Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases.

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

Educational attainment polygenic score predicts inhibitory control and academic skills in early and middle childhood.

Inhibitory control skills are important for academic outcomes across childhood, but it is unknown whether inhibitory control is implicated in the association between genetic variation and academic performance. This study examined the relationship between a GWAS-based (EduYears) polygenic score indexing educational attainment (EA PGS) and inhibitory control in early (Mage  = 3.80 years) and middle childhood (Mage  = 9.18 years), and whether inhibitory control in early childhood mediated the relation between EA PGS and academic skills. The sample comprised 731 low-income and racially/ethnically diverse children and their families from the longitudinal early steps multisite study. EA PGS predicted middle childhood inhibitory control (estimate = 0.09, SE = 0.05, p < 0.05) and academic skills (estimate = 0.18, SE = 0.05, p < 0.01) but did not predict early childhood inhibitory control (estimate = 0.08, SE = 0.05, p = 0.11); thus, mediation was not tested. Sensitivity analyses showed that effect sizes were similar across European and African American groups. This study suggests that inhibitory control could serve as a potential mechanism linking genetic differences to educational outcomes.

A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.

Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.

Widespread signatures of natural selection across human complex traits and functional genomic categories.

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k-547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of ~0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.

Economic Evaluation of a New Polygenic Risk Score to Predict Nephropathy in Adult Patients With Type 2 Diabetes.

OBJECTIVES: The current screening method for diabetic nephropathy (DN) is based on detection of albumin in the urine and decline of glomerular filtration rate. The latter usually occurs relatively late in the course of the disease. A polygenic risk score (PRS) was recently developed for early prediction of the risk for patients with type 2 diabetes (T2D) to develop DN. The aim of this study was to assess the economic impact of the implementation of the PRS for early prediction of DN in patients with T2D compared with usual screening methods in Canada. METHODS: A cost-utility analysis was developed using a Markov model. Health states include pre-end-stage renal disease (ESRD), ESRD and death. Model efficacy parameters were based on prediction of outcome data by polygenic risk testing of the genotyped participants in the Action in Diabetes and Vascular Disease PreterAx and DiamicronN Controlled Evaluation trial. Analyses were conducted from Canadian health-care and societal perspectives. Deterministic and probabilistic sensitivity analyses were conducted to assess results robustness. RESULTS: Over a lifetime horizon, the PRS was a dominant strategy, from both a health-care system and societal perspective. The PRS was less expensive and more efficacious in terms of quality-adjusted life-years compared with usual screening technics. Deterministic and probabilistic sensitivity analyses showed that results remained dominant in most simulations. CONCLUSIONS: This economic evaluation demonstrates that the PRS is a dominant option compared with usual screening methods for the prevention of DN in patients with T2D. Adoption of the PRS would reduce costs saving but would also help prevent ESRD and improve patients' quality of life.

Effects of Thyroid Status on Regional Brain Volumes: A Diagnostic and Genetic Imaging Study in UK Biobank.

BACKGROUND: Thyroid hormone is essential for optimal human neurodevelopment and may modify the risk of attention-deficit/hyperactivity disorder (ADHD). However, the brain structures involved are unknown and it is unclear if the adult brain is also susceptible to changes in thyroid status. METHODS: We used International Classification of Disease-10 codes, polygenic thyroid scores at different thresholds of association with thyroid traits (PT-values), and image-derived phenotypes in UK Biobank (n = 18 825) to investigate the effects of a recorded diagnosis of thyroid disease and genetic risk for thyroid status on cerebellar and subcortical gray matter volume. Regional genetic pleiotropy between thyroid status and ADHD was explored using the GWAS-pairwise method. RESULTS: A recorded diagnosis of hypothyroidism (n = 419) was associated with significant reductions in total cerebellar and pallidum gray matter volumes (ß [95% CI] = -0.14[-0.23, -0.06], P = 0.0005 and ß [95%CI] = -0.12 [-0.20, -0.04], P = 0.0042, respectively), mediated in part by increases in body mass index. While we found no evidence for total cerebellar volume alterations with increased polygenic scores for any thyroid trait, opposing influences of increased polygenic scores for hypo- and hyperthyroidism were found in the pallidum (PT < 1e-3: ß [95% CI] = -0.02 [-0.03, -0.01], P = 0.0003 and PT < 1e-7: ß [95% CI] = 0.02 [0.01, 0.03], P = 0.0003, respectively). Neither hypo- nor hyperthyroidism showed evidence of regional genetic pleiotropy with ADHD. CONCLUSIONS: Thyroid status affects gray matter volume in adults, particularly at the level of the cerebellum and pallidum, with potential implications for the regulation of motor, cognitive, and affective function.

