Prediction and interaction in complex disease genetics: experience in type 1 diabetes

A Genome-Wide Search for Gene-Environment Effects in Isolated Cleft Lip with or without Cleft Palate Triads Points to an Interaction between Maternal Periconceptional Vitamin Use and Variants in ESRRG

Background: It is widely accepted that cleft lip with or without cleft palate (CL/P) results from the complex interplay between multiple genetic and environmental factors. However, a robust investigation of these gene-environment (GxE) interactions at a genome-wide level is still lacking for isolated CL/P. Materials and Methods: We used our R-package Haplin to perform a genome-wide search for GxE effects in isolated CL/P. From a previously published GWAS, genotypes and information on maternal periconceptional cigarette smoking, alcohol intake, and vitamin use were available on 1908 isolated CL/P triads of predominantly European or Asian ancestry. A GxE effect is present if the relative risk estimates for gene-effects in the offspring are different across exposure strata. We tested this using the relative risk ratio (RRR). Besides analyzing all ethnicities combined ("pooled analysis"), separate analyses were conducted on Europeans and Asians to investigate ethnicity-specific effects. To control for multiple testing, q-values were calculated from the p-values. Results: We identified significant GxVitamin interactions with three SNPs in "Estrogen-related receptor gamma" (ESRRG) in the pooled analysis. The RRRs (95% confidence intervals) were 0.56 (0.45-0.69) with rs1339221 (q = 0.011), 0.57 (0.46-0.70) with rs11117745 (q = 0.011), and 0.62 (0.50-0.76) with rs2099557 (q = 0.037). The associations were stronger when these SNPs were analyzed as haplotypes composed of two-SNP and three-SNP combinations. The strongest effect was with the "t-t-t" haplotype of the rs1339221-rs11117745-rs2099557 combination [RRR = 0.50 (0.40-0.64)], suggesting that the effects observed with the other SNP combinations, including those in the single-SNP analyses, were mainly driven by this haplotype. Although there were potential GxVitamin effects with rs17734557 and rs1316471 and GxAlcohol effects with rs9653456 and rs921876 in the European sample, respectively, none of the SNPs was located in or near genes with strong links to orofacial clefts. GxAlcohol and GxSmoke effects were not assessed in the Asian sample because of a lack of observations for these exposures. Discussion/Conclusion: We identified significant interactions between vitamin use and variants in ESRRG in the pooled analysis. These GxE effects are novel and warrant further investigations to elucidate their roles in orofacial clefting. If validated, they could provide prospects for exploring the impact of estrogens and vitamins on clefting, with potential translational applications.

Genetic architecture: the shape of the genetic contribution to human traits and disease

Genetic architecture describes the characteristics of genetic variation that are responsible for heritable phenotypic variability. It depends on the number of genetic variants affecting a trait, their frequencies in the population, the magnitude of their effects and their interactions with each other and the environment. Defining the genetic architecture of a complex trait or disease is central to the scientific and clinical goals of human genetics, which are to understand disease aetiology and aid in disease screening, diagnosis, prognosis and therapy. Recent technological advances have enabled genome-wide association studies and emerging next-generation sequencing studies to begin to decipher the nature of the heritable contribution to traits and disease. Here, we describe the types of genetic architecture that have been observed, how architecture can be measured and why an improved understanding of genetic architecture is central to future advances in the field.

Predictive accuracy of combined genetic and environmental risk scores

The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.

Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank

BACKGROUND

Type 1 diabetes is typically considered a disease of children and young adults. Genetic susceptibility to young-onset type 1 diabetes is well defined and does not predispose to type 2 diabetes. It is not known how frequently genetic susceptibility to type 1 diabetes leads to a diagnosis of diabetes after age 30 years. We aimed to investigate the frequency and phenotype of type 1 diabetes resulting from high genetic susceptibility in the first six decades of life.

METHODS

In this cross-sectional analysis, we used a type 1 diabetes genetic risk score based on 29 common variants to identify individuals of white European descent in UK Biobank in the half of the population with high or low genetic susceptibility to type 1 diabetes. We used Kaplan-Meier analysis to evaluate the number of cases of diabetes in both groups in the first six decades of life. We genetically defined type 1 diabetes as the additional cases of diabetes that occurred in the high genetic susceptibility group compared with the low genetic susceptibility group. All remaining cases were defined as type 2 diabetes. We assessed the clinical characteristics of the groups with genetically defined type 1 or type 2 diabetes.

FINDINGS

13 250 (3·5%) of 379 511 white European individuals in UK Biobank had developed diabetes in the first six decades of life. 1286 more cases of diabetes were in the half of the population with high genetic susceptibility to type 1 diabetes than in the half of the population with low genetic susceptibility. These genetically defined cases of type 1 diabetes were distributed across all ages of diagnosis; 537 (42%) were in individuals diagnosed when aged 31-60 years, representing 4% (537/12 233) of all diabetes cases diagnosed after age 30 years. The clinical characteristics of the group diagnosed with type 1 diabetes when aged 31-60 years were similar to the clinical characteristics of the group diagnosed with type 1 diabetes when aged 30 years or younger. For individuals diagnosed with diabetes when aged 31-60 years, the clinical characteristics of type 1 diabetes differed from those of type 2 diabetes: they had a lower BMI (27·4 kg/m2 [95% CI 26·7-28·0] vs 32·4 kg/m2 [32·2-32·5]; p<0·0001), were more likely to use insulin in the first year after diagnosis (89% [476/537] vs 6% [648/11 696]; p<0·0001), and were more likely to have diabetic ketoacidosis (11% [61/537] vs 0·3% [30/11 696]; p<0·0001).

INTERPRETATION

Genetic susceptibility to type 1 diabetes results in non-obesity-related, insulin-dependent diabetes, which presents throughout the first six decades of life. Our results highlight the difficulty of identifying type 1 diabetes after age 30 years because of the increasing background prevalence of type 2 diabetes. Failure to diagnose late-onset type 1 diabetes can have serious consequences because these patients rapidly develop insulin dependency.

FUNDING

Wellcome Trust and Diabetes UK.

'25-Hydroxyvitamin D, Autoantigenic and Total Antibody Concentrations: Results from a Danish Case-control Study of Newly Diagnosed Patients with Childhood Type 1 Diabetes and their Healthy Siblings'

B cells have recently entered the stage as an important accessory player in type 1 diabetes (T1D) etiopathogenesis. Experimental studies suggest regulatory functions of vitamin D on B cells. However, only a few human studies, with considerable methodological limitations, have been conducted within this field. Our objective was to investigate whether higher 25-hydroxyvitamin D (25(OH)D) concentrations were inversely associated with β-cell autoantigens glutamic acid decarboxylase (isoform 65) (GADA) and insulinoma-associated antigen-2A (IA-2A). Further, we also wanted to examine the relationship between 25(OH)D and total antibody concentrations. We randomly selected 500 patients with newly diagnosed T1D and 500 siblings for 25(OH)D, antibody and genetic analysis from the population-based Danish Registry of Childhood and Adolescent Diabetes. The relative change (RC) in the mean concentration of GADA, IA-2A and antibody isotypes by a 10 nmol/l increase in 25(OH)D concentration was modelled by a robust log-normal regression model. We found no association between 25(OH)D and GADA [adjusted RC per 10 nmol/l increase: 1.00; 95% confidence interval (CI): 0.98-1.02] and IA-2A [adjusted RC per 10 nmol/l increase: 0.92; CI: 0.76-1.12]. Further, 25(OH)D was not associated with the total concentration of antibody isotypes [immunoglobulin (Ig)A, IgE, IgG and IgM]. All null findings were unaltered after adjustment for genetic variation in the vitamin D pathway. Physiological concentrations of 25(OH)D are unlikely to have a clinically important effect on antibody concentrations in a paediatric population of newly diagnosed patients with T1D and their healthy siblings.

Type 1 diabetes genome-wide association studies: not to be lost in translation

Genetic studies have identified >60 loci associated with the risk of developing type 1 diabetes (T1D). The vast majority of these are identified by genome-wide association studies (GWAS) using large case-control cohorts of European ancestry. More than 80% of the heritability of T1D can be explained by GWAS data in this population group. However, with few exceptions, their individual contribution to T1D risk is low and understanding their function in disease biology remains a huge challenge. GWAS on its own does not inform us in detail on disease mechanisms, but the combination of GWAS data with other omics-data is beginning to advance our understanding of T1D etiology and pathogenesis. Current knowledge supports the notion that genetic variation in both pancreatic β cells and in immune cells is central in mediating T1D risk. Advances, perspectives and limitations of GWAS are discussed in this review.

Levels of soluble TREM-1 in children with newly diagnosed type 1 diabetes and their siblings without type 1 diabetes: a Danish case-control study

BACKGROUND

Type 1 diabetes (T1D) is an organ-specific autoimmune disease with an increase in incidence worldwide including Denmark. The triggering receptor expressed on myeloid cells-1 (TREM-1) is a potent amplifier of pro-inflammatory responses and has been linked to autoimmunity, severe psychiatric disorders, sepsis, and cancer.

HYPOTHESIS

Our primary hypothesis was that levels of soluble TREM-1 (sTREM-1) differed between newly diagnosed children with T1D and their siblings without T1D.

METHODS

Since 1996, the Danish Childhood Diabetes Register has collected data on all patients who have developed T1D before the age of 18 years. Four hundred and eighty-one patients and 478 siblings with measurements of sTREM-1-blood samples were taken within 3 months after onset-were available for statistical analyses. Sample period was from 1997 through 2005. A robust log-normal regression model was used, which takes into account that measurements are left censored and accounts for correlation within siblings from the same family.

RESULTS

In the multiple regression model (case status, gender, age, HLA-risk, season, and period of sampling), levels of sTREM-1 were found to be significantly higher in patients (relative change [95%CI], 1.5 [1.1; 2.2],P = 0.02), but after adjustment for multiple testing our result was no longer statistically significant (P _adjust = 0.1). We observed a statistical significant temporal increase in levels of sTREM-1.

CONCLUSION

Our results need to be replicated by independent studies, but our study suggests that the TREM-1 pathway may have a role in T1D pathogenesis.

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes

AIM

It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies.

METHODS

Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D.

RESULTS

In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10^-92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime.

CONCLUSION

Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations.

Genetic Risk Scores for Type 1 Diabetes Prediction and Diagnosis

PURPOSE OF REVIEW

About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40 years towards differential diagnosis and risk assessment.

RECENT FINDINGS

T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.

Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data

Despite the many successes of genome-wide association studies (GWAS), the known susceptibility variants identified by GWAS have modest effect sizes, leading to notable skepticism about the effectiveness of building a risk prediction model from large-scale genetic data. However, in contrast to genetic variants, the family history of diseases has been largely accepted as an important risk factor in clinical diagnosis and risk prediction. Nevertheless, the complicated structures of the family history of diseases have limited their application in clinical practice. Here, we developed a new method that enables incorporation of the general family history of diseases with a liability threshold model, and propose a new analysis strategy for risk prediction with penalized regression analysis that incorporates both large numbers of genetic variants and clinical risk factors. Application of our model to type 2 diabetes in the Korean population (1846 cases and 1846 controls) demonstrated that single-nucleotide polymorphisms accounted for 32.5% of the variation explained by the predicted risk scores in the test data set, and incorporation of family history led to an additional 6.3% improvement in prediction. Our results illustrate that family medical history provides valuable information on the variation of complex diseases and improves prediction performance.

Genetics and its potential to improve type 1 diabetes care

PURPOSE OF REVIEW

The genetic basis of type 1 diabetes (T1D) is being characterized through DNA sequence variation and cell type specificity. This review discusses the current understanding of the genes and variants implicated in risk of T1D and how genetic information can be used in prediction, intervention and components of clinical care.

RECENT FINDINGS

Fine mapping and functional studies has provided resolution of the heritable basis of T1D risk, incorporating novel insights on the dominant role of human leukocyte antigen (HLA) genes as well as the lesser impact of non-HLA genes. Evaluation of T1D-associated single nucleotide polymorphisms (SNPs), there is enrichment of genetic effects restricted to specific immune cell types (CD4 and CD8 T cells, CD19 B cells and CD34 stem cells), suggesting pathways to improved prediction. In addition, T1D-associated SNPs have been used to generate genetic risk scores (GRS) as a tool to distinguish T1D from type 2 diabetes (T2D) and to provide prediagnostic data to target those for autoimmunity screening (e.g. islet autoantibodies) as a prelude for continuous monitoring and entry into intervention trials.

SUMMARY

Genetic susceptibility accounts for nearly one-half of the risk for T1D. Although the T1D-associated SNPs in white populations account for nearly 90% of the genetic risk, with high sensitivity and specificity, the low prevalence of T1D makes the T1D GRS of limited utility. However, identifying those with highest genetic risk may permit early and targeted immune monitoring to diagnose T1D months prior to clinical onset.

Further investigations of the W-test for pairwise epistasis testing

Background: In a recent paper, a novel W-test for pairwise epistasis testing was proposed that appeared, in computer simulations, to have higher power than competing alternatives. Application to genome-wide bipolar data detected significant epistasis between SNPs in genes of relevant biological function. Network analysis indicated that the implicated genes formed two separate interaction networks, each containing genes highly related to autism and neurodegenerative disorders.

