A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both A1C and glucose

Insights into obesity and diabetes at the intersection of mouse and human genetics

Many of our insights into obesity and diabetes come from studies in mice carrying natural or induced mutations. In parallel, genome-wide association studies (GWAS) in humans have identified numerous genes that are causally associated with obesity and diabetes, but discovering the underlying mechanisms required in-depth studies in mice. We discuss the advantages of studying natural variation in mice and summarize several examples where the combination of human and mouse genetics opened windows into fundamental physiological pathways. A noteworthy example is the melanocortin-4 receptor (MC4R) and its role in energy balance. The pathway was delineated by discovering the gene responsible for the Agouti mutation in mice. With more targeted phenotyping, we predict that additional pathways relevant to human pathophysiology will be discovered.

Genome-wide admixture and association study of serum iron, ferritin, transferrin saturation and total iron binding capacity in African Americans

Iron is an essential component of many important proteins and enzymes, including hemoglobin, which is responsible for carrying oxygen to the cells. African Americans (AAs) have a greater prevalence of iron deficiency compared with European Americans. We conducted genome-wide admixture-mapping and association studies for serum iron, serum ferritin, transferrin saturation (SAT) and total iron binding capacity (TIBC) in 2347 AAs participating in the Jackson Heart Study (JHS). Follow-up replication analyses for JHS iron-trait associated SNPs were conducted in 329 AA participants in the Healthy Aging in Neighborhoods of Diversity across the Life Span study (HANDLS). Higher estimated proportions of global African ancestry were significantly associated with lower levels of iron (P = 2.4 × 10(-5)), SAT (P = 0.0019) and TIBC (P = 0.042). We observed significant associations (P < 5 × 10(-8)) between serum TIBC levels and two independent SNPs around TF on chromosome 3, the first report of a genome-wide significant second independent signal in this region, and SNPs near two novel genes: HDGFL1 on chromosome 6 and MAF on chromosome 16. We also observed significant associations between ferritin levels and SNPs near GAB3 on chromosome X. We replicated our two independent associations at TF and our association at GAB3 in HANDLS. Our study provides evidence for both shared and unique genetic risk factors that are associated with iron-related measures in AAs. The top two variants in TF explain 11.2% of the total variation in TIBC levels in AAs after accounting for age, gender, body mass index and background ancestry.

WDR72 models of structure and function: a stage-specific regulator of enamel mineralization

Amelogenesis Imperfecta (AI) is a clinical diagnosis that encompasses a group of genetic mutations, each affecting processes involved in tooth enamel formation and thus, result in various enamel defects. The hypomaturation enamel phenotype has been described for mutations involved in the later stage of enamel formation, including Klk4, Mmp20, C4orf26, and Wdr72. Using a candidate gene approach we discovered a novel Wdr72 human mutation in association with AI to be a 5-base pair deletion (c.806_810delGGCAG; p.G255VfsX294). To gain insight into the function of WDR72, we used computer modeling of the full-length human WDR72 protein structure and found that the predicted N-terminal sequence forms two beta-propeller folds with an alpha-solenoid tail at the C-terminus. This domain iteration is characteristic of vesicle coat proteins, such as beta'-COP, suggesting a role for WDR72 in the formation of membrane deformation complexes to regulate intracellular trafficking. Our Wdr72 knockout mouse model (Wdr72(-/-)), containing a LacZ reporter knock-in, exhibited hypomineralized enamel similar to the AI phenotype observed in humans with Wdr72 mutations. MicroCT scans of Wdr72(-/-) mandibles affirmed the hypomineralized enamel phenotype occurring at the onset of the maturation stage. H&E staining revealed a shortened height phenotype in the Wdr72(-/-) ameloblasts with retained proteins in the enamel matrix during maturation stage. H(+)/Cl(-) exchange transporter 5 (CLC5), an early endosome acidifier, was co-localized with WDR72 in maturation-stage ameloblasts and decreased in Wdr72(-/-) maturation-stage ameloblasts. There were no obvious differences in RAB4A and LAMP1 immunostaining of Wdr72(-/-) mice as compared to wildtype controls. Moreover, Wdr72(-/-) ameloblasts had reduced amelogenin immunoreactivity, suggesting defects in amelogenin fragment resorption from the matrix. These data demonstrate that WDR72 has a major role in enamel mineralization, most notably during the maturation stage, and suggest a function involving endocytic vesicle trafficking, possibly in the removal of amelogenin proteins.

SORCS1 is necessary for normal insulin secretory granule biogenesis in metabolically stressed β cells

We previously positionally cloned Sorcs1 as a diabetes quantitative trait locus. Sorcs1 belongs to the Vacuolar protein sorting-10 (Vps10) gene family. In yeast, Vps10 transports enzymes from the trans-Golgi network (TGN) to the vacuole. Whole-body Sorcs1 KO mice, when made obese with the leptin(ob) mutation (ob/ob), developed diabetes. β Cells from these mice had a severe deficiency of secretory granules (SGs) and insulin. Interestingly, a single secretagogue challenge failed to consistently elicit an insulin secretory dysfunction. However, multiple challenges of the Sorcs1 KO ob/ob islets consistently revealed an insulin secretion defect. The luminal domain of SORCS1 (Lum-Sorcs1), when expressed in a β cell line, acted as a dominant-negative, leading to SG and insulin deficiency. Using syncollin-dsRed5TIMER adenovirus, we found that the loss of Sorcs1 function greatly impairs the rapid replenishment of SGs following secretagogue challenge. Chronic exposure of islets from lean Sorcs1 KO mice to high glucose and palmitate depleted insulin content and evoked an insulin secretion defect. Thus, in metabolically stressed mice, Sorcs1 is important for SG replenishment, and under chronic challenge by insulin secretagogues, loss of Sorcs1 leads to diabetes. Overexpression of full-length SORCS1 led to a 2-fold increase in SG content, suggesting that SORCS1 is sufficient to promote SG biogenesis.

