124 research outputs found
Correction: Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research
No full textCorrection to: Molecular Psychiatry 10.1038/s41380-024-02878-x, published online 27 December 2024
In this article the author’s name Wouter J. Peyrot was incorrectly written as Wouter Peyrout.
The original article has been corrected
Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS
No full textPsychiatric disorders are highly genetically correlated, but little research has been conducted on the genetic differences between disorders. We developed a new method (case–case genome-wide association study; CC-GWAS) to test for differences in allele frequency between cases of two disorders using summary statistics from the respective case–control GWAS, transcending current methods that require individual-level data. Simulations and analytical computations confirm that CC-GWAS is well powered with effective control of type I error. We applied CC-GWAS to publicly available summary statistics for schizophrenia, bipolar disorder, major depressive disorder and five other psychiatric disorders. CC-GWAS identified 196 independent case–case loci, including 72 CC-GWAS-specific loci that were not significant at the genome-wide level in the input case–control summary statistics; two of the CC-GWAS-specific loci implicate the genes KLF6 and KLF16 (from the Krüppel-like family of transcription factors), which have been linked to neurite outgrowth and axon regeneration. CC-GWAS loci replicated convincingly in applications to datasets with independent replication data
Genome-wide gene-environment interaction in depression: a systematic evaluation of candidate genes: the childhood trauma working-group of PGC-MDD
No full textGene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome-wide SNP data for a genome-wide case-control GxE model and GxE case-only analyses testing for an enrichment of associated SNPs. No genome-wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD.Sandra Van der Auwera, Wouter J. Peyrot, Yuri Milaneschi, Johannes Hertel, Bernhard Baune, Gerome Breen, Enda Byrne, Erin C. Dunn, Helen Fisher, Georg Homuth, Douglas Levinson, Cathryn Lewis, Natalie Mills, Niamh Mullins, Matthias Nauck, Giorgio Pistis, Martin Preisig, Marcella Rietschel, Stephan Ripke, Patrick Sullivan, Alexander Teumer, Henry Völzke, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Dorret I. Boomsma, Naomi R. Wray, Brenda Penninx, Hans Grab
The potential impact of falsely classified controls on the SNP-based heritability of depression
No full textCorrection: Genome-wide gene-environment analyses of major depressive disorder and reported lifetime traumatic experiences in UK Biobank
No full textGenetic correlations of polygenic disease traits: from theory to practice
No full textThe genetic correlation describes the genetic relationship between two traits and can contribute to a better understanding of the shared biological pathways and/or the causality relationships between them. The rarity of large family cohorts with recorded instances of two traits, particularly disease traits, has made it difficult to estimate genetic correlations using traditional epidemiological approaches. However, advances in genomic methodologies, such as genome-wide association studies, and widespread sharing of data now allow genetic correlations to be estimated for virtually any trait pair. Here, we review the definition, estimation, interpretation and uses of genetic correlations, with a focus on applications to human disease
Insulin therapy attitudes and beliefs of physicians in middle eastern arab countries
No full textBackground: Studies assessing attitudes and beliefs of physicians regarding insulin therapy in Arab countries are scant despite the high prevalence of type 2 diabetes mellitus (T2DM). Objective: This study examines family physicians' attitudes and beliefs towards insulin therapy in T2DM patients in the East Mediterranean Region of the World Organization of National Colleges, Academies and Academic Associations of General Practitioners-Family Physicians. Methods: This is a cross-sectional study conducted on 348 family physicians invited via email to fill an anonymous online questionnaire about their attitudes, beliefs and perceived barriers regarding insulin initiation in T2DM patients. Results: One hundred and twenty-two physicians completed the questionnaire. Of the 122 physicians, 73.6percent preferred to delay insulin initiation until it is absolutely essential and 59.0percent initiated it themselves. The majority agreed that T2DM patients benefit from insulin prior to the development of complications (85.7percent) and that patient