Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain

Comprehensive transcriptome analysis reveals altered mRNA splicing and post-transcriptional changes in the aged mouse brain

A comprehensive understanding of molecular changes during brain aging is essential to mitigate cognitive decline and delay neurodegenerative diseases. The interpretation of mRNA alterations during brain aging is influenced by the health and age of the animal cohorts studied. Here, we carefully consider these factors and provide an in-depth investigation of mRNA splicing and dynamics in the aging mouse brain, combining short- and long-read sequencing technologies with extensive bioinformatic analyses. Our findings encompass a spectrum of age-related changes, including differences in isoform usage, decreased mRNA dynamics and a module showing increased expression of neuronal genes. Notably, our results indicate a reduced abundance of mRNA isoforms leading to nonsense-mediated RNA decay and suggest a regulatory role for RNA-binding proteins, indicating that their regulation may be altered leading to the reshaping of the aged brain transcriptome. Collectively, our study highlights the importance of studying mRNA splicing events during brain aging.

Identifying novel age-modulating compounds and quantifying cellular aging using novel computational framework for evaluating transcriptional age

The differentiation of human pluripotent stem cells (hPSCs) provides access to most cell types and tissues. However, hPSC-derived lineages capture a fetal-stage of development and methods to accelerate progression to an aged identity are limited. Understanding the factors driving cellular age and rejuvenation is also essential for efforts aimed at extending human life and health span. A prerequisite for such studies is the development of methods to score cellular age and simple readouts to assess the relative impact of various age modifying strategies. Here we established a transcriptional score (RNAge) in young versus old primary fibroblasts, frontal cortex and substantia nigra tissue. We validated the score in independent RNA-seq datasets and demonstrated a strong cell and tissue specificity. In fibroblasts we observed a reset of RNAge during iPSC reprogramming while direct reprogramming of aged fibroblasts to induced neurons (iN) resulted in the maintenance of both a neuronal and a fibroblast aging signature. Increased RNAge in hPSC-derived neurons was confirmed for several age-inducing strategies such as SATB1 loss, progerin expression or chemical induction of senescence (SLO). Using RNAge as a probe set, we next performed an in-silico screen using the LINCS L1000 dataset. We identified and validated several novel age-inducing and rejuvenating compounds, and we observed that RNAage captures age-related changes associated with distinct cellular hallmarks of age. Our study presents a simple tool to score age manipulations and identifies compounds that greatly expand the toolset of age-modifying strategies in hPSC derived lineages.

Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia

Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.

Association between methylation in the promoter region of the GAD2 gene and opioid use disorder

DNA methylation is one of the epigenetic mechanisms involved in opioid use disorder. GAD2 is a key catalyticase in gamma amino butyric acid (GABA) synthesis from glutamate, that is implicated in opioid-induced rewarding effect. To reveal the relationship and the underlying mechanism between GAD2 gene methylation and opioid use disorder, we first examined and compared the methylation levels in the promoter region of the GAD2 gene in peripheral blood between 120 patients with opioid use disorder and 110 healthy controls by using a targeted approach. A diagnostic model with methylation biomarkers was established to distinguish opioid use disorder and healthy control groups. Correlations between methylation levels in the promoter region of the GAD2 gene and the duration and dosage of opioid use were then determined. Finally, the transcription factors that potentially bind to the target sequences including the detected CpG sites were predicted with the JASPAR database. Our results demonstrated that hypermethylation in the promoter region of the GAD2 gene was associated with opioid use disorder. A diagnostic model based on 10 methylation biomarkers could distinguish the opioid use disorder and healthy control groups. Several correlations between methylation levels in the GAD2 gene promoter and the duration and dosage of opioid use were observed. Transcription factors TFAP2A, Arnt and Runx1 were predicted to bind to the target sequences including several CpG sites detected in the present study in the GAD2 gene promoter. Our findings highlight and extend the role of DNA methylation in the GAD2 gene in opioid use disorder.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; P_HEIDI>0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR<0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

Single-cell RNA sequencing analysis of human Alzheimer's disease brain samples reveals neuronal and glial specific cells differential expression

Alzheimer's disease is the most common neurological disease worldwide. Unfortunately, there are currently no effective treatment methods nor early detection methods. Furthermore, the disease underlying molecular mechanisms are poorly understood. Global bulk gene expression profiling suggested that the disease is governed by diverse transcriptional regulatory networks. Thus, to identify distinct transcriptional networks impacted into distinct neuronal populations in Alzheimer, we surveyed gene expression differences in over 25,000 single-nuclei collected from the brains of two Alzheimer's in disease patients in Braak stage I and II and age- and gender-matched controls hippocampal brain samples. APOE status was not measured for this study samples (as well as CERAD and THAL scores). Our bioinformatic analysis identified discrete glial, immune, neuronal and vascular cell populations spanning Alzheimer's disease and controls. Astrocytes and microglia displayed the greatest transcriptomic impacts, with the induction of both shared and distinct gene programs.

Sex, Age, and Regional Differences in CHRM1 and CHRM3 Genes Expression Levels in the Human Brain Biopsies: Potential Targets for Alzheimer's Disease-related Sleep Disturbances

BACKGROUND

Cholinergic hypofunction and sleep disturbance are hallmarks of Alzheimer's disease (AD), a progressive disorder leading to neuronal deterioration. Muscarinic acetylcholine receptors (M1-5 or mAChRs), expressed in hippocampus and cerebral cortex, play a pivotal role in the aberrant alterations of cognitive processing, memory, and learning, observed in AD. Recent evidence shows that two mAChRs, M1 and M3, encoded by CHRM1 and CHRM3 genes, respectively, are involved in sleep functions and, peculiarly, in rapid eye movement (REM) sleep.

METHODS

We used twenty microarray datasets extrapolated from post-mortem brain tissue of nondemented healthy controls (NDHC) and AD patients to examine the expression profile of CHRM1 and CHRM3 genes. Samples were from eight brain regions and stratified according to age and sex.

RESULTS

CHRM1 and CHRM3 expression levels were significantly reduced in AD compared with ageand sex-matched NDHC brains. A negative correlation with age emerged for both CHRM1 and CHRM3 in NDHC but not in AD brains. Notably, a marked positive correlation was also revealed between the neurogranin (NRGN) and both CHRM1 and CHRM3 genes. These associations were modulated by sex. Accordingly, in the temporal and occipital regions of NDHC subjects, males expressed higher levels of CHRM1 and CHRM3, respectively, than females. In AD patients, males expressed higher levels of CHRM1 and CHRM3 in the temporal and frontal regions, respectively, than females.

CONCLUSION

Thus, substantial differences, all strictly linked to the brain region analyzed, age, and sex, exist in CHRM1 and CHRM3 brain levels both in NDHC subjects and in AD patients.

Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution

Human brain development is underpinned by cellular and molecular reconfigurations continuing into the third decade of life. To reveal cell dynamics orchestrating neural maturation, we profiled human prefrontal cortex gene expression and chromatin accessibility at single-cell resolution from gestation to adulthood. Integrative analyses define the dynamic trajectories of each cell type, revealing major gene expression reconfiguration at the prenatal-to-postnatal transition in all cell types followed by continuous reconfiguration into adulthood and identifying regulatory networks guiding cellular developmental programs, states, and functions. We uncover links between expression dynamics and developmental milestones, characterize the diverse timing of when cells acquire adult-like states, and identify molecular convergence from distinct developmental origins. We further reveal cellular dynamics and their regulators implicated in neurological disorders. Finally, using this reference, we benchmark cell identities and maturation states in organoid models. Together, this captures the dynamic regulatory landscape of human cortical development.

Limited evidence for blood eQTLs in human sexual dimorphism

BACKGROUND

The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits.

METHODS

To explore this scenario, we combined sex-specific whole blood RNA-seq eQTL data from 3447 European individuals included in BIOS Consortium and GWAS data from UK Biobank. Next, to test the presence of sex-biased causal effect of gene expression on complex traits, we performed sex-specific transcriptome-wide Mendelian randomization (TWMR) analyses on the two most sexually dimorphic traits, waist-to-hip ratio (WHR) and testosterone levels. Finally, we performed power analysis to calculate the GWAS sample size needed to observe sex-specific trait associations driven by sex-biased eQTLs.

RESULTS

Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-biased cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-biased eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-biased trait-associations. In addition, we observed that sex-specific causal effects of gene expression on complex traits are not driven by sex-specific eQTLs. Power analyses using real eQTL- and causal-effect sizes showed that millions of samples would be necessary to observe sex-biased trait associations that are fully driven by sex-biased cis-eQTLs. Compensatory effects may further hamper their detection.

CONCLUSIONS

Our results suggest that sex-specific eQTLs in whole blood do not translate to detectable sex-specific trait associations of complex diseases, and vice versa that the observed sex-specific trait associations cannot be explained by sex-specific eQTLs.

Integrated analysis of mRNA-single nucleotide polymorphism-microRNA interaction network to identify biomarkers associated with prostate cancer

Background: Prostate cancer is one of the most common malignancies among men worldwide currently. However, specific mechanisms of prostate cancer were still not fully understood due to lack of integrated molecular analyses. We performed this study to establish an mRNA-single nucleotide polymorphism (SNP)-microRNA (miRNA) interaction network by comprehensive bioinformatics analysis, and search for novel biomarkers for prostate cancer.

Materials and methods: mRNA, miRNA, and SNP data were acquired from Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify differentially expressed genes (DEGs) and miRNAs (DEMs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) analysis and expression quantitative trait loci (eQTL) analysis of DEGs were conducted. SNPs related to DEMs (miRSNPs) were downloaded from the open-source website MirSNP and PolymiRTS 3.0. TargetScan and miRDB databases were used for the target mRNA prediction of miRNA. The mRNA-SNP-miRNA interaction network was then constructed and visualized by Cytoscape 3.9.0. Selected key biomarkers were further validated using the Cancer Genome Atlas (TCGA) database. A nomogram model was constructed to predict the risk of prostate cancer.

Results: In our study, 266 DEGs and 11 DEMs were identified. KEGG pathway analysis showed that DEGs were strikingly enriched in focal adhesion and PI3K-Akt signaling pathway. A total of 60 mRNA-SNP-miRNAs trios were identified to establish the mRNA-SNP-miRNA interaction network. Seven mRNAs in mRNA-SNP-miRNA network were consistent with the predicted target mRNAs of miRNA. These results were largely validated by the TCGA database analysis. A nomogram was constructed that contained four variables (ITGB8, hsa-miR-21, hsa-miR-30b and prostate-specific antigen (PSA) value) for predicting the risk of prostate cancer.

Conclusion: Our study established the mRNA-SNP-miRNA interaction network in prostate cancer. The interaction network showed that hsa-miR-21, hsa-miR-30b, and ITGB8 may be utilized as new biomarkers for prostate cancer.

DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data

Deciphering the functional interactions of cells in tissues remains a major challenge. We describe DIALOGUE, a method to systematically uncover multicellular programs (MCPs) — combinations of coordinated cellular programs in different cell types that form higher-order functional units at the tissue level — from either spatial data or single-cell data obtained without spatial information. Tested on spatial datasets from the mouse hypothalamus, cerebellum, visual cortex, and neocortex, DIALOGUE identified MCPs associated with animal behavior and recovered spatial properties when tested on unseen data, while outperforming other methods and metrics. In spatial data from human lung cancer, DIALOGUE identified MCPs marking immune activation and tissue remodeling. Applied to scRNA-seq data across individuals or regions, DIALOGUE uncovered MCPs in Alzheimer’s disease, ulcerative colitis, and treatment with cancer immunotherapy. These programs were predictive of disease outcome and predisposition in independent cohorts and included risk genes from genome-wide association studies (GWAS). DIALOGUE enables the analysis of multicellular regulation in health and disease.

Coordinated gene programs spanning multiple different cell types are identified in healthy and diseased tissues.

Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases

The resolution of causal genetic variants informs understanding of disease biology. We used regulatory quantitative trait loci (QTLs) from the BLUEPRINT, GTEx and eQTLGen projects to fine-map putative causal variants for 12 immune-mediated diseases. We identify 340 unique loci that colocalize with high posterior probability (≥98%) with regulatory QTLs and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping credible sets derived from regulatory QTLs are smaller compared to disease summary statistics. Further, they are enriched for more functionally interpretable candidate causal variants and for putatively causal insertion/deletion (INDEL) polymorphisms. Finally, we use massively parallel reporter assays to evaluate candidate causal variants at the ITGA4 locus associated with inflammatory bowel disease. Overall, our findings suggest that fine-mapping applied to disease-colocalizing regulatory QTLs can enhance the discovery of putative causal disease variants and enhance insights into the underlying causal genes and molecular mechanisms.

Alu insertion variants alter gene transcript levels

Alu are high copy number interspersed repeats that have accumulated near genes during primate and human evolution. They are a pervasive source of structural variation in modern humans. Impacts that Alu insertions may have on gene expression are not well understood, although some have been associated with expression quantitative trait loci (eQTLs). Here, we directly test regulatory effects of polymorphic Alu insertions in isolation of other variants on the same haplotype. To screen insertion variants for those with such effects, we used ectopic luciferase reporter assays and evaluated 110 Alu insertion variants, including more than 40 with a potential role in disease risk. We observed a continuum of effects with significant outliers that up- or down-regulate luciferase activity. Using a series of reporter constructs, which included genomic context surrounding the Alu, we can distinguish between instances in which the Alu disrupts another regulator and those in which the Alu introduces new regulatory sequence. We next focused on three polymorphic Alu loci associated with breast cancer that display significant effects in the reporter assay. We used CRISPR to modify the endogenous sequences, establishing cell lines varying in the Alu genotype. Our findings indicate that Alu genotype can alter expression of genes implicated in cancer risk, including PTHLH, RANBP9, and MYC These data show that commonly occurring polymorphic Alu elements can alter transcript levels and potentially contribute to disease risk.

Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome

Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (rBMI = 0.11, PBMI = 2.0 × 10-51 and rTG = 0.13, PTG = 1.1 × 10-68), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones.

Network modules linking expression and methylation in prefrontal cortex of schizophrenia

Tremendous work has demonstrated the critical roles of genetics, epigenetics as well as their interplay in brain transcriptional regulations in the pathology of schizophrenia (SZ). There is great success currently in the dissection of the genetic components underlying risk-conferring transcriptomic networks. However, the study of regulating effect of epigenetics in the etiopathogenesis of SZ still faces many challenges. In this work, we investigated DNA methylation and gene expression from the dorsolateral prefrontal cortex (DLPFC) region of schizophrenia patients and healthy controls using weighted correlation network approach. We identified and replicated two expression and two methylation modules significantly associated with SZ. Among them, one pair of expression and methylation modules were significantly overlapped in the module genes which were significantly enriched in astrocyte-associated functional pathways, and specifically expressed in astrocytes. Another two linked expression-methylation module pairs were involved ageing process with module genes mostly related to oligodendrocyte development and myelination, and specifically expressed in oligodendrocytes. Further examination of underlying quantitative trait loci (QTLs) showed significant enrichment in genetic risk of most psychiatric disorders for expression QTLs but not for methylation QTLs. These results support the coherence between methylation and gene expression at the network level, and suggest a combinatorial effect of genetics and epigenetics in regulating gene expression networks specific to glia cells in relation to SZ and ageing process.

Sex-dependent monoamine oxidase isoforms expression patterns during human brain ageing

Human behavior is influenced by both genetic and environmental factors. Monoamine oxidase A (MAOA) is among the most investigated genetic determinants of violent behaviors, while the monoamine oxidase B (MAOB) is explored in Parkinson's disease. We collected twenty-four post-mortem brain tissue datasets of 3871 and 1820 non-demented males and females, respectively, who died from causes not attributable to neurodegenerative diseases. The gene expressions of MAOA and MAOB (MAO genes) were analyzed in these subjects, who were further stratified according to age into eleven groups ranging from late Infancy (5-9 months) to centenarians (>100 years). MAO genes were differently expressed in brains during the entire life span. In particular, maximal and minimal expression levels were found in early life and around the teen years. Females tended to have higher MAO gene levels throughout their lives than those found in age-matched males, even when expressions were separately measured in different brain regions. We demonstrated the existence of age- and sex- related variations in the MAO transcript levels in defined brain regions. More in-depth protein studies are needed to confirm our preliminary results obtained only on messenger RNAs in order to establish the role played by MAO genes in human development.

Causal Inference Methods to Integrate Omics and Complex Traits

Major biotechnological advances have facilitated a tremendous boost to the collection of (gen-/transcript-/prote-/methyl-/metabol-)omics data in very large sample sizes worldwide. Coordinated efforts have yielded a deluge of studies associating diseases with genetic markers (genome-wide association studies) or with molecular phenotypes. Whereas omics-disease associations have led to biologically meaningful and coherent mechanisms, the identified (non-germline) disease biomarkers may simply be correlates or consequences of the explored diseases. To move beyond this realm, Mendelian randomization provides a principled framework to integrate information on omics- and disease-associated genetic variants to pinpoint molecular traits causally driving disease development. In this review, we show the latest advances in this field, flag up key challenges for the future, and propose potential solutions.

Brain CHID1 Expression Correlates with NRGN and CALB1 in Healthy Subjects and AD Patients

Alzheimer’s disease is a progressive, devastating, and irreversible brain disorder that, day by day, destroys memory skills and social behavior. Despite this, the number of known genes suitable for discriminating between AD patients is insufficient. Among the genes potentially involved in the development of AD, there are the chitinase-like proteins (CLPs) CHI3L1, CHI3L2, and CHID1. The genes of the first two have been extensively investigated while, on the contrary, little information is available on CHID1. In this manuscript, we conducted transcriptome meta-analysis on an extensive sample of brains of healthy control subjects (n = 1849) (NDHC) and brains of AD patients (n = 1170) in order to demonstrate CHID1 involvement. Our analysis revealed an inverse correlation between the brain CHID1 expression levels and the age of NDHC subjects. Significant differences were highlighted comparing CHID1 expression of NDHC subjects and AD patients. Exclusive in AD patients, the CHID1 expression levels were correlated positively to calcium-binding adapter molecule 1 (IBA1) levels. Furthermore, both in NDHC and in AD patient’s brains, the CHID1 expression levels were directly correlated with calbindin 1 (CALB1) and neurogranin (NRGN). According to brain regions, correlation differences were shown between the expression levels of CHID1 in prefrontal, frontal, occipital, cerebellum, temporal, and limbic system. Sex-related differences were only highlighted in NDHC. CHID1 represents a new chitinase potentially involved in the principal processes underlying Alzheimer’s disease.

Molecular Mechanisms Underlying Synaptic and Axon Degeneration in Parkinson's Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disease that impairs movement as well as causing multiple other symptoms such as autonomic dysfunction, rapid eye movement (REM) sleep behavior disorder, hyposmia, and cognitive changes. Loss of dopamine neurons in the substantia nigra pars compacta (SNc) and loss of dopamine terminals in the striatum contribute to characteristic motor features. Although therapies ease the symptoms of PD, there are no treatments to slow its progression. Accumulating evidence suggests that synaptic impairments and axonal degeneration precede neuronal cell body loss. Early synaptic changes may be a target to prevent disease onset and slow progression. Imaging of PD patients with radioligands, post-mortem pathologic studies in sporadic PD patients, and animal models of PD demonstrate abnormalities in presynaptic terminals as well as postsynaptic dendritic spines. Dopaminergic and excitatory synapses are substantially reduced in PD, and whether other neuronal subtypes show synaptic defects remains relatively unexplored. Genetic studies implicate several genes that play a role at the synapse, providing additional support for synaptic dysfunction in PD. In this review article we: (1) provide evidence for synaptic defects occurring in PD before neuron death; (2) describe the main genes implicated in PD that could contribute to synapse dysfunction; and (3) show correlations between the expression of Snca mRNA and mouse homologs of PD GWAS genes demonstrating selective enrichment of Snca and synaptic genes in dopaminergic, excitatory and cholinergic neurons. Altogether, these findings highlight the need for novel therapeutics targeting the synapse and suggest that future studies should explore the roles for PD-implicated genes across multiple neuron types and circuits.

