Literature DB >> 27477491

Insights into kidney diseases from genome-wide association studies.

Matthias Wuttke1,2, Anna Köttgen1,2,3.   

Abstract

Over the past decade, genome-wide association studies (GWAS) have considerably improved our understanding of the genetic basis of kidney function and disease. Population-based studies, used to investigate traits that define chronic kidney disease (CKD), have identified >50 genomic regions in which common genetic variants associate with estimated glomerular filtration rate or urinary albumin-to-creatinine ratio. Case-control studies, used to study specific CKD aetiologies, have yielded risk loci for specific kidney diseases such as IgA nephropathy and membranous nephropathy. In this Review, we summarize important findings from GWAS and clinical and experimental follow-up studies. We also compare risk allele frequency, effect sizes, and specificity in GWAS of CKD-defining traits and GWAS of specific CKD aetiologies and the implications for study design. Genomic regions identified in GWAS of CKD-defining traits can contain causal genes for monogenic kidney diseases. Population-based research on kidney function traits can therefore generate insights into more severe forms of kidney diseases. Experimental follow-up studies have begun to identify causal genes and variants, which are potential therapeutic targets, and suggest mechanisms underlying the high allele frequency of causal variants. GWAS are thus a useful approach to advance knowledge in nephrology.

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Year:  2016        PMID: 27477491     DOI: 10.1038/nrneph.2016.107

Source DB:  PubMed          Journal:  Nat Rev Nephrol        ISSN: 1759-5061            Impact factor:   28.314


  83 in total

Review 1.  Genome-wide association studies of chronic kidney disease: what have we learned?

Authors:  Conall M O'Seaghdha; Caroline S Fox
Journal:  Nat Rev Nephrol       Date:  2011-12-06       Impact factor: 28.314

Review 2.  Pathogenesis of immunoglobulin A nephropathy: recent insight from genetic studies.

Authors:  Krzysztof Kiryluk; Jan Novak; Ali G Gharavi
Journal:  Annu Rev Med       Date:  2012-10-16       Impact factor: 13.739

3.  PLA2R-associated membranous glomerulopathy is modulated by common variants in PLA2R1 and HLA-DQA1 genes.

Authors:  M Saeed; M L Beggs; P D Walker; C P Larsen
Journal:  Genes Immun       Date:  2014-09-04       Impact factor: 2.676

4.  APOL1 genetic variants in focal segmental glomerulosclerosis and HIV-associated nephropathy.

Authors:  Jeffrey B Kopp; George W Nelson; Karmini Sampath; Randall C Johnson; Giulio Genovese; Ping An; David Friedman; William Briggs; Richard Dart; Stephen Korbet; Michele H Mokrzycki; Paul L Kimmel; Sophie Limou; Tejinder S Ahuja; Jeffrey S Berns; Justyna Fryc; Eric E Simon; Michael C Smith; Howard Trachtman; Donna M Michel; Jeffrey R Schelling; David Vlahov; Martin Pollak; Cheryl A Winkler
Journal:  J Am Soc Nephrol       Date:  2011-10-13       Impact factor: 10.121

5.  Performance of a genetic risk score for CKD stage 3 in the general population.

Authors:  Conall M O'Seaghdha; Qiong Yang; Hongsheng Wu; Shih-Jen Hwang; Caroline S Fox
Journal:  Am J Kidney Dis       Date:  2011-10-13       Impact factor: 8.860

6.  Admixture mapping of end stage kidney disease genetic susceptibility using estimated mutual information ancestry informative markers.

Authors:  Liran I Shlush; Sivan Bercovici; Walter G Wasser; Guennady Yudkovsky; Alan Templeton; Dan Geiger; Karl Skorecki
Journal:  BMC Med Genomics       Date:  2010-10-18       Impact factor: 3.063

7.  Overlap between common genetic polymorphisms underpinning kidney traits and cardiovascular disease phenotypes: the CKDGen consortium.

