Improved risk prediction for Crohn's disease with a multi-locus approach

Anti-inflammatory and anti-hyperalgesic effects induced by an aqueous aged black garlic extract in rodent models of ulcerative colitis and colitis-associated visceral pain

Inflammatory bowel disease (IBD) is a morbid condition characterized by relapsing-remitting inflammation of the colon, accompanied by persistent gut dysmotility and abdominal pain. Different reports demonstrated biological activities of aged black garlic (ABG), including anti-inflammatory and antioxidant effects. We aimed to investigate beneficial effects exerted by ABGE on colon inflammation by using ex vivo and in vivo experimental models. We investigated the anti-inflammatory effects of an ABG water extract (ABGE) on rat colon specimens exposed to E. coli lipopolysaccharide (LPS), a known ex vivo experimental model of ulcerative colitis. We determined gene expression of various biomarkers involved in inflammation, including interleukin (IL)-1β, IL-6, nuclear factor-kB (NF-kB), tumor necrosis factor (TNF)-α. Moreover, we studied the acute effects of ABGE on visceral pain associated with colitis induced by 2,4-di-nitrobenzene sulfonic acid (DNBS) injection in rats. ABGE suppressed LPS-induced gene expression of IL-1β, IL-6, NF-kB, and TNF-α. In addition, the acute administration of ABGE (0.03-1 g kg-1) dose-dependently relieved post-inflammatory visceral pain, with the higher dose (1 g kg-1) able to significantly reduce both the behavioral nociceptive response and the entity of abdominal contraction (assessed by electromyography) in response to colorectal distension after the acute administration in DNBS-treated rats. Present findings showed that ABGE could represent a potential strategy for treatment of colitis-associated inflammatory process and visceral pain. The beneficial effects induced by the extract could be related to the pattern of polyphenolic composition, with particular regard to gallic acid and catechin.

Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance

Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. We extracted information about the methods used and other differentiating features in genomic machine learning models. We used these features in linear regressions predicting model performance. We tested for univariate and multivariate associations as well as interactions between features. Of the models reviewed, 46% used feature selection methods that can lead to data leakage. Across our models, the number of hyperparameter optimizations reported, data leakage due to feature selection, model type, and modeling an autoimmune disorder were significantly associated with an increase in reported model performance. We found a significant, negative interaction between data leakage and training size. Our results suggest that methods susceptible to data leakage are prevalent among genomic machine learning research, resulting in inflated reported performance. Best practice guidelines that promote the avoidance and recognition of data leakage may help the field avoid biased results.

Systemic lupus erythematosus as a genetic disease

Systemic lupus erythematosus is the prototypical systemic autoimmune disease, as it is characterized both by protean multi-organ system manifestations and by the uniform presence of pathogenic autoantibodies directed against components of the nucleus. Prior to the modern genetic era, the diverse clinical manifestations of SLE suggested to many that SLE patients were unlikely to share a common genetic risk basis. However, modern genetic studies have revealed that SLE usually arises when an environmental exposure occurs in an individual with a collection of genetic risk variants passing a liability threshold. Here, we summarize the current state of the field aimed at: (1) understanding the genetic architecture of this complex disease, (2) synthesizing how this genetic risk architecture impacts cellular and molecular disease pathophysiology, (3) providing illustrative examples that highlight the rich complexity of the pathobiology of this prototypical autoimmune disease and (4) communicating this complex etiopathogenesis to patients.

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

BACKGROUND

DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions.

RESEARCH QUESTION

Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis?

STUDY DESIGN AND METHODS

Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.

RESULTS

Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10-17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10-18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC3 year, 0.88 [95% CI, 0.83-0.93]; and AUC5 year, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.

INTERPRETATION

The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

Blood-Derived DNA Methylation Signatures of Crohn's Disease and Severity of Intestinal Inflammation

BACKGROUND & AIMS

Crohn's disease is a relapsing and remitting inflammatory disorder with a variable clinical course. Although most patients present with an inflammatory phenotype (B1), approximately 20% of patients rapidly progress to complicated disease, which includes stricturing (B2), within 5 years. We analyzed DNA methylation patterns in blood samples of pediatric patients with Crohn's disease at diagnosis and later time points to identify changes that associate with and might contribute to disease development and progression.

METHODS

We obtained blood samples from 164 pediatric patients (1-17 years old) with Crohn's disease (B1 or B2) who participated in a North American study and were followed for 5 years. Participants without intestinal inflammation or symptoms served as controls (n = 74). DNA methylation patterns were analyzed in samples collected at time of diagnosis and 1-3 years later at approximately 850,000 sites. We used genetic association and the concept of Mendelian randomization to identify changes in DNA methylation patterns that might contribute to the development of or result from Crohn's disease.

