A strategy to search for common obesity and type 2 diabetes genes

The Multifunctional Role of Herbal Products in the Management of Diabetes and Obesity: A Comprehensive Review

Obesity and diabetes are the most demanding health problems today, and their prevalence, as well as comorbidities, is on the rise all over the world. As time goes on, both are becoming big issues that have a big impact on people’s lives. Diabetes is a metabolic and endocrine illness set apart by hyperglycemia and glucose narrow-mindedness because of insulin opposition. Heftiness is a typical, complex, and developing overall wellbeing worry that has for quite some time been connected to significant medical issues in individuals, all things considered. Because of the wide variety and low adverse effects, herbal products are an important hotspot for drug development. Synthetic compounds are not structurally diverse and lack drug-likeness properties. Thus, it is basic to keep on exploring herbal products as possible wellsprings of novel drugs. We conducted this review of the literature by searching Scopus, Science Direct, Elsevier, PubMed, and Web of Science databases. From 1990 until October 2021, research reports, review articles, and original research articles in English are presented. It provides top to bottom data and an examination of plant-inferred compounds that might be utilized against heftiness or potentially hostile to diabetes treatments. Our expanded comprehension of the systems of activity of phytogenic compounds, as an extra examination, could prompt the advancement of remedial methodologies for metabolic diseases. In clinical trials, a huge number of these food kinds or restorative plants, as well as their bioactive compounds, have been shown to be beneficial in the treatment of obesity.

Genetic association analysis of polymorphisms in PSD3 gene with obesity, type 2 diabetes, and HDL cholesterol

BACKGROUND

The pleckstrin and Sec7 domain-containing 3 (PSD3) gene has been linked to immune diseases. We examined whether the genetic variants within the PSD3 gene are associated with obesity, type 2 diabetes (T2D), and high-density lipoprotein (HDL) cholesterol level.

METHODS

Multiple logistic regression model and linear regression model were used to examine the associations of 259 single nucleotide polymorphisms (SNPs) within the PSD3 gene with obesity and T2D as binary traits, and HDL level as a continuous trait using the Marshfield data, respectively. A replication study of obesity was conducted using the Health Aging and Body Composition (Health ABC) sample.

RESULTS

23SNPs were associated with obesity (p<0.05) in the Marshfield sample and rs4921966 revealed the strongest association (p=3.97×10^-6). Of the 23SNPs, 20 were significantly associated with obesity in the meta-analysis of two samples (p<0.05). Furthermore, 6SNPs revealed associations with T2D in the Marshfield data (top SNP rs12156368 with p=3.05×10^-3); while two SNPs (rs6983992 and rs7843239) were associated with both obesity and T2D (p=0.0188 and 0.023 for obesity and p=8.47×10^-3 and 0.0128 for T2D, respectively). Furthermore, 11SNPs revealed associations with HDL level (top SNP rs13254772 with p=2.79×10^-3) in the Marshfield data; meanwhile rs7009615 was associated with both T2D (p=0.038) and HDL level (p=4.44×10^-3). In addition, haplotype analyses further supported the results of single SNP analysis.

CONCLUSIONS

Common variants in PSD3 were associated with obesity, T2D and HDL level. These findings add important new insights into the pathogenesis of obesity, T2D and HDL cholesterol.

Polymorphisms in PDLIM5 gene are associated with alcohol dependence, type 2 diabetes, and hypertension

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in alcohol dependence (AD), bipolar disorder, and major depressive disorder; however, no study has identified shared genetic variants within PDLIM5 gene among AD, type 2 diabetes (T2D), and hypertension. This study investigated the association of 72 single nucleotide polymorphism (SNPs) with AD (1066 AD cases and 1278 controls) in the Study of Addiction - Genetics and Environment (SAGE) sample and 47 SNPs with T2D (878 cases and 2686 non-diabetic) and hypertension (825 cases and 2739 non-hypertensive) in the Marshfield sample. Multiple logistic regression models in PLINK software were used to examine the associations of genetic variants with AD, T2D, and hypertension and SNP x alcohol consumption interactions for T2D and hypertension. Twenty-five SNPs were associated with AD in the SAGE sample (p < 0.05); rs1048627 showed the strongest association with AD (p = 5.53 × 10^-4). Of the 25 SNPs, 5 SNPs showed associations with both AD in the SAGE sample and T2D in the Marshfield sample (top SNP rs11097432 with p = 0.00107 for T2D and p = 0.0483 for AD) while 6 SNPs showed associations with both AD in the SAGE sample and hypertension in the Marshfield sample (top SNP rs12500426 with p = 0.0119 for hypertension and p = 1.51 × 10^-3 for AD). SNP (rs6532496) showed significant interaction with alcohol consumption for hypertension. Our results showed that several genetic variants in PDLIM5 gene influence AD, T2D and hypertension. These findings offer the potential for new insights into the pathogenesis of AD, T2D, and hypertension.

