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.     

Pathway analysis of genome‐wide association datasets of personality traits

Although several genome‐wide association (GWA) studies of human personality have been recently published, genetic variants that are highly associated with certain personality traits remain unknown, due to difficulty reproducing results. To further investigate these genetic variants, we assessed biological pathways using GWA datasets. Pathway analysis using GWA data was performed on 1089 Korean women whose personality traits were measured with the Revised NEO Personality Inventory for the 5‐factor model of personality. A total of 1042 pathways containing 8297 genes were included in our study. Of these, 14 pathways were highly enriched with association signals that were validated in 1490 independent samples. These pathways include association of: Neuroticism with axon guidance [L1 cell adhesion molecule (L1CAM) interactions]; Extraversion with neuronal system and voltage‐gated potassium channels; Agreeableness with L1CAM interaction, neurotransmitter receptor binding and downstream transmission in postsynaptic cells; and Conscientiousness with the interferon‐gamma and platelet‐derived growth factor receptor beta polypeptide pathways. Several genes that contribute to top‐ranked pathways in this study were previously identified in GWA studies or by pathway analysis in schizophrenia or other neuropsychiatric disorders. Here we report the first pathway analysis of all five personality traits. Importantly, our analysis identified novel pathways that contribute to understanding the etiology of personality traits.     

Association analysis of candidate genes for neuropsychiatric disease: the perpetual campaign

Association studies have been proposed to identify the genetic determinants of complex neuropsychiatric traits. Although such studies of candidate genes offer great potential to identify genetic variants that contribute to the expression of psychiatric disease, no consistent associations have been identified. Studies to date have focused on candidate genes that are selected for analysis on the basis of incomplete information about gene function in the brain, therefore the majority of genes expressed in the brain have been ignored. Additionally, most genetic determinants of psychiatric disease will probably be of modest effect and therefore require association studies of large samples. As genomic technologies advance, massive genotyping of large samples should allow identification of alleles that contribute to psychopathology.     

New insights from the last decade of research in psychiatric genetics: discoveries,challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.     

Inferences of genetic architecture of bill morphology in house sparrow using a high‐density SNP array point to a polygenic basis

Understanding the genetic architecture of quantitative traits can provide insights into the mechanisms driving phenotypic evolution. Bill morphology is an ecologically important and phenotypically variable trait, which is highly heritable and closely linked to individual fitness. Thus, bill morphology traits are suitable candidates for gene mapping analyses. Previous studies have revealed several genes that may influence bill morphology, but the similarity of gene and allele effects between species and populations is unknown. Here, we develop a custom 200K SNP array and use it to examine the genetic basis of bill morphology in 1857 house sparrow individuals from a large‐scale, island metapopulation off the coast of Northern Norway. We found high genomic heritabilities for bill depth and length, which were comparable with previous pedigree estimates. Candidate gene and genomewide association analyses yielded six significant loci, four of which have previously been associated with craniofacial development. Three of these loci are involved in bone morphogenic protein (BMP) signalling, suggesting a role for BMP genes in regulating bill morphology. However, these loci individually explain a small amount of variance. In combination with results from genome partitioning analyses, this indicates that bill morphology is a polygenic trait. Any studies of eco‐evolutionary processes in bill morphology are therefore dependent on methods that can accommodate polygenic inheritance of the phenotype and molecular‐scale evolution of genetic architecture.     

The genetics of neuroticism and human values

Human values and personality have been shown to share genetic variance in twin studies. However, there is a lack of evidence about the genetic components of this association. This study examined the interplay between genes, values and personality in the case of neuroticism, because polygenic scores were available for this personality trait. First, we replicated prior evidence of a positive association between the polygenic neuroticism score (PNS) and neuroticism. Second, we found that the PNS was significantly associated with the whole human value space in a sinusoidal waveform that was consistent with Schwartz's circular model of human values. These results suggest that it is useful to consider human values in the analyses of genetic contributions to personality traits. They also pave the way for an investigation of the biological mechanisms contributing to human value orientations.     

