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Human neuropsychiatric disorders are associated with genetic and environmental factors affecting the brain, which has been subjected to strong evolutionary pressures resulting in an enlarged cerebral cortex and improved cognitive performance. Thus, genes involved in human brain evolution may also play a role in neuropsychiatric disorders. We test whether genes associated with 7 neuropsychiatric phenotypes are enriched in genomic regions that have experienced rapid changes in human evolution (HARs) and importantly, whether HAR status interacts with developmental brain expression to predict associated genes. We used the most recent publicly available GWAS and gene expression data to test for enrichment of HARs, brain expression, and their interaction. These revealed significant interactions between HAR status and whole-brain expression across developmental stages, indicating that the relationship between brain expression and association with schizophrenia and intelligence is stronger among HAR than non-HAR genes. Follow-up regional analyses indicated that predicted HAR-expression interaction effects may vary substantially across regions and developmental stages. Although depression indicated significant enrichment of HAR genes, little support was found for HAR enrichment among bipolar, autism, ADHD, or Alzheimer's associated genes. Our results indicate that intelligence, schizophrenia, and depression-associated genes are enriched for those involved in the evolution of the human brain. These findings highlight promising candidates for follow-up study and considerations for novel drug development, but also caution careful assessment of the translational ability of animal models for studying neuropsychiatric traits in the context of HARs, and the importance of using humanized animal models or human-derived tissues when researching these traits.

Merging genetic risk, neurological phenotypes, and clinical presentation is a primary goal for psychiatry. Pursuing this goal, we tested association between phenotypes and overall and pathway-specific polygenic risk in patients with early-stage psychosis. Subjects included 206 demographically diverse cases with a psychotic disorder and 115 matched controls with comprehensive psychiatric and neurological phenotyping. DNA was extracted from blood and genotyped. We calculated polygenic scores (PGSs) for schizophrenia (SZ) and bipolar disorder (BP) using Psychiatric Genomics Consortium GWAS summary statistics. To dissect convergent mechanisms of symptoms, we calculated pathway PGSs (pPGSs) for SZ risk affecting each of four major neurotransmitter systems: glutamate, GABA, dopamine, and serotonin. Psychosis subjects had elevated SZ and BP PGS versus controls; cases with SZ or BP diagnoses had stronger SZ or BP risk, respectively. There was no significant association between individual symptom measures and overall PGS. However, neurotransmitter-specific pPGSs were significantly associated with specific symptoms; most notably, increased glutamatergic pPGS was associated with deficits in cognitive control and altered cortical activation during cognitive control task-based fMRI. Finally, unbiased symptom-driven clustering identified three diagnostically mixed case groups with distinct symptom profiles that separated on primary deficits of positive symptoms, negative symptoms, global functioning, and cognitive control. These clusters had specific genetic risk profiles and differential response to treatment, and outperformed diagnosis in predicting glutamate and GABA pPGS. Our findings suggest pathway-based PGS analysis may be a powerful path forward for identifying convergent mechanisms driving psychotic disorders and linking genetic risk with endophenotypes.

A randomized controlled trial tested whether mindfulness training would increase lab-based and in vivo spontaneous helping behaviors toward racial outgroup members. First, across conditions, those scoring higher in baseline trait mindfulness showed higher levels of preintervention lab-based and ecological momentary assessment (EMA)-based helping behavior. Next, short-term (4-day) training in mindfulness, relative to a well-matched sham meditation training, increased interracial helping behavior in a lab-based simulation. Finally, among people scoring lower in a basic form of trait mindfulness at baseline—that is, with greater room for improvement—mindfulness training predicted higher postintervention in vivo helping behavior reported via EMA. However, neither training condition alone attenuated preferential helping toward racial ingroup members. These findings indicate, for the first time, that mindfulness and its training fosters helping behavior toward strangers and acquaintances regardless of their racial ingroup or outgroup status, but preferential helping of racial ingroup members remains.

