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- Pardiñas, Antonio F., et al.
(författare)
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Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia
- 2022
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Ingår i: JAMA psychiatry. - Chicago, IL, United States : American Medical Association. - 2168-6238 .- 2168-622X. ; 79:3, s. 260-269
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Tidskriftsartikel (refereegranskat)abstract
- Importance About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts.Objective To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples.Design, Setting, and Participants Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]).Main Outcomes and Measures GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition.Results The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P = .04).Conclusions and Relevance In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.
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- Ashton, Nicholas J., et al.
(författare)
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A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer's disease.
- 2019
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Ingår i: Science advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 5:2
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Tidskriftsartikel (refereegranskat)abstract
- A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer's disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as Aβ negative or Aβ positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict Aβ-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting Aβ-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.
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- Lynham, J., et al.
(författare)
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Costly stakeholder participation creates inertia in marine ecosystems
- 2017
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Ingår i: Marine Policy. - : Elsevier BV. - 0308-597X .- 1872-9460. ; 76, s. 122-129
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Tidskriftsartikel (refereegranskat)abstract
- Ecosystems often shift abruptly and dramatically between different regimes in response to human or natural disturbances. When ecosystems tip from one regime to another, the suite of available ecosystem benefits changes, impacting the stakeholders who rely on these benefits. These changes often create some groups who stand to incur large losses if an ecosystem returns to a previous regime. When the participation cost in the decision-making process is extremely high, this can lock in ecosystem regimes, making it harder for policy and management to shift ecosystems out of what the majority of society views as the undesirable regime. Public stakeholder meetings often have high costs of participation, thus economic theory predicts they will be dominated by extreme views and often lead to decisions that do not represent the majority viewpoint. Such extreme viewpoints can create strong inertia even when there is broad consensus to manage an ecosystem towards a different regime. In the same manner that reinforcing ecological feedback loops make it harder to exit an ecosystem regime, there are decision-making feedback loops that contribute additional inertia.
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