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Abstract Background The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed. Results We derive an unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k-mer distributions. The formalism is implemented using a Markov Chain Monte Carlo approach in a k-mer feature space. We introduce a space transformation that reduces the dimensionality of the feature space and a genomic fragment divergence measure that strongly correlates with the method's performance. Pairwise analysis of over 1000 completely sequenced genomes reveals that the vast majority of genomes have sufficient genomic fragment divergence to be amenable for binning using the present formalism. Using a high-performance implementation, the binner is able to classify fragments as short as 400 nt with accuracy over 90% in simulations of low-complexity communities of 2 to 10 species, given sufficient genomic fragment divergence. The method is available as an open source package called LikelyBin. Conclusion An unsupervised binning method based on statistical signatures of short environmental sequences is a viable stand-alone binning method for low complexity samples. For medium and high complexity samples, we discuss the possibility of combining the current method with other methods as part of an iterative process to enhance the resolving power of sorting reads into taxonomic and/or functional bins.

Background: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods., Methods: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter., Results: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders., Conclusions: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis., (© 2023. The Author(s).)

Purpose: Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI)., Patients and Methods: This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances., Results: With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology., Conclusion: The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma., Competing Interests: Declaration of Competing Interest The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (Copyright © 2022. Published by Elsevier Masson SAS.)

Motivation: Sparse regularized regression methods are now widely used in genome-wide association studies (GWAS) to address the multiple testing burden that limits discovery of potentially important predictors. Linear mixed models (LMMs) have become an attractive alternative to principal components (PCs) adjustment to account for population structure and relatedness in high-dimensional penalized models. However, their use in binary trait GWAS rely on the invalid assumption that the residual variance does not depend on the estimated regression coefficients. Moreover, LMMs use a single spectral decomposition of the covariance matrix of the responses, which is no longer possible in generalized linear mixed models (GLMMs)., Results: We introduce a new method called pglmm, a penalized GLMM that allows to simultaneously select genetic markers and estimate their effects, accounting for between-individual correlations and binary nature of the trait. We develop a computationally efficient algorithm based on penalized quasi-likelihood estimation that allows to scale regularized mixed models on high-dimensional binary trait GWAS. We show through simulations that when the dimensionality of the relatedness matrix is high, penalized LMM and logistic regression with PC adjustment fail to select important predictors, and have inferior prediction accuracy compared to pglmm. Further, we demonstrate through the analysis of two polygenic binary traits in a subset of 6731 related individuals from the UK Biobank data with 320K SNPs that our method can achieve higher predictive performance, while also selecting fewer predictors than a sparse regularized logistic lasso with PC adjustment., Availability and Implementation: Our Julia package PenalizedGLMM.jl is publicly available on github: https://github.com/julstpierre/PenalizedGLMM., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2023. Published by Oxford University Press.)

