Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses

Genome Sequencing of Consanguineous Family Implicates Ubiquitin-Specific Protease 53 (USP53) Variant in Psychosis/Schizophrenia: Wild-Type Expression in Murine Hippocampal CA 1-3 and Granular Dentate with AMPA Synapse Interactions

Psychosis is a severe mental disorder characterized by abnormal thoughts and perceptions (e.g., hallucinations) occurring quintessentially in schizophrenia and in several other neuropsychiatric disorders. Schizophrenia is widely considered as a neurodevelopmental disorder that onsets during teenage/early adulthood. A multiplex consanguineous Pakistani family was afflicted with severe psychosis and apparent autosomal recessive transmission. The first-cousin parents and five children were healthy, whereas two teenage daughters were severely affected. Structured interviews confirmed the diagnosis of DSM-V schizophrenia. Probands and father underwent next-generation sequencing. All available relatives were subjected to confirmatory Sanger sequencing. Homozygosity mapping and directed a priori filtering identified only one rare variant [MAF < 5(10)-5] at a residue conserved across vertebrates. The variant was a non-catalytic deubiquitinase, USP53 (p.Cys228Arg), predicted in silico as damaging. Genome sequencing did not identify any other potentially pathogenic single nucleotide variant or structural variant. Since the literature on USP53 lacked relevance to mental illness or CNS expression, studies were conducted which revealed USP53 localization in regions of the hippocampus (CA 1-3) and granular dentate. The staining pattern was like that seen with GRIA2/GluA2 and GRIP2 antibodies. All three proteins coimmunoprecipitated. These findings support the glutamate hypothesis of schizophrenia as part of the AMPA-R interactome. If confirmed, USP53 appears to be one of the few Mendelian variants potentially causal to a common-appearing mental disorder that is a rare genetic form of schizophrenia.

The molecular pathology of schizophrenia: an overview of existing knowledge and new directions for future research

Despite enormous efforts employing various approaches, the molecular pathology in the schizophrenia brain remains elusive. On the other hand, the knowledge of the association between the disease risk and changes in the DNA sequences, in other words, our understanding of the genetic pathology of schizophrenia, has dramatically improved over the past two decades. As the consequence, now we can explain more than 20% of the liability to schizophrenia by considering all analyzable common genetic variants including those with weak or no statistically significant association. Also, a large-scale exome sequencing study identified single genes whose rare mutations substantially increase the risk for schizophrenia, of which six genes (SETD1A, CUL1, XPO7, GRIA3, GRIN2A, and RB1CC1) showed odds ratios larger than ten. Based on these findings together with the preceding discovery of copy number variants (CNVs) with similarly large effect sizes, multiple disease models with high etiological validity have been generated and analyzed. Studies of the brains of these models, as well as transcriptomic and epigenomic analyses of patient postmortem tissues, have provided new insights into the molecular pathology of schizophrenia. In this review, we overview the current knowledge acquired from these studies, their limitations, and directions for future research that may redefine schizophrenia based on biological alterations in the responsible organ rather than operationalized criteria.

Schizophrenia or schizoaffective disorder? A 50-year assessment of diagnostic stability based on a national case registry

BACKGROUND

Schizoaffective disorder (SAD) remains a controversial diagnosis in terms of necessity and reliability.

OBJECTIVES

We assessed diagnostic patterns of SAD and schizophrenia (SZ) among hospitalized psychiatric patients over a fifty-year period.

METHOD

Data from the Israeli National Psychiatric Registry on 16,341 adults diagnosed with SZ or SAD, hospitalized at least twice in 1963-2017, were analyzed. Stability between most-frequent, first and last diagnosis, and diagnostic-constancy (the same diagnosis in >75 % of a person's hospitalizations) were calculated. Three groups were compared: People with both SAD and SZ diagnoses over the years (SZ-SAD), and people with only one of these diagnoses (SZ-only; SAD-only). The incidence of SAD and SZ before and after DSM-5 publication was compared.

RESULTS

Reliability between last and first diagnosis was 60 % for SAD and 94 % for SZ. Agreement between first and most-frequent diagnosis was 86 % for SAD and 92 % for SZ. Diagnostic shifts differ between persons with SAD and with SZ. Diagnostic-constancy was observed for 50 % of SAD-only patients. In the SZ-SAD group, 9 % had a constant SAD diagnosis. Compared to the other groups, the SZ-SAD group exhibited a higher substance use prevalence, younger age at first-hospitalization, and more hospitalizations/person (p < 0.0001). The incidence of a first-hospitalization SAD diagnosis increased by 2.2 % in the 4-years after vs. prior to DSM-5.

CONCLUSIONS

A SAD diagnosis is less stable than SZ. The incidence of a SAD diagnosis increased after DSM-5, despite stricter diagnostic criteria. The SZ-SAD group exhibited the poorest outcomes. SAD may evolve over time necessitating periodic re-evaluation.

Rare genetic variants explain missing heritability in smoking

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Polygenic Architecture of Human Neuroanatomical Diversity

We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.

