Journal of the American Society of Nephrology
2007 JASN IMPACT FACTOR 7.111 HOME   AUTHOR INFO   EDITORIAL BOARD   SUBSCRIBE   FEEDBACK   ALERTS   HELP 
    advanced
CURRENT ISSUE ARCHIVES JASN Express ONLINE SUBMISSION


Published ahead of print on February 8, 2006
J Am Soc Nephrol 17: 831-836, 2006
© 2006 American Society of Nephrology
doi: 10.1681/ASN.2005050493
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
ASN.2005050493v1
17/3/831    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by McKnight, A. J.
Right arrow Articles by Savage, D. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by McKnight, A. J.
Right arrow Articles by Savage, D. A.

Human Genetics

A Genome-Wide DNA Microsatellite Association Screen to Identify Chromosomal Regions Harboring Candidate Genes in Diabetic Nephropathy

Amy Jayne McKnight*, A. Peter Maxwell*, Stephen Sawcer{dagger}, Alastair Compston{dagger}, Efrosini Setakis{ddagger}, Chris C. Patterson§, Hugh R. Brady|| and David A. Savage*

* Nephrology Research Group; § Department of Epidemiology & Public Health, Queen’s University Belfast, Belfast, Northern Ireland; {dagger} Department of Neurology, University of Cambridge, Cambridge, United Kingdom; {ddagger} Department of Epidemiology and Public Health, Imperial College, London, United Kingdom; and || Conway Institute, University College Dublin, Dublin, Ireland

Address correspondence to: Dr. David A. Savage, Nephrology Research Group, Queen’s University Belfast, c/o Medical Genetics, Floor A, Tower Block, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, Northern Ireland. Phone: +44-0-28-90329241 ext. 2149; Fax: +44-0-28-90236911; d.savage{at}qub.ac.uk

Received for publication May 12, 2005. Accepted for publication January 3, 2006.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
In an effort to accelerate the identification of susceptibility genes in diabetic nephropathy, the first genome-wide fluorescence-based DNA microsatellite (n = 6000) association screen was performed, using pools of genomic DNA derived from Irish patients with (cases; n = 200) and without (controls; n = 200) type 1 diabetic nephropathy. Allele image profiles were generated for 5353 (89.2%) microsatellite markers for both case and control pools. Allele counts (estimated from allele image profiles) were compared in case versus control groups, and empirical P values were generated. Markers then were ranked on the basis of their empirical P values (lowest to highest). Repeat PCR amplification and electrophoresis of pooled samples were performed systematically on ranked markers until the 50 most associated markers with consistent results were identified. DNA samples that composed the pools then were genotyped individually for these markers. Two markers on chromosome 10, D10S558 (Pcorrected = 0.005) and D10S1435 (Pcorrected = 0.016), revealed statistically significant associations with diabetic nephropathy. An additional four markers (D6S281, D4S2937, D2S291, and D17S515) also are worthy of further investigation. Relevant functional candidate genes have been identified in the vicinity of these markers, demonstrating the feasibility of low-resolution genome-wide microsatellite association screening to identify possible candidate genes for diabetic nephropathy.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Diabetic nephropathy (DN) is an extremely serious clinical complication of diabetes in which patients exhibit persistent proteinuria, hypertension, declining renal function, and an increased premature mortality largely as a result of cardiovascular disease. The pathophysiology of DN involves glomerular capillary hypertension, glomerular hyperfiltration, mesangial matrix expansion, and glomerulosclerosis (1–3). In Western populations, DN has emerged as the leading cause of ESRD (4,5), resulting in patients’ requiring dialysis and transplantation.

The risk for developing nephropathy in patients with diabetes becomes greater with increasing duration of diabetes and poor regulation of blood glucose and BP. Studies have shown, however, that irrespective of good glycemic control, some patients with diabetes still develop nephropathy (6,7). In addition, although administration of antihypertensive therapy to microalbuminuric patients may retard progression toward ESRD (8,9), irreversible structural changes in the kidney have already occurred by the time microalbuminuria is identified. Disease prevention therefore is an important goal.

