Risk Genes Research Articles

Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework

Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer-promoter interactions derived from multiple brain cell types at different developmental stages. The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. iGOAT can successfully leverage epigenomic data, phenotype-genotype associations, and protein-protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

Caveolae with GLP-1 and NMDA Receptors as Crossfire Points for the Innovative Treatment of Cognitive Dysfunction Associated with Neurodegenerative Diseases.

Some neurodegenerative diseases may be characterized by continuing behavioral and cognitive dysfunction that encompasses memory loss and/or apathy. Alzheimer's disease is the most typical type of such neurodegenerative diseases that are characterized by deficits of cognition and alterations of behavior. Despite the huge efforts against Alzheimer's disease, there has yet been no successful treatment for this disease. Interestingly, several possible risk genes for cognitive dysfunction are frequently expressed within brain cells, which may also be linked to cholesterol metabolism, lipid transport, exosomes, and/or caveolae formation, suggesting that caveolae may be a therapeutic target for cognitive dysfunctions. Interestingly, the modulation of autophagy/mitophagy with the alteration of glucagon-like peptide-1 (GLP-1) and N-methyl-d-aspartate (NMDA) receptor signaling may offer a novel approach to preventing and alleviating cognitive dysfunction. A paradigm showing that both GLP-1 and NMDA receptors at caveolae sites may be promising and crucial targets for the treatment of cognitive dysfunctions has been presented here, which may also be able to modify the progression of Alzheimer's disease. This research direction may create the potential to move clinical care toward disease-modifying treatment strategies with maximal benefits for patients without detrimental adverse events for neurodegenerative diseases.

Analysis of prognosis and drug sensitivity in lung cancer patients based on the integration of 18 programmed cell death modes and single-cell sequencing data

Objective: Programmed cell death (PCD) has therapeutic potential for a variety of malignant tumors, including lung cancer. In this study, we used PCD and bioinformatics to construct a prognostic model for lung cancer and explore new therapeutic strategies. Methods: Multiple bioinformatics algorithms (co-expression analysis, univariate Cox’s analysis, multivariate Cox’s analysis, and cross-validation) were used to screen PCD-related genes and construct a risk model. Lung cancer patients were divided into training and testing groups in a ratio of 7:3. The prognostic model was validated by comparing the risk scores of the high-risk and low-risk groups using receiver operating characteristic (ROC) curves, nomograms, and independent prognostic analyses. In addition, PCD patterns were classified and compared in terms of survival time, immune microenvironment and pathway regulation using consensus clustering methods with the validation of principal component analysis (PCA). Single-cell RNA sequencing (scRNA-seq) analysis was applied to validate screened PCD-related genes in this risk model. Results: Twelve risk genes were identified, including BIK, CDCP1, CHEK2, FADD, GLS2, IL33, ITPRIP, KRT8, MELK, MMP9, PTGIS and TRIB3, to construct prognostic risk model. In the process of lung cancer, the most significantly up-regulated gene was TRIML2, and the most down-regulated gene was GLS2. ROC curves, nomograms, and independent prognostic analyses confirmed the accuracy of risk model to predict the prognosis of lung cancer, indicating that it can be regarded as an independent prognostic model. In the immune cell infiltration, we found that patients with an increased M0 macrophage had a poorer prognosis. Drug sensitivity testing after reliable risk modeling identified three molecularly targeted drugs for lung cancer patients in the high-risk group, namely, Staurosporine, Luminespib and Docetaxel. scRNA-seq results further analyzed the reliability of ITPRIP and KRT8 as prognostic targets. Conclusion: This study identified twelve PCD-related genes and constructed an accurate risk model based on bioinformatics analysis, which can be used for prognostic prediction and design of clinical treatment strategies.

Alzheimer's disease risk gene CD2AP is a dose-sensitive determinant of synaptic structure and plasticity.

