Proteome-wide mendelian randomization identifies causal plasma proteins in venous thromboembolism development.

Genome-wide association studies (GWAS) have identified numerous risk loci for venous thromboembolism (VTE), but it is challenging to decipher the underlying mechanisms. We employed an integrative analytical pipeline to transform genetic associations to identify novel plasma proteins for VTE. Proteome-wide association studies (PWAS) were determined by functional summary-based imputation leveraging data from a genome-wide association analysis (14,429 VTE patients, 267,037 controls), blood proteomes (1348 cases), followed by Mendelian randomization, Bayesian colocalization, protein-protein interaction, and pathway enrichment analysis. Twenty genetically regulated circulating protein abundances (F2, F11, ABO, PLCG2, LRP4, PLEK, KLKB1, PROC, KNG1, THBS2, SERPINA1, RARRES2, CEL, GP6, SERPINE2, SERPINA10, OBP2B, EFEMP1, F5, and MSR1) were associated with VTE. Of these 13 proteins demonstrated Mendelian randomized correlations. Six proteins (F2, F11, PLEK, SERPINA1, RARRES2, and SERPINE2) had strong support in colocalization analysis. Utilizing multidimensional data, this study suggests PLEK, SERPINA1, and SERPINE2 as compelling proteins that may provide key hints for future research and possible diagnostic and therapeutic targets for VTE.

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy.

Background: Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy. Methods: A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (T pre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus T bio), and clinical variables and T pre (base model plus T pre) were developed separately in 1,168 patients with regular follow-up to evaluate whether T pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T pre. Results: The features selected by AUCRF for the T pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T pre was 0.82 (95% CI: 0.80-0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75-0.97). When the T bio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75-0.97) to 0.92 (95% CI: 0.85-0.98); P = 0.03]. There was no difference in AUC between the base model plus T pre and the base model plus T bio [0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T pre was 0.93 (95% CI: 0.87-0.99) in the external validation set. Conclusion: A pathology T-score prediction (T pre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.

Association between uric acid and risk of venous thromboembolism in East Asian populations: a cohort and Mendelian randomization study.

Background: Serum uric acid (SUA) levels have been associated with an increased risk and recurrence of venous thromboembolism (VTE) in European populations, but the potential causal relationship remains unclear. Large-scale studies on the association between SUA and VTE in East Asian populations are lacking, despite the high prevalence of hyperuricemia in this region. To address this, we conducted a cohort analysis and a two-sample Mendelian randomization (MR) study in East Asian populations. Methods: We collected data on VTE patients from the China Pulmonary Thromboembolism Registry Study (CURES) and compared them to controls obtained from the China Health and Retirement Longitudinal Survey (CHARLS). Propensity score matching (PSM) and cubic-spline models were applied to assess the effect of SUA on VTE risk while adjusting for multiple covariates. We also performed two-sample MR analyses to infer potential causality based on summary statistics from Genome-wide Association Studies (GWAS) of SUA and VTE in the East Asian population. Findings: We found that the SUA levels were higher in VTE patients (317.95 mmol/L) compared to the general population (295.75 mmol/L), and SUA ≥ 325 mmol/L was associated with an increased risk of VTE recurrence (P-value = 0.0001). The univariable MR suggested a causal relationship between elevated SUA and higher VTE risk (Pinverse variance weighted < 0.05), and multivariable MR showed that elevated SUA levels continued to promote the development of VTE after adjusting for multiple covariates (Pmultivariable residual < 0.05). Sensitivity analyses produced similar results for these estimations. Interpretation: Our study provides evidence supporting a robust positive association between SUA and VTE in the East Asian population, and MR analyses suggest that this association is likely to be causal. Our findings underscore the importance of monitoring SUA levels in VTE prevention and call for urgent action to address the growing burden of hyperuricemia in the Asia-Pacific region. Funding: This research was funded by Beijing Nova Program (No. Z211100002121057), National Natural Science Foundation of China (No. 82100065 and No. 62350004), CAMS Innovation Fund for Medical Sciences (No. 2021-I2M-1-061 and No. 2021-1-I2M-001), Elite Medical Professionals project of China-Japan Friendship Hospital (No. ZRJY2021-QM12), National Key Research and Development Project (No. 2021YFF1201200 and No. 2022YFC3341004).

Genome-wide association analyses identified novel susceptibility loci for pulmonary embolism among Han Chinese population.