Methods: Here we investigate further the properties and performance of the W-test via theoretical evaluation, computer simulations and application to real data.

Results: We demonstrate that, for common variants, the W-test is closely related to several existing tests of association allowing for interaction, including logistic regression on 8 degrees of freedom, although logistic regression can show inflated type I error for low minor allele frequencies,  whereas the W-test shows good/conservative type I error control. Although in some situations the W-test can show higher power, logistic regression is not limited to tests on 8 degrees of freedom but can instead be tailored to impose greater structure on the assumed alternative hypothesis, offering a power advantage when the imposed structure matches the true structure.

Conclusions: The W-test is a potentially useful method for testing for association - without necessarily implying interaction - between genetic variants disease, particularly when one or more of the genetic variants are rare. For common variants, the advantages of the W-test are less clear, and, indeed, there are situations where existing methods perform better. In our investigations, we further uncover a number of problems with the practical implementation and application of the W-test (to bipolar disorder) previously described, apparently due to inadequate use of standard data quality-control procedures. This observation leads us to urge caution in interpretation of the previously-presented results, most of which we consider are highly likely to be artefacts.

Genetics of primary sclerosing cholangitis and pathophysiological implications

Primary sclerosing cholangitis (PSC) is a chronic disease leading to fibrotic scarring of the intrahepatic and extrahepatic bile ducts, causing considerable morbidity and mortality via the development of cholestatic liver cirrhosis, concurrent IBD and a high risk of bile duct cancer. Expectations have been high that genetic studies would determine key factors in PSC pathogenesis to support the development of effective medical therapies. Through the application of genome-wide association studies, a large number of disease susceptibility genes have been identified. The overall genetic architecture of PSC shares features with both autoimmune diseases and IBD. Strong human leukocyte antigen gene associations, along with several susceptibility genes that are critically involved in T-cell function, support the involvement of adaptive immune responses in disease pathogenesis, and position PSC as an autoimmune disease. In this Review, we survey the developments that have led to these gene discoveries. We also elaborate relevant interpretations of individual gene findings in the context of established disease models in PSC, and propose relevant translational research efforts to pursue novel insights.

The impact of rare and low-frequency genetic variants in common disease

Despite thousands of genetic loci identified to date, a large proportion of genetic variation predisposing to complex disease and traits remains unaccounted for. Advances in sequencing technology enable focused explorations on the contribution of low-frequency and rare variants to human traits. Here we review experimental approaches and current knowledge on the contribution of these genetic variants in complex disease and discuss challenges and opportunities for personalised medicine.

Screening for interaction effects in gene expression data

Expression quantitative trait (eQTL) studies are a powerful tool for identifying genetic variants that affect levels of messenger RNA. Since gene expression is controlled by a complex network of gene-regulating factors, one way to identify these factors is to search for interaction effects between genetic variants and mRNA levels of transcription factors (TFs) and their respective target genes. However, identification of interaction effects in gene expression data pose a variety of methodological challenges, and it has become clear that such analyses should be conducted and interpreted with caution. Investigating the validity and interpretability of several interaction tests when screening for eQTL SNPs whose effect on the target gene expression is modified by the expression level of a transcription factor, we characterized two important methodological issues. First, we stress the scale-dependency of interaction effects and highlight that commonly applied transformation of gene expression data can induce or remove interactions, making interpretation of results more challenging. We then demonstrate that, in the setting of moderate to strong interaction effects on the order of what may be reasonably expected for eQTL studies, standard interaction screening can be biased due to heteroscedasticity induced by true interactions. Using simulation and real data analysis, we outline a set of reasonable minimum conditions and sample size requirements for reliable detection of variant-by-environment and variant-by-TF interactions using the heteroscedasticity consistent covariance-based approach.

Type 1 Diabetes Candidate Genes Linked to Pancreatic Islet Cell Inflammation and Beta-Cell Apoptosis

Type 1 diabetes (T1D) is a chronic immune-mediated disease resulting from the selective destruction of the insulin-producing pancreatic islet β-cells. Susceptibility to the disease is the result of complex interactions between environmental and genetic risk factors. Genome-wide association studies (GWAS) have identified more than 50 genetic regions that affect the risk of developing T1D. Most of these susceptibility loci, however, harbor several genes, and the causal variant(s) and gene(s) for most of the loci remain to be established. A significant part of the genes located in the T1D susceptibility loci are expressed in human islets and β cells and mounting evidence suggests that some of these genes modulate the β-cell response to the immune system and viral infection and regulate apoptotic β-cell death. Here, we discuss the current status of T1D susceptibility loci and candidate genes with focus on pancreatic islet cell inflammation and β-cell apoptosis.

Type I Interferon Is a Catastrophic Feature of the Diabetic Islet Microenvironment

A detailed understanding of the molecular pathways and cellular interactions that result in islet beta cell (β cell) destruction is essential for the development and implementation of effective therapies for prevention or reversal of type 1 diabetes (T1D). However, events that define the pathogenesis of human T1D have remained elusive. This gap in our knowledge results from the complex interaction between genetics, the immune system, and environmental factors that precipitate T1D in humans. A link between genetics, the immune system, and environmental factors are type 1 interferons (T1-IFNs). These cytokines are well known for inducing antiviral factors that limit infection by regulating innate and adaptive immune responses. Further, several T1D genetic risk loci are within genes that link innate and adaptive immune cell responses to T1-IFN. An additional clue that links T1-IFN to T1D is that these cytokines are a known constituent of the autoinflammatory milieu within the pancreas of patients with T1D. The presence of IFNα/β is correlated with characteristic MHC class I (MHC-I) hyperexpression found in the islets of patients with T1D, suggesting that T1-IFNs modulate the cross-talk between autoreactive cytotoxic CD8⁺ T lymphocytes and insulin-producing pancreatic β cells. Here, we review the evidence supporting the diabetogenic potential of T1-IFN in the islet microenvironment.

'Omics' biomarkers associated with chronic low back pain: protocol of a retrospective longitudinal study

INTRODUCTION

Chronic low back pain (CLBP) produces considerable direct costs as well as indirect burdens for society, industry and health systems. CLBP is characterised by heterogeneity, inclusion of several pain syndromes, different underlying molecular pathologies and interaction with psychosocial factors that leads to a range of clinical manifestations. There is still much to understand in the underlying pathological processes and the non-psychosocial factors which account for differences in outcomes. Biomarkers that may be objectively used for diagnosis and personalised, targeted and cost-effective treatment are still lacking. Therefore, any data that may be obtained at the '-omics' level (glycomics, Activomics and genome-wide association studies-GWAS) may be helpful to use as dynamic biomarkers for elucidating CLBP pathogenesis and may ultimately provide prognostic information too. By means of a retrospective, observational, case-cohort, multicentre study, we aim to investigate new promising biomarkers potentially able to solve some of the issues related to CLBP.

