Gualberto Ruaño

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 α‐7cholinergic 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. Hum Brain Mapp, 2009.

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

Significance Connectivity within the brain’s resting-state default mode network (DMN) has been shown to be compromised in multiple genetically complex/heritable neuropsychiatric disorders. Uncovering the source of such alterations will help in developing targeted treatments for these disorders. To our knowledge, this study is the first attempt to do so by using a multivariate data-driven fusion approach. We report five major DMN subnodes, all of which were found to be hypo-connected in probands with psychotic illnesses. Further, we found an overrepresentation of genes in major relevant pathways such as NMDA potentiation, PKA/immune response signalling, synaptogenesis, and axon guidance that influenced altered DMN connectivity in psychoses. The study thus identifies several putative genes and pathways related to an important biological marker known to be compromised in psychosis. 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.

Pharmacogenetic testing in psychiatry: a review of features and clinical realities.

This article focuses on the first generation of pharmacogenetic tests that are potentially useful in psychiatry. All pharmacogenetic tests currently on the market, or soon to be marketed in psychiatry, for which some information has been published in peer-reviewed journal articles (or abstracts), were selected. Five pharmacogenetic tests are reviewed in detail: the Roche AmpliChip CYP450 Test, the Luminex Tag-It Mutation Detection Kit, the LGC clozapine response test, the PGxPredict: Clozapine test, and the Genomas PhyzioType system. After reviewing these tests, three practical aspects of implementing pharmacogenetic testing in psychiatric clinical practice are briefly reviewed: (1) the evaluation of these tests in clinical practice, (2) cost-effectiveness, and (3) regulatory oversight. Finally, the future of these and other pharmacogenetic tests in psychiatry is discussed.

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.

Physiogenomic comparison of weight profiles of olanzapine- and risperidone-treated patients

Atypical antipsychotics induce pre-diabetic symptoms in some but not all patients, characterized most notably by elevated weight. The side effect profiles of the various drugs in the class differ, however, raising the possibility of drug-specific mechanisms for similar side effects. We used physiogenomic analysis, an approach previously employed to study the genetics of drug and diet response, to discover and compare genetic associations with weight profiles observed in patients treated with olanzapine and risperidone as an approach to unraveling contrasting mechanistic features of both drugs. A total of 29 single nucleotide polymorphisms (SNPs) were selected from 13 candidate genes relevant to two potential pharmacological axes of psychotropic-related weight profiles, appetite peptides and peripheral lipid homeostasis. We applied physiogenomic analysis to a cross-section of 67 and 101 patients being treated with olanzapine and risperidone, respectively, and assessed genetic associations with the weight profiles. Weight profiles in patients treated with olanzapine were significantly associated with SNPs in the genes for apolipoprotein E, apolipoprotein A4 and scavenger receptor class B, member 1. Weight profiles in patients treated with risperidone were significantly associated with SNPs in the genes for leptin receptor, neuropeptide Y receptor Y5 and paraoxonase 1. These results are consistent with contrasting mechanisms for the weight profile of patients treated with these drugs. Genes associated with olanzapine weight profiles may be related to peripheral lipid homeostatic axes, whereas those associated with risperidones may be related to brain appetite peptide regulation. Future physiogenomic studies will include neurotransmitter receptor SNPs and validation in independent samples.

Physiogenomic association of statin‐related myalgia to serotonin receptors

We employed physiogenomic analyses to investigate the relationship between myalgia and selected polymorphisms in serotonergic genes, based on their involvement with pain perception and transduction of nociceptive stimuli. We screened 195 hypercholesterolemic, statin‐treated patients, all of whom received either atorvastatin, simvastatin, or pravastatin. Patients were classified as having no myalgia, probable myalgia, or definite myalgia, and assigned a myalgia score of 0, 0.5, or 1, respectively. Fourteen single nucleotide polymorphisms (SNPs) were selected from candidates within the 5‐HT receptor gene families [5a‐hydroxytryptamine receptor genes (HTR) 1D, 2A, 2C, 3A, 3B, 5A, 6, 7] and the serotonin transporter gene (SLC6A4). SNPs in the HTR3B and HTR7 genes, rs2276307 and rs1935349, respectively, were significantly associated with the myalgia score. Individual differences in pain perception and nociception related to specific serotonergic gene variants may affect the development of myalgia in statin‐treated patients. Muscle Nerve, 2007

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.

Mechanisms of statin-induced myalgia assessed by physiogenomic associations

OBJECTIVE We investigated genetic variants predictive of muscular side effects in patients treated with statins. We utilized a physiogenomic approach to prototype a multi-gene panel correlated with statin-induced myalgia. BACKGROUND Statin-induced myalgia occurs in ∼10% of lipid clinic outpatients. Its clinical manifestation may depend in part upon gene variation from patient to patient. METHODS We genotyped 793 patients (377 with myalgia and 416 without) undergoing statin therapy at four U.S. outpatient clinic sites to evaluate 31 candidate genes from the literature for their association with statin-induced common myalgia. RESULTS Three previously hypothesized candidate genes were validated: COQ2 (rs4693570) encoding para-hydroxybenzoate-polyprenyltransferase, which participates in the biosynthesis of coenzyme Q10 (p<0.000041); ATP2B1 (rs17381194) which encodes a calcium transporting ATPase involved in calcium homeostasis (p<0.00079); and DMPK (rs672348) which encodes a protein kinase implicated in myotonic dystrophy (p<0.0016). CONCLUSIONS The candidate genes COQ2, ATP2B1, and DMPK, representing pathways involved in myocellular energy transfer, calcium homeostasis, and myotonic dystonia, respectively, were validated as markers for the common myalgia observed in patients receiving statin therapy. The three genes integrated into a physiogenomic predictive system could be relevant to myalgia diagnosis and prognosis in clinical practice.

Genetic Associations of Brain Structural Networks in Schizophrenia: A Preliminary Study

BACKGROUND Schizophrenia is a complex genetic disorder, with multiple putative risk genes and many reports of reduced cortical gray matter. Identifying the genetic loci contributing to these structural alterations in schizophrenia (and likely also to normal structural gray matter patterns) could aid understanding of schizophrenias pathophysiology. We used structural parameters as potential intermediate illness markers to investigate genomic factors derived from single nucleotide polymorphism (SNP) arrays. METHOD We used research quality structural magnetic resonance imaging (sMRI) scans from European American subjects including 33 healthy control subjects and 18 schizophrenia patients. All subjects were genotyped for 367 SNPs. Linked sMRI and genetic (SNP) components were extracted to reveal relationships between brain structure and SNPs, using parallel independent component analysis, a novel multivariate approach that operates effectively in small sample sizes. RESULTS We identified an sMRI component that significantly correlated with a genetic component (r = -.536, p < .00005); components also distinguished groups. In the sMRI component, schizophrenia gray matter deficits were in brain regions consistently implicated in previous reports, including frontal and temporal lobes and thalamus (p < .01). These deficits were related to SNPs from 16 genes, several previously associated with schizophrenia risk and/or involved in normal central nervous system development, including AKT, PI3K, SLC6A4, DRD2, CHRM2, and ADORA2A. CONCLUSIONS Despite the small sample size, this novel analysis method identified an sMRI component including brain areas previously reported to be abnormal in schizophrenia and an associated genetic component containing several putative schizophrenia risk genes. Thus, we identified multiple genes potentially underlying specific structural brain abnormalities in schizophrenia.

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.