Evidence from epidemiologic and family-based studies strongly suggest a role for both susceptibility and protective genes in the development of DN in type 1 diabetes. This is illustrated by the observation that the disease generally develops within 20 yr after diagnosis of type 1 diabetes, whereas the risk for developing nephropathy decreases in patients who have diabetes without nephropathy after 20 yr of duration of diabetes (10). Also, there is strong concordance for nephropathy status among siblings with diabetes (11,12), and there is evidence to support the involvement of genes with major and minor effects in the pathogenesis of this disease (12,13).

Identification of validated genetic markers for high and low risk for nephropathy will facilitate prediction of nephropathy in patients with diabetes; this information also could lead to the development of pharmacogenetic treatments to prevent this disease. Genome-wide linkage analysis, however, has been complicated largely by difficulties in obtaining sufficient numbers of sibling pairs who have diabetes and are concordant or discordant for nephropathy. Consequently, case-control association studies, using a candidate gene approach, have become the method of choice for identifying causal gene variants that contribute to DN. A number of such studies have been performed, but no consistent positive associations have been identified (14–18). Conflicting findings may be due to several factors, including genetic heterogeneity, inadequate sample sizes, and use of inappropriate selection criteria for patients (19).

In an attempt to identify chromosomal regions that harbor possible candidate genes for DN, we performed a low-resolution genome-wide microsatellite [i.e., anonymous nucleotide repeat units, e.g., (CA)n] association screen, using the same method and markers used in the Genetic Analysis of Multiple sclerosis in EuropeanS (GAMES) collaborative project (20–23; http://www-gene.cimr.cam.ac.uk/MSgenetics/GAMES). Separate pools of DNA, derived from Irish patients with type 1 diabetes and with (cases) and without (controls) nephropathy, were screened for association using 6000 fluorescently labeled microsatellite markers (the GAMES marker set). A comparison of allele image profiles (AIP; a series of peaks of varying heights reflecting the allele frequency distribution within each analyzed DNA pool) from cases and controls permitted ranking of markers according to empirical P values; individual genotyping then was performed on the 50 most associated markers. In this report, we describe the major findings of this genome-wide association screen and demonstrate the feasibility of this approach for identifying possible candidate genes in DN.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Patient Groups and Selection Criteria
Patients were recruited from Northern Ireland (n = 105 cases; n = 105 controls) and the Republic of Ireland (n = 95 cases, n = 95 controls). Parents and grandparents of patients were born in Ireland. All patients were white and had type 1 diabetes, defined as diabetes that was diagnosed before the age of 31 yr and required insulin from diagnosis. Cases with nephropathy were defined by development of persistent proteinuria (>0.5 g protein/24 h) at least 10 yr after diagnosis of diabetes, hypertension (BP >135/85 mmHg and/or treatment with antihypertensive agents), and presence of diabetic retinopathy. In contrast, controls were patients who had diabetes duration of at least 15 yr and urinary albumin in the normal range and were not receiving antihypertensive treatment. Patients with microalbuminuria were excluded from both groups. Ethical approval was obtained from the appropriate research ethics committees, and written informed consent was obtained from patients before this study was conducted.