education is important (99.1percent) and uncomplicated (74.7percent). Sixty-three per cent expressed reluctance to start insulin mostly because of perceived patients' reluctance. Referral to endocrinologists to initiate insulin therapy was associated with inadequate experience and concern about risks, particularly in elderly patients (backward logistic regression, P 0.05). Physicians' reluctance to initiate insulin therapy was associated with patients' perception of insulin initiation as a personal failure and threat to the quality of life (backward logistic regression, P 0.05). Conclusions: Although family physicians in the Arab world believe in the benefits of insulin therapy, many are reluctant to initiate it themselves. Further studies are needed per country, as well as multiple measures to minimize the physicians' barriers to insulin prescription.Brunton SA, 2005, J FAM PRACT S, V54, pS1; Calvert MJ, 2007, BRIT J GEN PRACT, V57, P455; DeWitt DE, 2003, JAMA-J AM MED ASSOC, V289, P2254; Hayes RP, 2008, INT J CLIN PRACT, V62, P860, DOI 10.1111-j.1742-1241.2008.01742.x; Hirbli KI, 2005, DIABETES CARE, V28, P1262, DOI 10.2337-diacare.28.5.1262; Inzucchi SE, 2012, DIABETES CARE, V35, P1364, DOI [10.2337-dc12-0413, 10.2337-dc12-041.3]; Jabbour S, 2008, INT J CLIN PRACT, V62, P845, DOI 10.1111-j.1742-1241.2008.01757.x; Kunt T, 2009, INT J CLIN PRACT, V63, P6, DOI 10.1111-j.1742-1241.2009.02176.x; Nichols GA, 2007, J GEN INTERN MED, V22, P453, DOI 10.1007-s11606-007-0139-y; Peyrot M, 2005, DIABETES CARE, V28, P2673, DOI 10.2337-diacare.28.11.2673; Peyrot Mark, 2010, Prim Care Diabetes, V4 Suppl 1, pS11, DOI 10.1016-S1751-9918(10)60004-6; Polonsky WH, 2005, DIABETES CARE, V28, P2543, DOI 10.2337-diacare.28.10.2543; Polonsky WH, 2011, CURR MED RES OPIN, V27, P1169, DOI 10.1185-03007995.2011.573623; Riddle MC, 2002, DIABETES METAB RES, V18, pS42, DOI 10.1002-dmrr.277; Rubino A, 2007, DIABETIC MED, V24, P1412, DOI 10.1111-j.1464-5491.2007.02279.x; Shah BR, 2005, DIABETES CARE, V28, P600, DOI 10.2337-diacare.28.3.600; Turner RC, 1998, LANCET, V352, P837; Valensi P, 2008, INT J CLIN PRACT, V62, P1809, DOI 10.1111-j.1742-1241.2008.01917.x; Wild S, 2004, DIABETES CARE, V27, P1047, DOI 10.2337-diacare.27.5.1047; Ziemer DC, 2005, DIABETES EDUCATOR, V31, P564, DOI 10.1177-01457217052790500
Exploring Boundaries for the Genetic Consequences of Assortative Mating for Psychiatric Traits
No full textImportance Considerable partner resemblances have been found for a wide range of psychiatric disorders, meaning that partners of affected individuals have an increased risk of being affected compared with partners of unaffected individuals. If this resemblance is reflected in genetic similarity between partners, genetic risk is anticipated to accumulate in offspring, but these potential consequences have not been quantified and have been left implicit. Observations The anticipated consequences of partner resemblance on prevalence and heritability of psychiatric traits in the offspring generation were modeled for disorders with varying heritabilities, population prevalence (lifetime risk), and magnitudes of partner resemblance. These models facilitate interpretation for a wide range of psychiatric disorders, such as autism, schizophrenia, and depression. The genetic consequences of partner resemblance are most pronounced when attributable to phenotypic assortment (driven by the psychiatric trait). Phenotypic assortment results in increased genetic variance in the offspring generation, which may result in increased heritability and population prevalence. These consequences add generation after generation to a limit, but assortative mating is unlikely to balance the impact of reduced fecundity of patients with psychiatric disorders in the long term. This modeling suggests that the heritabilities of psychiatric disorders are unlikely to increase by more than 5% from 1 generation of assortative mating (maximally 13% across multiple generations). The population prevalence will increase most for less common disorders with high heritability; for example, the prevalence of autism might increase by 1.5-fold after 1 generation of assortative mating (≥2.4-fold in the long term) depending on several assumptions. Conclusions and Relevance The considerable partner resemblances found for psychiatric disorders deserve more detailed interpretation than has been provided thus far. Although the limitations of modeling are emphasized, the anticipated consequences are at most modest for the heritability but may be considerable for the population prevalence of rare disorders with a high heritability
Genome-wide association studies of polygenic risk score-derived phenotypes may lead to inflated false positive rates