Mapping genetic determinants of host susceptibility to Pseudomonas aeruginosa lung infection in mice

Background

P. aeruginosa is one of the top three causes of opportunistic human bacterial infections. The remarkable variability in the clinical outcomes of this infection is thought to be associated with genetic predisposition. However, the genes underlying host susceptibility to P. aeruginosa infection are still largely unknown.

Results

As a step towards mapping these genes, we applied a genome wide linkage analysis approach to a mouse model. A large F2 intercross population, obtained by mating P. aeruginosa-resistant C3H/HeOuJ, and susceptible A/J mice, was used for quantitative trait locus (QTL) mapping. The F2 progenies were challenged with a P. aeruginosa clinical strain and monitored for the survival time up to 7 days post-infection, as a disease phenotype associated trait. Selected phenotypic extremes of the F2 distribution were genotyped with high-density single nucleotide polymorphic (SNP) markers, and subsequently QTL analysis was performed. A significant locus was mapped on chromosome 6 and was named P. aeruginosa infection resistance locus 1 (Pairl1). The most promising candidate genes, including Dok1, Tacr1, Cd207, Clec4f, Gp9, Gata2, Foxp1, are related to pathogen sensing, neutrophils and macrophages recruitment and inflammatory processes.

Conclusions

We propose a set of genes involved in the pathogenesis of P. aeruginosa infection that may be explored to complement human studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2676-4) contains supplementary material, which is available to authorized users.

Aberrant expression of long noncoding RNAs in cumulus cells isolated from PCOS patients

PURPOSE

To describe the long noncoding RNA (lncRNA) profiles in cumulus cells isolated from polycystic ovary syndrome (PCOS) patients by employing a microarray and in-depth bioinformatics analysis. This information will help us understand the occurrence and development of PCOS.

METHODS

In this study, we used a microarray to describe lncRNA profiles in cumulus cells isolated from ten patients (five PCOS and five normal women). Several differentially expressed lncRNAs were chosen to validate the microarray results by quantitative RT-PCR (qRT-PCR). Then, the differentially expressed lncRNAs were classified into three subgroups (HOX loci lncRNA, enhancer-like lncRNA, and lincRNA) to deduce their potential features. Furthermore, a lncRNA/mRNA co-expression network was constructed by using the Cytoscape software (V2.8.3, http://www.cytoscape.org/ ).

RESULTS

We observed that 623 lncRNAs and 260 messenger RNAs (mRNAs) were significantly up- or down-regulated (≥2-fold change), and these differences could be used to discriminate cumulus cells of PCOS from those of normal patients. Five differentially expressed lncRNAs (XLOC_011402, ENST00000454271, ENST00000433673, ENST00000450294, and ENST00000432431) were selected to validate the microarray results using quantitative RT-PCR (qRT-PCR). The qRT-PCR results were consistent with the microarray data. Further analysis indicated that many differentially expressed lncRNAs were transcribed from chromosome 2 and may act as enhancers to regulate their neighboring protein-coding genes. Forty-three lncRNAs and 29 mRNAs were used to construct the coding-non-coding gene co-expression network. Most pairs positively correlated, and one mRNA correlated with one or more lncRNAs.

CONCLUSIONS

Our study is the first to determine genome-wide lncRNA expression patterns in cumulus cells isolated from PCOS patients by microarray. The results show that clusters of lncRNAs were aberrantly expressed in cumulus cells of PCOS patients compared with those of normal women, which revealed that lncRNAs differentially expressed in PCOS and normal women may contribute to the occurrence of PCOS and affect oocyte development.

