Examining the relationship between genetic risk for depression and youth episodic stress exposure

BACKGROUND

Offspring of depressed mothers have elevated risk of developing depression because they are exposed to greater stress. While generally assumed that youth's increased exposure to stress is due to the environmental effects of living with a depressed parent, youth's genes may influence stress exposure through gene-environment correlations (rGEs). To understand the relationship between risk for depression and stress, we examined the effects of polygenic risk for depression on youth stress exposure.

METHODS

We examined the relations of a polygenic risk score (PRS) for depression (DEP-PRS), as well as PRSs for 5 other disorders, with youth stress exposure. Data were from a longitudinal study of a community sample of youth and their parents (n = 377) focusing on data collected at youth's aged 12 and 15 assessments.

RESULTS

Elevated youth DEP-PRS was robustly associated with increased dependent stress, particularly interpersonal events. Exploratory analyses indicated that findings were driven by major stress and were not moderated by maternal nor paternal history of depression, and of the 5 additional PRSs tested, only elevated genetic liability for bipolar I was associated with increased dependent stress-particularly non-interpersonal events.

LIMITATIONS

Like other PRS studies, we focused on those of European ancestry thus, generalizability of findings is limited.

CONCLUSION

Polygenic risk contributes to youth experiencing stressful life events which are dependent on their behavior. This rGE appears to be specific to genetic risk for mood disorders.

General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study

BACKGROUND

Polygenic risk scores (PRSs) capture genetic vulnerability to psychiatric conditions. However, PRSs are often associated with multiple mental health problems in children, complicating their use in research and clinical practice. The current study is the first to systematically test which PRSs associate broadly with all forms of childhood psychopathology, and which PRSs are more specific to one or a handful of forms of psychopathology.

METHODS

The sample consisted of 4717 unrelated children (mean age = 9.92, s.d. = 0.62; 47.1% female; all European ancestry). Psychopathology was conceptualized hierarchically as empirically derived general factor (p-factor) and five specific factors: externalizing, internalizing, neurodevelopmental, somatoform, and detachment. Partial correlations explored associations between psychopathology factors and 22 psychopathology-related PRSs. Regressions tested which level of the psychopathology hierarchy was most strongly associated with each PRS.

RESULTS

Thirteen PRSs were significantly associated with the general factor, most prominently Chronic Multisite Pain-PRS (r = 0.098), ADHD-PRS (r = 0.079), and Depression-PRS (r = 0.078). After adjusting for the general factor, Depression-PRS, Neuroticism-PRS, PTSD-PRS, Insomnia-PRS, Chronic Back Pain-PRS, and Autism-PRS were not associated with lower order factors. Conversely, several externalizing PRSs, including Adventurousness-PRS and Disinhibition-PRS, remained associated with the externalizing factor (|r| = 0.040-0.058). The ADHD-PRS remained uniquely associated with the neurodevelopmental factor (r = 062).

CONCLUSIONS

PRSs developed to predict vulnerability to emotional difficulties and chronic pain generally captured genetic risk for all forms of childhood psychopathology. PRSs developed to predict vulnerability to externalizing difficulties, e.g. disinhibition, tended to be more specific in predicting behavioral problems. The results may inform translation of existing PRSs to pediatric research and future clinical practice.

Polygenic risk scores for asthma and allergic disease associate with COVID-19 severity in 9/11 responders

BACKGROUND

Genetic factors contribute to individual differences in the severity of coronavirus disease 2019 (COVID-19). A portion of genetic predisposition can be captured using polygenic risk scores (PRS). Relatively little is known about the associations between PRS and COVID-19 severity or post-acute COVID-19 in community-dwelling individuals.

METHODS

Participants in this study were 983 World Trade Center responders infected for the first time with SARS-CoV-2 (mean age at infection = 56.06; 93.4% male; 82.7% European ancestry). Seventy-five (7.6%) responders were in the severe COVID-19 category; 306 (31.1%) reported at least one post-acute COVID-19 symptom at 4-week follow-up. Analyses were adjusted for population stratification and demographic covariates.

FINDINGS

The asthma PRS was associated with severe COVID-19 category (odds ratio [OR] = 1.61, 95% confidence interval: 1.17-2.21) and more severe COVID-19 symptomatology (β = .09, p = .01), independently of respiratory disease diagnosis. Severe COVID-19 category was also associated with the allergic disease PRS (OR = 1.97, [1.26-3.07]) and the PRS for COVID-19 hospitalization (OR = 1.35, [1.01-1.82]). PRS for coronary artery disease and type II diabetes were not associated with COVID-19 severity.

CONCLUSION

Recently developed polygenic biomarkers for asthma, allergic disease, and COVID-19 hospitalization capture some of the individual differences in severity and clinical course of COVID-19 illness in a community population.

Genome-wide association study meta-analysis of suicide death and suicidal behavior

Suicide is a worldwide health crisis. We aimed to identify genetic risk variants associated with suicide death and suicidal behavior. Meta-analysis for suicide death was performed using 3765 cases from Utah and matching 6572 controls of European ancestry. Meta-analysis for suicidal behavior using data across five cohorts (n = 8315 cases and 256,478 psychiatric or populational controls of European ancestry) was also performed. One locus in neuroligin 1 (NLGN1) passing the genome-wide significance threshold for suicide death was identified (top SNP rs73182688, with p = 5.48 × 10^-8 before and p = 4.55 × 10^-8 after mtCOJO analysis conditioning on MDD to remove genetic effects on suicide mediated by MDD). Conditioning on suicidal attempts did not significantly change the association strength (p = 6.02 × 10^-8), suggesting suicide death specificity. NLGN1 encodes a member of a family of neuronal cell surface proteins. Members of this family act as splice site-specific ligands for beta-neurexins and may be involved in synaptogenesis. The NRXN-NLGN pathway was previously implicated in suicide, autism, and schizophrenia. We additionally identified ROBO2 and ZNF28 associations with suicidal behavior in the meta-analysis across five cohorts in gene-based association analysis using MAGMA. Lastly, we replicated two loci including variants near SOX5 and LOC101928519 associated with suicidal attempts identified in the ISGC and MVP meta-analysis using the independent FinnGen samples. Suicide death and suicidal behavior showed positive genetic correlations with depression, schizophrenia, pain, and suicidal attempt, and negative genetic correlation with educational attainment. These correlations remained significant after conditioning on depression, suggesting pleiotropic effects among these traits. Bidirectional generalized summary-data-based Mendelian randomization analysis suggests that genetic risk for the suicidal attempt and suicide death are both bi-directionally causal for MDD.