CD2-Associated protein (CD2AP) is a candidate susceptibility gene for Alzheimer's disease, but its role in the mammalian central nervous system remains largely unknown. We show that CD2AP protein is broadly expressed in the adult mouse brain, including within cortical and hippocampal neurons, where it is detected at pre-synaptic terminals. Deletion of Cd2ap altered dendritic branching and spine density, and impaired ubiquitin-proteasome system activity. Moreover, in mice harboring either one or two copies of a germline Cd2ap null allele, we noted increased paired-pulse facilitation at hippocampal Schaffer-collateral synapses, consistent with a haploinsufficient requirement for pre-synaptic release. Whereas conditional Cd2ap knockout in the brain revealed no gross behavioral deficits in either 3.5- or 12-month-old mice, Cd2ap heterozygous mice demonstrated subtle impairments in discrimination learning using a touchscreen task. Based on unbiased proteomics, partial or complete loss of Cd2ap triggered perturbation of proteins with roles in protein folding, lipid metabolism, proteostasis, and synaptic function. Overall, our results reveal conserved, dose-sensitive requirements for CD2AP in the maintenance of neuronal structure and function, including synaptic homeostasis and plasticity, and inform our understanding of possible cell-type specific mechanisms in Alzheimer's Disease.

Comprehensive analysis of the functional impact of single nucleotide variants of human CHEK2.

Loss of function mutations in the checkpoint kinase gene CHEK2 are associated with increased risk of breast and other cancers. Most of the 3,188 unique amino acid changes that can result from non-synonymous single nucleotide variants (SNVs) of CHEK2, however, have not been tested for their impact on the function of the CHEK2-enocded protein (CHK2). One successful approach to testing the function of variants has been to test for their ability to complement mutations in the yeast ortholog of CHEK2, RAD53. This approach has been used to provide functional information on over 100 CHEK2 SNVs and the results align with functional assays in human cells and known pathogenicity. Here we tested all but two of the 4,887 possible SNVs in the CHEK2 open reading frame for their ability to complement RAD53 mutants using a high throughput technique of deep mutational scanning (DMS). Among the non-synonymous changes, 770 were damaging to protein function while 2,417 were tolerated. The results correlate well with previous structure and function data and provide a first or additional functional assay for all the variants of uncertain significance identified in clinical databases. Combined, this approach can be used to help predict the pathogenicity of CHEK2 variants of uncertain significance that are found in susceptibility screening and could be applied to other cancer risk genes.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes.

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. We performed GWAS meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and, for the first time, the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signalling and brain ageing-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and ADHD. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Integrative analysis of transcriptome-wide association study and mRNA expression profile identified risk genes for bipolar disorder

ObjectiveBipolar disorder (BD) is a debilitating neuropsychiatric disorder, which is associated with genetic variation through “vast but mixed” Genome-Wide Association Studies (GWAS). Transcriptome-Wide Association Study (TWAS) is more effective in explaining genetic factors that influence complex diseases and can help identifying risk genes more reliably. So, this study aims to identify potential BD risk genes in pedigrees with TWAS. MethodsWe conducted a TWAS analysis with expression quantitative trait loci (eQTL) analysis on extended BD pedigrees, and the BD genome-wide association study (GWAS) summary data acquired from the Psychiatric Genomics Consortium (PGC). Furthermore, the BD-associated genes identified by TWAS were validated by mRNA expression profiles from the Gene Expression Omnibus (GEO) Datasets (GSE23848 and GSE46416). Functional enrichment and annotation analysis were implemented by RStudio (version 4.2.0). ResultsTWAS identified 362 genes with P value < 0.05, and 18 genes remain significant after Bonferroni correction, such as SEMA3G (PTWAS=1.07 × 10-11), ALOX5AP (PTWAS=3.12 × 10-8), and PLEC (PTWAS=1.27 × 10-7). Further 6 overlapped genes were detected in integrative analysis, such as UQCRB (PTWAS=0.0020, PmRNA=0.0000), TMPRSS9 (PTWAS=0.0405, PmRNA=0.0032), and SNX10 (PTWAS=0.0104, PmRNA=0.0015). Using genes identified by TWAS, Gene Ontology (GO) enrichment analysis identified 40 significant GO terms, such as mitochondrial ATP synthesis coupled electron transport, mitochondrial respiratory, aerobic electron transport chain, oxidative phosphorylation, mitochondrial membrane proteins, and ubiquinone activity. The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis identified significant 15 pathways for BD, such as Oxidative phosphorylation, endocannabinoids signaling, neurodegeneration, and reactive oxide species. ConclusionsWe found a set of BD-associated genes and pathways, validating the important role of neurodevelopmental abnormalities, inflammatory responses, and mitochondrial dysfunction in the pathology of BD, offering novel information for comprehending the genetic basis of BD.

