{"doi":"10.1093/hmg/ddac219","title":"Human Molecular Genetics Review Issue 2022","abstract":"Recent remarkable advances in high-throughput genotyping and next-generation DNA-sequencing technologies have generated massive human genome sequencing datasets, fueling genome-wide association studies (1) and whole-genome/exome sequencing studies (2,3), which have greatly informed our understanding of the rare and common genetic contributions to disease (4). Most of these variants lie outside protein coding regions in presumed non-coding, regulatory regions (5,6). Thus, the incomplete regulatory annotation of the genome, whose effects are developmental stage and cell-type specific, complicates the identification of functional noncoding genetic variants and their causal association with disease (6,7). The challenge is further amplified by linkage disequilibrium, which hinders the identification of the true causal variants at each locus in genome-wide association studies (GWAS) (8,9). This has fueled the recent development of both computational methods (10) and highly parallelized experimental assays (4) that have opened new avenues to efficiently identify functional noncoding variation, therapeutic targets and diagnostic tools. In this special issue, we are very pleased to present a curated collection of timely, authoritative reviews of this rapidly moving field, which we expect will be of general interest to the genomic medicine community. Interrogation of the role and mechanisms of noncoding variants has been rapidly introduced into different subfields of genome medicine, including, evolution, discovery of disease-associated loci and genes, understanding of disease mechanisms, identifying diagnostic tools and drug targets. In this special issue, we compile an exciting group of comprehensive reviews from diverse perspectives on topics ranging from functional non-coding variant prediction and annotation, to discovery and validation of disease-associated loci and genes, to document the innovation, recent progress, and potential biomedical impact of computational and experimental approaches in this area. We start with computational approaches and resources, followed by functional assays. Kuksa et al. describe the latest scalable tools, databases and functional genomic resources to interpret non-coding variant findings from whole-genome sequencing data (11). They review both experimental data and in silico annotation (such as machine learning-based predictive models) for variants scoring and prioritization. Schipper and Posthuma comprehensively summarize recent computational tools and methods to generate in silico hypothesis related to non-coding GWAS variant function in human diseases and to prioritize targets for follow-up studies (12). Many of the tools they and colleagues have developed are readily accessible to researchers without substantial programming skills, making this area of genome annotation more readily available to biologists and geneticists. Castaldi et al. provide an overview of the latest approaches for genome-wide mapping of splicing quantitative trait loci (sQTL) (13). They present illustrative examples on how to use long-read sequencing to characterize sQTL effects on protein isoforms and the linkage of RNA isoforms to protein-level function impact by human disease-associated variants (13). Wang et al. review the latest work on noncoding RNA (noRNAs) and their functional roles using Alzheimer’s disease (ad) as example (14). In particular, they discuss how to identify biomarkers and therapeutic targets from ncRNA signatures, interactions between ncRNAs and mRNA, and ncRNA-regulated pathways in ad by leveraging data from multi-omic studies. Xu et al. review recent advances building gene regulatory elements to characterize gene expression impacted by non-coding variation (15). The authors summarize strengths and shortcomings of current functional assays and evolutionary analyses, and highlight future directions by leveraging new techniques to create new gene regulatory maps of ever-increasing resolution and comp","journal":"Human Molecular Genetics","year":2022,"id":284163,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6631,"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":21398,"name":"Daniel H. Geschwind","orcid":"0000-0003-2896-3450","position":1,"is_corresponding":false},{"id":69831,"name":"Feixiong Cheng","orcid":"0000-0002-1736-2847","position":0,"is_corresponding":true}],"reference_count":20,"raw_metadata":null,"created_at":"2026-07-19T00:29:32.702533Z","pmid":"36268970","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":[]}