GWAS identifies an NAT2 acetylator status tag single nucleotide polymorphism to be a major locus for skin fluorescence

AIMS/HYPOTHESIS

Skin fluorescence (SF) is a non-invasive marker of AGEs and is associated with the long-term complications of diabetes. SF increases with age and is also greater among individuals with diabetes. A familial correlation of SF suggests that genetics may play a role. We therefore performed parallel genome-wide association studies of SF in two cohorts.

METHODS

Cohort 1 included 1,082 participants, 35-67 years of age with type 1 diabetes. Cohort 2 included 8,721 participants without diabetes, aged 18-90 years.

RESULTS

rs1495741 was significantly associated with SF in Cohort 1 (p < 6 × 10(-10)), which is known to tag the NAT2 acetylator phenotype. The fast acetylator genotype was associated with lower SF, explaining up to 15% of the variance. In Cohort 2, the top signal associated with SF (p = 8.3 × 10(-42)) was rs4921914, also in NAT2, 440 bases upstream of rs1495741 (linkage disequilibrium r (2) = 1.0 for rs4921914 with rs1495741). We replicated these results in two additional cohorts, one with and one without type 1 diabetes. Finally, to understand which compounds are contributing to the NAT2-SF signal, we examined 11 compounds assayed from skin biopsies (n = 198): the fast acetylator genotype was associated with lower levels of the AGEs hydroimidazolones of glyoxal (p = 0.017).

CONCLUSIONS/INTERPRETATION

We identified a robust association between NAT2 and SF in people with and without diabetes. Our findings provide proof of principle that genetic variation contributes to interindividual SF and that NAT2 acetylation status plays a major role.

Dynamic pathway analysis of genes associated with blood pressure using whole genome sequence data

Groups of genes assigned to a pathway, also called a module, have similar functions. Finding such modules, and the topology of the changes of the modules over time, is a fundamental problem in understanding the mechanisms of complex diseases. Here we investigated an approach that categorized variants into rare or common and used a hierarchical model to jointly estimate the group effects of the variants in a pathway for identifying enriched pathways over time using whole genome sequencing data and blood pressure data. Our results suggest that the method can identify potentially biologically meaningful genes in modules associated with blood pressure over time.

Identification of novel risk genes associated with type 1 diabetes mellitus using a genome-wide gene-based association analysis

Aims/Introduction

Type 1 diabetes mellitus is a serious disorder characterized by destruction of pancreatic β-cells, culminating in absolute insulin deficiency. Genetic factors contribute to the susceptibility of type 1 diabetes mellitus. The aim of the present study was to identify more susceptibility genes of type 1 diabetes mellitus.

Materials and Methods

We carried out an initial gene-based genome-wide association study in a total of 4,075 type 1 diabetes mellitus cases and 2,604 controls by using the Gene-based Association Test using Extended Simes procedure. Furthermore, we carried out replication studies, differential expression analysis and functional annotation clustering analysis to support the significance of the identified susceptibility genes.

Results

We identified 452 genes associated with type 1 diabetes mellitus, even after adapting the genome-wide threshold for significance (P < 9.05E-04). Among these genes, 171 were newly identified for type 1 diabetes mellitus, which were ignored in single-nucleotide polymorphism-based association analysis and were not previously reported. We found that 53 genes have supportive evidence from replication studies and/or differential expression studies. In particular, seven genes including four non-human leukocyte antigen (HLA) genes (RASIP1, STRN4, BCAR1 and MYL2) are replicated in at least one independent population and also differentially expressed in peripheral blood mononuclear cells or monocytes. Furthermore, the associated genes tend to enrich in immune-related pathways or Gene Ontology project terms.

Conclusions

The present results suggest the high power of gene-based association analysis in detecting disease-susceptibility genes. Our findings provide more insights into the genetic basis of type 1 diabetes mellitus.

Genome-wide association study identifies common loci influencing circulating glycated hemoglobin (HbA1c) levels in non-diabetic subjects: the Long Life Family Study (LLFS)

OBJECTIVE

Glycated hemoglobin (HbA1c) is a stable index of chronic glycemic status and hyperglycemia associated with progressive development of insulin resistance and frank diabetes. It is also associated with premature aging and increased mortality. To uncover novel loci for HbA1c that are associated with healthy aging, we conducted a genome-wide association study (GWAS) using non-diabetic participants in the Long Life Family Study (LLFS), a study with familial clustering of exceptional longevity in the US and Denmark.