Molecular genetics of schizophrenia: past, present and future

Schizophrenia is a severe neuropsychiatric disorder with a polygenic mode of inheritance which is also governed by non-genetic factors. Candidate genes identified on the basis of biochemical and pharmacological evidence are being tested for linkage and association studies. Neurotransmitters, especially dopamine and serotonin have been widely implicated in its etiology. Genome scan of all human chromosomes with closely spaced polymorphic markers is being used for linkage studies. The completion and availability of the first draft of Human Genome Sequence has provided a treasure-trove that can be utilized to gain insight into the so far inaccessible regions of the human genome. Significant technological advances for identification of single nucleotide polymorphisms (SNPs) and use of microarrays have further strengthened research methodologies for genetic analysis of complex traits. In this review, we summarize the evolution of schizophrenia genetics from the past to the present, current trends and future direction of research.     

Testing structural models of psychopathology at the genomic level

Genome‐wide association studies (GWAS) have revealed hundreds of genetic loci associated with the vulnerability to major psychiatric disorders, and post‐GWAS analyses have shown substantial genetic correlations among these disorders. This evidence supports the existence of a higher‐order structure of psychopathology at both the genetic and phenotypic levels. Despite recent efforts by collaborative consortia such as the Hierarchical Taxonomy of Psychopathology (HiTOP), this structure remains unclear. In this study, we tested multiple alternative structural models of psychopathology at the genomic level, using the genetic correlations among fourteen psychiatric disorders and related psychological traits estimated from GWAS summary statistics. The best‐fitting model included four correlated higher‐order factors – externalizing, internalizing, thought problems, and neurodevelopmental disorders – which showed distinct patterns of genetic correlations with external validity variables and accounted for substantial genetic variance in their constituent disorders. A bifactor model including a general factor of psychopathology as well as the four specific factors fit worse than the above model. Several model modifications were tested to explore the placement of some disorders – such as bipolar disorder, obsessive‐compulsive disorder, and eating disorders – within the broader psychopathology structure. The best‐fitting model indicated that eating disorders and obsessive‐compulsive disorder, on the one hand, and bipolar disorder and schizophrenia, on the other, load together on the same thought problems factor. These findings provide support for several of the HiTOP higher‐order dimensions and suggest a similar structure of psychopathology at the genomic and phenotypic levels.     

Genetics of anorexia nervosa: An overview of genome-wide association studies and emerging biological links

Anorexia nervosa (AN) is a complex disorder with a strong genetic component. Comorbidities are frequent and there is substantial overlap with other disorders. The lack of understanding of the molecular and neuroanatomical causes has made it difficult to develop effective treatments and it is often difficult to treat in clinical practice. Recent advances in genetics have changed our understanding of polygenic diseases, increasing the possibility of understanding better how molecular pathways are intertwined. This review synthetizes the current state of genetic research providing an overview of genome-wide association studies (GWAS) findings in AN as well as overlap with other disorders, traits, pathways, and imaging results. This paper also discusses the different putative global pathways that are contributing to the disease including the evidence for metabolic and psychiatric origin of the disease.     

Mapping the adaptive landscape of a major agricultural pathogen reveals evolutionary constraints across heterogeneous environments

The adaptive potential of pathogens in novel or heterogeneous environments underpins the risk of disease epidemics. Antagonistic pleiotropy or differential resource allocation among life-history traits can constrain pathogen adaptation. However, we lack understanding of how the genetic architecture of individual traits can generate trade-offs. Here, we report a large-scale study based on 145 global strains of the fungal wheat pathogen Zymoseptoria tritici from four continents. We measured 50 life-history traits, including virulence and reproduction on 12 different wheat hosts and growth responses to several abiotic stressors. To elucidate the genetic basis of adaptation, we used genome-wide association mapping coupled with genetic correlation analyses. We show that most traits are governed by polygenic architectures and are highly heritable suggesting that adaptation proceeds mainly through allele frequency shifts at many loci. We identified negative genetic correlations among traits related to host colonization and survival in stressful environments. Such genetic constraints indicate that pleiotropic effects could limit the pathogen’s ability to cause host damage. In contrast, adaptation to abiotic stress factors was likely facilitated by synergistic pleiotropy. Our study illustrates how comprehensive mapping of life-history trait architectures across diverse environments allows to predict evolutionary trajectories of pathogens confronted with environmental perturbations.Subject terms: Population genetics, Plant sciences, Molecular evolution, Fungi     

