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The Encyclopedia of Clinical Psychology
Abstract
Endophenotypes, measurable components unseen by the unaided eye along the pathway between disease or other phenotypes and distal genomics, have emerged as an important concept in the study of complex neuropsychiatric diseases. The term was introduced to psychopathology in 1972 by Gottesman and Shields, and reinvigorated in 2003 by Gottesman and Gould. An endophenotype may be neurophysiological, biochemical, endocrinological, neuroanatomical, cognitive, or neuropsychological (including configured self-report data) in nature. Endophenotypes represent simpler clues, in principle, to genetic underpinnings than the disease syndrome itself, thus promoting the prescient view embodied in the recent emphasis on new research domain criteria or RDoC that psychiatric diagnoses can be optimally deconstructed in ways that allow discovery of their etiology by focusing on endophenotypic dimensions rather than categories. Endophenotypes differ from other biomarkers in a number of ways, most importantly, they are heritable and thus not the consequence of disease. They are heuristic in the development of animal models of psychopathology. For example, genes implicated in humans can be knocked out or in to observe their effects on animal behaviors and their endophenotypes.Keywords:behavioral genetics;Gottesman, Irving;nature-nurture controversy;neuroscience;research domain criteria (RDoC);Shields, James;systems biology;bipolar;schizophrenia;suicide
... He considered the concept of endophenotype as the most proximate to the goal of the RDoC project (). The concept of endophenotype (Gottesman & Shields, 1972;Gottesman & Gould, 2003;Gottesman & McGue, 2014;Gould & Gottesman, 2006;) refers to a measurable component that lies within the pathway to disease from distal genotype to manifest phenotype. Normally, an endophenotype is unseen. ...
... He considered the concept of endophenotype as the most proximate to the goal of the RDoC project (). The concept of endophenotype (Gottesman & Shields, 1972; Gottesman & Gould, 2003; Gottesman & McGue, 2014; Gould & Gottesman, 2006;) refers to a measurable component that lies within the pathway to disease from distal genotype to manifest phenotype. Normally, an endophenotype is unseen. ...
The search for endophenotypes that stand between genetics and disease has been applied to the diagnostic entity of Posttraumatic Stress Disorder (PTSD). Advances are being made in understanding the pathway to disorder in PTSD in terms of brain regions, neuronal networks, stress-related systems (e.g., the hypothalamic–pituitary–adrenal (HPA) axis), and their underlying genetic and neurogenetic bases. The latter are affected by gene–environmental interactions and epigenetic effects, and the environment and context reciprocally interrelate with them, as well. Therefore, a primary focus on (neuro)pathophysiological intermediates in the disease pathway, as appears emphasized in the research domain criteria (RDoC) approach to etiology of psychiatric disorder, and to which the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) subscribes, might detract from a more inclusive biopsychosocial approach that would be more applicable in the case of PTSD. The paper undertakes a comprehensive review of the recent literature in the areas of endophenotypes, neurogenetics, epigenetics, neural networks, HPA axis, neuronal networks, pathways, the PTSD five-factor model, allostasis, and the RDoC criteria for psychiatric diagnosis, and then returns to the topic of endophenotypes. Neuronal networks constitute one integrating area that could help in arriving at an appropriate model of PTSD endophenotype. Pathway analysis provides a rich field for discerning individual differences in PTSD development, more so than the static approach of using DSM-5 symptom criteria. A model of endophenotypes is presented, which considers these factors in relation to PTSD. The paper concludes with implications for the DSM-5, for practice and for court, especially that it would be premature to seek individual biomarkers of PTSD given the current state of knowledge, even if it is burgeoning.
... MDD, however, manifests as a combination of different symptom phenotypes, for example, anxiety, somatic symptoms, cognitive restraints. Respecting the dimensionality of those markers (e.g., neurophysiological, biochemical, neuroanatomical, cognitive data) rather than their categories, contributes to disentangle the full range of psychiatric disorders (Gottesman & McGue, 2014;Kozak & Cuthbert, 2016). ...
Aberrant brain structural connectivity in major depressive disorder (MDD) has been repeatedly reported, yet many previous studies lack integration of different features of MDD with structural connectivity in multivariate modeling approaches. In n = 595 MDD patients, we used structural equation modeling (SEM) to test the intercorrelations between anhedonia, anxiety, neuroticism, and cognitive control in one comprehensive model. We then separately analyzed diffusion tensor imaging (DTI) connectivity measures in association with those clinical variables, and finally integrated brain connectivity associations, clinical/cognitive variables into a multivariate SEM. We first confirmed our clinical/cognitive SEM. DTI analyses (FWE‐corrected) showed a positive correlation of anhedonia with fractional anisotropy (FA) in the right anterior thalamic radiation (ATR) and forceps minor/corpus callosum, while neuroticism was negatively correlated with axial diffusivity (AD) in the left uncinate fasciculus (UF) and inferior fronto‐occipital fasciculus (IFOF). An extended SEM confirmed the associations of ATR FA with anhedonia and UF/IFOF AD with neuroticism impacting on cognitive control. Our findings provide evidence for a differential impact of state and trait variables of MDD on brain connectivity and cognition. The multivariate approach shows feasibility of explaining heterogeneity within MDD and tracks this to specific brain circuits, thus adding to better understanding of heterogeneity on the biological level. In this article, we analyzed a large cohort of n = 595 major depressive disorder (MDD) patients using structural equation modeling of brain connectivity, clinical, and cognitive parameters to identify the relation of anhedonia, neuroticism, and state anxiety as well as cognitive control. Results show a brain structural overlap of anhedonia and cognitive control as well as of neuroticism and cognitive control, contributing to disconnection in MDD.
... environment, is often traced back to Francis Galton who in 1869 observed that "eminence" ran in families [5]. Perhaps because of a later association between Galton and eugenics, debates surrounding the relative roles of genes and environment in influencing behavior fell out of favor until the 1960s [6]. In the interim, the primary influence on child behaviors, such as eating behaviors, was often considered to be the home, and particularly the mother's parenting behaviors, with some notion that the school environment and, in older children, peer groups could also have an influence. ...
Purpose of Review
This narrative review describes the evidence for both genetic and environmental influences on child appetitive traits and suggests ways of thinking about how these interact and correlate to influence how a child eats.
Recent Findings
Emerging evidence from social network analysis, and from longitudinal studies questioning the direction of association between parent feeding behaviors and child obesity risk, suggest that children’s genes may shape the environmental risk for obesity that they are exposed to.
Summary
There is strong evidence that child appetitive traits are both heritable and shaped by the environment. Instead of thinking about how genetic and environmental factors operate independently on each appetitive trait, research needs to expand the current paradigm to examine how genes and environments interact and shape each other.
The first law providing for the permanent, involuntary institutionalization of “feeble‐minded” individuals was passed in Illinois in 1915. This bill represented the first eugenic commitment law in the United States. Focusing on the consequences of this 1915 commitment law within the context of intelligence testing, eugenics, and the progressive movement, this paper will argue that the then newly devised Binet–Simon intelligence test facilitated the definition and classification of feeble‐mindedness that validated feeble‐mindedness theory, enabled the state to legitimize the eugenic diagnosis and institutionalization of feeble‐minded individuals, and especially empowered psychologists to carve out a niche for themselves in the courtroom as “experts” when testifying as to the feeble‐mindedness of individuals.
