Genome-wide association studies: a primer

A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

[Genetic studies on forensic-psychiatric inpatients? : Clinical, ethical and legal considerations]

BACKGROUND

Research on people deprived of liberty raises serious questions, especially concerning behavioral genetic studies.

QUESTION

Does including criminally detained patients with mental disorders in genetic studies lead to a gain of new knowledge and can this be ethically and legally justified?

METHOD

Evaluation of existing literature and interdisciplinary reflection.

RESULTS

After a review of research ethics and legal norms, we consider the benefits and risks of behavioral genetic research, taking the unique situation of test persons deprived of their liberty into account. The fundamental right to freedom of research also justifies foundational research in forensic psychiatry and psychotherapy. The possible future benefits of improving treatment plans must be weighed against the risks resulting from potential data leaks and inappropriate public reception of research results. Then we analyze possible threats to voluntary and informed consent to study participation in more detail by the ethical concept of vulnerability. Alongside problems with grasping complex issues, above all dependencies and power dynamics in the correctional system play a pivotal role. Recommendations on the ethical and legal inclusion of this study population are given.

CONCLUSION

Including criminally detained study participants can be ethically and legally justified when autonomous consent is supported by specific organizational and legal procedures and measures, for example via a clear professional and organizational separation of correction and research.

Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population

This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.

Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control - the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering - adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies.

METHODS

This cross-sectional and correlational study recruited 650 adults (330 males) aged 18-69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online.

RESULTS

Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor.

CONCLUSIONS

Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.

Contrasting genes conferring short- and long-term biofilm adaptation in Listeria

Listeria monocytogenes is an opportunistic food-borne bacterium that is capable of infecting humans with high rates of hospitalization and mortality. Natural populations are genotypically and phenotypically variable, with some lineages being responsible for most human infections. The success of L. monocytogenes is linked to its capacity to persist on food and in the environment. Biofilms are an important feature that allow these bacteria to persist and infect humans, so understanding the genetic basis of biofilm formation is key to understanding transmission. We sought to investigate the biofilm-forming ability of L. monocytogenes by identifying genetic variation that underlies biofilm formation in natural populations using genome-wide association studies (GWAS). Changes in gene expression of specific strains during biofilm formation were then investigated using RNA sequencing (RNA-seq). Genetic variation associated with enhanced biofilm formation was identified in 273 genes by GWAS and differential expression in 220 genes by RNA-seq. Statistical analyses show that the number of overlapping genes flagged by either type of experiment is less than expected by random sampling. This novel finding is consistent with an evolutionary scenario where rapid adaptation is driven by variation in gene expression of pioneer genes, and this is followed by slower adaptation driven by nucleotide changes within the core genome.

Analysis of convergence of linkage and association studies in autism spectrum disorders

Autism spectrum disorder (ASD) is a clinically and genetically heterogeneous group of pervasive neurodevelopmental disorders with a strong hereditary component. Although genome-wide linkage studies (GWLS) and [genome-wide association studies (GWAS)] have previously identified hundreds of ASD risk gene loci, the results remain inconclusive. In this study, a genomic convergence approach of GWAS and GWLS for ASD was implemented for the first time in order to identify genomic loci supported by both methods. A database with 32 GWLS and five GWAS for ASD was created. Convergence was quantified as the proportion of significant GWAS markers located within linked regions. Convergence was not found to be significantly higher than expected by chance (z-test = 1,177, P = 0,239). Although convergence is supportive of genuine effects, the lack of agreement between GWLS and GWAS is also indicative that these studies are designed to answer different questions and are not equally well suited for deciphering the genetics of complex traits.

The Benefits, Barriers, and Risks of Big-Team Science

Progress in psychology has been frustrated by challenges concerning replicability, generalizability, strategy selection, inferential reproducibility, and computational reproducibility. Although often discussed separately, these five challenges may share a common cause: insufficient investment of intellectual and nonintellectual resources into the typical psychology study. We suggest that the emerging emphasis on big-team science can help address these challenges by allowing researchers to pool their resources together to increase the amount available for a single study. However, the current incentives, infrastructure, and institutions in academic science have all developed under the assumption that science is conducted by solo principal investigators and their dependent trainees, an assumption that creates barriers to sustainable big-team science. We also anticipate that big-team science carries unique risks, such as the potential for big-team-science organizations to be co-opted by unaccountable leaders, become overly conservative, and make mistakes at a grand scale. Big-team-science organizations must also acquire personnel who are properly compensated and have clear roles. Not doing so raises risks related to mismanagement and a lack of financial sustainability. If researchers can manage its unique barriers and risks, big-team science has the potential to spur great progress in psychology and beyond.

Building causal knowledge in behavior genetics

Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes such as educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects (ATEs) from randomized controlled trials (RCTs). Both ATEs from RCTs and within-family genetic effects are shallow causes: They operate within intricate causal systems (non-unitary), produce heterogeneous effects across individuals (non-uniform), and are not mechanistically informative (non-explanatory). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects.