The Genetic Architecture of High Bone Mass

The phenotypic trait of high bone mass (HBM) is an excellent example of the nexus between common and rare disease genetics. HBM may arise from carriage of many 'high bone mineral density [BMD]'-associated alleles, and certainly the genetic architecture of individuals with HBM is enriched with high BMD variants identified through genome-wide association studies of BMD. HBM may also arise as a monogenic skeletal disorder, due to abnormalities in bone formation, bone resorption, and/or bone turnover. Individuals with monogenic disorders of HBM usually, though not invariably, have other skeletal abnormalities (such as mandible enlargement) and thus are best regarded as having a skeletal dysplasia rather than just isolated high BMD. A binary etiological division of HBM into polygenic vs. monogenic, however, would be excessively simplistic: the phenotype of individuals carrying rare variants of large effect can still be modified by their common variant polygenic background, and by the environment. HBM disorders-whether predominantly polygenic or monogenic in origin-are not only interesting clinically and genetically: they provide insights into bone processes that can be exploited therapeutically, with benefits both for individuals with these rare bone disorders and importantly for the many people affected by the commonest bone disease worldwide-i.e., osteoporosis. In this review we detail the genetic architecture of HBM; we provide a conceptual framework for considering HBM in the clinical context; and we discuss monogenic and polygenic causes of HBM with particular emphasis on anabolic causes of HBM.

Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model

The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that 9.3%, 3.9%, 1.6%, and 12.5%, respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores (-log10p>7.3).

GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies

Motivation

Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using non-local priors in an iterative variable selection framework.

Results

We develop a variable selection method, named, iterative non-local prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors. The hallmark of our method is the introduction of 'structured screen-and-select' strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations and concatenates variable selection within that hierarchy. Extensive simulation studies with single nucleotide polymorphisms having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype.

Availability and implementation

An R-package for implementing the GWASinlps method is available at https://cran.r-project.org/web/packages/GWASinlps/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Using zebrafish to study skeletal genomics

While genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of skeletal diseases, animal models are required to identify causal mechanisms and to translate underlying biology into new therapies. Despite large-scale knockout mouse phenotyping efforts, the skeletal functions of most genes residing at GWAS-identified loci remain unknown, highlighting a need for complementary model systems to accelerate gene discovery. Over the past several decades, zebrafish (Danio rerio) has emerged as a powerful system for modeling the genetics of human diseases. In this review, our goal is to outline evidence supporting the utility of zebrafish for accelerating our understanding of human skeletal genomics, as well as gaps in knowledge that need to be filled for this purpose. We do this by providing a basic foundation of the zebrafish skeletal morphophysiology and phenotypes, and surveying evidence of skeletal gene homology and the use of zebrafish for post-GWAS analysis in other tissues and organs. We also outline challenges in translating zebrafish mutant phenotypes. Finally, we conclude with recommendations of future directions and how to leverage the large body of tools and knowledge of skeletal genetics in zebrafish for the needs of human skeletal genomic exploration. Due to their amenability to rapid genetic approaches, as well as the large number of conserved genetic and phenotypic features, there is a strong rationale supporting the use of zebrafish for human skeletal genomic studies.

Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls

BACKGROUND

Most studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.

METHODS

We assessed heritability based on data of Swedish siblings (N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.

RESULTS

Heritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50.

CONCLUSIONS

Given the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.

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.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0 and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20,000 individuals in the Generation Scotland family cohort genotyped for ~700,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWAS of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence, and education is consistent with mutation-selection balance.

Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.

Genotype scores predict drug efficacy in subtypes of female sexual interest/arousal disorder: A double-blind, randomized, placebo-controlled cross-over trial

Attempts to develop a drug treatment for female sexual interest/arousal disorder have so far been guided by the principle of ‘one size fits all’, and have failed to acknowledge the complexity of female sexuality. Guided by personalized medicine, we designed two on-demand drugs targeting two distinct hypothesized causal mechanisms for this sexual disorder. The objective of this study was to design and test a novel procedure, based on genotyping, that predicts which of the two on-demand drugs will yield a positive treatment response. In a double-blind, randomized, placebo-controlled cross-over experiment, 139 women with female sexual interest/arousal disorder received three different on-demand drug-combination treatments during three 2-week periods: testosterone 0.5 mg + sildenafil 50 mg, testosterone 0.5 mg + buspirone 10 mg, and matching placebo. The primary endpoint was change in satisfactory sexual events. Subjects’ genetic profile was assessed using a microarray chip that measures 300,000 single-nucleotide polymorphisms. A preselection of single-nucleotide polymorphisms associated with genes that are shown to be involved in sexual behaviour were combined into a Phenotype Prediction Score. The Phenotype Prediction Score demarcation formula was developed and subsequently validated on separate data sets. Prediction of drug-responders with the Phenotype Prediction Score demarcation formula gave large effect sizes (d = 0.66 through 1.06) in the true drug-responders, and medium effect sizes (d = 0.51 and d = 0.47) in all patients (including identified double, and non-responders). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the Phenotype Prediction Score demarcation formula were all between 0.78 and 0.79, and thus sufficient. The resulting Phenotype Prediction Score was validated and shown to effectively and reliably predict which women would benefit from which on-demand drug, and could therefore also be useful in clinical practice, as a companion diagnostic establishing the way to a true personalized medicine approach.

