Comprehensive mapping of cystic fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site.

Cystic Fibrosis (CF) is caused by mutations in the CFTR gene, of which over 2000 have been reported to date. Mutations have yet to be analyzed in aggregate to assess their distribution across the tertiary structure of the CFTR protein, an approach that could provide valuable insights into the structure-function relationship of CFTR. In addition, the binding site of Class I correctors (VX-809, VX-661, and C18) is not well understood. In this study, exonic CFTR mutations and mutant allele frequencies described in 3 curated databases (ABCMdb, CFTR1, and CFTR2, comprising >130 000 data points) were mapped to 2 different structural models: a homology model of full-length CFTR protein in the open-channel state, and a cryo-electron microscopy core-structure of CFTR in the closed-channel state. Accordingly, residue positions of 6 high-frequency mutant CFTR alleles were found to spatially co-localize in CFTR protein, and a significant cluster was identified at the NBD1:ICL4 interdomain interface. In addition, immunoblotting confirmed the approximate binding site of Class I correctors, demonstrating that these small molecules act via a similar mechanism in vitro, and in silico molecular docking generated binding poses for their complex with the cryo-electron microscopy structure to suggest the putative corrector binding site is a multi-domain pocket near residues F374-L375. These results confirm the significance of interdomain interfaces as susceptible to disruptive mutation, and identify a putative corrector binding site. The structural pharmacogenomics approach of mapping mutation databases to protein models shows promise for facilitating drug discovery and personalized medicine for monogenetic diseases.

Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474.

The investigational compound BIA 10-2474, designed as a long-acting and reversible inhibitor of fatty acid amide hydrolase for the treatment of neuropathic pain, led to the death of one participant and hospitalization of five others due to intracranial hemorrhage in a Phase I clinical trial. Putative off-target activities of BIA 10-2474 have been suggested to be major contributing factors to the observed neurotoxicity in humans, motivating our study's proteome-wide screening approach to investigate its polypharmacology. Accordingly, we performed an in silico screen against 80,923 protein structures reported in the Protein Data Bank. The resulting list of 284 unique human interactors was further refined using target-disease association analyses to a subset of proteins previously linked to neurological, intracranial, inflammatory, hemorrhagic or clotting processes and/or diseases. Eleven proteins were identified as potential targets of BIA 10-2474, and the two highest-scoring proteins, Factor VII and thrombin, both essential blood-clotting factors, were predicted to be inhibited by BIA 10-2474 and suggest a plausible mechanism of toxicity. Once this small molecule becomes commercially available, future studies will be conducted to evaluate the predicted inhibitory effect of BIA 10-2474 on blood clot formation specifically in the brain.

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia.

The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

Proteome-Scale Drug-Target Interaction Predictions: Approaches and Applications.

Drug-Target interaction predictions are an important cornerstone of computer-aided drug discovery. While predictive methods around individual targets have a long history, the application of proteome-scale models is relatively recent. In this overview, we will provide the context required to understand advances in this emerging field within computational drug discovery, evaluate emerging technologies for suitability to given tasks, and provide guidelines for the design and implementation of new drug-target interaction prediction models. We will discuss the validation approaches used, and propose a set of key criteria that should be applied to evaluate their validity. We note that we find widespread deficiencies in the existing literature, making it difficult to judge the practical effectiveness of some of the techniques proposed from their publications alone. We hope that this review may help remedy this situation and increase awareness of several sources of bias that may enter into commonly used cross-validation methods. © 2021 Cyclica Inc. Current Protocols published by Wiley Periodicals LLC.

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease.

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.

Exposure to non-therapeutic INR in a high risk cardiovascular patient: potential hazard reduction with genotype-guided warfarin (Coumadin) dosing.

