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.     

A Phylogeny-aware GWAS Framework to Correct for Heritable Pathogen Effects on Infectious Disease Traits

Infectious diseases are particularly challenging for genome-wide association studies (GWAS) because genetic effects from two organisms (pathogen and host) can influence a trait. Traditional GWAS assume individual samples are independent observations. However, pathogen effects on a trait can be heritable from donor to recipient in transmission chains. Thus, residuals in GWAS association tests for host genetic effects may not be independent due to shared pathogen ancestry. We propose a new method to estimate and remove heritable pathogen effects on a trait based on the pathogen phylogeny prior to host GWAS, thus restoring independence of samples. In simulations, we show this additional step can increase GWAS power to detect truly associated host variants when pathogen effects are highly heritable, with strong phylogenetic correlations. We applied our framework to data from two different host–pathogen systems, HIV in humans and X. arboricola in A. thaliana. In both systems, the heritability and thus phylogenetic correlations turn out to be low enough such that qualitative results of GWAS do not change when accounting for the pathogen shared ancestry through a correction step. This means that previous GWAS results applied to these two systems should not be biased due to shared pathogen ancestry. In summary, our framework provides additional information on the evolutionary dynamics of traits in pathogen populations and may improve GWAS if pathogen effects are highly phylogenetically correlated amongst individuals in a cohort.     

Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data

High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.     

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.     

Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability

Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (～1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein-protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS.     

A multilocus model of the genetic architecture of autoimmune thyroid disorder, with clinical implications

We report here a preliminary model of the genetic architecture of Autoimmune Thyroid Disorder (AITD). Using a flexible class of mathematical modeling techniques, applied to an established set of data and supplemented with information both from candidate-gene and genome-wide-association studies and from basic bioinformatics, we find strong statistical support for a model in which AITD is the result of "hits" along three distinct genetic pathways: affected individuals have (1) a genetic susceptibility to clinical AITD, along with (2) a separate predisposition to develop the autoantibodies characteristic of AITD, and they also have (3) a predisposition to develop high levels of autoantibodies once they occur. Genes underlying each of these factors then appear to interact with one another to cause clinical AITD. We also find that a genetic variant in CTLA4 that increases risk for AITD in some people might actually protect against AITD in others, depending on which additional risk variants an individual carries. Our data show that the use of statistical methods for the incorporation of information from multiple sources, combined with careful modeling of distinct intermediate phenotypes, can provide insights into the genetic architecture of complex diseases. This model has several clinical implications, which we believe will prove relevant to other complex diseases as well.     

The genetics of human personality

Personality traits are the relatively enduring patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances. Twin and family studies have showed that personality traits are moderately heritable, and can predict various lifetime outcomes, including psychopathology. The Research Domain Criteria characterizes psychiatric diseases as extremes of normal tendencies, including specific personality traits. This implies that heritable variation in personality traits, such as neuroticism, would share a common genetic basis with psychiatric diseases, such as major depressive disorder. Despite considerable efforts over the past several decades, the genetic variants that influence personality are only beginning to be identified. We review these recent and increasingly rapid developments, which focus on the assessment of personality via several commonly used personality questionnaires in healthy human subjects. Study designs covered include twin, linkage, candidate gene association studies, genome‐wide association studies and polygenic analyses. Findings from genetic studies of personality have furthered our understanding about the genetic etiology of personality, which, like neuropsychiatric diseases themselves, is highly polygenic. Polygenic analyses have showed genetic correlations between personality and psychopathology, confirming that genetic studies of personality can help to elucidate the etiology of several neuropsychiatric diseases.     

Comparison of Family History and SNPs for Predicting Risk of Complex Disease

The clinical utility of family history and genetic tests is generally well understood for simple Mendelian disorders and rare subforms of complex diseases that are directly attributable to highly penetrant genetic variants. However, little is presently known regarding the performance of these methods in situations where disease susceptibility depends on the cumulative contribution of multiple genetic factors of moderate or low penetrance. Using quantitative genetic theory, we develop a model for studying the predictive ability of family history and single nucleotide polymorphism (SNP)–based methods for assessing risk of polygenic disorders. We show that family history is most useful for highly common, heritable conditions (e.g., coronary artery disease), where it explains roughly 20%–30% of disease heritability, on par with the most successful SNP models based on associations discovered to date. In contrast, we find that for diseases of moderate or low frequency (e.g., Crohn disease) family history accounts for less than 4% of disease heritability, substantially lagging behind SNPs in almost all cases. These results indicate that, for a broad range of diseases, already identified SNP associations may be better predictors of risk than their family history–based counterparts, despite the large fraction of missing heritability that remains to be explained. Our model illustrates the difficulty of using either family history or SNPs for standalone disease prediction. On the other hand, we show that, unlike family history, SNP–based tests can reveal extreme likelihood ratios for a relatively large percentage of individuals, thus providing potentially valuable adjunctive evidence in a differential diagnosis.     

Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes

Complex diseases are often highly heritable. However, for many complex traits only a small proportion of the heritability can be explained by observed genetic variants in traditional genome-wide association (GWA) studies. Moreover, for some of those traits few significant SNPs have been identified. Single SNP association methods test for association at a single SNP, ignoring the effect of other SNPs. We show using a simple multi-locus odds model of complex disease that moderate to large effect sizes of causal variants may be estimated as relatively small effect sizes in single SNP association testing. This underestimation effect is most severe for diseases influenced by numerous risk variants. We relate the underestimation effect to the concept of non-collapsibility found in the statistics literature. As described, continuous phenotypes generated with linear genetic models are not affected by this underestimation effect. Since many GWA studies apply single SNP analysis to dichotomous phenotypes, previously reported results potentially underestimate true effect sizes, thereby impeding identification of true effect SNPs. Therefore, when a multi-locus model of disease risk is assumed, a multi SNP analysis may be more appropriate.     

The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to indirect genetic effects dominates heritable variation and response to selection

Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here, we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R₀ and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently, we investigate the population-level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.     

Identification of Medically Actionable Secondary Findings in the 1000 Genomes

The American College of Medical Genetics and Genomics (ACMG) recommends that clinical sequencing laboratories return secondary findings in 56 genes associated with medically actionable conditions. Our goal was to apply a systematic, stringent approach consistent with clinical standards to estimate the prevalence of pathogenic variants associated with such conditions using a diverse sequencing reference sample. Candidate variants in the 56 ACMG genes were selected from Phase 1 of the 1000 Genomes dataset, which contains sequencing information on 1,092 unrelated individuals from across the world. These variants were filtered using the Human Gene Mutation Database (HGMD) Professional version and defined parameters, appraised through literature review, and examined by a clinical laboratory specialist and expert physician. Over 70,000 genetic variants were extracted from the 56 genes, and filtering identified 237 variants annotated as disease causing by HGMD Professional. Literature review and expert evaluation determined that 7 of these variants were pathogenic or likely pathogenic. Furthermore, 5 additional truncating variants not listed as disease causing in HGMD Professional were identified as likely pathogenic. These 12 secondary findings are associated with diseases that could inform medical follow-up, including cancer predisposition syndromes, cardiac conditions, and familial hypercholesterolemia. The majority of the identified medically actionable findings were in individuals from the European (5/379) and Americas (4/181) ancestry groups, with fewer findings in Asian (2/286) and African (1/246) ancestry groups. Our results suggest that medically relevant secondary findings can be identified in approximately 1% (12/1092) of individuals in a diverse reference sample. As clinical sequencing laboratories continue to implement the ACMG recommendations, our results highlight that at least a small number of potentially important secondary findings can be selected for return. Our results also confirm that understudied populations will not reap proportionate benefits of genomic medicine, highlighting the need for continued research efforts on genetic diseases in these populations.     

Information requirements and regulatory approaches for heritable genetic risk assessment and risk communication

With the evolution of genetic toxicology as a scientific discipline and the formation of the Environmental Mutagen Society (EMS), much thought was given to the study of chemicals in the human environment for their mutagenic effects. The Society's goal was to promote scientific investigation and dissemination of information related to genetic toxicology. Subsequently, the concern for chemically induced genetic damage in human germ cells and its potential impact on genetic diseases was detailed in the Committee 17 Report (1975). With new information on the involvement of genetic alterations in disease and on the ramifications of possible effects of exposures to environmental mutagens, it is becoming increasingly necessary to again focus our attention on the assessment of heritable genetic effects. Clearly, strategies for communication of genetic hazard/risk assessments to exposed individuals and to those charged with regulating environmental agents need to be developed.     

Evolutionary meta-analysis of association studies reveals ancient constraints affecting disease marker discovery

Genome-wide disease association studies contrast genetic variation between disease cohorts and healthy populations to discover single nucleotide polymorphisms (SNPs) and other genetic markers revealing underlying genetic architectures of human diseases. Despite scores of efforts over the past decade, many reproducible genetic variants that explain substantial proportions of the heritable risk of common human diseases remain undiscovered. We have conducted a multispecies genomic analysis of 5,831 putative human risk variants for more than 230 disease phenotypes reported in 2,021 studies. We find that the current approaches show a propensity for discovering disease-associated SNPs (dSNPs) at conserved genomic positions because the effect size (odds ratio) and allelic P value of genetic association of an SNP relates strongly to the evolutionary conservation of their genomic position. We propose a new measure for ranking SNPs that integrates evolutionary conservation scores and the P value (E-rank). Using published data from a large case-control study, we demonstrate that E-rank method prioritizes SNPs with a greater likelihood of bona fide and reproducible genetic disease associations, many of which may explain greater proportions of genetic variance. Therefore, long-term evolutionary histories of genomic positions offer key practical utility in reassessing data from existing disease association studies, and in the design and analysis of future studies aimed at revealing the genetic basis of common human diseases.     

