Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

Developmental Trajectories of Irritability across the Transition to Toddlerhood: Associations with Effortful Control and Psychopathology.

Preschool-age irritability is a transdiagnostic marker of internalizing and externalizing problems. However, researchers have generally been reluctant to examine irritability within a clinically salient framework at younger ages due to some instability during the "terrible twos" period. Developmentally sensitive and dense measurements to capture intra- and inter-individual variability, as well as exploration of developmental processes that predict change, are needed. This study aimed to examine (1) the trajectories of irritability at the transition to toddlerhood (12-24 months of age) using repeated measures, (2) whether effortful control was associated with individual differences in level and growth rate of irritability, and (3) whether individual differences in the irritability trajectories were associated with later psychopathology. Families were recruited when the child was 12-18 months old (N = 333, 45.65% female). Mothers reported on their toddler's irritability at baseline and every two months until a follow-up laboratory assessment approximately one year later. Effortful control was measured at baseline. Clinical internalizing/externalizing symptoms were measured at the follow-up assessment. Hierarchical linear models revealed an increase in irritability over time, yet there was relatively little within-person variability. Effortful control was only associated with the level of irritability and not growth rate. Level of irritability was associated with internalizing, externalizing, and combined symptoms, but growth rate was not. Findings suggest intraindividual stability in irritability at the transition to toddlerhood and the possibility that screening for elevated irritability at toddler age is meaningful.

The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk.

Background: The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood. Results: We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level. Conclusion: The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.

Maternal perceived stress and infant behavior during the COVID-19 pandemic.

BACKGROUND: Maternal stress has negative consequences on infant behavioral development, and COVID-19 presented uniquely stressful situations to mothers of infants born during the pandemic. We hypothesized that mothers with higher levels of perceived stress during the pandemic would report higher levels of infant regulatory problems including crying and interrupted sleep patterns. METHODS: As part 6 sites of a longitudinal study, mothers of infants born during the pandemic completed the Perceived Stress Scale, the Brief Infant Sleep Questionnaire, and an Infant Crying survey at 6 (n = 433) and 12 (n = 344) months of infant age. RESULTS: Maternal perceived stress, which remained consistent at 6 and 12 months of infant age, was significantly positively correlated with time taken to settle infants. Although maternal perceived stress was not correlated with uninterrupted sleep length, time taken to put the infant to sleep was correlated. Perceived stress was also correlated with the amount of infant crying and fussiness reported at 6 months. CONCLUSIONS: Mothers who reported higher levels of perceived stress during the pandemic reported higher levels of regulatory problems, specifically at 6 months. Examining how varying levels of maternal stress and infant behaviors relate to overall infant developmental status over time is an important next step. IMPACT: Women giving birth during the COVID-19 pandemic who reported higher levels of stress on the Perceived Stress Scale also reported higher levels of infant fussiness and crying at 6 months old, and more disruptive sleep patterns in their infants at 6 months and 12 months old. Sleeping problems and excessive crying in infancy are two regulatory problems that are known risk factors for emotional and behavioral issues in later childhood. This paper is one of the first studies highlighting the associations between maternal stress and infant behaviors during the COVID-19 pandemic.

Single-neuron whole genome sequencing identifies increased somatic mutation burden in Alzheimer's disease related genes.

Accumulation of somatic mutations in human neurons is associated with aging and neurodegeneration. To shed light on the somatic mutational burden in Alzheimer's disease (AD) neurons and get more insight into the role of somatic mutations in AD pathogenesis, we performed single-neuron whole genome sequencing to detect genome-wide somatic mutations (single nucleotide variants (SNVs) and Indels) in 96 single prefrontal cortex neurons from 8 AD patients and 8 elderly controls. We found that the mutational burden is ∼3000 somatic mutations per neuron genome in elderly subjects. AD patients have increased somatic mutation burden in AD-related annotation categories, including AD risk genes and differentially expressed genes in AD neurons. Mutational signature analysis showed somatic SNVs (sSNVs) primarily caused by aging and oxidative DNA damage processes but no significant difference was detected between AD and controls. Additionally, functional somatic mutations identified in AD patients showed significant enrichment in several AD-related pathways, including AD pathway, Notch-signaling pathway and Calcium-signaling pathway. These findings provide genetic insights into how somatic mutations may alter the function of single neurons and exert their potential roles in the pathogenesis of AD.

Lability of prenatal stress during the COVID-19 pandemic links to negative affect in infancy.

The association between prenatal stress and children's socioemotional development is well established. The COVID-19 pandemic has been a particularly stressful period, which may impact the gestational environment. However, most studies to-date have examined prenatal stress at a single time point, potentially masking the natural variation in stress that occurs over time, especially during a time as uncertain as the pandemic. This study leveraged dense ecological momentary assessments from a prenatal randomized control trial to examine patterns of prenatal stress over a 14-week period (up to four assessments/day) in a U.S. sample of 72 mothers and infants. We first examined whether varied features of stress exposure (lability, mean, and baseline stress) differed depending on whether mothers reported on their stress before or during the pandemic. We next examined which features of stress were associated with 3-month-old infants' negative affect. We did not find differences in stress patterns before and during the pandemic. However, greater stress lability, accounting for baseline and mean stress, was associated with higher infant negative affect. These findings suggest that pathways from prenatal stress exposure to infant socioemotional development are complex, and close attention to stress patterns over time will be important for explicating these pathways.