The review is devoted to unsolvable problems of biology. 1) Problems unsolvable due to stochastic mutations occurring during DNA replication that make it impossible to create two identical organisms or even two identical complex cells (Sverdlov, E. D. (2009) Biochemistry (Moscow), 74, 939-944) and to "defeat" cancer. 2) Problems unsolvable due to multiple interactions in complex systems leading to the appearance of unpredictable emergent properties that prevent establishment of unambiguous relationships between the genetic architecture and phenotypic manifestation of the genome and make impossible to predict with certainty responses of the organism, its parts, or pathological processes to external factors. 3) Problems unsolvable because of the uncertainty principle and observer effect in biology, due to which it is impossible to obtain adequate information about cells in their tissue microenvironment by isolating and analyzing individual cells. In particular, we cannot draw conclusions on the properties of stem cells in their niches based on the properties of stem cell cultures. A strategy is proposed for constructing the pattern most closely approximated to the relationship of genotypes with their phenotypes by designing networks of intermediate phenotypes (endophenotypes).
Psychiatric disorders are common illnesses whose core features include disturbances of mood, perception, cognition and behaviour that cause distress or interfere with daily functioning. Genes contribute substantially to risk, but multiple genes and environmental factors are involved, leading to complex patterns of inheritance. Many genes and genetic markers have already been identified that contribute to schizophrenia, bipolar disorder and autism, but each individual gene plays a small role and much of the inherited risk for these disorders remains unexplained. Most of the identified genetic risk factors are common and each one has a small impact. Some rare genetic variants have also been discovered that have a larger impact on risk, especially for schizophrenia or autism. Ongoing studies are beginning to uncover how multiple genetic risk factors act together in the development of psychiatric disorders, but much more remains to be learned before we can use genetic discoveries to develop better methods of diagnosis and treatment.
Key Concepts
Most psychiatric disorders are strongly influenced by genes.
Genome‐wide association studies have identified numerous common genetic markers associated with major psychiatric disorders.
Each common allele confers little risk, but many add together to confer susceptibility to psychiatric illness.
Rare copy number variants, which influence the dosage of many genes, confer substantial risk for schizophrenia, autism spectrum disorders and intellectual disability.
Rare, gene‐damaging point mutations, most of which arise de novo , can also confer substantial risk for schizophrenia, autism spectrum disorders and intellectual disability.
The ultimate goal of gene identification for mental illness is to develop better methods of diagnosis and treatment.
Psychiatric diseases encompass some of the most complex and debilitating afflictions known to man. Common to several of these diseases, namely autism spectrum disorders (ASDs) and schizophrenia, are impairments in socio-communicative behavior. These impairments have been proposed as candidate endophenotypes of disease processes, being present in prodromal and active stages of disease, as well as in undiagnosed relatives. The genetic bases for these diseases and associated socio-communicative endophenotypes make them highly amenable to study using mouse models. In this review, socio-communicative endophenotypes of two psychiatric diseases—ASDs and schizophrenia—are discussed and translated to measurable behavior in mice. Social interaction and ultrasonic vocalization behavioral paradigms serve as reliable methods for assessing social and communicative functioning in mice, respectively. Several of these paradigms will be overviewed alongside evidence of abnormalities in genetic mouse models of psychiatric disease.
The two great projects in psychiatry concern the psychiatric diagnostic manual DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013) and the neuroscientific-based psychiatric research project RDoC (Insel et al., American Journal of Psychiatry, 167, 748–751, 2010; Insel & Lieberman, DSM-5 and RDoC: Shared interests. The National Institute of Mental Health, 2013). Insel et al. had considered the latter as a step in improving psychiatric understanding of mental disorder and eventually as a key for a new approach to psychiatric diagnosis. More recently, he considered the two projects as collaborative and mutually informing (Insel & Lieberman, DSM-5 and RDoC: Shared interests. The National Institute of Mental Health, 2013). This chapter first examines the multiple criticisms of the RDoC project, which are eerily similar to those applicable to the DSM-5. Both projects appear insular and focused on the neurobiology of mental disorder, in particular, despite protestations to the contrary.
Next, the chapter describes once more PTSD, but this time as treated in the DSM-5. I review its criteria, its factor structure, and so on, and present recent research and criticisms related to it. I describe my own research on PTSD, including on the excessive symptom combinations possible because of its polythetic structure, especially when possible comorbidities are considered (Young, Lareau, & Pierre, Psychological Injury and Law, 7, 61–74, 2014). It would appear that simplifying the approach to symptom organization for PTSD makes sense, and the same message applies to many DSM-5 disorders.
Objective:
Several lines of evidence indicate that white matter integrity is compromised in bipolar disorder, but the nature, extent, and biological causes remain elusive. To determine the extent to which white matter deficits in bipolar disorder are familial, the authors investigated white matter integrity in a large sample of bipolar patients, unaffected siblings, and healthy comparison subjects.
Method:
The authors collected diffusion imaging data for 64 adult bipolar patients, 60 unaffected siblings (including 54 discordant sibling pairs), and 46 demographically matched comparison subjects. Fractional anisotropy was compared between the groups using voxel-wise tract-based spatial statistics and by extracting mean fractional anisotropy from 10 regions of interest. Additionally, intraclass correlation coefficients were calculated between the sibling pairs as an index of familiality.
Results:
Widespread fractional anisotropy reductions in bipolar patients (>40,000 voxels) and more subtle reductions in their siblings, mainly restricted to the corpus callosum, posterior thalamic radiations, and left superior longitudinal fasciculus (>2,000 voxels) were observed. Similarly, region-of-interest analysis revealed significant reductions in most white matter regions in patients. In siblings, fractional anisotropy in the posterior thalamic radiation and the forceps was nominally reduced. Significant between-sibling correlations were found for mean fractional anisotropy across the tract-based spatial statistic skeleton, within significant clusters, and within nearly all regions of interest.
Conclusions:
These findings emphasize the relevance of white matter to neuropathology and familiality of bipolar disorder and encourage further use of white matter integrity markers as endophenotypes in genetic studies.
Endophenotypes for psychopathology have been conceived as latent, unobserved, but measureable manifestations of phenomena that causally connect genetic liability to clinical disorder. Several decades of research have led to refinement of the construct and identification of some candidate endophenotypes, but rather limited progress on finding the genes involved or the mechanisms by which endophenotypes are driven by genetic and environmental factors and in turn drive psychopathology. Currently promising avenues for research involve development of transdiagnostic concepts not limited to traditional diagnostic categories, measures of endophenotypic and manifest psychopathology that have higher validity than those categories, and methods for modeling complex relationships among diverse contributors to etiology. With more grounding in animal neuroscience and other aspects of basic biological and psychological science, exemplified in the Research Domain Criteria initiative, there is every reason to anticipate that the endophenotype concept will grow more central in the psychopathology literature. Expected final online publication date for the Annual Review of Clinical Psychology Volume 9 is March 26, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Estimation of narrow-sense heritability, h(2), from genome-wide SNPs genotyped in unrelated individuals has recently attracted interest and offers several advantages over traditional pedigree-based methods. With the use of this approach, it has been estimated that over half the heritability of human height can be attributed to the ∼300,000 SNPs on a genome-wide genotyping array. In comparison, only 5%-10% can be explained by SNPs reaching genome-wide significance. We investigated via simulation the validity of several key assumptions underpinning the mixed-model analysis used in SNP-based h(2) estimation. Although we found that the method is reasonably robust to violations of four key assumptions, it can be highly sensitive to uneven linkage disequilibrium (LD) between SNPs: contributions to h(2) are overestimated from causal variants in regions of high LD and are underestimated in regions of low LD. The overall direction of the bias can be up or down depending on the genetic architecture of the trait, but it can be substantial in realistic scenarios. We propose a modified kinship matrix in which SNPs are weighted according to local LD. We show that this correction greatly reduces the bias and increases the precision of h(2) estimates. We demonstrate the impact of our method on the first seven diseases studied by the Wellcome Trust Case Control Consortium. Our LD adjustment revises downward the h(2) estimate for immune-related diseases, as expected because of high LD in the major-histocompatibility region, but increases it for some nonimmune diseases. To calculate our revised kinship matrix, we developed LDAK, software for computing LD-adjusted kinships.
