Louis-Philippe Lemieux Perreault

Pharmacogenomic Determinants of the Cardiovascular Effects of Dalcetrapib

Background—Dalcetrapib did not improve clinical outcomes, despite increasing high-density lipoprotein cholesterol by 30%. These results differ from other evidence supporting high-density lipoprotein as a therapeutic target. Responses to dalcetrapib may vary according to patients’ genetic profile. Methods and Results—We conducted a pharmacogenomic evaluation using a genome-wide approach in the dal-OUTCOMES study (discovery cohort, n=5749) and a targeted genotyping panel in the dal-PLAQUE-2 imaging trial (support cohort, n=386). The primary endpoint for the discovery cohort was a composite of cardiovascular events. The change from baseline in carotid intima-media thickness on ultrasonography at 6 and 12 months was evaluated as supporting evidence. A single-nucleotide polymorphism was found to be associated with cardiovascular events in the dalcetrapib arm, identifying the ADCY9 gene on chromosome 16 (rs1967309; P=2.41×10–8), with 8 polymorphisms providing P<10–6 in this gene. Considering patients with genotype AA at rs1967309, there was a 39% reduction in the composite cardiovascular endpoint with dalcetrapib compared with placebo (hazard ratio, 0.61; 95% confidence interval, 0.41–0.92). In patients with genotype GG, there was a 27% increase in events with dalcetrapib versus placebo. Ten single-nucleotide polymorphism in the ADCY9 gene, the majority in linkage disequilibrium with rs1967309, were associated with the effect of dalcetrapib on intima-media thickness (P<0.05). Marker rs2238448 in ADCY9, in linkage disequilibrium with rs1967309 (r2=0.8), was associated with both the effects of dalcetrapib on intima-media thickness in dal-PLAQUE-2 (P=0.009) and events in dal-OUTCOMES (P=8.88×10–8; hazard ratio, 0.67; 95% confidence interval, 0.58–0.78). Conclusions—The effects of dalcetrapib on atherosclerotic outcomes are determined by correlated polymorphisms in the ADCY9 gene. Clinical Trial Information—URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00658515 and NCT01059682

Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits

Hematological traits are important clinical parameters. To test the effects of rare and low-frequency coding variants on hematological traits, we analyzed hemoglobin concentration, hematocrit levels, white blood cell (WBC) counts and platelet counts in 31,340 individuals genotyped on an exome array. We identified several missense variants in CXCR2 associated with reduced WBC count (gene-based P = 2.6 × 10−13). In a separate family-based resequencing study, we identified a CXCR2 frameshift mutation in a pedigree with congenital neutropenia that abolished ligand-induced CXCR2 signal transduction and chemotaxis. We also identified missense or splice-site variants in key hematopoiesis regulators (EPO, TFR2, HBB, TUBB1 and SH2B3) associated with blood cell traits. Finally, we were able to detect associations between a rare somatic JAK2 mutation (encoding p.Val617Phe) and platelet count (P = 3.9 × 10−22) as well as hemoglobin concentration (P = 0.002), hematocrit levels (P = 9.5 × 10−7) and WBC count (P = 3.1 × 10−5). In conclusion, exome arrays complement genome-wide association studies in identifying new variants that contribute to complex human traits.

An inherited duplication at the gene p21 Protein-Activated Kinase 7 (PAK7) is a risk factor for psychosis

Identifying rare, highly penetrant risk mutations may be an important step in dissecting the molecular etiology of schizophrenia. We conducted a gene-based analysis of large (>100 kb), rare copy-number variants (CNVs) in the Wellcome Trust Case Control Consortium 2 (WTCCC2) schizophrenia sample of 1564 cases and 1748 controls all from Ireland, and further extended the analysis to include an additional 5196 UK controls. We found association with duplications at chr20p12.2 (P = 0.007) and evidence of replication in large independent European schizophrenia (P = 0.052) and UK bipolar disorder case-control cohorts (P = 0.047). A combined analysis of Irish/UK subjects including additional psychosis cases (schizophrenia and bipolar disorder) identified 22 carriers in 11 707 cases and 10 carriers in 21 204 controls [meta-analysis Cochran–Mantel–Haenszel P-value = 2 × 10−4; odds ratio (OR) = 11.3, 95% CI = 3.7, ∞]. Nineteen of the 22 cases and 8 of the 10 controls carried duplications starting at 9.68 Mb with similar breakpoints across samples. By haplotype analysis and sequencing, we identified a tandem ∼149 kb duplication overlapping the gene p21 Protein-Activated Kinase 7 (PAK7, also called PAK5) which was in linkage disequilibrium with local haplotypes (P = 2.5 × 10−21), indicative of a single ancestral duplication event. We confirmed the breakpoints in 8/8 carriers tested and found co-segregation of the duplication with illness in two additional family members of one of the affected probands. We demonstrate that PAK7 is developmentally co-expressed with another known psychosis risk gene (DISC1) suggesting a potential molecular mechanism involving aberrant synapse development and plasticity.