Unique and joint associations of polygenic risk for major depression and opioid use disorder with endogenous opioid system function

Major depressive disorder (MDD) and opioid use disorder (OUD) are common, potentially fatal, polygenic disorders that are moderately heritable and often co-occur. We examined the unique and shared associations of polygenic risk scores (PRS) for these disorders with µ-opioid receptor (MOR) concentration and endogenous opioid response during a stressful stimulus. Participants were 144 healthy European-ancestry (EA) subjects (88 females) who underwent MOR quantification scans with [11C]carfentanil and PET and provided DNA for genotyping. MOR non-displaceable binding potential (BPND) was measured in 5 regions of interest (ROIs) related to mood and addiction. We examined associations of PRS both at baseline and following opioid release calculated as the ratio of baseline and stress-challenge scans, first in the entire sample and then separately by sex. MOR availability at baseline was positively associated with MDD PRS in the amygdala and ventral pallidum. MDD and OUD PRS were significantly associated with stress-induced opioid system activation in multiple ROIs, accounting for up to 14.5% and 5.4%, respectively, of the variance in regional activation. The associations were most robust among females, where combined they accounted for up to 25.0% of the variance among the ROIs. We conclude that there is a pathophysiologic link between polygenic risk for MDD and OUD and opioid system activity, as evidenced by PRS with unique and overlapping regional associations with this neurotransmitter system. This link could help to explain the high rate of comorbidity of MDD and OUD and suggests that opioid-modulating interventions could be useful in treating MDD and OUD, both individually and jointly.

Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms

OBJECTIVE

To investigate the genetic architecture of internalizing symptoms in childhood and adolescence.

METHOD

In 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument.

RESULTS

The meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, neffective = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (|rg| > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range |rg| = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa.

CONCLUSION

Genetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

Harnessing changes in open chromatin determined by ATAC-seq to generate insulin-responsive reporter constructs

BACKGROUND

Gene regulation is critical for proper cellular function. Next-generation sequencing technology has revealed the presence of regulatory networks that regulate gene expression and essential cellular functions. Studies investigating the epigenome have begun to uncover the complex mechanisms regulating transcription. Assay for transposase-accessible chromatin by sequencing (ATAC-seq) is quickly becoming the assay of choice for many epigenomic investigations. However, whether intervention-mediated changes in accessible chromatin determined by ATAC-seq can be harnessed to generate intervention-inducible reporter constructs has not been systematically assayed.

RESULTS

We used the insulin signaling pathway as a model to investigate chromatin regions and gene expression changes using ATAC- and RNA-seq in insulin-treated Drosophila S2 cells. We found correlations between ATAC- and RNA-seq data, especially when stratifying differentially-accessible chromatin regions by annotated feature type. In particular, our data demonstrated a weak but significant correlation between chromatin regions annotated to enhancers (1-2 kb from the transcription start site) and downstream gene expression. We cloned candidate enhancer regions upstream of luciferase and demonstrate insulin-inducibility of several of these reporters.

CONCLUSIONS

Insulin-induced chromatin accessibility determined by ATAC-seq reveals enhancer regions that drive insulin-inducible reporter gene expression.

Rare protein-coding variants implicate genes involved in risk of suicide death

Identification of genetic factors leading to increased risk of suicide death is critical to combat rising suicide rates, however, only a fraction of the genetic variation influencing risk has been accounted for. To address this limitation, we conducted the first comprehensive analysis of rare genetic variation in suicide death leveraging the largest suicide death biobank, the Utah Suicide Genetic Risk Study (USGRS). We conducted a single-variant association analysis of rare (minor allele frequency <1%) putatively functional single-nucleotide polymorphisms (SNPs) present on the Illumina PsychArray genotyping array in 2,672 USGRS suicide deaths of non-Finnish European (NFE) ancestry and 51,583 NFE controls from the Genome Aggregation Database. Secondary analyses used an independent control sample of 21,324 NFE controls from the Psychiatric Genomics Consortium. Five novel, high-impact, rare SNPs were identified with significant associations with suicide death (SNAPC1, rs75418419; TNKS1BP1, rs143883793; ADGRF5, rs149197213; PER1, rs145053802; and ESS2, rs62223875). 119 suicide decedents carried these high-impact SNPs. Both PER1 and SNAPC1 have other supporting gene-level evidence of suicide risk, and psychiatric associations exist for PER1 (bipolar disorder, schizophrenia), and for TNKS1BP1 and ESS2 (schizophrenia). Three of the genes (PER1, TNKS1BP1, and ADGRF5), together with additional genes implicated by genome-wide association studies on suicidal behavior, showed significant enrichment in immune system, homeostatic and signal transduction processes. No specific diagnostic phenotypes were associated with the subset of suicide deaths with the identified rare variants. These findings suggest an important role for rare variants in suicide risk and implicate genes and gene pathways for targeted replication.

Suicide and Psychosis: Results From a Population-Based Cohort of Suicide Death (N = 4380)

Approximately 5% of individuals with schizophrenia die from suicide. However, suicide in psychosis is still poorly characterized, partly due to a lack of adequate population-based clinical or genetic data on suicide death. The Utah Suicide Genetics Research Study (USGRS) provides a large population-based cohort of suicide deaths with medical record and genome-wide data (N = 4380). Examination of this cohort identified medical and genetic risks associated with type of suicide death and investigated the relative contributions of psychotic and affective symptoms to method of suicide. Key differences in method of suicide (common vs. atypical methods) were tested in relation to lifetime psychosis and genome-wide genetic risk for schizophrenia, major depressive disorder, and neuroticism. Consistent with previous studies, psychosis-spectrum disorders were observed to be common in suicide (15% of the cohort). Individuals with psychosis more frequently died from atypical methods, with rates of atypical suicide increasing across the schizophrenia spectrum. Genetic risk for schizophrenia was also associated with atypical suicide, regardless of clinical diagnosis, though this association weakened when filtering individuals with schizophrenia from the analysis. Follow-up examination indicated that high rates of atypical suicide observed in schizophrenia are not likely accounted for by restricted access to firearms. Overall, better accounting for the increased risk of atypical suicide methods in psychosis could lead to improved prevention strategies in a large portion of the suicide risk population.

Genetic association study of childhood aggression across raters, instruments, and age

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E-06), PCDH7 (P = 2.0E-06), and IPO13 (P = 2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range [Formula: see text]: 0.19-1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = ~-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range [Formula: see text]: 0.46-0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

Polygenic prediction of PTSD trajectories in 9/11 responders

BACKGROUND

Genetics hold promise of predicting long-term post-traumatic stress disorder (PTSD) outcomes following trauma. The aim of the current study was to test whether six hypothesized polygenic risk scores (PRSs) developed to capture genetic vulnerability to psychiatric conditions prospectively predict PTSD onset, severity, and 18-year course after trauma exposure.

