Case-control Dataset Research Articles

Exome-Wide Rare Loss-of-Function Variant Enrichment Study of 21,347 Han Chinese Individuals Identifies Four Susceptibility Genes for Psoriasis

Most psoriasis-related genes or loci identified by GWAS represent common clusters and are located in noncoding regions of the human genome, providing only limited evidence for the roles of rare coding variants in psoriasis. Two exome-wide case-control genotyping data sets (11,245 cases and 11,177 controls) were obtained from our previous study. Quality controls were established for each data set, and the markers remaining in each set were annotated using ANNOVAR. Gene-based analysis was performed on the annotation results. A total of 250 and 35 genes in the Exome_Fine and Exome_Asian array cohorts, respectively, exceeded the threshold (P < 4.43× 10-6). Merged gene-based analysis was then conducted on the same set of SNPs from seven genes common to both arrays, and the chi-square test was used to confirm all gene-based results. Ultimately, four susceptibility genes were identified: BBS7 (Pcombine= 1.38× 10-29), GSTCD (Pcombine= 8.35× 10-47), LIPK (Pcombine= 1.02× 10-19), and PPP4R3B (Pcombine= 1.79× 10-33).This study identified four susceptibility genes for psoriasis via a gene-based method using rare variants, contributing to our understanding of the pathogenesis of psoriasis.

ClearF: a supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction

BackgroundFeature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-theoretic approaches. However, most of these methods generally require a long processing time. In addition, information-theoretic methods discretize continuous features, which is a drawback that can lead to the loss of information.ResultsIn this paper, a novel supervised feature scoring method named ClearF is proposed. The proposed method is suitable for continuous-valued data, which is similar to the principle of feature selection using mutual information, with the added advantage of a reduced computation time. The proposed score calculation is motivated by the association between the reconstruction error and the information-theoretic measurement. Our method is based on class-wise low-dimensional embedding and the resulting reconstruction error. Given multi-class datasets such as a case-control study dataset, low-dimensional embedding is first applied to each class to obtain a compressed representation of the class, and also for the entire dataset. Reconstruction is then performed to calculate the error of each feature and the final score for each feature is defined in terms of the reconstruction errors. The correlation between the information theoretic measurement and the proposed method is demonstrated using a simulation. For performance validation, we compared the classification performance of the proposed method with those of various algorithms on benchmark datasets.ConclusionsThe proposed method showed higher accuracy and lower execution time than the other established methods. Moreover, an experiment was conducted on the TCGA breast cancer dataset, and it was confirmed that the genes with the highest scores were highly associated with subtypes of breast cancer.

Potential role of TP63 in apical periodontitis development.

To investigate the expression of TP63 in apical periodontitis (AP) tissues and the association of single nucleotide polymorphisms (SNPs) in the TP63 gene with AP using a case-control dataset. Expression of TP63 in human AP lesions (apical abscess, radicular cyst, periapical granuloma) was evaluated using immunohistochemistry. A case-control association study was performed to assess the association of TP63 polymorphisms in individuals having AP with or without associated pain. Cases were defined as subjects with deep caries and AP (n=151) and subjects with symptomatic apical periodontitis or acute apical abscess (n=124). Subjects without AP (n=169) and asymptomatic (n=196) were used as controls, respectively. Saliva samples were collected as source of genomic DNA. Twelve SNPs in the TP63 gene were selected for genotyping using Taqman chemistry in real-time PCR. Data analysis was performed using PLINK software. The Bonferroni method was applied to correct for multiple testing; α≤0.004 indicates significant differences between groups. TP63 expression was evident in apical abscesses and radicular cysts, while weaker expression was observed in periapical granulomas. Positive expression was observed in mononuclear cells in the granulation tissues of all AP lesions. Regarding the presence of AP, a trend for allelic association was observed for rs16864812 and rs9810322 (P=0.04) and rs9810322 genotypes were also nominally associated with AP under a dominant model (P=0.04). When considering the presence of periapical pain, a trend for allelic and genotypic association was observed for rs10155037 (P=0.03). Haplotypes were also associated with AP and periapical pain (P≤0.05). Apical periodontitis is a complex multifactorial condition and it is likely that multiple genes and environmental effects may influence its susceptibility, progression or both. TP63 variants may play a role in AP pathogenesis and susceptibility, individually or interactively with other genes. Additional studies in other populations and functional studies are needed to improve understanding of the role of TP63 in AP.