METHODS AND ANALYSIS

The study follows a two-phase, 1:2 case-control model. A total of 12 000 individuals (4000 cases and 8000 controls) will be enrolled; clinical data will be registered, with particular attention to pain characteristics and outcomes of pain treatments. Blood samples will be collected to perform -omics studies. The primary objective is to recognise genetic variants associated with CLBP; secondary objectives are to study glycomics and Activomics profiles associated with CLBP.

ETHICS AND DISSEMINATION

The study is part of the PainOMICS project funded by European Community in the Seventh Framework Programme. The study has been approved from competent ethical bodies and copies of approvals were provided to the European Commission before starting the study. Results of the study will be reviewed by the Scientific Board and Ethical Committee of the PainOMICS Consortium. The scientific results will be disseminated through peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT02037789; Pre-results.

The burden of multiple sclerosis variants in continental Italians and Sardinians

BACKGROUND

Recent studies identified > 100 non-HLA (human leukocyte antigen) multiple sclerosis (MS) susceptibility variants in Northern European populations, but their role in Southern Europeans is largely unexplored.

OBJECTIVE

We aimed to investigate the cumulative impact of those variants in two Mediterranean populations: Continental Italians and Sardinians.

METHODS

We calculated four weighted Genetic Risk Scores (wGRS), using up to 102 non-HLA MS risk variants and 5 HLA MS susceptibility markers in 1691 patients and 2194 controls from continental Italy; and 2861 patients and 3034 controls from Sardinia. We then assessed the differences between populations using Nagelkerke's R(2) and the area under the Receiver Operating Characteristic (ROC) curves.

RESULTS

As expected, the genetic burden (mean wGRS value) was significantly higher in MS patients than in controls, in both populations. Of note, the burden was significantly higher in Sardinians. Conversely, the proportion of variability explained and the predictive power were significantly higher in continental Italians. Notably, within the Sardinian patients, we also observed a significantly higher burden of non-HLA variants in individuals who do not carry HLA risk alleles.

CONCLUSIONS

The observed differences in MS genetic burden between the two Mediterranean populations highlight the need for more genetic studies in South Europeans, to further expand the knowledge of MS genetics.

A perspective on interaction effects in genetic association studies

The identification of gene–gene and gene–environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression‐based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns. Linking this components it appears first that the simplest biological interaction models—in which the magnitude of a genetic effect depends on a common exposure—are among the most difficult to identify. Second, I highlight the demerit of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome this issue. Finally, I explore the advantages and limitations of multivariate interaction models, when testing for interaction between multiple SNPs and/or multiple exposures, over univariate approaches. Together, these new insights can be leveraged for future method development and to improve our understanding of the genetic architecture of multifactorial traits.

Effects of the genome on immune regulation in type 1 diabetes

Type 1 diabetes (T1DM) is a complex disease, arising through the interaction of an incompletely defined combination of genetic susceptibility and environmental factors. It is well accepted that T1DM results from selective immune-mediated destruction of the insulin-producing β cells in the islets of langerhans. Genetic studies of T1DM have identified several regions of susceptibility and identified major networks and pathways contributing to risk. In this study, we have taken advantages of the Immunochip fine-mapping genotyping data to address different aspects of immune regulation in relation to T1DM. First, we confirm that dense single nucleotide polymorphism (SNP) genotyping of the major histocompatibility complex/human leukocyte antigen (MHC/HLA) region capture the complex genetic contribution of this region to disease risk. Furthermore, it is shown that Immunochip genotyping can translate into a limited number of DRB1 and DQB1 amino acid residues that account for most of the HLA-risk. Second, we use the Immunochip data to look for functional significance by correlation to circulating levels of chemokines and demonstrate that genetic variation at chromosome 2, 3, and 6 correlates with circulating CCL2 and CCL4 in recent onset T1DM patients. Finally, we report that genetic variants predict autoantibody positivity in T1DM cases.

Genetic risk factors for type 1 diabetes

Type 1 diabetes is diagnosed at the end of a prodrome of β-cell autoimmunity. The disease is most likely triggered at an early age by autoantibodies primarily directed against insulin or glutamic acid decarboxylase, or both, but rarely against islet antigen-2. After the initial appearance of one of these autoantibody biomarkers, a second, third, or fourth autoantibody against either islet antigen-2 or the ZnT8 transporter might also appear. The larger the number of β-cell autoantibody types, the greater the risk of rapid progression to clinical onset of diabetes. This association does not necessarily mean that the β-cell autoantibodies are pathogenic, but rather that they represent reproducible biomarkers of the pathogenesis. The primary risk factor for β-cell autoimmunity is genetic, mainly occurring in individuals with either HLA-DR3-DQ2 or HLA-DR4-DQ8 haplotypes, or both, but a trigger from the environment is generally needed. The pathogenesis can be divided into three stages: 1, appearance of β-cell autoimmunity, normoglycaemia, and no symptoms; 2, β-cell autoimmunity, dysglycaemia, and no symptoms; and 3, β-cell autoimmunity, dysglycaemia, and symptoms of diabetes. The genetic association with each one of the three stages can differ. Type 1 diabetes could serve as a disease model for organ-specific autoimmune disorders such as coeliac disease, thyroiditis, and Addison's disease, which show similar early markers of a prolonged disease process before clinical diagnosis.

The nature of genetic susceptibility to multiple sclerosis: constraining the possibilities

BACKGROUND

Epidemiological observations regarding certain population-wide parameters (e.g., disease-prevalence, recurrence-risk in relatives, gender predilections, and the distribution of common genetic-variants) place important constraints on the possibilities for the genetic-basis underlying susceptibility to multiple sclerosis (MS).

METHODS

Using very broad range-estimates for the different population-wide epidemiological parameters, a mathematical model can help elucidate the nature and the magnitude of these constraints.

