Dataset Information

Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

ABSTRACT: Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.

SUBMITTER: Iyengar SK

PROVIDER: S-EPMC4549309 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Publications

Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Iyengar Sudha K SK Sedor John R JR Freedman Barry I BI Kao W H Linda WH Kretzler Matthias M Keller Benjamin J BJ Abboud Hanna E HE Adler Sharon G SG Best Lyle G LG Bowden Donald W DW Burlock Allison A Chen Yii-Der Ida YD Cole Shelley A SA Comeau Mary E ME Curtis Jeffrey M JM Divers Jasmin J Drechsler Christiane C Duggirala Ravi R Elston Robert C RC Guo Xiuqing X Huang Huateng H Hoffmann Michael Marcus MM Howard Barbara V BV Ipp Eli E Kimmel Paul L PL Klag Michael J MJ Knowler William C WC Kohn Orly F OF Leak Tennille S TS Leehey David J DJ Li Man M Malhotra Alka A März Winfried W Nair Viji V Nelson Robert G RG Nicholas Susanne B SB O'Brien Stephen J SJ Pahl Madeleine V MV Parekh Rulan S RS Pezzolesi Marcus G MG Rasooly Rebekah S RS Rotimi Charles N CN Rotter Jerome I JI Schelling Jeffrey R JR Seldin Michael F MF Shah Vallabh O VO Smiles Adam M AM Smith Michael W MW Taylor Kent D KD Thameem Farook F Thornley-Brown Denyse P DP Truitt Barbara J BJ Wanner Christoph C Weil E Jennifer EJ Winkler Cheryl A CA Zager Philip G PG Igo Robert P RP Hanson Robert L RL Langefeld Carl D CD

PLoS genetics 20150825 8

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation o ...[more]

PMID: 26305897

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Project description:ObjectiveEstimated 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.MethodsGenome-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.ResultsThe 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.ConclusionThe 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.

Project description:ObjectiveTo compare the genetic architecture of susceptibility variants of IgA nephropathy (IgAN) in Chinese and Europeans.MethodsWe selected the independent genome-wide significant variants of IgAN in European population as candidate variants. Their associations, risk alleles, risk allele frequencies, odds ratios and population attributable risk scores were derived and calculated, then compared with those in the current Chinese population, including 1 194 IgAN patients and 902 controls. Using the significant variants, genetic risk scores were calculated and compared between the East Asians and the Europeans. The correlation between the genetic risk scores and clinical manifestations was also evaluated.ResultsThere were 16 independent single nucleotide polymorphisms (SNPs) located in 11 loci showing significantly association with susceptibility to IgAN in the Europeans. 93.75% (15/16) of them also showed significant associations in the Chinese (P<0.05). The effects of all the associated SNPs were in the same direction, either risk or being protective for IgAN, between the Chinese and the Europeans. On the contrary, remarkable higher risk allelic odds ratio (P=1.94×10-2), higher risk allele frequency (P=3.09×10-2), and higher population attributable risk (P=3.03×10-4) were observed for most of the associated SNPs in the Chinese than in the Europeans. Furthermore, genetic risk scores were significantly larger in the Asian populations compared with the Europeans (P=1.78×10-163). While there was no significance among the subpopulations in both the East Asians and the Europeans. Compared with the healthy controls, the genetic risk score in the IgAN patients was significantly larger (P=3.60×10-27). Clinical analysis showed the genetic risk score was positively associated with serum levels of IgA and IgA1, phases of chronic kidney disease and Haas grades.ConclusionOur study provides further evidence in the shared genetic architecture between Chinese and Europeans, while differences with respect to the effect sizes and risk allele frequencies across ethnicities, contributing partially to the differences of disease prevalence.

Project description:BACKGROUND:The presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample. RESULTS:A fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy. CONCLUSIONS:The identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations.

Dataset Information

Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Publications

Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

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