Objective: To compare the diagnostic performance and inter-reader agreement of the CT-based v2019 versus v2005 Bosniak classification systems for risk stratification of cystic renal lesions (CRL)., Methods: This retrospective study included adult patients with CRL identified on CT scan between 2005 and 2018. The reference standard was histopathology or a minimum 4-year imaging follow-up. The studies were reviewed independently by five readers (three senior, two junior), blinded to pathology results and imaging follow-up, who assigned Bosniak categories based on the 2005 and 2019 versions. Diagnostic performance of v2005 and v2019 Bosniak classifications for distinguishing benign from malignant lesions was calculated by dichotomizing CRL into the potential for ablative therapy (III-IV) or conservative management (I-IIF). Inter-reader agreement was calculated using Light's Kappa., Results: One hundred thirty-nine patients with 149 CRL (33 malignant) were included. v2005 and v2019 Bosniak classifications achieved similar diagnostic performance with a sensitivity of 91% vs 91% and a specificity of 89% vs 88%, respectively. Inter-reader agreement for overall Bosniak category assignment was substantial for v2005 (κ = 0.78) and v2019 (κ = 0.75) between senior readers but decreased for v2019 when the Bosniak classification was dichotomized to conservative management (I-IIF) or ablative therapy (III-IV) (0.80 vs 0.71, respectively). For v2019, wall thickness was the morphological feature with the poorest inter-reader agreement (κ = 0.43 and 0.18 for senior and junior readers, respectively)., Conclusion: No significant improvement in diagnostic performance and inter-reader agreement was shown between v2005 and v2019. The observed decrease in inter-reader agreement in v2019 when dichotomized according to management strategy may reflect the more stringent morphological criteria., Key Points: • Versions 2005 and 2019 Bosniak classifications achieved similar diagnostic performance, but the specificity of higher risk categories (III and IV) was not increased while one malignant lesion was downgraded to v2019 Bosniak category II (i.e., not subjected to further follow-up). • Inter-reader agreement was similar between v2005 and v2019 but moderately decreased for v2019 when the Bosniak classification was dichotomized according to the potential need for ablative therapies (I-II-IIF vs III-IV)., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Objective: To distinguish benign from malignant cystic renal lesions (CRL) using a contrast-enhanced CT-based radiomics model and a clinical decision algorithm., Methods: This dual-center retrospective study included patients over 18 years old with CRL between 2005 and 2018. The reference standard was histopathology or 4-year imaging follow-up. Training and testing datasets were acquired from two institutions. Quantitative 3D radiomics analyses were performed on nephrographic phase CT images. Ten-fold cross-validated LASSO regression was applied to the training dataset to identify the most discriminative features. A logistic regression model was trained to classify malignancy and tested on the independent dataset. Reported metrics included areas under the receiver operating characteristic curves (AUC) and balanced accuracy. Decision curve analysis for stratifying patients for surgery was performed in the testing dataset. A decision algorithm was built by combining consensus radiological readings of Bosniak categories and radiomics-based risks., Results: A total of 149 CRL (139 patients; 65 years [56-72]) were included in the training dataset-35 Bosniak(B)-IIF (8.6% malignancy), 23 B-III (43.5%), and 23 B-IV (87.0%)-and 50 CRL (46 patients; 61 years [51-68]) in the testing dataset-12 B-IIF (8.3%), 10 B-III (60.0%), and 9 B-IV (100%). The machine learning model achieved high diagnostic performance in predicting malignancy in the testing dataset (AUC = 0.96; balanced accuracy = 94%). There was a net benefit across threshold probabilities in using the clinical decision algorithm over management guidelines based on Bosniak categories., Conclusion: CT-based radiomics modeling accurately distinguished benign from malignant CRL, outperforming the Bosniak classification. The decision algorithm best stratified lesions for surgery and active surveillance., Key Points: • The radiomics model achieved excellent diagnostic performance in identifying malignant cystic renal lesions in an independent testing dataset (AUC = 0.96). • The machine learning-enhanced decision algorithm outperformed the management guidelines based on the Bosniak classification for stratifying patients to surgical ablation or active surveillance., (© 2021. The Author(s), under exclusive licence to European Society of Radiology.)

Considering the emergence of SARS-CoV-2 variants and low vaccine access and uptake, minimizing human interactions remains an effective strategy to mitigate the spread of SARS-CoV-2. Using a functional principal component analysis, we created a multidimensional mobility index (MI) using six metrics compiled by SafeGraph from all counties in Illinois, Ohio, Michigan and Indiana between January 1 to December 8, 2020. Changes in mobility were defined as a time-updated 7-day rolling average. Associations between our MI and COVID-19 cases were estimated using a quasi-Poisson hierarchical generalized additive model adjusted for population density and the COVID-19 Community Vulnerability Index. Individual mobility metrics varied significantly by counties and by calendar time. More than 50% of the variability in the data was explained by the first principal component by each state, indicating good dimension reduction. While an individual metric of mobility was not associated with surges of COVID-19, our MI was independently associated with COVID-19 cases in all four states given varying time-lags. Following the expiration of stay-at-home orders, a single metric of mobility was not sensitive enough to capture the complexity of human interactions. Monitoring mobility can be an important public health tool, however, it should be modelled as a multidimensional construct., (© 2022. The Author(s).)

Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences ( p -value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

Aim: Low anterior resection syndrome (LARS) refers to a constellation of bowel symptoms that affect the majority of patients following restorative proctectomy. LARS is associated with poorer quality of life (QoL), and can lead to distress, anxiety and isolation. Peer support could be an important resource for people living with LARS, helping them normalize and validate their experience. The aim of this work is to describe the development of an interactive online informational and peer support app for LARS and the protocol for a randomized controlled trial., Method: A multicentre, randomized, assessor-blind, parallel-groups pragmatic trial will involve patients from five large colorectal surgery practices across Canada. The trial will evaluate the impact of an interactive online informational and peer support app for LARS, consisting of LARS informational modules and a closed forum for peers and trained peer support mentors, on patient-reported outcomes of people living with LARS. The primary outcome will be global QoL at 6 months following app exposure. The treatment effect on global QoL will be modelled using generalized estimating equations. Secondary outcomes will include patient activation and bowel function as measured by LARS scores., Results: In order to better understand patients' interest and preferences for an online peer support intervention for LARS, we conducted a single institution cross-sectional survey study of rectal cancer survivors. In total, 35/69 (51%) participants reported interest in online peer support for LARS. Age <65 years (OR 9.1; 95% CI 2.3-50) and minor/major LARS (OR 20; 95% CI 4.2-100) were significant predictors of interest in LARS online peer support., Conclusion: There is significant interest in the use of online peer support for LARS among younger patients and those with significant bowel dysfunction. Based on results of the needs assessment study, the app content and features were modified reflect patients' needs and preferences. We are now in an optimal position to rigorously test the potential effects of this initiative on patient-centered outcomes using a randomized controlled trial., (© 2021 The Association of Coloproctology of Great Britain and Ireland.)

Background: The clinical implications of a polygenic risk score (PRS) for LDL-C (low-density lipoprotein cholesterol) are not well understood, both within the general population and individuals with familial hypercholesterolemia (FH)., Methods: We developed the LDL-C PRS using Least Absolute Shrinkage and Selection Operator regression in 377 286 White British participants from UK Biobank and tested its association with LDL-C according to FH variant carrier status in another 41 748 whole-exome sequenced individuals. Next, we tested for an enrichment of FH variant carriers among individuals with severe hypercholesterolemia and low LDL-C PRS. Last, we contrasted the effect of the LDL-C PRS, measured LDL-C and FH variant carrier status on risk of ischemic heart disease among 3010 cases and 38 738 controls., Results: Among the 41 748 whole-exome sequenced White British individuals, 1-SD increase in the LDL-C PRS was associated with elevated LDL-C among both FH variant carriers (0.34 [95% CI, 0.22-0.47] mmol/L) and noncarriers (0.42 [95% CI, 0.42-0.43] mmol/L). Among individuals with severe hypercholesterolemia, FH variant carriers were enriched in those with a low LDL-C PRS (odds ratio, 2.20 [95% CI, 1.66-2.71] per SD). Each SD increase in the LDL-C PRS was associated with risk of ischemic heart disease to the comparable magnitude as measured LDL-C (odds ratio, 1.24 [95% CI, 1.20-1.29] and odds ratio, 1.15 [95% CI, 1.09-1.23], respectively). The LDL-C PRS was not strongly associated with other traditional ischemic heart disease risk factors., Conclusions: An LDL-C PRS could be used to identify individuals with a higher probability of harboring FH variants. The association between ischemic heart disease and the LDL-C PRS was comparable to measured LDL-C, likely because the PRS reflects lifetime exposure to LDL-C levels.