RESULTS

We identified 1189 5'-cytosine-phosphate-guanosine-3' (CpG) sites that were differentially methylated between patients with Crohn's disease (at diagnosis) and controls. Methylation changes at these sites correlated with plasma levels of C-reactive protein. A comparison of methylation profiles of DNA collected at diagnosis of Crohn's disease vs during the follow-up period showed that, during treatment, alterations identified in methylation profiles at the time of diagnosis of Crohn's disease more closely resembled patterns observed in controls, irrespective of disease progression to B2. We identified methylation changes at 3 CpG sites that might contribute to the development of Crohn's disease. Most CpG methylation changes associated with Crohn's disease disappeared with treatment of inflammation and might be a result of Crohn's disease.

CONCLUSIONS

Methylation patterns observed in blood samples from patients with Crohn's disease accompany acute inflammation; with treatment, these change to resemble methylation patterns observed in patients without intestinal inflammation. These findings indicate that Crohn's disease-associated patterns of DNA methylation observed in blood samples are a result of the inflammatory features of the disease and are less likely to contribute to disease development or progression.

Collective effects of common single nucleotide polymorphisms and genetic risk prediction in type 1 diabetes

Type 1 diabetes (T1D) is a common autoimmune disease and may be related to multiple genetic and environmental risk factors. Previous genetic studies have focused on looking for individual polymorphic risk variants. Here, we studied the overall levels of genetic diversity in T1D patients by making use of a previously published study including 1865 cases and 2828 reference samples with genotyping data for 500 K common single nucleotide polymorphisms (SNPs). We determined the minor allele (MA) status of each SNP in the reference samples and calculated the total number of MAs or minor allele contents (MAC) of each individual. We found the average MAC of cases to be greater than that of the reference samples. By focusing on MAs with strong linkage to cases, we further identified a set of 112 SNPs that could predict 19.19% of cases. These results suggest that overall genetic variation over a threshold level may be a risk factor in T1D and provide a new genetic method for predicting the disorder.

Collective effects of common SNPs and risk prediction in lung cancer

Lung cancer is the leading cause of cancer deaths in both men and women in the US. While most sporadic lung cancer cases are related to environmental factors such as smoking, genetic susceptibility may also play an important role and a number of lung cancer associated single-nucleotide polymorphisms (SNPs) have been identified although many remain to be found. The collective effects of genome-wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in lung cancer using previously published SNPs data sets (US and Finland samples) and found higher MAC in cases relative to matched controls. A set of 5400 SNPs with MA (MAF < 0.5) more common in cases (P < 0.08) and linkage disequilibrium (LD) r² = 0.3 was found to have the best predictive accuracy. These results identify higher MAC in lung cancer susceptibility and provide a meaningful genetic method to identify those at risk of lung cancer.

Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population

Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D.

CAGI4 Crohn's exome challenge: Marker SNP versus exome variant models for assigning risk of Crohn disease

Understanding the basis of complex trait disease is a fundamental problem in human genetics. The CAGI Crohn's Exome challenges are providing insight into the adequacy of current disease models by requiring participants to identify which of a set of individuals has been diagnosed with the disease, given exome data. For the CAGI4 round, we developed a method that used the genotypes from exome sequencing data only to impute the status of genome wide association studies marker SNPs. We then used the imputed genotypes as input to several machine learning methods that had been trained to predict disease status from marker SNP information. We achieved the best performance using Naïve Bayes and with a consensus machine learning method, obtaining an area under the curve of 0.72, larger than other methods used in CAGI4. We also developed a model that incorporated the contribution from rare missense variants in the exome data, but this performed less well. Future progress is expected to come from the use of whole genome data rather than exomes.

Performance of risk prediction for inflammatory bowel disease based on genotyping platform and genomic risk score method

Background

Predicting risk of disease from genotypes is being increasingly proposed for a variety of diagnostic and prognostic purposes. Genome-wide association studies (GWAS) have identified a large number of genome-wide significant susceptibility loci for Crohn’s disease (CD) and ulcerative colitis (UC), two subtypes of inflammatory bowel disease (IBD). Recent studies have demonstrated that including only loci that are significantly associated with disease in the prediction model has low predictive power and that power can substantially be improved using a polygenic approach.