Prioritization of candidate genes for periodontitis using multiple computational tools

BACKGROUND

Both genetic and environmental factors contribute to the development of periodontitis. Genetic studies identified a variety of candidate genes for periodontitis. The aim of the present study is to identify the most promising candidate genes for periodontitis using an integrative gene ranking method.

METHODS

Seed genes that were confirmed to be associated with periodontitis were identified using text mining. Three types of candidate genes were then extracted from different resources (expression profiles, genome-wide association studies). Combining the seed genes, four freely available bioinformatics tools (ToppGene, DIR, Endeavour, and GPEC) were integrated for prioritization of candidate genes. Candidate genes that identified with at least three programs and ranked in the top 20 by each program were considered the most promising.

RESULTS

Prioritization analysis resulted in 21 promising genes involved or potentially involved in periodontitis. Among them, IL18 (interleukin 18), CD44 (CD44 molecule), CXCL1 (chemokine [CXC motif] ligand 1), IL6ST (interleukin 6 signal transducer), MMP3 (matrix metallopeptidase 3), MMP7, CCR1 (chemokine [C-C motif] receptor 1), MMP13, and TLR9 (Toll-like receptor 9) had been associated with periodontitis. However, the roles of other genes, such as CSF3 (colony stimulating factor 3 receptor), CD40, TNFSF14 (tumor necrosis factor receptor superfamily, member 14), IFNB1 (interferon-β1), TIRAP (toll-interleukin 1 receptor domain containing adaptor protein), IL2RA (interleukin 2 receptor α), ETS1 (v-ets avian erythroblastosis virus E26 oncogene homolog 1), GADD45B (growth arrest and DNA-damage-inducible 45 β), BIRC3 (baculoviral IAP repeat containing 3), VAV1 (vav 1 guanine nucleotide exchange factor), COL5A1 (collagen, type V, α1), and C3 (complement component 3), have not been investigated thoroughly in the process of periodontitis. These genes are mainly involved in bacterial infection, immune response, and inflammatory reaction, suggesting that further characterizing their roles in periodontitis will be important.

CONCLUSIONS

A combination of computational tools will be useful in mining candidate genes for periodontitis. These theoretical results provide new clues for experimental biologists to plan targeted experiments.

A potentially functional polymorphism in the regulatory region of let-7a-2 is associated with an increased risk for diabetic nephropathy

Diabetic nephropathy (DN) is a major diabetic complication. However, the initiating molecular events triggering DN are unknown. MicroRNAs (miRNAs) have recently been identified as regulators that modulate the target gene expression and are involved in DN. However, the evidence of the mechanism is still insufficient in human samples. In this study, microRNA microarray assay was used to study gene differential expression profiles in DN and diabetes mellitus (DM) patients. One of the specific differentially expressed microRNAs, let-7a, was down-expressed in DN. Additionally, the expression of let-7a was also decreased in DN by real-time RT PCR in the patients' samples. Moreover, single nucleotide polymorphism (SNP) analysis was used to evaluate the relationship between three SNPs in the regulatory region of let-7a-2 gene and the risk of DN in the Chinese Han population by means of PCR-restriction fragment length polymorphism (RFLP-PCR). Also, the genotype and allele frequencies of let-7a-2 polymorphism were tested in 274 individuals, including 108 DN, 104 DM patients and 62 health control individuals (CON). It was found that a variant rs1143770 and the distributions of CT/TT genotypes were significantly different in three groups, and the CT+TT genotypes frequencies were significantly higher in DN and DM groups than that in CON group. In conclusion, let-7a-2 might participate in the regulation of the occurrence of DN, and a potential variant rs1143770 was significantly associated with the increased risk for DN.

An unbiased evaluation of gene prioritization tools

MOTIVATION

Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates-so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined, and some of them have been implemented and made available through freely available web tools. In this study, we aim at comparing the predictive performance of eight publicly available prioritization tools on novel data. We have performed an analysis in which 42 recently reported disease-gene associations from literature are used to benchmark these tools before the underlying databases are updated.