A Genome-wide association study of metastatic prostate cancer.

160 Background: There are no biomarkers currently used in clinical practice that can predict which men will develop metastatic prostate cancer (mPrCa). Germline genetic variants that are highly associated with metastatic disease could be used for such prediction and may be identifiable from genome-wide association analyses (GWAS) conducted in appropriately scaled mPrCa case-control datasets. Methods: 451 mPrCa cases sequentially sampled from University of Utah/Huntsman Cancer Institute clinics were genotyped on the Illumina OmniExpress SNP platform and population-matched via principal components analysis to 1043 controls from dbGaP study phs000733 genotyped on the Illumina 5M SNP platform. The mPrCa cases and controls were analyzed via genome-wide association to identify SNPs highly associated with mPrCa. The dataset was then merged and re-analyzed with 176 lethal prostate cancer cases (identified in the Utah SEER Tumor Registry with a linked Utah death certificate including prostate cancer as a cause of death) in an attempt to identify a larger number of potential candidate SNPs associated with metastatic or lethal prostate disease. Results: The GWAS of mPrCa cases identified 4 significant SNPs at the 8q24.21 locus (p &lt; 5e-8), as well as 9 SNPs in 6 previously unreported regions (5q14.1, 6p24.1, 8q21.3, 15q13.3, 15q22.2, and 17q25.1) with suggestive evidence for association (p &lt; 1e-6). In addition, 30 SNPs with nominal evidence (p &lt; 0.001) in the mPrCa-only GWAS appeared with more extreme p-values when the 176 lethal subjects were included in the analysis, and may be appropriate candidate variants to test in external datasets. Conclusions: In this case-control GWAS study of 627 men with mPrCa or lethal PrCa, germline SNPs at the 8q24.21 locus and 6 previously unreported regions may be associated with mPrCa. These findings should be validated in external datasets of men with mPrCa.

Higher Breast Cancer Risk Among Immigrant Asian American Women Than Among US-Born Asian American Women.

IntroductionGiven rising rates of breast cancer in parts of Asia, immigrant Asian American women in the United States may have higher rates of breast cancer than previously anticipated. This study examined breast cancer risk among Asian American women by nativity and percentage of life lived in the United States, accounting for established breast cancer risk factors.MethodsWe analyzed a breast cancer case-control data set of Asian American women living in the San Francisco Bay Area; this data set included 132 cases of women with breast cancer selected from a Surveillance, Epidemiology, and End Results cancer registry and 438 Asian American women without diagnosed breast cancer matched to cases by age and country of origin. We used logistic regression to compare 3 Asian American groups: US-born, immigrants who lived 50% or more of their life in the United States, and immigrants who lived less than 50% of their life in the United States.ResultsIn the minimally adjusted and fully adjusted models, both groups of immigrant Asian American women had higher risk of breast cancer than US-born Asian American women. In the fully adjusted model, compared with US-born Asian American women, immigrant Asian American women who lived more than 50% of their life in United States were on average 3 times as likely (odds ratio = 3.00; 95% confidence interval, 1.56–5.75) and immigrants who lived less than 50% of their life in United States were on average 2.46 times as likely (odds ratio = 2.46; 95% confidence interval, 1.21–4.99) to have breast cancer. We found no difference in fully adjusted odds ratios of having breast cancer between the 2 immigrant groups.ConclusionThis study provides preliminary evidence that breast cancer risk among immigrant Asian American women may be higher than among their US-born counterparts.

Cardiovascular, antidepressant and immunosuppressive drug use in relation to risk of cutaneous melanoma: a protocol for a prospective case–control study

IntroductionThe incidence of cutaneous melanoma (hereafter melanoma) has increased dramatically among fair-skinned populations worldwide. In Norway, melanoma is the most rapidly growing type of cancer, with a 47% increase among...