A case to illustrate the utility of genetic screening in warfarin (Coumadin) management is reported. A 45 year-old woman of Puerto Rican ancestry was admitted to the emergency room twice within one month with chest pain. She was diagnosed with congestive heart failure, which was stabilized both times. At her second release, warfarin therapy was initiated at 5 mg/ day to prevent thrombus formation and was lowered to 3.75 mg/day at day 7 by her primary physician. International Normalized Ratio (INR) test results in the follow-up period at days 1, 7, and 10 of warfarin therapy were 4.5, 6.5, and 7.3, respectively-far in excess of the therapeutic range, despite the lower dosage in effect from day 7 onward. The patient achieved target INR over the next 43 days after downward adjustment of the dose to a dose of 1.5 mg/day by trial and error. DNA-typing specific for the CYP2C9*2,*3,*4,*5,*6 alleles and seven variants in the VKORC1 gene, including the VKORC1-1639 G > A polymorphism, revealed the presence of combinatorial CYP2C9*2/*3 and VKORC1-1639 G/A genotypes in this patient. Entering the patient's demographic and genotype status data into independent algorithms available in the public domain to predict effective warfarin dose yielded predicted doses which ranged from 1.5 to 1.8 mg/day. Notably, the prediction of 1.5 mg/day, which was generated by the online resource www.warfarindosing.org, coincided with the patient's actual effective warfarin dose. We conclude that the rapid rise in INR observed upon the initiation of warfarin therapy and the final effective warfarin dose of 1.5 mg/day, are attributable in some part to the presence of two minor alleles in CYP2C9, which together significantly reduce warfarin metabolism. Warfarin genotyping can therefore inform the clinician of the predicted effective warfarin dose. The results highlight the potential for warfarin genetic testing to improve patient care.

Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA.

There is current interest in understanding genetic influences on both healthy and disordered brain function. We assessed brain function with functional magnetic resonance imaging (fMRI) data collected during an auditory oddball task--detecting an infrequent sound within a series of frequent sounds. Then, task-related imaging findings were utilized as potential intermediate phenotypes (endophenotypes) to investigate genomic factors derived from a single nucleotide polymorphism (SNP) array. Our target is the linkage of these genomic factors to normal/abnormal brain functionality. We explored parallel independent component analysis (paraICA) as a new method for analyzing multimodal data. The method was aimed to identify simultaneously independent components of each modality and the relationships between them. When 43 healthy controls and 20 schizophrenia patients, all Caucasian, were studied, we found a correlation of 0.38 between one fMRI component and one SNP component. This fMRI component consisted mainly of parietal lobe activations. The relevant SNP component was contributed to significantly by 10 SNPs located in genes, including those coding for the nicotinic alpha-7 cholinergic receptor, aromatic amino acid decarboxylase, disrupted in schizophrenia 1, among others. Both fMRI and SNP components showed significant differences in loading parameters between the schizophrenia and control groups (P = 0.0006 for the fMRI component; P = 0.001 for the SNP component). In summary, we constructed a framework to identify interactions between brain functional and genetic information; our findings provide a proof-of-concept that genomic SNP factors can be investigated by using endophenotypic imaging findings in a multivariate format.

Prevalence of combinatorial CYP2C9 and VKORC1 genotypes in Puerto Ricans: implications for warfarin management in Hispanics.

Polymorphisms in the cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) genes significantly alter the effective warfarin dose. We determined the frequencies of alleles, single carriers, and double carriers of single nucleotide polymorphisms (SNPs) in the CYP2C9 and VKORC1 genes in a Puerto Rican cohort and gauged the impact of these polymorphisms on warfarin dosage using a published algorithm. A total of 92 DNA samples were genotyped using Luminex x-MAP technology. The polymorphism frequencies were 6.52%, 5.43% and 28.8% for CYP2C9 *2, *3 and VKORC1-1639 C>A polymorphisms, respectively. The prevalence of combinatorial genotypes was 16% for carriers of both the CYP2C9 and VKORC1 polymorphisms, 9% for carriers of CYP2C9 polymorphisms, 35% for carriers of the VKORC1 polymorphism, and the remaining 40% were non-carriers for either gene. Based on a published warfarin dosing algorithm, single, double and triple carriers of functionally deficient polymorphisms predict reductions of 1.0-1.6, 2.0-2.9, and 2.9-3.7 mg/day, respectively, in warfarin dose. Overall, 60% of the population carried at least a single polymorphism predicting deficient warfarin metabolism or responsiveness and 13% were double carriers with polymorphisms in both genes studied. Combinatorial genotyping of CYP2C9 and VKORC1 can allow for individualized dosing of warfarin among patients with gene polymorphisms, potentially reducing the risk of stroke or bleeding.

