Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA

Genetic architecture of ADHD and overlap with other psychiatric disorders and cognition-related phenotypes

Attention-deficit/hyperactivity disorder (ADHD) co-occurs with many other psychiatric disorders and traits. In this review, we summarize and interpret the existing literature on the genetic architecture of these comorbidities based on hypothesis-generating approaches. Quantitative genetic studies indicate that genetic factors play a substantial role in the observed co-occurrence of ADHD with many different disorders and traits. Molecular genetic correlations derived from genome-wide association studies and results of studies based on polygenic risk scores confirm the general pattern but provide effect estimates that are smaller than those from twin studies. The identification of the specific genetic variants and biological pathways underlying co-occurrence using genome-wide approaches is still in its infancy. The first analyses of causal inference using genetic data support causal relationships between ADHD and comorbid disorders, although bidirectional effects identified in some instances point to complex relationships. While several issues in the methodology and inferences from the results are still to be overcome, this review shows that the co-occurrence of ADHD with many psychiatric disorders and traits is genetically interpretable.

Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood

Multivariate heterogeneous responses and heteroskedasticity have attracted increasing attention in recent years. In genome-wide association studies, effective simultaneous modeling of multiple phenotypes would improve statistical power and interpretability. However, a flexible common modeling system for heterogeneous data types can pose computational difficulties. Here we build upon a previous method for multivariate probit estimation using a two-stage composite likelihood that exhibits favorable computational time while retaining attractive parameter estimation properties. We extend this approach to incorporate multivariate responses of heterogeneous data types (binary and continuous), and possible heteroskedasticity. Although the approach has wide applications, it would be particularly useful for genomics, precision medicine, or individual biomedical prediction. Using a genomics example, we explore statistical power and confirm that the approach performs well for hypothesis testing and coverage percentages under a wide variety of settings. The approach has the potential to better leverage genomics data and provide interpretable inference for pleiotropy, in which a locus is associated with multiple traits.

Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests

BACKGROUND

Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests.

METHODS

Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes.

RESULTS

We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission.

LIMITATIONS

The effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data.

CONCLUSION

We identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice.

Common effects of bipolar disorder medications on expression quantitative trait loci genes

The molecular mechanism(s) underpinning the clinical efficacy of the current drugs for bipolar disorder (BD) are largely unknown. This study evaluated the transcriptional perturbations potentially playing roles in the therapeutic efficacy of four commonly prescribed psychotropic drugs used to treat BD. NT2-N cells were treated with lamotrigine, lithium, quetiapine, valproate or vehicle control for 24 h. Genome-wide mRNA expression was quantified by RNA-sequencing. Incorporating drug-induced gene expression profiles with BD-associated transcriptional changes from post-mortem brains, we identified potential therapeutic-relevant genes associated with both drug treatments and BD pathophysiology and focused on expression quantitative trait loci (eQTL) genes with genome-wide association with BD. Each eQTL gene was ranked based on its potential role in the therapeutic effect across multiple drugs. The expression of highest-ranked eQTL genes were measured by RT-qPCR to confirm their transcriptional changes observed in RNA-seq. We found 775 genes for which at least 2 drugs reversed expression levels relative to the differential expression in post-mortem brains. Pathway analysis identified enriched biological processes highlighting mitochondrial and endoplasmic reticulum function. Differential expression of SRPK2 and CHDH was confirmed by RT-qPCR following multiple-dose treatments. We pinpointed potential genes involved in the beneficial effects of drugs used for BD and their main associated biological pathways. CHDH, which encodes a mitochondrial protein, had a significant dose-responsive downregulation following treatment with increasing doses of quetiapine and lamotrigine, which in combination with the enriched mitochondrial pathways suggests potential therapeutic roles and demand more studies on mitochondrial involvement in BD to identify novel treatment targets.