Neuroscientific research on pleasant touch has focused on the C-tactile pathway for gentle stroking and has successfully explained how these sensory fibers transmit information about affective social touch to the brain and induce sensations of pleasantness. The C-tactile social/affective touch hypothesis even proposes that C-tactile fibers form a privileged pathway underlying social touch. However, deep pressure is a type of touch commonly considered pleasant and calming, occurring in hugs, cuddling, and massage. In this paper we introduce a paradigm for studying pleasant deep pressure and propose that it constitutes another important form of social touch. We describe development of the oscillating compression sleeve (OCS) as one approach to administering deep pressure and demonstrate that this touch is perceived as pleasant and calming. Further, we show that deep pressure can be imaged with functional magnetic resonance imaging (MRI) using the air-pressure-driven OCS and that deep pressure activates brain regions highly similar to those that respond to C-tactile stroking, as well as regions not activated by stroking. We propose that deep pressure constitutes another social touch pathway of evolutionary importance signaling the close proximity of conspecifics.

Nonparametric maximum likelihood estimation encompasses a group of classic methods to estimate distribution-associated functions from potentially censored and truncated data, with extensive applications in survival analysis. These methods, including the Kaplan-Meier estimator and Turnbull's method, often result in overfitting, especially when the sample size is small. We propose an improvement to these methods by applying kernel smoothing to their raw estimates, based on a BIC-type loss function that balances the trade-off between optimizing model fit and controlling model complexity. In the context of a longitudinal study with repeated observations, we detail our proposed smoothing procedure and optimization algorithm. With extensive simulation studies over multiple realistic scenarios, we demonstrate that our smoothing-based procedure provides better overall accuracy in both survival function estimation and individual-level time-to-event prediction (imputation) by reducing overfitting. Our smoothing procedure decreases the bias (discrepancy between the estimated and true simulated survival function) using interval-censored data by up to 48% compared to the raw un-smoothed estimate, with similar improvements of up to 34% and 23% in within-sample and out-of-sample prediction, respectively. Our smoothing algorithm also demonstrates significant overall improvement across all three metrics when compared to a popular semiparametric B-splines estimation method. Finally, we apply our method to real data on censored breast cancer diagnosis, which similarly shows improvement when compared to empirical survival estimates from uncensored data. We provide an R package, SISE, for implementing our penalized likelihood method.

Disaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; Hwang et al. in PLoS Genet 16:e1009154, 2020) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body-mass index.

Schizophrenia is a severe neuropsychiatric disorder with core features including hallucinations, delusions, and cognition deficits. Accumulating evidence has implicated abnormal DNA methylation in the development of schizophrenia. However, the mechanisms by which DNA methylation changes alter the risk for schizophrenia remain largely unknown. We recently carried out a DNA methylome study of peripheral blood samples from 469 first-episode patients with schizophrenia and 476 age- and gender-matched healthy controls of Han Chinese origin. Genomic DNA methylation patterns were quantified using an Illumina Infinium Human MethylationEPIC BeadChip. We identified multiple differentially methylated positions (DMPs) and regions between patients and controls. The most significant DMPs were annotated to genes C17orf53, THAP1 and KCNQ4 (KV7.4), with Bonferroni-adjusted P values of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. In particular, KCNQ4 encodes a voltage-gated potassium channel of the KV7 family, which is linked to neuronal excitability. The genes associated with top-ranked DMPs also included many genes involved in nervous system development, such as LIMK2 and TMOD2. Gene ontology analysis of the differentially methylated genes further identified strong enrichment of neuronal networks, including neuron projection extension, axonogenesis and neuron apoptotic process. Finally, we provided evidence that schizophrenia-associated epigenetic alterations co-localize with genetic susceptibility loci. By focusing on first-episode schizophrenia patients, our investigation lends particularly strong support for an important role of DNA methylation in schizophrenia pathogenesis unconfounded by the effects of long-term antipsychotic medication or disease progression. The observed DNA methylation aberrations in schizophrenia patients could potentially provide a valuable resource for identifying diagnostic biomarkers and developing novel therapeutic targets to benefit schizophrenia patients.

A study examined whether trait mindfulness would increase spontaneous helping behavior toward racial outgroup members (vs ingroup members). Self-identifying White participants scoring higher in basic trait mindfulness more frequently helped both racial outgroup and ingroup members in two randomly assigned lab-based helping simulations: (1) giving one’s seat to a person on crutches or (2) aiding an experimenter in picking up dropped consent forms. Men were 3.70 times as likely to help than women. Discussion focuses on the role of individual differences in mindful attention deployment in helping.

