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ObjectivesTo determine whether clinically extremely vulnerable (CEV) children or children living with a CEV person in Wales were at greater risk of presenting with anxiety or depression in primary or secondary care during the COVID-19 pandemic compared with children in the general population, and to compare patterns of anxiety and depression during the pandemic (23rd March 2020–31st January 2021, referred to as 2020/21) and before the pandemic (March 23rd 2019–January 31st 2020, referred to as 2019/20), between CEV children and the general population.DesignPopulation-based cross-sectional cohort study using anonymised, linked, routinely collected health and administrative data held in the Secure Anonymised Information Linkage Databank. CEV individuals were identified using the COVID-19 Shielded Patient List.SettingPrimary and secondary healthcare settings covering 80% of the population of Wales.ParticipantsChildren aged 2–17 in Wales: CEV (3,769); living with a CEV person (20,033); or neither (415,009).Primary outcome measureFirst record of anxiety or depression in primary or secondary healthcare in 2019/20 and 2020/21, identified using Read and ICD-10 codes.ResultsA Cox regression model adjusted for demographics and history of anxiety or depression revealed that only CEV children were at greater risk of presenting with anxiety or depression during the pandemic compared with the general population (Hazard Ratio=2.27, 95% Confidence Interval=1.94–2.66, pConclusionsDifferences in the cumulative incidences of recorded anxiety or depression in healthcare between CEV children and the general population were largely driven by a reduction in presentations to healthcare services by children in the general population during the pandemic.Strengths and limitations of this studyStrengths of this study include its novelty, national focus and clinical relevance; to date this is the first population-based study examining the effects of the COVID-19 pandemic on healthcare use for anxiety or depression amongst clinically extremely vulnerable (CEV) children and children living with a CEV person in WalesWe compared 2020/21 data with pre-pandemic 2019/20 data for CEV children and children in the general population, to place the impact of the COVID-19 pandemic in the context of longer-term patterns of healthcare useWe used a novel approach and linked multiple datasets to identify a cohort of children living with a CEV person in Wales during the COVID-19 pandemicThere was heterogeneity within the Shielded Patient List that was used to create the cohorts of children identified as CEV or living with a CEV person, in terms of the type and severity of individuals’ underlying conditions; the manner in which people were added to the list; the time point that people were added to the list; and the extent to which people followed the shielding guidanceRoutinely collected healthcare data does not capture self-reported health, and is likely to underestimate the burden of common mental disorders in the population

Background Response to the early stages of the COVID-19 pandemic resulted in the temporary disruption of cancer screening in the UK, and strong public messaging to stay safe and to protect NHS capacity. Following reintroduction in services, we explored the impact on inequalities in uptake of the Bowel Screening Wales (BSW) programme to identify groups who may benefit from tailored interventions. Methods Records within the BSW were linked to electronic health records (EHR) and administrative data within the Secured Anonymised Information Linkage (SAIL) Databank. Ethnic group was obtained from a linked data method available within SAIL. We examined uptake for the first 3 months of invitations (August to October) following the reintroduction of BSW programme in 2020, compared to the same period in the preceding 3 years. Uptake was measured across a 6 month follow-up period. Logistic models were conducted to analyse variations in uptake by sex, age group, income deprivation quintile, urban/rural location, ethnic group, and clinically extremely vulnerable (CEV) status in each period; and to compare uptake within sociodemographic groups between different periods. Results Uptake during August to October 2020 (period 2020/21; 60.4%) declined compared to the same period in 2019/20 (62.7%) but remained above the 60% Welsh standard. Variation by sex, age, income deprivation, and ethnic groups was observed in all periods studied. Compared to the pre-pandemic period in 2019/20, uptake declined for most demographic groups, except for older individuals (70–74 years) and those in the most income deprived group. Uptake continues to be lower in males, younger individuals, people living in the most income deprived areas and those of Asian and unknown ethnic backgrounds. Conclusion Our findings are encouraging with overall uptake achieving the 60% Welsh standard during the first three months after the programme restarted in 2020 despite the disruption. Inequalities did not worsen after the programme resumed activities but variations in CRC screening in Wales associated with sex, age, deprivation and ethnic group remain. This needs to be considered in targeting strategies to improve uptake and informed choice in CRC screening to avoid exacerbating disparities in CRC outcomes as screening services recover from the pandemic.