Effects of caloric restriction on the antagonistic and integrative hallmarks of aging

Aging is a significant risk factor for cognitive decline associated with neurodegenerative diseases, which makes understanding what promotes 'healthy brain aging' very important. Studies suggest that caloric restriction (CR) is a non-genetic intervention that reliably extends life- and healthspan. Here, we review the CR literature related to both the subject of aging and alterations in cell cycle machinery, especially surrounding the regulation of the E2F/DP1 complex, to elucidate the cellular protection mechanisms in the brain induced via dietary applications. The alterations extending lifespan via CR appear to exert their effects by promoting survival of individual cells, downregulating cell proliferation, and inducing stem cell quiescence, which results in keeping the stem cell reserve for extreme needs. This survival instinct of cells is believed to cause some molecular adaptations for their maintenance of the system. Avoiding energy waste of proliferation machinery promotes the long term survival of the individual cells and this is due to adaptations to the limited nutrient supply in the environment. Such a protective mechanism induced by diet could be promoted via the downregulation of crucial cell cycle-related transcription activators. This review article aims to bring attention to the importance of molecular adaptations induced by diet that promote healthy brain aging. It will provide insights into alternative targets for new treatments or neuroprotective approaches against neurodegenerative pathophysiologies.

Allele-specific expression of Parkinson's disease susceptibility genes in human brain

Genome-wide association studies have identified genetic variation in genomic loci associated with susceptibility to Parkinson’s disease (PD), the most common neurodegenerative movement disorder worldwide. We used allelic expression profiling of genes located within PD-associated loci to identify cis-regulatory variation affecting gene expression. DNA and RNA were extracted from post-mortem superior frontal gyrus tissue and whole blood samples from PD patients and controls. The relative allelic expression of transcribed SNPs in 12 GWAS risk genes was analysed by real-time qPCR. Allele-specific expression was identified for 9 out of 12 genes tested (GBA, TMEM175, RAB7L1, NUCKS1, MCCC1, BCKDK, ZNF646, LZTS3, and WDHD1) in brain tissue samples. Three genes (GPNMB, STK39 and SIPA1L2) did not show significant allele-specific effects. Allele-specific effects were confirmed in whole blood for three genes (BCKDK, LZTS3 and MCCC1), whereas two genes (RAB7L1 and NUCKS1) showed brain-specific allelic expression. Our study supports the hypothesis that changes to the cis-regulation of gene expression is a major mechanism behind a large proportion of genetic associations in PD. Interestingly, allele-specific expression was also observed for coding variants believed to be causal variants (GBA and TMEM175), indicating that splicing and other regulatory mechanisms may be involved in disease development.

GNG13 Is a Potential Marker of the State of Health of Alzheimer's Disease Patients' Cerebellum

Brain regions such as the cerebellum (CB) have been neglected for a long time in the study of Alzheimer's disease (AD) pathogenesis. In reference to a new emerging hypothesis according to which there is an altered cerebellar synaptic processing in AD, we verified the possible role played by new biomarkers in the CB of AD patients compared with not-demented healthy control subjects (NDHS). Using a bioinformatics approach, we have collected several microarray datasets and obtained 626 cerebella sample biopsies belonging to subjects who did not die from causes related to neurological diseases and 199 cerebella belonging to AD. The analysis of logical relations between the transcriptome dataset highlighted guanine nucleotide-binding protein (G protein) gamma 13 (GNG13) as a potential new biomarker for Purkinje cells (PCs). We have correlated GNG13 expression levels with already widely existing bibliography of PC marker genes, such as Purkinje cell protein 2 (PCP2), Purkinje cell protein 4 (PCP4), and cerebellin 3 (CBLN3). We showed that expression levels of GNG13 and PCP2, PCP4, and CBLN3 were significantly correlated with each other in NDHS and in AD and significantly reduced in AD patients compared with NDHS subjects. In addition, we highlighted a negative correlation between the expression levels of PC biomarkers and age. From the outcome of our investigation, it is possible to conclude that the identification of GNG13 as a potentially biomarker in PCs represents also a state of health of CB, in association with the expression of PCP2, PCP4, and CBLN3.

Associations of attention distractibility with attention deficit and with variation in the KTN1 gene

Attention distractibility in a low load visual search experiment with a rare irrelevant distractor could be an objective continuous measure in adulthood that correlates well with the symptoms of attention deficit throughout lifespan. This was studied using a birth cohort representative sample in a longitudinal study. The expected correlations were not found between the distractor cost measured in the experiment in adulthood and the inattention questionnaire scores from ages 15-33. However, the coefficient of variability for RT (CVRT) correlated negatively with self-reported motor restlessness (age 15) and attention deficit (age 25). We suggest that hyperactivity in childhood improved motor control at age 33. Associations with the gene KTN1 rs945270 (found to affect putamen size) were explored. CVRT, motor restlessness at age 15 and attention deficit scores at age 25 were especially low for male C-allele carriers. A possible association with the volume of putamen of individual participants is considered.

Postsynaptic damage and microglial activation in AD patients could be linked CXCR4/CXCL12 expression levels

Alzheimer's disease (AD) is one of the most common forms of dementia with still unknown pathogenesis. Several cytokines and chemokines are involved in the pathogenesis of AD. Among the chemokines, the CXCR4/CXCL12 complex has been shown to play an important role in the pathogenetic development of AD. We investigated the expression levels of CXCR4 / CXCL12 in fifteen brain regions of healthy non-demented subjects (NDHC) (2139 sample) and AD patients (1170 sample) stratified according to sex and age. Furthermore, we correlated their expressions with the Neurogranin (NRGN) and CHI3L1 levels, two inflamm-aging markers. We highlighted that CXCR4 gene expression levels were age-correlated in the brain of NDHC subjects and that AD nullified this correlation. A similar trend, but diametrically opposite was observed for CXCL12. Its expression was decreased during the aging in both sexes, and in the brains of AD patients, it underwent an inversion of the trend, only and exclusively in females. Brains of AD patients expressed high CXCR4 and CHI3L1, and low CXCL12 and Neurogranin levels compared to NDHC subjects. Both CXCR4 and CXCL12 correlated significantly with CHI3L1 and Neurogranin expression levels, regardless of disease. Furthermore, we showed a selective modulation of CXCL12 and CXCR4 only in specific brain regions. Taken together our results demonstrate that CXCL12 and CXCR4 are linked to Neurogranin and CHI3L1 expression levels and the relationship between postsynaptic damage and microglial activation in AD could be shown using all these genes. Further confirmations are needed to demonstrate the close link between these genes.