Abstract TP514: The Effect of Statin Adherence on Cardiovascular Outcomes After Ischemic Stroke in Statin Benefit Group: A Nested Case-Control Study

Backgrounds: Irrespective of the indication for statin, statin adherence after ischemic stroke was associated with better clinical outcome. We studied the association between statin adherence and recurrent cardiovascular (CV) events among the patients with acute ischemic stroke and statin benefit. Methods: Using Comprehensive Registry Collaboration for Stroke in Korea (CRCS-K), patients with acute ischemic stroke were identified. Statin benefit was defined using the secondary prevention guidelines of American Stroke Association in 2014. The data for statin prescription after discharge were obtained from National Health Insurance Services (NHIS) claim data by matching with CRCS-K. Statin adherence was measured by the medication possession ratio (MPR), which was dichotomized, setting a threshold of MPR (&lt;80%) to identify patients who were nonadherent. A nested case-control design was used to evaluate the occurrence of recurrent stroke, myocardial infarction, and all-cause mortality for 1 year after discharge. Each case was matched to 4 controls by age, sex, initial stroke severity and registry entry time. Adjusted conditional logistic regression models were used to estimate the odds ratio (OR) of CV events. Results: Among 11,768 patients in NHIS-CRCS-K matching database, 5,504 patients were the statin benefit group and prescribed with statin on discharge. Overall MPR for 1 year after discharge was 73.1%. Overall CV events for 1 year were 569, which was more prevalent in low adherence group. (MPR &lt;80%, 18.0% vs. ≥80%, 6.5%, p&lt;0.0001) In nested case-control data set, lower adherence to statin was associated with a higher risk of CV events compared with higher adherence (adjusted OR 1.496 [1.174-1.904]) after adjusting hypertension, diabetes mellitus, atrial fibrillation, history of stroke, history of antiplatelet drug use and discharge modified Rankin Scale score. Statin discontinuation was associated with higher risk compared with adequate statin prescription. (adjusted OR 2.075[1.622-2.655]) Conclusion: Adequate statin adherence in the patients with acute ischemic stroke and statin benefit was significantly associated with recurrent CV events.

Genome and transcriptome analysis highlights the association of rs2180341 variant with breast cancer

Abstract In 2008, a breast cancer genetic variant rs2180341 in 6q22.33 was first identified in Ashkenazi Jews population with P=2.90E-08. However, other studies reported controversial findings. Here, we conducted a three-stage analysis. In stage 1, we used the genome-wide association study datasets and candidate gene study datasets to evaluate the potential association between rs2180341 and breast cancer. In stage 2, we conducted an expression quantitative trait loci (eQTLs) analysis to further evaluate whether rs2180341 is associated with the expression of nearby genes. In stage 3, we evaluated the potential different expression of these nearby genes using case-control gene expression dataset. Our findings showed significant heterogeneity in all these selected genome-wide association study datasets and candidate gene study datasets. The breast cancer variant rs2180341 identified in Ashkenazi Jews population may not be directly replicated in other populations. Meanwhile, eQTLs analysis provides strong evidence implicating rs2180341 as a risk variant regulating the expression of nearby genes involved in kinds of cancers. However, few nearby genes showed significant different expression in breast cancer cases and controls.

Developmental and Genetic Regulation of The Human Cortex Transcriptome In Schizophrenia

GWAS have identified over 108 loci that confer risk for schizophrenia, but risk mechanisms for individual loci are largely unknown. Using developmental, genetic, and illness-based RNA sequencing expression analysis, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and post-natal life. Within patients and controls, we implemented a novel algorithm for RNA quality adjustment, and identified 237 genes significantly associated with diagnosis that replicated in an independent case-control dataset. These genes implicated synaptic processes and were strongly regulated in early development (p < 10-20). Lastly, we found 42.5% of risk variants associate with nearby genes and diverse transcript features that converge on developmental regulation and subsequent dysregulation in illness and 34 loci show convergent directionality with illness association implicating specific causative transcripts. These data offer new targets for modeling schizophrenia risk in cellular systems.