METHODS

Participants were 1490 responders to the World Trade Center (WTC) disaster (mean age at 9/11 = 38.81 years, s.d. = 8.20; 93.5% male; 23.8% lifetime WTC-related PTSD diagnosis). Prospective longitudinal data on WTC-related PTSD symptoms were obtained from electronic medical records and modelled as PTSD trajectories using growth mixture model analysis. Independent regression models tested whether six hypothesized psychiatric PRSs (PTSD-PRS, Re-experiencing-PRS, Generalized Anxiety-PRS, Schizophrenia-PRS, Depression-PRS, and Neuroticism-PRS) are predictive of WTC-PTSD outcomes: lifetime diagnoses, average symptom severity, and 18-year symptom trajectory. All analyses were adjusted for population stratification, 9/11 exposure severity, and multiple testing.

RESULTS

Depression-PRS predicted PTSD diagnostic status (OR 1.37, CI 1.17-1.61, adjusted p = 0.001). All PRSs, except PTSD-PRS, significantly predicted average PTSD symptoms (β = 0.06-0.10, adjusted p < 0.05). Re-experiencing-PRS, Generalized Anxiety-PRS and Schizophrenia-PRS predicted the high severity PTSD trajectory class (ORs 1.21-1.28, adjusted p < 0.05). Finally, PRSs prediction was independent of 9/11 exposure severity and jointly accounted for 3.7 times more variance in PTSD symptoms than the exposure severity.

CONCLUSIONS

Psychiatric PRSs prospectively predicted WTC-related PTSD lifetime diagnosis, average symptom severity, and 18-year trajectory in responders to 9/11 disaster. Jointly, PRSs were more predictive of subsequent PTSD than the exposure severity. In the future, PRSs may help identify at-risk responders who might benefit from targeted prevention approaches.

Genome-Wide Association Study of Suicide Death and Polygenic Prediction of Clinical Antecedents

OBJECTIVE

Death by suicide is a highly preventable yet growing worldwide health crisis. To date, there has been a lack of adequately powered genomic studies of suicide, with no sizable suicide death cohorts available for analysis. To address this limitation, the authors conducted the first comprehensive genomic analysis of suicide death using previously unpublished genotype data from a large population-ascertained cohort.

METHODS

The analysis sample comprised 3,413 population-ascertained case subjects of European ancestry and 14,810 ancestrally matched control subjects. Analytical methods included principal component analysis for ancestral matching and adjusting for population stratification, linear mixed model genome-wide association testing (conditional on genetic-relatedness matrix), gene and gene set-enrichment testing, and polygenic score analyses, as well as single-nucleotide polymorphism (SNP) heritability and genetic correlation estimation using linkage disequilibrium score regression.

RESULTS

Genome-wide association analysis identified two genome-wide significant loci (involving six SNPs: rs34399104, rs35518298, rs34053895, rs66828456, rs35502061, and rs35256367). Gene-based analyses implicated 22 genes on chromosomes 13, 15, 16, 17, and 19 (q<0.05). Suicide death heritability was estimated at an h2SNP value of 0.25 (SE=0.04) and a value of 0.16 (SE=0.02) when converted to a liability scale. Notably, suicide polygenic scores were significantly predictive across training and test sets. Polygenic scores for several other psychiatric disorders and psychological traits were also predictive, particularly scores for behavioral disinhibition and major depressive disorder.

CONCLUSIONS

Multiple genome-wide significant loci and genes were identified and polygenic score prediction of suicide death case-control status was demonstrated, adjusting for ancestry, in independent training and test sets. Additionally, the suicide death sample was found to have increased genetic risk for behavioral disinhibition, major depressive disorder, depressive symptoms, autism spectrum disorder, psychosis, and alcohol use disorder compared with the control sample.

Molecular Genetic Risk for Psychosis Is Associated With Psychosis Risk Symptoms in a Population-Based UK Cohort: Findings From Generation Scotland

OBJECTIVE

Subthreshold psychosis risk symptoms in the general population may be associated with molecular genetic risk for psychosis. This study sought to optimize the association of risk symptoms with genetic risk for psychosis in a large population-based cohort in the UK (N = 9104 individuals 18-65 years of age) by properly accounting for population stratification, factor structure, and sex.

METHODS

The newly expanded Generation Scotland: Scottish Family Health Study includes 5391 females and 3713 males with age M [SD] = 45.2 [13] with both risk symptom data and genetic data. Subthreshold psychosis symptoms were measured using the Schizotypal Personality Questionnaire-Brief (SPQ-B) and calculation of polygenic risk for schizophrenia was based on 11 425 349 imputed common genetic variants passing quality control. Follow-up examination of other genetic risks included attention-deficit hyperactivity disorder (ADHD), autism, bipolar disorder, major depression, and neuroticism.

RESULTS

Empirically derived symptom factor scores reflected interpersonal/negative symptoms and were positively associated with polygenic risk for schizophrenia. This signal was largely sex specific and limited to males. Across both sexes, scores were positively associated with neuroticism and major depressive disorder.

CONCLUSIONS

A data-driven phenotypic analysis enabled detection of association with genetic risk for schizophrenia in a population-based sample. Multiple polygenic risk signals and important sex differences suggest that genetic data may be useful in improving future phenotypic risk assessment.

A methylation study of long-term depression risk

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10^-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Methylome-wide association findings for major depressive disorder overlap in blood and brain and replicate in independent brain samples

We present the first large-scale methylome-wide association studies (MWAS) for major depressive disorder (MDD) to identify sites of potential importance for MDD etiology. Using a sequencing-based approach that provides near-complete coverage of all 28 million common CpGs in the human genome, we assay methylation in MDD cases and controls from both blood (N = 1132) and postmortem brain tissues (N = 61 samples from Brodmann Area 10, BA10). The MWAS for blood identified several loci with P ranging from 1.91 × 10^-8 to 4.39 × 10^-8 and a resampling approach showed that the cumulative association was significant (P = 4.03 × 10^-10) with the signal coming from the top 25,000 MWAS markers. Furthermore, a permutation-based analysis showed significant overlap (P = 5.4 × 10^-3) between the MWAS findings in blood and brain (BA10). This overlap was significantly enriched for a number of features including being in eQTLs in blood and the frontal cortex, CpG islands and shores, and exons. The overlapping sites were also enriched for active chromatin states in brain including genic enhancers and active transcription start sites. Furthermore, three loci located in GABBR2, RUFY3, and in an intergenic region on chromosome 2 replicated with the same direction of effect in the second brain tissue (BA25, N = 60) from the same individuals and in two independent brain collections (BA10, N = 81 and 64). GABBR2 inhibits neuronal activity through G protein-coupled second-messenger systems and RUFY3 is implicated in the establishment of neuronal polarity and axon elongation. In conclusion, we identified and replicated methylated loci associated with MDD that are involved in biological functions of likely importance to MDD etiology.