Extended-amygdala intrinsic functional connectivity networks: A population study.

Pre-clinical and human neuroimaging research implicates the extended-amygdala (ExtA) (including the bed nucleus of the stria terminalis [BST] and central nucleus of the amygdala [CeA]) in networks mediating negative emotional states associated with stress and substance-use behaviours. The extent to which individual ExtA structures form a functionally integrated unit is controversial. We utilised a large sample (n > 1,000 healthy young adult humans) to compare the intrinsic functional connectivity networks (ICNs) of the BST and CeA using task-free functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. We assessed whether inter-individual differences within these ICNs were related to two principal components representing negative disposition and alcohol use. Building on recent primate evidence, we tested whether within BST-CeA intrinsic functional connectivity (iFC) was heritable and further examined co-heritability with our principal components. We demonstrate the BST and CeA to have discrete, but largely overlapping ICNs similar to previous findings. We found no evidence that within BST-CeA iFC was heritable; however, post hoc analyses found significant BST iFC heritability with the broader superficial and centromedial amygdala regions. There were no significant correlations or co-heritability associations with our principal components either across the ICNs or for specific BST-Amygdala iFC. Possible differences in phenotype associations across task-free, task-based, and clinical fMRI are discussed, along with suggestions for more causal investigative paradigms that make use of the now well-established ExtA ICNs.

Large-scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease.

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological processes underlying PD using the largest currently available cohorts of genetic and gene expression data from International Parkinson's Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership-Parkinson's disease initiative (AMP-PD), among other sources. We applied large-scale gene-set specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk focusing on publicly annotated gene sets representative of curated pathways. We nominated specific molecular sub-processes underlying protein misfolding and aggregation, post-translational protein modification, immune response, membrane and intracellular trafficking, lipid and vitamin metabolism, synaptic transmission, endosomal-lysosomal dysfunction, chromatin remodeling and apoptosis mediated by caspases among the main contributors to PD etiology. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data and found evidence for a burden of rare damaging alleles in a range of processes, including neuronal transmission-related pathways and immune response. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for dopaminergic neurons, serotonergic neurons, hypothalamic GABAergic neurons, and neural progenitors. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of PD patients, which revealed functional enrichment in inflammatory signaling pathways, cell death machinery related processes, and dysregulation of mitochondrial homeostasis. Our analyses highlight several specific promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

Partitioning heritability analyses unveil the genetic architecture of human brain multidimensional functional connectivity patterns.

Resting-state functional connectivity profiles have been increasingly shown to be important endophenotypes that are tightly linked to human cognitive functions and psychiatric diseases, yet the genetic architecture of this multidimensional trait is barely understood. Using a unique sample of 1,704 unrelated, young and healthy Chinese Han individuals, we revealed a significant heritability of functional connectivity patterns in the whole brain and several subnetworks. We further proposed a partitioned heritability analysis for multidimensional functional connectivity patterns, which revealed the common and unique enrichment patterns of the genetic contributions to brain connectivity patterns for several gene sets linked to brain functions, including the genes expressed preferentially in the central nervous system and those associated with intelligence, educational attainment, attention-deficit/hyperactivity disorder, and schizophrenia. These results for the first time reveal the genetic architecture of multidimensional brain connectivity patterns across different networks and advance our understanding of the complex relationship between gene sets, neural networks, and behaviors.