RESULTS

Cross-validation on retrospective data provides performance estimate likely to be overoptimistic because some of the data sources are contaminated with knowledge from disease-gene association. Our approach mimics a novel discovery more closely and thus provides more realistic performance estimates. There are, however, marked differences, and tools that rely on more advanced data integration schemes appear more powerful.

CONTACT

yves.moreau@esat.kuleuven.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prioritization of candidate genes for attention deficit hyperactivity disorder by computational analysis of multiple data sources

Attention deficit hyperactivity disorder (ADHD) is a common, highly heritable psychiatric disorder characterized by hyperactivity, inattention and increased impulsivity. In recent years, a large number of genetic studies for ADHD have been published and related genetic data has been accumulated dramatically. To provide researchers a comprehensive ADHD genetic resource, we previously developed the first genetic database for ADHD (ADHDgene). The abundant genetic data provides novel candidates for further study. Meanwhile, it also brings new challenge for selecting promising candidate genes for replication and verification research. In this study, we surveyed the computational tools for candidate gene prioritization and selected five tools, which integrate multiple data sources for gene prioritization, to prioritize ADHD candidate genes in ADHDgene. The prioritization analysis resulted in 16 prioritized candidate genes, which are mainly involved in several major neurotransmitter systems or in nervous system development pathways. Among these genes, nervous system development related genes, especially SNAP25, STX1A and the gene-gene interactions related with each of them deserve further investigations. Our results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.

A methodology based on molecular interactions and pathways to find candidate genes associated to diseases: its application to schizophrenia and Alzheimer's disease

Experimental techniques for the identification of genes associated with diseases are expensive and have certain limitations. In this scenario, computational methods are useful tools to identify lists of promising genes for further experimental verification. This paper describes a flexible methodology for the in silico prediction of genes associated with diseases combining the use of available tools for gene enrichment analysis, gene network generation and gene prioritization. A set of reference genes, with a known association to a disease, is used as bait to extract candidate genes from molecular interaction networks and enriched pathways. In a second step, prioritization methods are applied to evaluate the similarities between previously selected candidates and the set of reference genes. The top genes obtained by these programs are grouped into a single list sorted by the number of methods that have selected each gene. As a proof of concept, top genes reported a few years ago in SzGene and AlzGene databases were used as references to predict genes associated to schizophrenia and Alzheimer's disease, respectively. In both cases, we were able to predict a statistically significant amount of genes belonging to the updated lists.

Web tools for the prioritization of candidate disease genes

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

Follow-up association studies of chromosome region 9q and nonsyndromic cleft lip/palate

Cleft lip/palate comprises a large fraction of all human birth defects, and is notable for its significant lifelong morbidity and complex etiology. Several studies have shown that genetic factors appear to play a significant role in the etiology of cleft lip/palate. Human chromosomal region 9q21 has been suggested in previous reports to contain putative cleft loci. Moreover, a specific region (9q22.3-34.1) was suggested to present a approximately 45% probability of harboring a cleft susceptibility gene. Fine mapping of 50 SNPs across the 9q22.3-34.11 region was performed to test for association with cleft lip/palate in families from United States, Spain, Turkey, Guatemala, and China. We performed family-based analyses and found evidence of association of cleft lip/palate with STOM (rs306796) in Guatemalan families (P = 0.004) and in all multiplex families pooled together (P = 0.002). This same SNP also showed borderline association in the US families (P = 0.04). Under a nominal value of 0.05, other SNPs also showed association with cleft lip/palate and cleft subgroups. SNPs in STOM and PTCH genes and nearby FOXE1 were further associated with cleft phenotypes in Guatemalan and Chinese families. Gene prioritization analysis revealed PTCH and STOM ranking among the top fourteen candidates for cleft lip/palate among 339 genes present in the region. Our results support the hypothesis that the 9q22.32-34.1 region harbors cleft susceptibility genes. Additional studies with other populations should focus on these loci to further investigate the participation of these genes in human clefting.

Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics

Array CGH enables the detection of pathogenic copy number variants (CNVs) in 5-15% of individuals with intellectual disability (ID), making it a promising tool for uncovering ID candidate genes. However, most CNVs encompass multiple genes, making it difficult to identify key disease gene(s) underlying ID etiology. Using array CGH we identified 47 previously unreported unique CNVs in 45/255 probands. We prioritized ID candidate genes using five bioinformatic gene prioritization web tools. Gene priority lists were created by comparing integral genes from each CNV from our ID cohort with sets of training genes specific either to ID or randomly selected. Our findings suggest that different training sets alter gene prioritization only moderately; however, only the ID gene training set resulted in significant enrichment of genes with nervous system function (19%) in prioritized versus non-prioritized genes from the same de novo CNVs (7%, p < 0.05). This enrichment further increased to 31% when the five web tools were used in concert and included genes within mitogen-activated protein kinase (MAPK) and neuroactive ligand-receptor interaction pathways. Gene prioritization web tools enrich for genes with relevant function in ID and more readily facilitate the selection of ID candidate genes for functional studies, particularly for large CNVs.

Exploring Candidate Genes for Epilepsy by Computational Disease-Gene Identification Strategy

Epilepsy is a complex disease with a strong genetic component. So far, studies have focused on experimental validation or genome-wide linkage scans for epilepsy susceptibility genes in multiple populations. We have used four bioinformatic tools (SNPs3D, PROSPECTR and SUSPECTS, GenWanderer, PosMed) to analyze 16 susceptibility loci selected from a literature search. Pathways and regulatory network analyses were performed using the Ingenuity Pathways Analysis (IPA) software. We identified a subset of 48 candidate epilepsy susceptibility genes. Five significant canonical pathways, in four typical networks, were identified: GABA receptor signaling, interleukin-6 (IL-6) signaling, G-protein coupled receptor signaling, type 2 diabetes mellitus signaling and airway inflammation in asthma. We concluded that online analytical tools provide a powerful way to reveal candidate genes which can greatly reduce experimental time. Our study contributes to further experimental tests for epilepsy susceptibility genes.

Variants in neuropeptide Y receptor 1 and 5 are associated with nutrient-specific food intake and are under recent selection in Europeans

There is a large variation in caloric intake and macronutrient preference between individuals and between ethnic groups, and these food intake patterns show a strong heritability. The transition to new food sources during the agriculture revolution around 11,000 years ago probably created selective pressure and shaped the genome of modern humans. One major player in energy homeostasis is the appetite-stimulating hormone neuropeptide Y, in which the stimulatory capacity may be mediated by the neuropeptide Y receptors 1, 2 and 5 (NPY1R, NPY2R and NPY5R). We assess association between variants in the NPY1R, NPY2R and NPY5R genes and nutrient intake in a cross-sectional, single-center study of 400 men aged 40 to 80 years, and we examine whether genomic regions containing these genes show signatures of recent selection in 270 HapMap individuals (90 Africans, 90 Asians, and 90 Caucasians) and in 846 Dutch bloodbank controls. Our results show that derived alleles in NPY1R and NPY5R are associated with lower carbohydrate intake, mainly because of a lower consumption of mono- and disaccharides. We also show that carriers of these derived alleles, on average, consume meals with a lower glycemic index and glycemic load and have higher alcohol consumption. One of these variants shows the hallmark of recent selection in Europe. Our data suggest that lower carbohydrate intake, consuming meals with a low glycemic index and glycemic load, and/or higher alcohol consumption, gave a survival advantage in Europeans since the agricultural revolution. This advantage could lie in overall health benefits, because lower carbohydrate intake, consuming meals with a low GI and GL, and/or higher alcohol consumption, are known to be associated with a lower risk of chronic diseases.

