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

Publisher Correction: A practical guide to large-scale docking

Thrombin generation test based on a 96-channel pipettor for evaluation of FXIa procoagulant activity in pharmaceuticals

Universal platform for the generation of thermostabilized GPCRs that crystallize in LCP

Probing the free-state solution behavior of drugs and their tendencies to self-aggregate into nano-entities

Fabrication and use of silicon hollow-needle arrays to achieve tissue nanotransfection in mouse tissue in vivo

Use of stable isotope-tagged thymidine and multi-isotope imaging mass spectrometry (MIMS) for quantification of human cardiomyocyte division

Graphene- and metal-induced energy transfer for single-molecule imaging and live-cell nanoscopy with (sub)-nanometer axial resolution

Analyzing somatic mutations by single-cell whole-genome sequencing

Transcriptome-wide measurement of poly(A) tail length and composition at subnanogram total RNA sensitivity by PAIso-seq

3D human induced pluripotent stem cell–derived bioengineered skeletal muscles for tissue, disease and therapy modeling

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