No full textDissecting depression heterogeneity using cross-disorder genetic patterns
No full textA diagnosis of Major Depression (MD) can be made from many different constellations of symptoms1, meaning that depression is not a uniform construct, even though it is often treated as such. This heterogeneity complicates both fundamental research on the biological substrates of depression and results in a high variability of treatment success. Different subtypes of depression have been proposed that have been based on clinical features or identified with data-driven methods2–4. Differences in liability to depression are partly due to differences in genetic variation5 and different subtypes of depression have been shown to be related to partly different underlying genetic influences3,6,7. The high polygenicity of psychiatric disorders8 coupled with the large genetic overlap that they share with each other9 mean that it is unreasonable to expect that genetic influences for psychiatric disorders adhere to DSM-defined borders. Instead, individuals with a certain disorder will not only carry a genetic liability for that particular disorder, but will also have mixtures of liabilities to other disorders, even if those disorders are not expressed at a phenotypic level. These cross-disorder genetic influences have the potential to explain differences in symptoms expressed within disorders10. In line with the idea that genetic liability for psychiatric disorders do not occur in isolation from each other, five broad latent factors representing shared liability across disorders for neurodevelopmental, compulsive, thought, substance use and internalizing disorders (Figure 1) have been shown to underlie genetic correlations among major psychiatric disorders11. Here, we will assess whether these cross-disorder genetic factors can explain differences seen in symptomatology within depressive patients.
To do this, we will perform several analyses (details can be found in section 6 below) in the UK Biobank (UKB, n=500.000)12 with replication analyses in the Netherlands Study of Depression and Anxiety (NESDA, n=2981)13,14. In the UKB sample, we will first select individuals with a diagnosis of MD and categorize them into clinically meaningful subgroups. These subgroups largely follow those found in a previous study3 and are based on the presence of vegetative symptoms, depression severity, presence of comorbid anxiety, age at onset, recurrence of depression, presence of suicidality, level of impairment, presence of postpartum depression, presence of childhood trauma and the atypical/energy-related symptom profile (see section 4). We will perform genome-wide association studies (GWAS) on these subgroups, after which we can estimate the genetic correlation between the subgroups and the previously published cross-disorder latent factors11. A high genetic correlation would indicate biological concordance between a depression subtype and a cross-disorder latent factor. Due to limited sample sizes, the genetic correlations cannot be replicated in NESDA, as that dataset will not have power to perform meaningful GWAS. To overcome this, we will create polygenic scores (PGS) for the NESDA participants for each of the cross-disorder latent variables. For the findings that replicate in NESDA, we will do additional exploratory follow-up analyses to investigate phenotypic associations of interest with the rich secondary clinical phenotypes that are available in the NESDA dataset. The genetic correlation analyses in UKB and PGS analyses plus potential follow-up analyses in NESDA provide complimentary information utilizing both the power of UKB and the deep phenotyping of NESDA.
We will furthermore estimate the co-occurrence of the different subtypes, given that research shows that there is some structure to co-occurrence of symptoms1. We will therefore investigate whether the phenotypic subgroups can be summarized into fewer, overarching classes using latent class analysis (LCA). We will perform this analysis both in the UKB and in NESDA, allowing us to compare the general structures of these two different datasets. Lastly, we will gauge the future clinical utility of these subtypes. Genetic subtypes of depression are generally studied for two reasons: 1) to better understand the underlying biology, and 2) to use clinical distinctions between subtypes to guide treatment. We will compute genetic distances between subgroups, which gives a rough approximation of the potential for future disentangling of the subtypes based on genetic prediction
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