Using genome-wide pathway analysis to unravel the etiology of complex diseases

Several genome-wide association studies (GWAS) have been published on various complex diseases. Although, new loci are found to be associated with these diseases, still only very little of the genetic risk for these diseases can be explained. As GWAS are still underpowered to find small main effects, and gene-gene interactions are likely to play a role, the data might currently not be analyzed to its full potential. In this study, we evaluated alternative methods to study GWAS data. Instead of focusing on the single nucleotide polymorphisms (SNPs) with the highest statistical significance, we took advantage of prior biological information and tried to detect overrepresented pathways in the GWAS data. We evaluated whether pathway classification analysis can help prioritize the biological pathways most likely to be involved in the disease etiology. In this study, we present the various benefits and limitations of pathway-classification tools in analyzing GWAS data. We show multiple differences in outcome between pathway tools analyzing the same dataset. Furthermore, analyzing randomly selected SNPs always results in significantly overrepresented pathways, large pathways have a higher chance of becoming statistically significant and the bioinformatics tools used in this study are biased toward detecting well-defined pathways. As an example, we analyzed data from two GWAS on type 2 diabetes (T2D): the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC). Occasionally the results from the DGI and the WTCCC GWAS showed concordance in overrepresented pathways, but discordance in the corresponding genes. Thus, incorporating gene networks and pathway classification tools into the analysis can point toward significantly overrepresented molecular pathways, which cannot be picked up using traditional single-locus analyses. However, the limitations discussed in this study, need to be addressed before these methods can be widely used.

Thr54 allele of fatty-acid binding protein 2 gene is associated with obesity but not type 2 diabetes mellitus in a Caucasian population

AIM

The fatty acid-binding protein 2 (FABP2) A54T polymorphism has been associated with type 2 diabetes mellitus (T2DM) and obesity in many but not all studies. Our aim was to investigate possible associations of FABP2 A54T polymorphism with T2DM and/or obesity in a Greek Caucasian population.

METHODS

242 subjects with T2DM and 188 control subjects were genotyped for the FABP2 A54T polymorphism using PCR-RFLP method. Of the total subjects included in both groups, 172 were classified as obese (BMI >or= 30 kg/m(2)) and 258 were classified as nonobese (BMI <30 kg/m(2)).

RESULTS

In the whole population, 218 subjects (50.7%) were genotyped as AA, 175 subjects (40.7%) as AT, and 37 subjects (8.6%) as TT for the FABP2 A54T polymorphism. According to the dominant model, the frequency of AA genotype was significantly lower in obese than in nonobese subjects (43.0% vs 55.8%, p=0.009). No significant difference was observed in genotypes between diabetic and nondiabetic subjects. According to the additive model, the presence of TT genotype was significantly associated with obesity after adjusting for age, sex, and the presence of T2DM (OR 2.32, p=0.028).

CONCLUSION

FABP2 A54T polymorphism may help identify Caucasian subjects at risk for obesity.

Robust signaling networks of the adipose secretome

Type 2 diabetes is a prototypical complex systems disease that has a strong hereditary component and etiologic links with a sedentary lifestyle, overeating and obesity. Adipose tissue has been shown to be a central driver of type 2 diabetes progression, establishing and maintaining a chronic state of low-level inflammation. The number and diversity of identified endocrine factors from adipose tissue (adipokines) is growing rapidly. Here, I argue that a systems biology approach to understanding the robust multi-level signaling networks established by the adipose secretome will be crucial for developing efficient type 2 diabetes treatment. Recent advances in whole-genome association studies, global molecular profiling and quantitative modeling are currently fueling the emergence of this novel research strategy.

Nutrition and its contribution to obesity and diabetes: a life-course approach to disease prevention?

Whilst previously type 2 diabetes occurred in older adults, its incidence, together with obesity, has increased rapidly in children. An improved understanding of this disease pathway from a developmental view point is critical. It is likely that subtle changes in dietary patterns over an extended period of time contribute to diabetes, although this type of rationale is largely ignored in animal studies aimed at determining the mechanisms involved. Small-animal studies in which large, and often extreme, changes in the diet are imposed at different stages of the life cycle can have substantial effects on fat mass and/or pancreatic functions. These responses are not representative of the much more gradual changes seen in the human population. An increasing number of studies indicate that it is growth rate per se, rather than the type of dietary intervention that determines pancreatic function during development. Epigenetic mechanisms that regulate insulin secretion by the pancreas can be re-set by more extreme changes in dietary supply in early life. The extent to which these changes may contribute to more subtle modulations in glucose homeostasis that can accompany excess fat growth in childhood remains to be established. For human subjects there is much less information as to whether specific dietary components determine disease onset. Indeed, it is highly likely that genotype has a major influence, although recent data relating early diet to physical activity and the FTO gene indicate the difficulty of establishing the relative contribution of diet and changes in body mass to diabetes.

