiya, S. M. (2021) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12775 · Cited by 33
Genome-wide association study identifies new locus associated with OCDPreprint
Strom, N. I., Yu, D., Gerring, Z. F., Halvorsen, M., Abdellaoui, A., Rodriguez‐Fontenla, C., Sealock, J., Bigdeli, T. B., Coleman, J. R. I., Mahjani, B., et al. (2021) · medRxiv · doi:10.1101/2021.10.13.21261078 · Cited by 28
Association between Human Genetic Variants and the Vaginal Bacteriome of Pregnant Women
Fan, W. Q., Kan, H., Liu, H.-Y., Wang, T., He, Y., Zhang, M., Li, Y., Li, Y., Meng, W., Li, Q., et al. (2021) · mSystems · doi:10.1128/msystems.00158-21 · Cited by 31
Brain mRNA Expression Associated with Cortical Volume Alterations in Autism Spectrum Disorder
Xie, Y., Zhang, X., Liu, F., Qin, W., Fu, J., Xue, K., & Yu, C. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108137 · Cited by 29
CSEA-DB: an omnibus for human complex trait and cell type associations
Dai, Y., Hu, R., Manuel, A. M., Liu, A., Jia, P., & Zhao, Z. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1064 · Cited by 31
Annotating genetic variants to target genes using H-MAGMA
Sey, N. Y. A., Pratt, B. M., & Won, H. (2022) · Nature Protocols · doi:10.1038/s41596-022-00745-z · Cited by 27
Self-citationHall, A., Bandrés‐Ciga, S., Díez-Fairén, M., Quinn, J. P., & Billingsley, K. J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21197332 · Cited by 27
Sey, N. Y. A., Hu, B., Iskhakova, M., Lee, S., Sun, H., Shokrian, N., Hutta, G. B., Marks, J. A., Quach, B. C., Johnson, E. O., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01558-y · Cited by 28
Self-citationCurrent and Future Perspectives of Noncoding RNAs in Brain Function and Neuropsychiatric Disease
Kyzar, E. J., Bohnsack, J. P., & Pandey, S. C. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.08.013 · Cited by 27
3D genome organization links non-coding disease-associated variants to genes
Orozco, G., Schoenfelder, S., Walker, N. J., Eyre, S., & Fraser, P. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.995388 · Cited by 30
Linnér, R. K., Mallard, T., Barr, P. B., Sanchez-Roige, S., Madole, J., Driver, M. N., Poore, H., Grotzinger, A., Tielbeek, J., Johnson, E., et al. (2020) · bioRxiv · doi:10.1101/2020.10.16.342501 · Cited by 29
Convergence and Divergence of Rare Genetic Disorders on Brain Phenotypes
Raznahan, A., Won, H., Glahn, D. C., & Jacquemont, S. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.1450 · Cited by 29
Self-citationPergola, G., Parihar, M., Sportelli, L., Bharadwaj, R. A., Borcuk, C., Radulescu, E., Bellantuono, L., Blasi, G., Chen, Q., Kleinman, J. E., et al. (2023) · Science Advances · doi:10.1126/sciadv.ade2812 · Cited by 26
Zhang, S., Zhang, H., Forrest, M. P., Zhou, Y., Sun, X., Bagchi, V. A., Kozlova, A., Santos, M. D., Piguel, N. H., Dionisio, L. E., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100399 · Cited by 26
SATB2 organizes the 3D genome architecture of cognition in cortical neurons
Wahl, N., Espeso‐Gil, S., Chietera, P., Nagel, A., Laighneach, A., Morris, D. W., Rajarajan, P., Akbarian, S., Dechant, G., & Apostolova, G. (2024) · Molecular Cell · doi:10.1016/j.molcel.2023.12.024 · Cited by 24
Self-citationPal, A., Noble, M. A., Morales, M., Pal, R., Baumgartner, M., Yang, J. W., Yim, K., Uebbing, S., & Noonan, J. P. (2025) · Cell · doi:10.1016/j.cell.2025.01.007 · Cited by 38
Ageing promotes microglial accumulation of slow-degrading synaptic proteins
Guldner, I. H., Wagner, V., Losada, P. M., Shi, S. M., Golub, S. W., Hevler, J. F., Chen, K. S., Meese, B. T., Ghoochani, A., Pulido, E. H., et al. (2026) · Nature · doi:10.1038/s41586-025-09987-9 · Cited by 33
Wang, H., Yi, Z., & Shi, T. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1934-x · Cited by 26
Kozlova, A., Zhang, S., Kotlar, A., Jamison, B., Zhang, H., Shi, S. Q. Z., Forrest, M. P., McDaid, J., Cutler, D. J., Epstein, M. P., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.07.001 · Cited by 24
