Joseph L. McClay

Methylome-Wide Association Study of Schizophrenia: Identifying Blood Biomarker Signatures of Environmental Insults

IMPORTANCE Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management. OBJECTIVE To identify schizophrenia DNA methylation biomarkers in blood. DESIGN, SETTING, AND PARTICIPANTS The sample consisted of 759 schizophrenia cases and 738 controls (N = 1497) collected in Sweden. We used methyl-CpG-binding domain protein-enriched genome sequencing of the methylated genomic fraction, followed by next-generation DNA sequencing. We obtained a mean (SD) number of 68 (26.8) million reads per sample. This massive data set was processed using a specifically designed data analysis pipeline. Critical top findings from our methylome-wide association study (MWAS) were replicated in independent case-control participants using targeted pyrosequencing of bisulfite-converted DNA. MAIN OUTCOMES AND MEASURES Status of schizophrenia cases and controls. RESULTS Our MWAS suggested a considerable number of effects, with 25 sites passing the highly conservative Bonferroni correction and 139 sites significant at a false discovery rate of 0.01. Our top MWAS finding, which was located in FAM63B, replicated with P = 2.3 × 10-10. It was part of the networks regulated by microRNA that can be linked to neuronal differentiation and dopaminergic gene expression. Many other top MWAS results could be linked to hypoxia and, to a lesser extent, infection, suggesting that a record of pathogenic events may be preserved in the methylome. Our findings also implicated a site in RELN, one of the most frequently studied candidates in methylation studies of schizophrenia. CONCLUSIONS AND RELEVANCE To our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provides good coverage of methylation sites across the genome. Our results demonstrated one of the unique features of methylation studies that can capture signatures of environmental insults in peripheral tissues. Our MWAS suggested testable hypotheses about disease mechanisms and yielded biomarkers that can potentially be used to improve disease management.

Genomewide pharmacogenomic study of metabolic side effects to antipsychotic drugs

Understanding individual differences in the susceptibility to metabolic side effects as a response to antipsychotic therapy is essential to optimize the treatment of schizophrenia. Here, we perform genomewide association studies (GWAS) to search for genetic variation affecting the susceptibility to metabolic side effects. The analysis sample consisted of 738 schizophrenia patients, successfully genotyped for 492K single nucleotide polymorphisms (SNPs), from the genomic subsample of the Clinical Antipsychotic Trial of Intervention Effectiveness study. Outcomes included 12 indicators of metabolic side effects, quantifying antipsychotic-induced change in weight, blood lipids, glucose and hemoglobin A1c, blood pressure and heart rate. Our criterion for genomewide significance was a pre-specified threshold that ensures, on average, only 10% of the significant findings are false discoveries. A total of 21 SNPs satisfied this criterion. The top finding indicated that a SNP in Meis homeobox 2 (MEIS2) mediated the effects of risperidone on hip circumference (q=0.004). The same SNP was also found to mediate risperidones effect on waist circumference (q=0.055). Genomewide significant finding were also found for SNPs in PRKAR2B, GPR98, FHOD3, RNF144A, ASTN2, SOX5 and ATF7IP2, as well as in several intergenic markers. PRKAR2B and MEIS2 both have previous research indicating metabolic involvement, and PRKAR2B has previously been shown to mediate antipsychotic response. Although our findings require replication and functional validation, this study shows the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antipsychotic medication.

Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics

Schizophrenia is an often devastating neuropsychiatric illness. Understanding the genetic variation affecting response to antipsychotics is important to develop novel diagnostic tests to match individual schizophrenia patients to the most effective and safe medication. In this study, we use a genome-wide approach to detect genetic variation underlying individual differences in response to treatment with the antipsychotics olanzapine, quetiapine, risperidone, ziprasidone and perphenazine. Our sample consisted of 738 subjects with DSM-IV schizophrenia who took part in the Clinical Antipsychotic Trials of Intervention Effectiveness. Subjects were genotyped using the Affymetrix 500 K genotyping platform plus a custom 164 K chip to improve genome-wide coverage. Treatment outcome was measured using the Positive and Negative Syndrome Scale. Our criterion for genome-wide significance was a prespecified threshold that ensures that, on an average, only 10% of the significant findings are false discoveries. The top statistical result reached significance at our prespecified threshold and involved a single-nucleotide polymorphism (SNP) in an intergenic region on chromosome 4p15. In addition, SNPs in Ankyrin Repeat and Sterile Alpha Motif Domain-Containing Protein 1B (ANKS1B) and in the Contactin-Associated Protein-Like 5 gene (CNTNAP5), which mediated the effects of olanzapine and risperidone on Negative symptoms, were very close to our threshold for declaring significance. The most significant SNP in CNTNAP5 is nonsynonymous, giving rise to an amino-acid substitution. In addition to highlighting our top results, we provide all P-values for download as a resource for investigators with the requisite samples to carry out replication. This study demonstrates the potential of genome-wide association studies to discover novel genes that mediate the effects of antipsychotics, which could eventually help to tailor drug treatment to schizophrenic patients.

