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Article: Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations

TitleIdentification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations
Authors
KeywordsBlock Clustering
Genome-Wide Association Study
Osteoporosis
Phenomics
Pleiotropy
Issue Date2011
PublisherAmerican Society for Bone and Mineral Research. The Journal's web site is located at http://www.jbmr.org/view/0/index.html
Citation
Journal Of Bone And Mineral Research, 2011, v. 26 n. 6, p. 1261-1271 How to Cite?
AbstractGenome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene-set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axesâSNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP-phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross-sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene-set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis-related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk. © 2011 American Society for Bone and Mineral Research. Copyright © 2011 American Society for Bone and Mineral Research.
Persistent Identifierhttp://hdl.handle.net/10722/164330
ISSN
2015 Impact Factor: 5.622
2015 SCImago Journal Rankings: 2.773
PubMed Central ID
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorGupta, Men_HK
dc.contributor.authorCheung, CLen_HK
dc.contributor.authorHsu, YHen_HK
dc.contributor.authorDemissie, Sen_HK
dc.contributor.authorCupples, LAen_HK
dc.contributor.authorKiel, DPen_HK
dc.contributor.authorKarasik, Den_HK
dc.date.accessioned2012-09-20T07:58:00Z-
dc.date.available2012-09-20T07:58:00Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Bone And Mineral Research, 2011, v. 26 n. 6, p. 1261-1271en_HK
dc.identifier.issn0884-0431en_HK
dc.identifier.urihttp://hdl.handle.net/10722/164330-
dc.description.abstractGenome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene-set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axesâSNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP-phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross-sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene-set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis-related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk. © 2011 American Society for Bone and Mineral Research. Copyright © 2011 American Society for Bone and Mineral Research.en_HK
dc.languageengen_US
dc.publisherAmerican Society for Bone and Mineral Research. The Journal's web site is located at http://www.jbmr.org/view/0/index.htmlen_HK
dc.relation.ispartofJournal of Bone and Mineral Researchen_HK
dc.subjectBlock Clusteringen_HK
dc.subjectGenome-Wide Association Studyen_HK
dc.subjectOsteoporosisen_HK
dc.subjectPhenomicsen_HK
dc.subjectPleiotropyen_HK
dc.subject.meshGenetic Predisposition to Disease-
dc.subject.meshGenome, Human - genetics-
dc.subject.meshGenome-Wide Association Study-
dc.subject.meshOsteoporosis - genetics-
dc.subject.meshQuantitative Trait, Heritable-
dc.titleIdentification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associationsen_HK
dc.typeArticleen_HK
dc.identifier.emailCheung, CL: lung1212@hku.hken_HK
dc.identifier.authorityCheung, CL=rp01749en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1002/jbmr.333en_HK
dc.identifier.pmid21611967-
dc.identifier.pmcidPMC3312758-
dc.identifier.scopuseid_2-s2.0-79958839788en_HK
dc.identifier.hkuros208937en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79958839788&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume26en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1261en_HK
dc.identifier.epage1271en_HK
dc.identifier.isiWOS:000291109100012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridGupta, M=22134995700en_HK
dc.identifier.scopusauthoridCheung, CL=14520953400en_HK
dc.identifier.scopusauthoridHsu, YH=8071612400en_HK
dc.identifier.scopusauthoridDemissie, S=35292066400en_HK
dc.identifier.scopusauthoridCupples, LA=7007090535en_HK
dc.identifier.scopusauthoridKiel, DP=7005526959en_HK
dc.identifier.scopusauthoridKarasik, D=7004384589en_HK
dc.identifier.citeulike9248710-

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