A Genome-Wide Linkage Scan for Genes Controlling Variation in Renal Function Estimated by Serum Cystatin C Levels in Extended Families With Type 2 Diabetes

Placha, Grzegorz; Poznik, G. David; Dunn, Jonathon; Smiles, Adam M.; Krolewski, Bozena; Glew, Timothy; Puppala, Sobha; Schneider, Jennifer L.; Rogus, John; Rich, Stephen S.; Duggirala, Ravindranath; Warram, James H.; Królewski, Andrzej S.

doi:10.2337/db06-0781

Cited by 63 publications

(47 citation statements)

References 35 publications

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“…Heritability estimates for albuminuria were significant, ranging from 0.3 to 0.44 in Finnish (9), New England (10), and southeastern US (11) families enriched for members with type 2 diabetes, and the estimate was higher (h 2 ϭ 0.49) in Hypertension Genetic Epidemiology Network (Hyper-GEN) families enriched for multiple siblings with hypertension (12). Similarly, GFR was significantly heritable in families with diabetes and hypertension, ranging from 0.36 to 0.75 (11,13,14). These results are impressive, considering that albuminuria and GFR change during the course of DN.…”

Section: Familial Aggregation Of Dnmentioning

confidence: 97%

“…Six complete genome-wide linkage scans have been published in type 2 DN, with one in type 1 DN (and another partial genome scan in type 1 DN) (14,(23)(24)(25)(26)(27)(28)(29). Although most of these analyses evaluated small numbers of families from different ethnic groups, several consistent regions of linkage have been detected (Table 1).…”

Section: Genome-wide Linkage Scans For Dnmentioning

confidence: 99%

“…Several regions of linkage have been replicated among the various groups with type 2 DN, as well. The 18q22-23 linkage region was replicated in four populations (23,26,29), the 7q35-36 peak in three reports (25,26,31), and the 7p15 and 10q26 peaks in two each (14,23,32). Positional candidate genes for DN in these regions are now being sought and identified (Table 2).…”

Section: Genome-wide Linkage Scans For Dnmentioning

confidence: 99%

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Genetic Factors in Diabetic Nephropathy

Freedman

Bostrom

Daeihagh

et al. 2007

Clinical Journal of the American Society of Nephrology

174

125

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Several genes that predispose to type 2 diabetes have recently been identified. In addition to the recognized and powerful effects of environmental factors, there is abundant evidence in support of genetic susceptibility to the microvascular complication of nephropathy in individuals with both type 1 and type 2 diabetes. Familial aggregation of phenotypes such as end-stage renal disease, albuminuria, and chronic kidney disease have routinely been reported in populations throughout the world, and heritability estimates for albuminuria and glomerular filtration rate demonstrate strong contributions of inherited factors. Recent genome-wide linkage scans have identified several chromosomal regions that likely contain diabetic nephropathy susceptibility genes, and association analyses have evaluated positional candidate genes under these linkage peaks. These complimentary approaches have demonstrated that polymorphisms in the carnosinase 1 gene on chromosome 18q, the adiponectin gene on 3q, and the engulfment and cell motility gene on 7p are likely associated with susceptibility to diabetic nephropathy. Additional genes that seem to be of importance in renal phenotypes include manganese superoxide dismutase and angiotensin 1-converting enzyme, with nitric oxide synthase implicated in albuminuria. This article reviews the inherited aspects of diabetic kidney disease with particular emphasis on recently implicated genes and pathways. It seems likely that the risk for diabetes-associated kidney disease is magnified by inheriting risk alleles at several susceptibility loci, in the presence of hyperglycemia.Clin J Am Soc Nephrol 2: 1306 -1316, 2007. doi: 10.2215/CJN.02560607 I t was previously thought that individuals who had diabetes and developed progressive diabetic nephropathy (DN) and/or ESRD were simply exposed to long durations of diabetes with relatively poor glycemic control. In other words, all individuals with diabetes were assumed to be at essentially equivalent risk for developing nephropathy, allowing for differences in their ambient serum glucose concentration. In support of the concept that exposure to a hyperglycemic environment led to DN (often with coexisting obesity, metabolic syndrome, hypertension, and hyperlipidemia), clinical trials have conclusively demonstrated that improving glycemic control delays and, in some cases, may prevent the subsequent development of albuminuria, DN, and macrovascular complications such as coronary artery disease (1,2). The concept that select individuals with diabetes were at differential risk for developing nephropathy on the basis of familial aggregation of kidney disease was initially reported in 1989 but has only recently gained broad acceptance among nephrologists and diabetologists (3). Familial Aggregation of DNThe notion that genetic factors contribute to any complex disease rests on the demonstration of familial aggregation. Familial clustering of nephropathy could result from shared genes, environmental exposures, or their combination.After repeated demonstra...