A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects

The central importance of epigenetics to the aging process is increasingly being recognized. Here we perform a methylome-wide association study (MWAS) of aging in whole blood DNA from 718 individuals, aged 25-92 years (mean = 55). We sequenced the methyl-CpG-enriched genomic DNA fraction, averaging 67.3 million reads per subject, to obtain methylation measurements for the ∼27 million autosomal CpGs in the human genome. Following extensive quality control, we adaptively combined methylation measures for neighboring, highly-correlated CpGs into 4 344 016 CpG blocks with which we performed association testing. Eleven age-associated differentially methylated regions (DMRs) passed Bonferroni correction (P-value < 1.15 × 10(-8)). Top findings replicated in an independent sample set of 558 subjects using pyrosequencing of bisulfite-converted DNA (min P-value < 10(-30)). To examine biological themes, we selected 70 DMRs with false discovery rate of <0.1. Of these, 42 showed hypomethylation and 28 showed hypermethylation with age. Hypermethylated DMRs were more likely to overlap with CpG islands and shores. Hypomethylated DMRs were more likely to be in regions associated with polycomb/regulatory proteins (e.g. EZH2) or histone modifications H3K27ac, H3K4m1, H3K4m2, H3K4m3 and H3K9ac. Among genes implicated by the top DMRs were protocadherins, homeobox genes, MAPKs and ryanodine receptors. Several of our DMRs are at genes with potential relevance for age-related disease. This study successfully demonstrates the application of next-generation sequencing to MWAS, by interrogating a large proportion of the methylome and returning potentially novel age DMRs, in addition to replicating several loci implicated in previous studies using microarrays.

Genome-Wide Pharmacogenomic Study of Neurocognition As an Indicator of Antipsychotic Treatment Response in Schizophrenia

Neurocognitive deficits are a core feature of schizophrenia and, therefore, represent potentially critical outcome variables for assessing antipsychotic treatment response. We performed genome-wide association studies (GWAS) with 492K single nucleotide polymorphisms (SNPs) in a sample of 738 patients with schizophrenia from the Clinical Antipsychotic Trials of Intervention Effectiveness study. Outcome variables consisted of a neurocognitive battery administered at multiple time points over an 18-month period, measuring processing speed, verbal memory, vigilance, reasoning, and working memory domains. Genetic mediation of improvements in each of these five domains plus a composite neurocognitive measure was assessed for each of five antipsychotics (olanzapine, perphenazine, quetiapine, risperidone, and ziprasidone). Six SNPs achieved genome-wide significance using a pre-specified threshold that ensures, on average, only 1 in 10 findings is a false discovery. These six SNPs were located within, or in close proximity to, genes EHF, SLC26A9, DRD2, GPR137B, CHST8, and IL1A. The more robust findings, that is those significant across multiple neurocognitive domains and having adjacent SNPs showing evidence for association, were rs286913 at the EHF gene (p-value 6.99 × 10−8, q-value 0.034, mediating the effects of ziprasidone on vigilance), rs11240594 at SLC26A9 (p-value 1.4 × 10−7, q-value 0.068, mediating the effects of olanzapine on processing speed), and rs11677416 at IL1A (p-value 6.67 × 10−7, q-value 0.081, mediating the effects of olanzapine on working memory). This study has generated several novel candidate genes for antipsychotic response. However, our findings will require replication and functional validation. To facilitate replication efforts, we provide all GWAS p-values for download.

1H nuclear magnetic resonance metabolomics analysis identifies novel urinary biomarkers for lung function

Chronic obstructive pulmonary disease (COPD), characterized by chronic airflow limitation, is a serious public health concern. In this study, we used proton nuclear magnetic resonance ((1)H NMR) spectroscopy to identify and quantify metabolites associated with lung function in COPD. Plasma and urine were collected from 197 adults with COPD and from 195 without COPD. Samples were assayed using a 600 MHz NMR spectrometer, and the resulting spectra were analyzed against quantitative spirometric measures of lung function. After correcting for false discoveries and adjusting for covariates (sex, age, smoking) several spectral regions in urine were found to be significantly associated with baseline lung function. These regions correspond to the metabolites trigonelline, hippurate and formate. Concentrations of each metabolite, standardized to urinary creatinine, were associated with baseline lung function (minimum p-value = 0.0002 for trigonelline). No significant associations were found with plasma metabolites. Urinary hippurate and formate are often related to gut microflora. This could suggest that the microbiome varies between individuals with different lung function. Alternatively, the associated metabolites may reflect lifestyle differences affecting overall health. Our results will require replication and validation, but demonstrate the utility of NMR metabolomics as a screening tool for identifying novel biomarkers of pulmonary outcomes.