Background: Care home residents have complex healthcare needs but may have faced barriers to accessing hospital treatment during the first wave of the COVID-19 pandemic. Objective: To examine trends in the number of hospital admissions for care home residents during the first months of the COVID-19 outbreak. Methods: Retrospective analysis of a national linked dataset on hospital admissions for residential and nursing home residents in England (257,843 residents, 45% in nursing homes) between 20 January 2020 and 28 June 2020, compared to admissions during the corresponding period in 2019 (252,432 residents, 45% in nursing homes). Elective and emergency admission rates, normalised to the time spent in care homes across all residents, were derived across the first three months of the pandemic between 1 March and 31 May and primary admissions reasons for this period were compared across years. Results: Hospital admission rates rapidly declined during early March 2020 and remained substantially lower than in 2019 until the end of June. Between March and May, 2,960 admissions from residential homes (16.2%) and 3,295 admissions from nursing homes (23.7%) were for suspected or confirmed COVID-19. Rates of other emergency admissions decreased by 36% for residential and by 38% for nursing home residents (13,191 fewer admissions in total). Emergency admissions for acute coronary syndromes fell by 43% and 29% (105 fewer admission) and emergency admissions for stroke fell by 17% and 25% (128 fewer admissions) for residential and nursing home residents, respectively. Elective admission rates declined by 64% for residential and by 61% for nursing home residents (3,762 fewer admissions). Conclusions: This is the first study showing that care home residents’ hospital use declined during the first wave of COVID-19, potentially resulting in substantial unmet health need that will need to be addressed alongside ongoing pressures from COVID-19.

BackgroundThe medical burden in mood disorders is high; various factors are thought to drive this pattern. Little research has examined the role of childhood maltreatment and its effects on medical morbidity in adulthood among people with unipolar depression and bipolar disorder.AimsThis is the first study to explore the association between childhood maltreatment and medical morbidity in bipolar disorder and in unipolar depression, and examine whether the impact of abuse and neglect are distinct or combined.MethodThe participants consisted of 354 psychiatrically healthy controls, 248 participants with recurrent unipolar depression and 72 with bipolar disorder. Participants completed the Childhood Trauma Questionnaire and received a validated medical history interview.ResultsAny type of childhood maltreatment, child abuse and child neglect were significantly associated with the medical burden in bipolar disorder, but not unipolar depression or for controls. These associations worked in a dose–response fashion where participants with bipolar disorder with a history of two or more types of childhood maltreatment had the highest odds of having a medical illness relative to those without such history or those who reported one form. No such significant dose–response patterns were detected for participants with unipolar depression or controls.ConclusionsThese findings suggest that childhood maltreatment may play a stronger role in the development of medical illnesses in individuals with bipolar disorder relative to those with unipolar depression. Individuals who had been maltreated with a mood disorder, especially bipolar disorder may benefit most from prevention and intervention efforts surrounding physical health.Declaration of interestNone.

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

The accurate estimation of age using methylation data has proved a useful and heritable biomarker, with acceleration in epigenetic age predicting a number of age-related phenotypes. Measures of white matter integrity in the brain are also heritable and highly sensitive to both normal and pathological aging processes across adulthood. We consider the phenotypic and genetic interrelationships between epigenetic age acceleration and white matter integrity in humans. Our goal was to investigate processes that underlie interindividual variability in age-related changes in the brain. Using blood taken from a Mexican-American extended pedigree sample (n= 628; age = 23.28–93.11 years), epigenetic age was estimated using the method developed by Horvath (2013). Forn= 376 individuals, diffusion tensor imaging scans were also available. The interrelationship between epigenetic age acceleration and global white matter integrity was investigated with variance decomposition methods. To test for neuroanatomical specificity, 16 specific tracts were additionally considered. We observed negative phenotypic correlations between epigenetic age acceleration and global white matter tract integrity (ρpheno= −0.119,p= 0.028), with evidence of shared genetic (ρgene= −0.463,p= 0.013) but not environmental influences. Negative phenotypic and genetic correlations with age acceleration were also seen for a number of specific white matter tracts, along with additional negative phenotypic correlations between granulocyte abundance and white matter integrity. These findings (i.e., increased acceleration in epigenetic age in peripheral blood correlates with reduced white matter integrity in the brain and shares common genetic influences) provide a window into the neurobiology of aging processes within the brain and a potential biomarker of normal and pathological brain aging.SIGNIFICANCE STATEMENTEpigenetic measures can be used to predict age with a high degree of accuracy and so capture acceleration in biological age, relative to chronological age. The white matter tracts within the brain are also highly sensitive to aging processes. We show that increased biological aging (measured using epigenetic data from blood samples) is correlated with reduced integrity of white matter tracts within the human brain (measured using diffusion tensor imaging) with data from a large sample of Mexican-American families. Given the family design of the sample, we are also able to demonstrate that epigenetic aging and white matter tract integrity also share common genetic influences. Therefore, epigenetic age may be a potential, and accessible, biomarker of brain aging.

INTRODUCTION: It’s imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. METHODS: Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. RESULTS: All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. CONCLUSIONS: The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

IntroductionIt’s imperative to understand the specific and shared aetiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression.MethodsPolygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic aetiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in eleven data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank.ResultsAll cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression.ConclusionsThe phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic aetiology irrespective of the age depression first presents.

BACKGROUND Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to construct comprehensive patient profiles for delivering better health care. Reusing NLP models in new settings, however, remains cumbersome, as it requires validation and retraining on new data iteratively to achieve convergent results. OBJECTIVE The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. METHODS We formally define and analyze the model adaptation problem in phenotype-mention identification tasks. We identify “duplicate waste” and “imbalance waste,” which collectively impede efficient model reuse. We propose a phenotype embedding–based approach to minimize these sources of waste without the need for labelled data from new settings. RESULTS We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% waste (duplicate waste), that is, phenotype mentions without the need for validation and model retraining and with very good performance (93%-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% waste (imbalance waste), that is, the effort required in “blind” model-adaptation approaches. CONCLUSIONS Adapting pretrained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.

