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Montasser ME, Shimmin LC, Gu D, Chen J, Gu C, Kelly TN, Jaquish CE, Rice TK, Rao DC, Cao J, Chen J, Liu DP, Whelton PK, Hamm LL, He J, Hixson JE. Variation in genes that regulate blood pressure are associated with glomerular filtration rate in Chinese. PLoS One 2014; 9:e92468. [PMID: 24658007 PMCID: PMC3962404 DOI: 10.1371/journal.pone.0092468] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 02/21/2014] [Indexed: 01/13/2023] Open
Abstract
Chronic kidney disease (CKD) can be a consequence of diabetes, hypertension, immunologic disorders, and other exposures, as well as genetic factors that are still largely unknown. Glomerular filtration rate (GFR), which is widely used to measure kidney function, has a heritability ranging from 25% to 75%, but only 1.5% of this heritability is explained by genetic loci that have been identified to date. In this study we tested for associations between GFR and 234 SNPs in 26 genes from pathways of blood pressure regulation in 3,025 rural Chinese participants of the "Genetic Epidemiology Network of Salt Sensitivity" (GenSalt) study. We estimated GFR (eGFR) using baseline serum creatinine measurements obtained prior to dietary intervention. We identified significant associations between eGFR and 12 SNPs in 6 genes (ACE, ADD1, AGT, GRK4, HSD11B1, and SCNN1G). The cumulative effect of the protective alleles was an increase in mean eGFR of 4 mL/min per 1.73 m2, while the cumulative effect of the risk alleles was a decrease in mean eGFR of 3 mL/min per 1.73 m2. In addition, we identified a significant interaction between SNPs in CYP11B1 and ADRB2. We have identified common variants in genes from pathways that regulate blood pressure and influence kidney function as measured by eGFR, providing new insights into the genetic determinants of kidney function. Complex genetic effects on kidney function likely involve interactions among genes as we observed for CYP11B1 and ADRB2.
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Affiliation(s)
- May E. Montasser
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- * E-mail:
| | - Lawrence C. Shimmin
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Dongfeng Gu
- Cardiovascular Institute and Fu Wai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Chen
- Tulane University School of Medicine, New Orleans, Louisiana, United States of America
| | - Charles Gu
- Washington University in School of Medicine, St. Louis, Missouri, United States of America
| | - Tanika N. Kelly
- Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Cashell E. Jaquish
- National Heart, Lung and Blood Institute, National Institute of Health, Bethesda, Maryland, United States of America
| | - Treva K. Rice
- Washington University in School of Medicine, St. Louis, Missouri, United States of America
| | - Dabeeru C. Rao
- Washington University in School of Medicine, St. Louis, Missouri, United States of America
| | - Jie Cao
- Cardiovascular Institute and Fu Wai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Jichun Chen
- Cardiovascular Institute and Fu Wai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - De-Pei Liu
- National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China
| | - Paul K. Whelton
- Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Lotuce Lee Hamm
- Tulane University School of Medicine, New Orleans, Louisiana, United States of America
| | - Jiang He
- Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - James E. Hixson
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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Thameem F, Igo RP, Freedman BI, Langefeld C, Hanson RL, Schelling JR, Elston RC, Duggirala R, Nicholas SB, Goddard KAB, Divers J, Guo X, Ipp E, Kimmel PL, Meoni LA, Shah VO, Smith MW, Winkler CA, Zager PG, Knowler WC, Nelson RG, Pahl MV, Parekh RS, Kao WHL, Rasooly RS, Adler SG, Abboud HE, Iyengar SK, Sedor JR. A genome-wide search for linkage of estimated glomerular filtration rate (eGFR) in the Family Investigation of Nephropathy and Diabetes (FIND). PLoS One 2013; 8:e81888. [PMID: 24358131 PMCID: PMC3866106 DOI: 10.1371/journal.pone.0081888] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Accepted: 10/27/2013] [Indexed: 12/22/2022] Open
Abstract
Objective Estimated glomerular filtration rate (eGFR), a measure of kidney function, is heritable, suggesting that genes influence renal function. Genes that influence eGFR have been identified through genome-wide association studies. However, family-based linkage approaches may identify loci that explain a larger proportion of the heritability. This study used genome-wide linkage and association scans to identify quantitative trait loci (QTL) that influence eGFR. Methods Genome-wide linkage and sparse association scans of eGFR were performed in families ascertained by probands with advanced diabetic nephropathy (DN) from the multi-ethnic Family Investigation of Nephropathy and Diabetes (FIND) study. This study included 954 African Americans (AA), 781 American Indians (AI), 614 European Americans (EA) and 1,611 Mexican Americans (MA). A total of 3,960 FIND participants were genotyped for 6,000 single nucleotide polymorphisms (SNPs) using the Illumina Linkage IVb panel. GFR was estimated by the Modification of Diet in Renal Disease (MDRD) formula. Results The non-parametric linkage analysis, accounting for the effects of diabetes duration and BMI, identified the strongest evidence for linkage of eGFR on chromosome 20q11 (log of the odds [LOD] = 3.34; P = 4.4×10−5) in MA and chromosome 15q12 (LOD = 2.84; P = 1.5×10−4) in EA. In all subjects, the strongest linkage signal for eGFR was detected on chromosome 10p12 (P = 5.5×10−4) at 44 cM near marker rs1339048. A subsequent association scan in both ancestry-specific groups and the entire population identified several SNPs significantly associated with eGFR across the genome. Conclusion The present study describes the localization of QTL influencing eGFR on 20q11 in MA, 15q21 in EA and 10p12 in the combined ethnic groups participating in the FIND study. Identification of causal genes/variants influencing eGFR, within these linkage and association loci, will open new avenues for functional analyses and development of novel diagnostic markers for DN.