Detecting SNP–SNP Interactions in Imbalanced Case-Control Study

SNP-SNP interactions are particularly informative biomarkers regarding the genetic components of disease risk. However, SNP-SNP interaction identifications are yet limited in imbalanced case-control study. In this study, we proposed a multiobjective multifactor dimensionality reduction (MOMDR) based on three balancing approaches (BMOMDR), including (1) stratified K-fold cross-validation; (2) balanced estimation of ratio between cases and controls; (3) balanced measures of SNP-SNP interactions, to effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR was evaluated by extensive experiments on both simulated imbalanced case-control datasets and real genome-wide data from Wellcome Trust Case Control Consortium (WTCCC). For the simulated datasets, the results indicated that three balancing approaches can enhance the detection success rate of SNP-SNP interaction by MOMDR in imbalanced datasets. For WTCCC datasets, the results of SNP-SNP interaction detection obtained from BMOMDR revealed statistically significant (p <; 0.0001), revealing that BMOMDR can effectively identify SNP-SNP interaction in imbalanced case-control study. BMOMDR is freely available at http://shorturl.at/bluJS.

M20 - COMBINING POLYGENIC RISK SCORES ACROSS SEVERAL TRAITS CAN IMPROVE SCHIZOPHRENIA RISK PREDICTION

Background Schizophrenia is a highly heritable disorder and is known to have a substantial common polygenetic component [1] . The polygenic risk score approach [1] allows common genetic liability to the disorder to be directly estimated in individuals regardless of their affected status. In addition to weakly predicting schizophrenia affected status in independent case-control datasets, polygenic risk score approaches have revealed significant shared SNP heritability between schizophrenia and multiple psychiatric and behavioural traits [2 , 3] . We therefore postulated that combining risk score profiles derived from several traits might improve the prediction of schizophrenia risk. Methods Polygenic risk score profiles were calculated for the CLOZUK schizophrenia GWAS study [4] (11260 cases, 24542 controls) by training on six recent GWAS datasets: schizophrenia (independent of the CLOZUK set), bipolar disorder, bipolar vs schizophrenia [5] , major depression disorder, educational attainment [6] and neuroticism [7] . Principal component analysis (PCA) was employed to generate factor loadings for the six polygenic risk score profiles. The advantage of PCA is that it converts a set of possibly correlated variables (e.g. due to shared genetic liability) into a set of uncorrelated variables, with the first principal component accounting for the greatest degree of variance in the dataset outcome. We investigated the prediction accuracy of the first principal component compared to the schizophrenia polygenic risk score by means of area under the curve (AUC) analysis. Results Our analysis indicates that schizophrenia case prediction accuracy of the combined schizophrenia and neuropsychiatric-related first principal component is increased as compared to the AUC of schizophrenia polygenic risk scores alone. The first principal component also visually separates cases and controls in the CLOZUK individuals based upon their combined (schizophrenia and neuropsychiatric-related traits) profiles. Discussion We have proposed an approach to combine genetic evidence for schizophrenia and neuropsychiatric-related traits into one score and tested its prediction accuracy. This approach takes advantage of the shared genetic risk between schizophrenia, psychiatric disorders and behavioural traits, and at present, can improve the prediction of schizophrenia risk.

\u201cOmics\u201d data integration and functional analyses link Enoyl-CoA hydratase, short chain 1 to drug refractory dilated cardiomyopathy

