Trust Case Control Consortium Dataset Research Articles

Fuzzy-Based Multiobjective Multifactor Dimensionality Reduction for Epistasis Analysis.

Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality. However, the binary classification does not reflect the uncertainty of the H and L classification. In this study, we proposed an empirical fuzzy MOMDR (EFMOMDR) to address the limitations of binary classification using the degree of membership through an empirical fuzzy approach. The EFMOMDR can simultaneously consider two incorporated fuzzy-based measures, including correct classification rate and likelihood rate, and does not require parameter tuning. Simulation studies revealed that EFMOMDR has higher 7.14% detection success rates than MOMDR, indicating that the limitations of binary classification of MOMDR have been successfully improved by empirical fuzzy. Moreover, EFMOMDR was used to analyze coronary artery disease in the Wellcome Trust Case Control Consortium dataset.

Epistasis Analysis Using an Improved Fuzzy C-Means-Based Entropy Approach

Epistasis detection is vital to determining disease susceptibility in the human genome. With rapid advances in technology, multifactor dimensionality reduction (MDR) has become an effective algorithm for epistasis detection. Classification of high-risk (H) and low-risk (L) groups in MDR operations is a key topic, but it has not been thoroughly investigated. In this paper, we propose an improved fuzzy c-means-based entropy (FCME) approach to address the limitations of binary classification. For this approach, the degree of membership in MDR, referred to as FCMEMDR, was used. The FCME approach and MDR measure were integrated to enable more precise differentiation between similar frequencies of multifactor genotypes in the cases of possible epistasis. We used the MDR measures of correct classification rate and likelihood ratio. Numerous simulated datasets were applied, and the experimental results revealed two measures of FCMEMDR with higher detection rates than those of other MDR-based algorithms. Our analysis of binary and fuzzy classifications in MDR operations may offer insights into the problem of uncertainty in H/L classification. Two measures of FCMEMDR detected significant instances of epistasis associated with coronary artery disease in the Wellcome Trust Case Control Consortium dataset. FCMEMDR is freely available at https://gitlab.com/yudalinemail/fcmemdr .

Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions.

Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities. This study proposes a multiobjective MDR (MOMDR) method that is based on a contingency table of MDR as an objective function. MOMDR considers the incorporated measures, including correct classification and likelihood rates, to detect SSIs and adopts set theory to predict the most favorable SSIs with cross-validation consistency. MOMDR enables simultaneously using multiple measures to determine potential SSIs. Three simulation studies were conducted to compare the detection success rates of MOMDR and single-objective MDR (SOMDR), revealing that MOMDR had higher detection success rates than SOMDR. Furthermore, the Wellcome Trust Case Control Consortium dataset was analyzed by MOMDR to detect SSIs associated with coronary artery disease. Availability and implementation: MOMDR is freely available at https://goo.gl/M8dpDg. Supplementary data are available at Bioinformatics online.

Multiple-Criteria Decision Analysis-Based Multifactor Dimensionality Reduction for Detecting Gene-Gene Interactions.

Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.

Pathway Analysis Using Information from Allele-Specific Gene Methylation in Genome-Wide Association Studies for Bipolar Disorder

Bipolar disorder (BPD) is a complex psychiatric trait with high heritability. Despite efforts through conducting genome-wide association (GWA) studies, the success of identifying susceptibility loci for BPD has been limited, which is partially attributed to the complex nature of its pathogenesis. Pathway-based analytic strategy is a powerful tool to explore joint effects of gene sets within specific biological pathways. Additionally, to incorporate other aspects of genomic data into pathway analysis may further enhance our understanding for the underlying mechanisms for BPD. Patterns of DNA methylation play important roles in regulating gene expression and function. A commonly observed phenomenon, allele-specific methylation (ASM) describes the associations between genetic variants and DNA methylation patterns. The present study aimed to identify biological pathways that are involve in the pathogenesis of BPD while incorporating brain specific ASM information in pathway analysis using two large-scale GWA datasets in Caucasian populations. A weighting scheme was adopted to take ASM information into consideration for each pathway. After multiple testing corrections, we identified 88 and 15 enriched pathways for their biological relevance for BPD in the Genetic Association Information Network (GAIN) and the Wellcome Trust Case Control Consortium dataset, respectively. Many of these pathways were significant only when applying the weighting scheme. Three ion channel related pathways were consistently identified in both datasets. Results in the GAIN dataset also suggest for the roles of extracellular matrix in brain for BPD. Findings from Gene Ontology (GO) analysis exhibited functional enrichment among genes of non-GO pathways in activity of gated channel, transporter, and neurotransmitter receptor. We demonstrated that integrating different data sources with pathway analysis provides an avenue to identify promising and novel biological pathways for exploring the underlying molecular mechanisms for bipolar disorder. Further basic research can be conducted to target the biological mechanisms for the identified genes and pathways.

Follow-Up Analysis of Genome-Wide Association Data Identifies Novel Loci for Type 1 Diabetes

OBJECTIVE—Two recent genome-wide association (GWA) studies have revealed novel loci for type 1 diabetes, a common multifactorial disease with a strong genetic component. To fully utilize the GWA data that we had obtained by genotyping 563 type 1 diabetes probands and 1,146 control subjects, as well as 483 case subject–parent trios, using the Illumina HumanHap550 BeadChip, we designed a full stage 2 study to capture other possible association signals.RESEARCH DESIGN AND METHODS—From our existing datasets, we selected 982 markers with P < 0.05 in both GWA cohorts. Genotyping these in an independent set of 636 nuclear families with 974 affected offspring revealed 75 markers that also had P < 0.05 in this third cohort. Among these, six single nucleotide polymorphisms in five novel loci also had P < 0.05 in the Wellcome Trust Case-Control Consortium dataset and were further tested in 1,303 type 1 diabetes probands from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) plus 1,673 control subjects.RESULTS—Two markers (rs9976767 and rs3757247) remained significant after adjusting for the number of tests in this last cohort; they reside in UBASH3A (OR 1.16; combined P = 2.33 × 10−8) and BACH2 (1.13; combined P = 1.25 × 10−6).CONCLUSIONS—Evaluation of a large number of statistical GWA candidates in several independent cohorts has revealed additional loci that are associated with type 1 diabetes. The two genes at these respective loci, UBASH3A and BACH2, are both biologically relevant to autoimmunity.