Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project.

SNPs Translation to population health UKBB biobank genetics genome-wide association study polygenic risk score

Journal

Med (New York, N.Y.)
ISSN: 2666-6340
Titre abrégé: Med
Pays: United States
ID NLM: 101769215

Informations de publication

Date de publication:
21 Dec 2023
Historique:
received: 03 04 2023
revised: 29 09 2023
accepted: 03 12 2023
medline: 2 1 2024
pubmed: 2 1 2024
entrez: 29 12 2023
Statut: aheadofprint

Résumé

Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes. We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging. In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations. The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes. E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

Sections du résumé

BACKGROUND BACKGROUND
Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes.
METHODS METHODS
We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging.
FINDINGS RESULTS
In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations.
CONCLUSIONS CONCLUSIONS
The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes.
FUNDING BACKGROUND
E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

Identifiants

pubmed: 38157848
pii: S2666-6340(23)00400-2
doi: 10.1016/j.medj.2023.12.001
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests H.R., D.W.-V., T.S., and A.D. are employees of Pheno.AI, Ltd, a biomedical data science company from Tel-Aviv, Israel. A.W., A.K., and E.S. are paid consultants to Pheno.AI, Ltd.

Auteurs

Zachary Levine (Z)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.

Iris Kalka (I)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.

Dmitry Kolobkov (D)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.

Hagai Rossman (H)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel.

Anastasia Godneva (A)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.

Smadar Shilo (S)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.

Ayya Keshet (A)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel.

Daphna Weissglas-Volkov (D)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel.

Tal Shor (T)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel.

Alon Diament (A)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel.

Yeela Talmor-Barkan (Y)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel; Department of Cardiology, Rabin Medical Center, Petah-Tikva 49100, Israel.

Yaron Aviv (Y)

Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel; Department of Cardiology, Rabin Medical Center, Petah-Tikva 49100, Israel.

Tom Sharon (T)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.

Adina Weinberger (A)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Pheno.AI, Tel-Aviv, Israel.

Eran Segal (E)

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel. Electronic address: eran.segal@weizmann.ac.il.

Classifications MeSH