CHI3L2 Expression Levels Are Correlated with AIF1, PECAM1, and CALB1 in the Brains of Alzheimer's Disease Patients

Alzheimer's disease (AD) represents one of the main forms of dementia that afflicts our society. The expression of several genes has been associated with disease development. Despite this, the number of genes known to be capable of discriminating between AD patients according to sex remains deficient. In our study, we performed a transcriptomes meta-analysis on a large court of brains of healthy control subjects (n = 2139) (NDHC) and brains of AD patients (n = 1170). Our aim was to verify the brain expression levels of CHI3L2 and its correlation with genes associated with microglia-mediated neuroinflammation (IBA1), alteration of the blood-brain barrier (PECAM1), and neuronal damage (CALB1). We showed that the CHI3L2, IBA1, PECAM1, and CALB1 expression levels were modulated in the brains of patients with AD compared to NDHC subjects. Furthermore, both in NDHC and in AD patient's brains, the CHI3L2 expression levels were directly correlated with IBA1 and PECAM1 and inversely with CALB1. Additionally, the expression levels of CHI3L2, PECAM1, and CALB1 but not of IBA1 were sex-depended. By stratifying the samples according to age and sex, correlation differences emerged between the expression levels of CHI3L2, IBA1, PECAM1, and CALB1 and the age of NDHC subjects and AD patients. CHI3L2 represents a promising gene potentially involved in the key processes underlying Alzheimer's disease. Its expression in the brains of sex-conditioned AD patients opens up new possible sex therapeutic strategies aimed at controlling imbalance in disease progression.

Immune and Inflammatory Pathways Implicated by Whole Blood Transcriptomic Analysis in a Diverse Ancestry Alzheimer's Disease Cohort

BACKGROUND

Significant work has identified genetic variants conferring risk and protection for Alzheimer's disease (AD), but functional effects of these variants is lacking, particularly in under-represented ancestral populations. Expression studies performed in easily accessible tissue, such as whole blood, can recapitulate some transcriptional changes occurring in brain and help to identify mechanisms underlying neurodegenerative processes.

OBJECTIVE

We aimed to identify transcriptional differences between AD cases and controls in a cohort of diverse ancestry.

METHODS

We analyzed the protein coding transcriptome using RNA sequencing from peripheral blood collected from 234 African American (AA) (115 AD, 119 controls) and 240 non-Hispanic Whites (NHW) (121 AD, 119 controls). To identify case-control differentially expressed genes and pathways, we performed stratified, joint, and interaction analyses using linear regression models within and across ancestral groups followed by pathway and gene set enrichment analyses.

RESULTS

Overall, we identified 418 (291 upregulated, 127 downregulated) and 488 genes (352 upregulated, 136 downregulated) differentially expressed in the AA and NHW datasets, respectively, with only 16 genes commonly differentially expressed in both ancestral groups. Joint analyses provided greater power to detect case-control differences and identified 1,102 differentially expressed genes between cases and controls (812 upregulated, 290 downregulated). Interaction analysis identified only 27 genes with different effects in AA compared to NHW. Pathway and gene-set enrichment analyses revealed differences in immune response-related pathways that were enriched across the analyses despite different underlying gene sets.

CONCLUSION

These results support the hypothesis of converging underlying pathophysiological processes in AD across ancestral groups.

Genetic Predisposition to Glioma Mediated by a MAPKAP1 Enhancer Variant

Mitogen-activated protein kinase-associated protein 1 (MAPKAP1) is a unique component of the mechanistic target of rapamycin (MTOR) pathway which plays a pivotal role in carcinogenesis. The role of enhancer variant in carcinogenesis receives increased attentions. However, the significance of enhancer variants of MAPKAP1 in glioma has not yet been investigated. The associations of enhancer variants of MAPKAP1 with glioma susceptibility were evaluated in a cohort of 400 glioma patients and 651 controls. The function of glioma susceptibility locus was examined by a set of biochemical assays. We found that an enhancer variant of MAPKAP1 rs473426 was associated with a significantly increased risk of glioma in a dominant manner (OR 1.53, 95% CI 1.13-2.06; P = 0.006). The association for rs1339499 located in the same enhancer approached the borderline of significance after multiple testing correction (OR 0.74, 95% CI 0.56-0.98; P = 0.037). Furthermore, cumulative associations of rs473426 and rs1339499 with glioma risk were observed (P = 0.011). Functional analyses showed that the risk allele rs473426 C downregulated the regulatory activity of enhancer by reducing the binding affinity of a transcriptional activator NFΙC, which resulted in lower gene expression both in vitro and in vivo. These results demonstrate for the first time that enhancer variant of MAPKAP1 confers susceptibility to glioma by downregulation of MAPKAP1 expression, and provide further evidence highlighting MAPKAP1 as a cancer suppressor in glioma carcinogenesis.

Missing heritability in Parkinson's disease: the emerging role of non-coding genetic variation

Parkinson's disease (PD) is a neurodegenerative disorder caused by a complex interplay of genetic and environmental factors. For the stratification of PD patients and the development of advanced clinical trials, including causative treatments, a better understanding of the underlying genetic architecture of PD is required. Despite substantial efforts, genome-wide association studies have not been able to explain most of the observed heritability. The majority of PD-associated genetic variants are located in non-coding regions of the genome. A systematic assessment of their functional role is hampered by our incomplete understanding of genotype-phenotype correlations, for example through differential regulation of gene expression. Here, the recent progress and remaining challenges for the elucidation of the role of non-coding genetic variants is reviewed with a focus on PD as a complex disease with multifactorial origins. The function of gene regulatory elements and the impact of non-coding variants on them, and the means to map these elements on a genome-wide level, will be delineated. Moreover, examples of how the integration of functional genomic annotations can serve to identify disease-associated pathways and to prioritize disease- and cell type-specific regulatory variants will be given. Finally, strategies for functional validation and considerations for suitable model systems are outlined. Together this emphasizes the contribution of rare and common genetic variants to the complex pathogenesis of PD and points to remaining challenges for the dissection of genetic complexity that may allow for better stratification, improved diagnostics and more targeted treatments for PD in the future.

An atlas of genetic correlations between psychiatric disorders and human blood plasma proteome

BACKGROUND

Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins.

METHODS

The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins.

RESULTS

LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ).

CONCLUSIONS

This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.

Transcriptomic landscape, gene signatures and regulatory profile of aging in the human brain

The molecular characteristics of aging that lead to increased disease susceptibility remain poorly understood. Here we present a transcriptomic profile of the human brain associated with age and aging, derived from a systematic integrative analysis of four independent cohorts of genome-wide expression data from 2202 brain samples (cortex, hippocampus and cerebellum) of individuals of different ages (from young infants, 5-10 years old, to elderly people, up to 100 years old) categorized in age stages by decades. The study provides a signature of 1148 genes detected in cortex, 874 genes in hippocampus and 657 genes in cerebellum, that present significant differential expression changes with age according to a robust gamma rank correlation profiling. The signatures show a significant large overlap of 258 genes between cortex and hippocampus, and 63 common genes between the three brain regions. Focusing on cortex, functional enrichment analysis and cell-type analysis provided biological insight about the aging signature. Response to stress and immune response were up-regulated functions. Synapse, neurotransmission and calcium signaling were down-regulated functions. Cell analysis, derived from single-cell data, disclosed an increase of neuronal activity in the young stages of life and a decline of such activity in the old stages. A regulatory analysis identified the transcription factors (TF) associated with the signature of 258 genes, common to cortex and hippocampus; revealing the role of MEF2(A,D), PDX1, FOSL(1,2) and RFX(5,1) as candidate regulators of the signature. Finally, a deep-learning neural network algorithm was used to build a biological age predictor based on the aging signature. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Expression quantitative trait loci-derived scores and white matter microstructure in UK Biobank: a novel approach to integrating genetics and neuroimaging

