scholarly journals Polygenic risk scores applied to a single cohort reveal pleiotropy among hundreds of human phenotypes

2017 ◽  
Author(s):  
Adam Socrates ◽  
Tom Bond ◽  
Ville Karhunen ◽  
Juha Auvinen ◽  
Cornelius A. Rietveld ◽  
...  
Keyword(s):  
Epidemiological Study ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Convincing Evidence ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genetic Associations ◽  
Polygenic Risk ◽  
Moderate Sample Size ◽  
Shared Genetic

AbstractBackgroundThere is now convincing evidence that pleiotropy across the genome contributes to the correlation between human traits and comorbidity of diseases. The recent availability of genome-wide association study (GWAS) results have made the polygenic risk score (PRS) approach a powerful way to perform genetic prediction and identify genetic overlap among phenotypes.Methods and findingsHere we use the PRS method to assess evidence for shared genetic aetiology across hundreds of traits within a single epidemiological study – the Northern Finland Birth Cohort 1966 (NFBC1966). We replicate numerous recent findings, such as a genetic association between Alzheimer’s disease and lipid levels, while the depth of phenotyping in the NFBC1966 highlights a range of novel significant genetic associations between traits.ConclusionsThis study illustrates the power in taking a hypothesis-free approach to the study of shared genetic aetiology between human traits and diseases. It also demonstrates the potential of the PRS method to provide important biological insights using only a single well-phenotyped epidemiological study of moderate sample size (~5k), with important advantages over evaluating genetic correlations from GWAS summary statistics only.

scholarly journals Polygenic risk scores for late smoking initiation associated with the risk of schizophrenia

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Kazutaka Ohi ◽  
Daisuke Nishizawa ◽  
Yukimasa Muto ◽  
Shunsuke Sugiyama ◽  
Junko Hasegawa ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Smoking Initiation ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Intermediate Phenotypes ◽  
Smoking Behaviors

AbstractPatients with schizophrenia display characteristic smoking-related behaviors and genetic correlations between smoking behaviors and schizophrenia have been identified in European individuals. However, the genetic etiology of the association remains to be clarified. The present study investigated transethnic genetic overlaps between European-based smoking behaviors and the risk of Japanese schizophrenia by conducting polygenic risk score (PRS) analyses. Large-scale European genome-wide association study (GWAS) datasets (n = 24,114–74,035) related to four smoking-related intermediate phenotypes [(i) smoking initiation, (ii) age at smoking initiation, (iii) smoking quantity, and (iv) smoking cessation] were utilized as discovery samples. PRSs derived from these discovery GWASs were calculated for 332 Japanese subjects [schizophrenia patients, their unaffected first-degree relatives (FRs), and healthy controls (HCs)] as a target sample. Based on GWASs of European smoking phenotypes, we investigated the effects of PRSs on smoking phenotypes and the risk of schizophrenia in the Japanese population. Of the four smoking-related behaviors, the PRSs for age at smoking initiation in Europeans significantly predicted the age at smoking initiation (R2 = 0.049, p = 0.026) and the PRSs for smoking cessation significantly predicted the smoking cessation (R2 = 0.092, p = 0.027) in Japanese ever-smokers. Furthermore, the PRSs related to age at smoking initiation in Europeans were higher in Japanese schizophrenia patients than in the HCs and those of the FRs were intermediate between those of patients with schizophrenia and those of the HCs (R2 = 0.015, p = 0.015). In our target subjects, patients with schizophrenia had a higher mean age at smoking initiation (p = 0.018) and rate of daily smoking initiation after age 20 years (p = 0.023) compared with the HCs. A total of 60.6% of the patients started to smoke before the onset of schizophrenia. These findings suggest that genetic factors affecting late smoking initiation are associated with the risk of schizophrenia.

scholarly journals Polygenic Risk Scores Augment Stroke Subtyping

2021 ◽  
Vol 7 (2) ◽  
pp. e560
Author(s):  
Jiang Li ◽  
Durgesh P. Chaudhary ◽  
Ayesha Khan ◽  
Christoph Griessenauer ◽  
David J. Carey ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Genetic Correlation ◽  
Genome Wide Association Study ◽  
Low Frequency ◽  
European Ancestry ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Large Artery ◽  
Polygenic Risk ◽  
Gene Sets

