Association Between Circulating Vitamin K Levels, Gut Microbiome, and Type 1 Diabetes: A Mendelian Randomization Study.

Background/objectives: Nutritional deficiencies have been proposed as possible etiological causes for autoimmune diseases, among which type 1 diabetes (T1D). Vitamin K (VK) has potentially positive effects on type 2 diabetes, but its role on T1D in humans remains largely unknown. We aimed to examine the presence of a causal association between VK and T1D using a Mendelian randomization (MR) approach.

ExPheWas: a platform for cis-Mendelian randomization and gene-based association scans.

Establishing the relationship between protein-coding genes and phenotypes has the potential to inform on the molecular etiology of diseases. Here, we describe ExPheWas (exphewas.ca), a gene-based phenome-wide association study browser and platform that enables the conduct of gene-based Mendelian randomization.

A sex-specific evolutionary interaction between and .

Pharmacogenomic studies have revealed associations between rs1967309 in the adenylyl cyclase type 9 () gene and clinical responses to the cholesteryl ester transfer protein (CETP) modulator dalcetrapib, however, the mechanism behind this interaction is still unknown. Here, we characterized selective signals at the locus associated with the pharmacogenomic response in human populations and we show that rs1967309 region exhibits signatures of positive selection in several human populations. Furthermore, we identified a variant in , rs158477, which is in long-range linkage disequilibrium with rs1967309 in the Peruvian population.

Genetics of symptom remission in outpatients with COVID-19.

We conducted a genome-wide association study of time to remission of COVID-19 symptoms in 1723 outpatients with at least one risk factor for disease severity from the COLCORONA clinical trial. We found a significant association at 5p13.3 (rs1173773; P = 4.

Genetic meta-analysis of cancer diagnosis following statin use identifies new associations and implicates human leukocyte antigen (HLA) in women.

We sought to perform a genomic evaluation of the risk of incident cancer in statin users, free of cancer at study entry. Patients who previously participated in two phase IV trials (TNT and IDEAL) with genetic data were used (n = 11,196). A GWAS meta-analysis using Cox modeling for the prediction of incident cancer was conducted in the pooled cohort and sex-stratified.

Pharmacogenomics of the Efficacy and Safety of Colchicine in COLCOT.

Background: The randomized, placebo-controlled COLCOT (Colchicine Cardiovascular Outcomes Trial) has shown the benefits of colchicine 0.5 mg daily to lower the rate of ischemic cardiovascular events in patients with a recent myocardial infarction. Here, we conducted a post hoc pharmacogenomic study of COLCOT with the aim to identify genetic predictors of the efficacy and safety of treatment with colchicine.

A genetic model of ivabradine recapitulates results from randomized clinical trials.

Background: Naturally occurring human genetic variants provide a valuable tool to identify drug targets and guide drug prioritization and clinical trial design. Ivabradine is a heart rate lowering drug with protective effects on heart failure despite increasing the risk of atrial fibrillation. In patients with coronary artery disease without heart failure, the drug does not protect against major cardiovascular adverse events prompting questions about the ability of genetics to have predicted those effects.

Pharmacogenomics of blood lipid regulation.

Blood lipids are important modifiable risk factors for coronary heart disease and various drugs have been developed to target lipid fractions. Considerable efforts have been made to identify genetic variants that modulate responses to drugs in the hope of optimizing their use. Pharmacogenomics and new biotechnologies now allow for meaningful integration of human genetic findings and therapeutic development for increased efficiency and precision of lipid-lowering drugs.

Paternal Age Explains a Major Portion of De Novo Germline Mutation Rate Variability in Healthy Individuals.

De novo mutations (DNM) are an important source of rare variants and are increasingly being linked to the development of many diseases. Recently, the paternal age effect has been the focus of a number of studies that attempt to explain the observation that increasing paternal age increases the risk for a number of diseases. Using disease-free familial quartets we show that there is a strong positive correlation between paternal age and germline DNM in healthy subjects.

genipe: an automated genome-wide imputation pipeline with automatic reporting and statistical tools.

Unlabelled: Genotype imputation is now commonly performed following genome-wide genotyping experiments. Imputation increases the density of analyzed genotypes in the dataset, enabling fine-mapping across the genome. However, the process of imputation using the most recent publicly available reference datasets can require considerable computation power and the management of hundreds of large intermediate files.

Machine learning and data mining in complex genomic data--a review on the lessons learned in Genetic Analysis Workshop 19.

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses.

Simultaneous Electrophysiological Recording and Micro-injections of Inhibitory Agents in the Rodent Brain.

Here we describe a method for the construction of a single-use "injectrode" using commercially accessible and affordable parts. A probing system was developed that allows for the injection of a drug while recording electrophysiological signals from the affected neuronal population. This method provides a simple and economical alternative to commercial solutions.

Comparison of sequencing based CNV discovery methods using monozygotic twin quartets.

Background: The advent of high throughput sequencing methods breeds an important amount of technical challenges. Among those is the one raised by the discovery of copy-number variations (CNVs) using whole-genome sequencing data. CNVs are genomic structural variations defined as a variation in the number of copies of a large genomic fragment, usually more than one kilobase.

N-Heterocycles from chromium aminocarbenes.

The initial [2 + 2]-cycloadduct between a chromium aminocarbene and a tethered alkene undergoes a β-hydrogen elimination very efficiently when triphenylphosphine is added as a ligand. The reaction gives cyclic enamines or homoenamines depending on the substitution on the alkene.

One-pot synthesis of polyunsaturated fatty acid amides with anti-proliferative properties.

A one-pot environmentally friendly transamidation of ω-3 fatty acid ethyl esters to amides and mono- or diacylglycerols was investigated via the use of a polymer-supported lipase. The method was used to synthesize a library of fatty acid monoglyceryl esters and amides. These new derivatives were found to have potent growth inhibition effects against A549 lung cancer cells.

Comparison of genotype clustering tools with rare variants.

Background: Along with the improvement of high throughput sequencing technologies, the genetics community is showing marked interest for the rare variants/common diseases hypothesis. While sequencing can still be prohibitive for large studies, commercially available genotyping arrays targeting rare variants prove to be a reasonable alternative. A technical challenge of array based methods is the task of deriving genotype classes (homozygous or heterozygous) by clustering intensity data points.

pyGenClean: efficient tool for genetic data clean up before association testing.

Unlabelled: Genetic association studies making use of high-throughput genotyping arrays need to process large amounts of data in the order of millions of markers per experiment. The first step of any analysis with genotyping arrays is typically the conduct of a thorough data clean up and quality control to remove poor quality genotypes and generate metrics to inform and select individuals for downstream statistical analysis. We have developed pyGenClean, a bioinformatics tool to facilitate and standardize the genetic data clean up pipeline with genotyping array data.