Construction and Assessment of DNA Pools
Genomic DNA samples from cases and controls were quantified using the Picogreen method (Molecular Probes, Eugene, OR) and normalized to 10 ng/µl in 10 mM Tris/0.1 mM EDTA (pH 7.6). Equal volumes (200 µl) of individual DNA samples (10 ng/µl) from cases and controls were combined to generate the DNA pools. Before use in the genome-wide screen, the pools were validated by comparing estimated allele frequencies that were generated from pools (five replicate AIP in each case) with allele frequencies obtained by individual genotyping of samples that composed the pools, for two randomly selected microsatellite markers, D19S49 (dinucleotide) and D7S1821 (tetranucleotide), as follows. PCR reactions (15 µl in total) that contained 25 ng DNA, 5 pmol of each primer (forward primer for both markers was labeled with FAM), and 9 µl of True Allele PCR Premix (Applied Biosystems, Warrington, UK) were set up. All PCR reactions were performed using an MJ Tetrad thermal cycler using the following cycling conditions; 95°C for 12 min, 10 cycles at 94°C for 15 s, 55°C for 15 s, and 72°C for 30 s followed by 20 cycles of 89°C for 15 s, 55°C for 15 s, 72°C for 30 s, and finally 72°C for 20 min. Each PCR product was diluted 10-fold in ddH2O; 1 µl of diluted PCR product then was combined with 12 µl of ROX/HiDi (prepared by combining 0.3 µl of GS-400HD [ROX] and 11.7 µl of HiDi formamide, both supplied by Applied Biosystems), and the mixture was subjected to capillary electrophoresis on a 3100 genetic analyser (Applied Biosystems). The raw data were exported to ABI Genescan software to confirm size labeling. Files then were imported into ABI Genotyper software, where alleles were identified appropriately.

Genome-Wide Microsatellite Genotyping
The 6000 microsatellite markers that were used in the screening phase consist of 811 that make up the Applied Biosystems high-density linkage mapping set (LMS-HD5) together with an additional 5189 identified from the Co-operative for Human Linkage Centre database (http://gai.nci.nih.gov/CHLC), the Genethon database (http://www.genethon.fr), and the Genome database (http://www.gdb.org). The total number of markers from each chromosome was chosen to reflect the number of genes that map to that chromosome (according to the National Center for Biotechnology Information web site in December 1999, http://www.ncbi.nlm.nih.gov). The density of markers therefore is biased toward gene-rich and against gene-poor chromosomes. However, markers were not selected to be necessarily intragenic. Within each chromosome, markers were selected so as to make the distribution of markers as uniform as possible according to their location scores on the genetic location database (http://cedar.genetics.soton.ac.uk/public_html). The 6000 microsatellite markers that were used in this study resulted in an average intermarker spacing of 0.6 cM (or 0.5 Mb; Appendix 1); gender-averaged cM measurements were derived from the Marshfield Master Map (http://www.ncbi.nlm.nih.gov/mapview). There are 329 gaps >2 cM in gene-poor regions, 11 of which are >10 cM. These gaps correspond mainly to pericentromeric regions, particularly on acrocentric chromosomes, and sections of the genome that generally contain very few genes. In total, 4723 (79%) of the markers are dinucleotides, 1019 (17%) are tetranucleotides, and 258 (4%) are trinucleotides. The estimated average heterozygosity (according to data in the originating databases) of the 6000 markers is 70%. Details of the markers, including primer sequences, are available on the GAMES web site (http://www-gene.cimr.cam.ac.uk/MSgenetics/GAMES).


View this table:
[in this window]
[in a new window]

  		Appendix 1. Marker densities (cM and Mb) in each chromosome for 6000 microsatellite markersa

 
Aliquots of DNA (2.5 µl; 25 ng) from each pool were PCR amplified by individual primer pairs that flanked the 6000 microsatellite markers as described above for pool assessment. Amplification products were subjected to capillary electrophoresis on a 3700 genetic analyser (Applied Biosystems), either as a uniplex or a triplex essentially as described above. For PCR products with AIP peak heights above 10,000 (i.e., saturated), the products were diluted and run again. To minimize electrophoretic artifacts, we performed repeat electrophoresis on all PCR products; when contradictory results were observed, PCR products were run on a third occasion to assist in resolving ambiguities. Repeat PCR amplification and electrophoresis were performed systematically on ranked markers with the smallest empirical P values as described in the Statistical Analyses section to facilitate the identification of the 50 most associated markers.