BACKGROUND: A large proportion of pulmonary embolism (PE) heritability remains unexplained, particularly among the East Asian (EAS) population. Our study aims to expand the genetic architecture of PE and reveal more genetic determinants in Han Chinese. METHODS: We conducted the first genome-wide association study (GWAS) of PE in Han Chinese, then performed the GWAS meta-analysis based on the discovery and replication stages. To validate the effect of the risk allele, qPCR and Western blotting experiments were used to investigate possible changes in gene expression. Mendelian randomization (MR) analysis was employed to implicate pathogenic mechanisms, and a polygenic risk score (PRS) for PE risk prediction was generated. RESULTS: After meta-analysis of the discovery dataset (622 cases, 8853 controls) and replication dataset (646 cases, 8810 controls), GWAS identified 3 independent loci associated with PE, including the reported loci FGG rs2066865 (p-value = 3.81 × 10-14), ABO rs582094 (p-value = 1.16 × 10-10) and newly reported locus FABP2 rs1799883 (p-value = 7.59 × 10-17). Previously reported 10 variants were successfully replicated in our cohort. Functional experiments confirmed that FABP2-A163G(rs1799883) promoted the transcription and protein expression of FABP2. Meanwhile, MR analysis revealed that high LDL-C and TC levels were associated with an increased risk of PE. Individuals with the top 10% of PRS had over a fivefold increased risk for PE compared to the general population. CONCLUSIONS: We identified FABP2, related to the transport of long-chain fatty acids, contributing to the risk of PE and provided more evidence for the essential role of metabolic pathways in PE development.

Association between CES1 rs2244613 and the pharmacokinetics and safety of dabigatran: Meta-analysis and quantitative trait loci analysis.

Objective: To date, the influence of the carboxylesterase 1 (CES1) rs2244613 genotype on the pharmacokinetics (PKs) and safety of dabigatran remains controversial. Hence, a systematic review was performed to study the association between CES1 rs2244613 genotype and the PKs and safety of dabigatran and CES1 relative expression. Methods: In addition to the three English databases (Web of Science, PubMed, and Embase), two Chinese databases (CNKI and Wanfang) were thoroughly revised. The mean differences (MD) and corresponding 95% confidence intervals (CI) were applied to evaluate the differences in PKs between the CES1 rs2244613 genotype. Odds ratio (OR) was used to study the risk for bleeding events between the CES1 rs2244613 genotypes. Subsequent expression quantitative trait loci (eQTL) analyses were performed to evaluate genotype-specific expressions in human tissues. Results: Ten studies (n = 2,777) were included. CES1 rs2244613 G allele carriers exhibited significantly lower dabigatran trough concentrations compared to T allele carriers (MD: -8.00 ng/mL; 95% CI: -15.08 to -0.92; p = 0.03). The risk for bleeding events was significantly lower in carriers of the G allele compared to T allele carriers (OR: 0.65; 95% CI: 0.44-0.96; p = 0.03). Subsequent eQTL analysis showed significant genome-wide expressions in two human tissues, whole blood (p = 5.1 × 10-10) and liver (p = 6.2 × 10-43). Conclusion: Our meta-analysis indicated a definite relation between the CES1 rs2244613 genotype and tolerability variations or pharmacokinetic fluctuations. The carriers of T allele showed higher dabigatran concentrations; therefore, they would benefit from a dose reduction. Systematic review registration: [https://inplasy.com/inplasy-2022-6-0027/], identifier [NPLASY202260027].

Plasma p-tau181 Level Predicts Neurodegeneration and Progression to Alzheimer's Dementia: A Longitudinal Study.