METHODS

A total of 4088 non-diabetic subjects from the LLFS were used for GWAS discoveries, and a total of 8231 non-diabetic subjects from the Atherosclerosis Risk in Communities Study (ARIC, in the MAGIC Consortium) and the Health, Aging, and Body Composition Study (HABC) were used for GWAS replications. HbA1c was adjusted for age, sex, centers, 20 principal components, without and with BMI. A linear mixed effects model was used for association testing.

RESULTS

Two known loci at GCK rs730497 (or rs2908282) and HK1 rs17476364 were confirmed (p<5e-8). Of 25 suggestive (5e-8

CONCLUSIONS

The analysis reconfirmed two known GWAS loci (GCK, HK1) and identified 25 suggestive loci including one reconfirmed variant in G6PC2 and one replicated variant near OR10R3P/SPTA1. Future focused survey of sequence elements containing mainly functional and regulatory variants may yield additional findings.

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Key Words

• Genome-wide association study
• Glucose
• Insulin resistance and diabetes
• Non-enzymatic glycation
• Premature aging processes

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MESH Headings

• Aged
• Cohort Studies
• Denmark
• Diabetes Mellitus/blood
• Female
• Genome-Wide Association Study
• Genotype
• Glycated Hemoglobin/metabolism
• Humans
• Longevity
• Male
• Middle Aged
• Phenotype
• United States

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Grants

• R01 AG032319 NIA NIH HHS
• U01 AG023744 NIA NIH HHS
• R01 HL059367 NHLBI NIH HHS
• P30 AG034424 NIA NIH HHS
• HHSN268201100006C NHLBI NIH HHS
• U01AG023712 NIA NIH HHS
• HHSN268200782096C NHLBI NIH HHS
• K01DK076595 NIDDK NIH HHS
• R21 DK080294 NIDDK NIH HHS
• U19AG023122 NIA NIH HHS
• R01HL086694 NHLBI NIH HHS
• HHSN268201100012C NHLBI NIH HHS
• UL1RR025005 NCRR NIH HHS
• U01 AG023712 NIA NIH HHS
• HHSN268201100009I NHLBI NIH HHS
• P30 AG024827 NIA NIH HHS
• R01HL59367 NHLBI NIH HHS
• HHSN268201100010C NHLBI NIH HHS
• UL1 RR025005 NCRR NIH HHS
• N01 AG062101 NIA NIH HHS
• HHSN268201100008C NHLBI NIH HHS
• UL1 TR001079 NCATS NIH HHS
• HHSN268201100005G NHLBI NIH HHS
• U19 AG023122 NIA NIH HHS
• HHSN268201100008I NHLBI NIH HHS
• HHSN268201100007C NHLBI NIH HHS
• P50 AG008702 NIA NIH HHS
• K01067207 PHS HHS
• HHSN268201100011I NHLBI NIH HHS
• P30 DK079637 NIDDK NIH HHS
• HHSN268201100011C NHLBI NIH HHS
• R01 HL086694 NHLBI NIH HHS
• 1R01AG032098-01A1 NIA NIH HHS
• U01AG023746 NIA NIH HHS
• K24 AG025727 NIA NIH HHS
• N01 AG062106 NIA NIH HHS
• HHSN268200625226C PHS HHS
• U01 HG004402 NHGRI NIH HHS
• U01HG004402 NHGRI NIH HHS
• R01 AG032098 NIA NIH HHS
• U01AG023744 NIA NIH HHS
• K01 DK076595 NIDDK NIH HHS
• R01HL087641 NHLBI NIH HHS
• U01 AG023749 NIA NIH HHS
• R21 HL114237 NHLBI NIH HHS
• HHSN268201100005I NHLBI NIH HHS
• N01 AG062103 NIA NIH HHS
• U01AG023755 NIA NIH HHS
• R21HL114237 NHLBI NIH HHS
• U01 AG023755 NIA NIH HHS
• U01AG023749 NIA NIH HHS
• K24AG025727 NIA NIH HHS
• Intramural NIH HHS
• U01 AG023746 NIA NIH HHS
• HHSN268201100009C NHLBI NIH HHS
• HHSN268201100005C NHLBI NIH HHS
• HHSN268201100007I NHLBI NIH HHS
• R21DK080294 NIDDK NIH HHS
• R01AG032319 NIA NIH HHS
• R01 HL087641 NHLBI NIH HHS

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Affiliation(s)

Ping An Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA.
Iva Miljkovic University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Center for Aging and Population Health, Pittsburgh, PA, USA
Bharat Thyagarajan Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
Aldi T Kraja Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
E Warwick Daw Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
James S Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
Elizabeth Selvin Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
W H Linda Kao Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
Nisa M Maruthur Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
Micahel A Nalls Laboratory of Neurogenetics, NIA/NIH, Bethesda, MD, USA
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, USA
Tamara B Harris Laboratory of Epidemiology, Demography and Biometry, NIA/NIH, Bethesda, MD, USA
Joseph H Lee Gertrude H. Sergievsky Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York City, NY, USA
Ingrid B Borecki Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
Kaare Christensen Danish Aging Research Center, Epidemiology, University of Southern Denmark, Odense, Denmark; Department of Clinical Biochemistry and Pharmacology and Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
John H Eckfeldt Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
Richard Mayeux Gertrude H. Sergievsky Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York City, NY, USA
Thomas T Perls Division of Geriatrics, Department of Medicine, Boston University Medical Center, Boston, MA, USA
Anne B Newman University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Center for Aging and Population Health, Pittsburgh, PA, USA
Michael A Province Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA

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SORCS1 contributes to the development of renal disease in rats and humans

Many lines of evidence demonstrate that genetic variability contributes to chronic kidney disease susceptibility in humans as well as rodent models. Little progress has been made in discovering causal kidney disease genes in humans mainly due to genetic complexity. Here, we use a minimal congenic mapping strategy in the FHH (fawn hooded hypertensive) rat to identify Sorcs1 as a novel renal disease candidate gene. We investigated the hypothesis that genetic variation in Sorcs1 influences renal disease susceptibility in both rat and human. Sorcs1 is expressed in the kidney, and knocking out this gene in a rat strain with a sensitized genome background produced increased proteinuria. In vitro knockdown of Sorcs1 in proximal tubule cells impaired protein trafficking, suggesting a mechanism for the observed proteinuria in the FHH rat. Since Sorcs1 influences renal function in the rat, we went on to test this gene in humans. We identified associations between single nucleotide polymorphisms in SORCS1 and renal function in large cohorts of European and African ancestry. The experimental data from the rat combined with association results from different ethnic groups indicates a role for SORCS1 in maintaining proper renal function.

A possible mechanism behind autoimmune disorders discovered by genome-wide linkage and association analysis in celiac disease

Celiac disease is a common autoimmune disorder characterized by an intestinal inflammation triggered by gluten, a storage protein found in wheat, rye and barley. Similar to other autoimmune diseases such as type 1 diabetes, psoriasis and rheumatoid arthritis, celiac disease is the result of an immune response to self-antigens leading to tissue destruction and production of autoantibodies. Common diseases like celiac disease have a complex pattern of inheritance with inputs from both environmental as well as additive and non-additive genetic factors. In the past few years, Genome Wide Association Studies (GWAS) have been successful in finding genetic risk variants behind many common diseases and traits. To complement and add to the previous findings, we performed a GWAS including 206 trios from 97 nuclear Swedish and Norwegian families affected with celiac disease. By stratifying for HLA-DQ, we identified a new genome-wide significant risk locus covering the DUSP10 gene. To further investigate the associations from the GWAS we performed pathway analyses and two-locus interaction analyses. These analyses showed an over-representation of genes involved in type 2 diabetes and identified a set of candidate mechanisms and genes of which some were selected for mRNA expression analysis using small intestinal biopsies from 98 patients. Several genes were expressed differently in the small intestinal mucosa from patients with celiac autoimmunity compared to intestinal mucosa from control patients. From top-scoring regions we identified susceptibility genes in several categories: 1) polarity and epithelial cell functionality; 2) intestinal smooth muscle; 3) growth and energy homeostasis, including proline and glutamine metabolism; and finally 4) innate and adaptive immune system. These genes and pathways, including specific functions of DUSP10, together reveal a new potential biological mechanism that could influence the genesis of celiac disease, and possibly also other chronic disorders with an inflammatory component.

Spatiotemporal expression profile of a putative β propeller WDR72 in laying hens

The purpose of this study is to characterize the expression profile of a novel gene WDR72 in laying hens. Sixty-week old Hy-line Brown layers with similar laying sequence, egg weight, and shell strength, were selected and divided into 5 groups. The oviduct segments, such as magnum, white isthmus, and uterus, were sampled from each group of hens which were killed at 3 h post-oviposition (3 h P.O.), 4.15-4.5 h P.O., 8.5-9 h P.O., 12 h P.O. and 18 h P.O., respectively. To the 8.5-9 h P.O. hens, additional organs were also sampled besides oviduct tissues. Moreover, another group of hens with weak shell strength were selected and their oviduct segments were sampled at 12 h P.O. Then the expression profile of WDR72 was analyzed using real-time quantitative RT-PCR. The results showed as follows. (1) WDR72 transcripts specifically distributed in parts of organs investigated. At 8.5-9 h P.O., WDR72 appeared to be much more abundantly expressed in hens' oviduct sections, then followed in turn by brain, kidney, lung, glandular stomach and spleen. However, there were almost no WDR72 transcripts expressed in pectoral muscle, liver, heart and jejunum. (2) During the process of an "egg" passing through an oviduct, the expression of WDR72 in the magnum was greatly superior to that in the other two oviduct segments at 3 h P.O., 8.5-9 h P.O., and 12 h P.O.; while it was white isthmus in which WDR72 transcript levels were the highest at 4.15-4.5 h P.O. and 18 h P.O. (3) To any oviduct segment, not only uterus but also magnum and white isthmus, the expression of WDR72 in which was significantly up-regulated at the stages of active calcification. (4) WDR72 transcript levels in any oviduct segments of strong-shell hens were significantly higher than that of weak-shell layers (P < 0.01), which arose the possibility that WDR72 was positively associated with chicken eggshell strength. In conclusion, the expression profile of WDR72 gene in laying hens has been characterized, which would facilitate to further probe into its functions.