Vulnerability to suicidal behavior (SB) is likely mediated by an underlying genetic predisposition interacting with environmental and probable epigenetic factors throughout the lifespan to modify the function of neuronal circuits, thus rendering an individual more likely to engage in a suicidal act. Improving our understanding of the neuroscience underlying SBs, both attempts and completions, at all developmental stages is crucial for more effective preventive treatments and for better identification of vulnerable individuals. Recent studies have characterized SB using an endophenotype strategy, which aims to identify quantitative measures that reflect genetically influenced stable changes in brain function. In addition to aiding in the functional characterization of susceptibility genes, endophenotypic research strategies may have a wider impact in determining vulnerability to SB, as well as the translation of human findings to animal models, and vice versa. Endophenotypes associated with vulnerability to SB include impulsive/aggressive personality traits and disadvantageous decision making. Deficits in realistic risk evaluation represent key processes in vulnerability to SB. Serotonin dysfunction, indicated by neuroendocrine responses and neuroimaging, is also strongly implicated as a potential endophenotype and is linked with impulsive aggression and disadvantageous decision making. Specific endophenotypes may represent heritable markers for the identification of vulnerable patients and may be relevant targets for successful suicide prevention and treatments.
Background
Neuroimaging investigations of white matter abnormalities in subjects at genetic risk for bipolar disorders (BD) potentially predating the onset of BD offer several advantages. They are not confounded by the presence of illness duration or previous treatment with medication and may ultimately inform evaluation of risk for subsequent development of BD and subsequent therapeutic intervention.
Discussion
Although a number of imaging studies in subjects at genetic risk for BD are available the results are conflicting and no reliable structural markers of genetic liability to bipolar disorders have been proposed. We debate that white matter pathology may be central to the genetic risk to develop BD. Thus, white matter abnormalities detectable in HR subjects but not in controls may reflect genetically driven trait markers. Similar abnormalities may be also evident both in the HR and in BD, suggesting the possibility of genetic risk factors shared by both groups. Conversely, white matter alterations observed in BD patients but not in HR and controls can be interpreted as state markers.
Summary
We suggest that white matter alterations may represent endophenotypes and neurobiological markers intermediate between the underlying susceptibility genes and the clinical expression of BD.
We have used a translational convergent functional genomics (CFG) approach to identify and prioritize genes involved in schizophrenia, by gene-level integration of genome-wide association study data with other genetic and gene expression studies in humans and animal models. Using this polyevidence scoring and pathway analyses, we identify top genes (DISC1, TCF4, MBP, MOBP, NCAM1, NRCAM, NDUFV2, RAB18, as well as ADCYAP1, BDNF, CNR1, COMT, DRD2, DTNBP1, GAD1, GRIA1, GRIN2B, HTR2A, NRG1, RELN, SNAP-25, TNIK), brain development, myelination, cell adhesion, glutamate receptor signaling, G-protein-coupled receptor signaling and cAMP-mediated signaling as key to pathophysiology and as targets for therapeutic intervention. Overall, the data are consistent with a model of disrupted connectivity in schizophrenia, resulting from the effects of neurodevelopmental environmental stress on a background of genetic vulnerability. In addition, we show how the top candidate genes identified by CFG can be used to generate a genetic risk prediction score (GRPS) to aid schizophrenia diagnostics, with predictive ability in independent cohorts. The GRPS also differentiates classic age of onset schizophrenia from early onset and late-onset disease. We also show, in three independent cohorts, two European American and one African American, increasing overlap, reproducibility and consistency of findings from single-nucleotide polymorphisms to genes, then genes prioritized by CFG, and ultimately at the level of biological pathways and mechanisms. Finally, we compared our top candidate genes for schizophrenia from this analysis with top candidate genes for bipolar disorder and anxiety disorders from previous CFG analyses conducted by us, as well as findings from the fields of autism and Alzheimer. Overall, our work maps the genomic and biological landscape for schizophrenia, providing leads towards a better understanding of illness, diagnostics and therapeutics. It also reveals the significant genetic overlap with other major psychiatric disorder domains, suggesting the need for improved nosology.
Univariate genome-wide association analysis of quantitative and qualitative traits has been investigated extensively in the literature. In the presence of correlated phenotypes, it is more intuitive to analyze all phenotypes simultaneously. We describe an efficient likelihood-based approach for the joint association analysis of quantitative and qualitative traits in unrelated individuals. We assume a probit model for the qualitative trait, under which an unobserved latent variable and a prespecified threshold determine the value of the qualitative trait. To jointly model the quantitative and qualitative traits, we assume that the quantitative trait and the latent variable follow a bivariate normal distribution. The latent variable is allowed to be correlated with the quantitative phenotype. Simultaneous modeling of the quantitative and qualitative traits allows us to make more precise inference on the pleiotropic genetic effects. We derive likelihood ratio tests for the testing of genetic effects. An application to the Genetic Analysis Workshop 17 data is provided. The new method yields reasonable power and meaningful results for the joint association analysis of the quantitative trait Q1 and the qualitative trait disease status at SNPs with not too small MAF.
Why do memory abilities vary so greatly across individuals and cognitive domains? Although memory functions are highly heritable, what exactly is being genetically transmitted? Here we review evidence for the contribution of both common and partially independent inheritance of distinct aspects of memory function. We begin by discussing the assessment of long-term memory and its underlying neural and molecular basis. We then consider evidence for both specialist and generalist genes underlying individual variability in memory, indicating that carving memory into distinct subcomponents may yield important information regarding its genetic architecture. And finally we review evidence from both complex and single-gene disorders, which provide insight into the molecular mechanisms underlying the genetic basis of human memory function.
The purpose of this study was to find loci for major depression via linkage analysis of a large sibling pair sample.
The authors conducted a genome-wide linkage analysis of 839 families consisting of 971 affected sibling pairs with severe recurrent major depression, comprising waves I and II of the Depression Network Study cohort. In addition to examining affected status, linkage analyses in the full data set were performed using diagnoses restricted by impairment severity, and association mapping of hits in a large case-control data set was attempted.
The authors identified genome-wide significant linkage to chromosome 3p25-26 when the diagnoses were restricted by severity, which was a maximum LOD score of 4.0 centered at the linkage marker D3S1515. The linkage signal identified was genome-wide significant after correction for the multiple phenotypes tested, although subsequent association mapping of the region in a genome-wide association study of a U.K. depression sample did not provide significant results.
The authors report a genome-wide significant locus for depression that implicates genes that are highly plausible for involvement in the etiology of recurrent depression. Despite the fact that association mapping in the region was negative, the linkage finding was replicated by another group who found genome-wide-significant linkage for depression in the same region. This suggests that 3p25-26 is a new locus for severe recurrent depression. This represents the first report of a genome-wide significant locus for depression that also has an independent genome-wide significant replication.
