The presence of subclinical levels of psychosis in the general population may imply that schizophrenia is the extreme expression of more or less continuously distributed traits in the population. In a previous study, we identified five quantitative measures of schizophrenia (positive, negative, disorganisation, mania, and depression scores). The aim of this study is to examine the association between a direct measure of genetic risk of schizophrenia and the five quantitative measures of psychosis. Estimates of the log of the odds ratios of case/control allelic association tests were obtained from the Psychiatric GWAS Consortium (PGC) (minus our sample) which included genome-wide genotype data of 8,690 schizophrenia cases and 11,831 controls. These data were used to calculate genetic risk scores in 314 schizophrenia cases and 148 controls from the Netherlands for whom genotype data and quantitative symptom scores were available. The genetic risk score of schizophrenia was significantly associated with case-control status (p<0.0001). In the case-control sample, the five psychosis dimensions were found to be significantly associated with genetic risk scores; the correlations ranged between.15 and.27 (all p<.001). However, these correlations were not significant in schizophrenia cases or controls separately. While this study confirms the presence of a genetic risk for schizophrenia as categorical diagnostic trait...
Genome-wide association studies (GWAS) have demonstrated a significant polygenic contribution to bipolar disorder (BD) where disease risk is determined by the summation of many alleles of small individual magnitude. Modelling polygenic risk scores may be a powerful way of identifying disrupted brain regions whose genetic architecture is related to that of BD. We determined the extent to which common genetic variation underlying risk to BD affected neural activation during an executive processing/language task in individuals at familial risk of BD and healthy controls. Polygenic risk scores were calculated for each individual based on GWAS data from the Psychiatric GWAS Consortium Bipolar Disorder Working Group (PGC-BD) of over 16 000 subjects. The familial group had a significantly higher polygene score than the control group (P=0.04). There were no significant group by polygene interaction effects in terms of association with brain activation. However, we did find that an increasing polygenic risk allele load for BD was associated with increased activation in limbic regions previously implicated in BD, including the anterior cingulate cortex and amygdala, across both groups. The findings suggest that this novel polygenic approach to examine brain-imaging data may be a useful means of identifying genetically mediated traits mechanistically linked to the aetiology of BD.
We review critical trends in imaging genetics as applied to schizophrenia research, and then discuss some future directions of the field. A plethora of imaging genetics studies have investigated the impact of genetic variation on brain function, since the paradigm of a neuroimaging intermediate phenotype for schizophrenia first emerged. It was initially posited that the effects of schizophrenia susceptibility genes would be more penetrant at the level of biologically based neuroimaging intermediate phenotypes than at the level of a complex and phenotypically heterogeneous psychiatric syndrome. The results of many studies support this assumption, most of which show single genetic variants to be associated with changes in activity of localized brain regions, as determined by select cognitive controlled tasks. From these basic studies, functional neuroimaging analysis of intermediate phenotypes has progressed to more complex and realistic models of brain dysfunction, incorporating models of functional and effective connectivity, including the modalities of psycho-physiological interaction, dynamic causal modeling, and graph theory metrics. The genetic association approaches applied to imaging genetics have also progressed to more sophisticated multivariate effects...
A recent publication reported an exciting polygenic effect of schizophrenia (SCZ) risk variants, identified by a large genome-wide association study (GWAS), on total brain and white matter volumes in schizophrenic patients and, even more prominently, in healthy subjects. The aim of the present work was to replicate and then potentially extend these findings. According to the original publication, polygenic risk scores—using single nucleotide polymorphism (SNP) information of SCZ GWAS—(polygenic SCZ risk scores; PSS) were calculated in 122 healthy subjects, enrolled in a structural magnetic resonance imaging (MRI) study. These scores were computed based on P-values and odds ratios available through the Psychiatric GWAS Consortium. In addition, polygenic white matter scores (PWM) were calculated, using the respective SNP subset in the original publication. None of the polygenic scores, either PSS or PWM, were found to be associated with total brain, white matter or gray matter volume in our replicate sample. Minor differences between the original and the present study that might have contributed to lack of reproducibility (but unlikely explain it fully), are number of subjects, ethnicity, age distribution, array technology, SNP imputation quality and MRI scanner type. In contrast to the original publication...
Ruderfer, Douglas M.; Fanous, Ayman H.; Ripke, Stephan; McQuillin, Andrew; Amdur, Richard L.; ; ; ; Gejman, Pablo V.; O’Donovan, Michael C.; Andreassen, Ole A.; Djurovic, Srdjan; Hultman, Christina M.; Kelsoe, John R.; Jamain, Stephane; Landén, Mikael;
Bipolar disorder and schizophrenia are two often severe disorders with high heritabilities. Recent studies have demonstrated a large overlap of genetic risk loci between these disorders but diagnostic and molecular distinctions still remain. Here, we perform a combined GWAS of 19,779 BP and SCZ cases versus 19,423 controls, in addition to a direct comparison GWAS of 7,129 SCZ cases versus 9,252 BP cases. In our case-control analysis, we identify five previously identified regions reaching genome-wide significance (CACNA1C, IFI44L, MHC, TRANK1, MAD1L1) and a novel locus near PIK3C2A. We create a polygenic risk score that is significantly different between BP and SCZ and show a significant correlation between a BP polygenic risk score and the clinical dimension of mania in SCZ patients. Our results indicate that first, combining diseases with similar genetic risk profiles improves power to detect shared risk loci and second, that future direct comparisons of BP and SCZ are likely to identify loci with significant differential effects. Identifying these loci should aid in the fundamental understanding of how these diseases differ biologically. These findings also indicate that combining clinical symptom dimensions and polygenic signatures could provide additional information that may someday be used clinically.
Recently, polygenic risk scores have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether polygenic risk scores assist in the prediction of risk of common cancers is unknown. We built polygenic risk scores from lists of genetic markers prioritized by their association with breast or prostate cancer in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 breast cancer cases and 1,142 controls from the Nurses’ Health Study and 1,164 prostate cancer cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate polygenic risk scores with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both breast and prostate cancer, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that polygenic risk scores using common variants improved risk prediction for breast and prostate cancer over replicated SNP scores.
Alcohol problems represent a classic example of a complex behavioral outcome that is likely influenced by many genes of small effect. A polygenic approach, which examines aggregate measured genetic effects, can have predictive power in cases where individual genes or genetic variants do not. In the current study, we first tested whether polygenic risk for alcohol problems—derived from genome-wide association estimates of an alcohol problems factor score from the age 18 assessment of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 4304 individuals of European descent; 57% female)—predicted alcohol problems earlier in development (age 14) in an independent sample (FinnTwin12; n = 1162; 53% female). We then tested whether environmental factors (parental knowledge and peer deviance) moderated polygenic risk to predict alcohol problems in the FinnTwin12 sample. We found evidence for both polygenic association and for additive polygene-environment interaction. Higher polygenic scores predicted a greater number of alcohol problems (range of Pearson partial correlations 0.07–0.08, all p-values ≤ 0.01). Moreover, genetic influences were significantly more pronounced under conditions of low parental knowledge or high peer deviance (unstandardized regression coefficients (b)...
In this commentary I review the recent paper by Iyegbe et al. on "The emerging molecular architecture of schizophrenia, polygenic risk scores and the clinical implications for gXe research". I discuss how the paper advances our knowledge of polygenic risk scores for use, amongst others, in gene-environment interaction studies and the opportunities and challenges such approaches will bring to our understanding of the epidemiology of psychotic disorders, including schizophrenia.
It has been suggested that depression is a polygenic trait, arising from the influences of multiple loci with small individual effects. The aim of this study is to generate a polygenic risk score (PRS) to examine the association between genetic variation and depressive symptoms. Our analytic sample included N=10,091 participants ages 50+ from the Health and Retirement Study (HRS). Depressive symptoms were measured by CESD scores assessed on up to nine occasions across 18 years. We conducted a genome-wide association analysis for a discovery set (n=7,000) and used the top 11 single nucleotide polymorphisms, all with P<10−05 to generate a weighted PRS for our replication sample (n=3,091). Results showed the PRS was significantly associated with mean CESD score in the replication sample (β=.08, P=.002). The R2-change for the inclusion of the PRS was .003. Using a multinomial logistic regression model we also examined the association between genetic risk and chronicity of high (4+) CESD scores. We found that a one standard deviation increase in PRS was associated with a 36% increase in the odds of having chronically high CESD scores relative to never having had high CESD scores. Our findings are consistent with depression being a polygenic trait and suggest that the cumulative influence of multiple variants increase an individual’s susceptibility for chronically experiencing high levels of depressive symptoms.
Evidence suggests that there is shared genetic aetiology across the major psychiatric disorders conferred by additive effects of many common variants. Measuring their joint effects on brain function may identify common neural risk mechanisms. We investigated the effects of a cross-disorder polygenic risk score (PGRS), based on additive effects of genetic susceptibility to the five major psychiatric disorders, on brain activation during performance of a language-based executive task. We examined this relationship in healthy individuals with (n = 82) and without (n = 57) a family history of bipolar disorder to determine whether this effect was additive or interactive dependent on the presence of family history. We demonstrate a significant interaction for polygenic loading × group in left lateral frontal cortex (BA9, BA6). Further examination indicated that this was driven by a significant positive correlation in those without a family history (i.e. healthy unrelated volunteers), with no significant relationships in the familial group. We then examined the effect of the individual diagnoses contributing to the PGRS to determine evidence of disorder-specificity. We found a significant association with the schizophrenia polygenic score only...
With numbers of common variants identified mainly through genome-wide association studies (GWASs), there is great interest in incorporating the findings into screening individuals at high risk of psoriasis. The purpose of this study is to establish genetic prediction models and evaluate its discriminatory ability in psoriasis in Han Chinese population. We built the genetic prediction models through weighted polygenic risk score (PRS) using 14 susceptibility variants in 8,819 samples. We found the risk of psoriasis among individuals in the top quartile of PRS was significantly larger than those in the lowest quartile of PRS (OR = 28.20, P < 2.0×10-16). We also observed statistically significant associations between the PRS, family history and early age onset of psoriasis. We also built a predictive model with all 14 known susceptibility variants and alcohol consumption, which achieved an area under the curve statistic of ~ 0.88. Our study suggests that 14 psoriasis known susceptibility loci have the discriminating potential, as is also associated with family history and age of onset. This is the genetic predictive model in psoriasis with the largest accuracy to date.
Objective: The study investigated whether childhood cognitive ability moderates Type 2 diabetes polygenic risk manifestation in older age. Method: In 940 relatively healthy people (mean age 69.55 ± 0.85), we tested whether self-reported diabetes and hemoglobin HbA1c (HbA1c) levels were predicted by diabetes polygenic risk, cognitive ability measured about 60 years earlier, and their interaction. Polygenic risk scores aggregated the small effects of up to nearly 121,000 single-nucleotide polymorphisms (SNPs). Participants’ cognitive ability was measured at age 11. Results: Both polygenic risk and low childhood cognitive ability significantly predicted diabetes diagnosis. Polygenic risk interacted with cognitive ability (p = .02), predicting HbA1c levels more strongly in people with below-median cognitive ability (effect r = .21) than in people with above-median cognitive ability (effect r = .10). The interaction term was not significant for self-reported diabetes (p = .34), although the genetic risk-diabetes association showed a tendency of being stronger among those with below-median cognitive ability. Conclusions: Higher premorbid cognitive ability may provide some environmental protection against the manifestation of Type 2 diabetes genetic risk. This information may improve early identification of diabetes risk and inform intervention development.
Objective: Although attention deficit hyperactivity disorder (ADHD) is highly heritable, genome-wide association studies (GWAS) have not yet identified any common genetic variants that contribute to risk. There is evidence that aggression or conduct disorder in children with ADHD indexes higher genetic loading and clinical severity. The authors examine whether common genetic variants considered en masse as polygenic scores for ADHD are especially enriched in children with comorbid conduct disorder. Method Polygenic scores derived from an ADHD GWAS meta-analysis were calculated in an independent ADHD sample (452 case subjects, 5,081 comparison subjects). Multivariate logistic regression analyses were employed to compare polygenic scores in the ADHD and comparison groups and test for higher scores in ADHD case subjects with comorbid conduct disorder relative to comparison subjects and relative to those without comorbid conduct disorder. Association with symptom scores was tested using linear regression. Results: Polygenic risk for ADHD, derived from the meta-analysis, was higher in the independent ADHD group than in the comparison group. Polygenic score was significantly higher in ADHD case subjects with conduct disorder relative to ADHD case subjects without conduct disorder. ADHD polygenic score showed significant association with comorbid conduct disorder symptoms. This relationship was explained by the aggression items. Conclusions: Common genetic variation is relevant to ADHD...
Walter, Stefan; Glymour, M. Maria; Koenen, Karestan; Liang, Liming; Tchetgen Tchetgen, Eric J.; Cornelis, Marilyn; Chang, Shun-Chiao; Rimm, Eric; Kawachi, Ichiro; Kubzansky, Laura D.
Fonte: Public Library of SciencePublicador: Public Library of Science
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
Context Anxiety disorders are common, with a lifetime prevalence of 20% in the U.S., and are responsible for substantial burdens of disability, missed work days and health care utilization. To date, no causal genetic variants have been identified for anxiety, anxiety disorders, or related traits. Objective: To investigate whether a phobic anxiety symptom score was associated with 3 alternative polygenic risk scores, derived from external genome-wide association studies of anxiety, an internally estimated agnostic polygenic score, or previously identified candidate genes. Design: Longitudinal follow-up study. Using linear and logistic regression we investigated whether phobic anxiety was associated with polygenic risk scores derived from internal, leave-one out genome-wide association studies, from 31 candidate genes, and from out-of-sample genome-wide association weights previously shown to predict depression and anxiety in another cohort. Setting and Participants: Study participants (n = 11,127) were individuals from the Nurses' Health Study and Health Professionals Follow-up Study. Main Outcome Measure: Anxiety symptoms were assessed via the 8-item phobic anxiety scale of the Crown Crisp Index at two time points, from which a continuous phenotype score was derived. Results: We found no genome-wide significant associations with phobic anxiety. Phobic anxiety was also not associated with a polygenic risk score derived from the genome-wide association study beta weights using liberal p-value thresholds; with a previously published genome-wide polygenic score; or with a candidate gene risk score based on 31 genes previously hypothesized to predict anxiety. Conclusion: There is a substantial gap between twin-study heritability estimates of anxiety disorders ranging between 20–40% and heritability explained by genome-wide association results. New approaches such as improved genome imputations...
Major depressive disorder (MDD) and obesity are frequently co-morbid and this correlation is partly due to genetic factors. Although specific genetic risk variants are associated with body mass index (BMI) and with larger effect sizes in depressed individuals, the genetic overlap and interaction with depression has not been addressed using whole-genome data. Polygenic profile scores for MDD and BMI were created in 13 921 members of Generation Scotland: the Scottish Family Health Study and tested for their association with BMI, MDD, neuroticism and scores on the General Health Questionnaire (GHQ) (current psychological distress). The association between BMI polygenic profile scores and BMI was tested fitting GHQ, neuroticism or MDD status as an interaction term to test for a moderating effect of mood disorder. BMI polygenic profile scores were not associated with lifetime MDD status or neuroticism although a significant positive association with GHQ scores was found (P=0.0001, β=0.034, r2=0.001). Polygenic risk for MDD was not associated with BMI. A significant interaction between BMI polygenic profile scores and MDD (P=0.0003, β=0.064), GHQ (P=0.0005, β=0.027) and neuroticism (P=0.003, β=0.023) was found when BMI was the dependent variable. The effect of BMI-increasing alleles was greater in those with MDD...
Summary: A polygenic risk score (PRS) is a sum of trait-associated alleles across many genetic loci, typically weighted by effect sizes estimated from a genome-wide association study. The application of PRS has grown in recent years as their utility for detecting shared genetic aetiology among traits has become appreciated; PRS can also be used to establish the presence of a genetic signal in underpowered studies, to infer the genetic architecture of a trait, for screening in clinical trials, and can act as a biomarker for a phenotype. Here we present the first dedicated PRS software, PRSice (‘precise'), for calculating, applying, evaluating and plotting the results of PRS. PRSice can calculate PRS at a large number of thresholds (“high resolution”) to provide the best-fit PRS, as well as provide results calculated at broad P-value thresholds, can thin Single Nucleotide Polymorphisms (SNPs) according to linkage disequilibrium and P-value or use all SNPs, handles genotyped and imputed data, can calculate and incorporate ancestry-informative variables, and can apply PRS across multiple traits in a single run. We exemplify the use of PRSice via application to data on schizophrenia, major depressive disorder and smoking, illustrate the importance of identifying the best-fit PRS and estimate a P-value significance threshold for high-resolution PRS studies.
Late-onset Alzheimer's disease (AD) is 50–70% heritable with complex genetic underpinnings. In addition to Apoliprotein E (APOE) ε4, the major genetic risk factor, recent genome-wide association studies (GWAS) have identified a growing list of sequence variations associated with the disease. Building on a prior large-scale AD GWAS, we used a recently developed analytic method to compute a polygenic score that involves up to 26 independent common sequence variants and is associated with AD dementia, above and beyond APOE. We then examined the associations between the polygenic score and the magnetic resonance imaging–derived thickness measurements across AD-vulnerable cortex in clinically normal (CN) human subjects (N = 104). AD-specific cortical thickness was correlated with the polygenic risk score, even after controlling for APOE genotype and cerebrospinal fluid (CSF) levels of β-amyloid (Aβ1–42). Furthermore, the association remained significant in CN subjects with levels of CSF Aβ1–42 in the normal range and in APOE ε3 homozygotes. The observation that genetic risk variants are associated with thickness across AD-vulnerable regions of interest in CN older individuals, suggests that the combination of polygenic risk profile...
This is the author accepted manuscript. The final version is available from American association for Cancer Research via http://dx.doi.org/10.1158/1078-0432.CCR-15-1080; PURPOSE:
This study aims to quantify the probability of overdiagnosis of prostate cancer by polygenic risk.
We calculated the polygenic risk score based on 66 known prostate cancer susceptibility variants for 17,012 men aged 50-69 years (9,404 men identified with prostate cancer and 7,608 with no cancer) derived from three UK-based ongoing studies. We derived the probabilities of overdiagnosis by quartiles of polygenic risk considering that the observed prevalence of screen-detected prostate cancer is a combination of underlying incidence, mean sojourn time (MST), test sensitivity, and overdiagnosis.
Polygenic risk quartiles one to four had 9%, 18%, 25% and 48% of the cases respectively. For a PSA test sensitivity of 80% and MST of nine years, 43%, 30%, 25% and 19% of the prevalent screen-detected cancers in quartiles one to four, respectively, were likely to be overdiagnosed cancers. Overdiagnosis decreased with increasing polygenic risk, with 56% drop between the lowest and the highest polygenic risk quartiles.
Targeting screening to men at higher polygenic risk could reduce the problem of overdiagnosis and lead to a better benefit to harm balance in screening for prostate cancer.; NP is Cancer Research UK Clinician Scientist Fellow.
The COGS project was funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175)...
This is the author accepted manuscript. The final version is available via AACR at http://cebp.aacrjournals.org/content/early/2015/04/02/1055-9965.EPI-14-0317.long.; BACKGROUND: Genome-wide association studies have identified multiple genetic variants associated with prostate cancer (PrCa) risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of PrCa.
METHODS: We genotyped 25 PrCa susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical Odds Ratios for PrCa associated with different risk strata defined by PRS and derived age-specific absolute risks of developing PrCa by PRS stratum and family history.
RESULTS: The PrCa risk for men in the top 1% of the PRS distribution was 30.6 (95% CI 16.4-57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI 3.2-5.5) fold compared with the median risk. The absolute risk of PrCa by age 85 was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation=0.09).
CONCLUSIONS: Risk profiling can identify men at substantially increased or reduced risk of PrCa. The effect size...
Pashayan, Nora; Duffy, Stephen W.; Neal, David E.; Hamdy, Freddie C.; Donovan, Jenny L.; Martin, Richard M.; Harrington, Patricia; Benlloch, Sara; Al Olama, Ali Amin; Shah, Mitul; Kote-Jarai, Zsofia; Easton, Douglas F.; Eeles, Rosalind; Pharoah, Paul D. P
Fonte: Nature Publishing GroupPublicador: Nature Publishing Group
This is the final published version. It first appeared at http://www.nature.com/gim/journal/vaop/ncurrent/full/gim2014192a.html.; Purpose:
This study aimed to quantify the probability of overdiagnosis of prostate cancer by polygenic risk.
We calculated the polygenic risk score based on 66 known prostate cancer susceptibility variants for 17,012 men aged 50?69 years (9,404 men identified with prostate cancer and 7,608 with no cancer) derived from three UK-based ongoing studies. We derived the probabilities of overdiagnosis by quartiles of polygenic risk considering that the observed prevalence of screen-detected prostate cancer is a combination of underlying incidence, mean sojourn time (MST), test sensitivity, and overdiagnosis.
Polygenic risk quartiles 1 to 4 comprised 9, 18, 25, and 48% of the cases, respectively. For a prostate-specific antigen test sensitivity of 80% and MST of 9 years, 43, 30, 25, and 19% of the prevalent screen-detected cancers in quartiles 1 to 4, respectively, were likely to be overdiagnosed cancers. Overdiagnosis decreased with increasing polygenic risk, with 56% decrease between the lowest and the highest polygenic risk quartiles.
Targeting screening to men at higher polygenic risk could reduce the problem of overdiagnosis and lead to a better benefit-to-harm balance in screening for prostate cancer.; N.P. is a Cancer Research UK Clinician Scientist Fellow. The COGS
project was funded through a European Commission?s Seventh
Framework Programme grant (agreement number: 223175-