Novel putative drugs and key initiating genes for neurodegenerative disease determined using network-based genetic integrative analysis

Understanding the genetic causes of neurodegenerative disease (ND) can be useful for their prevention and treatment. Among the genetic variations responsible for ND, heritable germline variants have been discovered in genome-wide association studies (GWAS), and nonheritable somatic mutations have been discovered in sequencing projects. Distinguishing the important initiating genes in ND and comparing the importance of heritable and nonheritable genetic variants for treating ND are important challenges. In this study, we analysed GWAS results, somatic mutations and drug targets of ND from large databanks by performing directed network-based analysis considering a randomised network hypothesis testing procedure. A disease-associated biological network was created in the context of the functional interactome, and the nonrandom topological characteristics of directed-edge classes were interpreted. Hierarchical network analysis indicated that drug targets tend to lie upstream of somatic mutations and germline variants. Furthermore, using directed path length information and biological explanations, we provide information on the most important genes in these created node classes and their associated drugs. Finally, we identified nine germline variants overlapping with drug targets for ND, seven somatic mutations close to drug targets from the hierarchical network analysis and six crucial genes in controlling other genes from the network analysis. Based on these findings, some drugs have been proposed for treating ND via drug repurposing. Our results provide new insights into the therapeutic actionability of GWAS results and somatic mutations for ND. The interesting properties of each node class and the existing relationships between them can broaden our knowledge of ND.     

Contribution of rare and common variants determine complex diseases—Hirschsprung disease as a model

Finding genes for complex diseases has been the goal of many genetic studies. Most of these studies have been successful by searching for genes and mutations in rare familial cases, by screening candidate genes and by performing genome wide association studies. However, only a small fraction of the total genetic risk for these complex genetic diseases can be explained by the identified mutations and associated genetic loci. In this review we focus on Hirschsprung disease (HSCR) as an example of a complex genetic disorder. We describe the genes identified in this congenital malformation and postulate that both common ‘low penetrant’ variants in combination with rare or private ‘high penetrant’ variants determine the risk on HSCR, and likely, on other complex diseases. We also discuss how new technological advances can be used to gain further insights in the genetic background of complex diseases. Finally, we outline a few steps to develop functional assays in order to determine the involvement of these variants in disease development.     

Modellvorstellungen zur Genetik multifaktorieller Krankheiten

In contrast to monogenic diseases, a straightforward genotype–phenotype relationship is unlikely for multifactorial diseases because of a number of genetic and nongenetic factors, including genetic heterogeneity, gene–gene and gene–environment interactions, and epigenetic mechanisms. As a consequence, the relative risk of particular genetic variants will generally be small, which implies that large sample sizes are required for their initial identification. No conclusions as to the frequency and diversity of the causative genetic variation can generally be drawn from the prevalence of a disease alone. Homogenization of the genetic background of the study population and the use of simple and clearly defined phenotypes together with “educated guesses” in candidate gene and gene–environment studies appear to be the most promising way to identify the genetic factors underlying multifactorial diseases. Replication of initial disease association findings, particularly for rare variants, should be carried out in populations that are genetically as similar as possible to the original population.     

Functional Analysis of Variance for Association Studies

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods – SKAT and a previously proposed method based on functional linear models (FLM), – especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.     

Genotype-based databases for variants causing rare diseases

Inherited diseases are the result of DNA sequence changes. In recessive diseases, the clinical phenotype results from the combined functional effects of variants in both copies of the gene. In some diseases there is often considerable variability of clinical presentation or disease severity, which may be predicted by the genotype. Additional effects may be triggered by environmental factors, as well as genetic modifiers which could be nucleotide polymorphisms in related genes, e.g. maternal ApoE or ABCA1 genotypes which may have an influence on the phenotype of SLOS individuals. Here we report the establishment of genotype variation databases for various rare diseases which provide individual clinical phenotypes associated with genotypes and include data about possible genetic modifiers. These databases aim to be an easy public access to information on rare and private variants with clinical data, which will facilitate the interpretation of genetic variants.     

Acrylamide: a review of its genotoxicity and an assessment of heritable genetic risk

An updated review of the genotoxicity studies with acrylamide is provided. Then, using data from the studies generating quantitative information concerning heritability of genetic effects, an assessment of the heritable genetic risk presented by acrylamide is presented. The review offers a discussion of the reactions and possible mechanisms of genotoxic action by acrylamide and its epoxide metabolite glycidamide. Several genetic risk approaches are discussed, including the parallelogram, direct (actually a modified direct), and doubling dose approaches. Using data from the specific-locus and heritable translocation assays, the modified direct and doubling dose approaches are utilized to quantitate genetic risk. Exposures of male parents to acrylamide via inhalation, ingestion, and dermal routes are also quantitated. With these approaches and measurements and their underlying assumptions concerning extrapolation factors (including germ cell stage specificity, DNA repair variability, locus specificity), number of human loci associated with dominant disease alleles, and spontaneous mutation rates, an assessment of heritable genetic risk for humans is calculated for the three exposure scenarios. The calculated estimates for offspring from fathers exposed to acrylamide via drinking water are up to three offspring potentially affected with induced genetic disease per 10⁸ offspring. Estimates for inhalation or dermal exposures suggest higher risks for induced genetic disease in offspring from fathers exposed in occupational settings.