Exploring genetic variations that may be associated with the direct effects of some antipsychotics on lipid levels.

The goal of this study was to select some genes that may serve as good candidates for future studies of the direct effects (not explained by obesity) of some antipsychotics on hyperlipidemia. A search for single-nucleotide polymorphisms (SNPs) that may be associated with these direct effects was conducted. From a published cross-sectional sample, 357 patients on antipsychotics were genotyped using a DNA microarray with 384 SNPs. A total of 165 patients were taking olanzapine, quetiapine or chlorpromazine which may directly cause hypertriglyceridemia or hypercholesterolemia. Another 192 patients taking other antipsychotics were controls. A two-stage statistical analysis that included loglinear and logistic models was developed to select SNPs blindly. In a third stage, physiological knowledge was used to select promising SNPs. Known physiological mechanisms were supported for 3 associations found in patients taking olanzapine, quetiapine or chlorpromazine [acetyl-coenzyme A carboxylase alpha SNP (rs4072032) in the hypertriglyceridemia model, and for the neuropeptide Y (rs1468271) and ACCbeta, (rs2241220) in the hypercholesterolemia model]. These genes may be promising candidates for studies of the direct effects of some antipsychotics on hyperlipidemia.

Integrating genomic based information into clinical warfarin (Coumadin) management: an illustrative case report.

Warfarin is a well established oral anticoagulant for the treatment of thromboembolic disorders. Warfarin therapy is complicated by a narrow therapeutic index and marked inter-individual dose variability with therapeutic doses ranging from 1 mg to 10 mg/day. Recently genetic variation and resultant drug metabolizing polymorphisms have been found to contribute to warfarin dose variability with resultant hemorrhagic or thromboembolic complications. Cytochrome P4502C9 alters the rate of warfarin metabolism and clearance. A second enzyme, Vitamin K Epoxide Reductase Complex (VKORC) binds and reduces Vitamin K which is necessary for activation of clotting Factors II, VII, IX and X. The VKORC1 gene encodes for Vitamin K Epoxide Reductase Complex subunit 1, a key component of VKORC. The combination of physiologic factors (30%), CYP2C9 variations (20%) and VKORC1 variants (25%) accounts for approximately 75% of warfarin dose variability. This illustrative case report demonstrates the clinical importance of this new information. Clinicians need to incorporate these new genomic findings into appropriate management of warfarin dose anticoagulation.

VEGFA GENE variation influences hallucinations and frontotemporal morphology in psychotic disorders: a B-SNIP study.

Vascular endothelial growth factor A (VEGFA) dysfunction may contribute to a number of pathological processes that characterize psychotic disorders. However, the influence of VEGFA gene variants on clinical and neuroimaging phenotypes in psychotic disorders has yet to be shown. In the present study, we examined whether different VEGFA gene variants influence psychosis risk, symptom severity, cognition, and brain volume. The study group included 480 probands (Bipolar I disorder with psychosis, n = 205; Schizoaffective disorder, n = 112; Schizophrenia, n = 163) and 126 healthy controls that were recruited across six sites in the B-SNIP consortium. VEGFA variants identified for analysis (rs699947, rs833070, and rs2146323) were quantified via SNP chip array. We assessed symptoms and cognition using standardized clinical and neuropsychological batteries. The dorsolateral prefrontal cortex (DLPFC), medial temporal lobe, and hippocampal volumes were quantified using FreeSurfer. In our sample, VEGFA rs2146323 A- carriers showed reduced odds of being a proband (p = 0.037, OR = 0.65, 95% CI = 0.43-0.98) compared to noncarriers, but not for rs699947 or rs833070. In probands, rs2146323 A- carriers demonstrated fewer hallucinations (p = 0.035, Cohen's d = 0.194), as well as significantly greater DLPFC (p < 0.05, Cohen's d = -0.21) and parahippocampal volumes (p < 0.01, Cohen's d = -0.27). No clinical or neuroimaging associations were identified for rs699947 or rs833070. In general, we found that the three SNPs exhibited several significant negative relationships between psychosis symptoms and brain structure. In the probands and control groups, positive relationships were identified between several cognitive and brain volume measures. The findings suggest VEGFA effects in the DLPFC and hippocampus found in animals may also extend to humans. VEGFA variations may have important implications in identifying dimensional moderators of function that could be targeted through VEGFA-mediated interventions.