Signatures of negative selection in the genetic architecture of human complex traits

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

The genetic architecture of schizophrenia, bipolar disorder, obsessive-compulsive disorder and autism spectrum disorder

Considerable evidence suggests that autism spectrum disorders (ASD), schizophrenia (SCZ), bipolar disorder (BD) and obsessive-compulsive disorder (OCD) share a common molecular aetiology, despite their unique clinical diagnostic criteria. The aim of this study was therefore to determine and characterise the common and unique molecular architecture of ASD, SCZ, BD and OCD. Gene lists were obtained from previously published studies for ASD, BD, SCZ and for OCD. Genes identified to be common to all disorders, or unique to one specific disorder, were included for enrichment analyses using the web-server tool Enrichr. Ten genes were identified to be commonly associated with the aetiology of ASD, SCZ, BD and OCD. Enrichment analyses determined that these genes are predominantly involved in the dopaminergic and serotonergic pathways, the voltage-gated calcium ion channel gene network, folate metabolism, regulation of the hippo signaling pathway, and the regulation of gene silencing and expression. In addition to well-characterised and previously described pathways, regulation of the hippo signaling pathway was commonly associated with ASD, SCZ, BD and OCD, implicating neural development and neuronal maintenance as key in neuropsychiatric disorders. In contrast, a large number of previously associated genes were shown to be disorder-specific. And unique disorder-specific pathways and biological processes were presented for ASD, BD, SCZ and OCD aetiology. Considering the current global incidence and prevalence rates of mental health disorders, focus should be placed on cross-disorder commonalities in order to realise actionable and translatable results to combat mental health disorders.

Whole genome sequence study of cannabis dependence in two independent cohorts

Recent advances in genome wide sequencing techniques and analytical methods allow for more comprehensive examinations of the genome than microarray-based genome-wide association studies (GWAS). The present report provides the first application of whole genome sequencing (WGS) to identify low frequency variants involved in cannabis dependence across two independent cohorts. The present study used low-coverage whole genome sequence data to conduct set-based association and enrichment analyses of low frequency variation in protein-coding regions as well as regulatory regions in relation to cannabis dependence. Two cohorts were studied: a population-based Native American tribal community consisting of 697 participants nested within large multi-generational pedigrees and a family-based sample of 1832 predominantly European ancestry participants largely nested within nuclear families. Participants in both samples were assessed for Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) lifetime cannabis dependence, with 168 and 241 participants receiving a positive diagnosis in each sample, respectively. Sequence kernel association tests identified one protein-coding region, C1orf110 and one regulatory region in the MEF2B gene that achieved significance in a meta-analysis of both samples. A regulatory region within the PCCB gene, a gene previously associated with schizophrenia, exhibited a suggestive association. Finally, a significant enrichment of regions within or near genes with multiple splice variants or involved in cell adhesion or potassium channel activity were associated with cannabis dependence. This initial study demonstrates the potential utility of low pass whole genome sequencing for identifying genetic variants involved in the etiology of cannabis use disorders.

Induced Pluripotent Stem Cells in Disease Modeling and Gene Identification

Experimental modeling of human inherited disorders provides insight into the cellular and molecular mechanisms involved, and the underlying genetic component influencing, the disease phenotype. The breakthrough development of induced pluripotent stem cell (iPSC) technology represents a quantum leap in experimental modeling of human diseases, providing investigators with a self-renewing and, thus, unlimited source of pluripotent cells for targeted differentiation. In principle, the entire range of cell types found in the human body can be interrogated using an iPSC approach. Therefore, iPSC technology, and the increasingly refined abilities to differentiate iPSCs into disease-relevant target cells, has far-reaching implications for understanding disease pathophysiology, identifying disease-causing genes, and developing more precise therapeutics, including advances in regenerative medicine. In this chapter, we discuss the technological perspectives and recent developments in the application of patient-derived iPSC lines for human disease modeling and disease gene identification.

A Model of Compound Heterozygous, Loss-of-Function Alleles Is Broadly Consistent with Observations from Complex-Disease GWAS Datasets

The genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider multiple genetic and demographic models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait. In particular, the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests. Models with partial recessivity closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies without requiring homozygous effect sizes to be small. We highlight a particular gene-based model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations in a genomic region partially fail to complement one another. This model of gene-based recessivity predicts the empirically observed inconsistency between twin and SNP based estimated of dominance heritability. Furthermore, this model predicts considerable levels of unexplained variance associated with intralocus epistasis. Our results suggest a need for improved statistical tools for region based genetic association and heritability estimation.

Gene action determines how mutations affect phenotype. When placed in an evolutionary context, the details of the genotype-to-phenotype model can impact the maintenance of genetic variation for complex traits. Likewise, non-equilibrium demographic history may affect patterns of genetic variation. Here, we explore the impact of genetic model and population growth on distribution of genetic variance across the allele frequency spectrum underlying risk for a complex disease. Using forward-in-time population genetic simulations, we show that the genetic model has important impacts on the composition of variation for complex disease risk in a population. We explicitly simulate genome-wide association studies (GWAS) and perform heritability estimation on population samples. A particular model of gene-based partial recessivity, based on allelic non-complementation, aligns well with empirical results. This model is congruent with the dominance variance estimates from both SNPs and twins, and the minor allele frequency distribution of GWAS hits.

