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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.
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68 Own result · 45 Not checked yet · 16 Own interpretation · 7 Own method · 2 No basis given · 1 Couldn’t confirm · accuracy not yet measured
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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)
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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
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“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
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“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.
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“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.
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“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
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“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
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“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.
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“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
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“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
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“Fetal enrichment was more robust in neurodevelopmental disorders such as ADHD and ASD than in adult onset disorders including BD, SCZ, and MDD.”
§ 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.
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“whereas the number of SNPs assigned per gene was three-fold higher for cMAGMA (~244 SNPs per gene) than H-MAGMA (~73 SNPs per gene, Extended Data Fig. 1c).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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“Within brain tissue, psychiatric disorders showed stronger heritability enrichment in the fetal brain than in the adult brain, highlighting their neurodevelopmental origin (Fig. 2a).”
§ 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.
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“74.9% of coloc genes, Fisher’s exact test, OR=4.34, 95% CI=3.22–5.90, P=1.76x10–26”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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“The number of brain disorder risk genes (FDR<0.05) was comparable between H-MAGMA and cMAGMA (Extended Data Fig. 1b, Supplementary Data 1–2)”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Hi-C coupled MAGMA
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“We also observed Wnt/β-catenin pathway enrichment for a number of brain disorders including ASD, SCZ, MDD, PD, MS and ALS.”
§ 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.
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“MS-associated genes were enriched for dopaminergic signaling, disruption of which has been associated with immune malfunction in MS23.”
§ 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 [23] Marino, F. & Cosentino, M. Repurposing dopaminergic drugs for MS—the evidence mounts. Nat. Rev. Neurol. 12, 191–192 (2016). source
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“We observed pronounced immune related processes for degenerative disorders in contrast to psychiatric 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.
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In the paper
“Moreover, all degenerative disorders showed associations with genes involved in myelination and oligodendrocyte function, suggesting a potential role of oligodendrocytes in neurodegeneration.”
§ 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.
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In the paper
“ASD-associated genes were enriched for acetylcholinergic and serotonergic signaling, reflecting known biology of ASD20,21.”
§ 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 [20] Deutsch, S. I., Urbano, M. R., Burket, J. A., Herndon, A. L. & Winebarger, E. E. Pharmacotherapeutic implications of the association between genomic instability at chromosome 15q13.3 and autism spectrum disorders. Clin. Neuropharmacol. 34, 203–205 (2011). source
- Not checked yetcites [21] Muller, C. L., Anacker, A. M. J. & Veenstra-VanderWeele, J. The serotonin system in autism spectrum disorder: from biomarker to animal models. Neuroscience 321, 24–41 (2016). source
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In the paper
“All brain disorders showed enrichment for pathways involved in transcriptional and translational regulation (e.g. transcriptional regulators, RNA splicing, and DNA damage and repair pathways; 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.
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“SCZ and BD-associated genes were also enriched for acetylcholinergic signaling, supporting previous studies that altered cholinergic signaling contributes to SCZ and BD pathogenesis4,22.”
§ 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 [4] Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016). source
- Not checked yetcites [22] Berman, J. A., Talmage, D. A. & Role, L. W. Cholinergic circuits and signaling in the pathophysiology of schizophrenia. Int. Rev. Neurobiol. 78, 193–223 (2007). source
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“Neurogenesis was enriched in the majority of disorders except ASD and BD, consistent with an increasing number of studies elucidating the role of neurogenesis, differentiation, and neuronal apoptosis in brain disorders17,18.”
§ 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 [17] Marín, O. Developmental timing and critical windows for the treatment of psychiatric disorders. Nat. Med. 22, 1229–1238 (2016). source
- Not checked yetcites [18] Wegiel, J. et al. The neuropathology of autism: defects of neurogenesis and neuronal migration, and dysplastic changes. Acta Neuropathol. 119, 755–770 (2010). source
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“MDD-associated genes were enriched for a neurodevelopmental module (geneM16) upregulated in BD and SCZ.”
§ 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
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“Degenerative disorder risk genes showed pronounced enrichment for glial development and inflammatory responses.”
§ 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
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In the paper
“We also identified brain regions (e.g. cortex, hippocampus, substantia nigra, and hypothalamus) associated with multiple 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.
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“We found that a significant proportion of genes (20–40%) were detected in a cell-type specific fashion (Extended Data Fig. 5).”
§ 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
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“However, the overlap between DEG and gene association statistics was nominal (beta=0–0.04), and 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
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“Further, SCZ-associated genes were enriched for a synaptic module (geneM7) upregulated in BD and SCZ.”
§ 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
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“Since psychiatric disorders showed neurodevelopmental origin, we also measured cell-type specific expression profiles of psychiatric disorder-associated genes in the developing cortex and found convergence onto outer radial glia and excitatory neurons (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
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“In addition, ADHD-associated genes were enriched for ASD DEGs, recapitulating a shared genetic relationship between these two neurodevelopmental disorders41.”
§ 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
What the sources say
- Not checked yetcites [41] Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019). source
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In the paper
“We found a significant overlap between common variation affected genes and differentially expressed genes (DEG) in SCZ (Extended Data Fig. 6a).”