Test-statistic inflation in methylome-wide association studies

Recent years have seen a surge of methylome-wide association studies (MWAS). We observed that many of these studies suffer from test statistic inflation that is most likely caused by commonly used quality control (QC) pipelines not going far enough to remove technical artefacts. To support this claim, we reanalysed GEO datasets with an improved QC pipeline that reduced test-statistic inflation parameter lambda from the original mean/median of 20.16/15.17 to 3.07/1.14. Furthermore, the mean/median number of methylome-wide significant findings was reduced by 65,688/57,805 loci after more thorough QC. To avoid such false positives we argue for more extensive QC and that reporting the test-statistic inflation parameter lambda become standard for all MWAS allowing readers to better assess the risk of false discoveries.

Cell Type-Specific Methylome-wide Association Studies Implicate Neurotrophin and Innate Immune Signaling in Major Depressive Disorder

BACKGROUND

We sought to characterize methylation changes in brain and blood associated with major depressive disorder (MDD). As analyses of bulk tissue may obscure association signals and hamper the biological interpretation of findings, these changes were studied on a cell type-specific level.

METHODS

In 3 collections of human postmortem brain (n = 206) and 1 collection of blood samples (N = 1132) of MDD cases and controls, we used epigenomic deconvolution to perform cell type-specific methylome-wide association studies within subpopulations of neurons/glia for the brain data and granulocytes/T cells/B cells/monocytes for the blood data. Sorted neurons/glia from a fourth postmortem brain collection (n = 58) were used for validation purposes.

RESULTS

Cell type-specific methylome-wide association studies identified multiple findings in neurons/glia that were detected across brain collections and were reproducible in physically sorted nuclei. Cell type-specific analyses in blood samples identified methylome-wide significant associations in T cells, monocytes, and whole blood that replicated findings from a past methylation study of MDD. Pathway analyses implicated p75 neurotrophin receptor/nerve growth factor signaling and innate immune toll-like receptor signaling in MDD. Top results in neurons, glia, bulk brain, T cells, monocytes, and whole blood were enriched for genes supported by genome-wide association studies for MDD and other psychiatric disorders.

CONCLUSIONS

We both replicated and identified novel MDD-methylation associations in human brain and blood samples at a cell type-specific level. Our results provide mechanistic insights into how the immune system may interact with the brain to affect MDD susceptibility. Importantly, our findings involved associations with MDD in human samples that implicated many closely related biological pathways. These disease-linked sites and pathways represent promising new therapeutic targets for MDD.

Independent Methylome-Wide Association Studies of Schizophrenia Detect Consistent Case-Control Differences

Methylome-wide association studies (MWASs) are promising complements to sequence variation studies. We used existing sequencing-based methylation data, which assayed the majority of all 28 million CpGs in the human genome, to perform an MWAS for schizophrenia in blood, while controlling for cell-type heterogeneity with a recently generated platform-specific reference panel. Next, we compared the MWAS results with findings from 3 existing large-scale array-based schizophrenia methylation studies in blood that assayed up to ~450 000 CpGs. Our MWAS identified 22 highly significant loci (P < 5 × 10-8) and 852 suggestively significant loci (P < 1 × 10-5). The top finding (P = 5.62 × 10-11, q = 0.001) was located in MFN2, which encodes mitofusin-2 that regulates Ca2+ transfer from the endoplasmic reticulum to mitochondria in cooperation with DISC1. The second-most significant site (P = 1.38 × 10-9, q = 0.013) was located in ALDH1A2, which encodes an enzyme for astrocyte-derived retinoic acid-a key neuronal morphogen with relevance for schizophrenia. Although the most significant MWAS findings were not assayed on the arrays, we observed significant enrichment of overlapping findings with 2 of the 3 array datasets (P = 0.0315, 0.0045, 0.1946). Overrepresentation analysis of Gene Ontology terms for the genes in the significant overlaps suggested high similarity in the biological functions detected by the different datasets. Top terms were related to immune and/or stress responses, cell adhesion and motility, and a broad range of processes essential for neurodevelopment.

RaMWAS: fast methylome-wide association study pipeline for enrichment platforms

Motivation

Enrichment-based technologies can provide measurements of DNA methylation at tens of millions of CpGs for thousands of samples. Existing tools for methylome-wide association studies cannot analyze datasets of this size and lack important features like principal component analysis, combined analysis with SNP data and outcome predictions that are based on all informative methylation sites.

Results

We present a Bioconductor R package called RaMWAS with a full set of tools for large-scale methylome-wide association studies. It is free, cross-platform, open source, memory efficient and fast.

Availability and implementation

Release version and vignettes with small case study at bioconductor.org/packages/ramwas Development version at github.com/andreyshabalin/ramwas.

Supplementary information

Supplementary data are available at Bioinformatics online.

An empirical Bayes approach for multiple tissue eQTL analysis

Expression quantitative trait locus (eQTL) analyses identify genetic markers associated with the expression of a gene. Most up-to-date eQTL studies consider the connection between genetic variation and expression in a single tissue. Multi-tissue analyses have the potential to improve findings in a single tissue, and elucidate the genotypic basis of differences between tissues. In this article, we develop a hierarchical Bayesian model (MT-eQTL) for multi-tissue eQTL analysis. MT-eQTL explicitly captures patterns of variation in the presence or absence of eQTL, as well as the heterogeneity of effect sizes across tissues. We devise an efficient Expectation-Maximization (EM) algorithm for model fitting. Inferences concerning eQTL detection and the configuration of eQTL across tissues are derived from the adaptive thresholding of local false discovery rates, and maximum a posteriori estimation, respectively. We also provide theoretical justification of the adaptive procedure. We investigate the MT-eQTL model through an extensive analysis of a 9-tissue data set from the GTEx initiative.

