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...
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| Vydáno v: | Frontiers in genetics Ročník 13; s. 997302 |
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31.10.2022
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| Abstract | 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. |
|---|---|
| AbstractList | 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
. Functional characterization of known index SNPs and novel independent signals using colocalization of conditional eGFR association results and gene expression in
across 51 human tissues identified two potentially causal genes across kidney tissues:
and
, and three candidate genes across other tissues:
, and
. 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. 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. 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. 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.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. |
| Author | Becker, Tim Teumer, Alexander Grabe, Hans J. Ghasemi, Sahar |
| AuthorAffiliation | 3 DZHK (German Center for Cardiovascular Research), Partner Site Greifswald , Greifswald , Germany 2 Department of Psychiatry and Psychotherapy , University Medicine Greifswald , Greifswald , Germany 4 German Center for Neurodegenerative Diseases DZNE, Site Rostock/Greifswald , Greifswald , Germany 1 Institute for Community Medicine , University Medicine Greifswald , Greifswald , Germany |
| AuthorAffiliation_xml | – name: 1 Institute for Community Medicine , University Medicine Greifswald , Greifswald , Germany – name: 4 German Center for Neurodegenerative Diseases DZNE, Site Rostock/Greifswald , Greifswald , Germany – name: 2 Department of Psychiatry and Psychotherapy , University Medicine Greifswald , Greifswald , Germany – name: 3 DZHK (German Center for Cardiovascular Research), Partner Site Greifswald , Greifswald , Germany |
| Author_xml | – sequence: 1 givenname: Sahar surname: Ghasemi fullname: Ghasemi, Sahar – sequence: 2 givenname: Tim surname: Becker fullname: Becker, Tim – sequence: 3 givenname: Hans J. surname: Grabe fullname: Grabe, Hans J. – sequence: 4 givenname: Alexander surname: Teumer fullname: Teumer, Alexander |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36386835$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1001/jama.2010.39 10.1038/ki.2010.550 10.1016/j.clim.2013.08.005 10.1016/S2213-8587(15)00040-6 10.1097/01.asn.0000123691.46138.e2 10.2215/CJN.09741110 10.1038/s41588-018-0047-6 10.1159/000488946 10.1152/ajprenal.90257.2008 10.1371/journal.pgen.1004383 10.1016/j.ajhg.2010.11.011 10.2202/1544-6115.1437 10.1038/s41467-019-11576-0 10.1093/hmg/dds369 10.1038/ng.3190 10.1371/journal.pgen.1002584 10.1038/srep46835 10.1056/NEJMoa041031 10.1038/s41586-018-0579-z 10.1023/b:pham.0000029286.45788.ad 10.1172/JCI141801 10.1016/j.ajhg.2018.07.004 10.1038/ng.2352 10.1016/S0140-6736(13)60595-4 10.1038/ki.2009.85 10.1038/s41467-019-09861-z 10.1371/journal.pone.0194044 10.1038/s41467-019-11704-w 10.1007/s00467-009-1381-9 10.1038/ki.2010.531 10.1093/bioinformatics/btp596 10.1038/ng.568 10.12659/MSM.892141 10.1038/ncomms10023 10.1093/bioinformatics/btab332 10.1093/hmg/ddz263 10.1016/j.ajhg.2016.07.012 10.1038/s41467-018-07867-7 10.1038/s41588-019-0407-x 10.1016/j.ajhg.2017.05.004 10.1016/S0140-6736(10)60674-5 10.1098/rstb.2012.0362 |
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| Copyright | Copyright © 2022 Ghasemi, Becker, Grabe and Teumer. Copyright © 2022 Ghasemi, Becker, Grabe and Teumer. 2022 Ghasemi, Becker, Grabe and Teumer |
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| Keywords | expression quantitative trait loci (eQTL) SNP-specific alpha-level genome-wide association studies (GWAS) colocalization conditional association analysis estimated glomerular filtration rate (eGFR) |
| Language | English |
| License | Copyright © 2022 Ghasemi, Becker, Grabe and Teumer. 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. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Weiqiu Cheng, University of Oslo, Norway Edited by: Yaowu Liu, Southwestern University of Finance and Economics, China This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics Ryan Sun, University of Texas MD Anderson Cancer Center, United States Reviewed by: Elise Flynn, Columbia University, United States |
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Sci. doi: 10.1098/rstb.2012.0362 |
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| Snippet | A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a... |
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| SubjectTerms | colocalization conditional association analysis estimated glomerular filtration rate (eGFR) expression quantitative trait loci (eQTL) Genetics genome-wide association studies (GWAS) SNP-specific alpha-level |
| Title | Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method |
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