Genome-wide Characterization of Shared and Distinct Genetic Components that Influence Blood Lipid Levels in Ethnically Diverse Human Populations

Blood lipid concentrations are heritable risk factors associated with atherosclerosis and cardiovascular diseases. Lipid traits exhibit considerable variation among populations of distinct ancestral origin as well as between individuals within a population. We performed association analyses to identify genetic loci influencing lipid concentrations in African American and Hispanic American women in the Women’s Health Initiative SNP Health Association Resource. We validated one African-specific high-density lipoprotein cholesterol locus at CD36 as well as 14 known lipid loci that have been previously implicated in studies of European populations. Moreover, we demonstrate striking similarities in genetic architecture (loci influencing the trait, direction and magnitude of genetic effects, and proportions of phenotypic variation explained) of lipid traits across populations. In particular, we found that a disproportionate fraction of lipid variation in African Americans and Hispanic Americans can be attributed to genomic loci exhibiting statistical evidence of association in Europeans, even though the precise genes and variants remain unknown. At the same time, we found substantial allelic heterogeneity within shared loci, characterized both by population-specific rare variants and variants shared among multiple populations that occur at disparate frequencies. The allelic heterogeneity emphasizes the importance of including diverse populations in future genetic association studies of complex traits such as lipids; furthermore, the overlap in lipid loci across populations of diverse ancestral origin argues that additional knowledge can be gleaned from multiple populations.     

Using the tools of genetic epidemiology to understand sex differences in neuropsychiatric disorders

Many neuropsychiatric disorders exhibit differences in prevalence, age of onset, symptoms or course of illness between males and females. For the most part, the origins of these differences are not well understood. In this article, we provide an overview of sex differences in psychiatric disorders including autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), anxiety, depression, alcohol and substance abuse, schizophrenia, eating disorders and risk of suicide. We discuss both genetic and nongenetic mechanisms that have been hypothesized to underlie these differences, including ascertainment bias, environmental stressors, X‐ or Y‐linked risk loci, and differential liability thresholds in males and females. We then review the use of twin, family and genome‐wide association approaches to study potential genetic mechanisms of sex differences and the extent to which these designs have been employed in studies of psychiatric disorders. We describe the utility of genetic epidemiologic study designs, including classical twin and family studies, large‐scale studies of population registries, derived recurrence risks, and molecular genetic analyses of genome‐wide variation that may enhance our understanding sex differences in neuropsychiatric disorders.     

Family study designs in the age of genome-wide association studies: experience from the Framingham Heart Study

PURPOSE OF REVIEW: The past year has seen the publication of many genome-wide association studies, most of which are case-control studies. These publications are at the forefront of current research into the examination of genetic effects for numerous diseases, including diabetes, heart disease and cancer. Over the past 25 years the tour de force of genetics research has been in family studies, using segregation, linkage and association analyses. Are these approaches now passé? Here we discuss the role of family studies in modern genetics research, using results from the Framingham Heart Study as examples. RECENT FINDINGS: Family studies permit both linkage and association analyses. Importantly, family-based association tests that consider transmission of genetic variants within a family provide important information on the genetic etiology of disease traits and avoid the potential of false-positive findings due to population substructure. SUMMARY: Family-based study designs continue to contribute much to the modern era of genome-wide association studies.     