Emergency admission rates for ambulatory care-sensitive conditions (ACSCs) have been used by both researchers and policy makers as an indicator to assess healthcare systems.1–3 ACSCs are a set of conditions such as asthma and diabetes, where the need for emergency admissions is thought to be avoidable.4 5 They are designed to capture how ambulatory care might impact on rates of emergency admissions. However, in this issue of BMJ Quality and Safety , Lynch et al 6 question the merits of distinguishing ACSCs from other emergency admissions. Examining factors associated with county-level variation in emergency admissions in Ireland, they found similar factors were associated with both all emergency admissions and ACSCs. Lynch and colleagues concluded that ‘the distinction between potentially avoidable and all other emergency admissions may not be as useful as previously believed’.6 Here, we discuss the value of using ACSCs. We argue that they give important information on potentially avoidable demand for healthcare, but can only be used as an effective indicator after accounting for the drivers of demand for care that are common to all hospital admissions. Further, to reduce potentially avoidable emergency admissions, we must understand the mechanisms which link population factors such as deprivation to higher ACSC admission rates. In addition to understanding demand, ACSCs can provide indirect insights into the quality of ambulatory care provided, although it is important to recognise that they do not capture all facets of high quality care. Policy makers generally seek to reduce emergency hospital admission rates (ie, where a patient is admitted to hospital urgently and the admission is unplanned). Emergency admissions to hospitals carry risks to patients; for vulnerable patients, even short admissions can lead to infection, loss of mobility or increased frailty from inactivity. If treatment is unavoidable, many patients would prefer to be …

Background and aims While the prevalence of major depression is elevated among cannabis users, the role of genetics in this pattern of comorbidity is not clear. This study aimed to estimate the heritability of cannabis use and major depression, quantify the genetic overlap between these two traits and localize regions of the genome that segregate in families with cannabis use and major depression. Design Family-based univariate and bivariate genetic analysis. Setting San Antonio, Texas, USA. Participants Genetics of Brain Structure and Function study (GOBS) participants: 1284 Mexican Americans from 75 large multi-generation families and an additional 57 genetically unrelated spouses. Measurements Phenotypes of life-time history of cannabis use and major depression, measured using the semistructured MINI-Plus interview. Genotypes measured using ~1 M single nucleotide polymorphisms (SNPs) on Illumina BeadChips. A subselection of these SNPs were used to build multi-point identity-by-descent matrices for linkage analysis. Findings Both cannabis use [h2 = 0.614, P = 1.00 × 10−6, standard error (SE) = 0.151] and major depression (h2 = 0.349, P = 1.06 × 10−5, SE = 0.100) are heritable traits, and there is significant genetic correlation between the two (ρg = 0.424, P = 0.0364, SE = 0.195). Genome-wide linkage scans identify a significant univariate linkage peak for major depression on chromosome 22 [logarithm of the odds (LOD) = 3.144 at 2 centimorgans (cM)], with a suggestive peak for cannabis use on chromosome 21 (LOD = 2.123 at 37 cM). A significant pleiotropic linkage peak influencing both cannabis use and major depression was identified on chromosome 11 using a bivariate model (LOD = 3.229 at 112 cM). Follow-up of this pleiotropic signal identified a SNP 20 kb upstream of NCAM1 (rs7932341) that shows significant bivariate association (P = 3.10 × 10−5). However, this SNP is rare (seven minor allele carriers) and does not drive the linkage signal observed. Conclusions There appears to be a significant genetic overlap between cannabis use and major depression among Mexican Americans, a pleiotropy that appears to be localized to a region on chromosome 11q23 that has been linked previously to these phenotypes.

Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline., SCOPUS: ar.j, info:eu-repo/semantics/published

Background Much effort has been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records in order to construct comprehensive patient profiles for delivering better health care. Reusing NLP models in new settings, however, remains cumbersome, as it requires validation and retraining on new data iteratively to achieve convergent results. Objective The aim of this work is to minimize the effort involved in reusing NLP models on free-text medical records. Methods We formally define and analyze the model adaptation problem in phenotype-mention identification tasks. We identify “duplicate waste” and “imbalance waste,” which collectively impede efficient model reuse. We propose a phenotype embedding–based approach to minimize these sources of waste without the need for labelled data from new settings. Results We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype-mention identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% waste (duplicate waste), that is, phenotype mentions without the need for validation and model retraining and with very good performance (93%-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% waste (imbalance waste), that is, the effort required in “blind” model-adaptation approaches. Conclusions Adapting pretrained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype-mention embedding approach is an effective way to model language patterns for phenotype-mention identification tasks and that its use can guide efficient NLP model reuse.

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757-3770, 2017. © 2017 Wiley Periodicals, Inc.

Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h2 = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts.

BackgroundPsychiatric comorbidity is common among individuals with addictive disorders, with patients frequently suffering from anxiety disorders. While the genetic architecture of comorbid addictive and anxiety disorders remains unclear, elucidating the genes involved could provide important insights into the underlying etiology.MethodsHere we examine a sample of 1284 Mexican-Americans from randomly selected extended pedigrees. Variance decomposition methods were used to examine the role of genetics in addiction phenotypes (lifetime history of alcohol dependence, drug dependence or chronic smoking) and various forms of clinically relevant anxiety. Genome-wide univariate and bivariate linkage scans were conducted to localize the chromosomal regions influencing these traits.ResultsAddiction phenotypes and anxiety were shown to be heritable and univariate genome-wide linkage scans revealed significant quantitative trait loci for drug dependence (14q13.2-q21.2, LOD = 3.322) and a broad anxiety phenotype (12q24.32-q24.33, LOD = 2.918). Significant positive genetic correlations were observed between anxiety and each of the addiction subtypes (ρg = 0.550–0.655) and further investigation with bivariate linkage analyses identified significant pleiotropic signals for alcohol dependence-anxiety (9q33.1-q33.2, LOD = 3.054) and drug dependence-anxiety (18p11.23-p11.22, LOD = 3.425).ConclusionsThis study confirms the shared genetic underpinnings of addiction and anxiety and identifies genomic loci involved in the etiology of these comorbid disorders. The linkage signal for anxiety on 12q24 spans the location of TMEM132D, an emerging gene of interest from previous GWAS of anxiety traits, whilst the bivariate linkage signal identified for anxiety-alcohol on 9q33 peak coincides with a region where rare CNVs have been associated with psychiatric disorders. Other signals identified implicate novel regions of the genome in addiction genetics.

Background Obtaining sufficient statistical power has been a key issue in the study of genetics in depression; only recently have samples become large enough to identify any genome-wide significant hits for this phenotype. Researchers have used a range of different approaches to tackle the issue of balancing sample size with phenotypic depth, and here we evaluate the statistical power achieved with these different methods. Methods We collated details of published genome-wide association studies exploring major depression or depressive symptoms. For each study we assessed sampling methods, phenotyping criteria and sample size, and use GPC (Genetics Power Calculator, Purcell et al. 2003) and extension methods (Yang et al. 2010, Traylor et al. 2015) to calculate statistical power, and compare approaches within the literature. We compared the statistical power (as captured by the non-centrality parameter) of each cohort across a range of allele thresholds and genetic effect sizes, contrasting different methodologies of cohort ascertainment within the depression literature. We evaluate how statistical power is affected by the selection of potentially more homogenous but less prevalent depression phenotypes, the potential impact of misclassification in large cohorts which have employed brief phenotypic screening questions, and the value of dimensional measures of depression symptoms in population-based cohorts. Results We confirm the findings of Traylor et al. apply in the case of depression; that in the analysis of specific subtypes of depression performed in datasets including PGC2 (2013, secondary analysis of recurrent, early-onset recurrent and typical-like depression, observed in approximately 75%, 50% and 40% of cases respectively) and CONVERGE (2015, secondary analysis of melancholia observed in approximately 85% of cases), very modest increases in genotypic relative risk (GRR) are required to achieve equivalent power with the whole sample analyses, particularly for less common risk allele frequencies and lower index GRR. We also compare the statistical power of the PGC2 case-control sample (discovery sample n=18,759) and the study from the CHARGE Consortium (discovery sample n= 34,549, Hek et al. 2013) looking at depression symptoms, observing that with a sample size which is 54% of that in CHARGE, the PGC2 dataset has less than half the statistical power to detect genetic effects. Discussion Given range of approaches, here we give evaluation of expected statistical power across a range of approaches specific to the field of major depression genetics. We compare the statistical power achieved in the current literature and evaluate how different phenotypic approaches affect this. If the ultimate aim of our research is to improve outcomes for patients with depression, our phenotypic definitions must be informed by clinical relevance. But nevertheless, an understanding of how statistical power is impacted by these definitions is key to optimal study design for the identification of the genetic variants associated with depressive disorders.

Because its etiology remains largely unexplained, major depression, like other psychiatric diseases, is understood entirely on the basis of symptomatology. Major depression is the most common mental illness and is responsible for substantial mortality, morbidity, and disability. Arguably we know less about the root causes of major depression than about other major mental illnesses (eg, schizophrenia, bipolar disorder, autism). In the current chapter, we examine the literature on the prevalence, diagnostic heterogeneity, risk factors, neuroanatomy, neurophysiology, heritability, endophenotypes, and genetic architecture of major depressive disorder. In addition, we briefly discuss current treatments. Whereas epidemiological results stress the heterogeneity and complex nature of the illness, neuroimaging-based models typically ignore the diversity of clinical factors, potentially limiting their usefulness. Although certainly influenced by environmental factors, there is ample evidence for a genetic component to major depression. However, to date no specific genomic variant or gene has been implicated for depression.

