Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Investigating the phenotypic and genetic associations between personality traits and suicidal behavior across major mental health diagnoses.

Personality traits influence risk for suicidal behavior. We examined phenotype- and genotype-level associations between the Big Five personality traits and suicidal ideation and attempt in major depressive, bipolar and schizoaffective disorder, and schizophrenia patients (N = 3012) using fixed- and random-effects inverse variance-weighted meta-analyses. Suicidal ideations were more likely to be reported by patients with higher neuroticism and lower extraversion phenotypic scores, but showed no significant association with polygenic load for these personality traits. Our findings provide new insights into the association between personality and suicidal behavior across mental illnesses and suggest that the genetic component of personality traits is unlikely to have strong causal effects on suicidal behavior.

Mutations in the pancreatic secretory enzymes CPA1 and CPB1 are associated with pancreatic cancer.

To evaluate whether germline variants in genes encoding pancreatic secretory enzymes contribute to pancreatic cancer susceptibility, we sequenced the coding regions of CPB1 and other genes encoding pancreatic secretory enzymes and known pancreatitis susceptibility genes (PRSS1, CPA1, CTRC, and SPINK1) in a hospital series of pancreatic cancer cases and controls. Variants in CPB1, CPA1 (encoding carboxypeptidase B1 and A1), and CTRC were evaluated in a second set of cases with familial pancreatic cancer and controls. More deleterious CPB1 variants, defined as having impaired protein secretion and induction of endoplasmic reticulum (ER) stress in transfected HEK 293T cells, were found in the hospital series of pancreatic cancer cases (5/986, 0.5%) than in controls (0/1,045, P = 0.027). Among familial pancreatic cancer cases, ER stress-inducing CPB1 variants were found in 4 of 593 (0.67%) vs. 0 of 967 additional controls (P = 0.020), with a combined prevalence in pancreatic cancer cases of 9/1,579 vs. 0/2,012 controls (P < 0.01). More ER stress-inducing CPA1 variants were also found in the combined set of hospital and familial cases with pancreatic cancer than in controls [7/1,546 vs. 1/2,012; P = 0.025; odds ratio, 9.36 (95% CI, 1.15-76.02)]. Overall, 16 (1%) of 1,579 pancreatic cancer cases had an ER stress-inducing CPA1 or CPB1 variant, compared with 1 of 2,068 controls (P < 0.00001). No other candidate genes had statistically significant differences in variant prevalence between cases and controls. Our study indicates ER stress-inducing variants in CPB1 and CPA1 are associated with pancreatic cancer susceptibility and implicate ER stress in pancreatic acinar cells in pancreatic cancer development.

Allele-specific expression reveals interactions between genetic variation and environment.

Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on associations between environmental variables and allele-specific expression. Combining whole-blood RNA-seq with extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared with only four using standard GxE interaction testing. EAGLE provides new opportunities for researchers to identify GxE interactions using functional genomic data.

Design and Implementation of a Regional Inpatient Psychiatry Unit for Patients who are Positive for Asymptomatic SARS-CoV-2.

BACKGROUND: Patients with psychiatric illnesses are particularly vulnerable to highly contagious, droplet-spread organisms such as SARS-CoV-2. Patients with mental illnesses may not be able to consistently follow up behavioral prescriptions to avoid contagion, and they are frequently found in settings with close contact and inadequate infection control, such as group homes, homeless shelters, residential rehabilitation centers, and correctional facilities. Furthermore, inpatient psychiatry settings are generally designed as communal spaces, with heavy emphasis on group and milieu therapies. As such, inpatient psychiatry services are vulnerable to rampant spread of contagion. OBJECTIVE: With this in mind, the authors outline the decision process and ultimate design and implementation of a regional inpatient psychiatry unit for patients infected with asymptomatic SARS-CoV-2 and share key points for consideration in implementing future units elsewhere. CONCLUSION: A major takeaway point of the analysis is the particular expertise of trained experts in psychosomatic medicine for treating patients infected with SARS-CoV-2.

Targeted sequencing of the LRRTM gene family in suicide attempters with bipolar disorder.