A single nucleotide variant in microRNA-1269a promotes the occurrence and process of hepatocellular carcinoma by targeting to oncogenes SPATS2L and LRP6

Hepatocellular carcinoma (HCC) is one of the malignant and lethal cancers. Single nucleotide polymorphisms (SNPs) in microRNAs(miRNAs) can affect the expression and target identification of miRNAs and lead to the formation of malignant tumors. However, little is known about whether microRNA-1269a (miR-1269a) SNPs affect the susceptibility and progression of HCC or their specific mechanism. The association between microRNA-1269a rs73239138 and the susceptibility to HCC was verified by MassARRAY assay in a large case-control sample. The effect of miR-1269a and the variant on the proliferation and apoptosis of HCC cells was examined by flow cytometry (FCM), CCK8 assay and Western blot. The target of miR-1269a was identified by bioinformatics analysis and qRT-PCR and its role on cell proliferative capacity was examined by CCK8 assay. The expression level of miR-1269a was analyzed by qRT-PCR in HCC cells transfected with wild or variant type pre-miR-1269a plasmid.MiR-1269a produced a tumor suppressor effect by inhibiting cell proliferation and inducing apoptosis of human HCC cells, possibly via inhibiting the expression of its target genes SPATS2L and LRP6, which were tumor promoters. While, rs73239138 (G>A) in miR-1269a reduced the anticancer effect of miR-1269a possibly by attenuating its total amount in HCC cells or its target recognition, reduce its inhibition on target genes and promoted the susceptibility to HCC. Our findings for the first time proved that miR-1269a SNP plays a role in the occurrence and process of HCC and the relevant mechanism, in accompany with the discovery of the novel target genes of miR-1269a.

The polygenic risk for bipolar disorder influences brain regional function relating to visual and default state processing of emotional information

Genome-wise association studies have identified a number of common single-nucleotide polymorphisms (SNPs), each of small effect, associated with risk to bipolar disorder (BD). Several risk-conferring SNPs have been individually shown to influence regional brain activation thus linking genetic risk for BD to altered brain function. The current study examined whether the polygenic risk score method, which models the cumulative load of all known risk-conferring SNPs, may be useful in the identification of brain regions whose function may be related to the polygenic architecture of BD. We calculated the individual polygenic risk score for BD (PGR-BD) in forty-one patients with the disorder, twenty-five unaffected first-degree relatives and forty-six unrelated healthy controls using the most recent Psychiatric Genomics Consortium data. Functional magnetic resonance imaging was used to define task-related brain activation patterns in response to facial affect and working memory processing. We found significant effects of the PGR-BD score on task-related activation irrespective of diagnostic group. There was a negative association between the PGR-BD score and activation in the visual association cortex during facial affect processing. In contrast, the PGR-BD score was associated with failure to deactivate the ventromedial prefrontal region of the default mode network during working memory processing. These results are consistent with the threshold-liability model of BD, and demonstrate the usefulness of the PGR-BD score in identifying brain functional alternations associated with vulnerability to BD. Additionally, our findings suggest that the polygenic architecture of BD is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions.

COMT and DAT1 genes are associated with hyperactivity and inattention traits in the 1993 Pelotas Birth Cohort: evidence of sex-specific combined effect

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) symptoms are dimensionally distributed in the population. This study aimed to assess the role of the catechol-O-methyltransferase (COMT) and of the dopamine transporter (DAT1) genes on ADHD symptoms in the general population.

METHODS

We investigated 4101 individuals from the 1993 Pelotas Birth Cohort Study using the parent version of the Strengths and Difficulties Questionnaire (SDQ) at ages 11 and 15 years. The SDQ hyperactivity/inattention scores were the main outcomes.

RESULTS

Linear regression analyses demonstrated that the increasing number of COMT158Val and DAT1 10R alleles significantly predicted increasing SDQ hyperactivity/inattention scores in boys at both 11 and 15 years of age (β coefficient = 0.049, t = 2.189, p = 0.029, R² = 0.012, and β coefficient = 0.064, t = 2.832, p = 0.005, R² = 0.008, respectively). The presence of both COMT158Val and DAT1 10R alleles was also associated with full categorical ADHD diagnosis at 18 years of age in boys (χ² = 4.561, p = 0.033, odds ratio 2.473, 95% confidence interval 1.048-5.838) from this cohort. We did not observe these associations in girls.

LIMITATIONS

Our analyses of SDQ hyperactivity/inattention scores were not corrected for SDQ scores of conduct problems because these variables were highly correlated.

CONCLUSION

This study demonstrates a role for COMT and DAT1 genes on hyperactivity/inattention symptoms and provides further support for ADHD as the extreme of traits that vary in the population. It also confirms previous evidence for sexual dimorphism on COMT and DAT1 gene expression.

New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis

BACKGROUND

A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders.

METHODS

We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born.

RESULTS

We estimate a small but significant negative SNP-genetic correlation between SZ and RA (-0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (-0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090).

CONCLUSIONS

Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.

Genetic and family counselling for schizophrenia: Where do we stand now?

