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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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“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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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
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“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
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“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.
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“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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“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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“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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“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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“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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“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 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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“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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“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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“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.
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“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
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“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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“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
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“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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“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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“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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“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
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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
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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
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“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
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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
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“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
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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
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“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
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“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
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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
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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
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“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
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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
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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
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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
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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
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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
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- Also stated in: § Introduction · plain
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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
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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
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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.
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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
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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.
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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
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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
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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.
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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
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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
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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
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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.
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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
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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
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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.
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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.
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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
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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.
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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
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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.
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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.
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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.
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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.
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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.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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.
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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”
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Metadata
- Authors
- Sey, N. Y. A., Hu, B., Mah, W., Fauni, H., McAfee, J. C., Rajarajan, P., Brennand, K. J., Akbarian, S., & Won, H.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Won, Hyejung
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2020-03-09
- Volume
- 23
- Issue
- 4
- Pages
- 583-593
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC7131892
- OpenAlex
- W3009063121
- Semantic Scholar
- f35ffcf138c22bfc43a0a99a2bb24338a41bf44b
- Cited by
- 297
- References
- 93
- Citations by year
- 2026: 29 · 2025: 45 · 2024: 48 · 2023: 33 · 2022: 53 · 2021: 51 · 2020: 36
- FWCI
- 26.09
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (5T32NS007431)
- UNC | University of North Carolina at Chapel Hill (Helen Lyng White Fellowship)
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R56MH101454, R0MH106056, R00MH113823, DP2MH122403)
- Brain and Behavior Research Foundation (NARSAD Young Investigator)
- Simons Foundation (605259)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- magma, brain disorders, risk genes, chromatin interactions, Hi-C, non-coding variants, mental illness, neurodegenerative diseases, gene regulatory relationships, developmental timing, excitatory neurons, brain cell types, gene expression, genome-wide association study
- MeSH terms
- Brain, Brain Diseases, Chromatin, Humans, Risk Factors, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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