3D genome organization links non-coding disease-associated variants to genes

Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.

Heritability of Sleep and Its Disorders in Childhood and Adolescence

Purpose of Review

This review summarizes recent literature on the heritability of sleep and sleep disorders in childhood and adolescence. We also identify gaps in the literature and priorities for future research.

Recent Findings

Findings indicate that age, measurement method, reporter, and timing of sleep measurements can influence heritability estimates. Recent genome-wide association studies (GWAS) have identified differences in the heritability of sleep problems when ancestral differences are considered, but sample sizes are small compared to adult GWAS. Most studies focus on sleep variables in the full range rather than on disorder. Studies using objective measures of sleep typically comprised small samples.

Summary

Current evidence demonstrates a wide range of heritability estimates across sleep phenotypes in childhood and adolescence, but research in larger samples, particularly using objective sleep measures and GWAS, is needed. Further understanding of environmental mechanisms and the interaction between genes and environment is key for future research.

Technological readiness and implementation of genomic-driven precision medicine for complex diseases

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Critical review of the evidence base regarding theories conceptualising the aetiology of psychosis

A critical review of literature related to the aetiology of psychosis was conducted with specific emphasis on genetics. It was found that, although many published articles were retrieved via database searches, the format of the information was disparate in presentation leading to unnecessary inconsistences. This suggests the need for insightful collaboration by authors and standardisation of published articles to prevent academic and specialism barriers remaining as a discouragement to non-specialists wishing to access this information.

Heuristic hyperparameter optimization of deep learning models for genomic prediction

There is a growing interest among quantitative geneticists and animal breeders in the use of deep learning (DL) for genomic prediction. However, the performance of DL is affected by hyperparameters that are typically manually set by users. These hyperparameters do not simply specify the architecture of the model; they are also critical for the efficacy of the optimization and model-fitting process. To date, most DL approaches used for genomic prediction have concentrated on identifying suitable hyperparameters by exploring discrete options from a subset of the hyperparameter space. Enlarging the hyperparameter optimization search space with continuous hyperparameters is a daunting combinatorial problem. To deal with this problem, we propose using differential evolution (DE) to perform an efficient search of arbitrarily complex hyperparameter spaces in DL models, and we apply this to the specific case of genomic prediction of livestock phenotypes. This approach was evaluated on two pig and cattle datasets with real genotypes and simulated phenotypes (N = 7,539 animals and M = 48,541 markers) and one real dataset (N = 910 individuals and M = 28,916 markers). Hyperparameters were evaluated using cross-validation. We compared the predictive performance of DL models using hyperparameters optimized by DE against DL models with “best practice” hyperparameters selected from published studies and baseline DL models with randomly specified hyperparameters. Optimized models using DE showed a clear improvement in predictive performance across all three datasets. DE optimized hyperparameters also resulted in DL models with less overfitting and less variation in predictive performance over repeated retraining compared to non-optimized DL models.

GWA-based pleiotropic analysis identified potential SNPs and genes related to type 2 diabetes and obesity

Metabolic syndrome is a cluster of symptoms including excessive body fat and insulin resistance which may lead to obesity and type 2 diabetes (T2D). The physiological and pathological cross-talk between T2D and obesity is crucial and complex, meanwhile, the genetic connection between T2D and obesity is largely unknown. The purpose of this study is to identify pleiotropic SNPs and genes between these two associated conditions by applying genetic analysis incorporating pleiotropy and annotation (GPA) on two large genome-wide association studies (GWAS) data sets: a body mass index (BMI) data set containing 339,224 subjects and a T2D data set containing 110,452 subjects. In all, 5182 SNPs showed pleiotropy in both T2D and obesity. After further prioritization based on suggested local false discovery rates (FDR) by the GPA model, 2146 SNPs corresponding to 217 unique genes are significantly associated with both traits (FDR < 0.2), among which 187 are newly identified pleiotropic genes compare with original GWAS in individual traits. Subsequently, gene enrichment and pathway analyses highlighted several pleiotropic SNPs including rs849135 (FDR = 0.0002), rs2119812 (FDR = 0.0018), rs4506565 (FDR = 1.23E-08), rs1558902 (7.23E-10) and corresponding genes JAZF1, SYN2, TCF7L2, FTO which may play crucial rol5es in the etiology of both T2D and obesity. Additional evidences from expression data analysis of pleiotropic genes strongly supports that the pleiotropic genes including JAZF1 (p = 1.39E-05 and p = 2.13E-05), SYN2 (p = 5.49E-03 and p = 5.27E-04), CDKN2C (p = 1.99E-12 and p = 6.27E-11), RABGAP1 (p = 3.08E-03 and p = 7.46E-03), and UBE2E2 (p = 1.83E-04 and p = 8.22E-03) play crucial roles in both obesity and T2D pathogenesis. Pleiotropic analysis integrated with functional network identified several novel and causal SNPs and genes involved in both BMI and T2D which may be ignored in single GWAS.