Although several genome-wide significant loci have been localized for schizophrenia and bipolar disorder, these findings explain a fraction of the genetic variance predisposing these illnesses and, in general, have yet to result in true gene identifications. Furthermore, there have been no similar findings for the more common mental illnesses like depression and anxiety disorders. Yet, progress in elucidating the pathophysiology of mental illnesses is predicated on causal gene identification. Focusing on quantitative endophenotypes, traits that index genetic liability for an illness, rather than diagnoses alone, provides a complementary strategy for identifying risk genes. Quantitative endophenotypes established in family-based studies of clinical samples typically vary within the normal population, providing the opportunity to localize genes influencing these traits in unselected pedigrees. Such genes are then validated in case-control samples. This normal endophenotypic variation strategy has been successfully applied to identify disease-risk genes for heart disease, obesity, and diabetes and there is no a priori reason this strategy can not be as profitable for mental illnesses. Here, I will discuss our recent work with applying neurocognitive, neuroimage and transcriptional endophenotypes in search of risk genes for affective and psychotic illnesses. Specifically, I will present work from the “Genetics of Brain Structure and Function” study, which involves acquisition of behavioral, neurocognitive and neuroimaging endophenotypes in ~2000 Mexican Americans from randomly-selected extended pedigrees. All participants have high-density SNP arrays, transcriptional data from two time points and ~1000 individuals have genome-wide sequence data. Our approach involves localizing loci for an endophenotype via genome-wide linkage or association, identifying the non-synonymous or regulatory variants diving that effect with sequence data, and then demonstrating pleiotropy with published association studies. Our results provide clear examples of how endophenotypes can provide novel genetic insights for mental illness.

Background While the prevalence of major depression is known to be elevated amongst cannabis users, the causes of this comorbidity are not clear. Here we investigate the role of genetics in this relationship and identify genomic loci linked to these traits. Methods Using a sample of Mexican American extended families (n=1,284), we use variance decomposition methods to establish the degree of genetic correlation between cannabis use and major depression. Genome-wide univariate and bivariate linkage scans are conducted to localize the chromosomal regions influencing these traits and the comorbidity observed between them. Results Both major depression (h2=0.349, p=1.06x10-5, SE=0.100) and cannabis use (h2=0.614, p=1.00x10-6, SE=0.151) are heritable traits, and there is significant genetic correlation between the two (ρg=0.424, p=0.0364, SE=0.195). Genome-wide linkage scans identify a significant univariate linkage peak for major depression on chromosome 22 (LOD=3.144 at 2cM), with a suggestive peak for cannabis use on chromosome 21 (LOD=2.123 at 37cM). A significant pleiotropic linkage peak influencing both major depression and cannabis use was identified on chromosome 11, using a bivariate model (LOD=3.229 at 112cM). This location spans the NCAM1-TTC12-ANKK1-DRDR2 gene cluster. Follow-up of this pleiotropic signal provided tentative evidence implicating a rare SNP 20kb upstream of NCAM1 (s7932341); with peak-wide significant bivariate association with cannabis use and major depression (p=3.10x10-5). Discussion We show that genetic influences play an important role in the comorbidity between cannabis use and major depression.Specifically, we identify a pleiotropic locus on chromosome 11, spanning the NCAM1-TTC12-ANKK1-DRDR2 gene cluster, which has been previously implicated in both addiction and depression research.

Background Biological age acceleration as measured by aggregate epigenetic markers has been associated with a number of phenotypes including Parkinson’s, obesity and physical fitness in the elderly. Additionally, epigenetic indices of age acceleration are touted as molecular biomarkers of brain aging, based on findings linking epigenetic age of prefrontal cortex tissue with Alzheimer’s-related phenotypes. Yet, in order to develop a clinically useful brain age acceleration biomarker, peripheral tissue, rather than neural tissue, is preferable. Likewise, in vivo neuroimaging measures are desirable. To that end, we examined the relationship between blood-derived epigenetic measures of age acceleration and white matter integrity, as indexed with diffusion weighted imaging (DWI), in a large randomly ascertained family cohort. Our goal was to investigate processes that underlie inter-individual variability in age-related changes in the white matter tracts. DWI measures of white mater integrity are among the most sensitive imaging measures of aging with demonstrated heritabilities. Methods Using blood DNA methylation data taken from a Mexican-American extended pedigree sample (n=634; mean age=49.16y, range 28.11y-97.52y), epigenetic age was estimated using the method developed by S. Horvath (2013). Epigenetic age acceleration was calculated as epigenetic age regressed upon age, sex, age x sex, age2, age2 x sex, and blood cell count estimates. 379 of these individuals had available DWI scans collected on a Siemens 3T Trio MRI located at the Research Imaging Institute, UTHSCSA. An average fractional anisotropy (FA) map was derived for each subject and skeletonized using the TBSS algorithm, providing FA measures for each of 16 white matter tracts and a global index. Variance decomposition methods were then used to investigate the interrelationship between epigenetic age acceleration and white matter integrity and identify genetic influences on these two phenotypes. Results Consistent with previous reports, both epigenetic age acceleration and white matter integrity measures are heritable in this sample. We observe significant (FDR Discussion Here we demonstrate that age acceleration, as measured via methylation profiles from peripheral blood, is significantly correlated with white matter integrity in a number of tracts within the brain. We also demonstrate evidence of shared genetic influences acting on both age acceleration and white matter integrity. These findings provide an interesting window into the neurobiology of aging processes within the brain and a potential new biomarker of normal and pathological brain aging.

