Study of effect modifiers of genetically predicted CETP reduction.

Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and body mass index (BMI) to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher high-density lipoprotein cholesterol and lower low-density lipoprotein cholesterol for the same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex differences in cardiovascular outcomes in our data. Our results provide insight into the clinical effects of CETP inhibitors in the presence of effect modification based on genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.

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. The ExPheWas data repository includes sex-stratified and sex-combined gene-based association results from 26 616 genes with 1746 phenotypes measured in up to 413 133 individuals from the UK Biobank. Interactive visualizations are provided through a browser to facilitate data exploration supported by false discovery rate control, and it includes tools for enrichment analysis. The interactive Mendelian randomization module in ExPheWas allows the estimation of causal effects of a genetically predicted exposure on an outcome by using genetic variation in a single gene as the instrumental variable.

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 (npooled = 11,196). A GWAS meta-analysis using Cox modeling for the prediction of incident cancer was conducted in the pooled cohort and sex-stratified. rs13210472 (near HLA-DOA gene) was associated with higher risk of incident cancer amongst women with prevalent coronary artery disease (CAD) taking statins (hazard ratio [HR]: 2.66, 95% confidence interval [CI]: 1.88-3.76, P = 3.5 × 10-8). Using the UK Biobank and focusing exclusively on women statin users with CAD (nfemale = 2952), rs13210472 remained significantly associated with incident cancer (HR: 1.71, 95% CI: 1.14-2.56, P = 9.0 × 10-3). The association was not observed in non-statin users. In this genetic meta-analysis, we have identified a variant in women statin users with prevalent CAD that was associated with incident cancer, possibly implicating the human leukocyte antigen pathway.

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

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. We have developed genipe, a complete genome-wide imputation pipeline which includes automatic reporting, imputed data indexing and management, and a suite of statistical tests for imputed data commonly used in genetic epidemiology (Sequence Kernel Association Test, Cox proportional hazards for survival analysis, and linear mixed models for repeated measurements in longitudinal studies). AVAILABILITY AND IMPLEMENTATION: The genipe package is an open source Python software and is freely available for non-commercial use (CC BY-NC 4.0) at https://github.com/pgxcentre/genipe Documentation and tutorials are available at http://pgxcentre.github.io/genipe CONTACT: louis-philippe.lemieux.perreault@statgen.org or marie-pierre.dube@statgen.orgSupplementary information: Supplementary data are available at Bioinformatics online.

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. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

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. The performance of clustering tools for common polymorphisms is well established, while their performance when conducted with a large proportion of rare variants (where data points are sparse for genotypes containing the rare allele) is less known. We have compared the performance of four clustering tools (GenCall, GenoSNP, optiCall and zCall) for the genotyping of over 10,000 samples using the Illumina's HumanExome BeadChip, which includes 247,870 variants, 90% of which have a minor allele frequency below 5% in a population of European ancestry. Different reference parameters for GenCall and different initial parameters for GenoSNP were tested. Genotyping accuracy was assessed using data from the 1000 Genomes Project as a gold standard, and agreement between tools was measured. RESULTS: Concordance of GenoSNP's calls with the gold standard was below expectations and was increased by changing the tool's initial parameters. While the four tools provided concordance with the gold standard above 99% for common alleles, some of them performed poorly for rare alleles. The reproducibility of genotype calls for each tool was assessed using experimental duplicates which provided concordance rates above 99%. The inter-tool agreement of genotype calls was high for approximately 95% of variants. Most tools yielded similar error rates (approximately 0.02), except for zCall which performed better with a 0.00164 mean error rate. CONCLUSIONS: The GenoSNP clustering tool could not be run straight "out of the box" with the HumanExome BeadChip, as modification of hard coded parameters was necessary to achieve optimal performance. Overall, GenCall marginally outperformed the other tools for the HumanExome BeadChip. The use of experimental replicates provided a valuable quality control tool for genotyping projects with rare variants.

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. In conjunction with a source batch-queuing system, the tool minimizes data manipulation errors, accelerates the completion of the data clean up process and provides informative plots and metrics to guide decision making for statistical analysis. AVAILABILITY AND IMPLEMENTATION: pyGenClean is an open source Python 2.7 software and is freely available, along with documentation and examples, from http://www.statgen.org.

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.

A sex-specific evolutionary interaction between ADCY9 and CETP.

Pharmacogenomic studies have revealed associations between rs1967309 in the adenylyl cyclase type 9 (ADCY9) 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 CETP, rs158477, which is in long-range linkage disequilibrium with rs1967309 in the Peruvian population. The signal is mainly seen in males, a sex-specific result that is replicated in the LIMAA cohort of over 3400 Peruvians. Analyses of RNA-seq data further suggest an epistatic interaction on CETP expression levels between the two SNPs in multiple tissues, which also differs between males and females. We also detected interaction effects of the two SNPs with sex on cardiovascular phenotypes in the UK Biobank, in line with the sex-specific genotype associations found in Peruvians at these loci. We propose that ADCY9 and CETP coevolved during recent human evolution due to sex-specific selection, which points toward a biological link between dalcetrapib's pharmacogene ADCY9 and its therapeutic target CETP.