A clinical study of the association of antipsychotics with hyperlipidemia.

Following a prior Kentucky clinical practice study on metabolic syndrome, serum glucose and lipid levels were used in a new sample to determine whether after correcting for confounding factors, olanzapine hyperlipidemia risk may be higher under naturalistic non-randomized treatment. Serum glucose, total cholesterol, HDL cholesterol and triglyceride levels were assessed in 360 patients with severe mental illnesses. The initial goal was to focus on olanzapine lipid profiles, but visual data inspection indicated that quetiapine needed attention as well. Patients were divided into 3 groups: 57 (16%) on olanzapine, 105 (29%) on quetiapine, and 198 (55%) on other antipsychotics (risperidone, ziprasidone, aripiprazole or typicals). HDL and glucose levels were not significantly different across the three antipsychotic groups. When compared with other antipsychotics, olanzapine patients had a borderline significantly higher mean total serum cholesterol level (178 vs. 192 mg/dl, p=0.06) and mean triglyceride level (172 vs. 202 mg/dl, p=0.06). These differences became significant (p=0.006 and 0.03) after correcting for confounders. Quetiapine appeared overprescribed in patients with metabolic syndrome complications. When compared with other antipsychotics, quetiapine patients had a significantly higher mean total serum cholesterol level (178 vs. 194 mg/dl, p=0.004) and mean triglyceride level (172 vs. 225 mg/dl, p<0.001). These differences were significant (p=0.02 and <0.001) after correcting for confounders. This study is consistent with emerging literature that suggests that some antipsychotics may have direct and immediate effects on lipid levels beyond obesity effects. The effect sizes of olanzapine and quetiapine on hyperlipidemia were about 0.40 in this naturalistic study.

Somatic complications of psychotropic medications in a patient with multiple CYP2 drug metabolism deficiencies.

A 54-year-old woman presented with severe anxiety, multiple somatic complaints, medication intolerance and adverse drug reactions (ADRs) to numerous prescribed psychotropic medications. Multiple drug metabolizing deficiencies were suspected. Molecular analysis was performed for the CYP2 family of Cytochrome P450 (CYP450) drug metabolism isoenzymes by DNA typing CYP2D6, CYP2C9, and CYP2C19 genes. A multiple deficiency in CYP2 drug metabolism was discovered. The patient was a double carrier of null alleles for CYP2D6, a carrier of a null allele for CYP2C19 and a carrier of a deficient allele for CYP2C9. These alleles were confirmed by Mendelian inheritance in her nuclear family, where her brother had a similar multigene CYP2 deficiency. The patient improved clinically with discontinuation of psychotropic medications, suggesting that much of her symptomatology was drug-induced. DNA typing for multigene CYP2 deficiencies is diagnostically useful in individuals with histories of multiple ADRs, which could be avoided by DNA-guided individualized prescription.

Structural coverage of the proteome for pharmaceutical applications.

Structure-based computational drug discovery efforts have traditionally focused on the structure of a single, well-known drug target. Important applications, such as target deconvolution and the analysis of polypharmacology, require proteome-scale molecular docking and have been inaccessible to structure-based in silico approaches. One important reason for this inaccessibility was that the structure of most proteins was not known. Lately, this 'structure gap' has been closing rapidly, and proteome-scale molecular docking seems within reach. Here, we survey the current state of structural coverage of the human genome and find that coverage is truly proteome-wide, both overall and in most pharmaceutically relevant categories of proteins. The time is right for structure-based approaches to target deconvolution and polypharmacology.

Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses.