Expression quantitative trait loci (eQTL) are genetic variants associated with gene expression. Using genome-wide genotype data, it is now possible to impute gene expression using eQTL mapping efforts. This approach can be used to analyse previously unexplored relationships between gene expression and heritable in vivo measures of human brain structural connectivity. Using large-scale eQTL mapping studies, we computed 6457 gene expression scores (eQTL scores) using genome-wide genotype data in UK Biobank, where each score represents a genetic proxy measure of gene expression. These scores were then tested for associations with two diffusion tensor imaging measures, fractional anisotropy (N_FA = 14,518) and mean diffusivity (N_MD = 14,485), representing white matter structural integrity. We found FDR-corrected significant associations between 8 eQTL scores and structural connectivity phenotypes, including global and regional measures (β_absolute FA = 0.0339-0.0453; MD = 0.0308-0.0381) and individual tracts (β_absolute FA = 0.0320-0.0561; MD = 0.0295-0.0480). The loci within these eQTL scores have been reported to regulate expression of genes involved in various brain-related processes and disorders, such as neurite outgrowth and Parkinson's disease (DCAKD, SLC35A4, SEC14L4, SRA1, NMT1, CPNE1, PLEKHM1, UBE3C). Our findings indicate that eQTL scores are associated with measures of in vivo brain connectivity and provide novel information not previously found by conventional genome-wide association studies. Although the role of expression of these genes regarding white matter microstructural integrity is not yet clear, these results suggest it may be possible, in future, to map potential trait- and disease-associated eQTL to in vivo brain connectivity and better understand the mechanisms of psychiatric disorders and brain traits, and their associated imaging findings.

A practical view of fine-mapping and gene prioritization in the post-genome-wide association era

Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

An epistasis between dopaminergic and oxytocinergic systems confers risk of post-traumatic stress disorder in a traumatized Chinese cohort

Post-traumatic stress disorder (PTSD) is a psychiatric syndrome that occurs after trauma exposure. Neurotransmitters such as dopamine and oxytocin have been reported to be involved in neuropathology of PTSD. Previous studies indicated that the dopamine–oxytocin interaction may contribute to behavioral disorders. Thus, exploring the epistasis (gene–gene interaction) between oxytocinergic and dopaminergic systems might be useful to reveal the genetic basis of PTSD. In this study, we analyzed two functional single nucleotide polymorphisms (SNPs), rs2268498 for oxytocinergic gene OXTR and rs1801028 for dopaminergic gene DRD2 based on putative oxytocin receptor–dopamine receptor D2 (OTR–DR2) heterocomplex in a Chinese cohort exposed to the 2008 Wenchuan earthquake (156 PTSD cases and 978 controls). Statistical analyses did not find any single variant or gene–environment interaction (SNP × earthquake-related trauma exposure) associated with provisional PTSD diagnosis or symptoms. An OXTR–DRD2 interaction (rs2268498 × rs1801028) was identified to confer risk of provisional PTSD diagnosis (OR = 9.18, 95% CI = 3.07–27.46 and P = 7.37e-05) and further subset analysis indicated that rs2268498 genotypes controlled the association directions of rs1801028 and rs1801028 genotypes also controlled the association directions of rs2268498. Rs2268498 × rs1801028 is also associated with PTSD symptoms (P = 0.043). Our study uncovered a genetic and putative function-based contribution of dopaminergic–oxytocinergic system interaction to PTSD.

Brain microstructural abnormalities correlate with KCC2 downregulation in refractory epilepsy

Dysregulations in the expression level of Na-K-Cl cotransporter (NKCC1) and K-Cl cotransporter (KCC2) genes have been detected in the brain tissues of patients with refractory epilepsy. Given the importance of these proteins in the determination of Cl equilibrium potential (ECl), evaluation of the expression changes of these transporters might assist in optimizing the diagnostic approaches and therapeutic strategies. The present investigation evaluates the expression level chloride transporters in polymorphonuclear cells and their correlation with microstructural abnormalities. Thirty cases of drug-resistant epilepsy (confirmed with temporal lobe epilepsy diagnosis) fulfilled the considered inclusion criteria. Cases were divided into two groups, one with a detectable MRI lesion (19 participants; right side) and another with no MRI findings (11 participants). Whole-brain voxel-based analysis was performed on diffusion tensor imaging to measure fractional anisotropy and mean diffusivity; neurite orientation dispersion and density imaging was performed to map neurite density index and orientation dispersion index. Our results indicated that fractional anisotropy and mean diffusivity changed in temporal and extratemporal parts of the brain, whereas the changes in neurite density index and orientation dispersion index were exclusively obvious in the temporal lobe. Molecular studies revealed significantly lower levels of KCC2 expression in patients with epilepsy, a finding that remarkably correlated with microstructural changes as well. Our research showed that downregulation of KCC2 and microstructural abnormalities might contribute to the observed refractoriness in temporal lobe epilepsy.

Human gene expression variability and its dependence on methylation and aging

BACKGROUND

Phenotypic variability of human populations is partly the result of gene polymorphism and differential gene expression. As such, understanding the molecular basis for diversity requires identifying genes with both high and low population expression variance and identifying the mechanisms underlying their expression control. Key issues remain unanswered with respect to expression variability in human populations. The role of gene methylation as well as the contribution that age, sex and tissue-specific factors have on expression variability are not well understood.

RESULTS

Here we used a novel method that accounts for sampling error to classify human genes based on their expression variability in normal human breast and brain tissues. We find that high expression variability is almost exclusively unimodal, indicating that variance is not the result of segregation into distinct expression states. Genes with high expression variability differ markedly between tissues and we find that genes with high population expression variability are likely to have age-, but not sex-dependent expression. Lastly, we find that methylation likely has a key role in controlling expression variability insofar as genes with low expression variability are likely to be non-methylated.

CONCLUSIONS

We conclude that gene expression variability in the human population is likely to be important in tissue development and identity, methylation, and in natural biological aging. The expression variability of a gene is an important functional characteristic of the gene itself and the classification of a gene as one with Hyper-Variability or Hypo-Variability in a human population or in a specific tissue should be useful in the identification of important genes that functionally regulate development or disease.