A bivariate whole genome linkage study identified genomic regions influencing both BMD and bone structure

Areal BMD (aBMD) and areal bone size (ABS) are biologically correlated traits and are each important determinants of bone strength and risk of fractures. Studies showed that aBMD and ABS are genetically correlated, indicating that they may share some common genetic factors, which, however, are largely unknown. To study the genetic factors influencing both aBMD and ABS, bivariate whole genome linkage analyses were conducted for aBMD-ABS at the femoral neck (FN), lumbar spine (LS), and ultradistal (UD)-forearm in a large sample of 451 white pedigrees made up of 4498 individuals. We detected significant linkage on chromosome Xq27 (LOD = 4.89) for LS aBMD-ABS. In addition, we detected suggestive linkages at 20q11 (LOD = 3.65) and Xp11 (LOD = 2.96) for FN aBMD-ABS; at 12p11 (LOD = 3.39) and 17q21 (LOD = 2.94) for LS aBMD-ABS; and at 5q23 (LOD = 3.54), 7p15 (LOD = 3.45), Xq27 (LOD = 2.93), and 12p11 (LOD = 2.92) for UD-forearm aBMD-ABS. Subsequent discrimination analyses indicated that quantitative trait loci (QTLs) at 12p11 and 17q21 may have pleiotropic effects on aBMD and ABS. This study identified several genomic regions that may contain QTLs important for both aBMD and ABS. Further endeavors are necessary to follow these regions to eventually pinpoint the genetic variants affecting bone strength and risk of fractures.

ENDEAVOUR update: a web resource for gene prioritization in multiple species

Endeavour (http://www.esat.kuleuven.be/endeavourweb; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes. Using a training set of genes known to be involved in a biological process of interest, our approach consists of (i) inferring several models (based on various genomic data sources), (ii) applying each model to the candidate genes to rank those candidates against the profile of the known genes and (iii) merging the several rankings into a global ranking of the candidate genes. In the present article, we describe the latest developments of Endeavour. First, we provide a web-based user interface, besides our Java client, to make Endeavour more universally accessible. Second, we support multiple species: in addition to Homo sapiens, we now provide gene prioritization for three major model organisms: Mus musculus, Rattus norvegicus and Caenorhabditis elegans. Third, Endeavour makes use of additional data sources and is now including numerous databases: ontologies and annotations, protein–protein interactions, cis-regulatory information, gene expression data sets, sequence information and text-mining data. We tested the novel version of Endeavour on 32 recent disease gene associations from the literature. Additionally, we describe a number of recent independent studies that made use of Endeavour to prioritize candidate genes for obesity and Type II diabetes, cleft lip and cleft palate, and pulmonary fibrosis.

Prediction of osteoporosis candidate genes by computational disease-gene identification strategy

Osteoporosis is a complex disease with a strong genetic component. To date, more than 20 genome-wide linkage scans across multiple populations have been launched to hunt for osteoporosis susceptibility genes. Some significant or suggestive chromosomal regions of linkage to bone mineral density have been identified and replicated in genome-wide linkage screens. However, identification of key candidate genes within these confirmed regions is challenging. We used five freely available bioinformatics tools (Prioritizer, GeneSeeker, PROSPECTR and SUSPECTS, Disease Gene Prediction, and Endeavor) to analyze the 13 well-replicated osteoporosis susceptibility loci: 1p36, 1q21-25, 2p22-24, 3p14-25, 4q25-34, 6p21, 7p14-21, 11q14-25, 12q23-24, 13q14-34, 20p12, 2q24-32, and 5q12-21. Pathways and regulatory network analyses were performed using the Ingenuity Pathways Analysis (IPA) software. We identified a subset of most likely candidate osteoporosis susceptibility genes that are largely involved in transforming growth factor (TGF)-beta signaling, granulocyte-macrophage colony-stimulating factor (GM-CSF) signaling, axonal guidance signaling, peroxisome proliferator-activated receptor (PPAR) signaling, and Wnt/beta-catenin signaling pathway. Six nonoverlapping networks were generated by IPA 5.0 from 88 out of the 91 candidate genes. The list of most likely candidate genes and the associated pathway identified will assist researchers in prioritizing candidate disease genes for further empirical analysis and understanding the pathogenesis of osteoporosis.

