The molecular genetics of type 1 diabetes: new genes and emerging mechanisms

Susceptibility to type 1 diabetes (T1D) is determined by complex interactions between several genetic loci and environmental factors. Alleles at the human leukocyte antigen (HLA) locus explain up to 50% of the familial clustering of T1D, and the remainder is contributed to by multiple loci, of which only four were known until recently. First-stage results of genome-wide association (GWA) studies performed with high-density genotyping arrays have already produced four novel loci and the promise that, with the completion of the second stage of the GWA studies, most of the genetic basis of T1D will be known. We will review what is known to date about the mechanisms of genetic susceptibility to T1D, with special emphasis on possible diagnostic and therapeutic applications of these recent genetic findings.     

Snat: a SNP annotation tool for bovine by integrating various sources of genomic information

Background

Genome-wide association (GWA) study has recently become a powerful approach for detecting genetic variants for common diseases without prior knowledge of the variant's location or function. Generally, in GWA studies, the most significant single-nucleotide polymorphisms (SNPs) associated with top-ranked p values are selected in stage one, with follow-up in stage two. The value of selecting SNPs based on statistically significant p values is obvious. However, when minor allele frequencies (MAFs) are relatively low, less-significant p values can still correspond to higher odds ratios (ORs), which might be more useful for prediction of disease status. Therefore, if SNPs are selected using an approach based only on significant p values, some important genetic variants might be missed. We proposed a hybrid approach for selecting candidate SNPs from the discovery stage of GWA study, based on both p values and ORs, and conducted a simulation study to demonstrate the performance of our approach.

Results

The simulation results showed that our hybrid ranking approach was more powerful than the existing ranked p value approach for identifying relatively less-common SNPs. Meanwhile, the type I error probabilities of the hybrid approach is well-controlled at the end of the second stage of the two-stage GWA study.

Conclusions

In GWA studies, SNPs should be considered for inclusion based not only on ranked p values but also on ranked ORs.     

Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants

Background

Obesity is a major health problem. Although heritability is substantial, genetic mechanisms predisposing to obesity are not very well understood. We have performed a genome wide association study (GWA) for early onset (extreme) obesity.

Methodology/Principal Findings

a) GWA (Genome-Wide Human SNP Array 5.0 comprising 440,794 single nucleotide polymorphisms) for early onset extreme obesity based on 487 extremely obese young German individuals and 442 healthy lean German controls; b) confirmatory analyses on 644 independent families with at least one obese offspring and both parents. We aimed to identify and subsequently confirm the 15 SNPs (minor allele frequency ≥10%) with the lowest p-values of the GWA by four genetic models: additive, recessive, dominant and allelic. Six single nucleotide polymorphisms (SNPs) in FTO (fat mass and obesity associated gene) within one linkage disequilibrium (LD) block including the GWA SNP rendering the lowest p-value (rs1121980; log-additive model: nominal p = 1.13×10⁻⁷, corrected p = 0.0494; odds ratio (OR)_CT 1.67, 95% confidence interval (CI) 1.22–2.27; OR_TT 2.76, 95% CI 1.88–4.03) belonged to the 15 SNPs showing the strongest evidence for association with obesity. For confirmation we genotyped 11 of these in the 644 independent families (of the six FTO SNPs we chose only two representing the LD bock). For both FTO SNPs the initial association was confirmed (both Bonferroni corrected p<0.01). However, none of the nine non-FTO SNPs revealed significant transmission disequilibrium.

Conclusions/Significance

Our GWA for extreme early onset obesity substantiates that variation in FTO strongly contributes to early onset obesity. This is a further proof of concept for GWA to detect genes relevant for highly complex phenotypes. We concurrently show that nine additional SNPs with initially low p-values in the GWA were not confirmed in our family study, thus suggesting that of the best 15 SNPs in the GWA only the FTO SNPs represent true positive findings.     