Progress and promise in understanding the genetic basis of common diseases

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.     

Heritability of cardiovascular and personality traits in 6,148 Sardinians

In family studies, phenotypic similarities between relatives yield information on the overall contribution of genes to trait variation. Large samples are important for these family studies, especially when comparing heritability between subgroups such as young and old, or males and females. We recruited a cohort of 6,148 participants, aged 14–102 y, from four clustered towns in Sardinia. The cohort includes 34,469 relative pairs. To extract genetic information, we implemented software for variance components heritability analysis, designed to handle large pedigrees, analyze multiple traits simultaneously, and model heterogeneity. Here, we report heritability analyses for 98 quantitative traits, focusing on facets of personality and cardiovascular function. We also summarize results of bivariate analyses for all pairs of traits and of heterogeneity analyses for each trait. We found a significant genetic component for every trait. On average, genetic effects explained 40% of the variance for 38 blood tests, 51% for five anthropometric measures, 25% for 20 measures of cardiovascular function, and 19% for 35 personality traits. Four traits showed significant evidence for an X-linked component. Bivariate analyses suggested overlapping genetic determinants for many traits, including multiple personality facets and several traits related to the metabolic syndrome; but we found no evidence for shared genetic determinants that might underlie the reported association of some personality traits and cardiovascular risk factors. Models allowing for heterogeneity suggested that, in this cohort, the genetic variance was typically larger in females and in younger individuals, but interesting exceptions were observed. For example, narrow heritability of blood pressure was approximately 26% in individuals more than 42 y old, but only approximately 8% in younger individuals. Despite the heterogeneity in effect sizes, the same loci appear to contribute to variance in young and old, and in males and females. In summary, we find significant evidence for heritability of many medically important traits, including cardiovascular function and personality. Evidence for heterogeneity by age and sex suggests that models allowing for these differences will be important in mapping quantitative traits.     

Sex Differences in Genetic Architecture of Complex Phenotypes?

We examined sex differences in familial resemblance for a broad range of behavioral, psychiatric and health related phenotypes (122 complex traits) in children and adults. There is a renewed interest in the importance of genotype by sex interaction in, for example, genome-wide association (GWA) studies of complex phenotypes. If different genes play a role across sex, GWA studies should consider the effect of genetic variants separately in men and women, which affects statistical power. Twin and family studies offer an opportunity to compare resemblance between opposite-sex family members to the resemblance between same-sex relatives, thereby presenting a test of quantitative and qualitative sex differences in the genetic architecture of complex traits. We analyzed data on lifestyle, personality, psychiatric disorder, health, growth, development and metabolic traits in dizygotic (DZ) same-sex and opposite-sex twins, as these siblings are perfectly matched for age and prenatal exposures. Sample size varied from slightly over 300 subjects for measures of brain function such as EEG power to over 30,000 subjects for childhood psychopathology and birth weight. For most phenotypes, sample sizes were large, with an average sample size of 9027 individuals. By testing whether the resemblance in DZ opposite-sex pairs is the same as in DZ same-sex pairs, we obtain evidence for genetic qualitative sex-differences in the genetic architecture of complex traits for 4% of phenotypes. We conclude that for most traits that were examined, the current evidence is that same the genes are operating in men and women.     

Dopamine D4 receptors: beyond schizophrenia

Dopamine D4 receptors mediate a wide range of neuronal signal transduction cascades. Malfunctions of these mechanisms may contribute to the pathophysiology of neuropsychiatric disorders, and their modification underlies the actions of many psychotropic drugs. Postmortem neuropathological and genetic studies provide inconclusive associations between D4 receptors and schizophrenia. Clinical trials of partially selective lead D4 antagonists have proved them to be ineffective against psychotic symptoms in patients diagnosed with schizophrenia. However, associations are emerging between D4 receptors and other neuropsychiatric disorders, including attention-deficit hyperactivity disorder as well as specific personality traits such as novelty seeking. Preclinical studies indicate that D4 receptors play a pivotal role in the cellular mechanisms of hyperactivity, impulsivity, and working memory. Accordingly, D4 receptors have broader implications for human illnesses than has been suggested by early focus on psychotic illness as a clinical target, and selective D4 agents may yield clinically useful drugs for several neuropsychiatric disorders that require improved treatments.     