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.

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

Summary: 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. Contact: louis-philippe.lemieux.perreault@umontreal.ca or marie-pierre.dube@statgen.org

Partitioning of copy-number genotypes in pedigrees

BackgroundCopy number variations (CNVs) and polymorphisms (CNPs) have only recently gained the genetic communitys attention. Conservative estimates have shown that CNVs and CNPs might affect more than 10% of the genome and that they may be at least as important as single nucleotide polymorphisms in assessing human variability. Widely used tools for CNP analysis have been implemented in Birdsuite and PLINK for the purpose of conducting genetic association studies based on the unpartitioned total number of CNP copies provided by the intensities from Affymetrixs Genome-Wide Human SNP Array. Here, we are interested in partitioning copy number variations and polymorphisms in extended pedigrees for the purpose of linkage analysis on familial data.ResultsWe have developed CNGen, a new software for the partitioning of copy number polymorphism using the integrated genotypes from Birdsuite with the Affymetrix platform. The algorithm applied to familial trios or extended pedigrees can produce partitioned copy number genotypes with distinct parental alleles. We have validated the algorithm using simulations on a complex pedigree structure using frequencies calculated from a real dataset of 300 genotyped samples from 42 pedigrees segregating a congenital heart defect phenotype.ConclusionsCNGen is the first published software for the partitioning of copy number genotypes in pedigrees, making possible the use CNPs and CNVs for linkage analysis. It was implemented with the Python interpreter version 2.5.2. It was successfully tested on current Linux, Windows and Mac OS workstations.

Methylomic changes in individuals with psychosis, prenatally exposed to endocrine disrupting compounds: Lessons from diethylstilbestrol

Background In the Western world, between 1940 and 1970, more than 2 million people were exposed in utero to diethylstilbestrol (DES). In exposed individuals, and in their descendants, adverse outcomes have been linked to such exposure, including cancers, genital malformations, and less consistently, psychiatric disorders. We aimed to explore whether prenatal DES exposure would be associated with DNA methylation changes, and whether these epigenetic modifications would be associated with increased risk of psychosis. Methods From 247 individuals born from mothers exposed to DES, we selected 69 siblings from 30 families. In each family, at least one sibling was exposed in utero to DES. We performed a methylome-wide association study using HumanMethylation450 DNA Analysis BeadChip® in peripheral blood. We analyzed methylation changes at individual CpGs or regions in exposed (n = 37) versus unexposed individuals (n = 32). We also compared exposed individuals with (n = 7) and without psychosis (n = 30). Results There were more individuals with schizophrenia in the DES-exposed group. We found no significant differences between exposed and unexposed individuals with respect to differentially methylated CpGs or regions. The largest difference was in a region near the promoter of an ADAMTS proteoglycanase gene (ADAMTS9). Compared to exposed individuals without psychosis, exposed individuals with psychosis had differential methylation in the region encompassing the gene encoding the zinc finger protein 57 (ZFP57). Conclusions In utero exposure to DES was not associated with methylation changes at specific CpG or regions. In exposed individuals, however, psychosis was associated with specific methylomic modifications that could impact neurodevelopment and neuroplasticity.

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.

Comparison of genotype clustering tools with rare variants

BackgroundAlong 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.ResultsConcordance 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.ConclusionsThe 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.

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

Summary: 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.org Supplementary information: Supplementary data are available at Bioinformatics online.