Uncovering the underlying genetic component of any disease is key to the understanding of its pathophysiology and may open new avenues for development of therapeutic strategies and biomarkers. In the past several years, there has been an explosion of genome-wide association studies (GWAS) resulting in the discovery of novel candidate genes conferring risk for complex diseases, including neurodegenerative diseases. Despite this success, there still remains a substantial genetic component for many complex traits and conditions that is unexplained by the GWAS findings. Additionally, in many cases, the mechanism of action of the newly discovered disease risk variants is not inherently obvious. Furthermore, a genetic region with multiple genes may be identified via GWAS, making it difficult to discern the true disease risk gene. Several alternative approaches are proposed to overcome these potential shortcomings of GWAS, including the use of quantitative, biologically relevant phenotypes. Gene expression levels represent an important class of endophenotypes. Genetic linkage and association studies that utilize gene expression levels as endophenotypes determined that the expression levels of many genes are under genetic influence. This led to the postulate that there may exist many genetic variants that confer disease risk via modifying gene expression levels. Results from the handful of genetic studies which assess gene expression level endophenotypes in conjunction with disease risk suggest that this combined phenotype approach may both increase the power for gene discovery and lead to an enhanced understanding of their mode of action. This review summarizes the evidence in support of gene expression levels as promising endophenotypes in the discovery and characterization of novel candidate genes for complex diseases, which may also represent a novel approach in the genetic studies of Alzheimer's and other neurodegenerative diseases.
We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that ∼45%, ∼17%, ∼25% and ∼21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further ∼0.5-1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.
New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.
Current versions of the DSM and ICD have facilitated reliable clinical diagnosis and research. However, problems have increasingly been documented over the past several years, both in clinical and research arenas (e.g., 1, 2). Diagnostic categories based on clinical consensus fail to align with findings emerging from clinical neuroscience and genetics. The boundaries of these categories have not been predictive of treatment response. And, perhaps most important, these categories, based upon presenting signs and symptoms, may not capture fundamental underlying mechanisms of dysfunction. One consequence has been to slow the development of new treatments targeted to underlying pathophysiological mechanisms.
We performed integrated gene coexpression network analysis on two large microarray-based brain gene expression data sets generated from the prefrontal cortex obtained post-mortem from 101 subjects, 47 subjects with schizophrenia and 54 normal control subjects, ranging in age from 19 to 81 years. Twenty-eight modules of coexpressed genes with functional interpretations were detected in both normal subjects and those with schizophrenia. Significant overlap of "case" and "control" module composition was observed, indicating that extensive differences in underlying molecular connectivity are not likely driving pathology in schizophrenia. Modules of coexpressed genes were characterized according to disease association, cell type specificity, and the effects of aging. We find that genes with altered expression in schizophrenia clustered into distinct coexpression networks and that these were associated primarily with neurons. We further identified a robust effect of age on gene expression modules that differentiates normal subjects from those with schizophrenia. In particular, we report that normal age-related decreases in genes related to central nervous system developmental processes, including neurite outgrowth, neuronal differentiation, and dopamine-related cellular signaling, do not occur in subjects with schizophrenia during the aging process. Extrapolating these findings to earlier stages of development supports the concept that schizophrenia pathogenesis begins early in life and is associated with a failure of normal decreases in developmental-related gene expression. These findings provide a novel mechanism for the "developmental" hypothesis of schizophrenia on a molecular level.
This paper provides a conceptual analysis of the endophenotype (EP) construct that is having an increasing role in genetic strategies for unraveling the etiology of psychiatric disorders (PDs). We make six major points illustrated through the method of path analysis. First, it is important to distinguish between mediational and liability-index (or 'risk indicator') models for EP, as only the former requires genetic risk for PD to pass through EP. Second, the relative reliability of EP and PD can have a critical role in the interpretation of results. Ignoring them can lead to substantial errors of inference. Third, we need to consider bidirectional relationships between an EP and a PD, and the possibility that genetic effects on PD are only partially mediated by EP. Fourth, EP models typically assume that all genetic effects that have an impact on EP also alter risk for PD. However, among the genetic influences on EP and PD, it is also plausible that some will influence only EP, some only PD and some both. Fifth, we should also consider models incorporating multiple EPs and PDs, which can be well captured by multivariate genetic methods. Sixth, EPs may also reflect the impact of the environment on risk for PDs. The EP concept has important potential lessons for etiological research in PDs that can be optimized by considering it as a special case of a broader set of multivariate genetic models, which can be fitted using currently available methodology.
We carried out a genome-wide association study of schizophrenia (479 cases, 2,937 controls) and tested loci with P < 10(-5) in up to 16,726 additional subjects. Of 12 loci followed up, 3 had strong independent support (P < 5 x 10(-4)), and the overall pattern of replication was unlikely to occur by chance (P = 9 x 10(-8)). Meta-analysis provided strongest evidence for association around ZNF804A (P = 1.61 x 10(-7)) and this strengthened when the affected phenotype included bipolar disorder (P = 9.96 x 10(-9)).
The present study demonstrates that schizophrenics are impaired on spatial delayed-response tasks, analogous to those that have been used to assess the working memory function of the dorsolateral prefrontal cortex in rhesus monkeys. Schizophrenic patients and two control groups, normal subjects and bipolar psychiatric patients, were tested on the oculomotor version of the memory task, a haptic version of the same task, and two control tasks: a sensory task that did not require working memory and a digit span test. The schizophrenic patients showed marked deficits relative to the two control groups in both the oculomotor and haptic delayed-response tasks. They were not, however, impaired on the digit span test, which taps verbal working memory as well as voluntary attention, and on the sensory control task, in which their responses were guided by external cues rather than by spatial working memory. These findings provide direct evidence that schizophrenics suffer a loss in representational processing and that this deficit is modality independent. These data on spatial working memory add to the growing evidence for involvement of the dorsolateral prefrontal cortex in schizophrenic disease.
Studies in nonhuman primates provide evidence that intact spatial working memory depends on the integrity of specific areas in the prefrontal cortex. Patients with schizophrenia have been shown to be impaired on spatial working memory tasks. Relatives of schizophrenic patients show a range of cognitive deficits in the absence of clinical symptoms (eg, thought disorder, eye tracking dysfunctions). We predicted that a significant proportion of relatives of schizophrenic patients would show deficits in working memory as measured by a delayed response task.
In experiment 1, we tested 18 schizophrenic patients, 15 first-degree relatives of schizophrenic patients, and 18 normal control subjects on an oculomotor delayed response task. In experiment 2, we assessed the performance of another group of 12 first-degree relatives of schizophrenic patients and 16 different normal control subjects on a visual-manual delayed response task.
Relatives of schizophrenic patients showed significant deficits in working memory on both the oculomotor and visual-manual delayed response tasks.
Some relatives of schizophrenic patients are impaired on tasks that tap spatial working memory and that implicate the prefrontal system. The delayed response paradigm may be useful in elucidating the multidimensionality of the schizophrenic phenotype.
The nature and extent of interindividual and temporal variation in gene expression patterns in specific cells and tissues is an important and relatively unexplored issue in human biology. We surveyed variation in gene expression patterns in peripheral blood from 75 healthy volunteers by using cDNA microarrays. Characterization of the variation in gene expression in healthy tissue is an essential foundation for the recognition and interpretation of the changes in these patterns associated with infections and other diseases, and peripheral blood was selected because it is a uniquely accessible tissue in which to examine this variation in patients or healthy volunteers in a clinical setting. Specific features of interindividual variation in gene expression patterns in peripheral blood could be traced to variation in the relative proportions of specific blood cell subsets; other features were correlated with gender, age, and the time of day at which the sample was taken. An analysis of multiple sequential samples from the same individuals allowed us to discern donor-specific patterns of gene expression. These data help to define human individuality and provide a database with which disease-associated gene expression patterns can be compared.