Risperidone-induced changes in DNA methylation in peripheral blood from first-episode schizophrenia patients parallel changes in neuroimaging and cognitive phenotypes.

BACKGROUND: Second generation antipsychotics such as risperidone are first-line pharmacotherapy treatment choices for schizophrenia. However, our ability to reliably predict and monitor treatment reaction is impeded by the lack of relevant biomarkers. As a biomarker for the susceptibility of schizophrenia and clozapine treatment response, DNA methylation (DNAm) has been studied, but the impact of antipsychotics on DNAm has not been explored in drug-naïve patients. OBJECTIVE: The aim of the present study was to examine changes of DNAm after short-term antipsychotic therapy in first-episode drug-naïve schizophrenia (FES) to identify the beneficial and adverse effect of risperidone on DNAm and their relation to treatment outcome. METHODS: Thirty-eight never treated schizophrenia patients and 38 demographically matched individuals (healthy controls) were assessed at baseline and at 8-week follow-up with symptom ratings, and cognitive and functional imaging procedures, at which time a blood draw for DNAm studies was performed. During the 8-week period, patients received treatment with risperidone monotherapy. An independent data set was used as replication. RESULTS: We identified brain related pathways enriched in 4,888 FES-associated CpG sites relative to controls. Risperidone administration in patients altered DNAm in 5,979 CpG sites relative to baseline. Significant group differences in DNAm at follow-up were seen in FES patients at 6,760 CpG sites versus healthy controls. Through comparison of effect size, we found 87.54% out of the risperidone-associated changes in DNAm showed possible beneficial effect, while only 12.46% showed potential adverse effect. There were 580 DNAm sites in which changes shifted methylation levels to be indistinguishable from controls after risperidone treatment. The DNAm changes of some sites that normalized after treatment were correlated with treatment-related changes in symptom severity, spontaneous neurophysiological activity, and cognition. We replicated our results in an independent data set. CONCLUSION: The normalizing effect of risperidone monotherapy on gene DNAm, and its correlation with clinically relevant phenotypes, indicates that risperidone therapy is associated with DNAm changes that are related to changes in brain physiology, cognition and symptom severity.

Outcomes in patients hospitalized for COVID-19 among Asian, Pacific Islander, and Hispanic subgroups in the American Heart Association COVID-19 registry.

BACKGROUND: Coronavirus disease 2019 (COVID-19) data from race/ethnic subgroups remain limited, potentially masking subgroup-level heterogeneity. We evaluated differences in outcomes in Asian American/Pacific Islander (AAPI) and Hispanic/Latino subgroups compared with non-Hispanic White patients hospitalized with COVID-19. METHODS: In the American Heart Association COVID-19 registry including 105 US hospitals, mortality and major adverse cardiovascular events in adults age ≥18 years hospitalized with COVID-19 between March-November 2020 were evaluated. Race/ethnicity groups included AAPI overall and subgroups (Chinese, Asian Indian, Vietnamese, and Pacific Islander), Hispanic/Latino overall and subgroups (Mexican, Puerto Rican), compared with non-Hispanic White (NHW). RESULTS: Among 13,511 patients, 7% were identified as AAPI (of whom 17% were identified as Chinese, 9% Asian Indian, 8% Pacific Islander, and 7% Vietnamese); 35% as Hispanic (of whom 15% were identified as Mexican and 1% Puerto Rican); and 59% as NHW. Mean [SD] age at hospitalization was lower in Asian Indian (60.4 [17.4] years), Pacific Islander (49.4 [16.7] years), and Mexican patients (57.4 [16.9] years), compared with NHW patients (66.9 [17.3] years, p<0.01). Mean age at death was lower in Mexican (67.7 [15.5] years) compared with NHW patients (75.5 [13.5] years, p<0.01). No differences in odds of mortality or MACE in AAPI or Hispanic patients relative to NHW patients were observed after adjustment for age. CONCLUSIONS: Pacific Islander, Asian Indian, and Mexican patients hospitalized with COVID-19 in the AHA registry were significantly younger than NHW patients. COVID-19 infection leading to hospitalization may disproportionately burden some younger AAPI and Hispanic subgroups in the US.

Absence of coding somatic single nucleotide variants within well-known candidate genes in late-onset sporadic Alzheimer's Disease based on the analysis of multi-omics data.