Background: Plasma-based biomarkers would be potential biomarkers for early diagnosis of Alzheimer's disease (AD) because they are more available and cost-effective than cerebrospinal fluid (CSF) or neuroimaging. Therefore, we aimed to evaluate whether phosphorylated tau181 (p-tau181) in plasma could be an accurate AD predictor. Methods: Participants from the ADNI database included 185 cognitively unimpaired subjects with negative Aß (CU-), 66 subjects with pre-clinical AD (CU with positive Aß), 164 subjects with mild cognitive impairment with negative Aß (MCI-), 254 subjects with prodromal AD (MCI with positive Aß), and 98 subjects with dementia. Multiple linear regression models, linear mixed-effects models, and local regression were used to explore cross-sectional and longitudinal associations of plasma p-tau181 with cognition, neuroimaging, or CSF biomarkers adjusted for age, sex, education, and APOE genotype. Besides, Kaplan-Meier and adjusted Cox-regression model were performed to predict the risk of progression to dementia. Receiver operating characteristic analyses were performed to evaluate the predictive value of p-tau181. Results: Plasma p-tau181 level was highest in AD dementia, followed by prodromal AD and pre-clinical AD. In pre-clinical AD, plasma p-tau181 was negatively associated with hippocampal volume (ß = -0.031, p-value = 0.017). In prodromal AD, plasma p-tau181 was associated with decreased global cognition, executive function, memory, language, and visuospatial functioning (ß range -0.119 to -0.273, p-value < 0.05) and correlated with hippocampal volume (ß = -0.028, p-value < 0.005) and white matter hyperintensity volume (WMH) volume (ß = 0.02, p-value = 0.01). In AD dementia, increased plasma p-tau181 was associated with worse memory. In the whole group, baseline plasma p-tau181 was significantly associated with longitudinal increases in multiple neuropsychological test z-scores and correlated with AD-related CSF biomarkers and hippocampal volume (p-value < 0.05). Meanwhile, CU or MCI with high plasma p-tau181 carried a higher risk of progression to dementia. The area under the curve (AUC) of the adjusted model (age, sex, education, APOE genotype, and plasma p-tau181) was 0.78; that of additionally included CSF biomarkers was 0.84. Conclusions: Plasma p-tau181 level is related to multiple AD-associated cognitive domains and AD-related CSF biomarkers at the clinical stages of AD. Moreover, plasma p-tau181 level is related to the change rates of cognitive decline and hippocampal atrophy. Thus, this study confirms the utility of plasma p-tau181 as a non-invasive biomarker for early detection and prediction of AD.

Altered Cerebral Blood Flow in Alzheimer's Disease With Depression.

Background: Depression is common in Alzheimer's disease (AD) with an unclear neural mechanism. This study aimed to investigate the underlying cerebral perfusion associated with depression in AD and evaluate its clinical significance. Method: Twenty-one AD patients and 21 healthy controls (HCs) were enrolled in this study. The depressive symptom was defined according to the Hamilton Depression Rating Scale (HAMD). Nine patients were diagnosed as AD with depression symptoms (HAMD >7). Three-dimensional pseudocontinuous arterial spin labeling MR imaging was conducted to measure regional cerebral blood flow (CBF). Neuropsychological tests covered cognition and depressive scores. Between-group comparisons on clinical variables and regional CBFs, relationship between regional CBF and depressive score, and identification of AD patients with depression were performed using covariance analysis, linear regression, and receiver operating characteristic (ROC) analysis, respectively. Results: Compared with HCs, AD patients without depression exhibited lower gray matter CBF (p = 0.016); compared with AD patients without depression, AD patients with depression had higher CBF in the right supplementary motor area (39.23 vs. 47.91 ml/100 g/min, p = 0.017) and right supramarginal gyrus (35.54 vs. 43.85 ml/100 g/min, p = 0.034). CBF in the right supplementary motor area was correlated with depressive score (ß = 0.46, p = 0.025). The combination of CBF in the right supplementary motor area and supramarginal gyrus and age could identify AD patients with depression from those without depression with a specificity of 100%, sensitivity of 66.67%, accuracy of 85.71%, and area under the curve of 0.87. Conclusions: Our findings suggested that hyperperfusion of the right supplementary motor area and right supramarginal gyrus were associated with depression syndrome in AD, which could provide a potential neuroimaging marker to evaluate the depression state in AD.

W-Test for Genetic Epistasis Testing.

The genetic epistasis effect has been widely acknowledged as an essential contributor to genetic variation in complex diseases. In this chapter, we introduce a powerful and efficient statistical method, called W-test, for genetic epistasis testing. A wtest R package is developed for the implementation of the W-test method, which provides various functions to measure the main effect, pairwise interaction, higher-order interaction, and cis-regulation of SNP-CpG pairs in genetic and epigenetic data. It allows flexible stagewise and exhaustive association testing as well as diagnostic checking on the probability distributions in a user-friendly interface. The wtest package is available in CRAN at https://CRAN.R-project.org/package=wtest .

Whole-Genome Shotgun Sequencing for Nasopharyngeal Microbiome in Pre-school Children With Recurrent Wheezing.