A major component of variation in body height is due to genetic differences, but environmental factors have a substantial contributory effect. In this study we aimed to analyse whether the genetic architecture of body height varies between affluent western societies. We analysed twin data from eight countries comprising 30,111 complete twin pairs by using the univariate genetic model of the Mx statistical package. Body height and zygosity were self-reported in seven populations and measured directly in one population. We found that there was substantial variation in mean body height between countries; body height was least in Italy (177 cm in men and 163 cm in women) and greatest in the Netherlands (184 cm and 171 cm, respectively). In men there was no corresponding variation in heritability of body height, heritability estimates ranging from 0.87 to 0.93 in populations under an additive genes/unique environment (AE) model. Among women the heritability estimates were generally lower than among men with greater variation between countries, ranging from 0.68 to 0.84 when an additive genes/shared environment/unique environment (ACE) model was used. In four populations where an AE model fit equally well or better, heritability ranged from 0.89 to 0.93. This difference between the sexes was mainly due to the effect of the shared environmental component of variance, which appears to be more important among women than among men in our study populations. Our results indicate that, in general, there are only minor differences in the genetic architecture of height between affluent Caucasian populations, especially among men.
The endophenotype is central to modern developmental psychopathology studies. It is used in studies seeking to connect the genetic substrates of the panoply of major mental disorders with processes, tapped by laboratory and other assessment measures, in the genotype to a behavior/psychopathology pathway. Proposed originally by Gottesman and Shields (1972; Shields & Gottesman, 1973) 41 years ago, the endophenotype concept has gained widespread traction in psychopathology research since the Gottesman and Gould (2003) review. Other concepts broadly related to the endophenotype notion have also generated discussion in experimental and developmental psychopathology research. One is the intermediate phenotype, a concept proffered as a putative alternative formulation to the endophenotype. Another concept in this intellectual vein is biomarker. The terms endophenotype, intermediate phenotype, and biomarker have often been used interchangeably in the psychiatric literature, yielding conceptual confusion. However, these three terms are not fungible. The recent Research Domain Criteria proposal from the National Institute of Mental Health has emphasized selected underlying processes thought to be of developmental etiologic significance to psychopathology. These selected processes will be the focus of energetic future research efforts, many of which will make use of the endophenotype and biomarker research paradigms. In this context, the concepts of endophenotype, intermediate phenotype, and biomarker are examined critically and contrasted in terms of meaning, intention, clarity, and intellectual history. This analysis favors use of the endophenotype concept in genetically informed laboratory and neuroscience studies of psychopathology. The term intermediate phenotype is perhaps best restricted to its originally defined meaning in genetics. Biomarker is used to denote objectively measured biological antecedents or consequences of normal or pathogenic processes or a physiologic response to a therapeutic intervention.
It is well established that risk for developing psychosis is largely mediated by the influence of genes, but identifying precisely which genes underlie that risk has been problematic. Focusing on endophenotypes, rather than illness risk, is one solution to this problem. Impaired cognition is a well-established endophenotype of psychosis. Here we aimed to characterize the genetic architecture of cognition using phenotypically detailed models as opposed to relying on general IQ or individual neuropsychological measures. In so doing we hoped to identify genes that mediate cognitive ability, which might also contribute to psychosis risk. Hierarchical factor models of genetically clustered cognitive traits were subjected to linkage analysis followed by QTL region-specific association analyses in a sample of 1,269 Mexican American individuals from extended pedigrees. We identified four genome wide significant QTLs, two for working and two for spatial memory, and a number of plausible and interesting candidate genes. The creation of detailed models of cognition seemingly enhanced the power to detect genetic effects on cognition and provided a number of possible candidate genes for psychosis. © 2013 Wiley Periodicals, Inc.
To understand the organization and assembly of mammalian brain circuits, we need a comprehensive tool set that can address the challenges of cellular diversity, spatial complexity at synapse resolution, dynamic complexity of circuit operations, and multifaceted developmental processes rooted in the genome. Complementary to physics- and chemistry-based methods, genetic tools tap into intrinsic cellular and developmental mechanisms. Thus, they have the potential to achieve appropriate spatiotemporal resolution and the cellular-molecular specificity necessary for observing and probing the makings and inner workings of neurons and neuronal circuits. Furthermore, genetic analysis will be key to unraveling the intricate link from genes to circuits to systems, in part through systematic targeting and tracking of individual cellular components of neural circuits. Here we review recent progress in genetic tool development and advances in genetic analysis of neural circuits in the mouse. We also discuss future directions and implications for understanding brain disorders. Expected final online publication date for the Annual Review of Neuroscience Volume 36 is July 08, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Statistical genetic analysis of quantitative traits in large pedigrees is a formidable computational task due to the necessity of taking the nonindependence among relatives into account. With the growing awareness that rare sequence variants may be important in human quantitative variation, heritability and association study designs involving large pedigrees will increase in frequency due to the greater chance of observing multiple copies of rare variants among related individuals. Therefore, it is important to have statistical genetic test procedures that utilize all available information for extracting evidence regarding genetic association. Optimal testing for marker/phenotype association involves the exact calculation of the likelihood ratio statistic which requires the repeated inversion of potentially large matrices. In a whole genome sequence association context, such computation may be prohibitive. Toward this end, we have developed a rapid and efficient eigensimplification of the likelihood that makes analysis of family data commensurate with the analysis of a comparable sample of unrelated individuals. Our theoretical results which are based on a spectral representation of the likelihood yield simple exact expressions for the expected likelihood ratio test statistic (ELRT) for pedigrees of arbitrary size and complexity. For heritability, the ELRT is
Obesity is a major predisposing factor for the development of several chronic diseases including non-insulin dependent diabetes mellitus (NIDDM) and coronary heart disease (CHD). Leptin is a serum protein which is secreted by adipocytes1−4 and thought to play a role in the regulation of body fat5−8. Leptin levels in humans have been found to be highly correlated with an individual's total adiposity8,9. We performed a genome-wide scan and conducted multipoint linkage analysis using a general pedigree-based variance component approach to identify genes with measurable effects on quantitative variation in leptin levels in Mexican Americans. A microsatellite polymorphism, D2S1788, mapped to chromosome 2p21 (approximately 74 cM from the tip of the short arm) and showed strong evidence of linkage with serum leptin levels with a lod score of 4.95 (P = 9 10-7). This locus accounted for 47% of the variation in serum leptin levels, with a residual additive genetic component contributing an additional 24%. This region contains several potential candidate genes for obesity, including glucokinase regulatory protein (GCKR) and pro-opiomelanocortin (POMC). Our results show strong evidence of linkage of this region of chromosome 2 with serum leptin levels and indicate that this region could contain an important human obesity gene.
The tremendous heterogeneity in the clinical symptoms and cognitive/emotional deficits seen in patients with schizophrenia has made it challenging to determine the underlying pathogenesis of the illness. One leading hypothesis that has come to the forefront over the past several decades
is that schizophrenia is caused by aberrant connectivity between brain regions. In fact, a new field of connectomics has emerged to study the effects of brain connectivity in health and illness. It is known that schizophrenia is highly heritable, although in the search for the underlying genetic
factors we have only scratched the tips of the omics icebergs. One technique to help identify underlying genetic factors is the use of heritable intermediate phenotypes, or endophenotypes. Endophenotypes provide mechanisms to study the genetic underpinnings of the disorder by focusing on measureable
traits that are more proximal to gene regulation and expression than are symptoms. Thus, the goal of this paper is to conduct a critical review of the evidence linking both structural and functional connectivity as an endophenotype for schizophrenia.
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.