Recent Progress in Germplasm Evaluation and Gene Mapping to Enable Breeding of Drought-Tolerant Wheat

There is a need to increase wheat productivity to meet the food demands of the ever-growing human population. However, accelerated development of high yielding varieties is hindered by drought, which is worsening due to climate change. In this context, germplasm diversity is central to the development of drought-tolerant wheat. Extensive collections of these genetic resources are conserved in national and international genebanks. In addition to phenotypic assessments, the use of advanced molecular techniques (e.g., genotype by sequencing) to identify quantitative trait loci (QTLs) for drought tolerance related traits is useful for genome- and marker-assisted selection based approaches. Therefore, to assist wheat breeders at a critical time, we searched the recent peer-reviewed literature (2011-current), first, to identify wheat germplasm observed to be useful genetic sources for drought tolerance, and second, to report QTLs associated with drought tolerance. Though many breeders limit the parents used in breeding programs to a familiar core collection, the results of this review show that larger germplasm collections have been sources of useful genes for drought tolerance in wheat. The review also demonstrates that QTLs for drought tolerance in wheat are associated with diverse physio-morphological traits, at different growth stages. Here, we also briefly discuss the potential of genome engineering/editing to improve drought tolerance in wheat. The use of CRISPR-Cas9 and other gene-editing technologies can be used to fine-tune the expression of genes controlling drought adaptive traits, while high throughput phenotyping (HTP) techniques can potentially accelerate the selection process. These efforts are empowered by wheat researcher consortia.

The international postpartum depression: action towards causes and treatment (PACT) consortium

The International Postpartum depression: Action towards Causes and Treatment (PACT) Consortium was founded with the overarching goal of creating an international perinatal psychiatry consortium to conduct novel investigations with large sample sizes to understand the genetic signature of perinatal mood disorders. PACT uses a collaborative and team science approach that includes investigators across 19 institutions and seven continents. The large sample sizes allow for statistically rigorous analyses to investigate perinatal psychiatric disorders, with an initial focus on postpartum depression (PPD). Our current aims are to identify clinical sub-types of PPD that contribute diagnostic heterogeneity, and to elucidate the genetic basis of PPD by conducting the first large genome-wide association study of PPD. To accomplish the latter aim, we are partnering with the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. To date, our consortium members have recruited 17,912 participants and 11,344 participants have been identified using the PPD ACT mobile app, of which 8,432 are PPD cases. Ultimately, we hope this approach will improve detection, diagnosis, and treatment of women who suffer from perinatal psychiatric disorders.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are "polygenic"-that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture-the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.

Genetic Overlap Between Alzheimer's Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes

Background: Alzheimer's disease (AD) and bipolar disorder (BIP) are complex traits influenced by numerous common genetic variants, most of which remain to be detected. Clinical and epidemiological evidence suggest that AD and BIP are related. However, it is not established if this relation is of genetic origin. Here, we applied statistical methods based on the conditional false discovery rate (FDR) framework to detect genetic overlap between AD and BIP and utilized this overlap to increase the power to identify common genetic variants associated with either or both traits. Methods: We obtained genome wide association studies data from the International Genomics of Alzheimer's Project part 1 (17,008 AD cases and 37,154 controls) and the Psychiatric Genetic Consortium Bipolar Disorder Working Group (20,352 BIP cases and 31,358 controls). We used conditional QQ-plots to assess overlap in common genetic variants between AD and BIP. We exploited the genetic overlap to re-rank test-statistics for AD and BIP and improve detection of genetic variants using the conditional FDR framework. Results: Conditional QQ-plots demonstrated a polygenic overlap between AD and BIP. Using conditional FDR, we identified one novel genomic locus associated with AD, and nine novel loci associated with BIP. Further, we identified two novel loci jointly associated with AD and BIP implicating the MARK2 gene (lead SNP rs10792421, conjunctional FDR = 0.030, same direction of effect) and the VAC14 gene (lead SNP rs11649476, conjunctional FDR = 0.022, opposite direction of effect). Conclusion: We found polygenic overlap between AD and BIP and identified novel loci for each trait and two jointly associated loci. Further studies should examine if the shared loci implicating the MARK2 and VAC14 genes could explain parts of the shared and distinct features of AD and BIP.

Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders

Mental disorders account for around one-third of disability worldwide and cause enormous personal and societal burden. Current pharmacotherapies and nonpharmacotherapies do help many patients, but there are still high rates of partial or no response, delayed effect, and unfavorable adverse effects. The current diagnostic taxonomy of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders and the International Classification of Diseases relies on presenting signs and symptoms, but does not reflect evidence from neurobiological and behavioral systems. However, in the last decades, the understanding of biological mechanisms underlying mental disorders has grown and can be used for the development of precision medicine, that is, to deliver a patient-tailored individual treatment. Precision medicine may incorporate genetic variants contributing to the mental disorder and the response to pharmacotherapies, but also consider gene ¥ environment interactions, blood-based markers, neuropsychological tests, data from electronic health records, early life adversity, stressful life events, and very proximal factors such as lifestyle, nutrition, and sport. Methods such as artificial intelligence and the underlying machine learning and deep learning approaches provide the framework to stratify patients, initiate specific tailored treatments and thus increase response rates, reduce adverse effects and medical errors. In conclusion, precision medicine uses measurable health parameters to identify individuals at risk of a mental disorder, to improve the diagnostic process and to deliver a patient-tailored treatment.

Genetic Variation in Insect Vectors: Death of Typology?

The issue of typological versus population thinking in biology is briefly introduced and defined. It is then emphasized how population thinking is most relevant and useful in vector biology. Three points are made: (1) Vectors, as they exist in nature, are genetically very heterogeneous. (2) Four examples of how this is relevant in vector biology research are presented: Understanding variation in vector competence, GWAS, identifying the origin of new introductions of invasive species, and resistance to inbreeding. (3) The existence of high levels of vector genetic heterogeneity can lead to failure of some approaches to vector control, e.g., use of insecticides and release of sterile males (SIT). On the other hand, vector genetic heterogeneity can be harnessed in a vector control program based on selection for refractoriness.

Genome-wide association studies for corneal and refractive astigmatism in UK Biobank demonstrate a shared role for myopia susceptibility loci

Previous studies have suggested that naturally occurring genetic variation contributes to the risk of astigmatism. The purpose of this investigation was to identify genetic markers associated with corneal and refractive astigmatism in a large-scale European ancestry cohort (UK Biobank) who underwent keratometry and autorefraction at an assessment centre. Genome-wide association studies for corneal and refractive astigmatism were performed in individuals of European ancestry (N = 86,335 and 88,005 respectively), with the mean corneal astigmatism or refractive astigmatism in fellow eyes analysed as a quantitative trait (dependent variable). Genetic correlation between the two traits was calculated using LD Score regression. Gene-based and gene-set tests were carried out using MAGMA. Single marker-based association tests for corneal astigmatism identified four genome-wide significant loci (P < 5 × 10^-8) near the genes ZC3H11B (1q41), LINC00340 (6p22.3), HERC2/OCA2 (15q13.1) and NPLOC4/TSPAN10 (17q25.3). Three of these loci also demonstrated genome-wide significant association with refractive astigmatism: LINC00340, HERC2/OCA2 and NPLOC4/TSPAN10. The genetic correlation between corneal and refractive astigmatism was 0.85 (standard error = 0.068, P = 1.37 × 10^-35). Here, we have undertaken the largest genome-wide association studies for corneal and refractive astigmatism to date and identified four novel loci for corneal astigmatism, two of which were also novel loci for refractive astigmatism. These loci have previously demonstrated association with axial length (ZC3H11B), myopia (NPLOC4), spherical equivalent refractive error (LINC00340) and eye colour (HERC2). The shared role of these novel candidate genes for astigmatism lends further support to the shared genetic susceptibility of myopia and astigmatism.

Genetic Variations Linked to Hepatocellular Carcinoma: Personalized Medicine Takes a Step Forward

: There are strong associations between candidate genetic polymorphisms associated with intrahepatocyte lipid droplet accumulation and the development of cirrhosis, via a common sequence in which lipid accumulation is followed by chronic inflammatory injury and fibrogenesis. Hepatocellular carcinoma (HCC) has also been linked to genetic polymorphisms associated with intracellular lipid trafficking, although, as shown in the present study, not all the candidate polymorphisms implicated in fibrogenesis are also tied to HCC. Genetic analysis may identify at-risk persons who would most benefit from modifying life-style factors linked to cirrhosis and HCC, and also persons most likely to benefit from HCC surveillance.

Genetic risk for schizophrenia and psychosis in Alzheimer disease

Psychotic symptoms, defined as the occurrence of delusions or hallucinations, are frequent in Alzheimer disease (AD), affecting ~40 to 60% of individuals with AD (AD with psychosis (AD+P)). In comparison with AD subjects without psychosis, AD+P subjects have more rapid cognitive decline and poor outcomes. Prior studies have estimated the heritability of psychosis in AD at 61%, but the underlying genetic sources of this risk are not known. We evaluated a Discovery Cohort of 2876 AD subjects with (N=1761) or without psychosis (N=1115). All subjects were genotyped using a custom genotyping array designed to evaluate single-nucleotide polymorphisms (SNPs) with evidence of genetic association with AD+P and include SNPs affecting or putatively affecting risk for schizophrenia and AD. Results were replicated in an independent cohort of 2194 AD subjects with (N=734) or without psychosis (N=1460). We found that AD+P is associated with polygenic risk for a set of novel loci and inversely associated with polygenic risk for schizophrenia. Among the biologic pathways identified by the associations of schizophrenia SNPs with AD+P are endosomal trafficking, autophagy and calcium channel signaling. To the best of our knowledge, these findings provide the first clear demonstration that AD+P is associated with common genetic variation. In addition, they provide an unbiased link between polygenic risk for schizophrenia and a lower risk of psychosis in AD. This provides an opportunity to leverage progress made in identifying the biologic effects of schizophrenia alleles to identify novel mechanisms protecting against more rapid cognitive decline and psychosis risk in AD.