C911: A Bench-Level Control for Sequence Specific siRNA Off-Target Effects

Small interfering RNAs (siRNAs) have become a ubiquitous experimental tool for down-regulating mRNAs. Unfortunately, off-target effects are a significant source of false positives in siRNA experiments and an effective control for them has not previously been identified. We introduce two methods of mismatched siRNA design for negative controls based on changing bases in the middle of the siRNA to their complement bases. To test these controls, a test set of 20 highly active siRNAs (10 true positives and 10 false positives) was identified from a genome-wide screen performed in a cell-line expressing a simple, constitutively expressed luciferase reporter. Three controls were then synthesized for each of these 20 siRNAs, the first two using the proposed mismatch design methods and the third being a simple random permutation of the sequence (scrambled siRNA). When tested in the original assay, the scrambled siRNAs showed significantly reduced activity in comparison to the original siRNAs, regardless of whether they had been identified as true or false positives, indicating that they have little utility as experimental controls. In contrast, one of the proposed mismatch design methods, dubbed C911 because bases 9 through 11 of the siRNA are replaced with their complement, was able to completely distinguish between the two groups. False positives due to off-target effects maintained most of their activity when the C911 mismatch control was tested, whereas true positives whose phenotype was due to on-target effects lost most or all of their activity when the C911 mismatch was tested. The ability of control siRNAs to distinguish between true and false positives, if widely adopted, could reduce erroneous results being reported in the literature and save research dollars spent on expensive follow-up experiments.     

Genome-wide association analysis reveals putative Alzheimer's disease susceptibility loci in addition to APOE

Alzheimer's disease (AD) is a genetically complex and heterogeneous disorder. To date four genes have been established to either cause early-onset autosomal-dominant AD (APP, PSEN1, and PSEN2(1-4)) or to increase susceptibility for late-onset AD (APOE5). However, the heritability of late-onset AD is as high as 80%, (6) and much of the phenotypic variance remains unexplained to date. We performed a genome-wide association (GWA) analysis using 484,522 single-nucleotide polymorphisms (SNPs) on a large (1,376 samples from 410 families) sample of AD families of self-reported European descent. We identified five SNPs showing either significant or marginally significant genome-wide association with a multivariate phenotype combining affection status and onset age. One of these signals (p = 5.7 x 10(-14)) was elicited by SNP rs4420638 and probably reflects APOE-epsilon4, which maps 11 kb proximal (r2 = 0.78). The other four signals were tested in three additional independent AD family samples composed of nearly 2700 individuals from almost 900 families. Two of these SNPs showed significant association in the replication samples (combined p values 0.007 and 0.00002). The SNP (rs11159647, on chromosome 14q31) with the strongest association signal also showed evidence of association with the same allele in GWA data generated in an independent sample of approximately 1,400 AD cases and controls (p = 0.04). Although the precise identity of the underlying locus(i) remains elusive, our study provides compelling evidence for the existence of at least one previously undescribed AD gene that, like APOE-epsilon4, primarily acts as a modifier of onset age.     

Common Inherited Variation in Mitochondrial Genes Is Not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits

Mitochondrial dysfunction has been observed in skeletal muscle of people with diabetes and insulin-resistant individuals. Furthermore, inherited mutations in mitochondrial DNA can cause a rare form of diabetes. However, it is unclear whether mitochondrial dysfunction is a primary cause of the common form of diabetes. To date, common genetic variants robustly associated with type 2 diabetes (T2D) are not known to affect mitochondrial function. One possibility is that multiple mitochondrial genes contain modest genetic effects that collectively influence T2D risk. To test this hypothesis we developed a method named Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA; http://www.broadinstitute.org/mpg/magenta). MAGENTA, in analogy to Gene Set Enrichment Analysis, tests whether sets of functionally related genes are enriched for associations with a polygenic disease or trait. MAGENTA was specifically designed to exploit the statistical power of large genome-wide association (GWA) study meta-analyses whose individual genotypes are not available. This is achieved by combining variant association p-values into gene scores and then correcting for confounders, such as gene size, variant number, and linkage disequilibrium properties. Using simulations, we determined the range of parameters for which MAGENTA can detect associations likely missed by single-marker analysis. We verified MAGENTA''s performance on empirical data by identifying known relevant pathways in lipid and lipoprotein GWA meta-analyses. We then tested our mitochondrial hypothesis by applying MAGENTA to three gene sets: nuclear regulators of mitochondrial genes, oxidative phosphorylation genes, and ∼1,000 nuclear-encoded mitochondrial genes. The analysis was performed using the most recent T2D GWA meta-analysis of 47,117 people and meta-analyses of seven diabetes-related glycemic traits (up to 46,186 non-diabetic individuals). This well-powered analysis found no significant enrichment of associations to T2D or any of the glycemic traits in any of the gene sets tested. These results suggest that common variants affecting nuclear-encoded mitochondrial genes have at most a small genetic contribution to T2D susceptibility.     