Methods

We performed a comprehensive analysis of risk prediction models using large case-control cohorts genotyped for 909,763 GWAS SNPs or 123,437 SNPs on the custom designed Immunochip using four prediction methods (polygenic score, best linear genomic prediction, elastic-net regularization and a Bayesian mixture model). We used the area under the curve (AUC) to assess prediction performance for discovery populations with different sample sizes and number of SNPs within cross-validation.

Results

On average, the Bayesian mixture approach had the best prediction performance. Using cross-validation we found little differences in prediction performance between GWAS and Immunochip, despite the GWAS array providing a 10 times larger effective genome-wide coverage. The prediction performance using Immunochip is largely due to the power of the initial GWAS for its marker selection and its low cost that enabled larger sample sizes. The predictive ability of the genomic risk score based on Immunochip was replicated in external data, with AUC of 0.75 for CD and 0.70 for UC. CD patients with higher risk scores demonstrated clinical characteristics typically associated with a more severe disease course including ileal location and earlier age at diagnosis.

Conclusions

Our analyses demonstrate that the power of genomic risk prediction for IBD is mainly due to strongly associated SNPs with considerable effect sizes. Additional SNPs that are only tagged by high-density GWAS arrays and low or rare-variants over-represented in the high-density region on the Immunochip contribute little to prediction accuracy. Although a quantitative assessment of IBD risk for an individual is not currently possible, we show sufficient power of genomic risk scores to stratify IBD risk among individuals at diagnosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-017-0451-2) contains supplementary material, which is available to authorized users.

Functional variant in the promoter region of IL-27 alters gene transcription and confers a risk for ulcerative colitis in northern Chinese Han

Ulcerative colitis (UC) is a chronic inflammatory disorder of unknown etiology and a polygenic disease. IL-27 encodes p28, a subunit of IL-12 family cytokines, and has been implicated in the pathogenesis of UC. The aims of the present study were to evaluate the genetic association of a variant of the IL-27 gene with UC and to further characterize the functional variant in the IL-27 gene that influences the risk for UC. Our data demonstrated that the genetic variant rs153109 in the 5' upstream region of IL-27 is significantly associated with UC in Chinese Han individuals. Analysis of IL-27 transcripts demonstrated that individuals carrying the risk allele of rs153109 display reduced transcription of IL-27 in PBMCs. Luciferase activity assays demonstrated that the risk allele rs153109 results in decreased promoter activity compared to a non-risk allele in a tissue specific manner. Mechanistic characterization of histone modifications in the promoter region revealed that the risk haplotype tagged by the risk allele of rs153109 reduces the levels of H3K3me3 and H3K27ac.

Crohn's Disease Localization Displays Different Predisposing Genetic Variants

Background

Crohn’s disease (CD) is a pathologic condition with different clinical expressions that may reflect an interplay between genetics and environmental factors. Recently, it has been highlighted that three genetic markers, NOD2, MHC and MST1, were associated to distinct CD sites, supporting the concept that genetic variations may contribute to localize CD. Genetic markers, previously shown to be associated with inflammatory bowel disease (IBD), were tested in CD patients with the aim to better dissect the genetic relationship between ileal, ileocolonic and colonic CD and ascertain whether a different genetic background would support the three disease sites as independent entities.

Methods

A panel of 29 SNPs of 19 IBD loci were analyzed by TaqMan SNP allelic discrimination method both evaluating their distinct contribute and analyzing all markers jointly.

Results

Seven hundred and eight CD patients and 537 healthy controls were included in the study. Of the overall population of patients, 237 patients had an ileal involvement (L1), 171 a colonic localization (L2), and the 300 remaining an ileocolon location (L3). We confirmed the association for 23 of 29 variations (P < 0.05). Compared to healthy controls, 16 variations emerged as associated to an ileum disease, 7 with a colonic disease and 14 with an ileocolonic site (P < 0.05). Comparing ileum to colonic CD, 5 SNPs (17%) were differentially associated (P < 0.05). A genetic model score that aggregated the risks of 23 SNPs and their odds ratios (ORs), yielded an Area Under the Curve (AUC) of 0.70 for the overall CD patients. By analyzing each CD location, the AUC remained at the same level for the ileal and ileocolonic sites (0.73 and 0.72, respectively), but dropped to a 0,66 value in patients with colon localization.

Conclusions

Our findings reaffirm the existence of at least three different subgroups of CD patients, with a genetic signature distinctive for the three main CD sites.

Multi-locus genetic risk score predicts risk for Crohn's disease in Slovenian population

AIM

To develop a risk model for Crohn's disease (CD) based on homogeneous population.