ObjectiveTo determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.MethodsControls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.ResultsA moderate heritability (10%–20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the R2 increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling (R2 = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.ConclusionsWe provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.

scholarly journals Polygenic risk modeling with latent trait-related genetic components

2019 ◽  
Author(s):  
Matthew Aguirre ◽  
Yosuke Tanigawa ◽  
Guhan Ram Venkataraman ◽  
Rob Tibshirani ◽  
Trevor Hastie ◽  
...  
Keyword(s):  
Fat Mass ◽  
Genetic Risk ◽  
Complex Traits ◽  
Fat Free Mass ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genetic Associations ◽  
Polygenic Risk ◽  
Genetic Components ◽  
Mass Component

AbstractPolygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While models like polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits in the UK Biobank and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual’s genetic risk for a trait across DeGAs components, highlighting specific results for body mass index (BMI), myocardial infarction (heart attack), and gout in 337,151 white British individuals, with replication in a further set of 25,486 non-British white individuals from the Biobank. We find that BMI polygenic risk factorizes into components relating to fat-free mass, fat mass, and overall health indicators like physical activity measures. Most individuals with high dPRS for BMI have strong contributions from both a fat mass component and a fat-free mass component, whereas a few ‘outlier’ individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.

scholarly journals Association between substance use disorder and polygenic liability to schizophrenia

2017 ◽  
Author(s):  
Sarah M. Hartz ◽  
Amy Horton ◽  
Mary Oehlert ◽  
Caitlin E. Carey ◽  
Arpana Agrawal ◽  
...  
Keyword(s):  
Substance Use ◽  
Substance Use Disorder ◽  
Meta Analysis ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Individual Substance ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Individual Dataset ◽  
Shared Genetic

AbstractBackgroundThere are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.MethodsHere, we test the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: COGEND (ascertained for nicotine dependence n=918 cases, 988 controls), COGA (ascertained for alcohol dependence n=643 cases, 384 controls), and FSCD (ascertained for cocaine dependence n=210 cases, 317 controls). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to 1000 Genomes reference panel.ResultsIn each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo R2 range 0.8%-3.7%, minimum p=4×10-23).ConclusionsThese results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.

scholarly journals Findings from the Hispanic Community Health Study/Study of Latinos on the Importance of Sociocultural Environmental Interactors: Polygenic Risk Score-by-Immigration and Dietary Interactions

2021 ◽  
Vol 12 ◽  
Author(s):  
Cristin E. McArdle ◽  
Hassan Bokhari ◽  
Clinton C. Rodell ◽  
Victoria Buchanan ◽  
Liana K. Preudhomme ◽  
...  
Keyword(s):  
Community Health ◽  
Genome Wide Association Study ◽  
The United States ◽  
European Ancestry ◽  
Risk Scores ◽  
Health Study ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Hispanic Community

Introduction: Hispanic/Latinos experience a disproportionate burden of obesity. Acculturation to US obesogenic diet and practices may lead to an exacerbation of innate genetic susceptibility. We examined the role of gene–environment interactions to better characterize the sociocultural environmental determinants and their genome-scale interactions, which may contribute to missing heritability of obesity. We utilized polygenic risk scores (PRSs) for body mass index (BMI) to perform analyses of PRS-by-acculturation and other environmental interactors among self-identified Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).Methods: PRSs were derived using genome-wide association study (GWAS) weights from a publicly available, large meta-analysis of European ancestry samples. Generalized linear models were run using a set of a priori acculturation-related and environmental factors measured at visit 1 (2008–2011) and visit 2 (2014–2016) in an analytic subsample of 8,109 unrelated individuals with genotypic, phenotypic, and complete case data at both visits. We evaluated continuous measures of BMI and waist-to-hip ratio. All models were weighted for complex sampling design, combined, and sex-stratified.Results: Overall, we observed a consistent increase of BMI with greater PRS across both visits. We found the best-fitting model adjusted for top five principal components of ancestry, sex, age, study site, Hispanic/Latino background genetic ancestry group, sociocultural factors and PRS interactions with age at immigration, years since first arrival to the United States (p < 0.0104), and healthy diet (p < 0.0036) and explained 16% of the variation in BMI. For every 1-SD increase in PRS, there was a corresponding 1.10 kg/m2 increase in BMI (p < 0.001). When these results were stratified by sex, we observed that this 1-SD effect of PRS on BMI was greater for women than men (1.45 vs. 0.79 kg/m2, p < 0.001).Discussion: We observe that age at immigration and the adoption of certain dietary patterns may play a significant role in modifying the effect of genetic risk on obesity. Careful consideration of sociocultural and immigration-related factors should be evaluated. The role of nongenetic factors, including the social environment, should not be overlooked when describing the performance of PRS or for promoting population health in understudied populations in genomics.