Psychiatric Genomics: An Update and an Agenda

The Psychiatric Genomics Consortium (PGC) is the largest consortium in the history of psychiatry. This global effort is dedicated to rapid progress and open science, and in the past decade it has delivered an increasing flow of new knowledge about the fundamental basis of common psychiatric disorders. The PGC has recently commenced a program of research designed to deliver "actionable" findings-genomic results that 1) reveal fundamental biology, 2) inform clinical practice, and 3) deliver new therapeutic targets. The central idea of the PGC is to convert the family history risk factor into biologically, clinically, and therapeutically meaningful insights. The emerging findings suggest that we are entering a phase of accelerated genetic discovery for multiple psychiatric disorders. These findings are likely to elucidate the genetic portions of these truly complex traits, and this knowledge can then be mined for its relevance for improved therapeutics and its impact on psychiatric practice within a precision medicine framework. [AJP at 175: Remembering Our Past As We Envision Our Future November 1946: The Genetic Theory of Schizophrenia Franz Kallmann's influential twin study of schizophrenia in 691 twin pairs was the largest in the field for nearly four decades. (Am J Psychiatry 1946; 103:309-322 )].

A fast and powerful W-test for pairwise epistasis testing

Epistasis plays an essential role in the development of complex diseases. Interaction methods face common challenge of seeking a balance between persistent power, model complexity, computation efficiency, and validity of identified bio-markers. We introduce a novel W-test to identify pairwise epistasis effect, which measures the distributional difference between cases and controls through a combined log odds ratio. The test is model-free, fast, and inherits a Chi-squared distribution with data adaptive degrees of freedom. No permutation is needed to obtain the P-values. Simulation studies demonstrated that the W-test is more powerful in low frequency variants environment than alternative methods, which are the Chi-squared test, logistic regression and multifactor-dimensionality reduction (MDR). In two independent real bipolar disorder genome-wide associations (GWAS) datasets, the W-test identified significant interactions pairs that can be replicated, including SLIT3-CENPN, SLIT3-TMEM132D, CNTNAP2-NDST4 and CNTCAP2-RTN4R The genes in the pairs play central roles in neurotransmission and synapse formation. A majority of the identified loci are undiscoverable by main effect and are low frequency variants. The proposed method offers a powerful alternative tool for mapping the genetic puzzle underlying complex disorders.

Anorexia nervosa

Anorexia nervosa (AN) is a psychiatric condition characterized by severe weight loss and secondary problems associated with malnutrition. AN predominantly develops in adolescence in the peripubertal period. Without early effective treatment, the course is protracted with physical, psychological and social morbidity and high mortality. Despite these effects, patients are noted to value the beliefs and behaviours that contribute to their illness rather than regarding them as problematic, which interferes with screening, prevention and early intervention. Involving the family to support interventions early in the course of the illness can produce sustained changes; however, those with a severe and/or protracted illness might require inpatient nursing support and/or outpatient psychotherapy. Prevention programmes aim to moderate the overvaluation of 'thinness' and body dissatisfaction as one of the proximal risk factors. The low prevalence of AN limits the ability to identify risk factors and to study the timing and sex distribution of the condition. However, genetic profiles, premorbid features, and brain structures and functions of patients with AN show similarities with other psychiatric disorders and contrast with obesity and metabolic disorders. Such studies are informing approaches to address the neuroadaptation to starvation and the other various physical and psychosocial deficits associated with AN. This Primer describes the epidemiology, diagnosis, screening and prevention, aetiology, treatment and quality of life of patients with AN.

Decoding Advances in Psychiatric Genetics: A Focus on Neural Circuits in Rodent Models

Appropriately powered genome-wide association studies combined with deep-sequencing technologies offer the prospect of real progress in revealing the complex biological underpinnings of schizophrenia and other psychiatric disorders. Meanwhile, recent developments in genome engineering, including CRISPR, constitute better tools to move forward with investigating these genetic leads. This review aims to assess how these advances can inform the development of animal models for psychiatric disease, with a focus on schizophrenia and in vivo electrophysiological circuit-level measures with high potential as disease biomarkers.