METHODS

In our study were included 160 CD patients and 209 healthy individuals from Slovenia. The association study was performed for 112 single nucleotide polymorphisms (SNPs). We generated genetic risk scores (GRS) based on the number of risk alleles using weighted additive model. Discriminatory accuracy was measured by area under ROC curve (AUC). For risk evaluation, we divided individuals according to positive and negative likelihood ratios (LR) of a test, with LR > 5 for high risk group and LR < 0.20 for low risk group.

RESULTS

The highest accuracy, AUC of 0.78 was achieved with GRS combining 33 SNPs with optimal sensitivity and specificity of 75.0% and 72.7%, respectively. Individuals with the highest risk (GRS > 5.54) showed significantly increased odds of developing CD (OR = 26.65, 95%CI: 11.25-63.15) compared to the individuals with the lowest risk (GRS < 4.57) which is a considerably greater risk captured than in one SNP with the highest effect size (OR = 3.24). When more than 33 SNPs were included in GRS, discriminatory ability was not improved significantly; AUC of all 74 SNPs was 0.76.

CONCLUSION

The authors proved the possibility of building accurate genetic risk score based on 33 risk variants on Slovenian CD patients which may serve as a screening tool in the targeted population.

A comparison of genomic profiles of complex diseases under different models

Background

Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task.

Results

We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium.

Conclusions

In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0157-2) contains supplementary material, which is available to authorized users.

Variations in inflammatory genes as molecular markers for prediction of inflammatory bowel disease occurrence

OBJECTIVE

Research on inflammatory bowel disease (IBD) has highlighted genes involved in the regulation of inflammatory responses as contributors to disease pathogenesis. This study aimed to evaluate the associations between IBD and variations in NOD2, TLR4, TNF-α, IL-6, IL-1β and IL-1RN genes, and to use the genetic data obtained in predictive modeling.

METHODS

A total of 167 IBD patients and 101 healthy controls were genotyped by polymerase chain reaction-restriction fragment length polymorphism. Using the genotype data attained as the input to various classification algorithms, IBD prediction models were designed. The area under the receiver operating characteristic curve (AUROC) was used to measure their performance.

RESULTS

Significant associations were found between Crohn's disease (CD) and minor NOD2 variants, as well as TLR4 299Gly, TNF-α G-308A, IL-6 G-174C and IL-1RN VNTR A2 variants, while ulcerative colitis (UC) was associated only with IL-1RN VNTR A2 variants. CD and UC showed highly significant difference in the allelic distribution of TNF-α G-308A, where the A allele was found to be related to CD, and the G allele to UC. A combined effect of patients' gender and TLR4 variants was observed among CD patients. When all analyzed genotype and gender data were used, prediction performance achieved a maximum AUROC of 0.690 for CD and 0.601 for UC dataset.

CONCLUSION

Variations in the genes involved in immune regulation are genetic factors of importance in IBD susceptibility that could potentially be used as predictors of disease development.

Genetic sharing and heritability of paediatric age of onset autoimmune diseases

Autoimmune diseases (AIDs) are polygenic diseases affecting 7-10% of the population in the Western Hemisphere with few effective therapies. Here, we quantify the heritability of paediatric AIDs (pAIDs), including JIA, SLE, CEL, T1D, UC, CD, PS, SPA and CVID, attributable to common genomic variations (SNP-h(2)). SNP-h(2) estimates are most significant for T1D (0.863±s.e. 0.07) and JIA (0.727±s.e. 0.037), more modest for UC (0.386±s.e. 0.04) and CD (0.454±0.025), largely consistent with population estimates and are generally greater than that previously reported by adult GWAS. On pairwise analysis, we observed that the diseases UC-CD (0.69±s.e. 0.07) and JIA-CVID (0.343±s.e. 0.13) are the most strongly correlated. Variations across the MHC strongly contribute to SNP-h(2) in T1D and JIA, but does not significantly contribute to the pairwise rG. Together, our results partition contributions of shared versus disease-specific genomic variations to pAID heritability, identifying pAIDs with unexpected risk sharing, while recapitulating known associations between autoimmune diseases previously reported in adult cohorts.