Vps10 family proteins and the retromer complex in aging-related neurodegeneration and diabetes

Members of the vacuolar protein sorting 10 (Vps10) family of receptors (including sortilin, SorL1, SorCS1, SorCS2, and SorCS3) play pleiotropic functions in protein trafficking and intracellular and intercellular signaling in neuronal and non-neuronal cells. Interactions have been documented between Vps10 family members and the retromer coat complex, a key component of the intracellular trafficking apparatus that sorts cargo from the early endosome to the trans-Golgi network. In recent years, genes encoding several members of the Vps10 family of proteins, as well as components of the retromer coat complex, have been implicated as genetic risk factors for sporadic and autosomal dominant forms of neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobar degeneration, and Parkinson's disease, with risk for type 2 diabetes mellitus and atherosclerosis. In addition to their functions in protein trafficking, the Vps10 family proteins modulate neurotrophic signaling pathways. Sortilin can impact the intracellular response to brain-derived neurotrophic factor (BDNF) by regulating anterograde trafficking of Trk receptors to the synapse and direct control of BDNF levels, while both sortilin and SorCS2 function as cell surface receptors to mediate acute responses to proneurotrophins. This mini-review and symposium will highlight the emerging data from this rapidly growing area of research implicating the Vps10 family of receptors and the retromer in physiological intracellular trafficking signaling by neurotrophins and in the pathogenesis of neurodegeneration.

Bag of Naïve Bayes: biomarker selection and classification from genome-wide SNP data

BACKGROUND

Multifactorial diseases arise from complex patterns of interaction between a set of genetic traits and the environment. To fully capture the genetic biomarkers that jointly explain the heritability component of a disease, thus, all SNPs from a genome-wide association study should be analyzed simultaneously.

RESULTS

In this paper, we present Bag of Naïve Bayes (BoNB), an algorithm for genetic biomarker selection and subjects classification from the simultaneous analysis of genome-wide SNP data. BoNB is based on the Naïve Bayes classification framework, enriched by three main features: bootstrap aggregating of an ensemble of Naïve Bayes classifiers, a novel strategy for ranking and selecting the attributes used by each classifier in the ensemble and a permutation-based procedure for selecting significant biomarkers, based on their marginal utility in the classification process. BoNB is tested on the Wellcome Trust Case-Control study on Type 1 Diabetes and its performance is compared with the ones of both a standard Naïve Bayes algorithm and HyperLASSO, a penalized logistic regression algorithm from the state-of-the-art in simultaneous genome-wide data analysis.

CONCLUSIONS

The significantly higher classification accuracy obtained by BoNB, together with the significance of the biomarkers identified from the Type 1 Diabetes dataset, prove the effectiveness of BoNB as an algorithm for both classification and biomarker selection from genome-wide SNP data.

AVAILABILITY

Source code of the BoNB algorithm is released under the GNU General Public Licence and is available at http://www.dei.unipd.it/~sambofra/bonb.html.

From genotype to human β cell phenotype and beyond

Polygenic type 2 diabetes mellitus (T2DM) is a multi-factorial disease due to the interplay between genes and the environment. Over the years, several genes/loci have been associated with this type of diabetes, with the majority of them being related to β cell dysfunction. In this review, the available information on how polymorphisms in T2DM-associated genes/loci do directly affect the properties of human islet cells are presented and discussed, including some clinical implications and the role of epigenetic mechanisms.

Pilot genome-wide association search identifies potential loci for risk of erectile dysfunction in type 1 diabetes using the DCCT/EDIC study cohort

PURPOSE

We identified genetic predictors of diabetes associated erectile dysfunction using genome-wide and candidate gene approaches in a cohort of men with type 1 diabetes.

MATERIALS AND METHODS

We examined 528 white men with type 1 diabetes, including 125 with erectile dysfunction, from DCCT (Diabetes Control and Complications Trial) and its observational followup, the EDIC (Epidemiology of Diabetes Interventions and Complications) study. Erectile dysfunction was identified from a single International Index of Erectile Function item. A Human1M BeadChip (Illumina®) was used for genotyping. A total of 867,125 single nucleotide polymorphisms were subjected to analysis. Whole genome and candidate gene approaches were used to test the hypothesis that genetic polymorphisms may predispose men with type 1 diabetes to erectile dysfunction. Univariate and multivariate models were used, controlling for age, HbA1c, diabetes duration and prior randomization to intensive or conventional insulin therapy during DCCT. A stratified false discovery rate was used to perform the candidate gene approach.

RESULTS

Two single nucleotide polymorphisms located on chromosome 3 in 1 genomic loci were associated with erectile dysfunction with p <1 × 10(-6), including rs9810233 with p = 7 × 10(-7) and rs1920201 with p = 9 ×10(-7). The nearest gene to these 2 single nucleotide polymorphisms is ALCAM. Genetic association results at these loci were similar on univariate and multivariate analysis. No candidate genes met the criteria for statistical significance.

CONCLUSIONS

Two single nucleotide polymorphisms, rs9810233 and rs1920101, which are 25 kb apart, are associated with erectile dysfunction, although they do not meet the standard genome-wide association study significance criterion of p <5 × 10(-8). Other studies with larger sample sizes are required to determine whether ALCAM represents a novel gene in the pathogenesis of diabetes associated erectile dysfunction.