Local Phylogeny Mapping of Quantitative Traits: Higher Accuracy and Better Ranking Than Single-Marker Association in Genomewide Scans

We present a new method, termed QBlossoc, for linkage disequilibrium (LD) mapping of genetic variants underlying a quantitative trait. The method uses principles similar to a previously published method, Blossoc, for LD mapping of case/control studies. The method builds local genealogies along the genome and looks for a significant clustering of quantitative trait values in these trees. We analyze its efficiency in terms of localization and ranking of true positives among a large number of negatives and compare the results with single-marker approaches. Simulation results of markers at densities comparable to contemporary genotype chips show that QBlossoc is more accurate in localization of true positives as expected since it uses the additional information of LD between markers simultaneously. More importantly, however, for genomewide surveys, QBlossoc places regions with true positives higher on a ranked list than single-marker approaches, again suggesting that a true signal displays itself more strongly in a set of adjacent markers than a spurious (false) signal. The method is both memory and central processing unit (CPU) efficient. It has been tested on a real data set of height data for 5000 individuals measured at ~317,000 markers and completed analysis within 5 CPU days.     

Genome-wide searching of rare genetic variants in WTCCC data

Although they have demonstrated success in searching for common variants for complex diseases, genome-wide association (GWA) studies are less successful in detecting rare genetic variants because of the poor statistical power of most of current methods. We developed a two-stage method that can apply to GWA studies for detecting rare variants. Here we report the results of applying this two-stage method to the Wellcome Trust Case Control Consortium (WTCCC) dataset that include seven complex diseases: bipolar disorder, cardiovascular disease, hypertension (HT), rheumatoid arthritis, Crohn’s disease, type 1 diabetes and type 2 diabetes (T2D). We identified 24 genes or regions that reach genome wide significance. Eight of them are novel and were not reported in the WTCCC study. The cumulative risk (or protective) haplotype frequency for each of the 8 genes or regions is small, being at most 11%. For each of the novel genes, the risk (or protective) haplotype set cannot be tagged by the common SNPs available in chips (r ² < 0.32). The gene identified in HT was further replicated in the Framingham Heart Study, and is also significantly associated with T2D. Our analysis suggests that searching for rare genetic variants is feasible in current GWA studies and candidate gene studies, and the results can severe as guides to future resequencing studies to identify the underlying rare functional variants.     

Pathway-based analysis of a melanoma genome-wide association study: analysis of genes related to tumour-immunosuppression

Systemic immunosuppression is a risk factor for melanoma, and sunburn-induced immunosuppression is thought to be causal. Genes in immunosuppression pathways are therefore candidate melanoma-susceptibility genes. If variants within these genes individually have a small effect on disease risk, the association may be undetected in genome-wide association (GWA) studies due to low power to reach a high significance level. Pathway-based approaches have been suggested as a method of incorporating a priori knowledge into the analysis of GWA studies. In this study, the association of 1113 single nucleotide polymorphisms (SNPs) in 43 genes (39 genomic regions) related to immunosuppression have been analysed using a gene-set approach in 1539 melanoma cases and 3917 controls from the GenoMEL consortium GWA study. The association between melanoma susceptibility and the whole set of tumour-immunosuppression genes, and also predefined functional subgroups of genes, was considered. The analysis was based on a measure formed by summing the evidence from the most significant SNP in each gene, and significance was evaluated empirically by case-control label permutation. An association was found between melanoma and the complete set of genes (p(emp)=0.002), as well as the subgroups related to the generation of tolerogenic dendritic cells (p(emp)=0.006) and secretion of suppressive factors (p(emp)=0.0004), thus providing preliminary evidence of involvement of tumour-immunosuppression gene polymorphisms in melanoma susceptibility. The analysis was repeated on a second phase of the GenoMEL study, which showed no evidence of an association. As one of the first attempts to replicate a pathway-level association, our results suggest that low power and heterogeneity may present challenges.     