Nature vs nurture: are leaders born or made? A behavior genetic investigation of leadership style.

With the recent resurgence in popularity of trait theories of leadership, it is timely to consider the genetic determination of the multiple factors comprising the leadership construct. Individual differences in personality traits have been found to be moderately to highly heritable, and so it follows that if there are reliable personality trait differences between leaders and non-leaders, then there may be a heritable component to these individual differences. Despite this connection between leadership and personality traits, however, there are no studies of the genetic basis of leadership using modern behavior genetic methodology. The present study proposes to address the lack of research in this area by examining the heritability of leadership style, as measured by self-report psychometric inventories. The Multifactor Leadership Questionnaire (MLQ), the Leadership Ability Evaluation, and the Adjective Checklist were completed by 247 adult twin pairs (183 monozygotic and 64 same-sex dizygotic). Results indicated that most of the leadership dimensions examined in this study are heritable, as are two higher level factors (resembling transactional and transformational leadership) derived from an obliquely rotated principal components factors analysis of the MLQ. Univariate analyses suggested that 48% of the variance in transactional leadership may be explained by additive heritability, and 59% of the variance in transformational leadership may be explained by non-additive (dominance) heritability. Multivariate analyses indicated that most of the variables studied shared substantial genetic covariance, suggesting a large overlap in the underlying genes responsible for the leadership dimensions.     

Detection of disease-associated deletions in case–control studies using SNP genotypes with application to rheumatoid arthritis

Genomic deletions have long been known to play a causative role in microdeletion syndromes. Recent whole-genome genetic studies have shown that deletions can increase the risk for several psychiatric disorders, suggesting that genomic deletions play an important role in the genetic basis of complex traits. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. Likelihood-based statistical methods for identifying disease-associated deletions have recently been developed for familial studies of parent-offspring trios. The purpose of this study is to develop statistical approaches for detecting genomic deletions associated with complex disease in case–control studies. Our methods are designed to be used with dense single nucleotide polymorphism (SNP) genotypes to detect deletions in large-scale or whole-genome genetic studies. As more and more SNP genotype data for genome-wide association studies become available, development of sophisticated statistical approaches will be needed that use these data. Our proposed statistical methods are designed to be used in SNP-by-SNP analyses and in cluster analyses based on combined evidence from multiple SNPs. We found that these methods are useful for detecting disease-associated deletions and are robust in the presence of linkage disequilibrium using simulated SNP data sets. Furthermore, we applied the proposed statistical methods to SNP genotype data of chromosome 6p for 868 rheumatoid arthritis patients and 1,197 controls from the North American Rheumatoid Arthritis Consortium. We detected disease-associated deletions within the region of human leukocyte antigen in which genomic deletions were previously discovered in rheumatoid arthritis patients.     

Neurocognitive phenotypes and genetic dissection of disorders of brain and behavior

Elucidating the molecular mechanisms underlying quantitative neurocognitive phenotypes will further our understanding of the brain's structural and functional architecture and advance the diagnosis and treatment of the psychiatric disorders that these traits underlie. Although many neurocognitive traits are highly heritable, little progress has been made in identifying genetic variants unequivocally associated with these phenotypes. A major obstacle to such progress is the difficulty in identifying heritable neurocognitive measures that are precisely defined and systematically assessed and represent unambiguous mental constructs, yet are also amenable to the high-throughput phenotyping necessary to obtain adequate power for genetic association studies. In this perspective we compare the current status of genetic investigations of neurocognitive phenotypes to that of other categories of biomedically relevant traits and suggest strategies for genetically dissecting traits that may underlie disorders of brain and behavior.