BACKGROUND: Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder overlap with regard to symptoms, structural and functional brain abnormalities, and genetic risk factors. Neurobiological pathways connecting genes to clinical phenotypes across the spectrum from schizophrenia to psychotic bipolar disorder remain largely unknown. METHODS: We examined the relationship between structural brain changes and risk alleles across the psychosis spectrum in the multi-site Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cohort. Regional MRI brain volumes were examined in 389 subjects with a psychotic disorder (139 schizophrenia, 90 schizoaffective disorder, and 160 psychotic bipolar disorder) and 123 healthy controls. 451,701 single-nucleotide polymorphisms were screened and processed using parallel independent component analysis (para-ICA) to assess associations between genes and structural brain abnormalities in probands. RESULTS: 482 subjects were included after quality control (364 individuals with psychotic disorder and 118 healthy controls). Para-ICA identified four genetic components including several risk genes already known to contribute to schizophrenia and bipolar disorder and revealed three structural components that showed overlapping relationships with the disease risk genes across the three psychotic disorders. Functional ontologies representing these gene clusters included physiological pathways involved in brain development, synaptic transmission, and ion channel activity. CONCLUSIONS: Heritable brain structural findings such as reduced cortical thickness and surface area in probands across the psychosis spectrum were associated with somewhat distinct genes related to putative disease pathways implicated in psychotic disorders. This suggests that brain structural alterations might represent discrete psychosis intermediate phenotypes along common neurobiological pathways underlying disease expression across the psychosis spectrum.

Apolipoprotein A1 genotype affects the change in high density lipoprotein cholesterol subfractions with exercise training.

High density lipoprotein cholesterol (HDL-C) is a primary risk factor for cardiovascular disease. Apolipoprotein A-1 (apoA1) is the major HDL-associated apolipoprotein. The -75G/A single nucleotide polymorphism (SNP) in the apolipoprotein A1 gene (APOA1) promoter has been reported to be associated with HDL-C concentrations as well as HDL-C response to dietary changes in polyunsaturated fat intake. We examined the effect of this APOA1 SNP on exercise-induced changes in HDL subfraction distribution. From a cohort of healthy normolipidemic adults who volunteered for 6 months of supervised aerobic exercise, 75 subjects were genotyped for the -75G/A SNP. Of these, 53 subjects were G homozygotes (G/G) and 22 were A carriers (A/G and A/A). HDL subfractions were measured by nuclear magnetic resonance (NMR) spectroscopy by adding categories HDL-C 1+2 for the small subfraction, and HDL-C 3+4+5 for the large. The change in total HDL-C after exercise was 0.8+/-7.2 mg/dL (+1.7%), and was not statistically significant. HDL subfraction amounts also did not significantly change with exercise training in the total cohort or in G homozygotes or A carriers. The amount of the large HDL subfraction increased in the G homozygotes and decreased in the A carriers (mean+/-S.E.M., 1.8+/-6.6 mg/dL versus -6.1+/-2.3 mg/dL, p<0.0005). In contrast, the amount of the small HDL subfraction decreased in G homozygotes and increased in A carriers (-1.3+/-6.6 mg/dL versus 4.7+/-1.2 mg/dL, p<0.005). These results show that genetic variation at the APOA1 gene promoter is associated with HDL subfraction redistribution resulting from exercise training.

Laboratory Medicine in the Clinical Decision Support for Treatment of Hypercholesterolemia: Pharmacogenetics of Statins.

Statin responsiveness is an area of great research interest given the success of the drug class in the treatment of hypercholesterolemia and in primary and secondary prevention of cardiovascular disease. Interrogation of the patient's genome for gene variants will eventually guide anti-hyperlipidemic intervention. In this review, we discuss methodological approaches to discover genetic markers predictive of class-wide and drug-specific statin efficacy and safety. Notable pharmacogenetic findings are summarized from hypothesis-free genome wide and hypothesis-led candidate gene association studies. Physiogenomic models and clinical decision support systems will be required for DNA-guided statin therapy to reach practical use in medicine.

Multivariate Genetic Correlates of the Auditory Paired Stimuli-Based P2 Event-Related Potential in the Psychosis Dimension From the BSNIP Study.

