Three patients with homozygous familial hypercholesterolemia: Genomic sequencing and kindred analysis.

BackgroundHomozygous Familial Hypercholesterolemia (HoFH) is an inherited recessive condition associated with extremely high levels of low-density lipoprotein (LDL) cholesterol in affected individuals. It is usually caused by homozygous or compound heterozygous functional mutations in the LDL receptor (LDLR). A number of mutations causing FH have been reported in literature and such genetic heterogeneity presents great challenges for disease diagnosis.ObjectiveWe aim to determine the likely genetic defects responsible for three cases of pediatric HoFH in two kindreds.MethodsWe applied whole exome sequencing (WES) on the two probands to determine the likely functional variants among candidate FH genes. We additionally applied 10x Genomics (10xG) Linked-Reads whole genome sequencing (WGS) on one of the kindreds to identify potentially deleterious structural variants (SVs) underlying HoFH. A PCR-based screening assay was also established to detect the LDLR structural variant in a cohort of 641 patients with elevated LDL.ResultsIn the Caucasian kindred, the FH homozygosity can be attributed to two compound heterozygous LDLR damaging variants, an exon 12 p.G592E missense mutation and a novel 3kb exon 1 deletion. By analyzing the 10xG phased data, we ascertained that this deletion allele was most likely to have originated from a Russian ancestor. In the Mexican kindred, the strikingly elevated LDL cholesterol level can be attributed to a homozygous frameshift LDLR variant p.E113fs.ConclusionsWhile the application of WES can provide a cost-effective way of identifying the genetic causes of FH, it often lacks sensitivity for detecting structural variants. Our finding of the LDLR exon 1 deletion highlights the broader utility of Linked-Read WGS in detecting SVs in the clinical setting, especially when HoFH patients remain undiagnosed after WES

Identification of a Kinase Profile that Predicts Chromosome Damage Induced by Small Molecule Kinase Inhibitors

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113×290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity

Exploring the Use of Electronic Health Record-Linked Biorepositories for Pharmacogenomic Application and Discovery

Drug response is well documented to vary considerably among patient groups and populations, as well as within individual patients. Since drug prescribing is often based on population averages of drug response, many patients will not respond, and up to one-third may experience harmful toxicity. Genetics plays a large role in explaining the variability observed in response to different drugs and is an important factor driving precision medicine initiatives. Pharmacogenetic information can be useful in optimizing patient therapy, potentially reducing the cost of hospitalizations and treatment of adverse drug events. As part of the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH), we analyzed 102,979 members of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort with genetic information available, along with almost two decades of electronic health record (EHR) data, prescription records, and lifestyle survey results. In one of the largest, most ethnically diverse pharmacogene characterization studies to date, we assessed cohort metabolizer status phenotypes for 7 drug-gene interactions (DGIs) for which there is moderate to strong evidence suggesting the use of pharmacogenetic information to guide therapy. 89% of the cohort had at least one actionable allele for the 7 DGIs in this study, and we observed large variations among ethnicities. Additionally, 17,747 individuals had been prescribed a drug for which they had an actionable or high-risk metabolizer status phenotype. For these individuals, the availability of pharmacogenetic information at point-of-care may have potentially led to a more personalized drug or dosing regimen. Following this study, we assessed the utility of this resource for deriving two drug response phenotypes: weight gain induced by atypical antipsychotic use and major adverse cardiovascular events in clopiodgrel non-responders. Despite challenges in deriving phenotypes from the EHR, we were able to extract phenotypes that reflected observed estimates from previously published studies. Using these phenotypes, we performed candidate gene and genome-wide association studies to identify genetic variants associated with response. Altogether, this dissertation demonstrates the potential utility and clinical impact of integrating genetic data with EHRs for pharmacogenetic application and discovery, and provides the foundation for future studies in precision medicine

Exploring the Use of Electronic Health Record-Linked Biorepositories for Pharmacogenomic Application and Discovery

Drug response is well documented to vary considerably among patient groups and populations, as well as within individual patients. Since drug prescribing is often based on population averages of drug response, many patients will not respond, and up to one-third may experience harmful toxicity. Genetics plays a large role in explaining the variability observed in response to different drugs and is an important factor driving precision medicine initiatives. Pharmacogenetic information can be useful in optimizing patient therapy, potentially reducing the cost of hospitalizations and treatment of adverse drug events.As part of the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH), we analyzed 102,979 members of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort with genetic information available, along with almost two decades of electronic health record (EHR) data, prescription records, and lifestyle survey results. In one of the largest, most ethnically diverse pharmacogene characterization studies to date, we assessed cohort metabolizer status phenotypes for 7 drug-gene interactions (DGIs) for which there is moderate to strong evidence suggesting the use of pharmacogenetic information to guide therapy. 89% of the cohort had at least one actionable allele for the 7 DGIs in this study, and we observed large variations among ethnicities. Additionally, 17,747 individuals had been prescribed a drug for which they had an actionable or high-risk metabolizer status phenotype. For these individuals, the availability of pharmacogenetic information at point-of-care may have potentially led to a more personalized drug or dosing regimen. Following this study, we assessed the utility of this resource for deriving two drug response phenotypes: weight gain induced by atypical antipsychotic use and major adverse cardiovascular events in clopiodgrel non-responders. Despite challenges in deriving phenotypes from the EHR, we were able to extract phenotypes that reflected observed estimates from previously published studies. Using these phenotypes, we performed candidate gene and genome-wide association studies to identify genetic variants associated with response. Altogether, this dissertation demonstrates the potential utility and clinical impact of integrating genetic data with EHRs for pharmacogenetic application and discovery, and provides the foundation for future studies in precision medicine

HGV2012:Leveraging Next-Generation Technology and Large Datasets to Advance Disease Research

<p>The 13th International Meeting on Human Genome Variation and Complex Genome Analysis (HGV2012: Shanghai, China, 6th8th September 2012) was a stimulating workshop where researchers from academia and industry explored the latest progress, challenges, and opportunities in genome variation research. Key themes included advancements in next-generation sequencing (NGS) technology, investigation of common and rare diseases, employing NGS in the clinic, utilizing large datasets that leverage biobanks and population-specific cohorts, and exploration of genomic features.</p>