Glutamatergic signaling is the primary excitatory neurotransmission pathway in the brain, and its relationship to neuropsychiatric disorders is of considerable interest. Our previous attempted suicide genome-wide association study, and numerous studies investigating gene expression, genetic variation, and DNA methylation have implicated aberrant glutamatergic signaling in suicide risk. The glutamatergic pathway gene LRRTM4 was an associated gene identified in our attempted suicide genome-wide association study, with association support seen primarily in females. Recent evidence has also shown that glutamatergic signaling is partly regulated by sex-related hormones. The LRRTM gene family encodes neuronal leucine-rich transmembrane proteins that localize to and promote glutamatergic synapse development. In this study, we sequenced the coding and regulatory regions of all four LRRTM gene members plus a large intronic region of LRRTM4 in 476 bipolar disorder suicide attempters and 473 bipolar disorder nonattempters. We identified two male-specific variants, one female- and five male-specific haplotypes significantly associated with attempted suicide in LRRTM4. Furthermore, variants within significant haplotypes may be brain expression quantitative trait loci for LRRTM4 and some of these variants overlap with predicted hormone response elements. Overall, these results provide supporting evidence for a sex-specific association of genetic variation in LRRTM4 with attempted suicide.

Impact of the X Chromosome and sex on regulatory variation.

The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.

Genome-wide DNA methylation investigation of glucocorticoid exposure within buccal samples.

AIM: Glucocorticoids play a major role in regulating the stress response, and an imbalance of glucocorticoids has been implicated in stress-related disorders. Within mouse models, CpGs across the genome have been shown to be differentially methylated in response to glucocorticoid treatment, and using the Infinium 27K array, it was shown that humans given synthetic glucocorticoids had DNA methylation (DNAm) changes in blood. However, further investigation of the extent to which glucocorticoids affect DNAm across a larger proportion of the genome is needed. METHODS: Buccal samples were collected before and after synthetic glucocorticoid treatment in the context of a dental procedure. This included 30 tooth extraction surgery patients who received 10 mg of dexamethasone. Genome-wide DNAm was assessed with the Infinium HumanMethylationEPIC array. RESULTS: Five CpGs showed genome-wide significant DNAm changes that were >10%. These differentially methylated CpGs were in or nearest the following genes: ZNF438, KLHDC10, miR-544 or CRABP1, DPH5, and WDFY2. Using previously published datasets of human blood gene expression changes following dexamethasone exposure, a significant proportion of genes with false-discovery-rate-adjusted significant CpGs were also differentially expressed. A pathway analysis of the genes with false-discovery-rate-adjusted significant CpGs revealed significant enrichment of olfactory transduction, pentose and glucuronate interconversions, ascorbate and aldarate metabolism, and steroid hormone biosynthesis pathways. CONCLUSION: High-dose synthetic glucocorticoid administration in the setting of a dental procedure was significantly associated with DNAm changes within buccal samples. These findings are consistent with prior findings of an influence of glucocorticoids on DNAm in humans.

Differentially methylated regions in bipolar disorder and suicide.

The addition of a methyl group to, typically, a cytosine-guanine dinucleotide (CpG) creates distinct DNA methylation patterns across the genome that can regulate gene expression. Aberrant DNA methylation of CpG sites has been associated with many psychiatric disorders including bipolar disorder (BD) and suicide. Using the SureSelectXT system, Methyl-Seq, we investigated the DNA methylation status of CpG sites throughout the genome in 50 BD individuals (23 subjects who died by suicide and 27 subjects who died from other causes) and 31 nonpsychiatric controls. We identified differentially methylated regions (DMRs) from three analyses: (a) BD subjects compared to nonpsychiatric controls (BD-NC), (b) BD subjects who died by suicide compared to nonpsychiatric controls (BDS-NC), and (c) BDS subjects compared to BD subjects who died from other causes (BDS-BDNS). One DMR from the BDS-NC analysis, located in ARHGEF38, was significantly hypomethylated (23.4%) in BDS subjects. This finding remained significant after multiple testing (PBootstrapped = 9.0 × 10-3 ), was validated using pyrosequencing, and was more significant in males. A secondary analysis utilized Ingenuity Pathway Analysis to identify enrichment in nominally significant DMRs. This identified an association with several pathways including axonal guidance signaling, calcium signaling, ß-adrenergic signaling, and opioid signaling. Our comprehensive study provides further support that DNA methylation alterations influence the risk for BD and suicide. However, further investigation is required to confirm these associations and identify their functional consequences.

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder.

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.

A comprehensive review of genetic and epigenetic mechanisms that regulate BDNF expression and function with relevance to major depressive disorder.