Cortical Surfaces Mediate the Relationship Between Polygenic Scores for Intelligence and General Intelligence.

Recent large-scale, genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with general intelligence. The cumulative influence of these loci on brain structure is unknown. We examined if cortical morphology mediates the relationship between GWAS-derived polygenic scores for intelligence (PSi) and g-factor. Using the effect sizes from one of the largest GWAS meta-analysis on general intelligence to date, PSi were calculated among 10 P value thresholds. PSi were assessed for the association with g-factor performance, cortical thickness (CT), and surface area (SA) in two large imaging-genetics samples (IMAGEN N = 1651; IntegraMooDS N = 742). PSi explained up to 5.1% of the variance of g-factor in IMAGEN (F1,1640 = 12.2-94.3; P < 0.005), and up to 3.0% in IntegraMooDS (F1,725 = 10.0-21.0; P < 0.005). The association between polygenic scores and g-factor was partially mediated by SA and CT in prefrontal, anterior cingulate, insula, and medial temporal cortices in both samples (PFWER-corrected < 0.005). The variance explained by mediation was up to 0.75% in IMAGEN and 0.77% in IntegraMooDS. Our results provide evidence that cumulative genetic load influences g-factor via cortical structure. The consistency of our results across samples suggests that cortex morphology could be a novel potential biomarker for neurocognitive dysfunction that is among the most intractable psychiatric symptoms.

Standing genetic variation fuels rapid adaptation to ocean acidification.

Global climate change has intensified the need to assess the capacity for natural populations to adapt to abrupt shifts in the environment. Reductions in seawater pH constitute a conspicuous global change stressor that is affecting marine ecosystems globally. Here, we quantify the phenotypic and genetic modifications associated with rapid adaptation to reduced seawater pH in the Mediterranean mussel, Mytilus galloprovincialis. We reared a genetically diverse larval population in two pH treatments (pHT 8.1 and 7.4) and tracked changes in the shell-size distribution and genetic variation through settlement. Additionally, we identified differences in the signatures of selection on shell growth in each pH environment. Both phenotypic and genetic data show that standing variation can facilitate adaptation to declines in seawater pH. This work provides insight into the processes underpinning rapid evolution, and demonstrates the importance of maintaining variation within natural populations to bolster species' adaptive capacity as global change progresses.

Testing for correlation between traits under directional evolution.

Being confounding factors, directional trends are likely to make two quantitative traits appear as spuriously correlated. By determining the probability distributions of independent contrasts when traits evolve following Brownian motions with linear trends, we show that the standard independent contrasts can not be used to test for correlation in this situation. We propose a multiple regression approach which corrects the bias caused by directional evolution. We show that our approach is equivalent to performing a Phylogenetic Generalized Least Squares (PGLS) analysis with tip times as covariables by providing a new and more general proof of the equivalence between PGLS and independent contrasts methods. Our approach is assessed and compared with three previous correlation tests on data simulated in various situations and overall outperforms all the other methods. The approach is next illustrated on a real dataset to test for correlation between hominin cranial capacity and body mass.

Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression.