BackgroundLarge-scale “omics” datasets have not been leveraged and integrated with functional analyses to discover potential drivers of cardiomyopathy. This study addresses the knowledge gap.MethodsWe coupled RNA sequence (RNA-Seq) variant detection and transcriptome profiling with pathway analysis to model drug refractory dilated cardiomyopathy (drDCM) using the BaseSpace sequencing hub and Ingenuity Pathway Analysis. We used RNA-Seq case-control datasets (n = 6 cases, n = 4 controls), exome sequence familial DCM datasets (n = 3 Italians, n = 5 Italians, n = 5 Chinese), and controls from the HapMap project (n = 5 Caucasians, and n = 5 Asians) for disease modeling and putative mutation discovery. Variant replication datasets: n = 128 cases and n = 15 controls. Source of datasets: NCBI Sequence Read Archive. Statistics: Pairwise differential expression analyses to determine differentially expressed genes and t-tests to calculate p-values. We adjusted for false discovery rates and reported q-values. We used chi-square tests to assess independence among variables, the Fisher’s Exact Tests and overlap p-values for the pathways and p-scores to rank network.ResultsData revealed that ECHS1(enoyl-CoA hydratase, short chain 1(log2(foldchange) = 1.63329) hosts a mirtron, MIR3944 expressed in drDCM (FPKM = 5.2857) and not in controls (FPKM = 0). Has-miR3944-3p is a putative target of BAG1 (BCL2 associated athanogene 1(log2(foldchange) = 1.31978) and has-miR3944-5p of ITGAV (integrin subunit alpha V(log2(foldchange) = 1.46107) and RHOD (ras homolog family member D(log2(foldchange) = 1.28851). There is an association between ECHS1:11 V/A(rs10466126) and drDCM (p = 0.02496). The interaction (p = 2.82E-07) between ECHS1:75 T/I(rs1049951) and ECHS1:rs10466126 is associated with drDCM (p < 2.2e-16). ECHS1:rs10466126 and ECHS1:rs1049951 are in linkage disequilibrium (D’ = 1). The interaction (p = 7.84E-08) between ECHS1:rs1049951 and the novel ECHS1:c.41insT variant is associated with drDCM (p < 2.2e-16). The interaction (p = 0.001096) between DBT (Dihydrolipoamide branched chain transacylase E2):384G/S(rs12021720) and ECHS1:rs10466126 is associated with drDCM (p < 2.2e-16). At the mRNA level, there is an association between ECHS1 (log2(foldchange) = 1.63329; q = 0.013927) and DBT (log2(foldchange) = 0.955072; q = 0.0368792) with drDCM. ECHS1 is involved in valine (−log (p = 3.39E00)), isoleucine degradation (p = 0.00457), fatty acid β-oxidation (−log(p) = 2.83E00), and drug metabolism:cytochrome P450 (z-score = 2.07985196) pathways. The mitochondria (−log(p) = 8.73E00), oxidative phosphorylation (−log(p) = 5.35E00) and TCA-cycle II (−log(p) = 2.70E00) are dysfunctional.ConclusionsWe introduce an integrative data strategy that considers the interplay between the DNA, mRNA, and associated pathways, which represents a possible diagnostic, prognostic, biomarker, and personalized treatment discovery approach in genomically heterogeneous diseases.

MelaNostrum: a consensus questionnaire of standardized epidemiologic and clinical variables for melanoma risk assessment by the melanostrum consortium

Many melanoma observational studies have been carried out across different countries and geographic areas using heterogeneous assessments of epidemiologic risk factors and clinical variables. To develop a consensus questionnaire to standardize epidemiologic and clinical data collection for melanoma risk assessment. We used a stepwise strategy that included: compilation of variables from case-control datasets collected at various centres of the MelaNostrum Consortium; integration of variables from published case-control studies; consensus discussion of the collected items by MelaNostrum members; revision by independent experts; addition of online tools and image-based charts; questionnaire testing across centres and generation of a final draft. We developed a core consensus questionnaire (MelanoQ) that includes four separate sections: A. general and demographic data; B. phenotypic and ultraviolet radiation exposure risk factors and lifestyle habits; C. clinical examination, medical and family history; and D. diagnostic data on melanoma (cases only). Accompanying online tools, informative tables, and image-based charts aid standardization. Different subsections of the questionnaire are designed for self-administration, patient interviews performed by a physician or study nurse, and data collection from medical records. The MelanoQ questionnaire is a useful tool for the collection and standardization of epidemiologic and clinical data across different studies, centres, cultures and languages. This will expedite ongoing efforts to compile high-quality data for pooled analyses or meta-analyses and offer a solid base for the design of clinical, epidemiologic and translational studies on melanoma.

