Pharmacogenomic Variants and Drug Interactions Identified Through the Genetic Analysis of Clozapine Metabolism.

OBJECTIVE: Clozapine is the only effective medication for treatment-resistant schizophrenia, but its worldwide use is still limited because of its complex titration protocols. While the discovery of pharmacogenomic variants of clozapine metabolism may improve clinical management, no robust findings have yet been reported. This study is the first to adopt the framework of genome-wide association studies (GWASs) to discover genetic markers of clozapine plasma concentrations in a large sample of patients with treatment-resistant schizophrenia. METHODS: The authors used mixed-model regression to combine data from multiple assays of clozapine metabolite plasma concentrations from a clozapine monitoring service and carried out a genome-wide analysis of clozapine, norclozapine, and their ratio on 10,353 assays from 2,989 individuals. These analyses were adjusted for demographic factors known to influence clozapine metabolism, although it was not possible to adjust for all potential mediators given the available data. GWAS results were used to pinpoint specific enzymes and metabolic pathways and compounds that might interact with clozapine pharmacokinetics. RESULTS: The authors identified four distinct genome-wide significant loci that harbor common variants affecting the metabolism of clozapine or its metabolites. Detailed examination pointed to coding and regulatory variants at several CYP* and UGT* genes as well as corroborative evidence for interactions between the metabolism of clozapine, coffee, and tobacco. Individual effects of single single-nucleotide polymorphisms (SNPs) fine-mapped from these loci were large, such as the minor allele of rs2472297, which was associated with a reduction in clozapine concentrations roughly equivalent to a decrease of 50 mg/day in clozapine dosage. On their own, these single SNPs explained from 1.15% to 9.48% of the variance in the plasma concentration data. CONCLUSIONS: Common genetic variants with large effects on clozapine metabolism exist and can be found via genome-wide approaches. Their identification opens the way for clinical studies assessing the use of pharmacogenomics in the clinical management of patients with treatment-resistant schizophrenia.

Genetics of CD33 in Alzheimer's disease and acute myeloid leukemia.

The CD33 single-nucleotide polymorphism (SNP) rs3865444 has been associated with the risk of Alzheimer's disease (AD). Rs3865444 is in linkage disequilibrium with rs12459419 which has been associated with efficacy of an acute myeloid leukemia (AML) chemotherapeutic agent based on a CD33 antibody. We seek to evaluate the extent to which CD33 genetics in AD and AML can inform one another and advance human disease therapy. We have previously shown that these SNPs are associated with skipping of CD33 exon 2 in brain mRNA. Here, we report that these CD33 SNPs are associated with exon 2 skipping in leukocytes from AML patients and with a novel CD33 splice variant that retains CD33 intron 1. Each copy of the minor rs12459419T allele decreases prototypic full-length CD33 expression by ∼ 25% and decreases the AD odds ratio by ∼ 0.10. These results suggest that CD33 antagonists may be useful in reducing AD risk. CD33 inhibitors may include humanized CD33 antibodies such as lintuzumab which was safe but ineffective in AML clinical trials. Here, we report that lintuzumab downregulates cell-surface CD33 by 80% in phorbol-ester differentiated U937 cells, at concentrations as low as 10 ng/ml. Overall, we propose a model wherein a modest effect on RNA splicing is sufficient to mediate the CD33 association with AD risk and suggest the potential for an anti-CD33 antibody as an AD-relevant pharmacologic agent.

Allele-specific RNA interference rescues the long-QT syndrome phenotype in human-induced pluripotency stem cell cardiomyocytes.

AIMS: Long-QT syndromes (LQTS) are mostly autosomal-dominant congenital disorders associated with a 1:1000 mutation frequency, cardiac arrest, and sudden death. We sought to use cardiomyocytes derived from human-induced pluripotency stem cells (hiPSCs) as an in vitro model to develop and evaluate gene-based therapeutics for the treatment of LQTS. METHODS AND RESULTS: We produced LQTS-type 2 (LQT2) hiPSC cardiomyocytes carrying a KCNH2 c.G1681A mutation in a IKr ion-channel pore, which caused impaired glycosylation and channel transport to cell surface. Allele-specific RNA interference (RNAi) directed towards the mutated KCNH2 mRNA caused knockdown, while leaving the wild-type mRNA unaffected. Electrophysiological analysis of patient-derived LQT2 hiPSC cardiomyocytes treated with mutation-specific siRNAs showed normalized action potential durations (APDs) and K(+) currents with the concurrent rescue of spontaneous and drug-induced arrhythmias (presented as early-afterdepolarizations). CONCLUSIONS: These findings provide in vitro evidence that allele-specific RNAi can rescue diseased phenotype in LQTS cardiomyocytes. This is a potentially novel route for the treatment of many autosomal-dominant-negative disorders, including those of the heart.

Genetic variants influencing human aging from late-onset Alzheimer's disease (LOAD) genome-wide association studies (GWAS).

Genetics plays a crucial role in human aging with up to 30% of those living to the mid-80s being determined by genetic variation. Survival to older ages likely entails an even greater genetic contribution. There is increasing evidence that genes implicated in age-related diseases, such as cancer and neuronal disease, play a role in affecting human life span. We have selected the 10 most promising late-onset Alzheimer's disease (LOAD) susceptibility genes identified through several recent large genome-wide association studies (GWAS). These 10 LOAD genes (APOE, CLU, PICALM, CR1, BIN1, ABCA7, MS4A6A, CD33, CD2AP, and EPHA1) have been tested for association with human aging in our dataset (1385 samples with documented age at death [AAD], age range: 58-108 years; mean age at death: 80.2) using the most significant single nucleotide polymorphisms (SNPs) found in the previous studies. Apart from the APOE locus (rs2075650) which showed compelling evidence of association with risk on human life span (p = 5.27 × 10(-4)), none of the other LOAD gene loci demonstrated significant evidence of association. In addition to examining the known LOAD genes, we carried out analyses using age at death as a quantitative trait. No genome-wide significant SNPs were discovered. Increasing sample size and statistical power will be imperative to detect genuine aging-associated variants in the future. In this report, we also discuss issues relating to the analysis of genome-wide association studies data from different centers and the bioinformatic approach required to distinguish spurious genome-wide significant signals from real SNP associations.

Investigating statistical epistasis in complex disorders.

The missing heritability exhibited by late-onset Alzheimer's disease is unexplained and has been partly attributed to epistatic interaction. Methods available to explore this are often based on logistic regression and allow for determination of deviation from an expected outcome as a result of statistical epistasis. Three such methodologies including Synergy Factor and the PLINK modules, -epistasis and -fast-epistasis, were applied to study an epistatic interaction between interleukin-6 and interleukin-10. The models analyzed consisted of two synergistic interactions (SF ≈ 4.2 and 1.6) and two antagonistic interactions (SF ≈ 0.9 and 0.6). As with any statistical test, power to detect association is paramount; and most studies will be underpowered for the task. However, the availability of large sample sizes through genome-wide association studies make it feasible to examine approaches for determining epistatic interactions. This study documents the sample sizes needed to achieve a statistically significant outcome from each of the methods examined and discusses the limitations/advantages of the chosen approaches.