Enrichment of Minor Alleles of Common SNPs and Improved Risk Prediction for Parkinson's Disease

Parkinson disease (PD) is the second most common neurodegenerative disorder in the aged population and thought to involve many genetic loci. While a number of individual single nucleotide polymorphisms (SNPs) have been linked with PD, many remain to be found and no known markers or combinations of them have a useful predictive value for sporadic PD cases. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have recently been shown to be linked with quantitative variations of numerous complex traits in model organisms with higher MAC more likely linked with lower fitness. Here we found that PD cases had higher MAC than matched controls. A set of 37564 SNPs with MA (MAF < 0.4) more common in cases (P < 0.05) was found to have the best predictive accuracy. A weighted risk score calculated by using this set can predict 2% of PD cases (100% specificity), which is comparable to using familial PD genes to identify familial PD cases. These results suggest a novel genetic component in PD and provide a useful genetic method to identify a small fraction of PD cases.

Genetic studies of Crohn's disease: past, present and future

The exact aetiology of Crohn's disease is unknown, though it is clear from early epidemiological studies that a combination of genetic and environmental risk factors contributes to an individual's disease susceptibility. Here, we review the history of gene-mapping studies of Crohn's disease, from the linkage-based studies that first implicated the NOD2 locus, through to modern-day genome-wide association studies that have discovered over 140 loci associated with Crohn's disease and yielded novel insights into the biological pathways underlying pathogenesis. We describe on-going and future gene-mapping studies that utilise next generation sequencing technology to pinpoint causal variants and identify rare genetic variation underlying Crohn's disease risk. We comment on the utility of genetic markers for predicting an individual's disease risk and discuss their potential for identifying novel drug targets and influencing disease management. Finally, we describe how these studies have shaped and continue to shape our understanding of the genetic architecture of Crohn's disease.

Smoking quit success genotype score predicts quit success and distinct patterns of developmental involvement with common addictive substances

Genotype scores that predict relevant clinical outcomes may detect other disease features and help direct prevention efforts. We report data that validate a previously established v1.0 smoking cessation quit success genotype score and describe striking differences in the score in individuals who display differing developmental trajectories of use of common addictive substances. In a cessation study, v1.0 genotype scores predicted ability to quit with P=0.00056 and area under receiver-operating characteristic curve 0.66. About 43% vs 13% quit in the upper vs lower genotype score terciles. Latent class growth analyses of a developmentally assessed sample identified three latent classes based on substance use. Higher v1.0 scores were associated with (a) higher probabilities of participant membership in a latent class that displayed low use of common addictive substances during adolescence (P=0.0004) and (b) lower probabilities of membership in a class that reported escalating use (P=0.001). These results indicate that: (a) we have identified genetic predictors of smoking cessation success, (b) genetic influences on quit success overlap with those that influence the rate at which addictive substance use is taken up during adolescence and (c) individuals at genetic risk for both escalating use of addictive substances and poor abilities to quit may provide especially urgent focus for prevention efforts.

Genetics of proteasome diseases

The proteasome is a large, multiple subunit complex that is capable of degrading most intracellular proteins. Polymorphisms in proteasome subunits are associated with cardiovascular diseases, diabetes, neurological diseases, and cancer. One polymorphism in the proteasome gene PSMA6 (-8C/G) is associated with three different diseases: type 2 diabetes, myocardial infarction, and coronary artery disease. One type of proteasome, the immunoproteasome, which contains inducible catalytic subunits, is adapted to generate peptides for antigen presentation. It has recently been shown that mutations and polymorphisms in the immunoproteasome catalytic subunit PSMB8 are associated with several inflammatory and autoinflammatory diseases including Nakajo-Nishimura syndrome, CANDLE syndrome, and intestinal M. tuberculosis infection. This comprehensive review describes the disease-related polymorphisms in proteasome genes associated with human diseases and the physiological modulation of proteasome function by these polymorphisms. Given the large number of subunits and the central importance of the proteasome in human physiology as well as the fast pace of detection of proteasome polymorphisms associated with human diseases, it is likely that other polymorphisms in proteasome genes associated with diseases will be detected in the near future. While disease-associated polymorphisms are now readily discovered, the challenge will be to use this genetic information for clinical benefit.

Penalized Regression and Risk Prediction in Genome-Wide Association Studies

An important task in personalized medicine is to predict disease risk based on a person's genome, e.g. on a large number of single-nucleotide polymorphisms (SNPs). Genome-wide association studies (GWAS) make SNP and phenotype data available to researchers. A critical question for researchers is how to best predict disease risk. Penalized regression equipped with variable selection, such as LASSO and SCAD, is deemed to be promising in this setting. However, the sparsity assumption taken by the LASSO, SCAD and many other penalized regression techniques may not be applicable here: it is now hypothesized that many common diseases are associated with many SNPs with small to moderate effects. In this article, we use the GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) to investigate the performance of various unpenalized and penalized regression approaches under true sparse or non-sparse models. We find that in general penalized regression outperformed unpenalized regression; SCAD, TLP and LASSO performed best for sparse models, while elastic net regression was the winner, followed by ridge, TLP and LASSO, for non-sparse models.

Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease

We performed risk assessment for Crohn's disease (CD) and ulcerative colitis (UC), the two common forms of inflammatory bowel disease (IBD), by using data from the International IBD Genetics Consortium's Immunochip project. This data set contains ~17,000 CD cases, ~13,000 UC cases, and ~22,000 controls from 15 European countries typed on the Immunochip. This custom chip provides a more comprehensive catalog of the most promising candidate variants by picking up the remaining common variants and certain rare variants that were missed in the first generation of GWAS. Given this unprecedented large sample size and wide variant spectrum, we employed the most recent machine-learning techniques to build optimal predictive models. Our final predictive models achieved areas under the curve (AUCs) of 0.86 and 0.83 for CD and UC, respectively, in an independent evaluation. To our knowledge, this is the best prediction performance ever reported for CD and UC to date.

Risk prediction for complex diseases: application to Parkinson disease

PURPOSE

The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease.

METHODS

A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk prediction. Risk allele scores were calculated as the weighted count of the minor alleles. Five models were constructed. Discriminatory capability was evaluated using the area under the curve.

RESULTS

Both family history and genetic risk scores increased risk for Parkinson disease. Although the fullest model, which included both family history and genetic risk information, resulted in the highest area under the curve, there were no significant differences between models using family history alone and those using genetic information alone.

CONCLUSION

Adding genome-wide association study-derived genotypes, family history information, or both to standard demographic risk factors for Parkinson disease resulted in an improvement in discriminatory capacity. In the full model, the contributions of genotype data and family history information to discriminatory capacity were similar, and both were statistically significant. This suggests that there is limited overlap between genetic risk factors identified through genome-wide association study and unmeasured susceptibility variants captured by family history. Our results are similar to those of studies of other complex diseases and indicate that genetic risk prediction for Parkinson disease requires identification of additional genetic risk factors and/or better methods for risk prediction in order to achieve a degree of risk prediction that is clinically useful.Genet Med 2013:15(5):361-367.

Extended haplotype association study in Crohn's disease identifies a novel, Ashkenazi Jewish-specific missense mutation in the NF-κB pathway gene, HEATR3

The Ashkenazi Jewish population has a several-fold higher prevalence of Crohn’s disease compared to non-Jewish European ancestry populations and has a unique genetic history. Haplotype association is critical to Crohn’s disease etiology in this population, most notably at NOD2, in which three causal, uncommon, and conditionally independent NOD2 variants reside on a shared background haplotype. We present an analysis of extended haplotypes which showed significantly greater association to Crohn’s disease in the Ashkenazi Jewish population compared to a non-Jewish population (145 haplotypes and no haplotypes with P-value < 10⁻³, respectively). Two haplotype regions, one each on chromosomes 16 and 21, conferred increased disease risk within established Crohn’s disease loci. We performed exome sequencing of 55 Ashkenazi Jewish individuals and follow-up genotyping focused on variants in these two regions. We observed Ashkenazi Jewish-specific nominal association at R755C in TRPM2 on chromosome 21. Within the chromosome 16 region, R642S of HEATR3 and rs9922362 of BRD7 showed genome-wide significance. Expression studies of HEATR3 demonstrated a positive role in NOD2-mediated NF-κB signaling. The BRD7 signal showed conditional dependence with only the downstream rare Crohn’s disease-causal variants in NOD2, but not with the background haplotype; this elaborates NOD2 as a key illustration of synthetic association.

Power and predictive accuracy of polygenic risk scores

Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.

Recently there has been much interest in combining multiple genetic markers into a single score for predicting disease risk. Even if many of the individual markers have no detected effect, the combined score could be a strong predictor of disease. This has allowed researchers to demonstrate that some diseases have a strong genetic basis, even if few actual genes have been identified, and it has also revealed a common genetic basis for distinct diseases. These analyses have so far been performed opportunistically, with mixed results. Here I derive formulae based on the heritability of disease and size of the study, allowing researchers to plan their analyses from a more informed position. I show that discouraging results in some previous studies were due to the low number of subjects studied, but a modest increase in study size would allow more successful analysis. However, I also show that, for genetics to become useful for predicting individual risk of disease, hundreds of thousands of subjects may be needed to estimate the gene effects. This is larger than most existing studies, but will become more common in the near future, so that gene scores will become more useful for predicting disease than has appeared to date.