Statistical Analyses
Clinical characteristics of the patients were compared by the independent samples t test. The electrophoretic output for a given microsatellite marker using pooled DNA comprises a series of peaks, an AIP (Figure 1), where the height of each peak reflects the frequency of the corresponding microsatellite allele. Allele counts were estimated by normalizing the observed peak heights according to the number of alleles in the respective pool. Allele counts from cases and controls then were compared using a {chi}2 test. Because pooling introduces additional sources of variance above and beyond the expected sampling variance, exact P values cannot be determined for these statistics. We therefore used the observed distribution of results to calculate empirical P values for each marker and therefore enable their ranking (23), i.e., markers that were ranked according to lowest empirical P value showed the greatest difference between case and control AIP. To refine this ranking and reduce the effects of the additional sources of error introduced by pooling, we generated replicate AIP from both the case and control pools for each of the 63 most highly ranked markers; the total data then were reanalyzed, and the markers were reranked.


Figure 1
View larger version (7K):
[in this window]
[in a new window]
[as a PowerPoint slide]
  		Figure 1. Allele image profiles for D10S1435 marker in case and control pools.

 
Individual genotyping then was performed in the 50 most highly ranked markers to distinguish definitively those that showed genuine allele frequency differences from those that ranked highly as a result of pooling-induced variance. Results from the individual typing were tested for significance using a {chi}2 test after amalgamation of rare alleles. When any cell in the contingency table had an expected value <5, the associated allele was pooled with the next rarest allele and this process was repeated until no cell had an expected value <5. This analysis was performed using the T2 statistic in the CLUMP program (24). P values for individual genotyping were corrected for the total number of comparisons made in the initial analysis by DNA pooling (5353). Markers that attained significance were examined using the Clump T4 statistic; the alleles were pooled into two groups in such a way as to maximize the {chi}2 test statistic. This pooling enabled odds ratios (OR) and 95% confidence intervals (CI) to be obtained.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Clinical Characteristics of Patients
Table 1 lists the clinical characteristics of the case and control groups. There were a total of 84 and 124 women in the case and control groups, respectively. The age at diagnosis of diabetes was not significantly different between the groups. The mean duration of diabetes was at least 25 yr for both groups. As expected, the average BP was higher in cases versus controls, despite the use of antihypertensive drug treatment by patients with DN.


View this table:
[in this window]
[in a new window]

  		Table 1. Clinical characteristics of cases (n = 200) and controls (n = 200) who composed DNA poolsa

 
Assessment of DNA Pools and Genome-Wide Screen
Excellent concordance was found between allele frequencies that were determined by pooling versus individual genotyping methods for D19S49 and D7S1821, in both case and control pools (data not shown). Interpretable AIP were generated for both case and control pools in 5353 (89.2%) markers. A total of 647 markers were excluded because of PCR failures in case and/or control pools or difficulties in data interpretation (e.g., weak or diffuse peaks in AIP). Markers were ranked according to empirical P value (lowest to highest). To refine the marker ranking, we systematically performed repeat PCR amplification and electrophoresis of pooled samples on the 63 most highly ranked markers until 50 markers that showed consistent results were identified. Empirical P values that were obtained for these markers were not corrected. These markers then were genotyped individually in the same samples that composed the pools (Appendix 2). Of note, two markers on chromosome 10 (D10S558 and D10S1435) revealed significant differences in allele distribution between groups after correction by the stringent Bonferroni method (Table 2). The allele distributions for these two markers are presented in Table 3 and indicate that larger D10S558 alleles were associated with increased risk for nephropathy (OR 2.50; 95% CI 1.83 to 3.41), whereas the D10S1435 272 allele was associated with decreased risk (OR 0.44; 95% CI 0.32 to 0.61). Furthermore, four additional markers (D2S291, D4S2937, D6S281, and D17S515) gave small uncorrected P values, and although they failed to achieve statistical significance after Bonferroni correction, they warrant further consideration (Table 2).