Common Genetic Variants of Surfactant Protein-D (SP-D) Are Associated with Type 2 Diabetes

Context

Surfactant protein-D (SP-D) is a primordial component of the innate immune system intrinsically linked to metabolic pathways. We aimed to study the association of single nucleotide polymorphisms (SNPs) affecting SP-D with insulin resistance and type 2 diabetes (T2D).

Research Design and Methods

We evaluated a common genetic variant located in the SP-D coding region (rs721917, Met³¹Thr) in a sample of T2D patients and non-diabetic controls (n = 2,711). In a subset of subjects (n = 1,062), this SNP was analyzed in association with circulating SP-D concentrations, insulin resistance, and T2D. This SNP and others were also screened in the publicly available Genome Wide Association (GWA) database of the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC).

Results

We found the significant association of rs721917 with circulating SP-D, parameters of insulin resistance and T2D. Indeed, G carriers showed decreased circulating SP-D (p = 0.004), decreased fasting glucose (p = 0.0002), glycated hemoglobin (p = 0.0005), and 33% (p = 0.002) lower prevalence of T2D, estimated under a dominant model, especially among women. Interestingly, these differences remained significant after controlling for origin, age, gender, and circulating SP-D. Moreover, this SNP and others within the SP-D genomic region (i.e. rs10887344) were significantly associated with quantitative measures of glucose homeostasis, insulin sensitivity, and T2D, according to GWAS datasets from MAGIC.

Conclusions

SP-D gene polymorphisms are associated with insulin resistance and T2D. These associations are independent of circulating SP-D concentrations.     

The Null Distributions of Test Statistics in Genomewide Association Studies

Genomewide association (GWA) studies assay hundreds of thousands of single nucleotide polymorphisms (SNPs) simultaneously across the entire genome and associate them with diseases, other biological or clinical traits. The association analysis usually tests each SNP as an independent entity and ignores the biological information such as linkage disequilibrium. Although the Bonferroni correction and other approaches have been proposed to address the issue of multiple comparisons as a result of testing many SNPs, there is a lack of understanding of the distribution of an association test statistic when an entire genome is considered together. In other words, there are extensive efforts in hypothesis testing, and almost no attempt in estimating the density under the null hypothesis. By estimating the true null distribution, we can apply the result directly to hypothesis testing; better assess the existing approaches of multiple comparisons; and evaluate the impact of linkage disequilibrium on the GWA studies. To this end, we estimate the empirical null distribution of an association test statistic in GWA studies using simulated population data. We further propose a convenient and accurate method based on adaptive spline to estimate the empirical value in GWA studies and validate our findings using a real data set. Our method enables us to fully characterize the null distribution of an association test that not only can be used to test the null hypothesis of no association, but also provides important information about the impact of density of the genetic markers on the significance of the tests. Our method does not require users to perform computationally intensive permutations, and hence provides a timely solution to an important and difficult problem in GWA studies.     

BlackOPs: increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequencing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical artifact results from incorrectly aligning experimentally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We developed BlackOPs, an open-source tool that simulates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklists contain thousands of artifact variants that are indistinguishable from true variants and, for a given sample, are expected to be almost completely false positives. We show that these blacklist positions are specific to the alignment algorithm and read length used, and BlackOPs allows users to generate a blacklist specific to their experimental setup. We queried the dbSNP and COSMIC variant databases and found numerous variants indistinguishable from mapping errors. We demonstrate how filtering against blacklist positions reduces the number of potential false variants using an RNA-seq glioblastoma cell line data set. In summary, accounting for mapping-caused variants tuned to experimental setups reduces false positives and, therefore, improves genome characterization by high-throughput sequencing.     