Alzheimer's disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults.

Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE-ɛ4, APOE-ɛ2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age = 66 years, SD = 9.6) and an average of 5.3 years of MRI follow-up (max = 24 years). Atrophy on volumetric MRI scans was quantified in three ways: (i) a composite score of regions vulnerable to Alzheimer's disease (SPARE-AD); (ii) hippocampal volume; and (iii) a composite score of regions indexing advanced non-Alzheimer's disease-related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from fluid attenuated inversion recovery (FLAIR) MRI. Using linear mixed effects models, there was an APOE-ɛ4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ɛ4/ɛ4 carriers, followed by ɛ4 heterozygouts, and lowest among ɛ3 homozygouts and ɛ2/ɛ2 and ɛ2/ɛ3 carriers, who did not differ from one another. The negative associations of APOE-ɛ4 with atrophy were reduced among those with higher education (P < 0.04) and younger baseline ages (P < 0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD (P = 0.035) and the hippocampus (P = 0.014), independent of APOE-ɛ4 status. APOE-ɛ2 status (ɛ2/ɛ2 and ɛ2/ɛ3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA or the hippocampus, but was related to greater increases in white matter hyperintensities (P = 0.014). Additionally, there was an APOE-ɛ4 × AD-PRS interaction in relation to white matter hyperintensities (P = 0.038), with greater increases in white matter hyperintensities among APOE-ɛ4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE-ɛ4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer's disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE-ɛ2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer's disease influence atrophy in a regionally specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer's disease risk genes contribute to white matter hyperintensity formation.

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder.

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

Dysregulation of parvalbumin expression and neurotransmitter imbalance in the auditory cortex of the BTBR mouse model of autism spectrum disorder.

Individuals diagnosed with autism spectrum disorder (ASD) frequently exhibit abnormalities in auditory perception, a phenomenon potentially attributed to alterations in the excitatory and inhibitory cells constituting cortical circuits. However, the exact genetic factors and cell types affected by ASD remain unclear. The present study investigated the balance of excitatory and inhibitory activity in the auditory cortex using BTBR T+ Itpr3tf/J (BTBR) mice, a well-established model for autism research. Our investigation unveiled a reduction in parvalbumin-positive (PV+) neurons within the AC of BTBR mice. Remarkably, in vivo magnetic resonance spectroscopy studies disclosed an elevation in glutamate (Glu) levels alongside a decrement in γ-aminobutyric acid (GABA) levels in this cortical region. Additionally, transcriptomic analysis of the mouse model facilitated the classification of several ASD-associated genes based on their cellular function and pathways. By comparing autism risk genes with RNA transcriptome sequencing data from the ASD mouse model, we identified the recurrent target gene Scn1a and performed validation. Intriguingly, we uncovered the specific expression of Scn1a in cortical inhibitory neurons. These findings hold significant value for understanding the underlying neural mechanisms of abnormal sensory perception in animal models of ASD.

Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving the identification of high-specificity spatial proteomes. The resulting native proximity proteomes are enriched for human genes dysregulated in the brain of autistic individuals, and reveal proximity interactions between proteins from high-confidence risk genes with those of lower-confidence that may provide new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

Modified Electroconvulsive Therapy Normalizes Plasma GNA13 Following Schizophrenic Relapse.

GNA13 is an important member of the G protein family, and its coding gene GNA13 has been identified as one of the risk genes for schizophrenia (SCZ). This study aimed to investigate the relationship between GNA13 levels and the clinical symptoms of SCZ following treatment with modified electroconvulsive therapy (MECT). This study recruited 82 SCZ patients and 86 healthy controls (HCs). Each SCZ patient received 6 sessions of MECT. The Positive and Negative Syndrome Scale (PANSS) was used to assess SCZ symptom severity. Plasma levels of GNA13 were measured by enzyme-linked immunosorbent assay. Pretreatment, SCZ patients had a higher GNA13 level than HC (t = 8.199, P < 0.001). MECT reduced the GNA13 level significantly (t = 11.13, P < 0.001) and normalized the difference between SCZ and HC (t = 0.219, P = 0.827). After treatment, the downregulation of GNA13 (ΔGNA13) was negatively correlated with the positive symptoms score reduction rate (ΔP) (r = -0.379, P = 0.027) and positively correlated with the negative score reduction rate (ΔN) (r = 0.480, P = 0.004) in females. In both males and females, the receiver operating characteristic curve revealed that the pretreatment GNA13 level could help differentiate SCZ from HC (male: area under the curve = 0.792, P < 0.001; female: area under the curve = 0.814, P < 0.001). The reduced expression of GNA13 after MECT may be related to the exhibition of both negative and positive symptoms of SCZ in female patients.

