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"Smoller, Jordan W."
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The other side of normal : how biology is providing the clues to unlock the secrets of normal and abnormal behavior
\"Brilliant Harvard psychiatrist Jordan Smoller reveals the hidden side of our everyday behaviors and addresses one of society's most enduring questions: What do we mean by \"normal?\" And what does this tell us about \"abnormal?\"\"--Provided by publisher.
Book
The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders
Research into the causes of psychopathology has largely focused on two broad etiologic factors: genetic vulnerability and environmental stressors. An important role for familial/heritable factors in the etiology of a broad range of psychiatric disorders was established well before the modern era of genomic research. This review focuses on the genetic basis of three disorder categories-posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and the anxiety disorders-for which environmental stressors and stress responses are understood to be central to pathogenesis. Each of these disorders aggregates in families and is moderately heritable. More recently, molecular genetic approaches, including genome-wide studies of genetic variation, have been applied to identify specific risk variants. In this review, I summarize evidence for genetic contributions to PTSD, MDD, and the anxiety disorders including genetic epidemiology, the role of common genetic variation, the role of rare and structural variation, and the role of gene-environment interaction. Available data suggest that stress-related disorders are highly complex and polygenic and, despite substantial progress in other areas of psychiatric genetics, few risk loci have been identified for these disorders. Progress in this area will likely require analysis of much larger sample sizes than have been reported to date. The phenotypic complexity and genetic overlap among these disorders present further challenges. The review concludes with a discussion of prospects for clinical translation of genetic findings and future directions for research.
Journal Article
Polygenic prediction via Bayesian regression and continuous shrinkage priors
Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.
Polygenic risk scores (PRS) have the potential to predict complex diseases and traits from genetic data. Here, Ge et al. develop PRS-CS which uses a Bayesian regression framework, continuous shrinkage (CS) priors and an external LD reference panel for polygenic prediction of binary and quantitative traits from GWAS summary statistics.
Journal Article
Psychiatric genetics and the structure of psychopathology
For over a century, psychiatric disorders have been defined by expert opinion and clinical observation. The modern DSM has relied on a consensus of experts to define categorical syndromes based on clusters of symptoms and signs, and, to some extent, external validators, such as longitudinal course and response to treatment. In the absence of an established etiology, psychiatry has struggled to validate these descriptive syndromes, and to define the boundaries between disorders and between normal and pathologic variation. Recent advances in genomic research, coupled with large-scale collaborative efforts like the Psychiatric Genomics Consortium, have identified hundreds of common and rare genetic variations that contribute to a range of neuropsychiatric disorders. At the same time, they have begun to address deeper questions about the structure and classification of mental disorders: To what extent do genetic findings support or challenge our clinical nosology? Are there genetic boundaries between psychiatric and neurologic illness? Do the data support a boundary between disorder and normal variation? Is it possible to envision a nosology based on genetically informed disease mechanisms? This review provides an overview of conceptual issues and genetic findings that bear on the relationships among and boundaries between psychiatric disorders and other conditions. We highlight implications for the evolving classification of psychopathology and the challenges for clinical translation.
Journal Article
AI-assisted prediction of differential response to antidepressant classes using electronic health records
Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.
Journal Article
Mammalian brain glycoproteins exhibit diminished glycan complexity compared to other tissues
Glycosylation is essential to brain development and function, but prior studies have often been limited to a single analytical technique and excluded region- and sex-specific analyses. Here, using several methodologies, we analyze Asn-linked and Ser/Thr/Tyr-linked protein glycosylation between brain regions and sexes in mice. Brain N-glycans are less complex in sequence and variety compared to other tissues, consisting predominantly of high-mannose and fucosylated/bisected structures. Most brain O-glycans are unbranched, sialylated O-GalNAc and O-mannose structures. A consistent pattern is observed between regions, and sex differences are minimal compared to those in plasma. Brain glycans correlate with RNA expression of their synthetic enzymes, and analysis of glycosylation genes in humans show a global downregulation in the brain compared to other tissues. We hypothesize that this restricted repertoire of protein glycans arises from their tight regulation in the brain. These results provide a roadmap for future studies of glycosylation in neurodevelopment and disease.