Mulvey, B., & Dougherty, J. D. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01493-6 · Cited by 23
Gunturkun, M. H., Wang, T., Chitre, A. S., Martinez, A. G., Holl, K., Pierre, C. L. S., Bimschleger, H. V., Gao, J., Cheng, R., Polesskaya, O., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.790566 · Cited by 23
Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways
Pergola, G., Penzel, N., Sportelli, L., & Bertolino, A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.10.009 · Cited by 24
H‐MAGMA, inheriting a shaky statistical foundation, yields excess false positives
Yurko, R., Roeder, K., Devlin, B., & G'Sell, M. (2021) · Annals of Human Genetics · doi:10.1111/ahg.12412 · Cited by 23
The dynamics of chromatin architecture in brain development and function
Harabula, I., & Pombo, A. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.12.008 · Cited by 26
Zhong, W., Liu, W., Chen, J. C., Sun, Q., Hu, M., & Li, Y. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.957292 · Cited by 24
Silveira, P. P., & Meaney, M. J. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106008 · Cited by 26
Gerstner, N., Fröhlich, A. S., Matosin, N., Gagliardi, M., Cruceanu, C., Ködel, M., Rex‐Haffner, M., Tu, X., Mostafavi, S., Ziller, M. J., et al. (2025) · Science Advances · doi:10.1126/sciadv.adq2290 · Cited by 23
Navigating centriolar satellites: the role of PCM1 in cellular and organismal processes
Begar, E., Seyrek, E., & Firat‐Karalar, E. N. (2024) · FEBS Journal · doi:10.1111/febs.17194 · Cited by 23
Pei, G., Hu, R., Dai, Y., Manuel, A. M., Zhao, Z., & Jia, P. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1137 · Cited by 21
Schizophrenia-Linked Protein tSNARE1 Regulates Endosomal Trafficking in Cortical Neurons
Plooster, M., Rossi, G., Farrell, M. S., McAfee, J. C., Bell, J. L., Ye, M., Diering, G. H., Won, H., Gupton, S. L., & Brennwald, P. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0556-21.2021 · Cited by 22
Self-citationBarr, P. B., Mallard, T. T., Sanchez‐Roige, S., Poore, H. E., Linnér, R. K., Collaborators, C., Porjesz, B., HESSELBROCK, V. M., Foroud, T., Arpana Agrawal, A., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02171-x · Cited by 22
Massively parallel disruption of enhancers active in human neural stem cells
Geller, E. T., Noble, M. A., Morales, M., Gockley, J., Emera, D., Uebbing, S., Cotney, J. L., & Noonan, J. P. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113693 · Cited by 23
Metadata
- Authors
- Sey, N. Y. A., Hu, B., Mah, W., Fauni, H., McAfee, J. C., Rajarajan, P., Brennand, K. J., Akbarian, S., & Won, H.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Won, Hyejung
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2020-03-09
- Volume
- 23
- Issue
- 4
- Pages
- 583-593
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC7131892
- OpenAlex
- W3009063121
- Semantic Scholar
- f35ffcf138c22bfc43a0a99a2bb24338a41bf44b
- Cited by
- 297
- References
- 93
- Citations by year
- 2026: 29 · 2025: 45 · 2024: 48 · 2023: 33 · 2022: 53 · 2021: 51 · 2020: 36
- FWCI
- 26.09
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (5T32NS007431)
- UNC | University of North Carolina at Chapel Hill (Helen Lyng White Fellowship)
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R56MH101454, R0MH106056, R00MH113823, DP2MH122403)
- Brain and Behavior Research Foundation (NARSAD Young Investigator)
- Simons Foundation (605259)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- magma, brain disorders, risk genes, chromatin interactions, Hi-C, non-coding variants, mental illness, neurodegenerative diseases, gene regulatory relationships, developmental timing, excitatory neurons, brain cell types, gene expression, genome-wide association study
- MeSH terms
- Brain, Brain Diseases, Chromatin, Humans, Risk Factors, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genomics
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 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
