144 research outputs found
Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors
No full textGWAS of Suicide Attempt in Psychiatric Disorders and Association With Major Depression Polygenic Risk Scores
No full textShared Genetic Risk Between Psychiatric and Cognitive Symptoms in Huntington's Disease and in the General Population
No full textClassical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression
No full textFeasibility and application of polygenic score analysis to the morphology of human-induced pluripotent stem cells
No full textGenome-wide association studies have identified thousands of significant associations between genetic variants and complex traits. Inferring biological insights from these associations has been challenging. One approach attempted has been to examine the effects of individual variants in cellular models. Here, I demonstrate the feasibility of examining the aggregate effect of many variants on cellular phenotypes. I examine the effects of polygenic scores for cross-psychiatric disorder risk, schizophrenia, body mass index and height on cellular morphology, using 1.5 million induced pluripotent stem cells (iPSC) from 60 European-ancestry donors from the Human iPSC Initiative dataset. I show that measuring multiple cells per donor provides sufficient power for polygenic score analyses, and that cross-psychiatric disorder risk is associated with cell area (p = 0.004). Combined with emerging methods of high-throughput iPSC phenotyping, cellular polygenic scoring is a promising method for understanding potential biological effects of the polygenic component of complex traits. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00438-022-01905-2
Measuring equity in health care financing - reflections on (and alternatives to) the World Health Organization's fairness of financing index
Get PDFIn its latest World Health Report, The World Health Organization (WHO) argues that a key dimension of a health system's performance is the fairness of its financing system. The report discusses how policymakers can improve this aspect of performance, proposes an index of fairness, discusses how it should be put into operation, and presents a league table of countries, ranked by fairness with which their health services are financed. The author shows that the WHO index cannot discriminate between health financing systems that are regressive, and those that are progressive - and cannot discriminate between horizontal inequity, and progressiveness, or regressiveness. The index cannot tell policymakers whether it deviates from 1 (complete fairness) because households with similar incomes spend different amounts on health care (horizontal inequity), or because households with different incomes spend different proportions of their income on health care (vertical inequity, given the WHO's interpretation of the ability-to-pay principle) - although the two have different policy implications. With the WHO's index, progressiveness, and regressiveness are both treated as unfair. This makes no sense, because policymakers who may be strongly averse to regressive payments (which worsen income distribution) may in the name of fairness be quite receptive to progressive payments (requiring that the better-off, who may be willing to spend proportionately more on health care, are required to pay proportionately more). The author compares the WHO index with an alternative, and more illuminating approach developed in the income redistribution literature in the early 1990s, and used in the late 1990s, to study the fairness of various OECD health care financing systems. He illustrates the differences between the approaches with an empirical comparison, using data on out-of-pocket payments for health services in Vietnam for 1993 and 1998. This analysis is of some interest in its own right, given the large share of health spending from out-of-pocket payments in Vietnam, and the changes in fees, and drug prices over the 1990s.
No Evidence for Passive Gene-Environment Correlation or the Influence of Genetic Risk for Psychiatric Disorders on Adult Body Composition via the Adoption Design
No full textThe relationship between genetic and environmental risk is complex and for many traits, estimates of genetic effects may be inflated by passive gene-environment correlation. This arises because biological offspring inherit both their genotypes and rearing environment from their parents. We tested for passive gene-environment correlation in adult body composition traits using the ‘natural experiment’ of childhood adoption, which removes passive gene-environment correlation within families. Specifically, we compared 6165 adoptees with propensity score matched non-adoptees in the UK Biobank. We also tested whether passive gene-environment correlation inflates the association between psychiatric genetic risk and body composition. We found no evidence for inflation of heritability or polygenic scores in non-adoptees compared to adoptees for a range of body composition traits. Furthermore, polygenic risk scores for anorexia nervosa, attention-deficit/hyperactivity disorder and schizophrenia did not differ in their influence on body composition traits in adoptees and non-adoptees. These findings suggest that passive gene-environment correlation does not inflate genetic effects for body composition, or the influence of psychiatric disorder genetic risk on body composition. Our design does not look at passive gene-environment correlation in childhood, and does not test for ‘pure’ environmental effects or the effects of active and evocative gene-environment correlations, where child genetics directly influences home environment. However, these findings suggest that genetic influences identified for body composition in this adult sample are direct, and not confounded by the family environment provided by biological relatives.</p
Exploring the genetic heterogeneity in major depression across diagnostic criteria
No full textMajor depressive disorder (MDD) is defined differently across genetic research studies and this may be a key source of heterogeneity. While previous literature highlights differences between minimal and strict phenotypes, the components contributing to this heterogeneity have not been identified. Using the cardinal symptoms (depressed mood/anhedonia) as a baseline, we build MDD phenotypes using five components—(1) five or more symptoms, (2) episode duration, (3) functional impairment, (4) episode persistence, and (5) episode recurrence—to determine the contributors to such heterogeneity. Thirty-two depression phenotypes which systematically incorporate different combinations of MDD components were created using the mental health questionnaire data within the UK Biobank. SNP-based heritabilities and genetic correlations with three previously defined major depression phenotypes were calculated (Psychiatric Genomics Consortium (PGC) defined depression, 23andMe self-reported depression and broad depression) and differences between estimates analysed. All phenotypes were heritable (h2SNP range: 0.102–0.162) and showed substantial genetic correlations with other major depression phenotypes (Rg range: 0.651–0.895 (PGC); 0.652–0.837 (23andMe); 0.699–0.900 (broad depression)). The strongest effect on SNP-based heritability was from the requirement for five or more symptoms (1.4% average increase) and for a long episode duration (2.7% average decrease). No significant differences were noted between genetic correlations. While there is some variation, the two cardinal symptoms largely reflect the genetic aetiology of phenotypes incorporating more MDD components. These components may index severity, however, their impact on heterogeneity in genetic results is likely to be limited.</p
Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors
Get PDFBackground: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders
ukbtools: An R package to manage and query UK Biobank data
No full textAbstractSummaryThe UK Biobank is a resource that includes detailed health-related data on about 500,000 individuals and is available to the research community. ukbtools removes all the upfront data wrangling required to get a single dataset for statistical analysis, and provides tools to assist in quality control, query of disease diagnoses, and retrieval of genetic metadata.AvailabilityThe package is available for installation from the Comprehensive R Archive Network (CRAN), and includes a vignette describing the use of all functionality.Contact [email protected], https://github.com/kenhanscombe/ukbtools</jats:sec
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