OBJECTIVE: The complex molecular etiology of psychosis in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is not well defined, presumably due to their multifactorial genetic architecture. Neurobiological correlates of psychosis can be identified through genetic associations of intermediate phenotypes such as event-related potential (ERP) from auditory paired stimulus processing (APSP). Various ERP components of APSP are heritable and aberrant in SZ, PBP and their relatives, but their multivariate genetic factors are less explored. METHODS: We investigated the multivariate polygenic association of ERP from 64-sensor auditory paired stimulus data in 149 SZ, 209 PBP probands, and 99 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Multivariate association of 64-channel APSP waveforms with a subset of 16 999 single nucleotide polymorphisms (SNPs) (reduced from 1 million SNP array) was examined using parallel independent component analysis (Para-ICA). Biological pathways associated with the genes were assessed using enrichment-based analysis tools. RESULTS: Para-ICA identified 2 ERP components, of which one was significantly correlated with a genetic network comprising multiple linearly coupled gene variants that explained ~4% of the ERP phenotype variance. Enrichment analysis revealed epidermal growth factor, endocannabinoid signaling, glutamatergic synapse and maltohexaose transport associated with P2 component of the N1-P2 ERP waveform. This ERP component also showed deficits in SZ and PBP. CONCLUSIONS: Aberrant P2 component in psychosis was associated with gene networks regulating several fundamental biologic functions, either general or specific to nervous system development. The pathways and processes underlying the gene clusters play a crucial role in brain function, plausibly implicated in psychosis.

Physiogenomic analysis links serum creatine kinase activities during statin therapy to vascular smooth muscle homeostasis.

Statins are highly effective at reducing coronary disease risk. The main side effects of these medications are a variety of skeletal muscle complaints ranging from mild myalgia to frank rhabdomyolysis. To search for physiologic factors possibly influencing statin muscle toxicity, we screened for genetic associations with serum creatine kinase (CK) levels in 102 patients receiving statin therapy for hypercholesteremia. A total of 19 single nucleotide polymorphism (SNPs) were selected from ten candidate genes involved in vascular homeostasis. Multiple linear regression was used to rank the SNPs according to probability of association, and the most significant associations were analyzed in greater detail. SNPs in the angiotensin II Type 1 receptor (AGTR1) and nitric oxide synthase 3 (NOS3) genes were significantly associated with CK activity. These results demonstrate a strong association between CK activity during statin treatment and variability in genes related to vascular function, and suggest that vascular smooth muscle function may contribute to the muscle side effects of statins.

Genetic Sources of Subcomponents of Event-Related Potential in the Dimension of Psychosis Analyzed From the B-SNIP Study.

OBJECTIVE: Biological risk factors underlying psychosis are poorly understood. Biological underpinnings of the dimension of psychosis can be derived using genetic associations with intermediate phenotypes such as subcomponents of auditory event-related potentials (ERPs). Various ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder are heritable and are expressed in unaffected relatives, although studies investigating genetic contributions to ERP abnormalities are limited. The authors used a novel parallel independent component analysis (para-ICA) to determine which empirically derived gene clusters are associated with data-driven ERP subcomponents, assuming a complex etiology underlying psychosis. METHOD: The authors examined the multivariate polygenic association of ERP subcomponents from 64-channel auditory oddball data in 144 individuals with schizophrenia, 210 psychotic bipolar disorder probands, and 95 healthy individuals from the multisite Bipolar-Schizophrenia Network on Intermediate Phenotypes study. Data were reduced by principal components analysis to two target and one standard ERP waveforms. Multivariate association of compressed ERP waveforms with a set of 20,329 single-nucleotide polymorphisms (SNPs) (reduced from a 1-million-SNP array) was examined using para-ICA. Genes associated with SNPs were further examined using pathway analysis tools. RESULTS: Para-ICA identified four ERP components that were significantly correlated with three genetic components. Enrichment analysis revealed complement immune response pathway and multiple processes that significantly mediate ERP abnormalities in psychosis, including synaptic cell adhesion, axon guidance, and neurogenesis. CONCLUSIONS: This study identified three genetic components comprising multiple genes mediating ERP subcomponent abnormalities in schizophrenia and psychotic bipolar disorder. The data suggest a possible polygenic structure comprising genes influencing key neurodevelopmental processes, neural circuitry, and brain function mediating biological pathways plausibly associated with psychosis.