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Section: Familial Aggregation Of Dnmentioning

confidence: 97%

Section: Genome-wide Linkage Scans For Dnmentioning

confidence: 99%

Section: Genome-wide Linkage Scans For Dnmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Factors in Diabetic Nephropathy

Freedman

Bostrom

Daeihagh

et al. 2007

Clinical Journal of the American Society of Nephrology

174

125

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“…Estimated GFR was calculated by the reciprocal of serum cystatin C expressed in mg/l multiplied by 100 and defined as cystatin C-GFR [33].…”

Section: Study Populationmentioning

confidence: 99%

Relationship between ADIPOQ gene, circulating high molecular weight adiponectin and albuminuria in individuals with normal kidney function: evidence from a family-based study

et al. 2011

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Aims/hypothesis Insulin resistance is associated with reduced serum adiponectin and increased albuminuria levels. Thus, one would anticipate an inverse relationship between circulating adiponectin and albuminuria. However, several studies have described a 'paradoxical' elevation of serum adiponectin in patients with elevated albuminuria. These findings may have been confounded by the presence of diseases and related treatments known to affect circulating adiponectin and albuminuria. We therefore studied the relationship between circulating adiponectin and albuminuria in the absence of such confounders. Methods To this purpose, the relationship between adiponectin isoforms and albumin:creatinine ratio (ACR) was investigated in a family-based sample of 634 non-diabetic untreated white individuals with normal kidney function. We also investigated whether the two variables share a common genetic background and addressed the specific role of the gene encoding adiponectin on that background by genotyping several ADIPOQ single nucleotide polymorphisms (SNPs). Results ACR was directly associated with high molecular weight (HMW) adiponectin isoform (p=0.024). The two variables shared some genetic correlation (ρ g = 0.38, p=0.04). ADIPOQ promoter SNP rs17300539 was associated with HMW adiponectin (p=4.8×10 −5 ) and ACR (p=0.0027). The genetic correlation between HMW adiponectin and ACR was no longer significant when SNP rs17300539 was added to the model, thus reinforcing the role of this SNP in determining both traits.Conclusions/interpretation Our study shows a positive, independent correlation between HWM adiponectin and ACR. ADIPOQ variability is associated with HMW adiponectin and ACR, and explains some of the common genetic background shared by these traits, thus suggesting that ADIPOQ and HMW adiponectin modulate albuminuria levels.

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“…Regarding T2DM and its related traits, numerous studies have reported genetic contributions, ranging from simple heritability estimates to mapping of susceptibility genes using genome-wide linkage and association approaches, to phenotypic manifestations such as carotid artery wall thickness [10], glucose concentrations [11], insulin resistance [12], T2DM [13,14], renal function [15], lipids [16,19], obesity [17], retinopathy [18], systolic blood pressure [20] and metabolic syndrome and cardio-metabolic risk factors in children and adolescents [21]. Specifically, the recent GWASs have contributed greatly to an understanding of the complex genetic architectures of T2DM and its related traits.…”

mentioning

confidence: 99%

Emerging face of genetics, genomics and diabetes

Sridhar¹,

Duggirala

Padmanabhan

2013

Int J Diabetes Dev Ctries

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Type 2 diabetes mellitus (T2DM) has become a global public health issue encompassing even children and youth in recent decades. It is a complex disease influenced by genetic and environmental factors. Although much attention is paid to environmental factors since they are identifiable and potentially modifiable, beginning the turn of the 21st century, there has been an explosion of activities that are aimed to screen the genome for identifying T2DM susceptibility genes. This gained momentum with a confluence of different scientific disciplines that was showcased by the millennial Human Genome Project and its aftermath. Currently, the next generation sequencing accelerated the identification of potential causal genes/variants (especially low frequency and rare variants) influencing complex diseases such as T2DM. Also, the other technologies such as transcriptomics, proteomics, metabolomics and epigenomics have emerged as additional tools to identify the molecular factors underling the phenotypic expression of T2DM. The voluminous biological data generated by these studies has necessitated or contributed to bioinformatics, a confluence of biology and its various flavours, information technology, computational biology, algorithms, matching, statistics, mathematics, nanotechnology, ethics among others.The areas of analysis using bioinformatics in diabetes can be approached as employing datasets from genome or amino acid sequences, structures of biological molecules and functional genomics experiments. Nevertheless analyses extend to other types of data including evolutionary trees, metabolic pathways and the semantics of published literature.Mathematical models and analysis and statistical analyses are required to obtain critical information from the large amounts of data that is generated by genomic and other approaches. Newer algorithms to cluster data from a wide scatter will be relevant: for example, K-means can select best density in a self-adoptive manner and initialize r empirically; an improved method over K means, the Automatic Generation of Distance for Density based Clustering (AGDDC) was developed by Karteeka Pavan et al. to optimize number of clusters [1]. This was developed to find initial optimum centroids based on density clustering. It can be applied to relevant genes related to the pathogenesis of T2DM.The challenge in modern biology and of modern diabetes, is a profusion of data. Bioinformatic methods aid in curating and synthesizing the deluge [2]. The Human Genomics Project (HGP) which heralded the situation where data overran the capacity to cull out the information, simultaneously saw sequencing of genes of other species including vertebrates, invertebrates, fungi, bacteria and plants. The underlying theme was to know how genetic architecture and genes evolved over time. Considering that the basic building blocks were the four nucleotides across all life forms, it was logical to compare the genomes of different species to identify where and how divergence of genes occurred and how newer metabo...

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A Genome-Wide Linkage Scan for Genes Controlling Variation in Renal Function Estimated by Serum Cystatin C Levels in Extended Families With Type 2 Diabetes

Cited by 63 publications

References 35 publications

Genetic Factors in Diabetic Nephropathy

Genetic Factors in Diabetic Nephropathy

Relationship between ADIPOQ gene, circulating high molecular weight adiponectin and albuminuria in individuals with normal kidney function: evidence from a family-based study

Emerging face of genetics, genomics and diabetes

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