Building a schizophrenia genetic network: transcription factor 4 regulates genes involved in neuronal development and schizophrenia risk

The transcription factor 4 (TCF4) locus is a robust association finding with schizophrenia (SCZ), but little is known about the genes regulated by the encoded transcription factor. Therefore, we conducted chromatin immunoprecipitation sequencing (ChIP-seq) of TCF4 in neural-derived (SH-SY5Y) cells to identify genome-wide TCF4 binding sites, followed by data integration with SCZ association findings. We identified 11 322 TCF4 binding sites overlapping in two ChIP-seq experiments. These sites are significantly enriched for the TCF4 Ebox binding motif (>85% having ≥1 Ebox) and implicate a gene set enriched for genes downregulated in TCF4 small-interfering RNA (siRNA) knockdown experiments, indicating the validity of our findings. The TCF4 gene set was also enriched among (1) gene ontology categories such as axon/neuronal development, (2) genes preferentially expressed in brain, in particular pyramidal neurons of the somatosensory cortex and (3) genes downregulated in postmortem brain tissue from SCZ patients (odds ratio, OR = 2.8, permutation P < 4x10-5). Considering genomic alignments, TCF4 binding sites significantly overlapped those for neural DNA-binding proteins such as FOXP2 and the SCZ-associated EP300. TCF4 binding sites were modestly enriched among SCZ risk loci from the Psychiatric Genomic Consortium (OR = 1.56, P = 0.03). In total, 130 TCF4 binding sites occurred in 39 of the 108 regions published in 2014. Thirteen genes within the 108 loci had both a TCF4 binding site ±10kb and were differentially expressed in siRNA knockdown experiments of TCF4, suggesting direct TCF4 regulation. These findings confirm TCF4 as an important regulator of neural genes and point toward functional interactions with potential relevance for SCZ.

A Whole Methylome Study of Ethanol Exposure in Brain and Blood: An Exploration of the Utility of Peripheral Blood as Proxy Tissue for Brain in Alcohol Methylation Studies

BACKGROUND

Recent reviews have highlighted the potential use of blood-based methylation biomarkers as diagnostic and prognostic tools of current and future alcohol use and addiction. Due to the substantial overlap that often exists between methylation patterns across different tissues, including blood and brain, blood-based methylation may track methylation changes in brain; however, little work has explored the overlap in alcohol-related methylation in these tissues.

METHODS

To study the effects of alcohol on the brain methylome and identify possible biomarkers of these changes in blood, we performed a methylome-wide association study in brain and blood from 40 male DBA/2J mice that received either an acute ethanol (EtOH) or saline intraperitoneal injection. To investigate all 22 million CpGs in the mouse genome, we enriched for the methylated genomic fraction using methyl-CpG binding domain (MBD) protein capture followed by next-generation sequencing (MBD-seq). We performed association tests in blood and brain separately followed by enrichment testing to determine whether there was overlapping alcohol-related methylation in the 2 tissues.

RESULTS

The top result for brain was a CpG located in an intron of Ttc39b (p = 5.65 × 10^-08 ), and for blood, the top result was located in Espnl (p = 5.11 × 10^-08 ). Analyses implicated pathways involved in inflammation and neuronal differentiation, such as CXCR4, IL-7, and Wnt signaling. Enrichment tests indicated significant overlap among the top results in brain and blood. Pathway analyses of the overlapping genes converge on MAPKinase signaling (p = 5.6 × 10^-05 ) which plays a central role in acute and chronic responses to alcohol and glutamate receptor pathways, which can regulate neuroplastic changes underlying addictive behavior.

CONCLUSIONS

Overall, we have shown some methylation changes in brain and blood after acute EtOH administration and that the changes in blood partly mirror the changes in brain suggesting the potential for DNA methylation in blood to be biomarkers of alcohol use.

Enhancing Psychosis-Spectrum Nosology Through an International Data Sharing Initiative

The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.

Polygenic risk scoring and prediction of mental health outcomes

Psychiatric conditions are highly polygenic, meaning that genetic risk arises from many hundreds or thousands of genetic variants. Psychiatric genomics and psychological science are increasingly using polygenic risk scoring-the integration of all common genetic variant effects into a single risk metric-to model latent risk and to predict mental health outcomes. This review discusses the use of these scores in psychology and psychiatry to date, important methodological considerations, and potential of scoring methods for informing psychological science. Polygenic risk scores can easily be added to environmental and behavioral genetic models of latent risk, making them desirable metrics for use in psychological research.

Convergence of evidence from a methylome-wide CpG-SNP association study and GWAS of major depressive disorder

DNA methylation is an epigenetic modification that provides stability and diversity to the cellular phenotype. It is influenced by both genetic sequence variation and environmental factors, and can therefore potentially account for variation of heritable phenotypes and disorders. Therefore, methylome-wide association studies (MWAS) are promising complements to genome-wide association studies (GWAS) of genetic variants. Of particular interest are methylation sites (CpGs) that are created or destroyed by the alleles of single-nucleotide polymorphisms (SNPs), as these so-called CpG-SNPs may show variation in methylation levels on top of what can be explained by the sequence variation. Using sequencing-based data from 1132 major depressive disorder (MDD) cases and controls, we performed a MWAS of 970,414 common CpG-SNPs. The analysis identified 27 suggestively significant (P < 1.00 × 10^-5) CpG-SNPs associations. Furthermore, the MWAS results were over-represented (odds ratios ranging 1.36-5.00; P ranging 4.9 × 10^-3-8.1 × 10^-2) among findings from three recent GWAS for MDD-related phenotypes. Overlapping loci included, e.g., ROBO2, ASIC2, and DCC. As the CpG-SNP analysis accounts for the number of alleles that creates CpGs, the methylation differences could not be explained by differences in allele frequencies. Thus, the results show that the MWAS and GWASs provide independent lines of evidence for the involvement of these loci in MDD. In conclusion, our methylation study of MDD contributes novel information about loci of relevance that complements previous findings and generates new hypothesis about MDD etiology, such as that the functional effects of genetic association may be partly mediated and/or enhanced by the methylation status in these loci.

Epigenetic Aging in Major Depressive Disorder

OBJECTIVE

Major depressive disorder is associated with an increased risk of mortality and aging-related diseases. The authors examined whether major depression is associated with higher epigenetic aging in blood as measured by DNA methylation (DNAm) patterns, whether clinical characteristics of major depression have a further impact on these patterns, and whether the findings replicate in brain tissue.

METHOD

DNAm age was estimated using all methylation sites in blood of 811 depressed patients and 319 control subjects with no lifetime psychiatric disorders and low depressive symptoms from the Netherlands Study of Depression and Anxiety. The residuals of the DNAm age estimates regressed on chronological age were calculated to indicate epigenetic aging. Major depression diagnosis and clinical characteristics were assessed with questionnaires and psychiatric interviews. Analyses were adjusted for sociodemographic characteristics, lifestyle, and health status. Postmortem brain samples of 74 depressed patients and 64 control subjects were used for replication. Pathway enrichment analysis was conducted using ConsensusPathDB to gain insight into the biological processes underlying epigenetic aging in blood and brain.