scholarly journals Genome-wide analyses in 1,987,836 participants identify 39 genetic loci associated with sleep apnoea

2020 ◽  
Author(s):  
Adrian I Campos ◽  
Nathan Ingold ◽  
Yunru Huang ◽  
Pik Fang Kho ◽  
Xikun Han ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Sleep Apnoea ◽  
Risk Scores ◽  
Genetic Associations ◽  
Genome Wide ◽  
A Genome

Rationale: Sleep apnoea is a complex disorder characterised by periods of halted breathing during sleep. Despite its association with serious health conditions such as cardiovascular disease, the aetiology of sleep apnoea remains understudied, and previous genetic studies have failed to identify replicable genetic risk factors. Objective: To advance our understanding of factors that increase susceptibility to sleep apnoea by identifying novel genetic associations. Methods: We conducted a genome-wide association study (GWAS) meta-analysis of sleep apnoea across five cohorts, and a previously published GWAS of apnoea-hypopnea index (N Total =510,484). Further, we used multi-trait analysis of GWAS (MTAG) to boost statistical power, leveraging the high genetic correlations between apnoea, snoring and body mass index. Replication was performed in an independent sample from 23andMe, Inc (N Total =1,477,352; N cases =175,522). Results: Our results revealed 39 independent genomic loci robustly associated with sleep apnoea risk, and significant genetic correlations with multisite chronic pain, sleep disorders, diabetes, high blood pressure, osteoarthritis, asthma and BMI-related traits. We also derived polygenic risk scores for sleep apnoea in a leave-one-out independent cohort and predicted probable sleep apnoea in participants (OR=1.15 to 1.22; variance explained = 0.4 to 0.9%). Conclusions: We report novel genetic markers robustly associated with sleep apnoea risk and substantial molecular overlap with other complex traits, thus advancing our understanding of the underlying biological mechanisms of susceptibility to sleep apnoea.

scholarly journals Genome-wide association study of suicide attempt in psychiatric disorders identifies association with major depression polygenic risk scores

2018 ◽  
Author(s):  
Niamh Mullins ◽  
Tim B. Bigdeli ◽  
Anders D Børglum ◽  
Jonathan R I Coleman ◽  
Ditte Demontis ◽  
...  
Keyword(s):  
Major Depression ◽  
Suicide Attempt ◽  
Psychiatric Disorders ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Risk Scores ◽  
Genetic Associations ◽  
Suicide Attempters ◽  
Polygenic Risk ◽  
Genome Wide

AbstractObjectiveOver 90% of suicide attempters have a psychiatric diagnosis, however twin and family studies suggest that the genetic etiology of suicide attempt (SA) is partially distinct from that of the psychiatric disorders themselves. Here, we present the largest genome-wide association study (GWAS) on suicide attempt using major depressive disorder (MDD), bipolar disorder (BIP) and schizophrenia (SCZ) cohorts from the Psychiatric Genomics Consortium.MethodSamples comprise 1622 suicide attempters and 8786 non-attempters with MDD, 3264 attempters and 5500 non-attempters with BIP and 1683 attempters and 2946 non-attempters with SCZ. SA GWAS were performed comparing attempters to non-attempters in each disorder followed by meta-analysis across disorders. Polygenic risk scoring investigated the genetic relationship between SA and the psychiatric disorders.ResultsThree genome-wide significant loci for SA were found: one associated with SA in MDD, one in BIP, and one in the meta-analysis of SA in mood disorders. These associations were not replicated in independent mood disorder cohorts from the UK Biobank and iPSYCH. Polygenic risk scores for major depression were significantly associated with SA in MDD (P=0.0002), BIP (P=0.0006) and SCZ (P=0.0006).ConclusionsThis study provides new information on genetic associations and the genetic etiology of SA across psychiatric disorders. The finding that polygenic risk scores for major depression predict suicide attempt across disorders provides a possible starting point for predictive modelling and preventative strategies. Further collaborative efforts to increase sample size hold potential to robustly identify genetic associations and gain biological insights into the etiology of suicide attempt.

scholarly journals Polygenic Risk Score Effectively Predicts Risk of Depression Onset in Alzheimer’s Disease

2021 ◽  
Author(s):  
Suraj Upadhya ◽  
Hongliang Liu ◽  
Sheng Luo ◽  
Michael W. Lutz ◽  
Ornit Chiba-Falek
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Education ◽  
Genome Wide Association Study ◽  
Late Onset ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Event Analysis ◽  
Polygenic Risk ◽  
Significant Performance

Abstract Introduction: Depression is a common, though heterogenous, comorbidity in late-onset Alzheimer’s Disease (LOAD) patients. In addition, individuals with depression are at greater risk to develop LOAD. In previous work, we demonstrated shared genetic etiology between depression and LOAD. Collectively, this evidence suggested interactions between depression and LOAD. However, the underpinning genetic heterogeneity of depression co-occurrence with LOAD is largely unknown.Methods: Major Depressive Disorder (MDD) genome wide association study (GWAS) summary statistics were used to create polygenic risk scores (PRS). The Religious Orders Society and Rush Memory and Aging Project (ROSMAP) and National Alzheimer’s Coordinating Center (NACC) datasets were utilized to assess the PRS performance in predicting depression onset in LOAD patients.Results: The developed PRS showed marginal results in standalone models for predicting depression onset in both ROSMAP (AUC=0.540) and NACC (AUC=0.534). Full models, with baseline age, sex, education, and APOEε4 allele count, showed improved prediction of depression onset (ROSMAP AUC: 0.606, NACC AUC: 0.583). In time-to-event analysis, standalone PRS models showed significant effects in ROSMAP (P=0.0051), but not in NACC cohort. Full models showed significant performance in predicting depression in LOAD for both datasets (P<0.001 for all).Discussion: This study provided new insights into the genetic factors contributing to depression onset in LOAD and advanced our knowledge of the genetics underlying the heterogeneity of depression in LOAD. The developed PRS accurately predicted LOAD patients with depressive symptoms, thus, has clinical implications including, diagnosis of LOAD patients at high-risk to develop depression for early anti-depressant treatment.

Genome-Wide Association Study And Polygenic Risk Scores Of Serum Dheas Levels In A Chilean Children Cohort

2021 ◽  
Author(s):  
José Patricio Miranda ◽  
María Cecilia Lardone ◽  
Fernando Rodríguez ◽  
Gordon B Cutler Jr ◽  
José Luis Santos ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Genetic Regulation ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genetic Determinants ◽  
Adrenal Androgen ◽  
Full Cohort ◽  
Polygenic Risk ◽  
Developmental Growth ◽  
Genome Wide

Abstract Background Adrenarche reflects the developmental growth of the adrenal zona reticularis, which produces increasing adrenal androgen secretion (e.g., DHEA/DHEAS) from ages ~5-15 years. We hypothesized that the study of the genetic determinants associated with variations in serum DHEAS during adrenarche might detect genetic variants influencing the rate or timing of this process. Subjects and methods Genome-wide genotyping was performed in participants of the Chilean pediatric GOCS cohort (n=788). We evaluated the genetic determinants of DHEAS levels at genome-wide level and in targeted genes associated with steroidogenesis. To corroborate our findings, we evaluated a polygenic risk score for age at pubarche, based upon the discovered variants, in children from the same cohort. Results We identified one significant variant at the genome-wide level in the full cohort, close to the GALR1 gene (P = 3.81x10 -8). In addition, variants suggestive of association (P &lt;1x10 -5) were observed in PRLR, PITX1, PTPRD, NR1H4, and BCL11B. Stratifying by sex, we found variants suggestive of association in SERBP1 and CAMTA1/VAMP3 for boys and near ZNF98, TRPC6, and SULT2A1 for girls. We also found significant reductions in age at pubarche in those children with higher polygenic risk scores for greater DHEAS based on these newly identified variants. Conclusions Our results disclose one variant associated with DHEAS concentrations at the level of GWAS significance, and several variants with suggestive association, which may be involved in the genetic regulation of adrenarche.

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