Microbiome mediates early life immune deviation in asthma development. Recurrent wheeze (RW) in pre-school years is a risk factor for asthma diagnosis in school-age children. Dysbiosis exists in asthmatic airways, while its origin in pre-school years and relationship to RW is not clearly defined. This study investigated metagenomics of nasopharyngeal microbiome in pre-school children with RW. We applied whole-genome shotgun sequencing and human rhinovirus (HRV) detection on nasopharyngeal samples collected from three groups of pre-school children: (i) RW group: 16 children at-risk for asthma who were hospitalized for RW, (ii) inpatient control (IC): 18 subjects admitted for upper respiratory infection, and (iii) community control (CC): 36 children without respiratory syndromes. Sequence reads were analyzed by MetaPhlAn2 and HUMAnN2 algorithm for taxonomic and functional identification. Linear discriminant analysis effect size (LEfSe) analysis was used to identify discriminative features. We identified that Moraxella catarrhalis and Dolosigranulum pigrum were predominant species in nasopharynx. RW had lower alpha diversity (Shannon diversity index) than CC (0.48 vs. 1.07; P adj = 0.039), characterized by predominant Proteobacteria. LEfSe analysis revealed D. pigrum was the only discriminative species across groups (LDA = 5.57, P = 0.002), with its relative abundance in RW, IC, and CC being 9.6, 14.2, and 37.3%, respectively (P < 0.05). LEfSe identified five (ribo)nucleotides biosynthesis pathways to be group discriminating. Adjusting for HRV status, pre-school children with RW have lower nasopharyngeal biodiversity, which is associated with Proteobacteria predominance and lower abundance of D. pigrum. Along with discriminative pathways found in RW and CC, these microbial biomarkers help to understand RW pathogenesis.

Relationship between homocysteine levels and post-stroke cognitive impairment in female and male population: from a prospective multicenter study.

BACKGROUND AND OBJECTIVES: To investigate the relationship between homocysteine levels and post-stroke cognitive impairment (PSCI) in Chinese female and male populations with minor acute ischemic stroke or transient ischemic attack. MATERIALS AND METHODS: A total of 1070 participants with clinically confirmed acute minor ischemic stroke or transient ischemic attack and baseline homocysteine information from a nationwide multicenter prospective registry study in China were included in this study. Of these, 919 patients had cognitive assessments at 3-month follow-ups and 584 participants had cognitive assessments at 12-month follow-ups. The incidence of PSCI was defined as a Montreal Cognitive Assessment score ≤22. The differences in homocysteine levels and the incidence of PSCI were compared between female and male populations. Relationships between homocysteine levels and the incidence of PSCI in female and male populations were analyzed using multiple logistic regression, respectively. RESULTS: Females had lower baseline homocysteine levels than males. Compared to males, females had lower education levels, lower rates of smoking and alcohol intake, and higher rates of diabetes and hypertension. No relationship was observed between elevated homocysteine level and 3-month PSCI incidence in either females or males. After adjusting the confounders, elevated baseline homocysteine significantly increased the 12-month PSCI risk (odds ratio 3.28, 95% confidence interval 1.47-7.34, P = 0.004) in females, but not in males (odds ratio 0.86, 95% confidence interval 0.49-1.49, P = 0.586). CONCLUSION: Elevated homocysteine levels increased the 12-month PSCI risk in females, but not in males with minor acute ischemic stroke or transient ischemic attack.

A statistical framework for cross-tissue transcriptome-wide association analysis.

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.

Gene-methylation epistatic analyses via the W-test identifies enriched signals of neuronal genes in patients undergoing lipid-control treatment.

An increasing number of studies are focused on the epigenetic regulation of DNA to affect gene expression without modifications to the DNA sequence. Methylation plays an important role in shaping disease traits; however, previous studies were mainly experiment, based, resulting in few reports that measured gene-methylation interaction effects via statistical means. In this study, we applied the data set adaptive W-test to measure gene-methylation interactions. Performance was evaluated by the ability to detect a given set of causal markers in the data set obtained from the GAW20. Results from simulation data analyses showed that the W-test was able to detect most markers. The method was also applied to chromosome 11 of the experimental data set and identified clusters of genes with neuronal and retinal functions, including MPPED2I, GUCY2E, NAV2, and ZBTB16. Genes from the TRIM family were also identified; these genes are potentially related to the regulation of triglyceride levels. Our results suggest that the W-test could be an efficient and effective method to detect gene-methylation interactions. Furthermore, the identified genes suggest an interesting relationship between lipid levels and the etiology of neurological disorders.

Incorporating methylation genome information improves prediction accuracy for drug treatment responses.

BACKGROUND: An accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data into models for disease prediction. However, it is still a challenge to integrate different types of omics data into prediction models, and the contribution of methylation information to prediction remains to be fully clarified. RESULTS: A stratified drug-response prediction model was built based on an artificial neural network to predict the change in the circulating triglyceride level after fenofibrate intervention. Associated single-nucleotide polymorphisms (SNPs), methylation of selected cytosine-phosphate-guanine (CpG) sites, age, sex, and smoking status, were included as predictors. The model with selected SNPs achieved a mean 5-fold cross-validation prediction error rate of 43.65%. After adding methylation information into the model, the error rate dropped to 41.92%. The combination of significant SNPs, CpG sites, age, sex, and smoking status, achieved the lowest prediction error rate of 41.54%. CONCLUSIONS: Compared to using SNP data only, adding methylation data in prediction models slightly improved the error rate; further prediction error reduction is achieved by a combination of genome, methylation genome, and environmental factors.