RESULTS

Significantly higher epigenetic aging was observed in patients with major depression compared with control subjects (Cohen's d=0.18), with a significant dose effect with increasing symptom severity in the overall sample. In the depression group, epigenetic aging was positively and significantly associated with childhood trauma score. The case-control difference was replicated in an independent data set of postmortem brain samples. The top significantly enriched Gene Ontology terms included neuronal processes.

CONCLUSIONS

As compared with control subjects, patients with major depression exhibited higher epigenetic aging in blood and brain tissue, suggesting that they are biologically older than their corresponding chronological age. This effect was even more profound in the presence of childhood trauma.

Methyl-CpG-Binding Domain Sequencing: MBD-seq

Detailed biological knowledge about the potential importance of the methylome is typically lacking for common diseases. Therefore, methylome-wide association studies (MWAS) are critical to detect disease relevant methylation sites. Methyl-CpG-binding domain sequencing (MBD-seq) offers potential advantages compared to antibody-based enrichment, but performance depends critically on using an optimal protocol. Using an optimized protocol, MBD-seq can approximate the sensitivity/specificity obtained with whole-genome bisulfite sequencing, but at a fraction of the costs and time to complete the project. Thus, MBD-seq offers a comprehensive first pass at the CpG methylome and is economically feasible with the samples sizes required for MWAS.

A MBD-seq protocol for large-scale methylome-wide studies with (very) low amounts of DNA

We recently showed that, after optimization, our methyl-CpG binding domain sequencing (MBD-seq) application approximates the methylome-wide coverage obtained with whole-genome bisulfite sequencing (WGB-seq), but at a cost that enables adequately powered large-scale association studies. A prior drawback of MBD-seq is the relatively large amount of genomic DNA (ideally >1 µg) required to obtain high-quality data. Biomaterials are typically expensive to collect, provide a finite amount of DNA, and may simply not yield sufficient starting material. The ability to use low amounts of DNA will increase the breadth and number of studies that can be conducted. Therefore, we further optimized the enrichment step. With this low starting material protocol, MBD-seq performed equally well, or better, than the protocol requiring ample starting material (>1 µg). Using only 15 ng of DNA as input, there is minimal loss in data quality, achieving 93% of the coverage of WGB-seq (with standard amounts of input DNA) at similar false/positive rates. Furthermore, across a large number of genomic features, the MBD-seq methylation profiles closely tracked those observed for WGB-seq with even slightly larger effect sizes. This suggests that MBD-seq provides similar information about the methylome and classifies methylation status somewhat more accurately. Performance decreases with <15 ng DNA as starting material but, even with as little as 5 ng, MBD-seq still achieves 90% of the coverage of WGB-seq with comparable genome-wide methylation profiles. Thus, the proposed protocol is an attractive option for adequately powered and cost-effective methylome-wide investigations using (very) low amounts of DNA.

Enrichment methods provide a feasible approach to comprehensive and adequately powered investigations of the brain methylome

Methylome-wide association studies are typically performed using microarray technologies that only assay a very small fraction of the CG methylome and entirely miss two forms of methylation that are common in brain and likely of particular relevance for neuroscience and psychiatric disorders. The alternative is to use whole genome bisulfite (WGB) sequencing but this approach is not yet practically feasible with sample sizes required for adequate statistical power. We argue for revisiting methylation enrichment methods that, provided optimal protocols are used, enable comprehensive, adequately powered and cost-effective genome-wide investigations of the brain methylome. To support our claim we use data showing that enrichment methods approximate the sensitivity obtained with WGB methods and with slightly better specificity. However, this performance is achieved at <5% of the reagent costs. Furthermore, because many more samples can be sequenced simultaneously, projects can be completed about 15 times faster. Currently the only viable option available for comprehensive brain methylome studies, enrichment methods may be critical for moving the field forward.

Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence

BACKGROUND

Previous genomewide association studies (GWASs) have identified a number of putative risk loci for alcohol dependence (AD). However, only a few loci have replicated and these replicated variants only explain a small proportion of AD risk. Using an innovative approach, the goal of this study was to generate hypotheses about potentially causal variants for AD that can be explored further through functional studies.

METHODS

We employed targeted capture of 71 candidate loci and flanking regions followed by next-generation deep sequencing (mean coverage 78X) in 806 European Americans. Regions included in our targeted capture library were genes identified through published GWAS of alcohol, all human alcohol and aldehyde dehydrogenases, reward system genes including dopaminergic and opioid receptors, prioritized candidate genes based on previous associations, and genes involved in the absorption, distribution, metabolism, and excretion of drugs. We performed single-locus tests to determine if any single variant was associated with AD symptom count. Sets of variants that overlapped with biologically meaningful annotations were tested for association in aggregate.

RESULTS

No single, common variant was significantly associated with AD in our study. We did, however, find evidence for association with several variant sets. Two variant sets were significant at the q-value <0.10 level: a genic enhancer for ADHFE1 (p = 1.47 × 10^-5 ; q = 0.019), an alcohol dehydrogenase, and ADORA1 (p = 5.29 × 10^-5 ; q = 0.035), an adenosine receptor that belongs to a G-protein-coupled receptor gene family.

CONCLUSIONS

To our knowledge, this is the first sequencing study of AD to examine variants in entire genes, including flanking and regulatory regions. We found that in addition to protein coding variant sets, regulatory variant sets may play a role in AD. From these findings, we have generated initial functional hypotheses about how these sets may influence AD.

Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies

Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment.Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7 and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y.

A Whole Methylome CpG-SNP Association Study of Psychosis in Blood and Brain Tissue

Mutated CpG sites (CpG-SNPs) are potential hotspots for human diseases because in addition to the sequence variation they may show individual differences in DNA methylation. We performed methylome-wide association studies (MWAS) to test whether methylation differences at those sites were associated with schizophrenia. We assayed all common CpG-SNPs with methyl-CpG binding domain protein-enriched genome sequencing (MBD-seq) using DNA extracted from 1408 blood samples and 66 postmortem brain samples (BA10) of schizophrenia cases and controls. Seven CpG-SNPs passed our FDR threshold of 0.1 in the blood MWAS. Of the CpG-SNPs methylated in brain, 94% were also methylated in blood. This significantly exceeded the 46.2% overlap expected by chance (P-value < 1.0×10(-8)) and justified replicating findings from blood in brain tissue. CpG-SNP rs3796293 in IL1RAP replicated (P-value = .003) with the same direction of effects. This site was further validated through targeted bisulfite pyrosequencing in 736 independent case-control blood samples (P-value < 9.5×10(-4)). Our top result in the brain MWAS (P-value = 8.8×10(-7)) was CpG-SNP rs16872141 located in the potential promoter of ENC1. Overall, our results suggested that CpG-SNP methylation may reflect effects of environmental insults and can provide biomarkers in blood that could potentially improve disease management.