DNA methylation derived systemic inflammation indices are associated with head and neck cancer development and survival

ObjectivesHead and neck squamous cell carcinoma (HNSCC) is often associated with chronic systemic inflammation (SI). In the present study, we assessed if DNA methylation-derived SI (mdSI) indices: Neutrophil-to-Lymphocyte ratio (mdNLR) and Lymphocyte-to-Monocyte ratio (mdLMR) are associated with the presence of HNSCC and overall survival (OS). Materials and methodsWe used two peripheral blood DNA methylation datasets: an HNSCC case-control dataset (n = 183) and an HNSCC survival dataset (n = 407) to estimate mdSI indices. We then performed multivariate regressions to test the association between mdSI indices, HNSCC development and OS. ResultsMultivariate logistic regression revealed that elevated mdNLR was associated with increased odds of being an HNSCC case (OR = 3.25, 95% CI = 2.14–5.34, P = 4 × 10−7) while the converse was observed for mdLMR (OR = 0.88, 95% CI = 0.81–0.90, P = 2 × 10−3).In the HNSCC survival dataset, HPV16-E6 seropositive HNSCC cases had an elevated mdLMR (P = 9 × 10−5) and a lower mdNLR (P = 0.003) compared to seronegative patients. Multivariate Cox regression in the HNSCC survival dataset revealed that lower mdLMR (HR = 1.96, 95% CI = 1.30–2.95, P = 0.0013) but not lower mdNLR (HR = 0.68, 95% CI = 0.46–1.00, P = 0.0501) was associated with increased risk of death. ConclusionOur results indicate that mdSI estimated by DNA methylation data is associated with the presence of HNSCC and overall survival. The mdSI indices may be used as a valuable research tool to reliably estimate SI in the absence of cell-based estimates. Rigorous validation of our findings in large prospective studies is warranted in the future.

Alzheimer's Disease Risk Variant rs2373115 Regulates GAB2 and NARS2 Expression in Human Brain Tissues.

Genetic association studies have identified significant association between the GAB2 rs2373115 variant and Alzheimer's disease (AD). However, it is unknown whether rs2373115 affects the regulation of nearby genes. Here, we evaluate the potential effect of rs2373115 on gene expression using multiple eQTL (expression quantitative trait loci) datasets from human brain tissues from the Mayo Clinic brain expression genome-wide association study (eGWAS), the UK Brain Expression Consortium (UKBEC), the Genotype-Tissue Expression (GTEx) project, and the Brain xQTL Serve. Our findings indicate that the rs2373115 C allele is associated with increased NARS2 expression, and both reduced and increased GAB2 expression in human tissues. Using a large-scale AD case-control expression dataset, we found increased GAB2 expression and reduced NARS2 expression in AD cases compared with controls. We believe that our findings provide important information regarding the rs2373115 variant and expression of nearby genes with respect to AD risk.

Integrated profiling of phenotype and blood transcriptome for stress vulnerability and depression

Etiology of depression and its vulnerability remains elusive. Using a latent profile analysis on dimensional personality traits, we previously identified 3 different phenotypes in the general population, namely stress-resilient, -vulnerable, and -resistant groups. Here we performed microarray-based blood gene expression profiling of these 3 groups (n = 20 for each group) in order to identify genes involved in stress vulnerability as it relates to the risk of depression. Identified differentially expressed genes among the groups were most markedly enriched in ribosome-related pathways. These ribosomal genes, which included ribosomal protein L17 (RPL17) and ribosomal protein L34 (RPL34), were upregulated in relation to the stress vulnerability. Protein-protein interaction and correlational co-expression analyses of the differentially expressed genes/non-coding RNAs consistently showed that functional networks involving ribosomes were affected. The significant upregulation of RPL17 and RPL34 was also observed in depressed patients compared to healthy controls, as confirmed in 2 independent case-control datasets by using pooled microarray data and qPCR experiments (total number of subjects was 122 and 166, respectively). Moreover, the upregulation of RPL17 and RPL34 was most marked in DSM-IV major depressive disorder, followed by in bipolar disorder, and then in schizophrenia, suggesting some diagnostic specificity of these markers as well as their general roles in stress vulnerability. These results suggest that ribosomal genes, particularly RPL17 and RPL34, can play integral roles in stress vulnerability and depression across nonclinical and clinical conditions. This study presents an opportunity to understand how multiple psychological traits and underlying molecular mechanisms interact to render individuals vulnerable to depression.

ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information.