Mendelian randomization using GWAS summary statistics has become a popular method to infer causal relationships across complex diseases. However, the widespread pleiotropy observed in GWAS has made the selection of valid instrumental variables problematic, leading to possible violations of Mendelian randomization assumptions and thus potentially invalid inferences concerning causation. Furthermore, current MR methods can examine causation in only one direction, so that two separate analyses are required for bi-directional analysis. In this study, we propose a ststistical framework, MRCI (Mixture model Reciprocal Causation Inference), to estimate reciprocal causation between two phenotypes simultaneously using the genome-scale summary statistics of the two phenotypes and reference linkage disequilibrium information. Simulation studies, including strong correlated pleiotropy, showed that MRCI obtained nearly unbiased estimates of causation in both directions, and correct Type I error rates under the null hypothesis. In applications to real GWAS data, MRCI detected significant bi-directional and uni-directional causal influences between common diseases and putative risk factors.

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Disaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; [Hwang et al., 2020][1]) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body mass index. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-9

Disaggregation and estimation of genetic effects from offspring and parents has long been of interest to statistical geneticists. Recently, technical and methodological advances have made the genome-wide and loci-specific estimation of direct offspring and parental genetic nurture effects more possible. However, unbiased estimation using these methods requires datasets where both parents and at least one child have been genotyped, which are relatively scarce. Our group has recently developed a method and accompanying software (IMPISH; Hwang et al. in PLoS Genet 16:e1009154, 2020) which is able to impute missing parental genotypes from observed data on sibships and estimate their effects on an offspring phenotype conditional on the effects of genetic transmission. However, this method is unable to disentangle maternal and paternal effects, which may differ in magnitude and direction. Here, we introduce an extension to the original IMPISH routine which takes advantage of all available nuclear families to impute parent-specific missing genotypes and obtain asymptotically unbiased estimates of genetic effects on offspring phenotypes. We apply this this method to data from related individuals in the UK Biobank, showing concordance with previous estimates of maternal genetic effects on offspring birthweight. We also conduct the first GWAS jointly estimating offspring-, maternal-, and paternal-specific genetic effects on body-mass index.

A strong body of evidence supports a role for immune dysregulation across many psychiatric disorders including depression, the leading cause of global disability. Recent progress in the search for genetic variants associated with depression provides the opportunity to strengthen our current understanding of etiological factors contributing to depression and generate novel hypotheses. Here, we provide an overview of the literature demonstrating a role for immune dysregulation in depression, followed by a detailed discussion of the immune-related genes identified by the most recent genome-wide meta-analysis of depression. These genes represent strong evidence-based targets for future basic and translational research which aims to understand the role of the immune system in depression pathology and identify novel points for therapeutic intervention., Highlights • Dysregulated immune function has been strongly linked to depression. • Recent genome-wide association studies support this relationship. • These genes represent strong candidates for etiological and translational research.

Depression is the world's leading cause of disability. Greater understanding of the neurobiological basis of depression is necessary for developing novel treatments with improved efficacy and acceptance. Recently, major advances have been made in the search for genetic variants associated with depression which may help to elucidate etiological mechanisms. The present review has two major objectives. First, we offer a brief review of two major biological systems with strong evidence for involvement in depression pathology: neurotransmitter systems and the stress response. Secondly, we provide a synthesis of the functions of the 269 genes implicated by the most recent genome-wide meta-analysis, supporting the importance of these systems in depression and providing insights into other possible mechanisms involving neurodevelopment, neurogenesis, and neurodegeneration. Our goal is to undertake a broad, preliminary stock-taking of the most recent hypothesis-free findings and examine the weight of the evidence supporting these existing theories and highlighting novel directions. This qualitative review and accompanying gene function table provides a valuable resource and guide for basic and translational researchers, with suggestions for future mechanistic research, leveraging genetics to prioritize studies on the neurobiological processes involved in depression etiology and treatment.

Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to their children. However, given the relative paucity of large-scale family-based cohorts around the world, it is difficult to demonstrate parental genetic effects on human traits, particularly at individual loci. In this manuscript, we illustrate how parental genetic effects on offspring phenotypes, including late onset conditions, can be estimated at individual loci in principle using large-scale genome-wide association study (GWAS) data, even in the absence of parental genotypes. Our strategy involves creating “virtual” mothers and fathers by estimating the genotypic dosages of parental genotypes using physically genotyped data from relative pairs. We then utilize the expected dosages of the parents, and the actual genotypes of the offspring relative pairs, to perform conditional genetic association analyses to obtain asymptotically unbiased estimates of maternal, paternal and offspring genetic effects. We apply our approach to 19066 sibling pairs from the UK Biobank and show that a polygenic score consisting of imputed parental educational attainment SNP dosages is strongly related to offspring educational attainment even after correcting for offspring genotype at the same loci. We develop a freely available web application that quantifies the power of our approach using closed form asymptotic solutions. We implement our methods in a user-friendly software package IMPISH (IMputing Parental genotypes In Siblings and Half Siblings) which allows users to quickly and efficiently impute parental genotypes across the genome in large genome-wide datasets, and then use these estimated dosages in downstream linear mixed model association analyses. We conclude that imputing parental genotypes from relative pairs may provide a useful adjunct to existing large-scale genetic studies of parents and their offspring., Author summary Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to children. Estimating indirect parental genetic effects on offspring outcomes at the genotype level has been challenging because it requires large-scale, individual level genotypes from both parents and their offspring, and there is a paucity of cohorts around the world with this information. Here we present a new approach to estimate indirect parental genetic effects without the requirement of physically genotyped parents. Our method creates virtual parental genotypes based on the genotypes of offspring pairs, and then uses these virtual genotypes in downstream genetic association analyses. We developed a software package “IMPISH” that allows users to impute virtual parental genotypes in their own genome-wide datasets and then use these in downstream genome-wide association analyses, as well a series of power calculators to estimate the power to detect indirect parental genetic effects on offspring phenotypes. We apply our method to educational attainment data from the UK Biobank and show that indirect parental genetic effects are related to offspring educational attainment even after correcting for offspring genotype at the same loci.

Indirect parental genetic effects may be defined as the influence of parental genotypes on offspring phenotypes over and above that which results from the transmission of genes from parents to children. However, given the relative paucity of large-scale family-based cohorts around the world, it is difficult to demonstrate parental genetic effects on human traits, particularly at individual loci. In this manuscript, we illustrate how parental genetic effects on offspring phenotypes, including late onset diseases, can be estimated at individual loci in principle using large-scale genome-wide association study (GWAS) data, even in the absence of parental genotypes. Our strategy involves creating “virtual” mothers and fathers by estimating the genotypic dosages of parental genotypes using physically genotyped data from relative pairs. We then utilize the expected dosages of the parents, and the actual genotypes of the offspring relative pairs, to perform conditional genetic association analyses to obtain asymptotically unbiased estimates of maternal, paternal and offspring genetic effects. We develop a freely available web application that quantifies the power of our approach using closed form asymptotic solutions. We implement our methods in a user-friendly software package IMPISH (IMputing Parental genotypes In Siblings and Half-Siblings) which allows users to quickly and efficiently impute parental genotypes across the genome in large genome-wide datasets, and then use these estimated dosages in downstream linear mixed model association analyses. We conclude that imputing parental genotypes from relative pairs may provide a useful adjunct to existing large-scale genetic studies of parents and their offspring.

We present an important characteristic of trio models which may lead to bias and loss of power when one parent is unmodeled in trio analyses. Motivated by recent interest in estimating parental effects on postnatal and later-life phenotypes, we consider a causal model where each parent has both an effect on their child's phenotype which is mediated through the genotype transmitted to the child and a direct effect on the phenotype through the parentally provided environment. We derive the power and bias of models in which one parent's genotype is not modeled, showing that while the effect of the child's genotype is biased in the direction of the unmodeled parent's effect as expected, the estimated effect of the observed parent's genotype is also biased in the opposite direction. While this phenomenon may not be intuitive under the assumption of random mating, it can be explained by intermediate confounding of the child's genotype-phenotype effect. These observations have implications for the accurate estimation of maternal and paternal effects in trio data sets with missing genotype data.