View this table:
[in this window]
[in a new window]

  		Appendix 2. P values for individual genotyping of case and control DNA samples that composed pools for the 50 highest ranked markers derived from DNA pooling and listed by chromosome

 

View this table:
[in this window]
[in a new window]

  		Table 2. P values for individual genotyping of case and control DNA samples that composed pools for markers that revealed statistically significant differences between groups and those that warrant further investigation

 

View this table:
[in this window]
[in a new window]

  		Table 3. Comparison of allele distributions between cases (n = 200) and controls (n = 200) from individual genotyping for two markers that remained significant after Bonferroni correction

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Genetic association studies that have used a candidate gene approach have been unsuccessful to date in identifying single-nucleotide polymorphisms (SNP) that show a consistent association with DN in type 1 diabetes. In addition, high-resolution whole-genome SNP screening in DN currently is not feasible, partly because of the extremely high total cost of SNP genotyping and uncertainty as to the composition of an appropriate panel of SNP for a comprehensive scan. However, with recent advances in novel, low-cost, high-throughput genotyping technologies (25,26) and improvements in statistical methods for data analysis (27), as well as projects such as the International HapMap (28), such screening certainly will be feasible in the future. It is interesting that a recent Japanese study assessed >80,000 SNP for association with DN in type 2 diabetes and a significant SNP association was reported between the engulfment and cell motility (ELMO 1) gene and DN (29).

In this report, we applied a highly efficient but low-resolution approach for whole-genome association screening in DN that uses 6000 microsatellite markers and a DNA pooling strategy; this strategy originally was used in the GAMES collaborative project (20–23). Importantly, the GAMES project succeeded in identifying the well-established association of multiple sclerosis with the MHC region, confirming that the approach is able to identify microsatellites in linkage disequilibrium with genuine but modest susceptibility factors.

Strict phenotyping criteria were used in the selection of cases and controls in our study to reduce the possibility of false outcomes related to phenotype misclassification. In addition, all participants were white and of Irish descent, therefore reducing potential problems associated with population stratification. We identified the 50 most associated markers that showed consistent results on repeat analysis using DNA pools. Individual genotyping of D10S558 and D10S1435 markers on chromosome 10 revealed statistically significant differences in the distribution of marker alleles in cases compared with controls (Table 2). It is interesting that suggestive linkage of D10S1435 in early-onset nondiabetic ESRD was reported recently in black patients (30). Also, a recent study reported linkage of a locus on chromosome 6q (D6S281) to essential hypertension in UK patients (31). Relevant functional candidate genes are located within the vicinity of D10S558 and D10S1435, as well as for four other markers (D2S291, D4S2937, D6S281, and D17S515) that are worthy of further investigation (Table 2).

The discrepancies that were observed in allele frequency data that were obtained for DNA pooling and individual genotyping are not unexpected. However, this was minimized by careful construction and assessment of the pools and applying checks for false-positive outcomes that resulted from electrophoretic and/or PCR artifacts (e.g., stutter bands, length-dependent amplification). Recently, it was shown that multiple disjoined pools are preferable to single pools (32), although there are additional cost considerations associated with this strategy.

The power of our study is limited by the sample size used, the confounding produced by our use of the pooling approach, and the low density of markers used. We recognize that genes that exert small effects or are not in linkage disequilibrium with the markers used cannot be identified by this method. Nevertheless, we have shown that this genome-wide microsatellite screen, using pools of DNA, is a cost-effective strategy for identifying positive associations between microsatellite markers and DN. Furthermore, we have demonstrated the utility of the GAMES method for the identification of possible functional candidate genes for this disease. We intend to assess haplotype tag SNP (33) within these candidate genes for association with DN using relatively large case-control studies such as the Juvenile Diabetes Research Foundation–funded Genetics of Kidneys in Diabetes (United Kingdom) case-control collection and Diabetes UK Warren 3 family collection; replication studies will be performed in case and control collections derived from other populations, such the US Genetics of Kidneys in Diabetes collection.


   Acknowledgments
 
Some of these data were presented at the American Society of Nephrology, San Diego, CA, November 12 to 17, 2003.