Lack of association of genetic variation in chromosome region 15q14-22.1 with type 2 diabetes in a Japanese population

Background

Chromosome 15q14-22.1 has been linked to type 2 diabetes (T2D) and its related traits in Japanese and other populations. The presence of T2D disease susceptibility variant(s) was assessed in the 21.8 Mb region between D15S118 and D15S117 in a Japanese population using a region-wide case-control association test.

Methods

A two-stage association test was performed using Japanese subjects: The discovery panel (Stage 1) used 372 cases and 360 controls, while an independent replication panel (Stage 2) used 532 cases and 530 controls. A total of 1,317 evenly-spaced, common SNP markers with minor allele frequencies > 0.10 were typed for each stage. Captured genetic variation was examined in HapMap JPT SNPs, and a haplotype-based association test was performed.

Results

SNP2140 (rs2412747) (C/T) in intron 33 of the ubiquitin protein ligase E3 component n-recognin 1 (UBR1) gene was selected as a landmark SNP based on repeated significant associations in Stage 1 and Stage 2. However, the marginal p value (p = 0.0043 in the allelic test, OR = 1.26, 95% CI = 1.07–1.48 for combined samples) was weak in a single locus or haplotype-based association test. We failed to find any significant SNPs after correcting for multiple testing.

Conclusion

The two-stage association test did not reveal a strong association between T2D and any common variants on chromosome 15q14-22.1 in 1,794 Japanese subjects. A further association test with a larger sample size and denser SNP markers is required to confirm these observations.

Identification of type 2 diabetes genes in Mexican Americans through genome-wide association studies

OBJECTIVE

The objective of this study was to identify DNA polymorphisms associated with type 2 diabetes in a Mexican-American population.

RESEARCH DESIGN AND METHODS

We genotyped 116,204 single nucleotide polymorphisms (SNPs) in 281 Mexican Americans with type 2 diabetes and 280 random Mexican Americans from Starr County, Texas, using the Affymetrix GeneChip Human Mapping 100K set. Allelic association exact tests were calculated. Our most significant SNPs were compared with results from other type 2 diabetes genome-wide association studies (GWASs). Proportions of African, European, and Asian ancestry were estimated from the HapMap samples using structure for each individual to rule out spurious association due to population substructure.

RESULTS

We observed more significant allelic associations than expected genome wide, as empirically assessed by permutation (14 below a P of 1 x 10(-4) [8.7 expected]). No significant differences were observed between the proportion of ancestry estimates in the case and random control sets, suggesting that the association results were not likely confounded by substructure. A query of our top approximately 1% of SNPs (P < 0.01) revealed SNPs in or near four genes that showed evidence for association (P < 0.05) in multiple other GWAS interrogated: rs979752 and rs10500641 near UBQLNL and OR52H1 on chromosome 11, rs2773080 and rs3922812 in or near RALGPS2 on chromosome 1, and rs1509957 near EGR2 on chromosome 10.

CONCLUSIONS

We identified several SNPs with suggestive evidence for replicated association with type 2 diabetes that merit further investigation.

Array comparative genomic hybridization and computational genome annotation in constitutional cytogenetics: suggesting candidate genes for novel submicroscopic chromosomal imbalance syndromes

Genome-wide array comparative genomic hybridization screening is uncovering pathogenic submicroscopic chromosomal imbalances in patients with developmental disorders. In those patients, imbalances appear now to be scattered across the whole genome, and most patients carry different chromosomal anomalies. Screening patients with developmental disorders can be considered a forward functional genome screen. The imbalances pinpoint the location of genes that are involved in human development. Because most imbalances encompass regions harboring multiple genes, the challenge is to (1) identify those genes responsible for the specific phenotype and (2) disentangle the role of the different genes located in an imbalanced region. In this review, we discuss novel tools and relevant databases that have recently been developed to aid this gene discovery process. Identification of the functional relevance of genes will not only deepen our understanding of human development but will, in addition, aid in the data interpretation and improve genetic counseling.