{"doi":"10.1101/2024.06.26.24309531","title":"Genomic Exploration of Essential Hypertension in African-Brazilian Quilombo Populations: A Comprehensive Approach with Pedigree Analysis and Family-Based Association Studies","abstract":"ABSTRACT Background Essential Hypertension (EH) is a global health issue. Despite extensive research, much of EH heritability remains unexplained. We investigated the genetic basis of EH in African-derived individuals from partially isolated quilombo populations in Vale do Ribeira (SP-Brazil). Methods Samples from 431 individuals (167 affected, 261 unaffected, 3 unknown) were genotyped using a 650k SNP array. Estimated global ancestry proportions were 47% African, 36% European, and 16% Native American. We constructed six pedigrees using additional data from 673 individuals and created three non-overlapping SNP subpanels. We phased haplotypes and performed local ancestry analysis to account for admixture. Genome-wide linkage analysis (GWLA) and fine-mapping via family-based association studies (FBAS) were conducted, prioritizing EH-associated genes through systematic approach involving databases like PubMed, ClinVar, and GWAS Catalog. Results Linkage analysis identified 22 regions of interest (ROIs) with LOD scores ranging 1.45-3.03, encompassing 2,363 genes. Fine-mapping (FBAS) identified 60 EH-related candidate genes and 117 suggestive/significant variants. Among these, 14 genes, including PHGDH , S100A10 , MFN2 , and RYR2 , were strongly related to hypertension harboring 29 suggestive/significant SNPs. Conclusions Through a complementary approach — combining admixture-adjusted GWLA based on Markov chain Monte Carlo methods, FBAS on known and imputed data, and gene prioritizing — new loci, variants, and candidate genes were identified. These findings provide targets for future research, replication in other populations, facilitate personalized treatments, and improve public health towards African-derived underrepresented populations. Limitations include restricted SNP coverage, self-reported pedigree data, and lack of available EH genomic studies on admixed populations for independent validation, despite the performed genetic correlation analyses using summary statistics. NOVELTY AND RELEVANCE What Is New? This study applies a multi-level computational approach integrating admixture-adjusted genome-wide linkage analysis (GWLA), family-based association studies (FBAS), and fine-mapping strategies to investigate the genetic basis of essential hypertension (EH) in Brazilian Quilombo populations, a historically underrepresented group in genomic research. What Is Relevant? By focusing on admixed populations with high African ancestry, our findings address gaps in hypertension genetics by identifying 22 regions of interest (ROIs), 60 candidate genes, and 117 suggestive/significant variants, highlighting population-specific genetic factors that may contribute to EH risk. The study also emphasizes the need for ancestry-aware genomic analyses to improve the precision of genetic risk assessment in underrepresented populations. What Question Should Be Addressed Next? Future studies should focus on replicating these findings in independent admixed cohorts, conducting functional validation of prioritized genes, and integrating polygenic risk scores (PRS) adjusted for ancestry to enhance clinical applications for hypertension prevention and treatment in diverse populations.","journal":"medRxiv","year":2024,"id":499720,"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":0.0,"corpus_rank":10062,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.61,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":421764,"name":"Andréa R. V. R. Horimoto","orcid":"0000-0002-8573-5158","position":1,"is_corresponding":false},{"id":1348715,"name":"Ellen Marie Wijsman","orcid":null,"position":2,"is_corresponding":false},{"id":118078,"name":"Lilian Kimura","orcid":null,"position":3,"is_corresponding":false},{"id":484623,"name":"Kelly Nunes","orcid":"0000-0003-0864-2414","position":4,"is_corresponding":false},{"id":576967,"name":"Alejandro Q. Nato","orcid":"0000-0002-8745-9046","position":5,"is_corresponding":false},{"id":762932,"name":"Regina Célia Mingroni‐Netto","orcid":"0000-0001-9233-5227","position":6,"is_corresponding":false},{"id":1347659,"name":"Vinícius Magalhães Borges","orcid":"0000-0002-9502-2549","position":0,"is_corresponding":true}],"reference_count":146,"raw_metadata":null,"created_at":"2026-07-19T02:09:53.188165Z","pmid":"38978678","pmcid":"PMC11230341","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":[]}