{"doi":"10.1016/j.xgen.2022.100180","title":"Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative","abstract":"The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.","journal":"Cell Genomics","year":2022,"id":246343,"datarank":1.128597515258422,"base_score":3.713572066704308,"endowment":3.713572066704308,"self_citation_contribution":0.5570358100056463,"citation_network_contribution":0.5715617052527757,"self_endowment_contribution":0.5570358100056463,"citer_contribution":0.5715617052527757,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":28,"citers_with_citation_signal":20,"citers_with_endowment":20,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9413,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":805227,"name":"Jibril Hirbo","orcid":"0000-0002-8932-7311","position":1,"is_corresponding":false},{"id":441248,"name":"Dan Zhou","orcid":"0000-0002-5313-8164","position":2,"is_corresponding":false},{"id":262530,"name":"Wei Zhou","orcid":"0000-0001-7719-0859","position":3,"is_corresponding":false},{"id":78817,"name":"Jie Zheng","orcid":"0000-0002-6623-6839","position":4,"is_corresponding":false},{"id":3032,"name":"Masahiro Kanai","orcid":"0000-0001-5165-4408","position":5,"is_corresponding":false},{"id":731,"name":"Bogdan Paşaniuc","orcid":"0000-0002-0227-2056","position":6,"is_corresponding":false},{"id":15605,"name":"Eric R. Gamazon","orcid":"0000-0003-4204-8734","position":7,"is_corresponding":false},{"id":103608,"name":"Nancy J. Cox","orcid":"0000-0001-9315-0830","position":8,"is_corresponding":false},{"id":21368,"name":"Arjun Bhattacharya","orcid":"0000-0003-1196-4385","position":0,"is_corresponding":true}],"reference_count":99,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:23:43.438539Z","pmid":"36341024","pmcid":null,"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":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}