Endophenotypes are heritable quantitative traits that are associated with disease liability, can be measured in both affected
and unaffected individuals, and provide much greater power to localize and identify risk genes for mental illness than does
affection status alone. Traditionally, endophenotypic markers for psychiatric illnesses include in vivo neuroanatomic and functional magnetic resonance imaging measurements and indices of neurocognitive abilities. However, neurocognitive
and neuroimaging measures are by no means the only classes of endophenotypes that could be useful for identifying genes for
mental illness. Given the advantages of endophenotype-based strategies for elucidating the genetic underpinnings of psychiatric
disorders, it would seem prudent to develop a wide range of putative endophenotypes. In order for a measure to be considered
a valid endophenotype, it must meet a number of criteria. Specifically, the trait must (1) have moderate to high heritability,
(2) be associated with the illness, (3) be independent of clinical state, and (4) impairment must co-segregate with the illness
within a family, with non-affected family members showing impairment relative to the general population. While each of these
criteria is critical, the heritability and co-segregation requirements are really what differentiate an endophenotype from
a simple biomarker. At this time, one requires an experimental design that includes families to demonstrate both heritability
and co-segregation. The assertion that novel endophenotypes can not be fully established without family data does not preclude
work in unrelated individuals, rather that unrelated samples will only be able to nominate potential candidate endophenotypes
that subsequently need to be confirmed in family-based experiments.
Keywordsendophenotype–family studies–biomarker–schizophrenia–bipolar disorder
Although biomarker science is a field that is advancing rapidly in medicine as a whole, neurop-sychiatric disorders are still
characterized by an absence of the biomarkers and laboratory tests that will promote new diagnostic and prognostic procedures.
Recent advances in genomic, genetic, epige-netic, neuroscience, proteomic and metabolomic knowledge and technologies have
opened the way to searching for biomarkers, however, it is still a relatively new field for neuropsychiatry. In addition,
candidate endophenotypes, important trait markers widely used for genetic studies, are useful for the development of heritable
diagnostic and prognostic biomarkers (endo-phenotype strategy). This chapter provides definitions of biomarkers and endophenotypes,
elucidating their types and properties that will make them useful in neuropsychiatric research and practice. Recent results
in the schizophrenia and mood disorders literature that illustrate the usefulness of biomarkers and endo-phenotypes are also
reviewed. We predict that both biomarker and endophenotypic approaches will open new avenues for practically important applications
of genetics, neuroscience and “omics” advantages in neuropsychiatry.
Despite overwhelming evidence that major depression is highly heritable, recent studies have localized only a single depression-related locus reaching genome-wide significance and have yet to identify a causal gene. Focusing on family-based studies of quantitative intermediate phenotypes or endophenotypes, in tandem with studies of unrelated individuals using categorical diagnoses, should improve the likelihood of identifying major depression genes. However, there is currently no empirically derived statistically rigorous method for selecting optimal endophentypes for mental illnesses. Here, we describe the endophenotype ranking value, a new objective index of the genetic utility of endophenotypes for any heritable illness.
Applying endophenotype ranking value analysis to a high-dimensional set of over 11,000 traits drawn from behavioral/neurocognitive, neuroanatomic, and transcriptomic phenotypic domains, we identified a set of objective endophenotypes for recurrent major depression in a sample of Mexican American individuals (n = 1122) from large randomly selected extended pedigrees.
Top-ranked endophenotypes included the Beck Depression Inventory, bilateral ventral diencephalon volume, and expression levels of the RNF123 transcript. To illustrate the utility of endophentypes in this context, each of these traits were utlized along with disease status in bivariate linkage analysis. A genome-wide significant quantitative trait locus was localized on chromsome 4p15 (logarithm of odds = 3.5) exhibiting pleiotropic effects on both the endophenotype (lymphocyte-derived expression levels of the RNF123 gene) and disease risk.
The wider use of quantitative endophenotypes, combined with unbiased methods for selecting among these measures, should spur new insights into the biological mechanisms that influence mental illnesses like major depression.
Failure to obtain convincing results in psychiatric genetics can partly be attributed to the fact that progress in molecular biology and genetic epidemiology has not been followed by an equivalent development in phenotypic description. Instead of relying entirely on classical nosological approaches, we argue that identifying more homogeneous forms of diseases through a'candidate symptom approach' among affected subjects and an endophenotype approach that identifies sub-clinical traits among non-affected relatives might yield better results. Examples where these strategies have already been fruitful when applied to complex diseases are presented in this review. Focusing on vulnerability traits might stimulate the redefinition of traditional psychiatric syndromes and help to bridge the gap between clinical and experimental approaches.
Bipolar disorder is a familial psychiatric disorder associated with reduced white matter integrity, but it is not clear whether such abnormalities are present in young unaffected relatives and, if so, whether they have behavioral correlates. We investigated with whole brain diffusion tensor imaging whether increased genetic risk for bipolar disorder is associated with reductions in white matter integrity and whether these reductions are associated with cyclothymic temperament.
Diffusion tensor imaging data of 117 healthy unaffected relatives of patients with bipolar disorder and 79 control subjects were acquired. Cyclothymic temperament was measured with the cyclothymia scale of the Temperament Evaluation of Memphis, Pisa and San Diego auto-questionnaire. Voxel-wise between-group comparisons of fractional anisotropy (FA) and regression of cyclothymic temperament were performed with tract-based spatial statistics.
Compared to the control group, unaffected relatives had reduced FA in one large widespread cluster. Cyclothymic temperament was inversely related to FA in the internal capsules bilaterally and in left temporal white matter, regions also found to be reduced in high-risk subjects.
These results show that widespread white matter integrity reductions are present in unaffected relatives of bipolar patients and that more localized reductions might underpin cyclothymic temperament. These findings suggest that white matter integrity is an endophenotype for bipolar disorder with important behavioral associations previously linked to the etiology of the condition.
The small effect size of most individual risk factors for psychiatric disorders likely reflects biological heterogeneity and diagnostic imprecision, which has encouraged genetic studies of intermediate biological phenotypes that are closer to the molecular effects of risk genes than are the clinical symptoms. Neuroimaging-based intermediate phenotypes have emerged as particularly promising because they map risk associated gene effects onto physiological processes in brain that are altered in patients and in their healthy relatives. Recent evidence using this approach has elucidated discrete, dissociable biological mechanisms of risk genes at the level of neural circuitries, and their related cognitive functions. This approach may greatly contribute to our understanding of the genetics and pathophysiology of psychiatric disorders.
Late-onset Alzheimer disease (LOAD) is a common disorder with a substantial genetic component. We postulate that many disease susceptibility variants act by altering gene expression levels.
We measured messenger RNA (mRNA) expression levels of 12 LOAD candidate genes in the cerebella of 200 subjects with LOAD. Using the genotypes from our LOAD genome-wide association study for the cis-single nucleotide polymorphisms (SNPs) (n = 619) of these 12 LOAD candidate genes, we tested for associations with expression levels as endophenotypes. The strongest expression cis-SNP was tested for AD association in 7 independent case-control series (2,280 AD and 2,396 controls).
We identified 3 SNPs that associated significantly with IDE (insulin degrading enzyme) expression levels. A single copy of the minor allele for each significant SNP was associated with approximately twofold higher IDE expression levels. The most significant SNP, rs7910977, is 4.2 kb beyond the 3' end of IDE. The association observed with this SNP was significant even at the genome-wide level (p = 2.7 x 10(-8)). Furthermore, the minor allele of rs7910977 associated significantly (p = 0.0046) with reduced LOAD risk (OR = 0.81 with a 95% CI of 0.70-0.94), as expected biologically from its association with elevated IDE expression.
These results provide strong evidence that IDE is a late-onset Alzheimer disease (LOAD) gene with variants that modify risk of LOAD by influencing IDE expression. They also suggest that the use of expression levels as endophenotypes in genome-wide association studies may provide a powerful approach for the identification of disease susceptibility alleles.