§ 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
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“MS-associated genes were highly expressed in glioblastoma, consistent with the emerging view that astrocyte-mediated neuroinflammation is a key contributor to the MS pathogenesis39.”
§ 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
What the sources say
- Not checked yetcites [39] Brambilla, R. The contribution of astrocytes to the neuroinflammatory response in multiple sclerosis and experimental autoimmune encephalomyelitis. Acta Neuropathol. 137, 757–783 (2019). source
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In the paper
“We found that an interneuronal module (geneM23) downregulated in ASD were enriched for ADHD-associated genes.”
§ 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
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“For example, brain disorder risk genes derived from cell-type specific H-MAGMA were involved in transcriptional regulation, neurogenesis, and synaptic transmission (Supplementary Data 6).”
§ 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
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In the paper
“SCZ-associated genes were also enriched for ASD DEG.”
§ 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
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“One remarkable difference between cell-type specific and brain homogenate H-MAGMA was postnatal expression of MS-associated genes from astrocytic H-MAGMA, which was not detected in brain homogenate.”
§ 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
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“We found that the same set of genes, including synaptic genes (DLG2, SYNGAP1, SHANK1) and genes that encode transcriptional regulators (SETD1A, SMARCC2), are affected by both common and rare variation in SCZ.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Interplay between common and rare variation
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“Finally, pleiotropic genes showed strong excitatory neuronal enrichment for cortical projection neurons in cortical layers 2/3 (excitatory neuronal subtypes 1) and corticothalamic projection neurons in cortical layers 5/6 (excitatory neuronal subtype 7) (Fig. 5d).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Biological pathways underlying pleiotropy
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In the paper
“ADHD-associated genes also overlap with genes with rare de novo variation in ASD, supporting the shared genetic basis of neurodevelopmental disorders.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Interplay between common and rare variation
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In the paper
“Among psychiatric disorders, neurodevelopmental disorders (ADHD and ASD) and adult-onset psychiatric disorders (BD, SCZ, and MDD) showed strong overlaps, indicating shared neurobiological bases.”
§ 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
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In the paper
“Notably, pleiotropic genes showed higher enrichment for genes mapped to pleiotropic cross-disorder GWS loci than those mapped to non-pleiotropic (disease-specific) GWS loci14 (Fig. 5a).”
§ 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
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In the paper
“Common and rare variation in ASD also converge onto the same set of genes.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Interplay between common and rare variation
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In the paper
“In total, we found 1,841 genes (hereby referred to as pleiotropic genes) that are shared in more than four psychiatric disorders (Supplementary Data 7).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Biological pathways underlying pleiotropy
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In the paper
“MDD-associated genes overlap with genes that harbor rare de novo variation in ASD and developmental disorders (DD), suggesting that a recently reported genetic correlation between MDD and ASD GWAS43 may also apply for rare variation.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Interplay between common and rare variation
What the sources say
- Not checked yetcites [43] Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018). source
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In the paper
“The correlation between RRHO and genetic correlation was 0.79 (Extended Data Fig. 7b, P-value=8.08x10–9)”
§ 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
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In the paper
“They showed a distinct peak at mid-gestation, consistent with the overall developmental expression patterns of psychiatric disorder-associated genes (Fig. 5c).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Biological pathways underlying pleiotropy
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In the paper
“Supporting this claim, we found that H-MAGMA derived genes explain a significant proportion of heritability in addition to eQTL derived genes.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
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In the paper
“H-MAGMA selective SNPs (SNPs assigned to H-MAGMA selective genes in H-MAGMA – SNPs assigned to H-MAGMA selective genes in cMAGMA) explain a significant proportion of heritability.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
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In the paper
“By comparing H-MAGMA SCZ genes and coloc/TWAS SCZ genes, we obtained 3,004 H-MAGMA selective genes (genes identified by H-MAGMA but not by TWAS and/or coloc).”
§ 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
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In the paper
“Coloc identified 255 SCZ-associated genes (coloc SCZ genes) whose eQTLs co-localize with SCZ genome-wide significant (GWS) loci (PP4>(PP0+PP1+PP2+PP3)).”
§ 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
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“TWAS identified 708 SCZ-associated genes (TWAS SCZ genes) whose imputed expression values are correlated with SCZ (FDR<0.05).”
§ 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
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Psychiatric disorder-associated genes are highly expressed in neurons, while neurogenerative disorder-associated genes show glial signatures.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
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In the paper
“Psychiatric disorder-associated genes are highly expressed in neurons, while neurogenerative disorder-associated genes show glial signatures.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Heritability enrichment of brain disorders in active regulatory elements of multiple tissue/cell types.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Genetic correlations measured with Pearson’s correlation (rg) and gene-level overlaps (RRHO Z) are highly correlated, indicating that gene-level overlaps obtained by H-MAGMA recapitulate genetic correlations.”
§ 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.