Major depressive disorder (MDD) is a mood disorder that affects behavior and impairs cognition. A gene potentially important to this disorder is the brain derived neurotrophic factor (BDNF) as it is involved in processes controlling neuroplasticity. Various mechanisms exist to regulate BDNF's expression level, subcellular localization, and sorting to appropriate secretory pathways. Alterations to these processes by genetic factors and negative stressors can dysregulate its expression, with possible implications for MDD. Here, we review the mechanisms governing the regulation of BDNF expression, and discuss how disease-associated single nucleotide polymorphisms (SNPs) can alter these mechanisms, and influence MDD. As negative stressors increase the likelihood of MDD, we will also discuss the impact of these stressors on BDNF expression, the cellular effect of such a change, and its impact on behavior in animal models of stress. We will also describe epigenetic processes that mediate this change in BDNF expression. Similarities in BDNF expression between animal models of stress and those in MDD will be highlighted. We will also contrast epigenetic patterns at the BDNF locus between animal models of stress, and MDD patients, and address limitations to current clinical studies. Future work should focus on validating current genetic and epigenetic findings in tightly controlled clinical studies. Regions outside of BDNF promoters should also be explored, as should other epigenetic marks, to improve identification of biomarkers for MDD.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals.

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

Integrating GWASs and human protein interaction networks identifies a gene subnetwork underlying alcohol dependence.

Despite a significant genetic contribution to alcohol dependence (AD), few AD-risk genes have been identified to date. In the current study, we aimed to integrate genome-wide association studies (GWASs) and human protein interaction networks to investigate whether a subnetwork of genes whose protein products interact with one another might collectively contribute to AD. By using two discovery GWAS data sets of the Study of Addiction: Genetics and Environment (SAGE) and the Collaborative Study on the Genetics of Alcoholism (COGA), we identified a subnetwork of 39 genes that not only was enriched for genes associated with AD, but also collectively associated with AD in both European Americans (p < 0.0001) and African Americans (p = 0.0008). We replicated the association of the gene subnetwork with AD in three independent samples, including two samples of European descent (p = 0.001 and p = 0.006) and one sample of African descent (p = 0.0069). To evaluate whether the significant associations are likely to be false-positive findings and to ascertain their specificity, we examined the same gene subnetwork in three other human complex disorders (bipolar disorder, major depressive disorder, and type 2 diabetes) and found no significant associations. Functional enrichment analysis revealed that the gene subnetwork was enriched for genes involved in cation transport, synaptic transmission, and transmission of nerve impulses, all of which are biologically meaningful processes that may underlie the risk for AD. In conclusion, we identified a gene subnetwork underlying AD that is biologically meaningful and highly reproducible, providing important clues for future research into AD etiology and treatment.

A hybrid likelihood model for sequence-based disease association studies.

In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values < 0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing.

Whole-gene sequencing investigation of SAT1 in attempted suicide.

Suicidal behavior imposes a tremendous cost, with current US estimates reporting approximately 1.3 million suicide attempts and more than 40,000 suicide deaths each year. Several recent research efforts have identified an association between suicidal behavior and the expression level of the spermidine/spermine N1-acetyltransferase 1 (SAT1) gene. To date, several SAT1 genetic variants have been inconsistently associated with altered gene expression and/or directly with suicidal behavior. To clarify the role SAT1 genetic variation plays in suicidal behavior risk, we present a whole-gene sequencing effort of SAT1 in 476 bipolar disorder subjects with a history of suicide attempt and 473 subjects with bipolar disorder but no suicide attempts. Agilent SureSelect target enrichment was used to sequence all exons, introns, promoter regions, and putative regulatory regions identified from the ENCODE project within 10 kb of SAT1. Individual variant, haplotype, and collapsing variant tests were performed. Our results identified no variant or assessed region of SAT1 that showed a significant association with attempted suicide, nor did any assessment show evidence for replication of previously reported associations. Overall, no evidence for SAT1 sequence variation contributing to the risk for attempted suicide could be identified. It is possible that past associations of SAT1 expression with suicidal behavior arise from variation not captured in this study, or that causal variants in the region are too rare to be detected within our sample. Larger sample sizes and broader sequencing efforts will likely be required to identify the source of SAT1 expression level associations with suicidal behavior. © 2016 Wiley Periodicals, Inc.

A targeted sequencing study of glutamatergic candidate genes in suicide attempters with bipolar disorder.