Although genetic influences on bipolar disorder are well established, localization of genes that predispose to the illness has proven difficult. Given that genes predisposing to bipolar disorder may be transmitted without expression of the categorical clinical phenotype, a strategy for identifying risk genes is to identify and map quantitative intermediate phenotypes or endophenotypes.
To adjudicate neurocognitive endophenotypes for bipolar disorder.
All participants underwent diagnostic interviews and comprehensive neurocognitive evaluations. Neurocognitive measures found to be heritable were entered into analyses designed to determine which test results are impaired in affected individuals, are sensitive to the genetic liability for the illness, and are genetically correlated with affection status.
Central valley of Costa Rica; Mexico City, Mexico; and San Antonio, Texas.
Seven hundred nine Latino individuals participated in the study. Of these, 660 were members of extended pedigrees with at least 2 siblings diagnosed as having bipolar disorder (n = 230). The remaining subjects were community control subjects drawn from each site who did not have a personal or family history of bipolar disorder or schizophrenia.
Neurocognitive test performance.
Two of the 22 neurocognitive variables were not significantly heritable and were excluded from subsequent analyses. Patients with bipolar disorder were impaired on 6 cognitive measures compared with nonrelated healthy controls. Nonbipolar first-degree relatives were impaired on 5 of these, and the following 3 tests were genetically correlated with affection status: Digit Symbol Coding Task, Object Delayed Response Task, and immediate facial memory.
This large-scale extended pedigree study of cognitive functioning in bipolar disorder identifies measures of processing speed, working memory, and declarative (facial) memory as candidate endophenotypes for bipolar disorder.
Genome-wide association (GWA) study is becoming a powerful tool in deciphering genetic basis of complex human diseases/traits. Currently, the univariate analysis is the most commonly used method to identify genes associated with a certain disease/phenotype under study. A major limitation with the univariate analysis is that it may not make use of the information of multiple correlated phenotypes, which are usually measured and collected in practical studies. The multivariate analysis has proven to be a powerful approach in linkage studies of complex diseases/traits, but it has received little attention in GWA. In this study, we aim to develop a bivariate analytical method for GWA study, which can be used for a complex situation in which continuous trait and a binary trait are measured under study. Based on the modified extended generalized estimating equation (EGEE) method we proposed herein, we assessed the performance of our bivariate analyses through extensive simulations as well as real data analyses. In the study, to develop an EGEE approach for bivariate genetic analyses, we combined two different generalized linear models corresponding to phenotypic variables using a seemingly unrelated regression model. The simulation results demonstrated that our EGEE-based bivariate analytical method outperforms univariate analyses in increasing statistical power under a variety of simulation scenarios. Notably, EGEE-based bivariate analyses have consistent advantages over univariate analyses whether or not there exists a phenotypic correlation between the two traits. Our study has practical importance, as one can always use multivariate analyses as a screening tool when multiple phenotypes are available, without extra costs of statistical power and false-positive rate. Analyses on empirical GWA data further affirm the advantages of our bivariate analytical method.
We describe a variance-components method for multipoint linkage analysis that allows joint consideration of a discrete trait and a correlated continuous biological marker (e.g., a disease precursor or associated risk factor) in pedigrees of arbitrary size and complexity. The continuous trait is assumed to be multivariate normally distributed within pedigrees, and the discrete trait is modeled by a threshold process acting on an underlying multivariate normal liability distribution. The liability is allowed to be correlated with the quantitative trait, and the liability and quantitative phenotype may each include covariate effects. Bivariate discrete-continuous observations will be common, but the method easily accommodates qualitative and quantitative phenotypes that are themselves multivariate. Formal likelihood-based tests are described for coincident linkage (i.e., linkage of the traits to distinct quantitative-trait loci [QTLs] that happen to be linked) and pleiotropy (i.e., the same QTL influences both discrete-trait status and the correlated continuous phenotype). The properties of the method are demonstrated by use of simulated data from Genetic Analysis Workshop 10. In a companion paper, the method is applied to data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate linkage analysis of alcoholism diagnoses and P300 amplitude of event-related brain potentials.
Osteocalcin (OC) is an important constituent of bone that is synthesized by osteoblasts. Serum levels of OC have been used as a biochemical marker of bone turnover. To identify the genes influencing variation in serum OC levels, we conducted a genome-wide scan in 429 individuals comprising 10 large multigenerational families. OC levels were measured by immunoassay, and genetic markers were typed at approximately 10-cM intervals across the genome. Quantitative trait linkage was tested using a multipoint analysis based on variance component methodology, adjusting for the effects of age, sex, and oral contraceptive use. Significance levels for linkage were obtained empirically, by Monte Carlo simulation. The heritability of OC levels in this population was 62 +/- 8%. We detected significant evidence for linkage between a quantitative trait locus influencing serum OC levels and markers on chromosome 16q, and suggestive evidence for linkage of OC levels with markers on chromosome 20q. The multipoint lod scores peaked at 3.35 on chromosome 16 and 2.78 on chromosome 20, corresponding to P values of 0.00004 and 0.00017, respectively. A potential candidate gene for bone formation in the linked region on chromosome 20 is CDMP1, which encodes cartilage-derived morphogenetic protein 1. Future studies should evaluate whether variation in CDMP1 or in other genes in the linked regions on chromosomes 16 and 20 influence the rate of bone turnover.
Genetic influences on psychiatric disorders are well established. However, localization of the genes responsible for these effects has proved extremely difficult. One emerging strategy that may circumvent some of these difficulties is the use of quantitative risk factors, or endophenotypes, which are correlated with disease and may be closer to underlying genetic liability and to gene action than are diagnostic phenotypes. Genetic studies of quantitative endophenotypes require different sampling and analysis strategies than studies of disease state. The rationale for using quantitative risk factors as indicators of disease liability and the optimal study design for localizing genes influencing such risk factors are discussed.
The Edinburgh High Risk Study concerns 162 young people aged 16 to 25 at ascertainment who have at least two close relatives with schizophrenia. They are compared with two control groups (1) of age-matched well subjects and (2) of age-matched subjects with first schizophrenic episodes. The interim results show that schizophrenia has developed in 10 high-risk subjects and no controls and that all categories of psychopathology are more marked in the high-risk subjects. Psychopathology shows no relationships with measures of genetic liability. Neuropsychological measures are most impaired in the individuals with first-episode schizophrenia, with high-risk subjects performing better and well controls better still. The greater the genetic liability of the high-risk subjects, the poorer the neuropsychological performance. Neuropsychological impairments occurred in more high-risk subjects than are expected to develop schizophrenia. Structural brain scans show significant differences between those with first-episode schizophrenia, high-risk subjects, and well controls. Brain structure is related to genetic liability in that high-risk subjects with higher genetic liability have smaller right and left prefrontal lobes and smaller right and left thalami. In those high-risk subjects with two scans, there was a significantly greater reduction in temporal lobe size in those with psychotic symptoms than in those without. It is suggested that in high-risk subjects, the change from vulnerability to psychosis may be preceded by reduction in size and deteriorating function of the temporal lobe.