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In the paper
“Psychiatric disorder risk genes predicted by H-MAGMA are dysregulated in postmortem brains of individuals with psychiatric disorders.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Psychiatric disorder-associated genes are highly expressed in radial glia and excitatory neurons in the developing cortex.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Extended Data
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Since 3D chromatin loops are highly tissue-specific4, it is important to decide which Hi-C datasets are appropriate to identify target genes for each disorder.”
§ 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 [4] Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“These results highlight the importance of using functional genomic evidence in assigning non-coding SNPs to genes.”
§ 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.
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In the paper
“This result highlights mid-gestation as a critical window during neurodevelopment that may confer risk to multiple psychiatric disorders, consistent with recent results from cross-disorder GWAS14,15.”
§ 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 [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
- Not checked yetcites [15] Schork, A. J. et al. A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment. Nat. Neurosci. 22, 353–361 (2019). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Together with heritability enrichment, this finding of enriched immune response in degenerative, but less so in psychiatric disorders hints a possible explanation for genetic distinctions between psychiatric and degenerative disorders25.”
§ 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 [25] The Brainstorm Consortium et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“In line with this, single cell transcriptomic profiles in AD postmortem brains suggested altered molecular profiles in oligodendrocytes24.”
§ 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 [24] Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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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 indicates that H-MAGMA can provide cellular etiology that can be missed by cMAGMA.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Cell-type specificity
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“demonstrating that gene-level association statistics from H-MAGMA reflect shared genetic architecture, and hence can be further used to decipher the biological mechanisms underlying shared genetic architecture among psychiatric disorders.”
§ 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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Our previous work suggests a potential interplay between common and rare variation in the genetic architecture of brain disorders42.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Interplay between common and rare variation
What the sources say
- Not checked yetcites [42] Mah, W. & Won, H. The three-dimensional landscape of the genome in human brain tissue unveils regulatory mechanisms leading to schizophrenia risk. Schizophr. Res. https://doi.org/10.1016/j.schres.2019.03.007 (2019). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“First, Hi-C can provide comprehensive genome-wide maps for tissues or cell-types with limited access.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“These results collectively suggest that rare and common variation may impact same biological pathways42.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Result > Interplay between common and rare variation
What the sources say
- Not checked yetcites [42] Mah, W. & Won, H. The three-dimensional landscape of the genome in human brain tissue unveils regulatory mechanisms leading to schizophrenia risk. Schizophr. Res. https://doi.org/10.1016/j.schres.2019.03.007 (2019). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“this result suggests that the cellular context in which risk variants influence gene expression needs to be carefully considered in understanding the molecular complexity of brain disorders.”
§ 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
hedged- Asserted: passes — The authors put it forward as true.
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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
“An application of H-MAGMA to nine brain disorder GWAS enabled systematic delineation of pathogenic mechanisms of brain disorders.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
plaindirectn = 9- Asserted: passes — The authors put it forward as true.
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In the paper
“It is of note that H-MAGMA also has its shortcoming, as it does not capture gene regulatory mechanisms such as altered RNA splicing or the allelic effect (Hi-C cannot predict whether the SNPs will downregulate or upregulate the cognate genes).”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“These results collectively suggest that chromatin architecture such as Hi-C and chromatin accessibility may provide complementary regulatory phenotypes that can be missed by eQTLs.”
§ 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.
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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 demonstrate that the shared genetic basis of psychiatric disorders translates into shared neurobiological mechanisms.”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Discussion
boosteddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“our previous study comparing Hi-C data with eQTLs have demonstrated the gene regulatory potential of exon-level interactions3”
§ A computational tool (H-MAGMA) for improved prediction of brain disorder risk genes by incorporating brain chromatin interaction profiles > Methods > Development of Hi-C coupled MAGMA (H-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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Importantly, this characteristic was also observed for pleiotropic genes identified by meta-analysis of eight psychiatric disorders14.”
§ 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 [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
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“We also obtained 102 ASD risk genes (rare variation burden, FDR<0.1) from the Autism Sequencing Consortium (ASC) study65.”
§ 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 [65] Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584.e23 (2020). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Genes that harbor de novo protein disrupting variation in developmental disorders (DD) were obtained from the Deciphering Developmental Disorders Study (93 DD risk genes with genome-wide significance)64.”
§ 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 [64] Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433–438 (2017). source
plain- Asserted: passes — The authors put it forward as true.
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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
“Genetic correlations measured with Pearson’s correlation (rg) and gene-level overlaps (RRHO Z) are highly correlated, indicating that gene-level overlaps obtained by H-MAGMA recapitulate genetic correlations.”
§ 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.
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- It may state two separate claims as one. 40
- Probably not a substantial claim. 21
- The quote only partly states this claim. 1
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.
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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.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.
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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.
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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
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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.
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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
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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
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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.
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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.
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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
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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.
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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.
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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.
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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
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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.
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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
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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
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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None of the sources cited for this claim have been checked yet.
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
- Not checked yetcites [5] Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015). source
- 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
plain- Asserted: passes — The authors put it forward as true.
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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
- 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
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“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.
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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.
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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.
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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.
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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.
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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.
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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?
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]
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]
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]
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
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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…”
MethodInfluential
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
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