<p><strong>Background:</strong> Recent genetic findings have led to profound changes in genetic and family counselling for schizophrenia patients and their families.</p><p><strong>Objectives:</strong> The article gives an overview of the present knowledge regarding the genetic and family counselling for schizophrenia.</p><p><strong>Method:</strong> Literature searches were performed on the MEDLINE database (2011–2015) and African Healthline. A current alert service which provides the most recent literature on the topic on a monthly basis was also used in the study. A clinical case example is presented as is experienced in daily psychiatric practice.</p><p><strong>Results:</strong> Genetic risk communication has become the responsibility of the multiprofessional treatment team, moving away from specialists in the field. The treatment team provides information on a daily basis regarding risk predictors in the management of schizophrenia, including risk of relapse, suicide and comorbid substance use. Although genetic information is unique and has implications for blood relatives, genetic risk factors only rarely provide information that is inherently different from that provided by other risk predictors commonly used in healthcare. The common variant common disease and rare variant common disease models as contrasting hypothesis of the genetics of schizophrenia are discussed and debated. An example of a family counselled is given and the place of commercial companies that offer directly to the consumer affordable personal DNA testing for psychiatric illness is discussed. Ethical issues without resolution regarding genetic counselling of schizophrenia are debated.</p><p><strong>Conclusions:</strong> Recent genetic findings must lead to profound changes in genetic and family counselling in schizophrenia. Exposed attributable risk has immediate effects on genetic counselling of schizophrenia. Psychiatric risk counselling has thus changed from risk estimates based on family history to estimates based on test results in specific individuals.</p><p><strong><br /></strong></p>

Efficient Software for Multi-marker, Region-Based Analysis of GWAS Data

Genome-wide association studies (GWAS) have associated many single variants with complex disease, yet the better part of heritable complex disease risk remains unexplained. Analytical tools designed to work under specific population genetic models are needed. Rare variants are increasingly shown to be important in human complex disease, but most existing GWAS data do not cover rare variants. Explicit population genetic models predict that genes contributing to complex traits and experiencing recurrent, unconditionally deleterious, mutation will harbor multiple rare, causative mutations of subtle effect. It is difficult to identify genes harboring rare variants of large effect that contribute to complex disease risk via the single marker association tests typically used in GWAS. Gene/region-based association tests may have the power detect associations by combining information from multiple markers, but have yielded limited success in practice. This is partially because many methods have not been widely applied. Here, we empirically demonstrate the utility of a procedure based on the rank truncated product (RTP) method, filtered to reduce the effects of linkage disequilibrium. We apply the procedure to the Wellcome Trust Case Control Consortium (WTCCC) data set, and uncover previously unidentified associations, some of which have been replicated in much larger studies. We show that, in the absence of significant rare variant coverage, RTP based methods still have the power to detect associated genes. We recommend that RTP-based methods be applied to all existing GWAS data to maximize the usefulness of those data. For this, we provide efficient software implementing our procedure.

Exome Sequence Data From Multigenerational Families Implicate AMPA Receptor Trafficking in Neurocognitive Impairment and Schizophrenia Risk

Schizophrenia is a mental disorder characterized by impairments in behavior, thought, and neurocognitive performance. We searched for susceptibility loci at a quantitative trait locus (QTL) previously reported for abstraction and mental flexibility (ABF), a cognitive function often compromised in schizophrenia patients and their unaffected relatives. Exome sequences were determined for 134 samples in 8 European American families from the original linkage study, including 25 individuals with schizophrenia or schizoaffective disorder. At chromosome 5q32-35.3, we analyzed 407 protein-altering variants for association with ABF and schizophrenia status. For replication, significant, Bonferroni-corrected findings were tested against cognitive traits in Mexican American families (n = 959), as well as interrogated for schizophrenia risk using GWAS results from the Psychiatric Genomics Consortium (PGC). From the gene SYNPO, rs6579797 (MAF = 0.032) shows significant associations with ABF (P = .015) and schizophrenia (P = .040), as well as jointly (P = .0027). In the Mexican American pedigrees, rs6579797 exhibits significant associations with IQ (P = .011), indicating more global effects on neurocognition. From the PGC results, other SYNPO variants were identified with near significant effects on schizophrenia risk, with a local linkage disequilibrium block displaying signatures of positive selection. A second missense variant within the QTL, rs17551608 (MAF = 0.19) in the gene WWC1, also displays a significant effect on schizophrenia in our exome sequences (P = .038). Remarkably, the protein products of SYNPO and WWC1 are interaction partners involved in AMPA receptor trafficking, a brain process implicated in synaptic plasticity. Our study reveals variants in these genes with significant effects on neurocognition and schizophrenia risk, identifying a potential pathogenic mechanism for schizophrenia spectrum disorders.

DNA methylation in a Scottish family multiply affected by bipolar disorder and major depressive disorder

BACKGROUND

Bipolar disorder (BD) is a severe, familial psychiatric condition. Progress in understanding the aetiology of BD has been hampered by substantial phenotypic and genetic heterogeneity. We sought to mitigate these confounders by studying a multi-generational family multiply affected by BD and major depressive disorder (MDD), who carry an illness-linked haplotype on chromosome 4p. Within a family, aetiological heterogeneity is likely to be reduced, thus conferring greater power to detect illness-related changes. As accumulating evidence suggests that altered DNA methylation confers risk for BD and MDD, we compared genome-wide methylation between (i) affected carriers of the linked haplotype (ALH) and married-in controls (MIs), (ii) well unaffected haplotype carriers (ULH) and MI, (iii) ALH and ULH and (iv) all haplotype carriers (LH) and MI.