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 = 4.94 × 10-8) near the natriuretic peptide receptor 3 gene (NPR3). By day 15 of the study, 44%, 54% and 59% of participants with 0, 1, or 2 copies of the effect allele respectively, had symptom remission. In 851 participants not treated with colchicine (placebo), there was a significant association at 9q33.1 (rs62575331; P = 2.95 × 10-8) in interaction with colchicine (P = 1.19 × 10-5) without impact on risk of hospitalisations, highlighting a possibly shared mechanistic pathway. By day 15 of the study, 46%, 62% and 64% of those with 0, 1, or 2 copies of the effect allele respectively, had symptom remission. The findings need to be replicated and could contribute to the biological understanding of COVID-19 symptom remission.

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. METHODS: There were 1522 participants of European ancestry from the COLCOT trial available for the pharmacogenomic study of COLCOT trial. The pharmacogenomic study's primary cardiovascular end point was defined as for the main trial, as time to first occurrence of cardiovascular death, resuscitated cardiac arrest, myocardial infarction, stroke, or urgent hospitalization for angina requiring coronary revascularization. The safety end point was time to the first report of gastrointestinal events. Patients' DNA was genotyped using the Illumina Global Screening array followed by imputation. We performed a genome-wide association study in colchicine-treated patients. RESULTS: None of the genetic variants passed the genome-wide association study significance threshold for the primary cardiovascular end point conducted in 702 patients in the colchicine arm who were compliant to medication. The genome-wide association study for gastrointestinal events was conducted in all 767 patients in the colchicine arm and found 2 significant association signals, one with lead variant rs6916345 (hazard ratio, 1.89 [95% CI, 1.52-2.35], P=7.41×10-9) in a locus which colocalizes with Crohn disease, and one with lead variant rs74795203 (hazard ratio, 2.51 [95% CI, 1.82-3.47]; P=2.70×10-8), an intronic variant in gene SEPHS1. The interaction terms between the genetic variants and treatment with colchicine versus placebo were significant. CONCLUSIONS: We found 2 genomic regions associated with gastrointestinal events in patients treated with colchicine. Those findings will benefit from replication to confirm that some patients may have genetic predispositions to lower tolerability 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. This study evaluates the effect of a variant in HCN4, ivabradine's drug target, on safety and efficacy endpoints. METHODS: We used genetic association testing and Mendelian randomization to predict the effect of ivabradine and heart rate lowering on cardiovascular outcomes. RESULTS: Using data from the UK Biobank and large GWAS consortia, we evaluated the effect of a heart rate-reducing genetic variant at the HCN4 locus encoding ivabradine's drug target. These genetic association analyses showed increases in risk for atrial fibrillation (OR 1.09, 95% CI: 1.06-1.13, P = 9.3 ×10-9) in the UK Biobank. In a cause-specific competing risk model to account for the increased risk of atrial fibrillation, the HCN4 variant reduced incident heart failure in participants that did not develop atrial fibrillation (HR 0.90, 95% CI: 0.83-0.98, P = 0.013). In contrast, the same heart rate reducing HCN4 variant did not prevent a composite endpoint of myocardial infarction or cardiovascular death (OR 0.99, 95% CI: 0.93-1.04, P = 0.61). CONCLUSION: Genetic modelling of ivabradine recapitulates its benefits in heart failure, promotion of atrial fibrillation, and neutral effect on myocardial infarction.

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. Polygenic predictors of disease risk are also changing how patient populations can be stratified, enabling targeted therapeutic interventions to patients more likely to derive the highest benefit, marking a shift from single variant to genomic approaches in pharmacogenomics.

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. We also observed that germline CNVs do not follow the same trend, suggesting a different mechanism. Finally, we observed that DNM were not evenly distributed across the genome, which adds support to the existence of DNM hotspots.

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. A glass pipette was modified by combining it with a hypodermic needle and a silver filament. The injectrode is attached to commercial microsyringe pump for drug delivery. This results in a technique that provides real-time pharmacodynamics feedback through multi-unit extracellular signals originating from the site of drug delivery. As a proof of concept, we recorded neuronal activity from the superior colliculus elicited by flashes of light in rats, concomitantly with delivery of drugs through the injectrode. The injectrode recording capacity permits the functional characterization of the injection site favoring precise control over the localization of drug delivery. Application of this method also extends far beyond what is demonstrated here, as the choice of chemical substance loaded into the injectrode is vast, including tracing markers for anatomic experiments.

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. Here, we aim to compare different CNV calling methods in order to assess their ability to consistently identify CNVs by comparison of the calls in 9 quartets of identical twin pairs. The use of monozygotic twins provides a means of estimating the error rate of each algorithm by observing CNVs that are inconsistently called when considering the rules of Mendelian inheritance and the assumption of an identical genome between twins. The similarity between the calls from the different tools and the advantage of combining call sets were also considered. RESULTS: ERDS and CNVnator obtained the best performance when considering the inherited CNV rate with a mean of 0.74 and 0.70, respectively. Venn diagrams were generated to show the agreement between the different algorithms, before and after filtering out familial inconsistencies. This filtering revealed a high number of false positives for CNVer and Breakdancer. A low overall agreement between the methods suggested a high complementarity of the different tools when calling CNVs. The breakpoint sensitivity analysis indicated that CNVnator and ERDS achieved better resolution of CNV borders than the other tools. The highest inherited CNV rate was achieved through the intersection of these two tools (81%). CONCLUSIONS: This study showed that ERDS and CNVnator provide good performance on whole genome sequencing data with respect to CNV consistency across families, CNV breakpoint resolution and CNV call specificity. The intersection of the calls from the two tools would be valuable for CNV genotyping pipelines.

N-Heterocycles from chromium aminocarbenes.

The initial [2 + 2]-cycloadduct between a chromium aminocarbene and a tethered alkene undergoes a ß-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.