We thank the Northern Ireland Research and Development Office and the Northern Ireland Kidney Research Fund for supporting this project. We also thank Julia Gray and Tai Wai Yeo (Neurology Unit, University of Cambridge) and Nick Leaves (HGMP Resource Centre, Cambridge) for technical assistance.


   Footnotes
 
Published online ahead of print. Publication date available at www.jasn.org.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Hostetter TH: Mechanisms of diabetic nephropathy. Am J Kidney Dis 23 : 188 –192, 1994[Medline]
  2. Mauer SM: Structural-functional correlations of diabetic nephropathy. Kidney Int 45 : 612 –622, 1994[Medline]
  3. Mogensen CE: Glomerular hyperfiltration in human diabetes. Diabetes Care 17 : 770 –775, 1994[Medline]
  4. UK Renal Registry Report, 2002. Available: www.renalreg.com. Accessed March 2005
  5. United States Renal Data System: Annual Data Report 2003. Incidence and Prevalence of ESRD. Available: www.usrds.org/2003/. Accessed March 2005
  6. The Diabetes Control and Complications Research Group: The effect of intensive treatment of diabetes on the development and progression of long term complications in insulin dependent diabetes mellitus. N Engl J Med 329 : 977 –986, 1993[Abstract/Free Full Text]
  7. The Diabetes Control and Complications Research Group: Effect of intensive therapy on the development and progression of diabetic nephropathy in the Diabetes Control and Complications Trial. Kidney Int 47 : 1703 –1720, 1995[Medline]
  8. Viberti G, Mogensen CE, Groop LC, Pauls JF: Effect of captopril on progression to clinical proteinuria in patients with insulin-dependent diabetes mellitus and microalbuminuria. European Microalbuminuria Captopril Study Group. JAMA 271 : 275 –279, 1994[Abstract]
  9. The Euclid Study Group: Randomised placebo-controlled trial of lisinopril in normotensive patients with insulin-dependent diabetes and normoalbuminuria or microalbuminuria. Lancet 349 : 1787 –1792, 1997[CrossRef][Medline]
  10. Krolewski AS, Warram JH, Christlieb AR, Busick EJ, Kahn CR: The changing natural history of nephropathy in type I diabetes. Am J Med 78 : 785 –794, 1985[CrossRef][Medline]
  11. Seaquist ER, Goetz FC, Rich S, Barbosa J: Familial clustering of diabetic kidney disease. Evidence for genetic susceptibility to diabetic nephropathy. N Engl J Med 320 : 1161 –1165, 1989[Abstract]
  12. Quinn M, Angelico MC, Warram JH, Krolewski AS: Familial factors determine the development of diabetic nephropathy in patients with IDDM. Diabetologia 39 : 940 –945, 1996[Medline]
  13. Krolewski AS: Genetics of diabetic nephropathy: Evidence for major and minor gene effects. Kidney Int 55 : 1582 –1596, 1999[CrossRef][Medline]
  14. Heesom AE, Hibberd ML, Millward A, Demaine AG: Polymorphism in the 5'-end of the aldose reductase gene is strongly associated with development of diabetic nephropathy in type 1 diabetes. Diabetes 46 : 287 –291, 1997[Abstract]
  15. Neamat-Allah M, Feeney SA, Savage DA, Maxwell AP, Hanson RL, Knowler WC, El Nahas AM, Plater ME, Shaw J, Boulton AJM, Duff GW, Cox A: Analysis of the association between diabetic nephropathy and polymorphisms in the aldose reductase gene in type 1 and type 2 diabetes mellitus. Diabet Med 18 : 906 –914, 2001[Medline]
  16. Ng DP, Warram JH, Krolewski AS: TGF-beta 1 as a genetic susceptibility locus for advanced diabetic nephropathy in type 1 diabetes mellitus: An investigation of multiple known DNA sequence variants. Am J Kidney Dis 41 : 22 –28, 2003[CrossRef][Medline]
  17. Patel A, Scott WR, Lympany PA, Rippin JD, Gill GV, Barnett AH, Bain SC; Warren 3/UK GoKind Study Group: The TGF-beta 1 gene codon 10 polymorphism contributes to the genetic predisposition to nephropathy in type 1 diabetes. Diabet Med 22 : 69 –73, 2005[Medline]