Rationale Cytochrome P450 enzymes are important in the metabolism of antidepressants. The highly polymorphic nature of these enzymes has been linked to variability in antidepressant metabolism rates, leading to hope regarding the use of P450 genotyping to guide treatment. However, evidence that P450 genotypic differences underlie the variation in treatment outcomes is inconclusive. Objectives We explored the links between both P450 genotype and serum concentrations of antidepressant with antidepressant side effects, using data from the Genome-Based Therapeutic Drugs for Depression Project (GENDEP), which is a large (n = 868), pharmacogenetic study of depressed individuals treated with escitalopram or nortriptyline. Methods Patients were genotyped for the enzymes CYP2C19 and CYP2D6, and serum concentrations of both antidepressant and primary metabolite were measured after 8 weeks of treatment. Side effects were assessed weekly. We investigated associations between P450 genotypes, serum concentrations of antidepressants and side effects, as well as the relationship between P450 genotype and study discontinuation. Results P450 genotype did not predict total side effect burden (nortriptyline: n = 251, p = 0.5638, β = −0.133, standard error (SE) = 0.229; escitalopram: n = 340, p = 0.9627, β = −0.004, SE = 0.085), study discontinuation (nortriptyline n = 284, hazard ratio (HR) = 1.300, p = 0.174; escitalopram n = 376, HR = 0.870, p = 0.118) or specific side effects. Serum concentrations of antidepressant were only related to a minority of the specific side effects measured: dry mouth, dizziness and diarrhoea. Conclusions In this sample where antidepressant dosage is titrated using clinical judgement, P450 genotypes do not explain differences between patients in side effects with antidepressants. Serum drug concentrations appear to only explain variability in the occurrence of a minority of specific side effects. Electronic supplementary material The online version of this article (doi:10.1007/s00213-015-3898-x) contains supplementary material, which is available to authorized users.

The mechanisms by which antidepressants have their effects are not clear and the reasons for variability in treatment outcomes are also unknown. However, there is evidence from candidate gene research that indicates gene expression changes may be involved in antidepressant action. In this study, we examined antidepressant-induced alterations in gene expression on a transcriptome-wide scale, exploring associations with treatment response. Blood samples were taken from a subset of depressed patients from the GENDEP study (n=136) before and after eight weeks of treatment with either escitalopram or nortriptyline. Transcriptomic data were obtained from these samples using Illumina HumanHT-12 v4 Expression BeadChip microarrays. When analysing individual genes, we observed that changes in the expression of two genes (MMP28 and KXD1) were associated with better response to nortriptyline. Considering connectivity between genes, we identified modules of genes that were highly coexpressed. In the whole sample, changes in one of the ten identified coexpression modules showed significant correlation with treatment response (cor=0.27, p=0.0029). Using transcriptomic approaches, we have identified gene expression correlates of the therapeutic effects of antidepressants, highlighting possible molecular pathways involved in efficacious antidepressant treatment.

Changes in the blood expression levels of SAT1, PTEN, MAP3K3 and MARCKS genes have been reported as biomarkers of high versus low suicidality state (Le-Niculescu et al.). Here, we investigate these expression biomarkers in the Genome-Based Therapeutic Drugs for Depression (GENDEP) study, of patients with major depressive disorder on a 12-week antidepressant treatment. Blood gene expression levels were available at baseline and week 8 for patients who experienced suicidal ideation during the study (n=20) versus those who did not (n=37). The analysis is well powered to detect the effect sizes reported in the original paper. Within either group, there was no significant change in the expression of these four genes over the course of the study, despite increasing suicidal ideation or initiation of antidepressant treatment. Comparison of the groups showed that the gene expression did not differ between patients with or without treatment-related suicidality. This independent study does not support the validity of the proposed biomarkers., SCOPUS: ar.j, info:eu-repo/semantics/published