Suicidal behavior has been shown to have a heritable component that is partly driven by psychiatric disorders [Brent and Mann, 2005]. However, there is also an independent factor contributing to the heritability of suicidal behavior. We previously conducted a genome-wide association study (GWAS) of bipolar suicide attempters and bipolar non-attempters to assess this independent factor [Willour et al., 2012]. This GWAS implicated glutamatergic neurotransmission in attempted suicide. In the current study, we have conducted a targeted next-generation sequencing study of the glutamatergic N-methyl-D-aspartate (NMDA) receptor, neurexin, and neuroligin gene families in 476 bipolar suicide attempters and 473 bipolar non-attempters. The goal of this study was to gather sequence information from coding and regulatory regions of these glutamatergic genes to identify variants associated with attempted suicide. We identified 186 coding variants and 4,298 regulatory variants predicted to be functional in these genes. No individual variants were overrepresented in cases or controls to a degree that was statistically significant after correction for multiple testing. Additionally, none of the gene-level results were statistically significant following correction. While this study provides no direct support for a role of the examined glutamatergic candidate genes, further sequencing in expanded gene sets and datasets will be required to ultimately determine whether genetic variation in glutamatergic signaling influences suicidal behavior. © 2016 Wiley Periodicals, Inc.

Validation and assessment of variant calling pipelines for next-generation sequencing.

BACKGROUND: The processing and analysis of the large scale data generated by next-generation sequencing (NGS) experiments is challenging and is a burgeoning area of new methods development. Several new bioinformatics tools have been developed for calling sequence variants from NGS data. Here, we validate the variant calling of these tools and compare their relative accuracy to determine which data processing pipeline is optimal. RESULTS: We developed a unified pipeline for processing NGS data that encompasses four modules: mapping, filtering, realignment and recalibration, and variant calling. We processed 130 subjects from an ongoing whole exome sequencing study through this pipeline. To evaluate the accuracy of each module, we conducted a series of comparisons between the single nucleotide variant (SNV) calls from the NGS data and either gold-standard Sanger sequencing on a total of 700 variants or array genotyping data on a total of 9,935 single-nucleotide polymorphisms. A head to head comparison showed that Genome Analysis Toolkit (GATK) provided more accurate calls than SAMtools (positive predictive value of 92.55% vs. 80.35%, respectively). Realignment of mapped reads and recalibration of base quality scores before SNV calling proved to be crucial to accurate variant calling. GATK HaplotypeCaller algorithm for variant calling outperformed the UnifiedGenotype algorithm. We also showed a relationship between mapping quality, read depth and allele balance, and SNV call accuracy. However, if best practices are used in data processing, then additional filtering based on these metrics provides little gains and accuracies of >99% are achievable. CONCLUSIONS: Our findings will help to determine the best approach for processing NGS data to confidently call variants for downstream analyses. To enable others to implement and replicate our results, all of our codes are freely available at http://metamoodics.org/wes.

DNA methylation and expression of KCNQ3 in bipolar disorder.

OBJECTIVES: Accumulating evidence implicates the potassium voltage-gated channel, KQT-like subfamily, member 2 and 3 (KCNQ2 and KCNQ3) genes in the etiology of bipolar disorder (BPD). Reduced KCNQ2 or KCNQ3 gene expression might lead to a loss of inhibitory M-current and an increase in neuronal hyperexcitability in disease. The goal of the present study was to evaluate epigenetic and gene expression associations of the KCNQ2 and KCNQ3 genes with BPD. METHODS: DNA methylation and gene expression levels of alternative transcripts of KCNQ2 and KCNQ3 capable of binding the ankyrin G (ANK3) gene were evaluated using bisulfite pyrosequencing and the quantitative real-time polymerase chain reaction in the postmortem prefrontal cortex of subjects with BPD and matched controls from the McLean Hospital. Replication analyses of DNA methylation findings were performed using prefrontal cortical DNA obtained from the Stanley Medical Research Institute. RESULTS: Significantly lower expression was observed in KCNQ3, but not KCNQ2. DNA methylation analysis of CpGs within an alternative exonic region of KCNQ3 exon 11 demonstrated significantly lower methylation in BPD, and correlated significantly with KCNQ3 mRNA levels. Lower KCNQ3 exon 11 DNA methylation was observed in the Stanley Medical Research Institute replication cohort, although only after correcting for mood stabilizer status. Mood stabilizer treatment in rats resulted in a slight DNA methylation increase at the syntenic KCNQ3 exon 11 region, which subsequent analyses suggested could be the result of alterations in neuronal proportion. CONCLUSION: The results of the present study suggest that epigenetic alterations in the KCNQ3 gene may be important in the etiopathogenesis of BPD and highlight the importance of controlling for medication and cellular composition-induced heterogeneity in psychiatric studies of the brain.

A mega-analysis of genome-wide association studies for major depressive disorder.

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.