Genetic effects of sequence-conserved enhancer-like elements on human complex traits.

Data integration Enhancer Fine mapping Gene prioritization Genome-wide association study Heritability Sequence conservation Tissue specificity

Journal

Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660

Informations de publication

Date de publication:
02 Jan 2024
Historique:
received: 02 09 2022
accepted: 08 12 2023
medline: 4 1 2024
pubmed: 4 1 2024
entrez: 3 1 2024
Statut: epublish

Résumé

The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits. Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes. Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

Sections du résumé

BACKGROUND BACKGROUND
The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits.
RESULTS RESULTS
Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes.
CONCLUSIONS CONCLUSIONS
Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

Identifiants

pubmed: 38167462
doi: 10.1186/s13059-023-03142-1
pii: 10.1186/s13059-023-03142-1
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1

Subventions

Organisme : NIH HHS
ID : P50HG007735
Pays : United States
Organisme : NIH HHS
ID : R01HG010359
Pays : United States

Informations de copyright

© 2023. The Author(s).

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Auteurs

Xiang Zhu (X)

Department of Statistics, The Pennsylvania State University, 326 Thomas Building, University Park, 16802, PA, USA. xiangzhu@psu.edu.
Huck Institutes of the Life Sciences, The Pennsylvania State University, 201 Huck Life Sciences Building, University Park, 16802, PA, USA. xiangzhu@psu.edu.
Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA. xiangzhu@psu.edu.

Shining Ma (S)

Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA.
Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road MC5464, Stanford, 94305, CA, USA.

Wing Hung Wong (WH)

Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, 94305, CA, USA. whwong@stanford.edu.
Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road MC5464, Stanford, 94305, CA, USA. whwong@stanford.edu.

Classifications MeSH