A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles

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.”

What the sources say

• Supported

  The Discussion reports that variants associated with complex traits are highly enriched in epigenomic annotations of trait-relevant tissues. Section 8 gives examples of disease-associated variants enriched in enhancers of relevant tissues. This supports the tissue-enrichment point to which the citation is attached; the paper is not cited here as evidence about MAGMA. (LLM, conf=0.93)

  cites [5] Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015). source
• Partly supported

  The Results (Cell-type-specific analysis of 17 traits; page unavailable) report trait-specific enrichment in relevant cell types, including immune cells for three immunological disorders. This supports the tissue-relevance point, although the analysis measures SNP-heritability enrichment in functional annotations rather than directly showing that disease-risk SNPs are enriched in regulatory elements. The paper discusses stratified LD score regression, not whether MAGMA accounts for tissue-specific regulatory relationships. (LLM, conf=0.84)

  cites [6] Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015). sourceExperimental — accuracy not yet measured

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.”

What the sources say

• Partly supported

  The Materials and Methods sections on summary SNP statistics and gene-set analysis say that MAGMA combines SNP associations into gene statistics and, in competitive gene-set analysis, corrects for gene size and gene density. The correction is described at the gene-set stage, not as part of generating gene-level associations, and our copy does not identify minor allele frequency as a correction factor. (LLM, conf=0.88)

  cites [2] de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015). sourceExperimental — accuracy not yet measured

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).”

What the sources say

• Supported

  The QTL analysis (page not provided) describes a prefrontal-cortex eQTL resource, supporting the citation’s identification of the resource used. The 56% and 76% figures are presented as the citing authors’ own findings from using that resource, not as results attributed to this paper. (LLM, conf=0.88)

  cites [3] Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018). source

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).”

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.”

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).”

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).”

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).”

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).”

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

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).”

In the paper

“Further, while the majority of brain disorders displayed enrichment in glutamatergic signaling, ASD, SCZ, and ALS displayed enrichment in GABAergic signaling.”

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).”

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.”

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

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.”

In the paper

“Neuronal differentiation and neuronal apoptotic pathways were also enriched in all brain disorders.”

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.”

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

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).”

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).”

In the paper

“Cell-type specific H-MAGMA recapitulated biological processes, cell-type specificities, and developmental trajectories of brain homogenate H-MAGMA.”

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).”

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).”

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).”

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.”

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).”

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).”

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

In the paper

“We found that AD-associated genes were significantly enriched for DEGs in microglia and oligodendrocytes, but not in neurons (Fig. 4a).”

In the paper

“Pleiotropic genes were involved in gene regulation, synaptic function, and neuronal and dendritic development (Fig. 5b).”

In the paper

“Consistent with the previous finding3,11, we detected a substantial overlap.”

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

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.”

In the paper

“Since psychiatric disorder-associated genes showed neurodevelopmental and neuronal enrichment, we used fetal brain and neuronal H-MAGMA results.”

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.”

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).”

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.”

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

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.”

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

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.”

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.”

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

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.”

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.”

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

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.”

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.”

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

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.”

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.”

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

In the paper

“Microglia are increasingly recognized as a central cell type contributing to the etiology of AD32.”

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

In the paper

“Common variation in SCZ maps onto specific groups of cells including pyramidal neurons and medium spiny neurons31.”

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

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.”

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

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.”

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.”

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

In the paper

“(3) data collection and analysis were not performed blind to the conditions of the experiments.”

In the paper

“Since we used publicly available GWAS summary statistics, (1) no data points were excluded from analysis”

In the paper

“(2) no statistical methods were used to pre-determine the sample size”

In the paper

“Similar cell-type specificity was reported by the interactome study, demonstrating the robustness of the result using an orthogonal approach49.”

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

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.”

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.”

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

In the paper

“Differentially expressed genes (DEG) in postmortem brains with psychiatric disorders were obtained from Gandal et al., 201840 (FDR<0.05).”

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

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.”

In the paper

“Cell-type specific DEG in AD postmortem brains was obtained from Mathys et al., 201924.”

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

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.”

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).”

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.”

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

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.”

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).”

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