Recently, methods have been introduced using polygenic scores (PGS) to estimate the effects of genetic nurture, the environmentally-mediated effects of parental genotypes on the phenotype of their child above and beyond the effects of the alleles which are transmitted to the child. We introduce a simplified model for estimating genetic nurture effects and show, through simulation and analytical derivation, that our method provides unbiased estimates and offers an increase in power to detect genetic nurture of up to 1/3 greater than that of previous methods. Subsequently, we apply this method to data from the Avon Longitudinal Study of Parents and Children to estimate the effects of maternal genetic nurture on childhood body mass index (BMI) trajectories. Through mixed modeling, we observe a statistically significant age-dependent effect of maternal PGS on child BMI, such that the influence of maternal genetic nurture appears to increase throughout development.

The sensation of pressure allows us to feel sustained compression and body strain. While our understanding of cutaneous touch has grown significantly in recent years, how deep tissue sensations are detected remains less clear. Here, we use quantitative sensory evaluations of patients with rare sensory disorders, as well as nerve blocks in typical individuals, to probe the neural and genetic mechanisms for detecting non-painful pressure. We show that the ability to perceive innocuous pressures is lost when myelinated fiber function is experimentally blocked in healthy volunteers and that two patients lacking Aβ fibers are strikingly unable to feel innocuous pressures at all. We find that seven individuals with inherited mutations in the mechanoreceptor PIEZO2 gene, who have major deficits in touch and proprioception, are nearly as good at sensing pressure as healthy control subjects. Together, these data support a role for Aβ afferents in pressure sensation and suggest the existence of an unknown molecular pathway for its detection., The mechanisms underlying deep pressure sensing are not fully understood. Here the authors demonstrate that while two individuals lacking Aβ fibers demonstrate impaired deep pressure sensing, seven individuals with PIEZO2 loss of function mutations display normal deep pressure responses.

OBJECTIVE: To explore the relations between trauma exposure and anxiety and depression among college students, and to determine whether trait mindfulness may moderate these relations. PARTICIPANTS: Self-report survey data from 2,336 college sophomores was drawn from a larger university-wide study (“Spit for Science”). METHODS: We constructed multiple linear regression models using past-year trauma exposure, trait mindfulness, and their multiplicative interaction to predict current anxiety and depressive symptom severity, while controlling for covariates. RESULTS: Mindfulness was associated with lower levels of depression and anxiety symptom severity. Trauma was a significant predictor of anxiety, but not depression, and high levels of mindfulness attenuated the association between trauma exposure and higher anxiety symptom severity. CONCLUSIONS: These results have implications for the treatment and prevention of anxiety among trauma-exposed college students and provides a basis for further research into the mechanisms through which mindfulness may facilitate positive mental health.

Both light skin stroking (engaging the C-tactile afferent sensory pathway) and deep pressure touch (as in deep tissue massage) are commonly perceived as pleasant and relaxing. Opioid blockade has been found to increase receipt of grooming in monkeys, suggesting possible opioid modulation of touch pleasantness. Recently, human data [1] suggested that blocking opioid receptors might decrease the perceived intensity of gentle brushing but increase its pleasantness. The current study examined the role of endogenous opioids in the perceived intensity and pleasantness of pleasant touch. Using a double-blind cross-over design, 24 healthy adult participants received intravenous naloxone or saline (control) on separate days. Blocks of gentle brushing and deep pressure compression were administered on the lower left calf. Participants rated the intensity and pleasantness of each stimulus, as well as current mood and anxiety. Naloxone significantly reduced perceived intensity of gentle brushing (p = 0.039), but did not affect brushing pleasantness (p = 0.44) or intensity or pleasantness of deep pressure (p = 0.97; p = 0.85). The naloxone-induced reduction in gentle touch intensity replicates results previously seen in chronic pain patients [1]. The lack of effect on pleasantness in this study versus [1] could relate to differences in the location of touch (arm or leg), sex of the experimenters, or whether the touch followed pain tasks. However, in the current study, naloxone was marginally anxiolytic (p = 0.07), and naloxone-induced changes in touch pleasantness were significantly more correlated with subsequent mood and anxiety ratings than were changes during saline (mood p = 0.04; anxiety p