RESULTS

For MS no more than 8.5 % of the population can possibly be in the "genetically-susceptible" subset (defined as having a life-time MS-probability at least as high as the overall population average). Indeed, the expected MS-probability for this subset is more than 12 times that for every other person of the population who is not in this subset. Moreover, provided that those genetically susceptible persons (genotypes), who carry the well-established MS susceptibility allele (DRB1*1501), are equally or more likely to get MS than those susceptible persons, who don't carry this allele, then at least 84 % of MS-cases must come from this "genetically susceptible" subset. Finally, because men, compared to women, are at least as likely (and possibly more likely) to be susceptible, it can be demonstrated that women are more responsive to the environmental factors that are involved in MS-pathogenesis (whatever these are) and, thus, susceptible women are more likely actually to develop MS than susceptible men. Finally, in contrast to genetic susceptibility, more than 70 % of men (and likely also women) must have an environmental experience (including all of the necessary factors), which is sufficient to produce MS in a susceptible individual.

CONCLUSIONS

As a result, because of these constraints, it is possible to distinguish two classes of persons, indicating either that MS can be caused by two fundamentally different pathophysiological mechanisms or that the large majority of the population is at no risk of the developing this disease regardless of their environmental experience. Moreover, although environmental-factors would play a critical role in both mechanisms (if both exist), there is no reason to expect that these factors are the same (or even similar) between the two.

Polygenic Epidemiology

Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.

Genes affecting β-cell function in type 1 diabetes

Type 1 diabetes (T1D) is a multifactorial disease resulting from an immune-mediated destruction of the insulin-producing pancreatic β cells. Several environmental and genetic risk factors predispose to the disease. Genome-wide association studies (GWAS) have identified around 50 genetic regions that affect the risk of developing T1D, but the disease-causing variants and genes are still largely unknown. In this review, we discuss the current status of T1D susceptibility loci and candidate genes with focus on the β cell. At least 40 % of the genes in the T1D susceptibility loci are expressed in human islets and β cells, where they according to recent studies modulate the β-cell response to the immune system. As most of the risk variants map to noncoding regions of the genome, i.e., promoters, enhancers, intergenic regions, and noncoding genes, their possible involvement in T1D pathogenesis as gene regulators will also be addressed.

Why significant variables aren't automatically good predictors

Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.

Functional relevance for type 1 diabetes mellitus-associated genetic variants by using integrative analyses

AIM

Type 1 diabetes mellitus (type 1 DM) is an autoimmune disease. Although genome-wide association studies (GWAS) and meta-analyses have successfully identified numerous type 1 DM-associated susceptibility loci, the underlying mechanisms for these susceptibility loci are currently largely unclear.

METHODS

Based on publicly available datasets, we performed integrative analyses (i.e., integrated gene relationships among implicated loci, differential gene expression analysis, functional prediction and functional annotation clustering analysis) and combined with expression quantitative trait loci (eQTL) results to further explore function mechanisms underlying the associations between genetic variants and type 1 DM.

RESULTS

Among a total of 183 type 1 DM-associated SNPs, eQTL analysis showed that 17 SNPs with cis-regulated eQTL effects on 9 genes. All the 9 eQTL genes enrich in immune-related pathways or Gene Ontology (GO) terms. Functional prediction analysis identified 5 SNPs located in transcription factor (TF) binding sites. Of the 9 eQTL genes, 6 (TAP2, HLA-DOB, HLA-DQB1, HLA-DQA1, HLA-DRB5 and CTSH) were differentially expressed in type 1 DM-associated related cells. Especially, rs3825932 in CTSH has integrative functional evidence supporting the association with type 1 DM.

CONCLUSIONS

These findings indicated that integrative analyses can yield important functional information to link genetic variants and type 1 DM.

Chi8: a GPU program for detecting significant interacting SNPs with the Chi-square 8-df test

BACKGROUND

Determining interacting SNPs in genome-wide association studies is computationally expensive yet of considerable interest in genomics.

FINDINGS

We present a program Chi8 that calculates the Chi-square 8 degree of freedom test between all pairs of SNPs in a brute force manner on a Graphics Processing Unit. We analyze each of the seven WTCCC genome-wide association studies that have about 5000 total case and controls and 400,000 SNPs in an average of 9.6 h on a single GPU. We also study the power, false positives, and area under curve of our program on simulated data and provide a comparison to the GBOOST program. Our program source code is freely available from http://www.cs.njit.edu/usman/Chi8.

Predicting type 1 diabetes using biomarkers

Clinical type 1 diabetes is preceded by an asymptomatic phase that can be identified by serum islet autoantibodies. This perspective proposes that there is now sufficient evidence to allow a broader use of islet autoantibodies as biomarkers to diagnose type 1 diabetes that is already at an asymptomatic stage, so that attempts to prevent clinical hyperglycemia become a feature of disease management. Prediction would first, therefore, shift toward the use of genetic and other biomarkers to determine the likelihood that islet autoimmunity will develop in an infant, and second, toward metabolic assessment to stage and biomarkers to determine the rate of progression to hyperglycemia in children in whom islet autoimmunity is diagnosed. A case is presented for future comprehensive risk assessment that commences at birth and includes attempts to predict, stage, and prevent initiation and progression of the disease process at multiple stages. The biomarkers required achieving this level of sophistication and dissemination are discussed.

Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge.

Cardiovascular and autoimmune diseases in females: The role of microvasculature and dysfunctional endothelium

Cardiovascular (CV) diseases are becoming increasingly frequent and associated with a high incidence of CV events, disability and death. It is known that there is a relationship between CV burden and systemic autoimmune diseases (SADs) that is mainly due to inflammation and autoimmunity, but the other mechanisms underlying the high CV risk of SAD patients have not yet been fully clarified. The aim of this review article is to discuss some of the specific factors associated with the accelerated atherosclerosis (ATS) characterising SADs (female sex, the microcirculation and the endothelium) in order to highlight the importance of an early diagnosis and the prompt implementation of preventive measures, as well as the possible role of new therapeutic strategies such as vaccine immunomodulation. Finally, as the natural history of ATS begins with endothelial injury (a potentially reversible process that is influenced by various factors) and microvascular damage plays a central role in the etiopathogenesis of SADs, it underlines the crucial need for the development of reliable means of detecting sub-clinical abnormalities in the microcirculation, particularly coronary microcirculation dysfunction.

Genetics of liver disease: From pathophysiology to clinical practice

Paralleling the first 30 years of the Journal of Hepatology we have witnessed huge advances in our understanding of liver disease and physiology. Genetic advances have played no small part in that. Initial studies in the 1970s and 1980s identified the strong major histocompatibility complex associations in autoimmune liver diseases. During the 1990 s, developments in genomic technologies drove the identification of genes responsible for Mendelian liver diseases. Over the last decade, genome-wide association studies have allowed for the dissection of the genetic susceptibility to complex liver disorders, in which also environmental co-factors play important roles. Findings have allowed the identification and elaboration of pathophysiological processes, have indicated the need for reclassification of liver diseases and have already pointed to new disease treatments. In the immediate future genetics will allow further stratification of liver diseases and contribute to personalized medicine. Challenges exist with regard to clinical implementation of rapidly developing technologies and interpretation of the wealth of accumulating genetic data. The historical perspective of genetics in liver diseases illustrates the opportunities for future research and clinical care of our patients.