Our goal was to establish whether altered hippocampal morphology represents a trait marker for genetic vulnerability in schizophrenia. We outlined the hippocampi on high-resolution MR images obtained from matched samples of control and discordant monozygotic and dizygotic co-twins (N = 40 pairs). Hippocampal measures were used in statistical tests specifically designed to identify disease-associated genetic and nongenetic influences on morphology. 3D surface average maps of the hippocampus were additionally compared in biological risk groups. Smaller hippocampal volumes were confirmed in schizophrenia. Dizygotic affected co-twins showed smaller left hippocampi compared to their healthy siblings. Disease-associated effects were not present between monozygotic discordant co-twins. Monozygotic, but not dizygotic, unaffected co-twins exhibited smaller left hippocampi compared to control twins, supporting genetic influences. Surface areas and posterior volumes similarly revealed schizophrenia and genetic liability effects. Results suggest that hippocampal volume reduction may be a trait marker for identifying individuals possessing a genetic predisposition for schizophrenia.
Endophenotypes, measurable components unseen by the unaided eye along the pathway between disease and distal genotype, have emerged as an important concept in the study of complex neuropsychiatric diseases. An endophenotype may be neurophysiological, biochemical, endocrinological, neuroanatomical, cognitive, or neuropsychological (including configured self-report data) in nature. Endophenotypes represent simpler clues to genetic underpinnings than the disease syndrome itself, promoting the view that psychiatric diagnoses can be decomposed or deconstructed, which can result in more straightforward-and successful-genetic analysis. However, to be most useful, endophenotypes for psychiatric disorders must meet certain criteria, including association with a candidate gene or gene region, heritability that is inferred from relative risk for the disorder in relatives, and disease association parameters. In addition to furthering genetic analysis, endophenotypes can clarify classification and diagnosis and foster the development of animal models. The authors discuss the etymology and strategy behind the use of endophenotypes in neuropsychiatric research and, more generally, in research on other diseases with complex genetics.
Spatial working memory impairments are among the neurocognitive deficits that may mark genetic predisposition toward schizophrenia. We previously reported that impairment on the spatial span subtask of the Wechsler Adult Intelligence Scale-Revised increased in a dose-dependent manner with increasing genetic predisposition toward schizophrenia in a sample of discordant twins; however, it remains to be determined whether these deficits reflect difficulties with encoding, maintenance, manipulation, time-tagging of visual spatial information, storage capacity, or complex motor response.
We developed a spatial delayed response task in which memory set size was parametrically varied, holding constant manipulation and decision processes. We then reassessed 80 of the previously studied twins (17 probands with 8 monozygotic co-twins and 13 dizygotic co-twins, and 42 healthy twins).
The spatial delayed response task was sensitive to genetic loading for schizophrenia but did not provide evidence for capacity limitations in probands or their co-twins.
The findings suggest that deficits in the encoding or storage aspects of short-term spatial mnemonic processing may be an effective endophenotypic marker for schizophrenia.
The genetic basis of thrombosis is complex, involving multiple genes and environmental factors. The field of common complex disease genetics has progressed enormously over the past 10 years with the development of powerful new molecular and analytical strategies that enable localization and identification of the causative genetic variants. During the course of these advances, a major paradigmatic change has been taking place that focuses on the genetic analysis of measurable quantitative traits that are correlated with disease risk vs. the previous emphasis on the analysis of the much less informative dichotomous disease trait. Because of their closer proximity to direct gene action, disease-related quantitative phenotypes represent our best chance to identify the underlying quantitative trait loci (QTLs) that influence disease susceptibility. This approach works best when data can be collected on extended families. Unfortunately, family-based designs are still relatively rare in thrombosis/hemostasis studies. In this review, we detail the reasons why the field would benefit from a more vigorous pursuit of modern family-based genetic studies.
Alcoholism is a complex disorder involving both genetic and environmental factors and interactions between them. Localizing and characterizing the genetic influences on susceptibility to alcohol dependence may provide new insights into pathology and new avenues for treatment and prevention. However, because of the complex nature of the disorder, the binary categorization of individuals as affected or unaffected may be a poor indicator of their underlying genetic susceptibility. Quantitative risk factors, or endophenotypes, that differentiate levels of severity among affected individuals and levels of susceptibility among unaffected individuals, provide one solution to this problem. Genetic studies of such quantitative risk factors in families of probands with alcohol dependence may help to disentangle the complex genetic architecture of this disorder.
To investigate whether a deficient response inhibition is a cognitive endophenotype of attention-deficit/hyperactivity disorder (ADHD). The authors hypothesized that nonaffected siblings of ADHD probands would have a response inhibition between that of ADHD probands and normal controls, although they resembled the controls at a behavioral level.
Participants were 25 ADHD probands with a family history of ADHD, their nonaffected siblings (n = 25), and 48 normal controls matched for age and IQ. All participants were between 6 and 17 years of age. The nonaffected siblings were compared with their ADHD siblings and with controls on measures reflecting different types of response inhibition.
The nonaffected siblings had results similar to those of the ADHD probands, who differed from the controls on all inhibition measures (p <.05).
Siblings of ADHD probands, while not behaviorally expressing the disorder, have ADHD-associated deficits in response inhibition. This suggests that subtyping based on measures of response inhibition can help identify genetic susceptibility to ADHD. Children with a genetic vulnerability to ADHD may have hidden cognitive deficits in the absence of manifest behavioral symptoms. Therefore, they should be monitored to detect possible learning problems.
We performed a genome scan using BMD data of the forearm and hip on 664 individuals in 29 Mexican-American families. We obtained evidence for QTL on chromosome 4p, affecting forearm BMD overall, and on chromosomes 2p and 13q, affecting hip BMD in men.
The San Antonio Family Osteoporosis Study (SAFOS) was designed to identify genes and environmental factors that influence bone mineral density (BMD) using data from large Mexican-American families.
We performed a genome-wide linkage analysis using 416 highly polymorphic microsatellite markers spaced approximately 9.5 cM apart to locate and identify quantitative trait loci (QTL) that affect BMD of the forearm and hip. Multipoint variance components linkage analyses were done using data on all 664 subjects, as well as two subgroups of 259 men and 261 premenopausal women, from 29 families for which genotypic and phenotypic data were available.
We obtained significant evidence for a QTL affecting forearm (radius midpoint) BMD in men and women combined on chromosome 4p near D4S2639 (maximum LOD = 4.33, genomic p = 0.006) and suggestive evidence for a QTL on chromosome 12q near locus D12S2070 (maximum conditional LOD = 2.35). We found suggestive evidence for a QTL influencing trochanter BMD on chromosome 6 (maximum LOD = 2.27), but no evidence for QTL affecting the femoral neck in men and women combined. In men, we obtained evidence for QTL affecting neck and trochanter BMD on chromosomes 2p near D2S1780 (maximum LOD = 3.98, genomic p = 0.013) and 13q near D13S788 (maximum LOD = 3.46, genomic p = 0.039), respectively. We found no evidence for QTL affecting forearm or hip BMD in premenopausal women.
These results provide strong evidence that a QTL on chromosome 4p affects radius BMD in Mexican-American men and women, as well as evidence that QTL on chromosomes 2p and 13q affect hip BMD in men. Our results are consistent with some reports in humans and mice. J Bone Miner Res 2003;18:2245-2252
The scientific process of localization and subsequent identification of genes influencing risk of common diseases is still in its infancy. Initial localization of disease-related loci has traditionally been performed using family-based linkage methods to scan the genome. Early pronouncements of the failure of this approach for common diseases were premature and based on comparing suboptimal linkage designs with overly optimistic and empirically unproven association-based designs. On the contrary, substantial recent progress in the positional cloning of genes influencing such complex phenotypes suggests that modern approaches based around a family-based linkage paradigm will be successful. In particular, the rapidly growing emphasis on the analysis of the genetic basis of quantitative correlates of disease risk represents a novel and promising approach in which initial localization is performed using linkage and subsequent identification utilizes association approaches in positional candidate genes.