Quality control, imputation and analysis of genome-wide genotyping data from the Illumina HumanCoreExome microarray

The decreasing cost of performing genome-wide association studies has made genomics widely accessible. However, there is a paucity of guidance for best practice in conducting such analyses. For the results of a study to be valid and replicable, multiple biases must be addressed in the course of data preparation and analysis. In addition, standardizing methods across small, independent studies would increase comparability and the potential for effective meta-analysis. This article provides a discussion of important aspects of quality control, imputation and analysis of genome-wide data from a low-coverage microarray, as well as a straight-forward guide to performing a genome-wide association study. A detailed protocol is provided online, with example scripts available at https://github.com/JoniColeman/gwas_scripts.

Genetics and Epigenetics of Eating Disorders

Eating disorders (EDs) are serious psychiatric conditions influenced by biological, psychological, and sociocultural factors. A better understanding of the genetics of these complex traits and the development of more sophisticated molecular biology tools have advanced our understanding of the etiology of EDs. The aim of this review is to critically evaluate the literature on the genetic research conducted on three major EDs: anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). We will first review the diagnostic criteria, clinical features, prevalence, and prognosis of AN, BN, and BED, followed by a review of family, twin, and adoption studies. We then review the history of genetic studies of EDs covering linkage analysis, candidate gene association studies, genome-wide association studies, and the study of rare variants in EDs. Our review also incorporates a translational perspective by covering animal models of ED-related phenotypes. Finally, we review the nascent field of epigenetics of EDs and a look forward to future directions for ED genetic research.

CHALLENGES FOR IMPLEMENTING A PTSD PREVENTIVE GENOMIC SEQUENCING PROGRAM IN THE U.S. MILITARY

There is growing interest in using the quickly developing field of genomics to contribute to military readiness and effectiveness. Specifically, influential military advisory panels have recommended that the U.S. military apply genomics to help treat, prevent, or minimize the risk for post-traumatic stress disorder (PTSD) among service members. This article highlights some important scientific, legal, and ethical challenges regarding the development and deployment of a preventive genomic sequencing (PGS) program to predict the risk of PTSD among military service members.

Genetic determinants of depression: recent findings and future directions

LEARNING OBJECTIVES

After participating in this activity, learners should be better able to: 1. Evaluate current evidence regarding the genetic determinants of depression 2. Assess findings from studies of gene-environment interaction 3. Identify challenges to gene discovery in depression Depression is one of the most prevalent, disabling, and costly mental health conditions in the United States and also worldwide. One promising avenue for preventing depression and informing its clinical treatment lies in uncovering the genetic and environmental determinants of the disorder as well as their interaction (G × E). The overarching goal of this review article is to translate recent findings from studies of genetic association and G × E related to depression, particularly for readers without in-depth knowledge of genetics or genetic methods. The review is organized into three major sections. In the first, we summarize what is currently known about the genetic determinants of depression, focusing on findings from genome-wide association studies (GWAS). In the second section, we review findings from studies of G × E, which seek to simultaneously examine the role of genes and exposure to specific environments or experiences in the etiology of depression. In the third section, we describe the challenges to genetic discovery in depression and promising strategies for future progress.

Candidate gene-environment interaction research: reflections and recommendations

Studying how genetic predispositions come together with environmental factors to contribute to complex behavioral outcomes has great potential for advancing the understanding of the development of psychopathology. It represents a clear theoretical advance over studying these factors in isolation. However, research at the intersection of multiple fields creates many challenges. We review several reasons why the rapidly expanding candidate gene-environment interaction (cG×E) literature should be considered with a degree of caution. We discuss lessons learned about candidate gene main effects from the evolving genetics literature and how these inform the study of cG×E. We review the importance of the measurement of the gene and environment of interest in cG×E studies. We discuss statistical concerns with modeling cG×E that are frequently overlooked. Furthermore, we review other challenges that have likely contributed to the cG×E literature being difficult to interpret, including low power and publication bias. Many of these issues are similar to other concerns about research integrity (e.g., high false-positive rates) that have received increasing attention in the social sciences. We provide recommendations for rigorous research practices for cG×E studies that we believe will advance its potential to contribute more robustly to the understanding of complex behavioral phenotypes.

Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology

Genome-wide association studies (GWASs) have become an increasingly important approach for eukaryotic geneticists, facilitating the identification of hundreds of genetic polymorphisms that are responsible for inherited diseases. Despite the relative simplicity of bacterial genomes, the application of GWASs to identify polymorphisms responsible for important bacterial phenotypes has only recently been made possible through advances in genome sequencing technologies. Bacterial GWASs are now about to come of age thanks to the availability of massive datasets, and because of the potential to bridge genomics and traditional genetic approaches that is provided by improving validation strategies. A small number of pioneering GWASs in bacteria have been published in the past 2 years, examining from 75 to more than 3,000 strains. The experimental designs have been diverse, taking advantage of different processes in bacteria for generating variation. Analysis of data from bacterial GWASs can, to some extent, be performed using software developed for eukaryotic systems, but there are important differences in genome evolution that must be considered. The greatest experimental advantage of bacterial GWASs is the potential to perform downstream validation of causality and dissection of mechanism. We review the recent advances and remaining challenges in this field and propose strategies to improve the validation of bacterial GWASs.