Authors:  Matthias Olden; Alexander Teumer; Murielle Bochud; Cristian Pattaro; Anna Köttgen; Stephen T Turner; Rainer Rettig; Ming-Huei Chen; Abbas Dehghan; Francois Bastardot; Reinhold Schmidt; Peter Vollenweider; Heribert Schunkert; Muredach P Reilly; Myriam Fornage; Lenore J Launer; Germaine C Verwoert; Gary F Mitchell; Joshua C Bis; Christopher J O'Donnell; Ching-Yu Cheng; Xueling Sim; David S Siscovick; Josef Coresh; W H Linda Kao; Caroline S Fox; Conall M O'Seaghdha
Journal:  Am J Kidney Dis       Date:  2013-03-06       Impact factor: 8.860

8.  Anti-PLA2R antibodies measured by ELISA predict long-term outcome in a prevalent population of patients with idiopathic membranous nephropathy.

Authors:  Durga Kanigicherla; Jennet Gummadova; Edward A McKenzie; Stephen A Roberts; Shelley Harris; Milind Nikam; Kay Poulton; Lorna McWilliam; Colin D Short; Michael Venning; Paul E Brenchley
Journal:  Kidney Int       Date:  2013-01-30       Impact factor: 10.612

Review 9.  Targets, trends, excesses, and deficiencies: refocusing clinical investigation to improve patient outcomes.

Authors:  Adeera Levin; William Lancashire; Robert G Fassett
Journal:  Kidney Int       Date:  2013-03-20       Impact factor: 10.612

Review 10.  Genetic and epigenetic risk factors for diabetic kidney disease.

Authors:  Amy Jayne McKnight; Gareth J McKay; Alexander P Maxwell
Journal:  Adv Chronic Kidney Dis       Date:  2014-05       Impact factor: 3.620

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  44 in total

Review 1.  Machine learning, the kidney, and genotype-phenotype analysis.

Authors:  Rachel S G Sealfon; Laura H Mariani; Matthias Kretzler; Olga G Troyanskaya
Journal:  Kidney Int       Date:  2020-04-01       Impact factor: 10.612

Review 2.  The UMOD Locus: Insights into the Pathogenesis and Prognosis of Kidney Disease.

Authors:  Olivier Devuyst; Cristian Pattaro
Journal:  J Am Soc Nephrol       Date:  2017-11-27       Impact factor: 10.121

Review 3.  Genetics of kidney diseases in 2017: Unveiling the genetic architecture of kidney disease.

Authors:  Olivier Devuyst
Journal:  Nat Rev Nephrol       Date:  2018-01-08       Impact factor: 28.314

Review 4.  Using Genetic and Species Diversity to Tackle Kidney Disease.

Authors:  Michael R Garrett; Ron Korstanje
Journal:  Trends Genet       Date:  2020-04-30       Impact factor: 11.639

Review 5.  Genome-wide association studies of albuminuria: towards genetic stratification in diabetes?

Authors:  Cristian Pattaro
Journal:  J Nephrol       Date:  2017-09-16       Impact factor: 3.902

6.  Using Large Datasets to Understand CKD.

Authors:  Thomas A Drysdale
Journal:  J Am Soc Nephrol       Date:  2018-04-11       Impact factor: 10.121

7.  Susceptibility to Hypertensive Renal Disease in the Spontaneously Hypertensive Rat Is Influenced by 2 Loci Affecting Blood Pressure and Immunoglobulin Repertoire.

Authors:  Isha S Dhande; Stacy M Cranford; Yaming Zhu; Sterling C Kneedler; M John Hicks; Scott E Wenderfer; Michael C Braun; Peter A Doris
Journal:  Hypertension       Date:  2018-02-05       Impact factor: 10.190

8.  Genome-Wide Association Studies of CKD and Related Traits.

Authors:  Adrienne Tin; Anna Köttgen
Journal:  Clin J Am Soc Nephrol       Date:  2020-05-14       Impact factor: 8.237

Review 9.  Towards precision nephrology: the opportunities and challenges of genomic medicine.

Authors:  Jordan G Nestor; Emily E Groopman; Ali G Gharavi
Journal:  J Nephrol       Date:  2017-10-17       Impact factor: 3.902

Review 10.  Uromodulin: from physiology to rare and complex kidney disorders.

Authors:  Olivier Devuyst; Eric Olinger; Luca Rampoldi
Journal:  Nat Rev Nephrol       Date:  2017-08-07       Impact factor: 28.314

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