Statistical testing of covariate effects in conditional copula

model

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Beyond simplified pair-copula constructions

AbstractPair-copula constructions (PCCs) offer great flexibility in modeling multivariate dependence. For inference purposes, however, conditional pair-copulas are often assumed to depend on the conditioning variables only indirectly through the conditional margins. The authors show here that this assumption can be misleading. To assess its validity in trivariate PCCs, they propose a visual tool based on a local likelihood estimator of the conditional copula parameter which does not rely on the simplifying assumption. They establish the consistency of the estimator and assess its performance in finite samples via Monte Carlo simulations. They also provide a real data application

Predictors of Symptomatic Hemorrhage After Endovascular Treatment for Anterior Circulation Occlusions: Turkish Endovascular Stroke Registry

We evaluated the predictive factors of symptomatic intracranial hemorrhage (SICH) in endovascular treatment of stroke. We included 975 ischemic stroke patients with anterior circulation occlusion. Patients that had hemorrhage and an increase of >= 4 points in their National Institutes of Health Stroke Scale (NIHSS) after the treatment were considered as SICH. The mean age of patients was 65.2 +/- 13.1 years and 469 (48.1%) were women. The median NIHSS was 16 (13-18) and Alberta Stroke Program Early CT 9 (8-10). In 420 patients (43.1%), modified Rankin Scale was favorable (0-2) and mortality was observed in 234 (24%) patients at the end of the third month. Patients with high diastolic blood pressure (P<.05) had significantly higher SICH. SICH was significantly higher in those with high NIHSS scores (P<.001), high blood glucose (P<.001), and leukocyte count at admission (P<.05). Diabetes mellitus (DM) (OR 1.90; P<.001), NIHSS (OR 1.07; P<.05), adjuvant intra-arterial thrombolytic therapy (IA-rtPA) (OR, 1.60; P<.05), and puncture-recanalization time (OR 1.01; P<.05) were independent factors of SICH. Higher baseline NIHSS score, longer procedure time, multiple thrombectomy maneuvers, administration of IA-rtPA, and the history of DM are independent predictors of SICH in anterior circulation occlusion

Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density.

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease