scholarly journals Polygenic risk scores based on European GWAS correlate to disease prevalence differences around the world

2021 ◽  
Author(s):  
Pritesh R Jain ◽  
Myson C Burch ◽  
Melanie B Martinez ◽  
Pablo Mir ◽  
Jakub Fichna ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Disease Risk ◽  
Disease Prevalence ◽  
Risk Scores ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
Complex Disorders ◽  
The World ◽  
Different Populations ◽  
European Populations

Background: Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. GWAS can help identify common variants that underlie disease risk. However, despite their increasing number, the vast majority of studies focuses on European populations, leading to questions regarding the transferability of findings to non-Europeans. Here, we investigated whether PRS based on European GWAS correlates to disease prevalence within Europe and around the world. Results: GWAS summary statistics of 20 different disorders were used to estimate Polygenic Risk Scores (PRS) in nine European and 24 worldwide reference populations. We estimated the correlation between average genetic risk for each of the 20 disorders and their prevalence in Europe and around the world. A clear variation in genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders both within European and global regions. We also found significant correlations between worldwide disease prevalence and PRS for 13 of the studied disorders with Obesity genetic risk having the highest correlation to disease prevalence. For these 13 disorders we also found that the loci used in PRS are significantly more conserved across the different populations compared to randomly selected SNPs as revealed by Fst and linkage disequilibrium structure. Conclusion: Our results show that PRS of world populations calculated based on European GWAS data can significantly capture differences in disease risk and identify populations with the highest genetic liability to develop various conditions. Our findings point to the potential transferability of European-based GWAS results to non-European populations and provide further support for the validity of GWAS.

scholarly journals Impute.me: an open source, non-profit tool for using data from DTC genetic testing to calculate and interpret polygenic risk scores

2019 ◽  
Author(s):  
Lasse Folkersen ◽  
Oliver Pain ◽  
Andres Ingasson ◽  
Thomas Werge ◽  
Cathryn M. Lewis ◽  
...  
Keyword(s):  
Open Source ◽  
Public Perception ◽  
Disease Risk ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Complex Disorders ◽  
Risk Variants ◽  
Genome Wide ◽  
The Impact

AbstractTo date, interpretation of genomic information has focused on single variants conferring disease risk, but most disorders of major public concern have a polygenic architecture. Polygenic risk scores (PRS) give a single measure of disease liability by summarising disease risk across hundreds of thousands of genetic variants. They can be calculated in any genome-wide genotype data-source, using a prediction model based on genome-wide summary statistics from external studies.As genome-wide association studies increase in power, the predictive ability for disease risk will also increase. While PRS are unlikely ever to be fully diagnostic, they may give valuable medical information for risk stratification, prognosis, or treatment response prediction.Public engagement is therefore becoming important on the potential use and acceptability of PRS. However, the current public perception of genetics is that it provides ‘Yes/No’ answers about the presence/absence of a condition, or the potential for developing a condition, which in not the case for common, complex disorders with of polygenic architecture.Meanwhile, unregulated third-party applications are being developed to satisfy consumer demand for information on the impact of lower risk variants on common diseases that are highly polygenic. Often applications report results from single SNPs and disregard effect size, which is highly inappropriate for common, complex disorders where everybody carries risk variants.Tools are therefore needed to communicate our understanding of genetic predisposition as a continuous trait, where a genetic liability confers risk for disease. Impute.me is one such a tool, whose focus is on education and information on common, complex disorders with polygenetic architecture. Its research-focused open-source website allows users to upload consumer genetics data to obtain PRS, with results reported on a population-level normal distribution. Diseases can only be browsed by ICD10-chapter-location or alphabetically, thus prompting the user to consider genetic risk scores in a medical context of relevance to the individual.Here we present an overview of the implementation of the impute.me site, along with analysis of typical usage-patterns, which may advance public perception of genomic risk and precision medicine.

scholarly journals Distribution of 54 polygenic risk scores for common diseases in long lived individuals and their offspring

2021 ◽  
Author(s):  
Sophia Gunn ◽  
Michael Wainberg ◽  
Zeyuan Song ◽  
Stacy Andersen ◽  
Robert Boudreau ◽  
...  
Keyword(s):  
General Population ◽  
Genetic Risk ◽  
Disease Risk ◽  
Association Studies ◽  
Risk Scores ◽  
Human Longevity ◽  
Genome Wide Association Studies ◽  
Predictive Values ◽  
Polygenic Risk ◽  
Polygenic Scores

Background: A surprising and well-replicated result in genetic studies of human longevity is that centenarians appear to carry disease-associated variants in numbers similar to the general population. With the proliferation of large genome-wide association studies (GWAS) in recent years, investigators have turned to polygenic scores to leverage GWAS results into a measure of genetic risk that can better predict risk of disease than individual significant variants alone. Methods: We selected 54 polygenic risk scores (PRSs) developed for a variety of outcomes and we calculated their values in individuals from the New England Centenarian Study (NECS, N = 4886) and the Long Life Family Study (LLFS, N = 4577). We compared the distribution of these PRSs among exceptionally long-lived individuals (ELLI), their offspring and controls and we also examined their predictive values, using t-tests and regression models adjusting for sex and principal components reflecting ancestral background of the individuals (PCs). In our analyses we controlled for multiple testing using a Bonferroni-adjusted threshold for 54 traits. Results: We found that only 4 of the 54 PRSs differed between ELLIs and controls in both cohorts. ELLIs had significantly lower mean PRSs for Alzheimer's disease (AD), coronary artery disease (CAD) and systemic lupus than controls, suggesting genetic predisposition to extreme longevity may be mediated by reduced susceptibility to these traits. ELLIs also had significantly higher mean PRSs for improved cognitive function. In addition, the PRS for AD was associated with higher risk of dementia among controls but not ELLIs (p = 0.0004, 0.3 in NECS, p = 0.03, 0.93 in LLFS respectively). Interestingly, ELLIs did not have a larger number of homozygous risk genotypes for AD (TNECS = -1.72, TLLFs = 0.83) and CAD (TNECS = -5.08, TLLFs = -0.31) in both cohorts, but did have significantly larger number of homozygous protective genotypes than controls for the two traits (AD: TNECS =3.10, TLLFs = 2.2, CAD: TNECS = 6.57, TLLFs =2.36, respectively). Conclusions: ELLIs have a similar burden of genetic disease risk as the general population for most traits, but have significantly lower genetic risk of AD, CAD, and lupus. The lack of association between AD PRS and dementia among ELLIs suggests that their genetic risk for AD is somehow buffered by protective genetic or environmental factors.

scholarly journals Pathway specific polygenic risk scores identify pathways and patient clusters associated with inflammatory bowel disease risk, severity and treatment response

2021 ◽  
Author(s):  
Corneliu A Bodea ◽  
Michael Macoritto ◽  
Yingchun Liu ◽  
Wenliang Zhang ◽  
Jozsef Karman ◽  
...  
Keyword(s):  
Treatment Response ◽  
Genetic Risk ◽  
Disease Risk ◽  
Clinical Care ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Stratification Method ◽  
Inflammatory Bowel

