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Clinical end-point traits are often characterized by quantitative and/or qualitative precursors and it has been argued that it may be statistically a more powerful strategy to analyze a multivariate phenotype comprising these precursor traits to decipher the genetic architecture of the underlying complex end-point trait. We (Majumdar et al., 2015) recently developed a Binomial Regression framework that models the conditional distribution of the allelic count at a SNP given a vector of phenotypes. The model does not require a priori assumptions on the probability distributions of the phenotypes. Moreover, it provides the flexibility of incorporating both quantitative and qualitative phenotypes simultaneously. However, it may often arise in practice that data may not be available on all phenotypes for a particular individual. In this study, we explore methodologies to estimate missing phenotypes conditioned on the available ones and carry out the Binomial Regression based test for association on the “complete” data. We partition the vector of phenotypes into three subsets: continuous, count and categorical phenotypes. For each missing continuous phenotype, the trait value is estimated using a conditional normal model. For each missing count phenotype, the trait value is estimated using a conditional Poisson model. For each missing categorical phenotype, the risk of the phenotype status is estimated using a conditional logistic model. We carry out simulations under a wide spectrum of multivariate phenotype models and assess the effect of the proposed imputation strategy on the power of the association test vis-a-vis the ideal situation with no missing data as well as analyses based only on individuals with complete data. We illustrate an application of our method using data on Coronary Artery Disease.

ABSTRACT The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods’ susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

To investigate the repeatability of a combined Dual-Scheimpflug placido disc corneal tomographer/topographer (Ziemer Galilei G4) with respect to keratometric indices used to monitor progression of keratoconus (KCN). Patients with KCN were prospectively enrolled. For each eye lacking history of corneal surgery, 5 measurements were taken in succession. Eyes in which 3 or more measurements could be obtained (defined by the device's 4 image quality metrics) were included in the analysis. The repeatability limits (RL) and interclass correlation coefficients (ICC) were calculated for various parameters. Thirty-two eyes from 25 patients met all image quality metrics, and 54 eyes from 38 patients met at least 3/4 criteria (all except the placido image quality metric). RLs for key parameters when 4/4 or ≥3/4 image quality metrics were met included: 0.37 and 0.77 diopters (D) for steep simulated keratometry, 0.79 and 1.65 D for maximum keratometry, 13.80 and 13.88 degrees for astigmatism axis, 0.64 and 0.56 µm for vertical coma magnitude, and 3.76 and 3.84 µm for thinnest pachymetry, respectively. The ICCs for all parameters were excellent (above 0.87) except for spherical aberration (0.77), which was still considered good. The dual-Scheimpflug placido disc corneal tomographer/topographer is highly repeatable in quantifying parameters used in monitoring KCN. Excellent placido images are difficult to capture in eyes with KCN, but when available, increase the reliability of the measurements. When clinicians find that a topographic index changes by more than the RLs defined herein, they can have confidence that this represents real change and may appropriately recommend interventions such as corneal cross-linking or intrastromal corneal ring segments.

Invasion plasticity allows malignant cells to toggle between collective, mesenchymal and amoeboid phenotypes while traversing extracellular matrix (ECM) barriers. Current dogma holds that collective and mesenchymal invasion programs trigger the mobilization of proteinases that digest structural barriers dominated by type I collagen, while amoeboid activity allows cancer cells to marshal mechanical forces to traverse tissues independently of ECM proteolysis. Here, we use cancer spheroid-3-dimensional matrix models, single-cell RNA sequencing, and human tissue explants to identify the mechanisms controlling mesenchymal versus amoeboid invasion. Unexpectedly, collective/mesenchymal- and amoeboid-type invasion programs - though distinct - are each characterized by active tunneling through ECM barriers, with expression of matrix-degradative metalloproteinases. CRISPR/Cas9-mediated targeting of a single membrane-anchored collagenase, MMP14/MT1-MMP, ablates tissue-invasive activity while co-regulating cancer cell transcriptional programs. Though changes in matrix architecture, nuclear rigidity, and metabolic stress as well as the presence of cancer-associated fibroblasts are proposed to support amoeboid activity, none of these changes restore invasive activity of MMP14-targeted cancer cells. While a requirement for MMP14 is bypassed in low-density collagen hydrogels, invasion by the proteinase-deleted cells is associated with nuclear envelope and DNA damage, highlighting a proteolytic requirement for maintaining nuclear integrity. Nevertheless, when cancer cells confront explants of live human breast tissue, MMP14 is again required to support invasive activity. Corroborating these results, spatial transcriptomic and immunohistological analyses of invasive human breast cancers identified clear expression of MMP14 in invasive cells that were further associated with degraded collagen, underlining the pathophysiologic importance of this proteinase in directing invasive activity .

Hidradenitis Suppurativa (HS) is a chronic, scarring, inflammatory skin disease affecting hair follicles in axillae, inguinal, and anogenital regions. Dysbiosis in HS patients compared to healthy subjects is documented. However, whether dysbiosis is specific to particular body sites or skin niches is unknown. We investigated the follicular and skin surface microbiome of the axilla and groin of HS patients (n=11) and healthy individuals (n=10) using 16S rRNA gene sequencing (V3-V4). We sampled non-lesional (HSN) and lesional skin (HSL) of HS patients. β-diversity was significantly decreased (p<0.05) in HSN and HSL skin compared to normal skin with loss of body site and skin niche heterogeneity in HS samples. The relative bacterial abundance of specific microbes was also significantly different between normal and HSN (15 genera) or HSL (21 genera) skin. Smoking and alcohol use influenced the β-diversity (p<0.08) in HS skin. We investigated metabolic profiles of bacterial communities in HS and normal skin using a computational approach. Metabolism, Genetic Information Processing, and Environmental Information Processing were significantly different between normal and HS samples. Altered metabolic pathways associated with dysbiosis of HS skin suggest mechanisms underlying the disease pathology and information about treatment with drugs targeting those pathways.