The role of intracellular trafficking and the VPS10d receptors in Alzheimer's disease

In Alzheimer's disease, the key pathological culprit is the amyloid-β protein, which is generated through β- and γ-secretase cleavage of the amyloid-β precursor protein (APP). Both the secretases and amyloid-β precursor protein are transmembrane proteins that are sorted via the trans-Golgi network and the endosome through multiple membranous compartments of the cell. The coat complex clathrin controls the sorting from the cell surface and the trans-Golgi network to the endosome. Instead, the retromer controls the reverse transport from the endosome to the trans-Golgi network. The retromer contains two subprotein complexes: the cargo-selective subcomplex consisting of VPS35, VPS29 and VPS26 and the membrane deformation subcomplex consisting of Vps5p, Vps17p, SNX 1/2 and possibly SNX 5/6 or SNX 32 in mammals. Cargo molecules of the retromer include the VPS10 receptor proteins SORL1, SORT1, SORCS1, SORCS2 and SORCS3. There is increasing evidence through cell biology and animal and genetic studies that components of the retromer and the VPS10d receptor family play a role in the etiology of Alzheimer's disease. This article reviews and summarizes this current evidence.

Validation of candidate genes associated with cardiovascular risk factors in psychiatric patients

The purpose of this study was to identify genetic variants predictive of cardiovascular risk factors in a psychiatric population treated with second generation antipsychotics (SGA). 924 patients undergoing treatment for severe mental illness at four US hospitals were genotyped at 1.2 million single nucleotide polymorphisms. Patients were assessed for fasting serum lipid (low density lipoprotein cholesterol [LDLc], high density lipoprotein cholesterol [HDLc], and triglycerides) and obesity phenotypes (body mass index, BMI). Thirteen candidate genes from previous studies of the same phenotypes in non-psychiatric populations were tested for association. We confirmed 8 of the 13 candidate genes at the 95% confidence level. An increased genetic effect size was observed for triglycerides in the psychiatric population compared to that in the cardiovascular population.

Genetic determinants of variability in glycated hemoglobin (HbA(1c)) in humans: review of recent progress and prospects for use in diabetes care

Glycated hemoglobin A(1c) (HbA(1c)) indicates the percentage of total hemoglobin that is bound by glucose, produced from the nonenzymatic chemical modification by glucose of hemoglobin molecules carried in erythrocytes. HbA(1c) represents a surrogate marker of average blood glucose concentration over the previous 8 to 12 weeks, or the average lifespan of the erythrocyte, and thus represents a more stable indicator of glycemic status compared with fasting glucose. HbA(1c) levels are genetically determined, with heritability of 47% to 59%. Over the past few years, inroads into understanding genetic predisposition by glycemic and nonglycemic factors have been achieved through genomewide analyses. Here I review current research aimed at discovering genetic determinants of HbA(1c) levels, discussing insights into biologic factors influencing variability in the general and diabetic population, and across different ethnicities. Furthermore, I discuss briefly the relevance of findings for diabetes monitoring and diagnosis.

The NIDDK Central Repository at 8 years--ambition, revision, use and impact

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository makes data and biospecimens from NIDDK-funded research available to the broader scientific community. It thereby facilitates: the testing of new hypotheses without new data or biospecimen collection; pooling data across several studies to increase statistical power; and informative genetic analyses using the Repository's well-curated phenotypic data. This article describes the initial database plan for the Repository and its revision using a simpler model. Among the lessons learned were the trade-offs between the complexity of a database design and the costs in time and money of implementation; the importance of integrating consent documents into the basic design; the crucial need for linkage files that associate biospecimen IDs with the masked subject IDs used in deposited data sets; and the importance of standardized procedures to test the integrity data sets prior to distribution. The Repository is currently tracking 111 ongoing NIDDK-funded studies many of which include genotype data, and it houses over 5 million biospecimens of more than 25 types including serum, plasma, stool, urine, DNA, red blood cells, buffy coat and tissue. Repository resources have supported a range of biochemical, clinical, statistical and genetic research (188 external requests for clinical data and 31 for biospecimens have been approved or are pending). Genetic research has included GWAS, validation studies, development of methods to improve statistical power of GWAS and testing of new statistical methods for genetic research. We anticipate that the future impact of the Repository's resources on biomedical research will be enhanced by (i) cross-listing of Repository biospecimens in additional searchable databases and biobank catalogs; (ii) ongoing deployment of new applications for querying the contents of the Repository; and (iii) increased harmonization of procedures, data collection strategies, questionnaires etc. across both research studies and within the vocabularies used by different repositories.

Loss of PDGF-B activity increases hepatic vascular permeability and enhances insulin sensitivity

Hepatic vasculature is not thought to pose a permeability barrier for diffusion of macromolecules from the bloodstream to hepatocytes. In contrast, in extrahepatic tissues, the microvasculature is critically important for insulin action, because transport of insulin across the endothelial cell layer is rate limiting for insulin-stimulated glucose disposal. However, very little is known concerning the role in this process of pericytes, the mural cells lining the basolateral membrane of endothelial cells. PDGF-B is a growth factor involved in the recruitment and function of pericytes. We studied insulin action in mice expressing PDGF-B lacking the proteoglycan binding domain, producing a protein with a partial loss of function (PDGF-B(ret/ret)). Insulin action was assessed through measurements of insulin signaling and insulin and glucose tolerance tests. PDGF-B deficiency enhanced hepatic vascular transendothelial transport. One outcome of this change was an increase in hepatic insulin signaling. This correlated with enhanced whole body glucose homeostasis and increased insulin clearance from the circulation during an insulin tolerance test. In obese mice, PDGF-B deficiency was associated with an 80% reduction in fasting insulin and drastically reduced insulin secretion. These mice did not have significantly higher glucose levels, reflecting a dramatic increase in insulin action. Our findings show that, despite already having a high permeability, hepatic transendothelial transport can be further enhanced. To the best of our knowledge, this is the first study to connect PDGF-B-induced changes in hepatic sinusoidal transport to changes in insulin action, demonstrating a link between PDGF-B signaling and insulin sensitivity.

