{"doi":"10.1101/2023.11.10.566586","title":"Methyl-TWAS: A powerful method for <i>in silico</i> transcriptome-wide association studies (TWAS) using long-range DNA methylation","abstract":"ABSTRACT In silico transcriptome-wide association studies (TWAS) are commonly used to test whether expression of specific genes is linked to a complex trait. However, genotype-based in silico TWAS such as PrediXcan, exhibit low prediction accuracy for a majority of genes because genotypic data lack tissue- and disease-specificity and are not affected by the environment. Because methylation is tissue-specific and, like gene expression, can be modified by environment or disease status, methylation should predict gene expression with more accuracy than SNPs. Therefore, we propose Methyl-TWAS, the first approach that utilizes long-range methylation markers to impute gene expression for in silico TWAS through penalized regression. Methyl-TWAS 1) predicts epigenetically regulated/associated expression (eGReX), which incorporates tissue-specific expression and both genetically- (GReX) and environmentally-regulated expression to identify differentially expressed genes (DEGs) that could not be identified by genotype-based methods; and 2) incorporates both cis- and trans- CpGs, including various regulatory regions to identify DEGs that would be missed using cis -methylation only. Methyl-TWAS outperforms PrediXcan and two other methods in imputing gene expression in the nasal epithelium, particularly for immunity-related genes and DEGs in atopic asthma. Methyl-TWAS identified 3,681 (85.2%) of the 4,316 DEGs identified in a previous TWAS of atopic asthma using measured expression, while PrediXcan could not identify any gene. Methyl-TWAS also outperforms PrediXcan for expression imputation as well as in silico TWAS in white blood cells. Methyl-TWAS is a valuable tool for in silico TWAS, leveraging a growing body of publicly available genome-wide DNA methylation data for a variety of human tissues.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":413997,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9546,"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":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":833426,"name":"Yidi Qin","orcid":null,"position":1,"is_corresponding":false},{"id":409933,"name":"Hyun Jung Park","orcid":"0000-0002-8324-2624","position":2,"is_corresponding":false},{"id":732198,"name":"Molin Yue","orcid":"0000-0002-3339-892X","position":3,"is_corresponding":false},{"id":279533,"name":"Zhongli Xu","orcid":"0000-0001-6843-6212","position":4,"is_corresponding":false},{"id":277431,"name":"Erick Forno","orcid":"0000-0001-6497-9885","position":5,"is_corresponding":false},{"id":109275,"name":"Wei Chen","orcid":"0000-0001-7196-8703","position":6,"is_corresponding":false},{"id":88795,"name":"Juan C. Celedón","orcid":"0000-0002-6139-5320","position":7,"is_corresponding":false},{"id":409930,"name":"Soyeon Kim","orcid":"0000-0003-1573-2733","position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:22:01.321790Z","pmid":"38014125","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":[]}