Genetic risk prediction in a small cohort of healthy adults in Atlanta

Compared with single markers, polygenic scores that evaluate the joint effects of multiple trait-associated variants are more effective in explaining the variance of traits and risk of diseases. In total, 182 CHDWB (Emory-Georgia Tech Center for Health Discovery and Well Being study) adults were genotyped to investigate the common variant contributions to three traits (height, BMI, serum triglycerides) and three diseases (coronary artery disease (CAD), type 2 diabetes (T2D) and asthma). Association was contrasted between weighted and simple allelic sum polygenic scores with quantitative traits, and with the Framingham risk scores for CAD and T2D. Although the cohort size is two or three orders of magnitude smaller than typical discovery cohorts, we were able to detect significant associations and to explain up to 5% of the traits by the genetic risk scores, despite a strong influence of outliers. An unexpected finding was that CAD-associated single nucleotide polymorphisms (SNPs) explain a significant amount of the variation for total serum cholesterol. Forward step-wise sequential addition of SNPs into the regression model showed that the top-ranked SNPs explain a large proportion of variance, whereas inclusion of gender and ethnicity also affect the performance of polygenic scores.

HapBoost: a fast approach to boosting haplotype association analyses in genome-wide association studies

Genome-wide association study (GWAS) has been successful in identifying genetic variants that are associated with complex human diseases. In GWAS, multilocus association analyses through linkage disequilibrium (LD), named haplotype-based analyses, may have greater power than single-locus analyses for detecting disease susceptibility loci. However, the large number of SNPs genotyped in GWAS poses great computational challenges in the detection of haplotype associations. We present a fast method named HapBoost for finding haplotype associations, which can be applied to quickly screen the whole genome. The effectiveness of HapBoost is demonstrated by using both synthetic and real data sets. The experimental results show that the proposed approach can achieve comparably accurate results while it performs much faster than existing methods.

Have genomic discoveries in inflammatory bowel disease translated into clinical progress?

Inflammatory bowel disease (IBD) is a heterogeneous disease that can be challenging to diagnose and manage. As a result, significant efforts have been made in attempting to identify clinical, genomic, and serologic markers of disease that can aid in patient assessment and treatment. Recent genomic discoveries have the potential to change clinical practice by identifying those susceptible to IBD, predict natural history and guide choice of therapy. Panels of genetic and genomic markers are more likely to emerge as clinical tools, as opposed to individual allelic variants. Serology and biomarkers are already being used and guiding management but await integration with genomic panels before achieving their maximal potential. This article reviews the current state of IBD genetics and evolving molecular approaches that may have potential clinical impact.

Genetics of spondyloarthritis--beyond the MHC

Ankylosing spondylitis (AS), psoriasis and inflammatory bowel disease (IBD) often coexist in the same patient and in their families. In AS, genes within the MHC region, in particular HLA-B27, account for nearly 25% of disease hereditability, with additional small contributions from genes outside of the MHC locus, including those involved in intracellular antigen processing (that is, ERAP1, which interacts with HLA-B27) and cytokine genes such as those involved in the IL-17-IL-23 pathway. Similar to AS, the strongest genetic signal of susceptibility to psoriasis and psoriatic arthritis also emanates from the MHC region (attributable mostly to HLA-C(*)06:02 although other genes have been implicated), and gene-gene interaction of HLA-C with ERAP1. The remaining hereditary load is from genes involved in cytokine production, specifically genes in the IL-17-IL-23 pathway, the NFκB pathway and the type 2 T-helper pathway. In IBD, similar genetic influences are operative. Indeed, genes important in the regulation of the IL-17-IL-23 pathway and, in Crohn's disease, genes important for autophagy (that is, NOD2 and ATG16L1 and IRGM) have a role in conferring susceptibility of individuals to these diseases. Thus, AS, psoriasis and IBD seem to share similar pathogenic mechanisms of aberrant intracellular antigen processing or elimination of intracellular bacteria and cytokine production, especially in the IL-17-IL-23 pathway.

Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!

One of the most popular modeling approaches to genetic risk prediction is to use a summary of risk alleles in the form of an unweighted or a weighted genetic risk score, with weights that relate to the odds for the phenotype in carriers of the individual alleles. Recent contributions have proposed the use of Bayesian classification rules using Naïve Bayes classifiers. We examine the relation between the two approaches for genetic risk prediction and show that the methods are mathematically related. In addition, we study the properties of the two approaches and describe how they can be generalized to include various models of inheritance.

