Cargando…

Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method

A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a heritable trait, and recent application of genome-wide association studies (GWAS) successfully identified multiple eGFR-associated genetic loci. To i...

Descripción completa

Detalles Bibliográficos
Autores principales: Ghasemi, Sahar, Becker, Tim, Grabe, Hans J., Teumer, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9660290/
https://www.ncbi.nlm.nih.gov/pubmed/36386835
http://dx.doi.org/10.3389/fgene.2022.997302
_version_ 1784830389803548672
author Ghasemi, Sahar
Becker, Tim
Grabe, Hans J.
Teumer, Alexander
author_facet Ghasemi, Sahar
Becker, Tim
Grabe, Hans J.
Teumer, Alexander
author_sort Ghasemi, Sahar
collection PubMed
description A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a heritable trait, and recent application of genome-wide association studies (GWAS) successfully identified multiple eGFR-associated genetic loci. To increase statistical power for detecting independent associations in GWAS loci, we improved our recently developed quasi-adaptive method estimating SNP-specific alpha levels for the conditional analysis, and applied it to the GWAS meta-analysis results of eGFR among 783,978 European-ancestry individuals. Among known eGFR loci, we revealed 19 new independent association signals that were subsequently replicated in the United Kingdom Biobank (n = 408,608). These associations have remained undetected by conditional analysis using the established conservative genome-wide significance level of 5 × 10(–8). Functional characterization of known index SNPs and novel independent signals using colocalization of conditional eGFR association results and gene expression in cis across 51 human tissues identified two potentially causal genes across kidney tissues: TSPAN33 and TFDP2, and three candidate genes across other tissues: SLC22A2, LRP2, and CDKN1C. These colocalizations were not identified in the original GWAS. By applying our improved quasi-adaptive method, we successfully identified additional genetic variants associated with eGFR. Considering these signals in colocalization analyses can increase the precision of revealing potentially functional genes of GWAS loci.
format Online
Article
Text
id pubmed-9660290
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-96602902022-11-15 Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method Ghasemi, Sahar Becker, Tim Grabe, Hans J. Teumer, Alexander Front Genet Genetics A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a heritable trait, and recent application of genome-wide association studies (GWAS) successfully identified multiple eGFR-associated genetic loci. To increase statistical power for detecting independent associations in GWAS loci, we improved our recently developed quasi-adaptive method estimating SNP-specific alpha levels for the conditional analysis, and applied it to the GWAS meta-analysis results of eGFR among 783,978 European-ancestry individuals. Among known eGFR loci, we revealed 19 new independent association signals that were subsequently replicated in the United Kingdom Biobank (n = 408,608). These associations have remained undetected by conditional analysis using the established conservative genome-wide significance level of 5 × 10(–8). Functional characterization of known index SNPs and novel independent signals using colocalization of conditional eGFR association results and gene expression in cis across 51 human tissues identified two potentially causal genes across kidney tissues: TSPAN33 and TFDP2, and three candidate genes across other tissues: SLC22A2, LRP2, and CDKN1C. These colocalizations were not identified in the original GWAS. By applying our improved quasi-adaptive method, we successfully identified additional genetic variants associated with eGFR. Considering these signals in colocalization analyses can increase the precision of revealing potentially functional genes of GWAS loci. Frontiers Media S.A. 2022-10-31 /pmc/articles/PMC9660290/ /pubmed/36386835 http://dx.doi.org/10.3389/fgene.2022.997302 Text en Copyright © 2022 Ghasemi, Becker, Grabe and Teumer. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Ghasemi, Sahar
Becker, Tim
Grabe, Hans J.
Teumer, Alexander
Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method
title Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method
title_full Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method
title_fullStr Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method
title_full_unstemmed Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method
title_short Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method
title_sort discovery of novel egfr-associated multiple independent signals using a quasi-adaptive method
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9660290/
https://www.ncbi.nlm.nih.gov/pubmed/36386835
http://dx.doi.org/10.3389/fgene.2022.997302
work_keys_str_mv AT ghasemisahar discoveryofnovelegfrassociatedmultipleindependentsignalsusingaquasiadaptivemethod
AT beckertim discoveryofnovelegfrassociatedmultipleindependentsignalsusingaquasiadaptivemethod
AT grabehansj discoveryofnovelegfrassociatedmultipleindependentsignalsusingaquasiadaptivemethod
AT teumeralexander discoveryofnovelegfrassociatedmultipleindependentsignalsusingaquasiadaptivemethod