The 18p11.22 locus is associated with never smoker non-small cell lung cancer susceptibility in Korean populations

The proportion of never smoker non-small cell lung cancer (NSCLC) in Asia is about 30-40%. Despite the striking demographics and high prevalence of never smoker NSCLC, the exact causes still remain undetermined. Although several genome wide association (GWA) studies were conducted to find susceptibility loci for lung cancer in never smokers, no regions were replicated except for 5p15.33, suggesting locus heterogeneity and different environmental toxic effects. To identify genetic loci associated with susceptibility of lung cancer in never smokers, we performed a GWA analysis using the Affymetrix 6.0 SNP array. For discovery GWA set, we recruited 446 never smoking Korean patients with NSCLC and 497 normal subjects. We tested association of SNPs with lung cancer susceptibility using the Cochran-Armitage trend test. For validation, 39 SNPs were selected from the top 50 SNPs and five additional SNPs were selected in the DAB1 gene region which showed significant associations in the GWA analysis. The validation SNPs were genotyped in an independent sample including 434 patients and 1,000 controls. Among the 44 validation SNPs, two SNPs (rs11080466 and rs11663246) near the APCDD1, NAPG and FAM38B genes in the 18p11.22 region were replicated. P value of rs11080466 was 1.08 × 10(-6) in the combined sets (2.68 × 10(-5) in the discovery set and 2.60 × 10(-3) in the validation set) and odds ratio was 0.68 (0.58-0.79). We observed similar association for rs11663246. Our result suggests the 18p11.22 region as a novel lung cancer susceptibility locus in never smokers.     

Extreme Evolutionary Disparities Seen in Positive Selection across Seven Complex Diseases

Positive selection is known to occur when the environment that an organism inhabits is suddenly altered, as is the case across recent human history. Genome-wide association studies (GWASs) have successfully illuminated disease-associated variation. However, whether human evolution is heading towards or away from disease susceptibility in general remains an open question. The genetic-basis of common complex disease may partially be caused by positive selection events, which simultaneously increased fitness and susceptibility to disease. We analyze seven diseases studied by the Wellcome Trust Case Control Consortium to compare evidence for selection at every locus associated with disease. We take a large set of the most strongly associated SNPs in each GWA study in order to capture more hidden associations at the cost of introducing false positives into our analysis. We then search for signs of positive selection in this inclusive set of SNPs. There are striking differences between the seven studied diseases. We find alleles increasing susceptibility to Type 1 Diabetes (T1D), Rheumatoid Arthritis (RA), and Crohn''s Disease (CD) underwent recent positive selection. There is more selection in alleles increasing, rather than decreasing, susceptibility to T1D. In the 80 SNPs most associated with T1D (p-value <7.01×10⁻⁵) showing strong signs of positive selection, 58 alleles associated with disease susceptibility show signs of positive selection, while only 22 associated with disease protection show signs of positive selection. Alleles increasing susceptibility to RA are under selection as well. In contrast, selection in SNPs associated with CD favors protective alleles. These results inform the current understanding of disease etiology, shed light on potential benefits associated with the genetic-basis of disease, and aid in the efforts to identify causal genetic factors underlying complex disease.     

Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements

PSI-BLAST is an iterative program to search a database for proteins with distant similarity to a query sequence. We investigated over a dozen modifications to the methods used in PSI-BLAST, with the goal of improving accuracy in finding true positive matches. To evaluate performance we used a set of 103 queries for which the true positives in yeast had been annotated by human experts, and a popular measure of retrieval accuracy (ROC) that can be normalized to take on values between 0 (worst) and 1 (best). The modifications we consider novel improve the ROC score from 0.758 ± 0.005 to 0.895 ± 0.003. This does not include the benefits from four modifications we included in the ‘baseline’ version, even though they were not implemented in PSI-BLAST version 2.0. The improvement in accuracy was confirmed on a small second test set. This test involved analyzing three protein families with curated lists of true positives from the non-redundant protein database. The modification that accounts for the majority of the improvement is the use, for each database sequence, of a position-specific scoring system tuned to that sequence’s amino acid composition. The use of composition-based statistics is particularly beneficial for large-scale automated applications of PSI-BLAST.     