  18. Chowdhury TA, Dyer PH, Kumar S, Gibson SP, Rowe BR, Davies SJ, Marshall SM, Morris PJ, Gill GV, Feeney S, Maxwell P, Savage D, Boulton AJM, Todd JA, Dunger D, Barnett AH, Bain SC: Association of apolipoprotein epsilon2 allele with diabetic nephropathy in Caucasian subjects with IDDM. Diabetes 47 : 278 –280, 1998[Medline]
  19. Colhoun HM, McKeigue PM, Davey-Smith G: Problems of reporting genetic associations with complex outcomes. Lancet 361 : 865 –872, 2003[CrossRef][Medline]
  20. Sawcer S, Maranian M, Setakis E, Curwen V, Akesson E, Hensiek A, Coraddu F, Roxburgh R, Sawcer D, Gray J, Deans J, Goodfellow PN, Walker N, Clayton D, Compston A: A whole genome screen for linkage disequilibrium in multiple sclerosis confirms disease associations with regions previously linked to susceptibility. Brain 125 : 1337 –1347, 2002[Abstract/Free Full Text]
  21. Goedde R, Sawcer S, Boehringer S, Miterski B, Sindern E, Haupts M, Schimrigk S, Compston A, Epplen JT: A genome screen for linkage disequilibrium in HLA-DRB1*15-positive Germans with multiple sclerosis based on 4666 microsatellite markers. Hum Genet 111 : 270 –277, 2002[CrossRef][Medline]
  22. Sawcer S, Compston A: The genetic analysis of multiple sclerosis in Europeans: Concepts and design. J Neuroimmunol 143 : 1 –6, 2003[CrossRef][Medline]
  23. Setakis E: Statistical analysis of the GAMES studies. J Neuroimmunol 143 : 47 –52, 2003[CrossRef][Medline]
  24. Sham PC, Curtis D: Monte Carlo tests for associations between disease and alleles at highly polymorphic loci. Ann Hum Genet 59 : 97 –105, 1995[Medline]
  25. Oliphant A, Barker DL, Stuelpnagel JR, Chee MS: BeadArray technology: Enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques 32 [Suppl]: 56 -58, 60–61, 2002
  26. Hardenbol P, Baner J, Jain M, Nilsson M, Namsaraev EA, Karlin-Neumann GA, Fakhrai-Rad H, Ronaghi M, Willis TD, Landegren U, Davis RW: Multiplexed genotyping with sequence-tagged molecular inversion probes. Nat Biotechnol 21 : 673 –678, 2003[CrossRef][Medline]
  27. Lowe CE, Cooper JD, Chapman JM, Barratt BJ, Twells RC, Green EA, Savage DA, Guja C, Ionescu-Tirgoviste C, Tuomilehto-Wolf E, Tuomilehto J, Todd JA, Clayton DG: Cost-effective analysis of candidate genes using htSNPs: A staged approach. Genes Immun 5 : 301 –305, 2004[CrossRef][Medline]
  28. The International HapMap Consortium: The International HapMap Project. Nature 426 : 789 –796, 2003[CrossRef][Medline]
  29. Shimazaki A, Kawamura Y, Kanazawa A, Sekine A, Saito S, Tsunoda T, Koya D, Babazono T, Tanaka Y, Matsuda M, Kawai K, Iiizumi T, Imanishi M, Shinosaki T, Yanagimoto T, Ikeda M, Omachi S, Kashiwagi A, Kaku K, Iwamoto Y, Kawamori R, Kikkawa R, Nakajima M, Nakamura Y, Maeda S: Genetic variations in the gene encoding ELMO1 are associated with susceptibility to diabetic nephropathy. Diabetes 54 : 1171 –1178, 2005[Abstract/Free Full Text]
  30. Freedman BI, Rich SS, Hongrun Y, Bong HR, Bowden DW: Linkage heterogeneity of end-stage renal disease on human chromosome 10. Kidney Int 62 : 770 –774, 2002[CrossRef][Medline]
  31. Caulfield M, Munroe P, Pembroke J, Samani N, Dominiczak A, Brown M, Benjamin N, Webster J, Ratcliffe P, O’Shea S, Papp J, Taylor E, Dobson R, Knight J, Newhouse S, Hooper J, Lee W, Brain N, Clayton D, Lathrop GM, Farrall M, Connell J; for The MRC British Genetics of Hypertension Study: Genome-wide mapping of human loci for essential hypertension. Lancet 361 : 2118 –2123, 2003[CrossRef][Medline]
  32. Barratt BJ, Payne F, Rance HE, Nutland S, Todd JA, Clayton DG: Identification of the sources of error in allele frequency estimations from pooled DNA indicates an optimal experimental design. Ann Hum Genet 66 : 393 –405, 2002[CrossRef][Medline]
  33. Johnson GC, Esposito L, Barratt BJ, Smith AN, Heward J, Di Genova G, Ueda H, Cordell HJ, Eaves IA, Dudbridge F, Twells RC, Payne F, Hughes W, Nutland S, Stevens H, Carr P, Tuomilehto-Wolf E, Tuomilehto J, Gough SC, Clayton DG, Todd JA: Haplotype tagging for the identification of common disease genes. Nat Genet 29 : 233 –237, 2001[CrossRef][Medline]