Deciphering Genome Environment Wide Interactions Using Exposed Subjects Only

The recent successes of genome-wide association studies (GWAS) have renewed interest in genome environment wide interaction studies (GEWIS) to discover genetic factors that modulate penetrance of environmental exposures to human diseases. Indeed, gene-environment interactions (G × E), which have not been emphasized in the GWAS era, could be a source contributing to the missing heritability, a major bottleneck limiting continuing GWAS successes. In this manuscript, we describe a design and analytic strategy to focus on G × E using only exposed subjects, dubbed as e-GEWIS. Operationally, an e-GEWIS analysis is equivalent to a GWAS analysis on exposed subjects only, and it has actually been used in some earlier GWAS without being explicitly identified as such. Through both analytics and simulations, e-GEWIS has been shown better efficiency than the usual cross-product-based analysis of G × E interaction with both cases and controls (cc-GEWIS), and they have comparable efficiency to case-only analysis of G × E (c-GEWIS), with potentially smaller sample sizes. The formalization of e-GEWIS here provides a theoretical basis to legitimize this framework for routine investigation of G × E, for more efficient G × E study designs, and for improvement of reproducibility in replicating GEWIS findings. As an illustration, we apply e-GEWIS to a lung cancer GWAS data set to perform a GEWIS, focusing on gene and smoking interaction. The e-GEWIS analysis successfully uncovered positive genetic associations on chromosome 15 among current smokers, suggesting a gene-smoking interaction. Although this signal was detected earlier, the current finding here serves as a positive control in support of this e-GEWIS strategy.

Genome-wide interaction analysis reveals replicated epistatic effects on brain structure

The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.

Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes

AIMS/HYPOTHESIS

More than 40 regions of the human genome confer susceptibility for type 1 diabetes and could be used to establish population screening strategies. The aim of our study was to identify weighted sets of SNP combinations for type 1 diabetes prediction.

METHODS

We applied multivariable logistic regression and Bayesian feature selection to the Type 1 Diabetes Genetics Consortium (T1DGC) dataset with genotyping of HLA plus 40 SNPs within other type 1 diabetes-associated gene regions in 4,574 cases and 1,207 controls. We tested the weighted models in an independent validation set (765 cases, 423 controls), and assessed their performance in 1,772 prospectively followed children.

RESULTS

The inclusion of 40 non-HLA gene SNPs significantly improved the prediction of type 1 diabetes over that provided by HLA alone (p = 3.1 × 10(-25)), with a receiver operating characteristic AUC of 0.87 in the T1DGC set, and 0.84 in the validation set. Feature selection identified HLA plus nine SNPs from the PTPN22, INS, IL2RA, ERBB3, ORMDL3, BACH2, IL27, GLIS3 and RNLS genes that could achieve similar prediction accuracy as the total SNP set. Application of this ten SNP model to prospectively followed children was able to improve risk stratification over that achieved by HLA genotype alone.

CONCLUSIONS

We provided a weighted risk model with selected SNPs that could be considered for recruitment of infants into studies of early type 1 diabetes natural history or appropriately safe prevention.

Detection and correction of artefacts in estimation of rare copy number variants and analysis of rare deletions in type 1 diabetes

Copy number variants (CNVs) have been proposed as a possible source of ‘missing heritability’ in complex human diseases. Two studies of type 1 diabetes (T1D) found null associations with common copy number polymorphisms, but CNVs of low frequency and high penetrance could still play a role. We used the Log-R-ratio intensity data from a dense single nucleotide polymorphism (SNP) array, ImmunoChip, to detect rare CNV deletions (rDELs) and duplications (rDUPs) in 6808 T1D cases, 9954 controls and 2206 families with T1D-affected offspring. Initial analyses detected CNV associations. However, these were shown to be false-positive findings, failing replication with polymerase chain reaction. We developed a pipeline of quality control (QC) tests that were calibrated using systematic testing of sensitivity and specificity. The case–control odds ratios (OR) of CNV burden on T1D risk resulting from this QC pipeline converged on unity, suggesting no global frequency difference in rDELs or rDUPs. There was evidence that deletions could impact T1D risk for a small minority of cases, with enrichment for rDELs longer than 400 kb (OR = 1.57, P = 0.005). There were also 18 de novo rDELs detected in affected offspring but none for unaffected siblings (P = 0.03). No specific CNV regions showed robust evidence for association with T1D, although frequencies were lower than expected (most less than 0.1%), substantially reducing statistical power, which was examined in detail. We present an R-package, plumbCNV, which provides an automated approach for QC and detection of rare CNVs that can facilitate equivalent analyses of large-scale SNP array datasets.

Gene-environment dependence creates spurious gene-environment interaction

Gene-environment interactions have the potential to shed light on biological processes leading to disease and to improve the accuracy of epidemiological risk models. However, relatively few such interactions have yet been confirmed. In part this is because genetic markers such as tag SNPs are usually studied, rather than the causal variants themselves. Previous work has shown that this leads to substantial loss of power and increased sample size when gene and environment are independent. However, dependence between gene and environment can arise in several ways including mediation, pleiotropy, and confounding, and several examples of gene-environment interaction under gene-environment dependence have recently been published. Here we show that under gene-environment dependence, a statistical interaction can be present between a marker and environment even if there is no interaction between the causal variant and the environment. We give simple conditions under which there is no marker-environment interaction and note that they do not hold in general when there is gene-environment dependence. Furthermore, the gene-environment dependence applies to the causal variant and cannot be assessed from marker data. Gene-gene interactions are susceptible to the same problem if two causal variants are in linkage disequilibrium. In addition to existing concerns about mechanistic interpretations, we suggest further caution in reporting interactions for genetic markers.

ABCC5 transporter is a novel type 2 diabetes susceptibility gene in European and African American populations

Numerous functional studies have implicated PARL in relation to type 2 diabetes (T2D). We hypothesised that conflicting human association studies may be due to neighbouring causal variants being in linkage disequilibrium (LD) with PARL. We conducted a comprehensive candidate gene study of the extended LD genomic region that includes PARL and transporter ABCC5 using three data sets (two European and one African American), in relation to healthy glycaemic variation, visceral fat accumulation and T2D disease. We observed no evidence for previously reported T2D association with Val262Leu or PARL using array and fine-map genomic and expression data. By contrast, we observed strong evidence of T2D association with ABCC5 (intron 26) for European and African American samples (P = 3E-07) and with ABCC5 adipose expression in Europeans [odds ratio (OR) = 3.8, P = 2E-04]. The genomic location estimate for the ABCC5 functional variant, associated with all phenotypes and expression data (P = 1E-11), was identical for all samples (at Chr3q 185,136 kb B36), indicating that the risk variant is an expression quantitative trait locus (eQTL) with increased expression conferring risk of disease. That the association with T2D is observed in populations of disparate ancestry suggests the variant is a ubiquitous risk factor for T2D.

Implementation and utilization of genetic testing in personalized medicine

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.

Gender differences in autoimmune disease

Autoimmune diseases are a range of diseases in which the immune response to self-antigens results in damage or dysfunction of tissues. Autoimmune diseases can be systemic or can affect specific organs or body systems. For most autoimmune diseases there is a clear sex difference in prevalence, whereby females are generally more frequently affected than males. In this review, we consider gender differences in systemic and organ-specific autoimmune diseases, and we summarize human data that outlines the prevalence of common autoimmune diseases specific to adult males and females in countries commonly surveyed. We discuss possible mechanisms for sex specific differences including gender differences in immune response and organ vulnerability, reproductive capacity including pregnancy, sex hormones, genetic predisposition, parental inheritance, and epigenetics. Evidence demonstrates that gender has a significant influence on the development of autoimmune disease. Thus, considerations of gender should be at the forefront of all studies that attempt to define mechanisms that underpin autoimmune disease.

Genetic Counseling for Diabetes Mellitus

Most diabetes is polygenic in etiology, with (type 1 diabetes, T1DM) or without (type 2 diabetes, T2DM) an autoimmune basis. Genetic counseling for diabetes generally focuses on providing empiric risk information based on family history and/or the effects of maternal hyperglycemia on pregnancy outcome. An estimated one to five percent of diabetes is monogenic in nature, e.g., maturity onset diabetes of the young (MODY), with molecular testing and etiology-based treatment available. However, recent studies show that most monogenic diabetes is misdiagnosed as T1DM or T2DM. While efforts are underway to increase the rate of diagnosis in the diabetes clinic, genetic counselors and clinical geneticists are in a prime position to identify monogenic cases through targeted questions during a family history combined with working in conjunction with diabetes professionals to diagnose and assure proper treatment and familial risk assessment for individuals with monogenic diabetes.

A genome-wide assessment of the role of untagged copy number variants in type 1 diabetes

Genome-wide association studies (GWAS) for type 1 diabetes (T1D) have successfully identified more than 40 independent T1D associated tagging single nucleotide polymorphisms (SNPs). However, owing to technical limitations of copy number variants (CNVs) genotyping assays, the assessment of the role of CNVs has been limited to the subset of these in high linkage disequilibrium with tag SNPs. The contribution of untagged CNVs, often multi-allelic and difficult to genotype using existing assays, to the heritability of T1D remains an open question. To investigate this issue, we designed a custom comparative genetic hybridization array (aCGH) specifically designed to assay untagged CNV loci identified from a variety of sources. To overcome the technical limitations of the case control design for this class of CNVs, we genotyped the Type 1 Diabetes Genetics Consortium (T1DGC) family resource (representing 3,903 transmissions from parents to affected offspring) and used an association testing strategy that does not necessitate obtaining discrete genotypes. Our design targeted 4,309 CNVs, of which 3,410 passed stringent quality control filters. As a positive control, the scan confirmed the known T1D association at the INS locus by direct typing of the 5′ variable number of tandem repeat (VNTR) locus. Our results clarify the fact that the disease association is indistinguishable from the two main polymorphic allele classes of the INS VNTR, class I-and class III. We also identified novel technical artifacts resulting into spurious associations at the somatically rearranging loci, T cell receptor, TCRA/TCRD and TCRB, and Immunoglobulin heavy chain, IGH, loci on chromosomes 14q11.2, 7q34 and 14q32.33, respectively. However, our data did not identify novel T1D loci. Our results do not support a major role of untagged CNVs in T1D heritability.

For many complex traits, and in particular type 1 diabetes (T1D), the genome-wide association study (GWAS) design has been successful at detecting a large number of loci that contribute disease risk. However, in the case of T1D as well as almost all other traits, the sum of these loci does not fully explain the heritability estimated from familial studies. This observation raises the possibility that additional variants exist but have not yet been found because they have not effectively been targeted by the GWAS design. Here, we focus on a specific class of large deletions/duplications called copy number variants (CNVs), and more precisely to the subset of these loci that mutate rapidly, which are highly polymorphic. A consequence of this high level of polymorphism is that these variants have typically not been captured by previous GWAS studies. We use a family based design that is optimized to capture these previously untested variants. We then perform a genome-wide scan to assess their contribution to T1D. Our scan was technically successful but did not identify novel associations. This suggests that little was missed by the GWAS strategy, and that the remaining heritability of T1D is most likely driven by a large number of variants, either rare of common, but with a small individual contribution to disease risk.

Multiple sclerosis genetics

Genome-wide association studies have revolutionised the genetic analysis of multiple sclerosis. Through international collaborative efforts involving tens of thousands of cases and controls, more than 100 associated common variants have now been identified. These variants consistently implicate genes associated with immunological processes, overwhelmingly lie in regulatory rather than coding regions, and are frequently associated with other autoimmune diseases. The functional implications of these associated variants are mostly unknown; however, early work has shown that several variants have effects on splicing that result in meaningful changes in the balance between different isoforms in relevant tissues. Including the well established risk attributable to variants in genes encoding human leucocyte antigens, only about a quarter of reported heritability can now be accounted for, suggesting that a substantial potential for further discovery remains.

Genetics of diabetes--are we missing the genes or the disease?

Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of different organs, especially the eyes, kidneys, nerves, heart, and blood vessels. Several pathogenic processes are involved in the development of diabetes. These range from autoimmune destruction of the beta-cells of the pancreas with consequent insulin deficiency to abnormalities that result in resistance to insulin action (American Diabetes Association, 2011). The vast majority of cases of diabetes fall into two broad categories. In type 1 diabetes (T1D), the cause is an absolute deficiency of insulin secretion, whereas in type 2 diabetes (T2D), the cause is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response. However, the subdivision into two main categories represents a simplification of the real situation, and research during the recent years has shown that the disease is much more heterogeneous than a simple subdivision into two major subtypes assumes. Worldwide prevalence figures estimate that there are 280 million diabetic patients in 2011 and more than 500 million in 2030 (http://www.diabetesatlas.org/). In Europe, about 6-8% of the population suffer from diabetes, of them about 90% has T2D and 10% T1D, thereby making T2D to the fastest increasing disease in Europe and worldwide. This epidemic has been ascribed to a collision between the genes and the environment. While our knowledge about the genes is clearly better for T1D than for T2D given the strong contribution of variation in the HLA region to the risk of T1D, the opposite is the case for T2D, where our knowledge about the environmental triggers (obesity, lack of exercise) is much better than the understanding of the underlying genetic causes. This lack of knowledge about the underlying genetic causes of diabetes is often referred to as missing heritability (Manolio et al., 2009) which exceeds 80% for T2D but less than 25% for T1D. In the following review, we will discuss potential sources of this missing heritability which also includes the possibility that our definition of diabetes and its subgroups is imprecise and thereby making the identification of genetic causes difficult.