A regulatory variant of CHRM3 is associated with cannabis-induced hallucinations in European Americans

Cannabis, the most widely used illicit drug, can induce hallucinations. Our understanding of the biology of cannabis-induced hallucinations (Ca-HL) is limited. We used the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) to identify cannabis-induced hallucinations (Ca-HL) among long-term cannabis users (used cannabis ≥1 year and ≥100 times). A genome-wide association study (GWAS) was conducted by analyzing European Americans (EAs) and African Americans (AAs) in Yale-Penn 1 and 2 cohorts individually, then meta-analyzing the two cohorts within population. In the meta-analysis of Yale-Penn EAs (n = 1917), one genome-wide significant (GWS) signal emerged at the CHRM3 locus, represented by rs115455482 (P = 1.66 × 10^-10), rs74722579 (P = 2.81 × 10^-9), and rs1938228 (P = 1.57 × 10^-8); signals were GWS in Yale-Penn 1 EAs (n = 1092) and nominally significant in Yale-Penn 2 EAs (n = 825). Two SNPs, rs115455482 and rs74722579, were available from the Collaborative Study on the Genetics of Alcoholism data (COGA; 3630 long-term cannabis users). The signals did not replicate, but when meta-analyzing Yale-Penn and COGA EAs, the two SNPs' association signals were increased (meta-P-values 1.32 × 10^-10 and 2.60 × 10^-9, respectively; n = 4291). There were no significant findings in AAs, but in the AA meta-analysis (n = 3624), nominal significance was seen for rs74722579. The rs115455482*T risk allele was associated with lower CHRM3 expression in the thalamus. CHRM3 was co-expressed with three psychosis risk genes (GABAG2, CHRNA4, and HRH3) in the thalamus and other human brain tissues and mouse GABAergic neurons. This work provides strong evidence for the association of CHRM3 with Ca-HL and provides insight into the potential involvement of thalamus for this trait.

Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene-trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer

Background

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported.

Methods

Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them.

Results

Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD.

Conclusions

Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer.

Electronic supplementary material

The online version of this article (10.1186/s13229-019-0262-8) contains supplementary material, which is available to authorized users.

Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder

Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.

Translating Alzheimer's disease-associated polymorphisms into functional candidates: a survey of IGAP genes and SNPs

The International Genomics of Alzheimer's Project (IGAP) is a consortium for characterizing the genetic landscape of Alzheimer's disease (AD). The identified and/or confirmed 19 single-nucleotide polymorphisms (SNPs) associated with AD are located on non-coding DNA regions, and their functional impacts on AD are as yet poorly understood. We evaluated the roles of the IGAP SNPs by integrating data from many resources, based on whether the IGAP SNP was (1) a proxy for a coding SNP or (2) associated with altered mRNA transcript levels. For (1), we confirmed that 12 AD-associated coding common SNPs and five nonsynonymous rare variants are in linkage disequilibrium with the IGAP SNPs. For (2), the IGAP SNPs in CELF1 and MS4A6A were associated with expression of their neighboring genes, MYBPC3 and MS4A6A, respectively, in blood. The IGAP SNP in DSG2 was an expression quantitative trait loci (eQTL) for DLGAP1 and NETO1 in the human frontal cortex. The IGAP SNPs in ABCA7, CD2AP, and CD33 each acted as eQTL for AD-associated genes in brain. Our approach for identifying proxies and examining eQTL highlighted potentially impactful, novel gene regulatory phenomena pertinent to the AD phenotype.

Genome-wide Association Study Identifies a Regulatory Variant of RGMA Associated With Opioid Dependence in European Americans

BACKGROUND

Opioid dependence (OD) is at epidemic levels in the United States. Genetic studies can provide insight into its biology.

METHODS

We completed an OD genome-wide association study in 3058 opioid-exposed European Americans, 1290 of whom met criteria for a DSM-IV diagnosis of OD. Analysis used DSM-IV criterion count.

RESULTS

By meta-analysis of four cohorts, Yale-Penn 1 (n = 1388), Yale-Penn 2 (n = 996), Yale-Penn 3 (n = 98), and SAGE (Study of Addiction: Genetics and Environment) (n = 576), we identified a variant on chromosome 15, rs12442183, near RGMA, associated with OD (p = 1.3 × 10^-8). The association was also genome-wide significant in Yale-Penn 1 taken individually and nominally significant in two of the other three samples. The finding was further supported in a meta-analysis of all available opioid-exposed African Americans (n = 2014 [1106 meeting DSM-IV OD criteria]; p = 3.0 × 10^-3) from three cohorts; there was nominal significance in two of these samples. Thus, of seven subsamples examined in two populations, one was genome-wide significant, and four of six were nominally (or nearly) significant. RGMA encodes repulsive guidance molecule A, which is a central nervous system axon guidance protein. Risk allele rs12442183*T was correlated with higher expression of a specific RGMA transcript variant in frontal cortex (p = 2 × 10^-3). After chronic morphine injection, the homologous mouse gene (Rgma) was upregulated in C57BL/6J striatum. Coexpression analysis of 1301 brain samples revealed that RGMA messenger RNA expression was associated with that of four genes implicated in other psychiatric disorders, including GRIN1.

CONCLUSIONS

This is the first study to demonstrate an association of RGMA with OD. It provides a new lead into our understanding of OD pathophysiology.

Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean

White mould of soya bean, caused by Sclerotinia sclerotiorum (Lib.) de Bary, is a necrotrophic fungus capable of infecting a wide range of plants. To dissect the genetic architecture of resistance to white mould, a high-density customized single nucleotide polymorphism (SNP) array (52 041 SNPs) was used to genotype two soya bean diversity panels. Combined with resistance variation data observed in the field and greenhouse environments, genome-wide association studies (GWASs) were conducted to identify quantitative trait loci (QTL) controlling resistance against white mould. Results showed that 16 and 11 loci were found significantly associated with resistance in field and greenhouse, respectively. Of these, eight loci localized to previously mapped QTL intervals and one locus had significant associations with resistance across both environments. The expression level changes in genes located in GWAS-identified loci were assessed between partially resistant and susceptible genotypes through a RNA-seq analysis of the stem tissue collected at various time points after inoculation. A set of genes with diverse biological functionalities were identified as strong candidates underlying white mould resistance. Moreover, we found that genomic prediction models outperformed predictions based on significant SNPs. Prediction accuracies ranged from 0.48 to 0.64 for disease index measured in field experiments. The integrative methods, including GWAS, RNA-seq and genomic selection (GS), applied in this study facilitated the identification of causal variants, enhanced our understanding of mechanisms of white mould resistance and provided valuable information regarding breeding for disease resistance through genomic selection in soya bean.

Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes

It is widely assumed that cells must be physically isolated to study their molecular profiles. However, intact tissue samples naturally exhibit variation in cellular composition, which drives covariation of cell-class-specific molecular features. By analyzing transcriptional covariation in 7,221 intact CNS samples from 840 neurotypical individuals, representing billions of cells, we reveal the core transcriptional identities of major CNS cell classes in humans. By modeling intact CNS transcriptomes as a function of variation in cellular composition, we identify cell-class-specific transcriptional differences in Alzheimer's disease, among brain regions, and between species. Among these, we show that PMP2 is expressed by human but not mouse astrocytes and significantly increases mouse astrocyte size upon ectopic expression in vivo, causing them to more closely resemble their human counterparts. Our work is available as an online resource ( http://oldhamlab.ctec.ucsf.edu/ ) and provides a generalizable strategy for determining the core molecular features of cellular identity in intact biological systems.