The functional connectivity between thalamic medio-dorsal nucleus (MD) and cortical regions, especially the dorsolateral prefrontal cortex (DLPFC), is implicated in attentional processing and is anomalous in schizophrenia, a brain disease associated with polygenic risk and attentional deficits. However, the molecular and genetic underpinnings of thalamic connectivity anomalies are unclear. Given that gene co-expression across brain areas promotes synchronous interregional activity, our aim was to investigate whether coordinated expression of genes relevant to schizophrenia in MD and DLPFC may reflect thalamic connectivity anomalies in an attention-related network including the DLPFC. With this aim, we identified in datasets of post-mortem prefrontal mRNA expression from healthy controls a gene module with robust overrepresentation of genes with coordinated MD-DLPFC expression and enriched for schizophrenia genes according to the largest genome-wide association study to date. To link this gene cluster with imaging phenotypes, we computed a Polygenic Co-Expression Index (PCI) combining single-nucleotide polymorphisms predicting module co-expression. Finally, we investigated the association between PCI and thalamic functional connectivity during attention through fMRI Independent Component Analysis in 265 healthy participants. We found that PCI was positively associated with connectivity strength of a thalamic region overlapping with the MD within an attention brain circuit. These findings identify a novel association between schizophrenia-related genes and thalamic functional connectivity. Furthermore, they highlight the association between gene expression co-regulation and brain connectivity, such that genes with coordinated MD-DLPFC expression are associated with coordinated activity between the same brain regions. We suggest that gene co-expression is a plausible mechanism underlying biological phenotypes of schizophrenia.

Polygenic scores for education, health, and personality as predictors of subjective age among older individuals of European ancestry: Evidence from the Health and Retirement Study.

The present study aimed to identify whether polygenic scores (PGSs) for education, health and psychological factors are related to subjective age in a large sample of older adults. Participants were 7,763 individuals of European ancestry (57% women, Mean age = 69.15, SD = 10.18) from the Health and Retirement Study who were genotyped and provided subjective age data. Higher PGSs for educational achievement and well-being were related to a younger subjective age, whereas higher PGSs for neuroticism, body mass index, waist circumference, and depressive symptoms were associated with an older subjective age. This study provides new evidence on the potential genetic underpinnings of subjective age. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Gene × Environment contributions to autonomic stress reactivity in youth.

Dysregulated physiological stress reactivity has been suggested to impact the development of children and adolescents with important health consequences throughout the life span. Both environmental adversity and genetic predispositions can lead to physiological imbalances in stress systems, which in turn lead to developmental differences. We investigated genetic and environmental contributions to autonomic nervous system reactivity to a psychosocial stressor. Furthermore, we tested whether these effects were consistent with the differential susceptibility framework. Composite measures of adverse life events combined with socioeconomic status were constructed. Effects of these adversity scores in interaction with a polygenic score summarizing six genetic variants, which were hypothesized to work as susceptibility factors, were tested on autonomic nervous system measures as indexed by heart rate and heart rate variability. Results showed that carriers of more genetic variants and exposed to high adversity manifested enhanced heart rate variability reactivity to a psychosocial stressor compared to carriers of fewer genetic variants. Conversely, the stress procedure elicited a more moderate response in these individuals compared to carriers of fewer variants when adversity was low.

Detecting past and ongoing natural selection among ethnically Tibetan women at high altitude in Nepal.

Adaptive evolution in humans has rarely been characterized for its whole set of components, i.e. selective pressure, adaptive phenotype, beneficial alleles and realized fitness differential. We combined approaches for detecting polygenic adaptations and for mapping the genetic bases of physiological and fertility phenotypes in approximately 1000 indigenous ethnically Tibetan women from Nepal, adapted to high altitude. The results of genome-wide association analyses and tests for polygenic adaptations showed evidence of positive selection for alleles associated with more pregnancies and live births and evidence of negative selection for those associated with higher offspring mortality. Lower hemoglobin level did not show clear evidence for polygenic adaptation, despite its strong association with an EPAS1 haplotype carrying selective sweep signals.

Alternative assembly processes from trait-mediated co-evolution in mutualistic communities.