Overlap of genetic susceptibility to type 1 diabetes, type 2 diabetes, and latent autoimmune diabetes in adults

Despite the notion that there is a degree of commonality to the biological etiology of type 1 diabetes (T1D) and type 2 diabetes (T2D), the lack of overlap in the genetic factors underpinning each of them suggests very distinct mechanisms. A disorder considered to be at the "intersection" of these two diseases is "latent autoimmune diabetes in adults" (LADA). Interestingly, genetic signals from both T1D and T2D are also seen in LADA, including the key HLA and transcription factor 7-like 2 (TCF7L2) loci, but the magnitudes of these effects are more complex than just pointing to LADA as being a simple admixture of T1D and T2D. We review the current status of the understanding of the genetics of LADA and place it in the context of what is known about the genetics of its better-studied "cousins," T1D and T2D, especially with respect to the myriad of discoveries made over the last decade through genome-wide association studies.

A strategy to find gene combinations that identify children who progress rapidly to type 1 diabetes after islet autoantibody seroconversion

We recently developed a novel approach capable of identifying gene combinations to obtain maximal disease risk stratification. Type 1 diabetes has a preclinical phase including seroconversion to autoimmunity and subsequent progression to diabetes. Here, we applied our gene combination approach to identify combinations that contribute either to islet autoimmunity or to the progression from islet autoantibodies to diabetes onset. We examined 12 type 1 diabetes susceptibility genes (INS, ERBB3, PTPN2, IFIH1, PTPN22, KIAA0350, CD25, CTLA4, SH2B3, IL2, IL18RAP, IL10) in a cohort of children of parents with type 1 diabetes and prospectively followed from birth. The most predictive combination was subsequently applied to a smaller validation cohort. The combinations of genes only marginally contributed to the risk of developing islet autoimmunity, but could substantially modify risk of progression to diabetes in islet autoantibody-positive children. The greatest discrimination was provided by risk allele scores of five genes, INS, IFIH1, IL18RAP, CD25, and IL2 genes, which could identify 80 % of islet autoantibody-positive children who progressed to diabetes within 6 years of seroconversion and discriminate high risk (63 % within 6 years; 95 % CI 45-81 %) and low risk (11 % within 6 years; 95 % CI 0.1-22 %; p = 4 × 10(-5)) antibody-positive children. Risk stratification by these five genes was confirmed in a second cohort of islet autoantibody children. These findings highlight genes that may affect the rate of the beta-cell destruction process once autoimmunity has initiated and may help to identify islet autoantibody-positive subjects with rapid progression to diabetes.

Wellness and health omics linked to the environment: the WHOLE approach to personalized medicine

The WHOLE approach to personalized medicine represents an effort to integrate clinical and genomic profiling jointly into preventative health care and the promotion of wellness. Our premise is that genotypes alone are insufficient to predict health outcomes, since they fail to account for individualized responses to the environment and life history. Instead, integrative genomic approaches incorporating whole genome sequences and transcriptome and epigenome profiles, all combined with extensive clinical data obtained at annual health evaluations, have the potential to provide more informative wellness classification. As with traditional medicine where the physician interprets subclinical signs in light of the person's health history, truly personalized medicine will be founded on algorithms that extract relevant information from genomes but will also require interpretation in light of the triggers, behaviors, and environment that are unique to each person. This chapter discusses some of the major obstacles to implementation, from development of risk scores through integration of diverse omic data types to presentation of results in a format that fosters development of personal health action plans.

A robust model-free approach for rare variants association studies incorporating gene-gene and gene-environmental interactions

Recently more and more evidence suggest that rare variants with much lower minor allele frequencies play significant roles in disease etiology. Advances in next-generation sequencing technologies will lead to many more rare variants association studies. Several statistical methods have been proposed to assess the effect of rare variants by aggregating information from multiple loci across a genetic region and testing the association between the phenotype and aggregated genotype. One limitation of existing methods is that they only look into the marginal effects of rare variants but do not systematically take into account effects due to interactions among rare variants and between rare variants and environmental factors. In this article, we propose the summation of partition approach (SPA), a robust model-free method that is designed specifically for detecting both marginal effects and effects due to gene-gene (G×G) and gene-environmental (G×E) interactions for rare variants association studies. SPA has three advantages. First, it accounts for the interaction information and gains considerable power in the presence of unknown and complicated G×G or G×E interactions. Secondly, it does not sacrifice the marginal detection power; in the situation when rare variants only have marginal effects it is comparable with the most competitive method in current literature. Thirdly, it is easy to extend and can incorporate more complex interactions; other practitioners and scientists can tailor the procedure to fit their own study friendly. Our simulation studies show that SPA is considerably more powerful than many existing methods in the presence of G×G and G×E interactions.

Genetic and epigenetic factors in etiology of diabetes mellitus type 1

Diabetes mellitus type 1 (T1D) is a complex disease resulting from the interplay of genetic, epigenetic, and environmental factors. Recent progress in understanding the genetic basis of T1D has resulted in an increased recognition of childhood diabetes heterogeneity. After the initial success of family-based linkage analyses, which uncovered the strong linkage and association between HLA gene variants and T1D, genome-wide association studies performed with high-density single-nucleotide polymorphism genotyping platforms provided evidence for a number of novel loci, although fine mapping and characterization of these new regions remains to be performed. T1D is one of the most heritable common diseases, and among autoimmune diseases it has the largest range of concordance rates in monozygotic twins. This fact, coupled with evidence of various epigenetic modifications of gene expression, provides convincing proof of the complex interplay between genetic and environmental factors. In T1D, epigenetic phenomena, such as DNA methylation, histone modifications, and microRNA dysregulation, have been associated with altered gene expression. Increasing epidemiologic and experimental evidence supports the role of genetic and epigenetic alterations in the etiopathology of diabetes. We discuss recent results related to the role of genetic and epigenetic factors involved in development of T1D.