Clinical interpretation of indices of quality of glycemic control and glycemic variability

The practicing physician is faced with the task of interpreting>2 dozen indices of quality of glycemic control and glycemic variability. It would be desirable to have reference data from relevant patient populations (eg, patients with the same type of diabetes, duration of diabetes, therapeutic regimen, or glycated hemoglobin [HbA1c] levels). The physician can then select the appropriate reference set for interpretation of results for each patient. Institutions and clinics may wish to develop their own reference data. Results can be interpreted as excellent, good, fair, or poor, corresponding with quartiles of their distributions. Each index of glycemic control and variability can be given a numerical score in terms of its percentile within the selected reference population. One can then compute the mean and standard deviation of the percentile scores to obtain an integrated measure of the quality of glycemic control or variability. We calculated quartiles for measures of quality of glycemic control and variability. One can use the percent coefficient of variation (%CV) with criteria that apply irrespective of the HbA1c level as a general rule for interpretation of glycemic variability. For example, a %CV<33.5% can be regarded as excellent, a %CV between 33.5% to 36.8% as good, a %CV between 36.8% to 40.6% as fair, and a %CV>40.6% as poor. A graphical display can be used to make more accurate assessments for narrow HbA1c ranges, as the percentiles of the %CV can change systematically with HbA1c level or with mean glucose level.

Comparative analysis of methods for detecting interacting loci

Background

Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted.

Results

We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs.

Conclusion

This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.

BR-squared: a practical solution to the winner's curse in genome-wide scans

The detrimental effects of the winner's curse, including overestimation of the genetic effects of associated variants and underestimation of sufficient sample sizes for replication studies are well-recognized in genome-wide association studies (GWAS). These effects can be expected to worsen as the field moves from GWAS into whole genome sequencing. To date, few studies have reported statistical adjustments to the naive estimates, due to the lack of suitable statistical methods and computational tools. We have developed an efficient genome-wide non-parametric method that explicitly accounts for the threshold, ranking, and allele frequency effects in whole genome scans. Here, we implement the method to provide bias-reduced estimates via bootstrap re-sampling (BR-squared) for association studies of both disease status and quantitative traits, and we report the results of applying BR-squared to GWAS of psoriasis and HbA1c. We observed over 50% reduction in the genetic effect size estimation for many associated SNPs. This translates into a greater than fourfold increase in sample size requirements for successful replication studies, which in part explains some of the apparent failures in replicating the original signals. Our analysis suggests that adjusting for the winner's curse is critical for interpreting findings from whole genome scans and planning replication and meta-GWAS studies, as well as in attempts to translate findings into the clinical setting.

Evaluation of four novel genetic variants affecting hemoglobin A1c levels in a population-based type 2 diabetes cohort (the HUNT2 study)

BACKGROUND

Chronic hyperglycemia confers increased risk for long-term diabetes-associated complications and repeated hemoglobin A1c (HbA1c) measures are a widely used marker for glycemic control in diabetes treatment and follow-up. A recent genome-wide association study revealed four genetic loci, which were associated with HbA1c levels in adults with type 1 diabetes. We aimed to evaluate the effect of these loci on glycemic control in type 2 diabetes.

METHODS

We genotyped 1,486 subjects with type 2 diabetes from a Norwegian population-based cohort (HUNT2) for single-nucleotide polymorphisms (SNPs) located near the BNC2, SORCS1, GSC and WDR72 loci. Through regression models, we examined their effects on HbA1c and non-fasting glucose levels individually and in a combined genetic score model.

RESULTS

No significant associations with HbA1c or glucose levels were found for the SORCS1, BNC2, GSC or WDR72 variants (all P-values > 0.05). Although the observed effects were non-significant and of much smaller magnitude than previously reported in type 1 diabetes, the SORCS1 risk variant showed a direction consistent with increased HbA1c and glucose levels, with an observed effect of 0.11% (P = 0.13) and 0.13 mmol/l (P = 0.43) increase per risk allele for HbA1c and glucose, respectively. In contrast, the WDR72 risk variant showed a borderline association with reduced HbA1c levels (β = -0.21, P = 0.06), and direction consistent with decreased glucose levels (β = -0.29, P = 0.29). The allele count model gave no evidence for a relationship between increasing number of risk alleles and increasing HbA1c levels (β = 0.04, P = 0.38).

CONCLUSIONS

The four recently reported SNPs affecting glycemic control in type 1 diabetes had no apparent effect on HbA1c in type 2 diabetes individually or by using a combined genetic score model. However, for the SORCS1 SNP, our findings do not rule out a possible relationship with HbA1c levels. Hence, further studies in other populations are needed to elucidate whether these novel sequence variants, especially rs1358030 near the SORCS1 locus, affect glycemic control in type 2 diabetes.