Introduction: Low anterior resection syndrome (LARS) is described as disordered bowel function after rectal resection that leads to a detriment in quality of life, and affects the majority of individuals following restorative proctectomy for rectal cancer. The management of LARS includes personalised troubleshooting and effective self-management behaviours. Thus, affected individuals need to be well informed and appropriately engaged in their own LARS management. This manuscript describes the development of a LARS patient-centred programme (LPCP) and the study protocol for its evaluation in a randomised controlled trial., Methods and Analysis: This will be a multicentre, randomised, assessor-blind, parallel-groups, pragmatic trial evaluating the impact of an LPCP, consisting of an informational booklet, patient diaries and nurse support, on patient-reported outcomes after restorative proctectomy for rectal cancer. The informational booklet was developed by a multidisciplinary LARS team, and was vetted in a focus group and semistructured interviews involving patients, caregivers, and healthcare professionals. The primary outcome will be global quality of life (QoL), as measured by the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (QLQ-C30), at 6 months after surgery. The treatment effect on global QoL will be modelled using generalised estimating equations. Secondary outcomes include symptom change, patient activation, bowel function measures, emotional distress, knowledge about LARS and satisfaction with the LPCP., Ethics and Dissemination: The Research Ethics Committee (REC) at the Integrated Health and Social Services Network for West-Central Montreal (health network responsible for the Jewish General Hospital) is the overseeing REC for all Quebec sites. They have granted ethical approval (MP-05-2019-1628) for all Quebec hospitals (Jewish General Hospital, McGill University Health Center, CHU de Quebec) and have granted full authorisation to begin research at the Jewish General Hospital. Patient recruitment will not begin at the other Quebec sites until inter-institutional contracts are finalised and feasibility/authorisation for research is granted by their respective REC. The results of this study will be presented at national and international conferences, and a manuscript with results will be submitted for publication in a high-impact peer-reviewed journal., Trial Registration Number: NCT03828318; Pre-results., Competing Interests: Competing interests: ASL receives travel stipends from Merck and Servier, and is on the advisory committee of Novadaq. JFFJ received a research grant from Merck and fees for consulting from Shionogi., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects' relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix)., Competing Interests: The authors have declared that no competing interests exist.

Background: Clostridium difficile colitis is an increasingly important cause of morbidity and mortality. Fulminant C. difficile colitis (FCDC) is a severe form of the colitis driven by a significant systemic inflammatory response, and managed with a total abdominal colectomy. Despite surgery, postoperative mortality rates remain high. The aim of this study was to develop a bedside calculator to predict the risk of 30-day postoperative mortality for patients with FCDC., Methods: After institutional review board approval, the American College of Surgeons National Surgical Quality Improvement Program database (2005-2015) was used to include adult patients who underwent emergency surgery for FCDC. A priori preoperative predictors of mortality were selected from the literature: age, immunosuppression, preoperative shock, intubation, and laboratory values. The predictive accuracy of different logistic regression models was measured by calculating the area under the receiver-operating characteristic curve. A cohort of 124 patients from Québec was used to validate the developed mortality calculator., Results: A total of 557 patients met the inclusion criteria, and the overall mortality was 44%. After developing the calculator, no statistically significant differences were found in comparison with the American College of Surgeons National Surgical Quality Improvement Program probability of mortality available in the database (area under the receiver operating curve, 75.61 vs. 75.14; p = 0.79). External validation with the cohort of patients from Quebec showed an area under the curve of 74.0% (95% confidence interval, 65.0-82.9)., Conclusion: A clinically applicable calculator using preoperative variables to predict postoperative mortality for patients with FCDC was developed and externally validated. This calculator may help guide preoperative decision making., Level of Evidence: Prognostic and epidemiological study, level III.

Objective: Hepatitis C virus (HCV) treatment may reduce liver-related mortality but with competing risks, other causes of mortality may undermine benefits. We examined changes in cause-specific mortality among HIV-HCV coinfected patients before and after scale-up of HCV treatment., Design: Prospective multicentre HIV-HCV cohort study in Canada., Methods: Cause-specific deaths, classified using a modified 'Coding of Cause of Death in HIV' protocol, were determined for two time periods, 2003-2012 and 2013-2017, stratified by age (20-49; 50-80 years). Comparison of trends between periods was performed using Poisson regression. To account for competing risks, multinomial regression was used to estimate the cause-specific hazard ratios of time and age on cause of death, from which end-stage liver disease (ESLD)-specific 5-year cumulative incidence functions were estimated., Results: Overall, 1634 participants contributed 8248 person-years of follow-up; 273 (17%) died. Drug overdose was the most common cause of death overall, followed by ESLD and smoking-related deaths. In 2013-2017, ESLD was surpassed by drug overdose and smoking-related deaths among those aged 20-49 and 50-80, respectively. After accounting for competing risks, comparing 2003-2012 to 2013-2017, ESLD deaths declined (adjusted hazards ratio: 0.18, 95% confidence interval 0.05-0.62). However, both early and late period cumulative incidence functions demonstrated increased risk of death from ESLD for patients with poor HIV control and advanced fibrosis., Conclusion: The gains made in overall mortality with HCV therapy may be thwarted if modifiable harms are not addressed. Although ESLD-related deaths have decreased over time, treatment should be further expanded, prioritizing those with advanced fibrosis.