Whole-genome sequencing identifies novel genes for autism in Chinese trios.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high genetic heritability but heterogeneity. Fully understanding its genetics requires whole-genome sequencing (WGS), but the ASD studies utilizing WGS data in Chinese population are limited. In this study, we present a WGS study for 334 individuals, including 112 ASD patients and their non-ASD parents. We identified 146 de novo variants in coding regions in 85 cases and 60 inherited variants in coding regions. By integrating these variants with an association model, we identified 33 potential risk genes (P<0.001) enriched in neuron and regulation related biological process. Besides the well-known ASD genes (SCN2A, NF1, SHANK3, CHD8 etc.), several high confidence genes were highlighted by a series of functional analyses, including CTNND1, DGKZ, LRP1, DDN, ZNF483, NR4A2, SMAD6, INTS1, and MRPL12, with more supported evidence from GO enrichment, expression and network analysis. We also integrated RNA-seq data to analyze the effect of the variants on the gene expression and found 12 genes in the individuals with the related variants had relatively biased expression. We further presented the clinical phenotypes of the proband carrying the risk genes in both our samples and Caucasian samples to show the effect of the risk genes on phenotype. Regarding variants in non-coding regions, a total of 74 de novo variants and 30 inherited variants were predicted as pathogenic with high confidence, which were mapped to specific genes or regulatory features. The number of de novo variants found in patient was significantly associated with the parents' ages at the birth of the child, and gender with trend. We also identified small de novo structural variants in ASD trios. The results in this study provided important evidence for understanding the genetic mechanism of ASD.

Exome-wide association study identifies KDELR3 mutations in extreme myopia

Extreme myopia (EM), defined as a spherical equivalent (SE) ≤ −10.00 diopters (D), is one of the leading causes of sight impairment. Known EM-associated variants only explain limited risk and are inadequate for clinical decision-making. To discover risk genes, we performed a whole-exome sequencing (WES) on 449 EM individuals and 9606 controls. We find a significant excess of rare protein-truncating variants (PTVs) in EM cases, enriched in the retrograde vesicle-mediated transport pathway. Employing single-cell RNA-sequencing (scRNA-seq) and a single-cell polygenic burden score (scPBS), we pinpointed PI16 + /SFRP4+ fibroblasts as the most relevant cell type. We observed that KDELR3 is highly expressed in scleral fibroblast and involved in scleral extracellular matrix (ECM) organization. The zebrafish model revealed that kdelr3 downregulation leads to elongated ocular axial length and increased lens diameter. Together, our study provides insight into the genetics of EM in humans and highlights KDELR3’s role in EM pathogenesis.

Natural killer cell-associated prognosis model characterizes immune landscape and treatment efficacy of diffuse large B cell lymphoma

PurposeNK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL. MethodsBioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient’s prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples. ResultsWe identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased. ConclusionThe NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer’s disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson’s disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.

Developmental trajectory and sex differences in auditory processing in a PTEN-deletion model of autism spectrum disorders