Gene-based mediation analysis in epigenetic studies

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

Epigenome-wide analysis uncovers a blood-based DNA methylation biomarker of lifetime cannabis use

Cannabis use is highly prevalent and is associated with adverse and beneficial effects. To better understand the full spectrum of health consequences, biomarkers that accurately classify cannabis use are needed. DNA methylation (DNAm) is an excellent candidate, yet no blood-based epigenome-wide association studies (EWAS) in humans exist. We conducted an EWAS of lifetime cannabis use (ever vs. never) using blood-based DNAm data from a case-cohort study within Sister Study, a prospective cohort of women at risk of developing breast cancer (Discovery N = 1,730 [855 ever users]; Replication N = 853 [392 ever users]). We identified and replicated an association with lifetime cannabis use at cg15973234 (CEMIP): combined p = 3.3 × 10^-8 . We found no overlap between published blood-based cis-meQTLs of cg15973234 and reported lifetime cannabis use-associated single nucleotide polymorphism (SNPs; p < .05), suggesting that the observed DNAm difference was driven by cannabis exposure. We also developed a multi-CpG classifier of lifetime cannabis use using penalized regression of top EWAS CpGs. The resulting 50-CpG classifier produced an area under the curve (AUC) = 0.74 (95% CI [0.72, 0.76], p = 2.00 × 10^-5 ) in the discovery sample and AUC = 0.54 ([0.51, 0.57], p = 2.87 × 10^-2 ) in the replication sample. Our EWAS findings provide evidence that blood-based DNAm is associated with lifetime cannabis use.

Psychiatric symptoms and syndromes transcending diagnostic boundaries in Indian multiplex families: The cohort of ADBS study

Syndromes of schizophrenia, bipolar disorder, obsessive-compulsive disorder, substance use disorders and Alzheimer's dementia are highly heritable. About 10-20% of subjects have another affected first degree relative (FDR), and thus represent a 'greater' genetic susceptibility. We screened 3583 families to identify 481 families with multiple affected members, assessed 1406 individuals in person, and collected information systematically about other relatives. Within the selected families, a third of all FDRs were affected with serious mental illness. Although similar diagnoses aggregated within families, 62% of the families also had members with other syndromes. Moreover, 15% of affected individuals met criteria for co-occurrence of two or more syndromes, across their lifetime. Using dimensional assessments, we detected a range of symptom clusters in both affected and unaffected individuals, and across diagnostic categories. Our findings suggest that in multiplex families, there is considerable heterogeneity of clinical syndromes, as well as sub-threshold symptoms. These families would help provide an opportunity for further research using both genetic analyses and biomarkers.

Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis

Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.

Identifying Shared Risk Genes for Asthma, Hay Fever, and Eczema by Multi-Trait and Multiomic Association Analyses

Asthma, hay fever and eczema are three comorbid diseases with high prevalence and heritability. Their common genetic architectures have not been well-elucidated. In this study, we first conducted a linkage disequilibrium score regression analysis to confirm the strong genetic correlations between asthma, hay fever and eczema. We then integrated three distinct association analyses (metaCCA multi-trait association analysis, MAGMA genome-wide and MetaXcan transcriptome-wide gene-based tests) to identify shared risk genes based on the large-scale GWAS results in the GeneATLAS database. MetaCCA can detect pleiotropic genes associated with these three diseases jointly. MAGMA and MetaXcan were performed separately to identify candidate risk genes for each of the three diseases. We finally identified 150 shared risk genes, in which 60 genes are novel. Functional enrichment analysis revealed that the shared risk genes are enriched in inflammatory bowel disease, T cells differentiation and other related biological pathways. Our work may provide help on treatment of asthma, hay fever and eczema in clinical applications.

Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis

Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10-6) and 1,044 pleotropic genes (P < 4.34 × 10-6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.

Effect of non-normality and low count variants on cross-phenotype association tests in GWAS

Many complex human diseases, such as type 2 diabetes, are characterized by multiple underlying traits/phenotypes that have substantially shared genetic architecture. Multivariate analysis of correlated traits has the potential to increase the power of detecting underlying common genetic loci. Several cross-phenotype association methods have been proposed-some require individual-level data on traits and genotypes, while the others require only summary-level data. In this article, we explore whether non-normality of multivariate trait distribution affects the inference from some of the existing multi-trait methods and how that effect is dependent on the allele count of the genetic variant being tested. We find that most of these tests are susceptible to biases that lead to spurious association signals. Even after controlling for confounders that may contribute to non-normality and then applying inverse normal transformation on the residuals of each trait, these tests may have inflated type I errors for variants with low minor allele counts (MACs). A likelihood ratio test of association based on the ordinal regression of individual-level genotype conditional on the traits seems to be the least biased and can maintain type I error when the MAC is reasonably large (e.g., MAC > 30). Application of these methods to publicly available summary statistics of eight amino acid traits on European samples seem to exhibit systematic inflation (especially for variants with low MAC), which is consistent with our findings from simulation experiments.