Large-scale genomics unveils the genetic architecture of psychiatric disorders

Family study results are consistent with genetic effects making substantial contributions to risk of psychiatric disorders such as schizophrenia, yet robust identification of specific genetic variants that explain variation in population risk had been disappointing until the advent of technologies that assay the entire genome in large samples. We highlight recent progress that has led to a better understanding of the number of risk variants in the population and the interaction of allele frequency and effect size. The emerging genetic architecture implies a large number of contributing loci (that is, a high genome-wide mutational target) and suggests that genetic risk of psychiatric disorders involves the combined effects of many common variants of small effect, as well as rare and de novo variants of large effect. The capture of a substantial proportion of genetic risk facilitates new study designs to investigate the combined effects of genes and the environment.

GWAS and drug targets

BACKGROUND

Genome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may be leveraged by network information to discover known and new drug targets.

RESULTS

We find that only a very small fraction of drug targets are directly detected in the relevant GWAS studies. We investigate two possible explanations for this observation. First, we find evidence of negative selection acting on drug target genes as a consequence of strong coupling with the disease phenotype, so reducing the incidence of SNPs linked to the disease. Second, we find that GWAS genes are substantially longer on average than drug targets and than all genes, suggesting there is a length related bias in GWAS results. In spite of the low direct relationship between drug targets and GWAS reported genes, we found these two sets of genes are closely coupled in the human protein network. As a consequence, machine-learning methods are able to recover known drug targets based on network context and the set of GWAS reported genes for the same disease. We show the approach is potentially useful for identifying drug repurposing opportunities.

CONCLUSIONS

Although GWA studies do not directly identify most existing drug targets, there are several reasons to expect that new targets will nevertheless be discovered using these data. Initial results on drug repurposing studies using network analysis are encouraging and suggest directions for future development.

The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases

Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of “non-cases”) reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.

Overview of Behavioral Genetics Research for Family Researchers

This article provides an overview of the methods, assumptions, and key findings of behavioral genetics methodology for family researchers with a limited background. We discuss how family researchers can utilize and contribute to the behavioral genetics field, particularly in terms of conducting research that seeks to explain shared environmental effects. This can be done, in part, by theoretically controlling for genetic confounds in research that seeks to determine cause-and-effect relationships among family variables and individual outcomes. Gene-environment correlation and interaction are especially promising areas for the family researcher to address. Given the methodological advancements in the field, we also briefly comment on new methods in molecular genetics for studying psychological mental health disorders.

Genetics of bipolar disorder

Studies of families and twins show the importance of genetic factors affecting susceptibility to bipolar disorder and suggest substantial genetic and phenotypic complexity. Robust and replicable genome-wide significant associations have recently been reported in genome-wide association studies at several common polymorphisms, including variants within the genes CACNA1C, ODZ4, and NCAN. Strong evidence exists for a polygenic contribution to risk (ie, many risk alleles of small effect). A notable finding is the overlap of susceptibility between bipolar disorder and schizophrenia for several individual risk alleles and for the polygenic risk. By contrast, genomic structural variation seems to play a smaller part in bipolar disorder than it does in schizophrenia. Together, these genetic findings suggest directions for future studies to delineate the aetiology and pathogenesis of bipolar disorder, indicate the need to re-evaluate our diagnostic classifications, and might eventually pave the way for major improvements in clinical management.

Genes and environments in schizophrenia: The different pieces of a manifold puzzle

Genetic research targeting schizophrenia has undergone tremendous development during recent years. Supported by recently developed high-throughput genotyping technologies, both rare and common genetic variants have been identified that show consistent association with schizophrenia. These results have been replicated by independent studies and refined in meta-analyses. The genetic variation uncovered consists of common alleles, i.e. single nucleotide polymorphisms (SNPs) conveying small effects (odds ratios below 1.1) on disease risk. The source of rare variants is copy number variations (CNVs), only detectable in a small proportion of patients (3-5% for all known CNVs) with schizophrenia, furthermore extremely rare de novo mutations captured by next generation sequencing, the most recent technological advancement in the field. Despite these findings, the search for the genetic architecture underlying schizophrenia continues since these variants explain only a small proportion of the overall phenotypic variance. Gene-environment interactions provide a compelling model for resolving this paradox and interpreting the risk factors of schizophrenia. Epidemiologically proven risk factors, such as prenatal infection, obstetric complications, urbanicity, cannabis, and trauma have been demonstrated to interact with genetic risk, giving rise to higher prevalence rates or more severe symptomatology in individuals with direct or indirect genetic predisposition for schizophrenia. Further research will have to explain how the different forms of genetic variation interact and how environmental factors modulate their effects. Moreover, the challenging question lying ahead of us is how genetic and environmental factors translate to molecular disease pathways. New approaches, including animal studies and in vitro disease modeling, as well as innovative real-world environment assessment methods, will help to understand the complex etiology of schizophrenia.

TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.

The genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS methods are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score, which frequently results in a considerable loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. We present a new multivariate method called TATES (Trait-based Association Test that uses Extended Simes procedure). Extensive simulations show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests of composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES uncovers both genetic variants that are common to multiple phenotypes as well as phenotype specific variants. TATES thus provides a more complete view of the genetic architecture of complex traits and constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants.

The evolution of nutrition research

"The doctor of the future will no longer treat the human frame with drugs, but will rather cure and prevent disease with nutrition". Thomas Edison's contemplation may come to fruition if the nutritional revolution continues in its current course. Two realizations have propelled the world into a new age of personalized nutrition: (i) food can provide benefits beyond its intrinsic nutrient content, and (ii) we are not all created equal in our ability to realize to these benefits. Nutrigenomics is concerned with delineating genomic propensities to respond to various nutritional stimuli and the resulting impact on individual health. This review will examine the current technologies utilized by nutrigeneticists, the available literature regarding nutrient-gene interactions, and the translation of this new awareness into public health.

Genome-wide association studies in psychiatry: what have we learned?

Genome-wide association studies (GWAS) have been the focus of considerable effort in psychiatry. These efforts have markedly increased knowledge of the genetic basis of psychiatric disorders, and yielded empirical data on genetic architecture critical to addressing long-standing debates in the field. There is a now a clear path to increased knowledge of the 'parts lists' for these disorders.

Molecular genomic research designs

Genetic and genomic research approaches have the capability to expand our understanding of the complex pathophysiology of disease susceptibility, susceptibility to complications related to disease, trajectory of recovery from acquired injuries and infections, patient response to interventions and therapeutics, as well as informing diagnoses and prognoses. Nurse scientists are actively involved in all of these fields of inquiry, and the goal of this chapter is to assist with incorporation of genetic and genomic trajectories into their research and facilitate the design and execution of these studies. New studies that are going to embark on recruitment, phenotyping, and sample collection will benefit from forethought about research design to ensure that it addresses the research questions or hypotheses being tested. Studies that will use existing data or samples will also benefit from forethought about research design for the same reason but will also address the fact that some designs may not be feasible with the available data or samples. This chapter discusses candidate gene association, genome-wide association, candidate gene expression, global gene expression, and epigenetic/epigenomic study designs. Information provided includes rationale for selecting an appropriate study design, important methodology considerations for each design, key technologies available to accomplish each type of study, and online resources available to assist in executing each type of study design.

Genetic architectures of psychiatric disorders: the emerging picture and its implications

Psychiatric disorders are among the most intractable enigmas in medicine. In the past 5 years, there has been unprecedented progress on the genetics of many of these conditions. In this Review, we discuss the genetics of nine cardinal psychiatric disorders (namely, Alzheimer's disease, attention-deficit hyperactivity disorder, alcohol dependence, anorexia nervosa, autism spectrum disorder, bipolar disorder, major depressive disorder, nicotine dependence and schizophrenia). Empirical approaches have yielded new hypotheses about aetiology and now provide data on the often debated genetic architectures of these conditions, which have implications for future research strategies. Further study using a balanced portfolio of methods to assess multiple forms of genetic variation is likely to yield many additional new findings.

Modelling the contribution of family history and variation in single nucleotide polymorphisms to risk of schizophrenia: a Danish national birth cohort-based study

BACKGROUND

Epidemiological studies indicate that having any family member with schizophrenia increases the risk of schizophrenia in the probands. However, genome-wide association studies (GWAS) have accounted for little of this variation. The aim of this study was to use a population-based sample to explore the influence of single-nucleotide polymorphisms (SNPs) on the excess schizophrenia risk in offspring of parents with a psychotic, bipolar affective or other psychiatric disorder.

METHOD

A nested case-control study with 739 cases with schizophrenia and 800 controls. Their parents and siblings. Information from national health registers and GWAS data from the national neonatal biobank.

RESULTS

Offspring schizophrenia risk was elevated in those whose mother, father or siblings had been diagnosed with schizophrenia or related psychosis, bipolar affective disorder or any other psychiatric disorder. The rate ratio was 9.31 (3.85; 22.44) in offspring whose 1st degree relative was diagnosed with schizophrenia. This rate ranged between 8.31 and 11.34 when adjusted for each SNP individually and shrank to 8.23 (3.13; 21.64) when adjusted for 25% of the SNP-variation in candidate genes. The percentage of the excess risk associated with a family history of schizophrenia mediated through genome-wide SNP-variation ranged between -6.1%(-17.0%;2.6%) and 4.1%(-3.9%;15.2%). Analogous results were seen for each parent and for histories of bipolar affective and other psychiatric diagnoses.

CONCLUSIONS

The excess risk of schizophrenia in offspring of parents who have a psychotic, bipolar affective or other psychiatric disorder is not currently explained by the SNP variation included in this study in accordance with findings from published genetic studies.