Ecological and evolutionary dynamics observed in mutualistic communities can be shaped by several mechanisms, including ecological interactions and their co-evolutionary consequences. Here we explore how intra and interspecific competition, together with mutualistic interactions, can affect community assembly through their effects on adaptive diversification and the emergence of biodiversity. To capture both ecological and evolutionary processes simultaneously, we used the adaptive dynamics approach based on a Lotka-Volterra framework and simulated the ecological dynamics of populations as well as the evolutionary dynamics of phenotypic traits. Depending on the initial trait values, two possible alternative evolutionary regimes emerged: traits evolve towards either optimal utilization of environmental resources or maximizing the benefits from mutualistic interactions. Diversification and overall biodiversity are mostly driven by frequency-dependent competition, while mutualism plays an important role in enhancing ecosystem productivity and evolutionary stability. Because different initial trait values in a community can lead to alternative evolutionary regimes, species loss and biological invasions could not only alter ecological dynamics but also push the system onto an alternative successional climax or evolutionary end point. It thus becomes essential to clarify the past evolutionary dynamics so as to draw conclusions on key community assembly processes.

Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism.

BACKGROUND: A statistical pipeline was developed and used for determining candidate genes and candidate gene coexpression networks involved in 2 alcohol (i.e., ethanol [EtOH]) metabolism phenotypes, namely alcohol clearance and acetate area under the curve in a recombinant inbred (RI) (HXB/BXH) rat panel. The approach was also used to provide an indication of how EtOH metabolism can impact the normal function of the identified networks. METHODS: RNA was extracted from alcohol-naïve liver tissue of 30 strains of HXB/BXH RI rats. The reconstructed transcripts were quantitated, and data were used to construct gene coexpression modules and networks. A separate group of rats, comprising the same 30 strains, were injected with EtOH (2 g/kg) for measurement of blood EtOH and acetate levels. These data were used for quantitative trait loci (QTL) analysis of the rate of EtOH disappearance and circulating acetate levels. The analysis pipeline required calculation of the module eigengene values, the correction of these values with EtOH metabolism rates and acetate levels across the rat strains, and the determination of the eigengene QTLs. For a module to be considered a candidate for determining phenotype, the module eigengene values had to have significant correlation with the strain phenotypic values and the module eigengene QTLs had to overlap the phenotypic QTLs. RESULTS: Of the 658 transcript coexpression modules generated from liver RNA sequencing data, a single module satisfied all criteria for being a candidate for determining the alcohol clearance trait. This module contained 2 alcohol dehydrogenase genes, including the gene whose product was previously shown to be responsible for the majority of alcohol elimination in the rat. This module was also the only module identified as a candidate for influencing circulating acetate levels. This module was also linked to the process of generation and utilization of retinoic acid as related to the autonomous immune response. CONCLUSIONS: We propose that our analytical pipeline can successfully identify genetic regions and transcripts which predispose a particular phenotype and our analysis provides functional context for coexpression module components.

Predicting loneliness with polygenic scores of social, psychological and psychiatric traits.

Loneliness is a heritable trait that accompanies multiple disorders. The association between loneliness and mental health indices may partly be due to inherited biological factors. We constructed polygenic scores for 27 traits related to behavior, cognition and mental health and tested their prediction for self-reported loneliness in a population-based sample of 8798 Dutch individuals. Polygenic scores for major depressive disorder (MDD), schizophrenia and bipolar disorder were significantly associated with loneliness. Of the Big Five personality dimensions, polygenic scores for neuroticism and conscientiousness also significantly predicted loneliness, as did the polygenic scores for subjective well-being, tiredness and self-rated health. When including all polygenic scores simultaneously into one model, only 2 major depression polygenic scores remained as significant predictors of loneliness. When controlling only for these 2 MDD polygenic scores, only neuroticism and schizophrenia remain significant. The total variation explained by all polygenic scores collectively was 1.7%. The association between the propensity to feel lonely and the susceptibility to psychiatric disorders thus pointed to a shared genetic etiology. The predictive power of polygenic scores will increase as the power of the genome-wide association studies on which they are based increases and may lead to clinically useful polygenic scores that can inform on the genetic predisposition to loneliness and mental health.

Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.

Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P = .019) and visual memory (P = .036) and risk scores developed for verbal (P = .042) and working memory (P = .037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype-phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships.