Schizophrenia polygenic risk and experiences of childhood adversity: a systematic review and meta-analysis

Background and Hypothesis Schizophrenia has been robustly associated with multiple genetic and environmental risk factors. Childhood adversity is one of the most widely replicated environmental risk factors for schizophrenia, but it is unclear if schizophrenia genetic risk alleles contribute to this association. Study Design In this systematic review and meta-analysis, we assessed the evidence for gene-environment correlation (genes influence likelihood of environmental exposure) between schizophrenia polygenic risk score (PRS) and reported childhood adversity. We also assessed the evidence for a gene-environment interaction (genes influence sensitivity to environmental exposure) in relation to the outcome of schizophrenia and/or psychosis. This study was registered on PROSPERO (CRD42020182812). Following PRISMA guidelines, a search for relevant literature was conducted using Cochrane, MEDLINE, PsycINFO, Web of Science, and Scopus databases until February 2022. All studies that examined the association between schizophrenia PRS and childhood adversity were included. Study Results Seventeen of 650 identified studies met the inclusion criteria and were assessed against the Newcastle-Ottawa Scale for quality. The meta-analysis found evidence for gene-environment correlation between schizophrenia PRS and childhood adversity (r = .02; 95% CI = 0.01, 0.03; P = .001), but the effect was small and therefore likely to explain only a small proportion of the association between childhood adversity and psychosis. The 4 studies that investigated a gene-environment interaction between schizophrenia PRS and childhood adversity in increasing risk of psychosis reported inconsistent results. Conclusions These findings suggest that a gene-environment correlation could explain a small proportion of the relationship between reported childhood adversity and psychosis

Investigating genetic liability and phenotypic presentations in schizophrenia

Common genetic liability to schizophrenia has provided insights into the clinical heterogeneity of schizophrenia, specifically within the context of environmental risk factors, diagnostic definitions and phenotypic presentations. I conducted a systematic review including the first meta-analysis of the association between schizophrenia polygenic risk score (PRS) and experiences of childhood adversity, a known environmental risk factor for schizophrenia. The meta-analysis of 14 studies found a small yet significant association between schizophrenia PRS and childhood adversity (r=0.02; 95% CI=0.01,0.03; P=0.001), indicating that common genetic liability for schizophrenia explains a small proportion of the relationship between childhood adversity and psychosis. Next, I assessed the validity of a self-reported schizophrenia diagnosis and compared phenotypic and genetic variables across participants defined by self-report and by research interview diagnosis in a clinically ascertained sample and by medical record diagnosis in UK Biobank. A self-reported schizophrenia diagnosis had a moderate to high positive predictive value (PPV) compared to a research interview diagnosis of schizophrenia (PPV=70). There were no differences in schizophrenia PRS in participants who only had a self-reported diagnosis compared to a research interview diagnosis (OR=0.97; 95%CI=0.86,1.09; p=0.59) or a medical record diagnosis (OR=1.01; 95%CI=-0.87,1.19; p=0.85), although phenotypic differences in age, education and employment status were found. Lastly, I recruited and interviewed 101 existing Cardiff University participants from the top and bottom 30% of the schizophrenia polygenic distribution. I found that participants with a higher schizophrenia PRS were more likely to experience negative symptoms (β=0.43, 95% CI=0.05,0.81, p=0.03) and have poorer cognitive performance (β= -0.20, 95% CI = -0.36, - 0.04, p=0.014). This thesis contributes to the current understanding of gene-environment associations in schizophrenia, provides novel evidence for using a self-reported schizophrenia diagnosis in psychiatric research and finds preliminary evidence for different clinical presentations in participants sampled from the polygenic extremes

The importance of lake-specific characteristics for water quality across the continental United States

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency’s 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to- volume ratio) were important for explaining water quality (54–60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28–39% variance explained). Basin-scale land use and land cover explained between 45–62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers

The importance of lake-specific characteristics for water quality across the continental United States

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency’s 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54–60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28–39% variance explained). Basin-scale land use and land cover explained between 45–62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Ecological Society of America. The published article can be found at: http://www.esajournals.org/loi/ecap. The appendix is available online at: http://dx.doi.org/10.1890/14-0935.1.sm. Additional supplemental material can be located at: http://water.epa.gov/type/lakes/NLA_data.cfm; http://water.epa.gov; http://www.mrlc.gov/nlcd06_data.php; http://nhd.usgs.gov; ftp://ftp2.census.gov/geo/tiger/TIGER2012/ROADS/; https://portal.lternet.edu/nis/mapbrowse?scope=knb-lter-ntl&identifier=10000&revision=1;  http://www.gpo.gov/fdsys/granule/FR-2003-01-06/03-176Keywords: land use, catchment geology, landscape limnology, nutrients, turbidity, National Lakes Assessment, morphology, hydrologic connectivity, conductivity, hydrogeology, water quality, drainage densit

Appendix A. Model and predictor variable descriptions.

Model and predictor variable descriptions