High Intensity Interval Training Improves Glycaemic Control and Pancreatic β Cell Function of Type 2 Diabetes Patients

Physical activity improves the regulation of glucose homeostasis in both type 2 diabetes (T2D) patients and healthy individuals, but the effect on pancreatic β cell function is unknown. We investigated glycaemic control, pancreatic function and total fat mass before and after 8 weeks of low volume high intensity interval training (HIIT) on cycle ergometer in T2D patients and matched healthy control individuals. Study design/method: Elderly (56 yrs±2), non-active T2D patients (n = 10) and matched (52 yrs±2) healthy controls (CON) (n = 13) exercised 3 times (10×60 sec. HIIT) a week over an 8 week period on a cycle ergometer. Participants underwent a 2-hour oral glucose tolerance test (OGTT). On a separate day, resting blood pressure measurement was conducted followed by an incremental maximal oxygen uptake (V˙O_2max) cycle ergometer test. Finally, a whole body dual X-ray absorptiometry (DXA) was performed. After 8 weeks of training, the same measurements were performed. Results: in the T2D-group, glycaemic control as determined by average fasting venous glucose concentration (p = 0.01), end point 2-hour OGTT (p = 0.04) and glycosylated haemoglobin (p = 0.04) were significantly reduced. Pancreatic homeostasis as determined by homeostatic model assessment of insulin resistance (HOMA-IR) and HOMA β cell function (HOMA-%β) were both significantly ameliorated (p = 0.03 and p = 0.03, respectively). Whole body insulin sensitivity as determined by the disposition index (DI) was significantly increased (p = 0.03). During OGTT, the glucose continuum was significantly reduced at -15 (p = 0.03), 30 (p = 0.03) and 120 min (p = 0.03) and at -10 (p = 0.003) and 0 min (p = 0.003) with an additional improvement (p = 0.03) of its 1^st phase (30 min) area under curve (AUC). Significant abdominal fat mass losses were seen in both groups (T2D: p = 0.004 and CON: p = 0.02) corresponding to a percentage change of -17.84%±5.02 and -9.66%±3.07, respectively. Conclusion: these results demonstrate that HIIT improves overall glycaemic control and pancreatic β cell function in T2D patients. Additionally, both groups experienced abdominal fat mass losses. These findings demonstrate that HIIT is a health beneficial exercise strategy in T2D patients.

Trial Registration

ClinicalTrials.gov NCT02333734 http://clinicaltrials.gov/ct2/show/NCT02333734     

Development of an Umami Taste Sensitivity Test and Its Clinical Use

There is a close relationship between perception of umami, which has become recognized as the fifth taste, and the human physical condition. We have developed a clinical test for umami taste sensitivity using a filter paper disc with a range of six monosodium glutamate (MSG) concentrations. We recruited 28 patients with taste disorders (45–78 years) and 184 controls with no taste disorders (102 young [18–25 years] and 82 older [65–89 years] participants). Filter paper discs (5 mm dia.) were soaked in aqueous MSG solutions (1, 5, 10, 50, 100 and 200 mM), then placed on three oral sites innervated by different taste nerves. The lowest concentration participants correctly identified was defined as the recognition threshold (RT) for MSG. This test showed good reproducibility for inter- and intra-observer variability. We concluded that: (1) The RT of healthy controls differed at measurement sites innervated by different taste nerves; that is, the RT of the anterior tongue was higher than that of either the posterior tongue or the soft palate in both young and older individuals. (2) No significant difference in RT was found between young adults and older individuals at any measurement site. (3) The RT of patients with taste disorders was higher before treatment than that of the healthy controls at any measurement site. (4) The RT after treatment in these patients improved to the same level as that of the healthy controls. (5) The cutoff values of RT, showing the highest diagnostic accuracy (true positives + true negatives), were 200 mM MSG for AT and 50 mM MSG for PT and SP. The diagnostic accuracy at these cutoff values was 0.92, 0.87 and 0.86 for AT, PT and SP, respectively. Consequently, this umami taste sensitivity test is useful for discriminating between normal and abnormal umami taste sensations.     