An integrated map of structural variation in 2,504 human genomes

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

Deep Sequencing of Three Loci Implicated in Large-Scale Genome-Wide Association Study Smoking Meta-Analyses

INTRODUCTION

Genome-wide association study meta-analyses have robustly implicated three loci that affect susceptibility for smoking: CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6 and EGLN2\CYP2A6. Functional follow-up studies of these loci are needed to provide insight into biological mechanisms. However, these efforts have been hampered by a lack of knowledge about the specific causal variant(s) involved. In this study, we prioritized variants in terms of the likelihood they account for the reported associations.

METHODS

We employed targeted capture of the CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6, and EGLN2\CYP2A6 loci and flanking regions followed by next-generation deep sequencing (mean coverage 78×) to capture genomic variation in 363 individuals. We performed single locus tests to determine if any single variant accounts for the association, and examined if sets of (rare) variants that overlapped with biologically meaningful annotations account for the associations.

RESULTS

In total, we investigated 963 variants, of which 71.1% were rare (minor allele frequency < 0.01), 6.02% were insertion/deletions, and 51.7% were catalogued in dbSNP141. The single variant results showed that no variant fully accounts for the association in any region. In the variant set results, CHRNB4 accounts for most of the signal with significant sets consisting of directly damaging variants. CHRNA6 explains most of the signal in the CHRNB3\CHRNA6 locus with significant sets indicating a regulatory role for CHRNA6. Significant sets in CYP2A6 involved directly damaging variants while the significant variant sets suggested a regulatory role for EGLN2.

CONCLUSIONS

We found that multiple variants implicating multiple processes explain the signal. Some variants can be prioritized for functional follow-up.

Combined Whole Methylome and Genomewide Association Study Implicates CNTN4 in Alcohol Use

BACKGROUND

Methylome-wide association (MWAS) studies present a new way to advance the search for biological correlates for alcohol use. A challenge with methylation studies of alcohol involves the causal direction of significant methylation-alcohol associations. One way to address this issue is to combine MWAS data with genomewide association study (GWAS) data.

METHODS

Here, we combined MWAS and GWAS results for alcohol use from 619 individuals. Our MWAS data were generated by next-generation sequencing of the methylated genomic DNA fraction, producing over 60 million reads per subject to interrogate methylation levels at ~27 million autosomal CpG sites in the human genome. Our GWAS included 5,571,786 single nucleotide polymorphisms (SNPs) imputed with 1000 Genomes.

RESULTS

When combining the MWAS and GWAS data, our top finding was a region in an intron of CNTN4 (p = 2.55 × 10(-8) ), located between chr3: 2,555,403 and 2,555,524, encompassing SNPs rs1382874 and rs1382875. This finding was then replicated in an independent sample of 730 individuals. We used bisulfite pyrosequencing to measure methylation and found significant association with regular alcohol use in the same direction as the MWAS (p = 0.021). Rs1382874 and rs1382875 were genotyped and found to be associated in the same direction as the GWAS (p = 0.008 and p = 0.009). After integrating the MWAS and GWAS findings from the replication sample, we replicated our combined analysis finding (p = 0.0017) in CNTN4.

CONCLUSIONS

Through combining methylation and SNP data, we have identified CNTN4 as a risk factor for regular alcohol use.

Refinement of schizophrenia GWAS loci using methylome-wide association data

Recent genome-wide association studies (GWAS) have made substantial progress in identifying disease loci. The next logical step is to design functional experiments to identify disease mechanisms. This step, however, is often hampered by the large size of loci identified in GWAS that is caused by linkage disequilibrium between SNPs. In this study, we demonstrate how integrating methylome-wide association study (MWAS) results with GWAS findings can narrow down the location for a subset of the putative casual sites. We use the disease schizophrenia as an example. To handle "data analytic" variation, we first combined our MWAS results with two GWAS meta-analyses (N = 32,143 and 21,953), that had largely overlapping samples but different data analysis pipelines, separately. Permutation tests showed significant overlapping association signals between GWAS and MWAS findings. This significant overlap justified prioritizing loci based on the concordance principle. To further ensure that the methylation signal was not driven by chance, we successfully replicated the top three methylation findings near genes SDCCAG8, CREB1 and ATXN7 in an independent sample using targeted pyrosequencing. In contrast to the SNPs in the selected region, the methylation sites were largely uncorrelated explaining why the methylation signals implicated much smaller regions (median size 78 bp). The refined loci showed considerable enrichment of genomic elements of possible functional importance and suggested specific hypotheses about schizophrenia etiology. Several hypotheses involved possible variation in transcription factor-binding efficiencies.

Resolving the polymorphism-in-probe problem is critical for correct interpretation of expression QTL studies

Polymorphisms in the target mRNA sequence can greatly affect the binding affinity of microarray probe sequences, leading to false-positive and false-negative expression quantitative trait locus (QTL) signals with any other polymorphisms in linkage disequilibrium. We provide the most complete solution to this problem, by using the latest genome and exome sequence reference data to identify almost all common polymorphisms (frequency >1% in Europeans) in probe sequences for two commonly used microarray panels (the gene-based Illumina Human HT12 array, which uses 50-mer probes, and exon-based Affymetrix Human Exon 1.0 ST array, which uses 25-mer probes). We demonstrate the impact of this problem using cerebellum and frontal cortex tissues from 438 neuropathologically normal individuals. We find that although only a small proportion of the probes contain polymorphisms, they account for a large proportion of apparent expression QTL signals, and therefore result in many false signals being declared as real. We find that the polymorphism-in-probe problem is insufficiently controlled by previous protocols, and illustrate this using some notable false-positive and false-negative examples in MAPT and PRICKLE1 that can be found in many eQTL databases. We recommend that both new and existing eQTL data sets should be carefully checked in order to adequately address this issue.

Computational tools for discovery and interpretation of expression quantitative trait loci

Expression quantitative trait locus (eQTL) analysis is rapidly moving from a cutting-edge concept in genomics to a mature area of investigation, with important connections to genome-wide association studies for human disease, pharmacogenomics and toxicogenomics. Despite the importance of the topic, many investigators must develop their own code or use tools not specifically suited for eQTL analysis. Convenient computational tools are becoming available, but they are not widely publicized, and investigators who are interested in discovery or eQTL, or in using them to interpret genome-wide association study results may have difficulty navigating the available resources. The purpose of this review is to help investigators find appropriate programs for eQTL analysis and interpretation.