Identifying single nucleotide polymorphism (SNP) interactions is considered as a popular and crucial way for explaining the missing heritability of complex diseases in genome-wide association studies (GWAS). Many approaches have been proposed to detect SNP interactions. However, existing approaches generally suffer from the high computational complexity resulting from the explosion of candidate high-order interactions. In this paper, we propose a two-stage approach (called ClusterMI) to detect high-order genome-wide SNP interactions based on significant pairwise SNP combinations. In the screening stage, to alleviate the huge computational burden, ClusterMI firstly applies a clustering algorithm combined with mutual information to divide SNPs into different clusters. Then, ClusterMI utilizes conditional mutual information to screen significant pairwise SNP combinations in each cluster. In this way, there is a higher probability of identifying significant two-locus combinations in each group, and the computational load for the follow-up search can be greatly reduced. In the search stage, two different search strategies (exhaustive search and improved ant colony optimization search) are provided to detect high-order SNP interactions based on the cardinality of significant two-locus combinations. Extensive simulation experiments show that ClusterMI has better performance than other related and competitive approaches. Experiments on two real case-control datasets from Wellcome Trust Case Control Consortium (WTCCC) also demonstrate that ClusterMI is more capable of identifying high-order SNP interactions from genome-wide data.

Mendelian randomization study shows no causal relationship between circulating urate levels and Parkinson's disease.

ObjectiveObservational studies have shown that increased plasma urate is associated with lower risk of Parkinson’s disease (PD), but these studies were not designed to test causality. If a causal relationship exists, then modulating plasma urate levels could be a potential preventive avenue for PD. We used a large two-sample Mendelian randomization (MR) design to assess for a causal relationship between plasma urate and PD risk.MethodsWe used a genetic instrument consisting of 31 independent loci for plasma urate on a case-control genome-wide association study data set, which included 13,708 PD cases and 95,282 controls. Individual effect estimates for each SNP were combined using the inverse-variance weighted (IVW) method. Two additional methods, MR-Egger and a penalized weighted median (PWM)-based approach, were used to assess potential bias attributed to pleiotropy or invalid instruments.ResultsWe found no evidence for a causal relationship between urate and PD, with an effect estimate from the IVW method of odds ratio (OR) 1.03 (95% confidence interval [CI], 0.88–1.20) per 1-standard-deviation increase in plasma urate levels. MR Egger and PWM analyses yielded similar estimates (OR, 0.99 [95% CI, 0.83–1.17] and 0.99 [95% CI, 0.86−1.14], respectively).InterpretationWe did not find evidence for a linear causal protective effect by urate on PD risk. The associations observed in previous observational studies may be, in part, attributed to confounding or reverse causality. In the context of the present findings, strategies to elevate circulating urate levels may not reduce overall PD risk.

Genetic interaction effects reveal lipid-metabolic and inflammatory pathways underlying common metabolic disease risks

BackgroundCommon metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body’s metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors.MethodsWe analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group.ResultsIn contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing β-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension.ConclusionsOur results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

The Relationship Between Health and Homelessness in Scotland

BackgroundHomeless people are among the most vulnerable and socially excluded in society, exhibiting higher premature mortality and multiple morbidity. This study links health and homelessness data for the first time at a national level in Scotland.&#x0D; ObjectivesThis study adds to the evidence base on the relationship between homelessness and health needs, including how homeless people exhibit multiple or complex needs.&#x0D; MethodThis study matches those who have experienced homelessness in Scotland since 2002 (n=430,000) with two people of the same age and sex – at random, one control is chosen from the 20% least deprived areas in Scotland and another from the 20% most deprived. This gives 1.3 million people in total. Each cohort is linked to a wide range of health datasets. More detail on the methodology is presented in a separate talk (Challenges of Analysing Case-Control Datasets: A Health andHomelessness Case Study) at this conference.&#x0D; FindingsAt the 2018 ADRN Health and Wellbeing session, we wish to present on our findings that we are publishing shortly before the conference. Large and complex differences were found between the cohorts in health service use and outcomes, particularly for the homeless. Analysis is presented on the different health datasets used, including acute and mental-health admissions, A&amp;E attendances, outpatient appointments, prescriptions and drugs misuse assessments. We also discuss findings for activity related to particular issues including drugs and alcohol. Furthermore, notable differences were also found between once only and repeat homeless individuals.&#x0D; ConclusionsThe range of linked health data used in this study has enabled invaluable evidence to be collected on the scale, severity and complexity of the health needs of homeless populations in Scotland.