Background: The unwanted side effects associated with antidepressants are key determinants of treatment adherence in depression; propensity to experience these adverse drug reactions (ADRs) may be influenced by genetic variation. However, previous work attempting to ascertain the genetic variants involved has had limited success, in part due to the range of ADRs reported with antidepressants. Method: ADRs reported with antidepressant treatment were categorised using their likely pharmacological basis; adrenergic, cholinergic, serotonergic and histaminergic. To identify genetic predictors of susceptibility to each group of ADRs, a candidate gene analysis was performed with data from 431 depressed patients (from a total sample size of 811 patients) enrolled in the Genome-Based Therapeutic Drugs for Depression (GENDEP) project, who were randomly allocated to receive treatment with escitalopram or nortriptyline. Data from 474 patients treated with citalopram or reboxetine in the GenPod project (total sample of 601 patients) were used for replication of significant findings. Results: We found no significant predictors of presumed adrenergic, cholinergic and histaminergic ADRs. Putative serotonergic ADRs were significantly associated with variation in the gene encoding the serotonin 2C receptor ( HTR2C, rs6644093, odds ratio (OR)=1.72, 95% confidence interval (CI)=1.31−2.25, p=7.43×10−5) in GENDEP. However, this finding was not replicated in GenPod. Conclusions: The association between serotonergic side effects and variation in the HTR2C gene in the GENDEP sample supports the hypothesis that serotonin receptor-mediated mechanisms underlie these adverse reactions, however this finding was not replicated in GenPod.

Aims: Antidepressant response varies between patients, possibly due to differences in the rate cytochrome P450 enzymes metabolise antidepressants into inactive compounds. Drug metabolism rates are influenced by common variants in the genes encoding these enzymes. However, it remains unclear whether treatment outcomes can be predicted by either CYP450 genotype or antidepressant serum concentration. Methods: In GENDEP (a pharmacogenetic study of depressed individuals treated with either escitalopram or nortriptyline), serum concentrations of antidepressants and their primary metabolite were measured after eight weeks treatment and variants in CYP2D6 and CYP2C19 were genotyped. Results: Amongst patients taking escitalopram (n=223), the genotype CYP2C19 was significantly associated with escitalopram serum concentrations and desmethylescitalopram:escitalopram ratio. For those taking nortriptyline (n=161), the CYP2D6 genotype was significantly associated with nortriptyline and 10-hydroxynortriptyline serum concentrations and 10-hydroxynortriptyline:nortrip-tyline ratio. CYP450 genotypes conferring greater enzyme activity were linked to lower drug serum concentrations and higher metabolite:drug ratios. Nonetheless, no significant association was found between either CYP450 genotype or antidepressant serum concentration and treatment response. Conclusions: While there is a significant relationship between the CYP450 genotype and serum concentrations of escitalopram and nortriptyline, the genotypes are not predictive of differences in treatment response for either drug. Furthermore, differences in antidepressant serum concentrations are not associated with variability in treatment response.

We test the hypothesis that the functional Val66Met polymorphism of BDNF interacts with recent life events to produce onset of new depressive episodes. We also explore the possibility that the Met allele of this polymorphism interacts with childhood maltreatment to increase the risk of chronic depression.In a risk-enriched combined sample of unrelated women, childhood maltreatment and current life events were measured with the Childhood Experience of Care and Abuse, and Life Events and Difficulties Schedule interviews. Chronic episodes of depression (12 months or longer) during adulthood and onset of a major depressive episode during a 12-month follow-up were established with the Schedules for Clinical Assessment in Neuropsychiatry interview.Met alleles of BDNF moderated the relationship between recent life events and adult onsets of depression in a significant gene-environment interaction (interaction risk difference 0.216, 95% CI 0.090-0.342; P =.0008). BDNF did not significantly influence the effect of childhood maltreatment on chronic depression in the present sample.The Met allele of BDNF increases the risk of a new depressive episode following a severe life event. The BDNF and the serotonin transporter gene length polymorphism (5-HTTLPR) and BDNF may contribute to depression through distinct mechanisms involving interactions with childhood and adulthood adversity respectively, which may, in combination, be responsible for a substantial proportion of depression burden in the general population.

Since the publication of the working draft of the human genome just over a decade ago, there have been dramatic advances in our understanding of the role genetics play in both normal human functioning as well as in disease. The identification of genes, which influence an individual's susceptibility to depression, is not only an intriguing scientific endeavour in its own right, but further, if a gene can be confidently implicated in depression, then this could shed light on the aetiological processes involved in the disease. Moreover, a genetic association with depression may identify targets for consideration in the development of novel treatments for the illness. This chapter will summarise the current research into the genetic basis of depression. A number of genes of interest have been highlighted, although a genetic variant, that is unequivocally associated with increased risk for the disease, is yet to be identified. However, technologies and methodologies are evolving rapidly, and genetic approaches have helped shape how we conceptualise depression as an illness.

Humans bargaining over money tend to reject unfair offers, whilst chimpanzees bargaining over primary rewards of food do not show this same motivation to reject. Whether such reciprocal fairness represents a predominantly human motivation has generated considerable recent interest. We induced either moderate or severe thirst in humans using intravenous saline, and examined responses to unfairness in an Ultimatum Game with water. We ask if humans also reject unfair offers for primary rewards. Despite the induction of even severe thirst, our subjects rejected unfair offers. Further, our data provide tentative evidence that this fairness motivation was traded-off against the value of the primary reward to the individual, a trade-off determined by the subjective value of water rather than by an objective physiological metric of value. Our data demonstrate humans care about fairness during bargaining with primary rewards, but that subjective self-interest may limit this fairness motivation.