RESULTS

Nominally significant differences in DNA methylation were observed in all comparisons, with differences withstanding correction for multiple testing when the ALH or LH group was compared to the MIs. In both comparisons, we observed increased methylation at a locus in FANCI, which was accompanied by increased FANCI expression in the ALH group. FANCI is part of the Fanconi anaemia complementation (FANC) gene family, which are mutated in Fanconi anaemia and participate in DNA repair. Interestingly, several FANC genes have been implicated in psychiatric disorders. Regional analyses of methylation differences identified loci implicated in psychiatric illness by genome-wide association studies, including CACNB2 and the major histocompatibility complex. Gene ontology analysis revealed enrichment for methylation differences in neurologically relevant genes.

CONCLUSIONS

Our results highlight altered DNA methylation as a potential mechanism by which the linked haplotype might confer risk for mood disorders. Differences in the phenotypic outcome of haplotype carriers might, in part, arise from additional changes in DNA methylation that converge on neurologically important pathways. Further work is required to investigate the underlying mechanisms and functional consequences of the observed differences in methylation.

Increased co-expression of genes harboring the damaging de novo mutations in Chinese schizophrenic patients during prenatal development

Schizophrenia is a heritable, heterogeneous common psychiatric disorder. In this study, we evaluated the hypothesis that de novo variants (DNVs) contribute to the pathogenesis of schizophrenia. We performed exome sequencing in Chinese patients (N = 45) with schizophrenia and their unaffected parents (N = 90). Forty genes were found to contain DNVs. These genes had enriched transcriptional co-expression profile in prenatal frontal cortex (Bonferroni corrected p < 9.1 × 10⁻³), and in prenatal temporal and parietal regions (Bonferroni corrected p < 0.03). Also, four prenatal anatomical subregions (VCF, MFC, OFC and ITC) have shown significant enrichment of connectedness in co-expression networks. Moreover, four genes (LRP1, MACF1, DICER1 and ABCA2) harboring the damaging de novo mutations are strongly prioritized as susceptibility genes by multiple evidences. Our findings in Chinese schizophrenic patients indicate the pathogenic role of DNVs, supporting the hypothesis that schizophrenia is a neurodevelopmental disease.

Overview of genetic research in anorexia nervosa: The past, the present and the future

BACKGROUND

Even though the evidence supporting the presence of a heritable component in the aetiology of anorexia nervosa (AN) is strong, the underlying genetic mechanisms remain poorly understood. The recent publication of a genome-wide association study (GWAS) of AN (Boraska, Mol Psychiatry, 2014) was an important step in genetic research in AN.

OBJECTIVE

To briefly sum up strengths and weaknesses of candidate-gene and genome-wide approaches, to discuss the genome-wide association studies of AN and to make predictions about the genetic architecture of AN by comparing it to that of schizophrenia (since the diseases share some similarities and genetic research in schizophrenia is more advanced).

METHOD

Descriptive literature review.

RESULTS

Despite remarkable efforts, the gene-association studies in AN did not advance our knowledge as much as had been hoped, although some results still await replication.

DISCUSSION

Continuous effort of participants, clinicians and researchers remains necessary to ensure that genetic research in AN follows a similarly successful path as in schizophrenia. Identification of genetic susceptibility loci provides a basis for follow-up studies.

Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection

The power of genome-wide association studies (GWAS) for mapping complex traits with single-SNP analysis (where SNP is single-nucleotide polymorphism) may be undermined by modest SNP effect sizes, unobserved causal SNPs, correlation among adjacent SNPs, and SNP-SNP interactions. Alternative approaches for testing the association between a single SNP set and individual phenotypes have been shown to be promising for improving the power of GWAS. We propose a Bayesian latent variable selection (BLVS) method to simultaneously model the joint association mapping between a large number of SNP sets and complex traits. Compared with single SNP set analysis, such joint association mapping not only accounts for the correlation among SNP sets but also is capable of detecting causal SNP sets that are marginally uncorrelated with traits. The spike-and-slab prior assigned to the effects of SNP sets can greatly reduce the dimension of effective SNP sets, while speeding up computation. An efficient Markov chain Monte Carlo algorithm is developed. Simulations demonstrate that BLVS outperforms several competing variable selection methods in some important scenarios.

Common variants in the ARC gene are not associated with cognitive abilities

INTRODUCTION

The Activity-Regulated Cytoskeleton-associated (ARC) gene encodes a protein that is critical for the consolidation of synaptic plasticity and long-term memory formation. Given ARC's key role in synaptic plasticity, we hypothesized that genetic variations in ARC may contribute to interindividual variability in human cognitive abilities or to attention-deficit hyperactivity disorder (ADHD) susceptibility, where cognitive impairment often accompanies the disorder.

METHODS

We tested whether ARC variants are associated with six measures of cognitive functioning in 670 healthy subjects in the Norwegian Cognitive NeuroGenetics (NCNG) by extracting data from its Genome-Wide Association Study (GWAS). In addition, the Swedish Betula sample of 1800 healthy subjects who underwent similar cognitive testing was also tested for association with 19 tag SNPs.