Protein glycosylation is critical in brain development and disease. Here, the authors characterize brain glycans in detail, showing that they are simpler and more homogenous than glycans from other tissues and providing a basis for future studies of brain glycosylation.
Journal Article
Childhood Adversity Is Associated with Adult Theory of Mind and Social Affiliation, but Not Face Processing
People vary substantially in their ability to acquire and maintain social ties. Here, we use a combined epidemiological and individual differences approach to understand the childhood roots of adult social cognitive functioning. We assessed exposure to 25 forms of traumatic childhood experiences in over 5000 adults, along with measures of face discrimination, face memory, theory of mind, social motivation, and social support. Retrospectively-reported experiences of parental maltreatment in childhood (particularly physical abuse) were the most broadly and robustly associated with adult variations in theory of mind, social motivation, and social support. Adult variations in face discrimination and face memory, on the other hand, were not significantly associated with exposure to childhood adversity. Our findings indicate domains of social cognition that may be particularly vulnerable to the effects of adverse childhood environments, and suggest mechanisms whereby environmental factors might influence the development of social abilities.
Journal Article
Genome-wide polygenic score to predict chronic kidney disease across ancestries
Chronic kidney disease (CKD) is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has not been optimized for ancestrally diverse populations. By combining
APOL1
risk genotypes with genome-wide association studies (GWAS) of kidney function, we designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (
n
= 97,050), 6 cohorts of African ancestry (
n
= 14,544), 4 cohorts of Asian ancestry (
n
= 8,625) and 2 admixed Latinx cohorts (
n
= 3,625). We demonstrated score transferability with reproducible performance across all tested cohorts. The top 2% of the GPS was associated with nearly threefold increased risk of CKD across ancestries. In African ancestry cohorts, the
APOL1
risk genotype and polygenic component of the GPS had additive effects on the risk of CKD.
A new study generated and optimized a polygenic score for chronic kidney disease with reproducible performance across 15 cohorts of different ancestries, and identified potentially clinically relevant thresholds with predicted effects comparable to having a family history of the disease.
Journal Article
Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis
Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) and parsimoniously define five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. We validate these factors by demonstrating the generalizability of the model to a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that broad aspects of cognitive function are associated with a general factor for SA and that psychiatric associations are null. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex.
The current study identifies five genomic subclusters of brain regions for cortical thickness and surface area characterized by high levels of shared genetic signal. These subclusters map onto biological and functional parcellations of the cortex.
Journal Article
Investigating the relationship between depression and breast cancer: observational and genetic analyses
Background
Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity and mortality among women, and previous studies have observed a potential depression-BC link. We aimed to comprehensively characterize the phenotypic and genetic relationships between depression and BC.
Methods
We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank (
N
= 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest genome-wide association study of European individuals conducted for depression (
N
= 500,199), BC (
N
= 247,173), and its subtypes based on the status of estrogen receptor (ER + :
N
= 175,475; ER − :
N
= 127,442).
Results
Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10, 95%CIs = 0.95–1.26). A positive genetic correlation between depression and overall BC was observed (
r
g
= 0.08,
P
= 3.00 × 10
–4
), consistent across ER + (
r
g
= 0.06,
P
= 6.30 × 10
–3
) and ER − subtypes (
r
g
= 0.08,
P
= 7.20 × 10
–3
). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and four loci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC. TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression was causally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04–1.19), but risk of BC was not causally associated with risk of depression.
Conclusions
Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationship between depression and BC, highlighting a biological link underlying the observed phenotypic relationship; these findings may provide important implications for future studies aimed reducing BC risk.
Journal Article