Autism Spectrum Disorders (ASD) encompass a wide array of debilitating symptoms, including severe sensory deficits and abnormal language development. Sensory deficits early in development may lead to broader symptomatology in adolescents and adults. The mechanistic links between ASD risk genes, sensory processing and language impairment are unclear. There is also a sex bias in ASD diagnosis and symptomatology. The current study aims to identify the developmental trajectory and genotype- and sex-dependent differences in auditory sensitivity and temporal processing in a Pten-deletion (phosphatase and tensin homolog missing on chromosome 10) mouse model of ASD. Auditory temporal processing is crucial for speech recognition and language development and deficits will cause language impairments. However, very little is known about the development of temporal processing in ASD animal models, and if there are sex differences. To address this major gap, we recorded epidural electroencephalography (EEG) signals from the frontal (FC) and auditory (AC) cortex in developing and adult Nse-cre PTEN mice, in which Pten is deleted in specific cortical layers (layers III-V) (PTEN conditional knock-out (cKO). We quantified resting EEG spectral power distribution, auditory event related potentials (ERP) and temporal processing from awake and freely moving male and female mice. Temporal processing is measured using a gap-in-noise-ASSR (auditory steady state response) stimulus paradigm. The experimental manipulation of gap duration and modulation depth allows us to measure cortical entrainment to rapid gaps in sounds. Temporal processing was quantified using inter-trial phase clustering (ITPC) values that account for phase consistency across trials. The results show genotype differences in resting power distribution in PTEN cKO mice throughout development. Male and female cKO mice have significantly increased beta power but decreased high frequency oscillations in the AC and FC. Both male and female PTEN cKO mice show diminished ITPC in their gap-ASSR responses in the AC and FC compared to control mice. Overall, deficits become more prominent in adult (p60) mice, with cKO mice having significantly increased sound evoked power and decreased ITPC compared to controls. While both male and female cKO mice demonstrated severe temporal processing deficits across development, female cKO mice showed increased hypersensitivity compared to males, reflected as increased N1 and P2 amplitudes. These data identify a number of novel sensory processing deficits in a PTEN-ASD mouse model that are present from an early age. Abnormal temporal processing and hypersensitive responses may contribute to abnormal development of language function in ASD.

Single-cell spatial transcriptomics reveals distinct patterns of dysregulation in non-neuronal and neuronal cells induced by the Trem2R47H Alzheimer's risk gene mutation.

The R47H missense mutation of the TREM2 gene is a known risk factor for development of Alzheimer's Disease. In this study, we analyze the impact of the Trem2R47H mutation on specific cell types in multiple cortical and subcortical brain regions in the context of wild-type and 5xFAD mouse background. We profile 19 mouse brain sections consisting of wild-type, Trem2R47H, 5xFAD and Trem2R47H; 5xFAD genotypes using MERFISH spatial transcriptomics, a technique that enables subcellular profiling of spatial gene expression. Spatial transcriptomics and neuropathology data are analyzed using our custom pipeline to identify plaque and Trem2R47H-induced transcriptomic dysregulation. We initially analyzecell type-specific transcriptomic alterations induced by plaque proximity. Next, we analyze spatial distributions of disease associated microglia and astrocytes, and how they vary between 5xFAD and Trem2R47H; 5xFAD mouse models. Finally, we analyze the impact of the Trem2R47H mutationon neuronal transcriptomes. The Trem2R47H mutation induces consistent upregulation of Bdnf and Ntrk2 across many cortical excitatory neuron types, independent of amyloid pathology. Spatial investigation of genotype enriched subclusters identified spatially localized neuronal subpopulations reduced in 5xFAD and Trem2R47H; 5xFAD mice. Overall, our MERFISH spatial transcriptomics analysis identifies glial and neuronal transcriptomic alterations induced independently by 5xFAD and Trem2R47H mutations, impacting inflammatory responses in microglia and astrocytes, and activity and BDNF signaling in neurons.

Bayesian Rare Variant Analysis Identifies Novel Schizophrenia Putative Risk Genes.

The genetics of schizophrenia is so complex that it involves both common variants and rare variants. Rare variant association studies of schizophrenia are challenging because statistical methods for rare variant analysis are under-powered due to the rarity of rare variants. The recent Schizophrenia Exome meta-analysis (SCHEMA) consortium, the largest consortium in this field to date, has successfully identified 10 schizophrenia risk genes from ultra-rare variants by burden test, while more risk genes remain to be discovered by more powerful rare variant association test methods. In this study, we use a recently developed Bayesian rare variant association method that is powerful for detecting sparse rare risk variants that implicates 88 new candidate risk genes associated with schizophrenia from the SCHEMA case-control sample. These newly identified genes are significantly enriched in autism risk genes and GO enrichment analysis indicates that new candidate risk genes are involved in mechanosensory behavior, regulation of cell size, neuron projection morphogenesis, and plasma-membrane-bounded cell projection morphogenesis, that may provide new insights on the etiology of schizophrenia.