What are the baselines for protein fold recognition?

MOTIVATION: What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein-ranging from the length of the sequence to a knowledge of its secondary structure-to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications? EXPERIMENTS PERFORMED: A number of rapid automatic methods which score similarities between protein domains were devised and tested. These methods ranged from those that incorporated no secondary structure information, such as measuring absolute differences in sequence lengths, to more complex alignments of secondary structure elements. Each method was assessed for accuracy by comparison with the Class Architecture Topology Homology (CATH) classification. Methods were rated against both a random baseline fold assignment method as a lower control and FSSP as an upper control. Similarity trees were constructed in order to evaluate the accuracy of optimum methods at producing a classification of structure. RESULTS: Using a rigorous comparison of methods with CATH, the random fold assignment method set a lower baseline of 11% true positives allowing for 3% false positives and FSSP set an upper benchmark of 47% true positives at 3% false positives. The optimum secondary structure alignment method used here achieved 27% true positives at 3% false positives. Using a less rigorous Critical Assessment of Structure Prediction (CASP)-like sensitivity measurement the random assignment achieved 6%, FSSP-59% and the optimum secondary structure alignment method-32%. Similarity trees produced by the optimum method illustrate that these methods cannot be used alone to produce a viable protein structural classification system. CONCLUSIONS: Simple methods that use perfect secondary structure information to assign folds cannot produce an accurate protein taxonomy, however they do provide useful baselines for fold recognition. In terms of a typical CASP assessment our results suggest that approximately 6% of targets with folds in the databases could be assigned correctly by randomly guessing, and as many as 32% could be recognised by trivial secondary structure comparison methods, given knowledge of their correct secondary structures.     

Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value

Background

Recently, several Genome Wide Association (GWA) studies in populations of European descent have identified and validated novel single nucleotide polymorphisms (SNPs), highly associated with type 2 diabetes (T2D). Our aims were to validate these markers in other European and non-European populations, then to assess their combined effect in a large French study comparing T2D and normal glucose tolerant (NGT) individuals.

Methodology/Principal Findings

In the same French population analyzed in our previous GWA study (3,295 T2D and 3,595 NGT), strong associations with T2D were found for CDKAL1 (OR_rs7756992 = 1.30[1.19–1.42], P = 2.3×10⁻⁹), CDKN2A/2B (OR_rs10811661 = 0.74[0.66–0.82], P = 3.5×10⁻⁸) and more modestly for IGFBP2 (OR_rs1470579 = 1.17[1.07–1.27], P = 0.0003) SNPs. These results were replicated in both Israeli Ashkenazi (577 T2D and 552 NGT) and Austrian (504 T2D and 753 NGT) populations (except for CDKAL1) but not in the Moroccan population (521 T2D and 423 NGT). In the overall group of French subjects (4,232 T2D and 4,595 NGT), IGFBP2 and CXCR4 synergistically interacted with (LOC38776, SLC30A8, HHEX) and (NGN3, CDKN2A/2B), respectively, encoding for proteins presumably regulating pancreatic endocrine cell development and function. The T2D risk increased strongly when risk alleles, including the previously discovered T2D-associated TCF7L2 rs7903146 SNP, were combined (8.68-fold for the 14% of French individuals carrying 18 to 30 risk alleles with an allelic OR of 1.24). With an area under the ROC curve of 0.86, only 15 novel loci were necessary to discriminate French individuals susceptible to develop T2D.

Conclusions/Significance

In addition to TCF7L2, SLC30A8 and HHEX, initially identified by the French GWA scan, CDKAL1, IGFBP2 and CDKN2A/2B strongly associate with T2D in French individuals, and mostly in populations of Central European descent but not in Moroccan subjects. Genes expressed in the pancreas interact together and their combined effect dramatically increases the risk for T2D, opening avenues for the development of genetic prediction tests.