Antidepressants are among the most commonly prescribed drugs, and a range of medications are available. However, treatment response to a particular drug varies greatly between patients, with only 30% of patients responding well to the first treatment administered. Given evidence that antidepressant treatment response is a heritable trait, together with technological advances in genetic research, three recently published genome-wide investigations into antidepressant responses have examined the determinants of variability in treatment outcomes between depressed patients. Here, we review these studies within the context of wider research efforts to identify treatment response predictors. Some interesting genes have been implicated, but no variants have yet been robustly and reliably linked to response. This may suggest that genetic effect sizes are smaller than originally anticipated. Candidate gene approaches in these samples have lent support to the involvement of serotonergic, glutamatergic and stress-response systems in treatment response, although corroborative evidence from genome-wide analyses indicates these results should be interpreted cautiously. Closer examination of antidepressant response, considering it as a complex trait, has indicated that multiple genes of small effect are likely to be involved. Furthermore, there is some evidence that genetic influence on response to treatment may vary between patients with different symptom profiles or environmental exposures. This has implications for the translation of pharmacogenetic findings into clinical practice: genotypic information from multiple loci and data on non-genetic factors are likely to be needed to tailor antidepressant treatment to the individual patient.

Value-based choices are influenced both by risk in potential outcomes and by whether outcomes reflect potential gains or losses. These variables are held to be related in a specific fashion, manifest in risk aversion for gains and risk seeking for losses. Instead, we hypothesized that there are independent impacts of risk and loss on choice such that, depending on context, subjects can show either risk aversion for gains and risk seeking for losses or the exact opposite. We demonstrate this independence in a gambling task, by selectively reversing a loss-induced effect (causing more gambling for gains than losses and the reverse) while leaving risk aversion unaffected. Consistent with these dissociable behavioral impacts of risk and loss, fMRI data revealed dissociable neural correlates of these variables, with parietal cortex tracking risk and orbitofrontal cortex and striatum tracking loss. Based on our neural data, we hypothesized that risk and loss influence action selection through approach–avoidance mechanisms, a hypothesis supported in an experiment in which we show valence and risk-dependent reaction time effects in line with this putative mechanism. We suggest that in the choice process risk and loss can independently engage approach–avoidance mechanisms. This can provide a novel explanation for how risk influences action selection and explains both classically described choice behavior as well as behavioral patterns not predicted by existing theory.

Since the publication of the working draft of the human genome just over a decade ago, there have been dramatic advances in our understanding of the role genetics play in both normal human functioning as well as in disease. The identification of genes, which influence an individual’s susceptibility to depression, is not only an intriguing scientific endeavour in its own right, but further, if a gene can be confidently implicated in depression, then this could shed light on the aetiological processes involved in the disease. Moreover, a genetic association with depression may identify targets for consideration in the development of novel treatments for the illness. This chapter will summarise the current research into the genetic basis of depression. A number of genes of interest have been highlighted, although a genetic variant, that is unequivocally associated with increased risk for the disease, is yet to be identified. However, technologies and methodologies are evolving rapidly, and genetic approaches have helped shape how we conceptualise depression as an illness.

model of depression based on the observation that exposure to uncontrollable stressors induces behavior deficits. Adult male rats were submitted to LH pre-test session (40 inescapable footshocks: 0.4mA, 10 s duration, 30−90 s interval) and were treated with vehicle, imipramine 15mg/kg (IMI) or desipramine 25mg/kg (DES) for seven days, and were tested 1 hour after the last injection. The test session consisted of 30 escapable footshocks (0.4mA, 10 s duration, 30−90 s interval) preceded by a tone (60dB, 670Hz) that started 5 s before each shock and lasted until its end. Animals could avoid the shock during sound presentation or interrupt its presentation by crossing to the opposite side of the chamber (escape). Absence of one of these behaviors was considered an escape failure. An independent group of rats was submitted to LH pre-test session (stressed group) or habituation in the same apparatus (non-stressed group), and then the rats were treated with vehicle, IMI or DES for seven days, and killed 1 hour after the last injection in order to have their dorsal hippocampus dissected. P2RX7 expression in hippocampus was determined by western blotting. IMI (t14= 2.84; p< 0.05) or DES (t17= 3.16; p< 0.05) treatment decreased the number of escape failures of rats submitted to the LH paradigm. Both, IMI [F2,20 = 5.47; p< 0.05] and DES [F2,14 = 4.61; p< 0.05] attenuated the stress-induced P2RX7 expression in the dorsal hippocampus of rats submitted to PT. The post-hoc analysis indicates that IMI-, but not DES-treated animals show significant difference from vehicle-treated groups, but both showed no difference from naive animals (NewmanKeuls, p< 0.05). Our data suggests that the behavioral effects induced by stress are associated to increased expression of P2RX7 in hippocampus, and imipramine treatment blunted both molecular and behavioral effects of stress. On the other hand, treatment with desipramine only partially attenuated the levels of P2X7R. Additional experiments are necessary to elucidate if the differential effects of imipramine and desipramine are related to the preferential effect of this drugs upon serotonergic/noradrenergic signaling.