RESULTS

No ARC variants show association at the study-wide level, but several markers show a trend toward association with human cognitive functions. We also tested for association between ARC SNPs and ADHD in a Norwegian sample of cases and controls, but found no significant associations.

CONCLUSION

This study suggests that common genetic variants located in ARC do not account for variance in human cognitive abilities, though small effects cannot be ruled out.

Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index

We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60-70% for height and 30-40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.

Genetics of smoking behaviour

It is now well-established that smoking-related behaviours are under a substantial degree of genetic influence. Efforts are now focused on identifying the specific genetic variants which underlie these behaviours. Within this chapter, we introduce a variety of established and emerging methods employed to identify such variants, ranging from candidate gene to whole genome sequencing approaches, and highlight what these techniques have taught us about the genetic architecture of smoking-related behaviours. Further, we discuss how phenotype refinement has developed our understanding of these relationships, affording us insight into the specific mechanisms linking genetic variants to smoking-related behaviours.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Genetics in child and adolescent psychiatry: methodological advances and conceptual issues

Discovering the genetic basis of early-onset psychiatric disorders has been the aim of intensive research during the last decade. We will first selectively summarize results of genetic research in child and adolescent psychiatry by using examples from different disorders and discuss methodological issues, emerging questions and future directions. In the second part of this review, we will focus on how to link genetic causes of disorders with physiological pathways, discuss the impact of genetic findings on diagnostic systems, prevention and therapeutic interventions. Finally we will highlight some ethical aspects connected to genetic research in child and adolescent psychiatry. Advances in molecular genetic methods have led to insights into the genetic architecture of psychiatric disorders, but not yet provided definite pathways to pathophysiology. If replicated, promising findings from genetic studies might in some cases lead to personalized treatments. On the one hand, knowledge of the genetic basis of disorders may influence diagnostic categories. On the other hand, models also suggest studying the genetic architecture of psychiatric disorders across diagnoses and clinical groups.

A robust GWSS method to simultaneously detect rare and common variants for complex disease

The rapid advances in sequencing technologies and the resulting next-generation sequencing data provide the opportunity to detect disease-associated variants with a better solution, in particular for low-frequency variants. Although both common and rare variants might exert their independent effects on the risk for the trait of interest, previous methods to detect the association effects rarely consider them simultaneously. We proposed a class of test statistics, the generalized weighted-sum statistic (GWSS), to detect disease associations in the presence of common and rare variants with a case-control study design. Information of rare variants was aggregated using a weighted sum method, while signal directions and strength of the variants were considered at the same time. Permutations were performed to obtain the empirical p-values of the test statistics. Our simulation showed that, compared to the existing methods, the GWSS method had better performance in most of the scenarios. The GWSS (in particular VDWSS-t) method is particularly robust for opposite association directions, association strength, and varying distributions of minor-allele frequencies. It is therefore promising for detecting disease-associated loci. For empirical data application, we also applied our GWSS method to the Genetic Analysis Workshop 17 data, and the results were consistent with the simulation, suggesting good performance of our method. As re-sequencing studies become more popular to identify putative disease loci, we recommend the use of this newly developed GWSS to detect associations with both common and rare variants.

The nature of individual differences in inhibited temperament and risk for psychiatric disease: A review and meta-analysis

What makes us different from one another? Why does one person jump out of airplanes for fun while another prefers to stay home and read? Why are some babies born with a predisposition to become anxious? Questions about individual differences in temperament have engaged the minds of scientists, psychologists, and philosophers for centuries. Recent technological advances in neuroimaging and genetics provide an unprecedented opportunity to answer these questions. Here we review the literature on the neurobiology of one of the most basic individual differences-the tendency to approach or avoid novelty. This trait, called inhibited temperament, is innate, heritable, and observed across species. Importantly, inhibited temperament also confers risk for psychiatric disease. Here, we provide a comprehensive review of inhibited temperament, including neuroimaging and genetic studies in human and non-human primates. We conducted a meta-analysis of neuroimaging findings in inhibited humans that points to alterations in a fronto-limbic-basal ganglia circuit; these findings provide the basis of a model of inhibited temperament neurocircuitry. Lesion and neuroimaging studies in non-human primate models of inhibited temperament highlight roles for the amygdala, hippocampus, orbitofrontal cortex, and dorsal prefrontal cortex. Genetic studies highlight a role for genes that regulate neurotransmitter function, such as the serotonin transporter polymorphisms (5-HTTLPR), as well as genes that regulate stress response, such as corticotropin-releasing hormone (CRH). Together these studies provide a foundation of knowledge about the genetic and neural substrates of this most basic of temperament traits. Future studies using novel imaging methods and genetic approaches promise to expand upon these biological bases of inhibited temperament and inform our understanding of risk for psychiatric disease.

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.

