{"doi":"10.1371/journal.pone.0065602","title":"The Geometric Increase in Meta-Analyses from China in the Genomic Era","abstract":"Meta-analyses are increasingly popular. It is unknown whether this popularity is driven by specific countries and specific meta-analyses types. PubMed was used to identify meta-analyses since 1995 (last update 9/1/2012) and catalogue their types and country of origin. We focused more on meta-analyses from China (the current top producer of meta-analyses) versus the USA (top producer until recently). The annual number of meta-analyses from China increased 40-fold between 2003 and 2011 versus 2.4-fold for the USA. The growth of Chinese meta-analyses was driven by genetics (110-fold increase in 2011 versus 2003). The HuGE Navigator identified 612 meta-analyses of genetic association studies published in 2012 from China versus only 109 from the USA. We compared in-depth 50 genetic association meta-analyses from China versus 50 from USA in 2012. Meta-analyses from China almost always used only literature-based data (92%), and focused on one or two genes (94%) and variants (78%) identified with candidate gene approaches (88%), while many USA meta-analyses used genome-wide approaches and raw data. Both groups usually concluded favorably for the presence of genetic associations (80% versus 74%), but nominal significance (P<0.05) typically sufficed in the China group. Meta-analyses from China typically neglected genome-wide data, and often included candidate gene studies published in Chinese-language journals. Overall, there is an impressive rise of meta-analyses from China, particularly on genetic associations. Since most claimed candidate gene associations are likely false-positives, there is an urgent global need to incorporate genome-wide data and state-of-the art statistical inferences to avoid a flood of false-positive genetic meta-analyses.","journal":"PLoS ONE","year":2013,"id":7375,"datarank":4.437281470201715,"base_score":4.204692619390966,"endowment":4.204692619390966,"self_citation_contribution":0.6307038929086449,"citation_network_contribution":3.8065775772930706,"self_endowment_contribution":0.6307038929086449,"citer_contribution":3.8065775772930706,"corpus_percentile":70.70789259560618,"corpus_rank":361,"citation_count":68,"citer_count":48,"citers_with_citation_signal":41,"citers_with_endowment":41,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7586,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2013-06-12","fair_score":31.0417,"fair_percentile":12.620932277924362,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":65854,"name":"Christine Q. Chang","orcid":"0000-0002-4624-898X","position":1,"is_corresponding":false},{"id":6427,"name":"Tram Kim Lam","orcid":"0000-0002-9192-2013","position":2,"is_corresponding":false},{"id":6450,"name":"Sheri D. Schully","orcid":"0000-0003-1550-9224","position":3,"is_corresponding":false},{"id":526,"name":"Muin J. Khoury","orcid":"0000-0002-9887-443X","position":4,"is_corresponding":false},{"id":148,"name":"John P. A. Ioannidis","orcid":"0000-0003-3118-6859","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":"23776510","pmcid":"PMC3680482","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":"gold","license":"cc-by","views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":52.5,"fair_a":42.5,"fair_i":12.5,"fair_r":16.6667,"fair_zscore":-1.2817,"fair_rationale":{"fair_score":31.04,"has_llm":true,"dimensions":{"F":{"name":"Findable","score":52.5,"criteria":[{"key":"f_has_doi","label":"Has a persistent DOI","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"DOI present","rationale":null},{"key":"f_repository_presence","label":"Indexed in repositories / literature DBs","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"datacite=0, pmcid=True, pmid=True","rationale":null},{"key":"f_persistent_ids","label":"Resolvable scholarly identifiers (OpenAlex)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no OpenAlex id","rationale":null},{"key":"f_metadata_richness","label":"Rich, machine-readable metadata","kind":"llm","weight":1.0,"fraction":0.25,"signal":null,"rationale":"The paper provides no metadata beyond PubMed tagging (e.g., no structured data files, no provenance metadata), and the citation information is limited to author lists and affiliations, which are not machine-actionable."}]},"A":{"name":"Accessible","score":42.5,"criteria":[{"key":"a_open_access","label":"Open Access / files deposited","kind":"deterministic","weight":1.5,"fraction":1.0,"signal":"Open Access","rationale":null},{"key":"a_retrievable","label":"Free full text retrievable","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"0 OA location(s)","rationale":null},{"key":"a_access_protocol","label":"Clear data/code access protocol","kind":"llm","weight":1.0,"fraction":0.25,"signal":null,"rationale":"No access protocol for underlying data (e.g., PubMed search results, HuGE Navigator counts, or the 100 meta-analyses sample) is provided; the paper only describes search strategies without a link to the data."}]},"I":{"name":"Interoperable","score":12.5,"criteria":[{"key":"i_linked_data","label":"Linked datasets / DataCite relations","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"linked_datasets=0, datacite=0","rationale":null},{"key":"i_standard_ids","label":"References data via standard accessions","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"accessions=0, trials=0","rationale":null},{"key":"i_standards","label":"Standard formats, vocabularies & identifiers","kind":"llm","weight":1.0,"fraction":0.25,"signal":null,"rationale":"The paper uses standard medical classification terms (e.g., MeSH via PubMed) but does not specify formal ontologies, controlled vocabularies, or persistent identifiers for the meta-analyses or gene variants studied."}]},"R":{"name":"Reusable","score":16.67,"criteria":[{"key":"r_license","label":"Clear, open reuse license","kind":"deterministic","weight":1.5,"fraction":0.0,"signal":"no license","rationale":null},{"key":"r_downloads","label":"Demonstrated reuse (downloads)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"downloads=0","rationale":null},{"key":"r_version","label":"Versioned / maintained","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no version chain","rationale":null},{"key":"r_dataset","label":"Classified as a data resource","kind":"deterministic","weight":0.5,"fraction":1.0,"signal":"is_dataset","rationale":null},{"key":"r_reusability","label":"Data-availability statement, license & reproducibility","kind":"llm","weight":2.0,"fraction":0.167,"signal":null,"rationale":"No data-availability statement is present, no license for reuse of the synthesized data is given, and the study does not provide the list of included meta-analyses or a reproducible code/query."}]}},"suggestions":["Deposit the list of 100 meta-analyses with PubMed IDs and all extracted characteristics in a public repository with a persistent identifier (e.g., Zenodo).","Provide the exact PubMed search queries and HuGE Navigator query parameters in a machine-readable format (e.g., JSON or R code).","Include a formal data-availability statement specifying a Creative Commons license (CC0 or CC-BY) and a link to the repository.","Use standard interoperable identifiers for genes, variants, and phenotypes (e.g., HGNC, dbSNP rsIDs, and UMLS CUIDs) in the supplementary data.","Publish the full extraction form (or codebook) used for the 100 meta-analyses evaluation to enable independent replication."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v2","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v2","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-06-18T00:42:02.181572Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}