Annotating genetic variants to target genes using H-MAGMA

Nancy Y. A. Sey, Brandon M. Pratt, Hyejung Won

Published: 2022-10-25 DOI: 10.1038/s41596-022-00745-z

Hi-C

Genetic variants

H-MAGMA

Genomic annotation

Complex traits

AI 解读

Quantitative nucleotide resolution profiling of RNA cytidine acetylation by ac4C-seq

Purification of infection-associated macropinosomes by magnetic isolation for proteomic characterization

Surface photovoltage microscopy for mapping charge separation on photocatalyst particles

Author Correction: Reliable detection of somatic mutations in solid tissues by laser-capture microdissection and low-input DNA sequencing

Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility

Spatial visualization of comprehensive brain neurotransmitter systems and neuroactive substances by selective in situ chemical derivatization mass spectrometry imaging

Preparation, validation and use of a vasoactive tryptophan-derived hydroperoxide and relevant control compounds

Genome-wide and sister chromatid-resolved profiling of protein occupancy in replicated chromatin with ChOR-seq and SCAR-seq

Optimal13C NMR investigation of intrinsically disordered proteins at 1.2 GHz

Synthesis and functionalization of scalable and versatile 2D protein films via amyloid-like aggregation

查看全部

Sections

Figures

References

An outstanding goal in modern genomics is to systematically predict the functional outcome of noncoding variation associated with complex traits. To address this, we developed Hi-C-coupled multi-marker analysis of genomic annotation (H-MAGMA), which build
Introduction
Materials
Procedure
Troubleshooting
Timing
Anticipated results
- References
- References
References
ACKNOWLEDGMENTS

Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110 (2015).
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).
Sanyal, A., Lajoie, B. R., Jain, G. & Dekker, J. The long-range interaction landscape of gene promoters. Nature 489, 109–113 (2012).
Pratt, B. M. & Won, H. Advances in profiling chromatin architecture shed light on the regulatory dynamics underlying brain disorders. Semin. Cell Dev. Biol. 121, 153–160 (2022).
Sey, N. Y. A. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat. Neurosci. 23, 583–593 (2020).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).
Jung, I. et al. A compendium of promoter-centered long-range chromatin interactions in the human genome. Nat. Genet. 51, 1442–1449 (2019).
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).
Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).
Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).
Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).
Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).
Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).
Andlauer, T. F. M. et al. Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation. Sci. Adv. 2, e1501678 (2016).
Van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).
Constantino, J. N. & Marrus, N. The early origins of autism. Child Adolesc. Psychiatr. Clin. N. Am. 26, 555–570 (2017).
Yurko, R., Roeder, K., Devlin, B. & G’Sell, M. H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives. Ann. Hum. Genet. 85, 97–100 (2021).
de Leeuw, C., Sey, N. Y. A., Posthuma, D. & Won, H. A response to Yurko et al: H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives. Preprint at https://www.biorxiv.org/content/10.1101/2020.09.25.310722v1(2020)
Matoba, N. et al. Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. Transl. Psychiatry 10, 265 (2020).
Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
Quach, B. C. et al. Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits. Nat. Commun. 11, 5562 (2020).
Song, M. et al. Cell-type-specific 3D epigenomes in the developing human cortex. Nature 587, 644–649 (2020).
Feleke, R. et al. Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases. Acta Neuropathol. 142, 449–474 (2021).
Hu, B. et al. Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders. Nat. Commun. 12, 3968 (2021).
Sey, N. Y. A. et al. Chromatin architecture in addiction circuitry identifies risk genes and potential biological mechanisms underlying cigarette smoking and alcohol use traits. Mol. Psychiatry 27, 3085–3094 (2022).
Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).
Fang, R. et al. Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq. Cell Res. 26, 1345–1348 (2016).
Sey, N., Pratt, B. & Won, H. H-MAGMA Protocol. Annotating genetic variants to target genes using H-MAGMA. Available at https://zenodo.org/record/6382668#.YwglBuzMIq0 (2021).

Annotating genetic variants to target genes using H-MAGMA

推荐阅读