Recent genetic findings in schizophrenia and their therapeutic relevance

Over 100 loci are now associated with schizophrenia risk as identified by single nucleotide polymorphisms (SNPs) in genome-wide association studies. These findings mean that 'genes for schizophrenia' have unquestionably been found. However, many questions remain unanswered, including several which affect their therapeutic significance. The SNPs individually have minor effects, and even cumulatively explain only a modest fraction of the genetic predisposition. The remainder likely results from many more loci, from rare variants, and from gene-gene and gene-environment interactions. The risk SNPs are almost all non-coding, meaning that their biological significance is unclear; probably their effects are mediated via an influence on gene regulation, and emerging evidence suggests that some key molecular events occur during early brain development. The loci include novel genes of unknown function as well as genes and pathways previously implicated in the pathophysiology of schizophrenia, e.g. NMDA receptor signalling. Genes in the latter category have the clearer therapeutic potential, although even this will be a challenging process because of the many complexities concerning the genetic architecture and mediating mechanisms. This review summarises recent schizophrenia genetic findings and some key issues they raise, particularly with regard to their implications for identifying and validating novel drug targets.

Functional implications of a psychiatric risk variant within CACNA1C in induced human neurons

Psychiatric disorders have clear heritable risk. Several large-scale genome-wide association studies have revealed a strong association between susceptibility for psychiatric disorders, including bipolar disease, schizophrenia and major depression, and a haplotype located in an intronic region of the L-type voltage-gated calcium channel (VGCC) subunit gene CACNA1C (peak associated SNP rs1006737), making it one of the most replicable and consistent associations in psychiatric genetics. In the current study, we used induced human neurons to reveal a functional phenotype associated with this psychiatric risk variant. We generated induced human neurons, or iN cells, from more than 20 individuals harboring homozygous risk genotypes, heterozygous or homozygous non-risk genotypes at the rs1006737 locus. Using these iNs, we performed electrophysiology and quantitative PCR experiments that demonstrated increased L-type VGCC current density as well as increased mRNA expression of CACNA1C in iNs homozygous for the risk genotype, compared with non-risk genotypes. These studies demonstrate that the risk genotype at rs1006737 is associated with significant functional alterations in human iNs, and may direct future efforts at developing novel therapeutics for the treatment of psychiatric disease.

GWAS-based pathway analysis differentiates between fluid and crystallized intelligence

Cognitive abilities vary among people. About 40-50% of this variability is due to general intelligence (g), which reflects the positive correlation among individuals' scores on diverse cognitive ability tests. g is positively correlated with many life outcomes, such as education, occupational status and health, motivating the investigation of its underlying biology. In psychometric research, a distinction is made between general fluid intelligence (gF) - the ability to reason in novel situations - and general crystallized intelligence (gC) - the ability to apply acquired knowledge. This distinction is supported by developmental and cognitive neuroscience studies. Classical epidemiological studies and recent genome-wide association studies (GWASs) have established that these cognitive traits have a large genetic component. However, no robust genetic associations have been published thus far due largely to the known polygenic nature of these traits and insufficient sample sizes. Here, using two GWAS datasets, in which the polygenicity of gF and gC traits was previously confirmed, a gene- and pathway-based approach was undertaken with the aim of characterizing and differentiating their genetic architecture. Pathway analysis, using genes selected on the basis of relaxed criteria, revealed notable differences between these two traits. gF appeared to be characterized by genes affecting the quantity and quality of neurons and therefore neuronal efficiency, whereas long-term depression (LTD) seemed to underlie gC. Thus, this study supports the gF-gC distinction at the genetic level and identifies functional annotations and pathways worthy of further investigation.

Psychiatrists' views of the genetic bases of mental disorders and behavioral traits and their use of genetic tests

We examined how 372 psychiatrists view genetic aspects of mental disorders and behaviors and use genetic tests (GTs). Most thought that the genetic contribution was moderate/high for bipolar disorder, schizophrenia, depression, Alzheimer's, intelligence, creativity, anxiety, and suicidality. In the past 6 months, 14.1% ordered GTs, 18.3% discussed prenatal testing with patients, 36.0% initiated discussions about other GTs, 41.6% had patients ask about GTs, and 5.3% excluded GT results from patient records. Many thought that GTs; were available for schizophrenia (24.3%) and major depression (19.6%). Women were more likely to report that patients asked about GTs; and were less certain about the degree of genetic contribution to several disorders. Psychiatrists perceive strong genetic bases for numerous disorders and traits, and many have discussed and ordered tests for GTs, but have relatively limited knowledge about available tests. These data suggest possible sex differences in psychiatrists' beliefs about genetic contributions to disorders and have implications for future research, education, policy, and care.

Review of statistical methodologies for the detection of parent-of-origin effects in family trio genome-wide association data with binary disease traits

The detection of parent-of-origin effects aims to identify whether the functionality of alleles, and in turn associated phenotypic traits, depends on the parental origin of the alleles. Different parent-of-origin effects have been identified through a variety of mechanisms and a number of statistical methodologies for their detection have been proposed, in particular for genome-wide association studies (GWAS). GWAS have had limited success in explaining the heritability of many complex disorders and traits, but successful identification of parent-of-origin effects using trio (mother, father and offspring) GWAS may help shed light on this missing heritability. However, it is important to choose the most appropriate parent-of-origin test or methodology, given knowledge of the phenotype, amount of available data and the type of parent-of-origin effect(s) being considered. This review brings together the parent-of-origin detection methodologies available, comparing them in terms of power and type I error for a number of different simulated data scenarios, and finally offering guidance as to the most appropriate choice for the different scenarios.