Background We investigated whether visceral adiposity is associated with more aggressive disease at prostatectomy. Materials and methods Four hundred and seventy-four patients referred for postoperative adjuvant or salvage radiotherapy were included in this study. Primary endpoints were positive surgical margins (pSM) or extracapsular extension (ECE). Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were manually contoured. Univariate and multivariate logistic regression was performed. Results In univariate analysis, VAT volume (p = 0.006), adipose tissue ratio (VAT/SAT, p = 0.003), density of the SAT (p = 0.04), as well as age (p < 0.001) were associated with pSM. In the univariate analysis, SAT density was associated with a trend towards a higher rate of ECE (p = 0.051) but visceral fat volume (p = 0.01), as well as the adipose tissue ratio (p = 0.03) were both protective factors. None of the adipose tissue measurements or BMI had an influence on biochemical recurrence or overall survival (all p ≥ 0.5). Conclusions SAT-volume and increased SAT-density were generally associated with more aggressive prostate cancers whereas VAT as a protective factor. These findings emphasize a possible mechanism for the association between obesity and prostate cancer aggressiveness.

Predicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in the analysis of high-dimensional (HD) data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly used two-step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype. It is known that important exposure variables can alter correlation patterns between clusters of HD variables, that is, alter network properties of the variables. However, it is not well understood whether such altered clustering is informative in prediction. Here, assuming there is a binary exposure with such network-altering effects, we explore whether the use of exposure-dependent clustering relationships in dimension reduction can improve predictive modeling in a two-step framework. Hence, we propose a modeling framework called ECLUST to test this hypothesis, and evaluate its performance through extensive simulations. With ECLUST, we found improved prediction and variable selection performance compared to methods that do not consider the environment in the clustering step, or to methods that use the original data as features. We further illustrate this modeling framework through the analysis of three data sets from very different fields, each with HD data, a binary exposure, and a phenotype of interest. Our method is available in the eclust CRAN package., (© 2018 WILEY PERIODICALS, INC.)

Introduction: Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci., Methods: We apply a recently developed multivariate rare-variant association test to the Genetic Analysis Workshop 19 data in order to test associations between genetic variants and multiple blood pressure phenotypes simultaneously. We also compare this multivariate test with a widely used univariate test that analyzes phenotypes separately., Results: The multivariate test identified 2 genetic variants that have been previously reported as associated with hypertension or coronary artery disease. In addition, our region-based analyses also show that the multivariate test tends to give smaller p values than the univariate test., Conclusions: Hence, the multivariate test has potential to improve test power, especially when multiple phenotypes are correlated.

A statistical departure from Mendel's law of segregation is known as transmission ratio distortion. Although well documented in many other organisms, the extent of transmission ratio distortion and its influence in the human genome remains incomplete. Using Genetic Analysis Workshop 19 whole genome sequence data from 20 large Mexican American pedigrees, our goal was to identify potentially distorted regions in the genome using family-based association methods such as the transmission disequilibrium test, the pedigree disequilibrium test, and the family-based association test. Preliminary results showed an unusually high number of transmission ratio distortion signals identified by the transmission disequilibrium test, but this phenomenon could not be replicated by the pedigree disequilibrium test or family-based association test. Applying these tests to different subsets of the data, we found the transmission disequilibrium test to be very sensitive to imputed genotypes. Regression analysis of transmission ratio distortion test p values controlling for minor allele frequency and quality control checks showed that Hardy Weinberg p values are associated with this inflation. Although the transmission disequilibrium test appears confounded by imputation of single nucleotide polymorphisms, the pedigree disequilibrium test and family-based association test seem to offer more robust alternatives when searching for transmission ratio distortion loci in whole genome sequence data from extended families.