Genetic profiling using genome-wide significant coronary artery disease risk variants does not improve the prediction of subclinical atherosclerosis: the Cardiovascular Risk in Young Finns Study, the Bogalusa Heart Study and the Health 2000 Survey--a meta-analysis of three independent studies

Background

Genome-wide association studies (GWASs) have identified a large number of variants (SNPs) associating with an increased risk of coronary artery disease (CAD). Recently, the CARDIoGRAM consortium published a GWAS based on the largest study population so far. They successfully replicated twelve already known associations and discovered thirteen new SNPs associating with CAD. We examined whether the genetic profiling of these variants improves prediction of subclinical atherosclerosis – i.e., carotid intima-media thickness (CIMT) and carotid artery elasticity (CAE) – beyond classical risk factors.

Subjects and Methods

We genotyped 24 variants found in a population of European ancestry and measured CIMT and CAE in 2001 and 2007 from 2,081, and 2,015 subjects (aged 30–45 years in 2007) respectively, participating in the Cardiovascular Risk in Young Finns Study (YFS). The Bogalusa Heart Study (BHS; n = 1179) was used as a replication cohort (mean age of 37.5). For additional replication, a sub-sample of 5 SNPs was genotyped for 1,291 individuals aged 46–76 years participating in the Health 2000 population survey. We tested the impact of genetic risk score (GRS_24SNP/CAD) calculated as a weighted (by allelic odds ratios for CAD) sum of CAD risk alleles from the studied 24 variants on CIMT, CAE, the incidence of carotid atherosclerosis and the progression of CIMT and CAE during a 6-year follow-up.

Results

CIMT or CAE did not significantly associate with GRS_24SNP/CAD before or after adjusting for classical CAD risk factors (p>0.05 for all) in YFS or in the BHS. CIMT and CAE associated with only one SNP each in the YFS. The findings were not replicated in the replication cohorts. In the meta-analysis CIMT or CAE did not associate with any of the SNPs.

Conclusion

Genetic profiling, by using known CAD risk variants, should not improve risk stratification for subclinical atherosclerosis beyond conventional risk factors among healthy young adults.

A two-marker haplotype in the IRF5 gene is associated with inflammatory bowel disease in a North American cohort

Interferon regulatory factor 5 (IRF5) located on human chromosome 7q32 is associated with many chronic inflammatory disorders. IRF5 is the key regulator of proinflammatory cytokines and type I interferons. We surveyed two cohorts of inflammatory bowel disease (IBD) patients from a North American Consortium. Six single-nucleotide polymorphisms and a 5-base-pair (bp) insertion-deletion (CGGGG indel)polymorphism were investigated. Cytokine secretion was measured in primary lymphocytes after toll-like receptor 9 stimulation. Two-marker haplotypes containing the pairs (rs4728142-CGGGG indel) and (CGGGG indel-rs7808907) were associated with IBD protection (P=2.89 × 10(-6), P=9.32 × 10(-4) (non-Jewish ancestry) and P=4.68 × 10(-8), P=2.50 × 10(-8) (Jewish ancestry)) and IBD risk (P=0.004, P=0.003 (Jewish ancestry), respectively. IRF5 polymorphisms were risk factors for IBD in a single cohort. Interleukin-12-p70 cytokine production was higher (P=0.04) in lymphocytes from controls with two alleles of the 5-bp insertion. IRF5 polymorphisms contribute to the risk profile for Crohn's disease and ulcerative colitis along with ancestry and NOD2 genotypes.

Genetic risk prediction in complex disease

Attempting to classify patients into high or low risk for disease onset or outcomes is one of the cornerstones of epidemiology. For some (but by no means all) diseases, clinically usable risk prediction can be performed using classical risk factors such as body mass index, lipid levels, smoking status, family history and, under certain circumstances, genetics (e.g. BRCA1/2 in breast cancer). The advent of genome-wide association studies (GWAS) has led to the discovery of common risk loci for the majority of common diseases. These discoveries raise the possibility of using these variants for risk prediction in a clinical setting. We discuss the different ways in which the predictive accuracy of these loci can be measured, and survey the predictive accuracy of GWAS variants for 18 common diseases. We show that predictive accuracy from genetic models varies greatly across diseases, but that the range is similar to that of non-genetic risk-prediction models. We discuss what factors drive differences in predictive accuracy, and how much value these predictions add over classical predictive tests. We also review the uses and pitfalls of idealized models of risk prediction. Finally, we look forward towards possible future clinical implementation of genetic risk prediction, and discuss realistic expectations for future utility.