{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:27Z","timestamp":1772138007173,"version":"3.50.1"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T00:00:00Z","timestamp":1606176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932008"],"award-info":[{"award-number":["61932008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772368"],"award-info":[{"award-number":["61772368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2018SHZDZX01"],"award-info":[{"award-number":["2018SHZDZX01"]}]},{"name":"Shanghai Science and Technology Innovation Fund","award":["19511101404"],"award-info":[{"award-number":["19511101404"]}]},{"name":"Zhangjiang Lab"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Motivation: Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account.<\/jats:p>\n                  <jats:p>Results: We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.<\/jats:p>","DOI":"10.1093\/bib\/bbaa298","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T15:09:39Z","timestamp":1602169779000},"source":"Crossref","is-referenced-by-count":11,"title":["nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6275-6095","authenticated-orcid":false,"given":"Anyi","family":"Yang","sequence":"first","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2454-0058","authenticated-orcid":false,"given":"Jingqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing-Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"ISTBI, KLCNBI and RIICS, Fudan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,11,24]]},"reference":[{"key":"2021072117035450600_ref1","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/s41588-018-0059-2","article-title":"Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection","volume":"50","author":"Pardinas","year":"2018","journal-title":"Nat Genet"},{"key":"2021072117035450600_ref2","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1038\/nature13595","article-title":"Biological insights from 108 schizophrenia-associated genetic loci","volume":"511","author":"Schizophrenia Working Group of the 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