Genome-wide pharmacogenomic study of citalopram- induced side effects in STAR*D

Affecting about 1 in 12 Americans annually, depression is a leading cause of the global disease burden. While a range of effective antidepressants are now available, failure and relapse rates remain substantial, with intolerable side effect burden the most commonly cited reason for discontinuation. Thus, understanding individual differences in susceptibility to antidepressant therapy side effects will be essential to optimize depression treatment. Here we perform genome-wide association studies (GWAS) to identify genetic variation influencing susceptibility to citalopram-induced side effects. The analysis sample consisted of 1762 depression patients, successfully genotyped for 421K single-nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Outcomes included five indicators of citalopram side effects: general side effect burden, overall tolerability, sexual side effects, dizziness and vision/hearing side effects. Two SNPs met our genome-wide significance criterion (q<0.1), ensuring that, on average, only 10% of significant findings are false discoveries. In total, 12 additional SNPs demonstrated suggestive associations (q<0.5). The top finding was rs17135437, an intronic SNP within EMID2, mediating the effects of citalopram on vision/hearing side effects (P=3.27 × 10−8, q=0.026). The second genome-wide significant finding, representing a haplotype spanning ∼30 kb and eight genotyped SNPs in a gene desert on chromosome 13, was associated with general side effect burden (P=3.22 × 10−7, q=0.096). Suggestive findings were also found for SNPs at LAMA1, AOX2P, EGFLAM, FHIT and RTP2. Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antidepressant medications.

A region of 35 kb containing the trace amine associate receptor 6 (TAAR6) gene is associated with schizophrenia in the Irish study of high-density schizophrenia families.

The TAAR6 gene has been previously associated with schizophrenia in 192 pedigrees of European and African ancestry. To replicate these findings we performed an association study of TAAR6 in 265 pedigrees of the Irish Study of High-Density Schizophrenia Families (ISHDSF). Of the 24 genotyped single-nucleotide polymorphisms only rs12189813 and rs9389011 provided single-marker evidence for association (0.0094⩽P⩽0.03). Two-marker haplotypes (rs7772821 and rs12189813; 0.0071⩽P⩽0.0023) and four-marker haplotypes (rs8192622, rs7772821, rs12189813 and rs9389011; 0.0047⩽P⩽0.018) gave strongest evidence for association. The associated high-risk (HR) haplotype in the ISHDSF is defined by the major alleles at rs7772821 and rs12189813 (0.00097⩽P⩽0.023). The associated HR remains positive in a case only test of association by Operational Criteria score analysis in which significant association was observed only with the highest threshold for delusions (P<0.009). When analysis was restricted to affected individuals under the core schizophrenia (D2) diagnosis, the observed associations for HR were most significant in the highest threshold for delusions (P<0.004) and hallucinations (P<0.0004), supporting the family-based association with schizophrenia.

A Genomewide Association Study of Citalopram Response in Major Depressive Disorder—A Psychometric Approach

We read with interest Garriock and colleagues’ article “A Genomewide Association Study of Citalopram Response in Major Depressive Disorder” (1). This work is an important step in analyzing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) genomewide SNP data. Given STAR*D’s complexity, many additional analyses will undoubtedly be required to realize the full potential of this valuable resource. In this spirit, we quantitatively model response as measured by the Quick Inventory of Depressive Symptoms (QIDS). The gains to quantitatively modeling response are evident in our results—unlike the initial analysis, we identify multiple genomewide significant SNPs.

Genome-wide and gene-based association studies of anxiety disorders in european and african american samples

Anxiety disorders (ADs) are common mental disorders caused by a combination of genetic and environmental factors. Since ADs are highly comorbid with each other, partially due to shared genetic basis, studying AD phenotypes in a coordinated manner may be a powerful strategy for identifying potential genetic loci for ADs. To detect these loci, we performed genome-wide association studies (GWAS) of ADs. In addition, as a complementary approach to single-locus analysis, we also conducted gene- and pathway-based analyses. GWAS data were derived from the control sample of the Molecular Genetics of Schizophrenia (MGS) project (2,540 European American and 849 African American subjects) genotyped on the Affymetrix GeneChip 6.0 array. We applied two phenotypic approaches: (1) categorical case-control comparisons (CC) based upon psychiatric diagnoses, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. Linear and logistic models were used to analyse the association with ADs using FS and CC traits, respectively. At the single locus level, no genome-wide significant association was found. A trans-population gene-based meta-analysis across both ethnic subsamples using FS identified three genes (MFAP3L on 4q32.3, NDUFAB1 and PALB2 on 16p12) with genome-wide significance (false discovery rate (FDR] <5%). At the pathway level, several terms such as transcription regulation, cytokine binding, and developmental process were significantly enriched in ADs (FDR <5%). Our approaches studying ADs as quantitative traits and utilizing the full GWAS data may be useful in identifying susceptibility genes and pathways for ADs.