{"doi":"10.1371/journal.pgen.1010597","title":"Transfer learning with false negative control improves polygenic risk prediction","abstract":"Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the genome and estimates an individual's genetic predisposition for a given trait. PRS analysis typically contains two input data sets: base data for effect size estimation and target data for individual-level prediction. Given the availability of large-scale base data, it becomes more common that the ancestral background of base and target data do not perfectly match. In this paper, we treat the GWAS summary information obtained in the base data as knowledge learned from a pre-trained model, and adopt a transfer learning framework to effectively leverage the knowledge learned from the base data that may or may not have similar ancestral background as the target samples to build prediction models for target individuals. Our proposed transfer learning framework consists of two main steps: (1) conducting false negative control (FNC) marginal screening to extract useful knowledge from the base data; and (2) performing joint model training to integrate the knowledge extracted from base data with the target training data for accurate trans-data prediction. This new approach can significantly enhance the computational and statistical efficiency of joint-model training, alleviate over-fitting, and facilitate more accurate trans-data prediction when heterogeneity level between target and base data sets is small or high.","journal":"PLoS Genetics","year":2023,"id":366440,"datarank":0.37638510602457603,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.08449858366627899,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.08449858366627899,"corpus_percentile":52.13893401407906,"corpus_rank":6188,"citation_count":6,"citer_count":5,"citers_with_citation_signal":3,"citers_with_endowment":3,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7364,"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":62.5,"fair_percentile":81.0149801284011,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":732696,"name":"Yifei Hu","orcid":"0000-0003-2870-7772","position":1,"is_corresponding":false},{"id":1122660,"name":"Vaishnavi Venkat","orcid":"0009-0004-8206-9403","position":2,"is_corresponding":false},{"id":395428,"name":"Tzu‐Pin Lu","orcid":"0000-0003-3697-0386","position":3,"is_corresponding":false},{"id":524049,"name":"Jung‐Ying Tzeng","orcid":"0000-0002-5505-1775","position":4,"is_corresponding":false},{"id":1122659,"name":"X. Jessie Jeng","orcid":"0000-0002-2182-133X","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-19T01:14:55.198227Z","pmid":"38011285","pmcid":"PMC10723713","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":38.8889,"fair_a":62.5,"fair_i":40.0,"fair_r":75.0,"fair_zscore":1.1103,"fair_rationale":{"fair_score":62.5,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":38.89,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No persistent identifier string from a recognized scheme is given for the paper's own dataset. 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A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For genomics / sequencing data, deposit in GEO (GSE accession), SRA (SRP/SRR) or ENA/BioProject (PRJEB/PRJNA).","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For genomics / sequencing data, deposit in GEO (GSE accession), SRA (SRP/SRR) or ENA/BioProject (PRJEB/PRJNA).","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. 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