Diabetes-associated SorCS1 regulates Alzheimer's amyloid-beta metabolism: evidence for involvement of SorL1 and the retromer complex

SorCS1 and SorL1/SorLA/LR11 belong to the sortilin family of vacuolar protein sorting-10 (Vps10) domain-containing proteins. Both are genetically associated with Alzheimer's disease (AD), and SORL1 expression is decreased in the brains of patients suffering from AD. SORCS1 is also genetically associated with types 1 and 2 diabetes mellitus (T1DM, T2DM). We have undertaken a study of the possible role(s) for SorCS1 in metabolism of the Alzheimer's amyloid-β peptide (Aβ) and the Aβ precursor protein (APP), to test the hypothesis that Sorcs1 deficiency might be a common genetic risk factor underlying the predisposition to AD that is associated with T2DM. Overexpression of SorCS1cβ-myc in cultured cells caused a reduction (p = 0.002) in Aβ generation. Conversely, endogenous murine Aβ(40) and Aβ(42) levels were increased (Aβ(40), p = 0.044; Aβ(42), p = 0.007) in the brains of female Sorcs1 hypomorphic mice, possibly paralleling the sexual dimorphism that is characteristic of the genetic associations of SORCS1 with AD and DM. Since SorL1 directly interacts with Vps35 to modulate APP metabolism, we investigated the possibility that SorCS1cβ-myc interacts with APP, SorL1, and/or Vps35. We readily recovered SorCS1:APP, SorCS1:SorL1, and SorCS1:Vps35 complexes from nontransgenic mouse brain. Notably, total Vps35 protein levels were decreased by 49% (p = 0.009) and total SorL1 protein levels were decreased by 29% (p = 0.003) in the brains of female Sorcs1 hypomorphic mice. From these data, we propose that dysfunction of SorCS1 may contribute to both the APP/Aβ disturbance underlying AD and the insulin/glucose disturbance underlying DM.

Pitfalls in the use of HbA₁(c) as a diagnostic test: the ethnic conundrum

The purpose of a diagnostic test is to identify individuals who have a disorder and reassure those who do not. An HbA₁(c)-based diagnosis of diabetes mellitus or prediabetes fails to meet that purpose. Diabetes mellitus is a disorder of glucose, not HbA₁(c), metabolism. Microvascular complications in diabetes mellitus are driven by chronic hyperglycemia. The correlation of these complications with HbA₁(c) levels is convenient; however, unlike the direct information provided by glucose, HbA₁(c) values reflect glycemic and nonglycemic factors. The latter include modulators of glucose transport across the erythrocyte membrane, intracellular protein glycation and deglycation, erythrocyte turnover, systemic illness and hematological and medical disorders, among others. Genetic rather than glycemic factors explain most of the variance in HbA₁(c) levels. Finally, HbA₁(c) values are misleading as a measure of average blood glucose among persons of African, Asian, Hispanic and other non-European ancestry. Given the numerous pitfalls, the use of HbA₁(c) levels for diagnosing diabetes mellitus or prediabetes is ill-advised.

BTBR Ob/Ob mutant mice model progressive diabetic nephropathy

There remains a need for robust mouse models of diabetic nephropathy (DN) that mimic key features of advanced human DN. The recently developed mouse strain BTBR with the ob/ob leptin-deficiency mutation develops severe type 2 diabetes, hypercholesterolemia, elevated triglycerides, and insulin resistance, but the renal phenotype has not been characterized. Here, we show that these obese, diabetic mice rapidly develop morphologic renal lesions characteristic of both early and advanced human DN. BTBR ob/ob mice developed progressive proteinuria beginning at 4 weeks. Glomerular hypertrophy and accumulation of mesangial matrix, characteristic of early DN, were present by 8 weeks, and glomerular lesions similar to those of advanced human DN were present by 20 weeks. By 22 weeks, we observed an approximately 20% increase in basement membrane thickness and a >50% increase in mesangial matrix. Diffuse mesangial sclerosis (focally approaching nodular glomerulosclerosis), focal arteriolar hyalinosis, mesangiolysis, and focal mild interstitial fibrosis were present. Loss of podocytes was present early and persisted. In summary, BTBR ob/ob mice develop a constellation of abnormalities that closely resemble advanced human DN more rapidly than most other murine models, making this strain particularly attractive for testing therapeutic interventions.

Metabolic memory and diabetic nephropathy: potential role for epigenetic mechanisms

Many clinical studies have shown that intensive glycemic control in patients with diabetes can reduce the incidence and progression of diabetic nephropathy and can also reduce the incidence of other complications. These beneficial effects persist after patients return to usual (often worse) glycemic control. The Diabetes Control and Complications Trial was the first to refer to this phenomenon as 'metabolic memory'. Many patients with diabetes, however, still develop diabetic nephropathy despite receiving intensified glycemic control. Preliminary work in endothelial cells has shown that transient episodes of hyperglycemia can induce changes in gene expression that are dependent on modifications to histone tails (for example, methylation), and that these changes persist after return to normoglycemia. The persistence of such modifications cannot yet be fully explained, but certain epigenetic changes, as well as biochemical mechanisms such as advanced glycation, may provide new and interesting clues towards explaining the pathogenesis of this phenomenon. Further elucidation of the molecular events that enable prior glycemic control to result in end-organ protection in diabetes may lead to the development of new approaches for reducing the burden of diabetic nephropathy.