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Affiliation(s)
- Farook Thameem
- Department of Medicine, The University of Texas Health Science Center, San Antonio, Texas, United States of America
| | - Robert P. Igo
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Barry I. Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Carl Langefeld
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Robert L. Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, United States of America
| | - Jeffrey R. Schelling
- Department of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Robert C. Elston
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Ravindranath Duggirala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Susanne B. Nicholas
- Department of Medicine, University of California, Los Angeles, California, United States of America
| | - Katrina A. B. Goddard
- Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon, United States of America
| | - Jasmin Divers
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Xiuqing Guo
- Department of Pediatrics, Harbor-University of California Los Angeles Medical Center, Torrance, California, United States of America
| | - Eli Ipp
- Department of Medicine, Harbor-University of California Los Angeles Medical Center, Torrance, California, United States of America
| | - Paul L. Kimmel
- National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Lucy A. Meoni
- Department of Epidemiology and Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Vallabh O. Shah
- University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Michael W. Smith
- National Human Genome Research Institute, NIH, Bethesda, Maryland, United States of America
| | - Cheryl A. Winkler
- Center for Cancer Research, National Cancer Institute, NIH, Frederick, Maryland, United States of America
| | - Philip G. Zager
- University of New Mexico, Albuquerque, New Mexico, United States of America
| | - William C. Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, United States of America
| | - Robert G. Nelson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, United States of America
| | - Madeline V. Pahl
- Department of Medicine, University of California, Irvine, California, United States of America
| | - Rulan S. Parekh
- Department of Epidemiology and Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Pediatrics, University of Toronto, Toronto, Canada
| | - W. H. Linda Kao
- Department of Epidemiology and Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Rebekah S. Rasooly
- National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sharon G. Adler
- Department of Medicine, Harbor-University of California Los Angeles Medical Center, Torrance, California, United States of America
| | - Hanna E. Abboud
- Department of Medicine, The University of Texas Health Science Center, San Antonio, Texas, United States of America
| | - Sudha K. Iyengar
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
| | - John R. Sedor
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
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Susceptibility gene search for nephropathy and related traits in Mexican-Americans. Mol Biol Rep 2013; 40:5769-79. [PMID: 24057238 DOI: 10.1007/s11033-013-2680-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Accepted: 09/14/2013] [Indexed: 02/06/2023]
Abstract
The rising global epidemic of diabetic nephropathy (DN) will likely lead to increase in the prevalence of cardiovascular morbidity and mortality posing a serious burden for public health care. Despite greater understanding of the etiology of diabetes and the development of novel treatment strategies to control blood glucose levels, the prevalence and incidence rate of DN is increasing especially in minority populations including Mexican-Americans. Mexican-Americans with type 2 diabetes (T2DM) are three times more likely to develop microalbuminuria, and four times more likely to develop clinical proteinuria compared to non-Hispanic whites. Furthermore, Mexican-Americans have a sixfold increased risk of developing renal failure secondary to T2DM compared to Caucasians. Prevention and better treatment of DN should be a high priority for both health-care organizations and society at large. Pathogenesis of DN is multi-factorial. Familial clustering of DN-related traits in MAs show that DN and related traits are heritable and that genes play a susceptibility role. While, there has been some progress in identifying genes which when mutated influence an individual's risk, major gene(s) responsible for DN are yet to be identified. Knowledge of the genetic causes of DN is essential for elucidation of its mechanisms, and for adequate classification, prognosis, and treatment. Self-identification and collaboration among researchers with suitable genomic and clinical data for meta-analyses in Mexican-Americans is critical for progress in replicating/identifying DN risk genes in this population. This paper reviews the approaches and recent efforts made to identify genetic variants contributing to risk for DN and related phenotypes in the Mexican-American population.
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Rao M, Mottl AK, Cole SA, Umans JG, Freedman BI, Bowden DW, Langefeld CD, Fox CS, Yang Q, Cupples A, Iyengar SK, Hunt SC, Trikalinos TA. Meta-analysis of genome-wide linkage scans for renal function traits. Nephrol Dial Transplant 2011; 27:647-56. [PMID: 21622988 DOI: 10.1093/ndt/gfr255] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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.
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Affiliation(s)
- Madhumathi Rao
- Division of Nephrology, Tufts Medical Center, Boston, MA, USA
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McKnight AJ, Currie D, Maxwell AP. Unravelling the genetic basis of renal diseases; from single gene to multifactorial disorders. J Pathol 2010; 220:198-216. [PMID: 19882676 DOI: 10.1002/path.2639] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Chronic kidney disease is common with up to 5% of the adult population reported to have an estimated glomerular filtration rate of < 60 ml/min/1.73 m(2). A large number of pathogenic mutations have been identified that are responsible for 'single gene' renal disorders, such as autosomal dominant polycystic kidney disease and X-linked Alport syndrome. These single gene disorders account for < 15% of the burden of end-stage renal disease that requires dialysis or kidney transplantation. It has proved more difficult to identify the genetic susceptibility underlying common, complex, multifactorial kidney conditions, such as diabetic nephropathy and hypertensive nephrosclerosis. This review describes success to date and explores strategies currently employed in defining the genetic basis for a number of renal disorders. The complementary use of linkage studies, candidate gene and genome-wide association analyses are described and a collation of renal genetic resources highlighted.
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Affiliation(s)
- Amy J McKnight
- Nephrology Research Group, Queen's University of Belfast, Belfast BT9 7AB, Northern Ireland, UK
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