Crohn's disease (CD) and ulcerative colitis (UC) are inflammatory bowel diseases (IBD) with a strong genetic component. Genome-wide association studies (GWAS) have successfully identified over 240 genetic loci that are statistically associated with risk of developing IBD, and these associations provide valuable insights into disease pathobiology. Building on GWAS findings, conventional polygenic risk scores (cPRS) aim to quantify the aggregated disease risk based on DNA variation, and these scores can identify individuals at high risk. While stratifying individuals based on cPRS has the potential to inform clinical care, the development of novel therapeutics requires deep insight into how aggregated genetic risk leads to disruption of specific biological pathways. Here, we developed a pathway-specific PRS (pPRS) methodology to assess IBD common variant genetic risk burden across 31 manually curated pathways. We first prioritized 206 genes based on comprehensive fine-mapping and eQTL colocalization analyses of genome-wide significant IBD GWAS loci and 58 highly penetrant genes based on their involvement in early onset IBD or autoimmunity-related colitis. These 264 genes were assigned to at least one of the 31 pathways based on Gene Ontology annotations and manual curation. Finally, we integrated these inputs into a novel pPRS model and performed an extensive investigation of IBD disease risk, severity, complications, and anti-TNF treatment response by applying our pPRS approach to three complementary datasets encompassing IBD cases and controls. Our analysis identified multiple promising pathways that can inform drug target discovery and provides a patient stratification method that offers insights into the biology of treatment response.

scholarly journals Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

2021 ◽  
pp. 1-12
Author(s):  
Simon Schmitt ◽  
Tina Meller ◽  
Frederike Stein ◽  
Katharina Brosch ◽  
Kai Ringwald ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mental Disorders ◽  
Cortical Thickness ◽  
Genetic Risk ◽  
Right Hemisphere ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Grey Matter Volume ◽  
Polygenic Risk ◽  
Risk For Depression

Abstract Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

scholarly journals An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

2018 ◽  
Author(s):  
Tom G. Richardson ◽  
Sean Harrison ◽  
Gibran Hemani ◽  
George Davey Smith
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Genome Wide Association Studies ◽  
Genetic Liability ◽  
Polygenic Risk ◽  
The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

scholarly journals Multiplex melanoma families are enriched for polygenic risk

2020 ◽  
Vol 29 (17) ◽  
pp. 2976-2985
Author(s):  
Matthew H Law ◽  
Lauren G Aoude ◽  
David L Duffy ◽  
Georgina V Long ◽  
Peter A Johansson ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Cumulative Effect ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Single Family ◽  
Polygenic Risk ◽  
Complex Disorders ◽  
Risk Variants ◽  
High Penetrance ◽  
Genetic Risks

Abstract Cancers, including cutaneous melanoma, can cluster in families. In addition to environmental etiological factors such as ultraviolet radiation, cutaneous melanoma has a strong genetic component. Genetic risks for cutaneous melanoma range from rare, high-penetrance mutations to common, low-penetrance variants. Known high-penetrance mutations account for only about half of all densely affected cutaneous melanoma families, and the causes of familial clustering in the remainder are unknown. We hypothesize that some clustering is due to the cumulative effect of a large number of variants of individually small effect. Common, low-penetrance genetic risk variants can be combined into polygenic risk scores. We used a polygenic risk score for cutaneous melanoma to compare families without known high-penetrance mutations with unrelated melanoma cases and melanoma-free controls. Family members had significantly higher mean polygenic load for cutaneous melanoma than unrelated cases or melanoma-free healthy controls (Bonferroni-corrected t-test P = 1.5 × 10−5 and 6.3 × 10−45, respectively). Whole genome sequencing of germline DNA from 51 members of 21 families with low polygenic risk for melanoma identified a CDKN2A p.G101W mutation in a single family but no other candidate high-penetrance melanoma susceptibility genes. This work provides further evidence that melanoma, like many other common complex disorders, can arise from the joint action of multiple predisposing factors, including rare high-penetrance mutations, as well as via a combination of large numbers of alleles of small effect.

scholarly journals Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiangming Sun ◽  
Yunpeng Wang ◽  
Lasse Folkersen ◽  
Yan Borné ◽  
Inge Amlien ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Complex Disease ◽  
Disease Risk ◽  
Disease Status ◽  
Risk Scores ◽  
Correct Prediction ◽  
Confidence Bounds ◽  
Conformal Prediction ◽  
Polygenic Risk ◽  
Individual Level

AbstractA promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual’s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.

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