{"doi":"10.1093/bib/bbad437","title":"A benchmark study on current GWAS models in admixed populations","abstract":"OBJECTIVE: The performances of popular genome-wide association study (GWAS) models have not been examined yet in a consistent manner under the scenario of genetic admixture, which introduces several challenging aspects: heterogeneity of minor allele frequency (MAF), wide spectrum of case-control ratio, varying effect sizes, etc. METHODS: We generated a cohort of synthetic individuals (N = 19 234) that simulates (i) a large sample size; (ii) two-way admixture (Native American and European ancestry) and (iii) a binary phenotype. We then benchmarked three popular GWAS tools [generalized linear mixed model associated test (GMMAT), scalable and accurate implementation of generalized mixed model (SAIGE) and Tractor] by computing inflation factors and power calculations under different MAFs, case-control ratios, sample sizes and varying ancestry proportions. We also employed a cohort of Peruvians (N = 249) to further examine the performances of the testing models on (i) real genetic and phenotype data and (ii) small sample sizes. RESULTS: In the synthetic cohort, SAIGE performed better than GMMAT and Tractor in terms of type-I error rate, especially under severe unbalanced case-control ratio. On the contrary, power analysis identified Tractor as the best method to pinpoint ancestry-specific causal variants but showed decreased power when the effect size displayed limited heterogeneity between ancestries. In the Peruvian cohort, only Tractor identified two suggestive loci (P-value $\\le 1\\ast{10}^{-5}$) associated with Native American ancestry. DISCUSSION: The current study illustrates best practice and limitations for available GWAS tools under the scenario of genetic admixture. Incorporating local ancestry in GWAS analyses boosts power, although careful consideration of complex scenarios (small sample sizes, imbalance case-control ratio, MAF heterogeneity) is needed.","journal":"Briefings in Bioinformatics","year":2023,"id":341944,"datarank":0.5383615233613736,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.12247321502540631,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.12247321502540631,"corpus_percentile":63.88179778757639,"corpus_rank":4670,"citation_count":15,"citer_count":12,"citers_with_citation_signal":8,"citers_with_endowment":8,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7952,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":66.6667,"fair_percentile":86.48731274839498,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":48092,"name":"Basilio Cieza","orcid":null,"position":1,"is_corresponding":false},{"id":413237,"name":"Dolly Reyes‐Dumeyer","orcid":"0000-0003-3572-8248","position":2,"is_corresponding":false},{"id":262293,"name":"Rosa Montesinos","orcid":"0000-0002-9342-8756","position":3,"is_corresponding":false},{"id":262311,"name":"Marcio Soto‐Añari","orcid":"0000-0002-9121-3284","position":4,"is_corresponding":false},{"id":262259,"name":"Nilton Custodio","orcid":"0000-0002-8025-3272","position":5,"is_corresponding":false},{"id":560402,"name":"Giuseppe Tosto","orcid":"0000-0001-7075-8245","position":6,"is_corresponding":false},{"id":1077988,"name":"Zikun Yang","orcid":"0000-0002-8834-6377","position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-19T01:11:08.077724Z","pmid":"38037235","pmcid":"PMC10689347","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":83.3333,"fair_a":68.75,"fair_i":0.0,"fair_r":33.3333,"fair_zscore":1.2752,"fair_rationale":{"fair_score":66.67,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":83.33,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"The synthetic data used in the simulation study are available at https://www.ebi.ac.uk/biostudies/studies/S-BSST936 .","grounded":true,"rationale":"The synthetic dataset is assigned a BioStudies accession (S-BSST936), which is a persistent identifier scheme.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":1.0,"verdict":"yes","evidence":"The synthetic data used in the simulation study are available at https://www.ebi.ac.uk/biostudies/studies/S-BSST936 .","grounded":true,"rationale":"The synthetic data are held in BioStudies, a curated repository registered in re3data. 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Describe the dataset as an object in its own right, not as a by-product of the findings — this is what makes it discoverable to someone who is not looking for your paper.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"We generated a cohort of synthetic individuals ( N = 19 234) that simulates (i) a large sample size; (ii) two-way admixture (Native American and European ancestry) and (iii) a binary phenotype.","why":"The dataset content is described in running prose rather than in an itemised section, table, or list.","gain":0.0,"priority":"essential","scored":false},{"key":"a_access_conditions_stated","dimension":"A","label":"Access level labelled","action":"State the access level in words, using the standard vocabulary: 'These data are open access' / 'These data are controlled access'. 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For sensitive/human clinical / human-subjects data, use a controlled-access repository such as dbGaP or EGA.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Request for genetic and phenotype data of Peruvian cohort can be submitted to gt2260@cumc.columbia.edu .","why":"The gatekeeper for the sensitive human Peruvian cohort data is a natural person (the corresponding author).","gain":0.0,"priority":"useful","scored":false},{"key":"i_qualified_references","dimension":"I","label":"Identifiers for the resources the data depend on","action":"Cite by identifier every resource the data depend on — the source datasets' accessions, the reference build (GRCh38 / GCA_000001405.28), the cohort application number, the code DOI — and register those relations on the dataset record (IsDerivedFrom, IsSupplementTo). 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A dataset that needs a €2,000 licence to open is not reusable.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list.","Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset.","Add a 'Data Records' section: itemise every file in the deposit and every variable or sample it holds, with counts and units. 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