Basal-like Breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival

Breast cancer is a heterogeneous disease with known expression-defined tumor subtypes. DNA copy number studies have suggested that tumors within gene expression subtypes share similar DNA Copy number aberrations (CNA) and that CNA can be used to further sub-divide expression classes. To gain further insights into the etiologies of the intrinsic subtypes, we classified tumors according to gene expression subtype and next identified subtype-associated CNA using a novel method called SWITCHdna, using a training set of 180 tumors and a validation set of 359 tumors. Fisher's exact tests, Chi-square approximations, and Wilcoxon rank-sum tests were performed to evaluate differences in CNA by subtype. To assess the functional significance of loss of a specific chromosomal region, individual genes were knocked down by shRNA and drug sensitivity, and DNA repair foci assays performed. Most tumor subtypes exhibited specific CNA. The Basal-like subtype was the most distinct with common losses of the regions containing RB1, BRCA1, INPP4B, and the greatest overall genomic instability. One Basal-like subtype-associated CNA was loss of 5q11-35, which contains at least three genes important for BRCA1-dependent DNA repair (RAD17, RAD50, and RAP80); these genes were predominantly lost as a pair, or all three simultaneously. Loss of two or three of these genes was associated with significantly increased genomic instability and poor patient survival. RNAi knockdown of RAD17, or RAD17/RAD50, in immortalized human mammary epithelial cell lines caused increased sensitivity to a PARP inhibitor and carboplatin, and inhibited BRCA1 foci formation in response to DNA damage. These data suggest a possible genetic cause for genomic instability in Basal-like breast cancers and a biological rationale for the use of DNA repair inhibitor related therapeutics in this breast cancer subtype.

Matrix eQTL: ultra fast eQTL analysis via large matrix operations

MOTIVATION

Expression quantitative trait loci (eQTL) analysis links variations in gene expression levels to genotypes. For modern datasets, eQTL analysis is a computationally intensive task as it involves testing for association of billions of transcript-SNP (single-nucleotide polymorphism) pair. The heavy computational burden makes eQTL analysis less popular and sometimes forces analysts to restrict their attention to just a small subset of transcript-SNP pairs. As more transcripts and SNPs get interrogated over a growing number of samples, the demand for faster tools for eQTL analysis grows stronger.

RESULTS

We have developed a new software for computationally efficient eQTL analysis called Matrix eQTL. In tests on large datasets, it was 2-3 orders of magnitude faster than existing popular tools for QTL/eQTL analysis, while finding the same eQTLs. The fast performance is achieved by special preprocessing and expressing the most computationally intensive part of the algorithm in terms of large matrix operations. Matrix eQTL supports additive linear and ANOVA models with covariates, including models with correlated and heteroskedastic errors. The issue of multiple testing is addressed by calculating false discovery rate; this can be done separately for cis- and trans-eQTLs.

seeQTL: a searchable database for human eQTLs

SUMMARY

seeQTL is a comprehensive and versatile eQTL database, including various eQTL studies and a meta-analysis of HapMap eQTL information. The database presents eQTL association results in a convenient browser, using both segmented local-association plots and genome-wide Manhattan plots.

AVAILABILITY AND IMPLEMENTATION

seeQTL is freely available for non-commercial use at http://www.bios.unc.edu/research/genomic_software/seeQTL/.

CONTACT

fred_wright@unc.edu; kxia@bios.unc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sex-specific gene expression in the BXD mouse liver

Differences in clinical phenotypes between the sexes are well documented and have their roots in differential gene expression. While sex has a major effect on gene expression, transcription is also influenced by complex interactions between individual genetic variation and environmental stimuli. In this study, we sought to understand how genetic variation affects sex-related differences in liver gene expression by performing genetic mapping of genomewide liver mRNA expression data in a genetically defined population of naive male and female mice from C57BL/6J, DBA/2J, B6D2F1, and 37 C57BL/6J x DBA/2J (BXD) recombinant inbred strains. As expected, we found that many genes important to xenobiotic metabolism and other important pathways exhibit sexually dimorphic expression. We also performed gene expression quantitative trait locus mapping in this panel and report that the most significant loci that appear to regulate a larger number of genes than expected by chance are largely sex independent. Importantly, we found that the degree of correlation within gene expression networks differs substantially between the sexes. Finally, we compare our results to a recently released human liver gene expression data set and report on important similarities in sexually dimorphic liver gene expression between mouse and human. This study enhances our understanding of sex differences at the genome level and between species, as well as increasing our knowledge of the molecular underpinnings of sex differences in responses to xenobiotics.

FastMap: fast eQTL mapping in homozygous populations

Motivation: Gene expression Quantitative Trait Locus (eQTL) mapping measures the association between transcript expression and genotype in order to find genomic locations likely to regulate transcript expression. The availability of both gene expression and high-density genotype data has improved our ability to perform eQTL mapping in inbred mouse and other homozygous populations. However, existing eQTL mapping software does not scale well when the number of transcripts and markers are on the order of 10⁵ and 10⁵–10⁶, respectively.

Results: We propose a new method, FastMap, for fast and efficient eQTL mapping in homozygous inbred populations with binary allele calls. FastMap exploits the discrete nature and structure of the measured single nucleotide polymorphisms (SNPs). In particular, SNPs are organized into a Hamming distance-based tree that minimizes the number of arithmetic operations required to calculate the association of a SNP by making use of the association of its parent SNP in the tree. FastMap's tree can be used to perform both single marker mapping and haplotype association mapping over an m-SNP window. These performance enhancements also permit permutation-based significance testing.

Availability: The FastMap program and source code are available at the website: http://cebc.unc.edu/fastmap86.html

Contact:iir@unc.edu; nobel@email.unc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Merging two gene-expression studies via cross-platform normalization

MOTIVATION

Gene-expression microarrays are currently being applied in a variety of biomedical applications. This article considers the problem of how to merge datasets arising from different gene-expression studies of a common organism and phenotype. Of particular interest is how to merge data from different technological platforms.

RESULTS

The article makes two contributions to the problem. The first is a simple cross-study normalization method, which is based on linked gene/sample clustering of the given datasets. The second is the introduction and description of several general validation measures that can be used to assess and compare cross-study normalization methods. The proposed normalization method is applied to three existing breast cancer datasets, and is compared to several competing normalization methods using the proposed validation measures.

AVAILABILITY

The supplementary materials and XPN Matlab code are publicly available at website: https://genome.unc.edu/xpn