Somatic mutations arise randomly or are induced by environmental factors, which may increase the risk of Alzheimer's disease (AD). Identifying somatic mutations in sporadic AD (SAD) may provide new insight of the disease. To evaluate the potential contribution of somatic single nucleotide variations (SNVs), particularly that of well-known AD-candidate genes, we investigated sequencing data sets from four platforms: whole-genome sequencing (WGS), deep whole-exome sequencing (WES) on paired brain and liver samples, RNA sequencing (RNA-seq), and single-cell whole-genome sequencing (scWGS) of brain samples from 16 AD patients and 16 non-AD individuals. We found that the average number, mean variant allele fractions (VAFs) and mutational signatures of somatic SNVs have similar distributions between AD brains and non-AD brains. We did not identify any somatic SNVs within coding regions of the APP, PSEN1, PSEN2, nor in APOE. This study shows that somatic SNVs within the coding region of AD-candidate genes are unlikely to be a common causal factor for SAD.

Sex-differential DNA methylation and associated regulation networks in human brain implicated in the sex-biased risks of psychiatric disorders.

Many psychiatric disorders are characterized by a strong sex difference, but the mechanisms behind sex-bias are not fully understood. DNA methylation plays important roles in regulating gene expression, ultimately impacting sexually different characteristics of the human brain. Most previous literature focused on DNA methylation alone without considering the regulatory network and its contribution to sex-bias of psychiatric disorders. Since DNA methylation acts in a complex regulatory network to connect genetic and environmental factors with high-order brain functions, we investigated the regulatory networks associated with different DNA methylation and assessed their contribution to the risks of psychiatric disorders. We compiled data from 1408 postmortem brain samples in 3 collections to identify sex-differentially methylated positions (DMPs) and regions (DMRs). We identified and replicated thousands of DMPs and DMRs. The DMR genes were enriched in neuronal related pathways. We extended the regulatory networks related to sex-differential methylation and psychiatric disorders by integrating methylation quantitative trait loci (meQTLs), gene expression, and protein-protein interaction data. We observed significant enrichment of sex-associated genes in psychiatric disorder-associated gene sets. We prioritized 2080 genes that were sex-biased and associated with psychiatric disorders, such as NRXN1, NRXN2, NRXN3, FDE4A, and SHANK2. These genes are enriched in synapse-related pathways and signaling pathways, suggesting that sex-differential genes of these neuronal pathways may cause the sex-bias of psychiatric disorders.

DRAMS: A tool to detect and re-align mixed-up samples for integrative studies of multi-omics data.

Studies of complex disorders benefit from integrative analyses of multiple omics data. Yet, sample mix-ups frequently occur in multi-omics studies, weakening statistical power and risking false findings. Accurately aligning sample information, genotype, and corresponding omics data is critical for integrative analyses. We developed DRAMS (https://github.com/Yi-Jiang/DRAMS) to Detect and Re-Align Mixed-up Samples to address the sample mix-up problem. It uses a logistic regression model followed by a modified topological sorting algorithm to identify the potential true IDs based on data relationships of multi-omics. According to tests using simulated data, the more types of omics data used or the smaller the proportion of mix-ups, the better that DRAMS performs. Applying DRAMS to real data from the PsychENCODE BrainGVEX project, we detected and corrected 201 (12.5% of total data generated) mix-ups. Of the 21 mix-ups involving errors of racial identity, DRAMS re-assigned all data to the correct racial group in the 1000 Genomes project. In doing so, quantitative trait loci (QTL) (FDR<0.01) increased by an average of 1.62-fold. The use of DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of the results. Even though very limited studies have multi-omics data in place, we expect such data will increase quickly with the needs of DRAMS.

The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders.

Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease. We built genome-wide coexpression networks that included microRNAs (miRNAs). We identified a coexpression network module that was differentially expressed in the brain tissue from patients compared to healthy control individuals. This module contained genes that were principally involved in glial and neural cell genesis and glial cell differentiation, and included schizophrenia risk genes carrying rare variants. This module included five miRNAs and 545 mRNAs, with six transcription factors serving as hub genes in this module. We found that the most connected transcription factor gene POU3F2, also identified on a genome-wide association study for bipolar disorder, could regulate the miRNA hsa-miR-320e and other putative target mRNAs. These regulatory relationships were replicated using PsychENCODE/BrainGVEX datasets and validated by knockdown and overexpression experiments in SH-SY5Y cells and human neural progenitor cells in vitro. Thus, we identified a brain gene expression module that was enriched for rare coding variants in genes associated with schizophrenia and that contained the putative bipolar disorder risk gene POU3F2 The transcription factor POU3F2 may be a key regulator of gene expression in this disease-associated gene coexpression module.

Positional effects revealed in Illumina methylation array and the impact on analysis.

AIM: We aimed to prove the existence of positional effects in the Illumina methylation beadchip data and to find an optimal correction method. MATERIALS & METHODS: Three HumanMethylation450, three HumanMethylation27 datasets and two EPIC datasets were analyzed. ComBat, linear regression, functional normalization and single-sample Noob were used for minimizing positional effects. The corrected results were evaluated by four methods. RESULTS: We detected 52,988 CpG loci significantly associated with sample positions, 112 remained after ComBat correction in the primary dataset. The pre- and postcorrection comparisons indicate the positional effects could alter the measured methylation values and downstream analysis results. CONCLUSION: Positional effects exist in the Illumina methylation array and may bias the analyses. Using ComBat to correct positional effects is recommended.