The challenge of detecting genotype-by-methylation interaction: GAW20.

BACKGROUND: GAW20 working group 5 brought together researchers who contributed 7 papers with the aim of evaluating methods to detect genetic by epigenetic interactions. GAW20 distributed real data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, including single-nucleotide polymorphism (SNP) markers, methylation (cytosine-phosphate-guanine [CpG]) markers, and phenotype information on up to 995 individuals. In addition, a simulated data set based on the real data was provided. RESULTS: The 7 contributed papers analyzed these data sets with a number of different statistical methods, including generalized linear mixed models, mediation analysis, machine learning, W-test, and sparsity-inducing regularized regression. These methods generally appeared to perform well. Several papers confirmed a number of causative SNPs in either the large number of simulation sets or the real data on chromosome 11. Findings were also reported for different SNPs, CpG sites, and SNP-CpG site interaction pairs. CONCLUSIONS: In the simulation (200 replications), power appeared generally good for large interaction effects, but smaller effects will require larger studies or consortium collaboration for realizing a sufficient power.

Genetic Test, Risk Prediction, and Counseling.

Advancement in technology has nurtured the new era of genetic tests for personalized medicine. In this chapter, we will introduce the current development, challenges, and the outlook of genetic test, disease risk prediction, and genetic counseling. In the first section, we will present the success cases in the areas of molecular classification of tumors, pharmacogenomics, and Mendelian disorders, and the challenges of genetic tests implementations. In the second section, common methods for genetic risk prediction models and evaluation measures will be introduced, as well as challenges in feature reliability, risk model stability, and clinical utility. In the final section, key components of genetic counseling will be introduced, covering individual communications, psychosocial concerns, risk assessments, and follow-ups. Current evidences have shown a promising future for genetic testing and risk prediction; we expect that the advancement of analytical methods, technology, integration of omics data, and the increasing clinical implementation and regulation will continue to pave the way for precision medicine in future.

A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests.

MOTIVATION: Increasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power. RESULTS: We propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data. AVAILABILITY AND IMPLEMENTATION: The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html. CONTACT: maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A W-test collapsing method for rare-variant association testing in exome sequencing data.

Advancement in sequencing technology enables the study of association between complex disorder phenotypes and single-nucleotide polymorphisms with rare mutations. However, the rare genetic variant has extremely small variance and impairs testing power of traditional statistical methods. We introduce a W-test collapsing method to evaluate rare-variant association by measuring the distributional differences between cases and controls through combined log of odds ratio within a genomic region. The method is model-free and inherits chi-squared distribution with degrees of freedom estimated from bootstrapped samples of the data, and allows for fast and accurate P-value calculation without the need of permutations. The proposed method is compared with the Weighted-Sum Statistic and Sequence Kernel Association Test on simulation datasets, and showed good performances and significantly faster computing speed. In the application of real next-generation sequencing dataset of hypertensive disorder, it identified genes of interesting biological functions associated to metabolism disorder and inflammation, including the MACROD1, NLRP7, AGK, PAK6, and APBB1. The proposed method offers an efficient and effective way for testing rare genetic variants in whole exome sequencing datasets.

A fast and powerful W-test for pairwise epistasis testing.

Epistasis plays an essential role in the development of complex diseases. Interaction methods face common challenge of seeking a balance between persistent power, model complexity, computation efficiency, and validity of identified bio-markers. We introduce a novel W-test to identify pairwise epistasis effect, which measures the distributional difference between cases and controls through a combined log odds ratio. The test is model-free, fast, and inherits a Chi-squared distribution with data adaptive degrees of freedom. No permutation is needed to obtain the P-values. Simulation studies demonstrated that the W-test is more powerful in low frequency variants environment than alternative methods, which are the Chi-squared test, logistic regression and multifactor-dimensionality reduction (MDR). In two independent real bipolar disorder genome-wide associations (GWAS) datasets, the W-test identified significant interactions pairs that can be replicated, including SLIT3-CENPN, SLIT3-TMEM132D, CNTNAP2-NDST4 and CNTCAP2-RTN4R The genes in the pairs play central roles in neurotransmission and synapse formation. A majority of the identified loci are undiscoverable by main effect and are low frequency variants. The proposed method offers a powerful alternative tool for mapping the genetic puzzle underlying complex disorders.