This article has been cited by other articles:


Home page
 Therapeutic Advances in Cardiovascular DiseaseHome page
S. Maeda
Review: Genetics of diabetic nephropathy
Therapeutic Advances in Cardiovascular Disease, October 1, 2008; 2(5): 363 - 371.
[Abstract] [PDF]

Home page
 DiabetesHome page
H. Al-Kateb, A. P. Boright, L. Mirea, X. Xie, R. Sutradhar, A. Mowjoodi, B. Bharaj, M. Liu, J. M. Bucksa, V. L. Arends, et al.
Multiple Superoxide Dismutase 1/Splicing Factor Serine Alanine 15 Variants Are Associated With the Development and Progression of Diabetic Nephropathy: The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Genetics Study
Diabetes, January 1, 2008; 57(1): 218 - 228.
[Abstract] [Full Text] [PDF]

Home page
 CJASNHome page
B. I. Freedman, M. Bostrom, P. Daeihagh, and D. W. Bowden
Genetic Factors in Diabetic Nephropathy
Clin. J. Am. Soc. Nephrol., November 1, 2007; 2(6): 1306 - 1316.
[Abstract] [Full Text] [PDF]

Home page
 DiabetesHome page
S. K. Iyengar, H. E. Abboud, K. A.B. Goddard, M. F. Saad, S. G. Adler, N. H. Arar, D. W. Bowden, R. Duggirala, R. C. Elston, R. L. Hanson, et al.
Genome-Wide Scans for Diabetic Nephropathy and Albuminuria in Multiethnic Populations: The Family Investigation of Nephropathy and Diabetes (FIND)
Diabetes, June 1, 2007; 56(6): 1577 - 1585.
[Abstract] [Full Text] [PDF]

Home page
 HypertensionHome page
B. I. Freedman, D. W. Bowden, M. M. Sale, C. D. Langefeld, and S. S. Rich
Genetic Susceptibility Contributes to Renal and Cardiovascular Complications of Type 2 Diabetes Mellitus
Hypertension, July 1, 2006; 48(1): 8 - 13.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
ASN.2005050493v1
17/3/831    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by McKnight, A. J.
Right arrow Articles by Savage, D. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by McKnight, A. J.
Right arrow Articles by Savage, D. A.

HOME CURRENT ISSUE ARCHIVES JASN Express ONLINE SUBMISSION AUTHOR INFO
EDITORIAL BOARD SUBSCRIBE FEEDBACK ALERTS HELP