Human Genetics of Addiction: New Insights and Future Directions

PURPOSE OF REVIEW

With the advent of the genome-wide association study (GWAS), our understanding of the genetics of addiction has made significant strides forward. Here, we summarize genetic loci containing variants identified at genome-wide statistical significance (P < 5 × 10-8) and independently replicated, review evidence of functional or regulatory effects for GWAS-identified variants, and outline multi-omics approaches to enhance discovery and characterize addiction loci.

RECENT FINDINGS

Replicable GWAS findings span 11 genetic loci for smoking, eight loci for alcohol, and two loci for illicit drugs combined and include missense functional variants and noncoding variants with regulatory effects in human brain tissues traditionally viewed as addiction-relevant (e.g., prefrontal cortex [PFC]) and, more recently, tissues often overlooked (e.g., cerebellum). GWAS analyses have discovered several novel, replicable variants contributing to addiction. Using larger sample sizes from harmonized datasets and new approaches to integrate GWAS with multiple 'omics data across human brain tissues holds great promise to significantly advance our understanding of the biology underlying addiction.

Integrating genome-wide association study and expression quantitative trait locus study identifies multiple genes and gene sets associated with schizophrenia

Schizophrenia is a serious mental disease with high heritability. To better understand the genetic basis of schizophrenia, we conducted a large scale integrative analysis of genome-wide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data was derived from a published GWAS of schizophrenia, containing 9394 schizophrenia patients and 12,462 healthy controls. The eQTLs dataset was obtained from an eQTLs meta-analysis of 5311 subjects, containing 923,021 cis-eQTLs for 14,329 genes and 4732 trans-eQTLs for 2612 genes. Genome-wide single gene expression association analysis was conducted by SMR software. The SMR analysis results were further subjected to gene set enrichment analysis (GSEA) to identify schizophrenia associated gene sets. SMR detected 49 genes significantly associated with schizophrenia. The top five significant genes were CRELD2 (p value=1.65×10^-11), DIP2B (p value=3.94×10^-11), ZDHHC18 (p value=1.52×10^-10) and ZDHHC5 (p value=7.45×10^-10), C11ORF75 (p value=3.70×10^-9). GSEA identified 80 gene sets with p values <0.01. The top five significant gene sets were COWLING_MYCN_TARGETS (p value <0.001) and CHR16P11 (p value <0.001), ACTACCT_MIR196A_MIR196B (p value=0.002), CELLULAR_COMPONENT_DISASSEMBLY (p value=0.002) and GRAESSMANN_RESPONSE_TO_MC_AND_DOXORUBICIN_DN (p value=0.002). Our results provide useful information for revealing the genetic basis of schizophrenia.

Identification of expression quantitative trait loci of MTOR associated with the progression of glioma

Mechanistic target of rapamycin (MTOR) encodes a key modulator of cell growth, proliferation, and apoptosis. Previous studies have demonstrated that the dysregulation of MTOR is involved in the development and progression of several types of cancer, including glioma. In the present study, a comprehensive analysis was conducted to examine whether the expression quantitative trait loci (eQTLs) of MTOR are associated with the progression of glioma. Candidate eQTLs of MTOR were obtained from the Genotype-Tissue Expression eQTL Browser. The Kaplan-Meier method and multivariate Cox model were used to analyze the progression-free survival time of glioma patients. Based on the analysis of 138 glioma patients, one eQTL of MTOR, rs4845964, was demonstrated to be significantly associated with the progression of glioma in a dominant manner. The adjusted hazard ratios (HRs) for patients with the AG or AA genotype at rs4845964 were 2.82 [95% confidence interval (CI), 1.27-6.27; P=0.0111] and 2.79 (95% CI, 1.10-7.07; P=0.0312), respectively, compared with those with the GG genotype. When the rs4845964 AG and AA genotypes were combined for analysis, the HR was 2.70 (95% CI, 1.25-5.82; P=0.0114) vs. the GG genotype. Stratified analyses revealed similar associations between the rs4845964 genotypes and the progression of glioma in all subgroups (following stratification by age, sex and tumor grade). These results demonstrate for the first time that the MTOR eQTL rs4845964 is associated with the progression of glioma.

Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus

Emerging evidence emphasizes the strong impact of regulatory genomic elements in neurodevelopmental processes and the complex pathways of brain disorders. The present genome-wide quantitative trait loci analyses explore the cis-regulatory effects of single-nucleotide polymorphisms (SNPs) on DNA methylation (meQTL) and gene expression (eQTL) in 110 human hippocampal biopsies. We identify cis-meQTLs at 14,118 CpG methylation sites and cis-eQTLs for 302 3'-mRNA transcripts of 288 genes. Hippocampal cis-meQTL-CpGs are enriched in flanking regions of active promoters, CpG island shores, binding sites of the transcription factor CTCF and brain eQTLs. Cis-acting SNPs of hippocampal meQTLs and eQTLs significantly overlap schizophrenia-associated SNPs. Correlations of CpG methylation and RNA expression are found for 34 genes. Our comprehensive maps of cis-acting hippocampal meQTLs and eQTLs provide a link between disease-associated SNPs and the regulatory genome that will improve the functional interpretation of non-coding genetic variants in the molecular genetic dissection of brain disorders.

Inherited variation in circadian rhythm genes and risks of prostate cancer and three other cancer sites in combined cancer consortia

Circadian disruption has been linked to carcinogenesis in animal models, but the evidence in humans is inconclusive. Genetic variation in circadian rhythm genes provides a tool to investigate such associations. We examined associations of genetic variation in nine core circadian rhythm genes and six melatonin pathway genes with risk of colorectal, lung, ovarian and prostate cancers using data from the Genetic Associations and Mechanisms in Oncology (GAME-ON) network. The major results for prostate cancer were replicated in the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial, and for colorectal cancer in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). The total number of cancer cases and controls was 15,838/18,159 for colorectal, 14,818/14,227 for prostate, 12,537/17,285 for lung and 4,369/9,123 for ovary. For each cancer site, we conducted gene-based and pathway-based analyses by applying the summary-based Adaptive Rank Truncated Product method (sARTP) on the summary association statistics for each SNP within the candidate gene regions. Aggregate genetic variation in circadian rhythm and melatonin pathways were significantly associated with the risk of prostate cancer in data combining GAME-ON and PLCO, after Bonferroni correction (ppathway  < 0.00625). The two most significant genes were NPAS2 (pgene  = 0.0062) and AANAT (pgene  = 0.00078); the latter being significant after Bonferroni correction. For colorectal cancer, we observed a suggestive association with the circadian rhythm pathway in GAME-ON (ppathway  = 0.021); this association was not confirmed in GECCO (ppathway  = 0.76) or the combined data (ppathway  = 0.17). No significant association was observed for ovarian and lung cancer. These findings support a potential role for circadian rhythm and melatonin pathways in prostate carcinogenesis. Further functional studies are needed to better understand the underlying biologic mechanisms.