Meta-analysis of genome-wide linkage scans for renal function traits. [Review]

MedStar author(s):
Citation: Nephrology Dialysis Transplantation. 27(2):647-56, 2012 Feb.PMID: 21622988Institution: MedStar Health Research InstituteForm of publication: Journal ArticleMedline article type(s): Journal Article | Meta-Analysis | Research Support, N.I.H., Extramural | Research Support, Non-U.S. Gov't | ReviewSubject headings: *Genetic Linkage | *Genetic Predisposition to Disease/ep [Epidemiology] | *Genome-Wide Association Study/mt [Methods] | *Kidney Failure, Chronic/ge [Genetics] | Female | Glomerular Filtration Rate/ge [Genetics] | Humans | Kidney Failure, Chronic/di [Diagnosis] | Kidney Failure, Chronic/ep [Epidemiology] | Male | Sensitivity and SpecificityYear: 2012Local holdings: Available online from MWHC library: 1996 - present, Available in print through MWHC library: 1999 - 2006ISSN:
  • 0931-0509
Name of journal: Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal AssociationAbstract: BACKGROUND: Several genome scans have explored the linkage of chronic kidney disease phenotypes to chromosomic regions with disparate results. Genome scan meta-analysis (GSMA) is a quantitative method to synthesize linkage results from independent studies and assess their concordance.CONCLUSIONS: While heterogeneity across populations, methodologies and study designs likely explain this lack of agreement, it is possible that linkage scan methodologies lack the resolution for investigating complex traits. Combining family-based linkage studies with genome-wide association studies may be a powerful approach to detect private mutations contributing to complex renal phenotypes.METHODS: We searched PubMed to identify genome linkage analyses of renal function traits in humans, such as estimated glomerular filtration rate (GFR), albuminuria, serum creatinine concentration and creatinine clearance. We contacted authors for numerical data and extracted information from individual studies. We applied the GSMA nonparametric approach to combine results across 14 linkage studies for GFR, 11 linkage studies for albumin creatinine ratio, 11 linkage studies for serum creatinine and 4 linkage studies for creatinine clearance.RESULTS: No chromosomal region reached genome-wide statistical significance in the main analysis which included all scans under each phenotype; however, regions on Chromosomes 7, 10 and 16 reached suggestive significance for linkage to two or more phenotypes. Subgroup analyses by disease status or ethnicity did not yield additional information.All authors: Bowden DW, Cole SA, Cupples A, Fox CS, Freedman BI, Hunt SC, Iyengar SK, Langefeld CD, Mottl AK, Rao M, Trikalinos TA, Umans JG, Yang QFiscal year: FY2012Digital Object Identifier: Date added to catalog: 2013-09-17
Holdings
Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 21622988 Available 21622988

Available online from MWHC library: 1996 - present, Available in print through MWHC library: 1999 - 2006

BACKGROUND: Several genome scans have explored the linkage of chronic kidney disease phenotypes to chromosomic regions with disparate results. Genome scan meta-analysis (GSMA) is a quantitative method to synthesize linkage results from independent studies and assess their concordance.

CONCLUSIONS: While heterogeneity across populations, methodologies and study designs likely explain this lack of agreement, it is possible that linkage scan methodologies lack the resolution for investigating complex traits. Combining family-based linkage studies with genome-wide association studies may be a powerful approach to detect private mutations contributing to complex renal phenotypes.

METHODS: We searched PubMed to identify genome linkage analyses of renal function traits in humans, such as estimated glomerular filtration rate (GFR), albuminuria, serum creatinine concentration and creatinine clearance. We contacted authors for numerical data and extracted information from individual studies. We applied the GSMA nonparametric approach to combine results across 14 linkage studies for GFR, 11 linkage studies for albumin creatinine ratio, 11 linkage studies for serum creatinine and 4 linkage studies for creatinine clearance.

RESULTS: No chromosomal region reached genome-wide statistical significance in the main analysis which included all scans under each phenotype; however, regions on Chromosomes 7, 10 and 16 reached suggestive significance for linkage to two or more phenotypes. Subgroup analyses by disease status or ethnicity did not yield additional information.

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