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- Publisher Website: 10.3389/fgene.2021.644419
- Scopus: eid_2-s2.0-85103510340
- PMID: 33815478
- WOS: WOS:000634952400001
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Article: An Omnibus Test for Detecting Multiple Phenotype Associations Based on GWAS Summary Level Data
Title | An Omnibus Test for Detecting Multiple Phenotype Associations Based on GWAS Summary Level Data |
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Authors | |
Keywords | multiple phenotypes summary statistics the generalized higher criticism the generalized Berk-Jones test the aggregated Cauchy association test |
Issue Date | 2021 |
Publisher | Frontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/genetics |
Citation | Frontiers in Genetics, 2021, v. 12, p. article no. 644419 How to Cite? |
Abstract | Abundant Genome-wide association study (GWAS) findings have reflected the sharing of genetic variants among multiple phenotypes. Exploring the association between genetic variants and multiple traits can provide novel insights into the biological mechanism of complex human traits. In this article, we proposed to apply the generalized Berk-Jones (GBJ) test and the generalized higher criticism (GHC) test to identify the genetic variants that affect multiple traits based on GWAS summary statistics. To be more robust to different gene-multiple traits association patterns across the whole genome, we proposed an omnibus test (OMNI) by using the aggregated Cauchy association test. We conducted extensive simulation studies to investigate the type one error rates and compare the powers of the proposed tests (i.e., the GBJ, GHC and OMNI tests) and the existing tests (i.e., the minimum of the p-values (MinP) and the cross-phenotype association test (CPASSOC) in a wide range of simulation settings. We found that all of these methods could control the type one error rates well and the proposed OMNI test has robust power. We applied those methods to the summary statistics dataset from Global Lipids Genetics Consortium and identified 19 new genetic variants that were missed by the original single trait association analysis. |
Persistent Identifier | http://hdl.handle.net/10722/298676 |
ISSN | 2023 Impact Factor: 2.8 2023 SCImago Journal Rankings: 0.853 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, W | - |
dc.contributor.author | GUO, Y | - |
dc.contributor.author | Liu, Z | - |
dc.date.accessioned | 2021-04-12T03:01:49Z | - |
dc.date.available | 2021-04-12T03:01:49Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Frontiers in Genetics, 2021, v. 12, p. article no. 644419 | - |
dc.identifier.issn | 1664-8021 | - |
dc.identifier.uri | http://hdl.handle.net/10722/298676 | - |
dc.description.abstract | Abundant Genome-wide association study (GWAS) findings have reflected the sharing of genetic variants among multiple phenotypes. Exploring the association between genetic variants and multiple traits can provide novel insights into the biological mechanism of complex human traits. In this article, we proposed to apply the generalized Berk-Jones (GBJ) test and the generalized higher criticism (GHC) test to identify the genetic variants that affect multiple traits based on GWAS summary statistics. To be more robust to different gene-multiple traits association patterns across the whole genome, we proposed an omnibus test (OMNI) by using the aggregated Cauchy association test. We conducted extensive simulation studies to investigate the type one error rates and compare the powers of the proposed tests (i.e., the GBJ, GHC and OMNI tests) and the existing tests (i.e., the minimum of the p-values (MinP) and the cross-phenotype association test (CPASSOC) in a wide range of simulation settings. We found that all of these methods could control the type one error rates well and the proposed OMNI test has robust power. We applied those methods to the summary statistics dataset from Global Lipids Genetics Consortium and identified 19 new genetic variants that were missed by the original single trait association analysis. | - |
dc.language | eng | - |
dc.publisher | Frontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/genetics | - |
dc.relation.ispartof | Frontiers in Genetics | - |
dc.rights | This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | multiple phenotypes | - |
dc.subject | summary statistics | - |
dc.subject | the generalized higher criticism | - |
dc.subject | the generalized Berk-Jones test | - |
dc.subject | the aggregated Cauchy association test | - |
dc.title | An Omnibus Test for Detecting Multiple Phenotype Associations Based on GWAS Summary Level Data | - |
dc.type | Article | - |
dc.identifier.email | Liu, Z: zhhliu@hku.hk | - |
dc.identifier.authority | Liu, Z=rp02429 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3389/fgene.2021.644419 | - |
dc.identifier.pmid | 33815478 | - |
dc.identifier.pmcid | PMC8009968 | - |
dc.identifier.scopus | eid_2-s2.0-85103510340 | - |
dc.identifier.hkuros | 321991 | - |
dc.identifier.volume | 12 | - |
dc.identifier.spage | article no. 644419 | - |
dc.identifier.epage | article no. 644419 | - |
dc.identifier.isi | WOS:000634952400001 | - |
dc.publisher.place | Switzerland | - |