Meta-analysis of genome-wide linkage scans for renal function traits

Madhumathi Rao, Amy K. Mottl, Shelley A. Cole, Jason G. Umans, Barry I. Freedman, Donald W. Bowden, Carl D. Langefeld, Caroline S. Fox, Qiong Yang, Adrienne Cupples, Sudha K. Iyengar, Steven Hunt, Thomas A. Trikalinos

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

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. 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.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.

Original languageEnglish
Pages (from-to)647-656
Number of pages10
JournalNephrology Dialysis Transplantation
Volume27
Issue number2
DOIs
Publication statusPublished - Feb 2012
Externally publishedYes

Fingerprint

Meta-Analysis
Creatinine
Genome
Kidney
Phenotype
Glomerular Filtration Rate
Chromosomes, Human, Pair 16
Chromosomes, Human, Pair 10
Chromosomes, Human, Pair 7
Albuminuria
Genome-Wide Association Study
Population Characteristics
Serum
Chronic Renal Insufficiency
PubMed
Albumins
Mutation

Keywords

  • albuminuria
  • chronic kidney disease
  • glomerular filtration rate
  • linkage scans
  • meta-analysis

ASJC Scopus subject areas

  • Nephrology
  • Transplantation

Cite this

Rao, M., Mottl, A. K., Cole, S. A., Umans, J. G., Freedman, B. I., Bowden, D. W., ... Trikalinos, T. A. (2012). Meta-analysis of genome-wide linkage scans for renal function traits. Nephrology Dialysis Transplantation, 27(2), 647-656. https://doi.org/10.1093/ndt/gfr255

Meta-analysis of genome-wide linkage scans for renal function traits. / Rao, Madhumathi; Mottl, Amy K.; Cole, Shelley A.; Umans, Jason G.; Freedman, Barry I.; Bowden, Donald W.; Langefeld, Carl D.; Fox, Caroline S.; Yang, Qiong; Cupples, Adrienne; Iyengar, Sudha K.; Hunt, Steven; Trikalinos, Thomas A.

In: Nephrology Dialysis Transplantation, Vol. 27, No. 2, 02.2012, p. 647-656.

Research output: Contribution to journalArticle

Rao, M, Mottl, AK, Cole, SA, Umans, JG, Freedman, BI, Bowden, DW, Langefeld, CD, Fox, CS, Yang, Q, Cupples, A, Iyengar, SK, Hunt, S & Trikalinos, TA 2012, 'Meta-analysis of genome-wide linkage scans for renal function traits', Nephrology Dialysis Transplantation, vol. 27, no. 2, pp. 647-656. https://doi.org/10.1093/ndt/gfr255
Rao M, Mottl AK, Cole SA, Umans JG, Freedman BI, Bowden DW et al. Meta-analysis of genome-wide linkage scans for renal function traits. Nephrology Dialysis Transplantation. 2012 Feb;27(2):647-656. https://doi.org/10.1093/ndt/gfr255
Rao, Madhumathi ; Mottl, Amy K. ; Cole, Shelley A. ; Umans, Jason G. ; Freedman, Barry I. ; Bowden, Donald W. ; Langefeld, Carl D. ; Fox, Caroline S. ; Yang, Qiong ; Cupples, Adrienne ; Iyengar, Sudha K. ; Hunt, Steven ; Trikalinos, Thomas A. / Meta-analysis of genome-wide linkage scans for renal function traits. In: Nephrology Dialysis Transplantation. 2012 ; Vol. 27, No. 2. pp. 647-656.
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