Abstract 175: Performance of a Multilocus Genetic Risk Score Derived from Top Signals in the Cardiogram+C4d Consortium in Predicting Incident Coronary Disease in the Atherosclerosis Risk in Communities Study

2012 ◽  
Vol 32 (suppl_1) ◽  
Author(s):  
Themistocles L Assimes ◽  
Benjamin Goldstein ◽  
Keyword(s):  
Risk Prediction ◽  
Risk Score ◽  
Genetic Risk ◽  
Association Studies ◽  
Genetic Risk Score ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genotype Information ◽  
Atherosclerosis Risk

Genome wide association studies (GWAS) to date have identified 30 CAD susceptibility loci but the ability to use this information to improve risk prediction remains limited. A meta-analysis of the GWAS and Cardio Metabochip data produced by the CARDIoGRAM+C4D consortium representing 63,253 cases and 126,820 controls has identified 1885 SNPs passing a False Discovery Rate (FDR) threshold of 0.5%. We hypothesized that an expanded multi locus genetic risk score (GRS) incorporating genotype information at all loci below an FDR of 0.5% would perform better than a GRS restricted to 42 loci reaching genome wide significance and tested this hypothesis in subjects of European ancestry participating in the Atherosclerosis Risk in the Community (ARIC) study. Models testing the GRS were either minimally (age and sex) or fully adjusted for traditional risk factors (TRFs). The Figure shows the hazard ratio (HZ) and 95% CI for incident events comparing each quintile of GRS to the middle quintile. The GRS including genotype information at all loci with an FDR of 0.5% noticeably improves risk prediction over the GRS restricted to genome wide significant loci in both the minimally and fully adjusted models based on several metrics including i) HR per GRS quintile, ii) the HR per SD of the GRS, and iii) the logistic regression pseudo R2, and iv) the c statistic. The HR per GRS quintile and per SD of GRS were all lower in the fully adjusted models compared to the respective minimally adjusted models but the reduction of the HR was more striking for the models that tested the more expansive GRS. These findings suggest that a larger proportion of novel GWAS CAD loci are mediating their effects through TRFs. While these findings demonstrate some progress in risk prediction using GWAS loci, both the limited and the expanded GRS continues to explain a relatively small proportion of the overall variance compared to TRF. Thus, the clinical utility of a CAD GRS remains to be determined.

scholarly journals Personalized Risk Prediction for Type 2 Diabetes: the Potential of Genetic Risk Scores

2016 ◽  
Author(s):  
Kristi Lall ◽  
Reedik Magi ◽  
Andrew Morris ◽  
Andres Metspalu ◽  
Krista Fischer
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Risk Score ◽  
Cardiovascular Mortality ◽  
Genetic Risk ◽  
Association Studies ◽  
Genetic Risk Score ◽  
Genome Wide Association Studies ◽  
Net Reclassification Improvement

Purpose: The study aims to develop a Genetic Risk Score (GRS) for the prediction of Type 2 Diabetes (T2D) that could be used for risk assessment in general population. Methods: Using the results of genome-wide association studies, we develop a doubly-weighted GRS for the prediction of T2D risk, aiming to capture the effect of 1000 single nucleotide polymorphisms. The GRS is evaluated in the Estonian Biobank cohort (n=10273), analysing its effect on prevalent and incident T2D, while adjusting for other predictors. We assessed the effect of GRS on all-cause and cardiovascular mortality and its association with other T2D risk factors, and conducted the reclassification analysis. Results: The adjusted hazard for incident T2D is 1.90 (95% CI 1.48, 2.44) times higher and for cardiovascular mortality 1.27 (95% CI 1.10, 1.46) times higher in the highest GRS quintile compared to the rest of the cohort. No significant association between BMI and GRS is found in T2D-free individuals. Adding GRS to the prediction model for 5-year T2D risks results in continuous Net Reclassification Improvement of 0.26 (95% CI 0.15, 0.38). Conclusion: The proposed GRS would considerably improve the accuracy of T2D risk prediction when added to the set of predictors used so far. Keywords: genetic risk score, Type 2 Diabetes, risk prediction, genetic risk, precision medicine

scholarly journals Interaction between Genetic Risk Scores for reduced pulmonary function and smoking, asthma and endotoxin

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-215624
Author(s):  
Sinjini Sikdar ◽  
Annah B Wyss ◽  
Mi Kyeong Lee ◽  
Thanh T Hoang ◽  
Marie Richards ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Genetic Risk ◽  
Association Studies ◽  
European Ancestry ◽  
Risk Scores ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Inverse Association ◽  
Genetic Risk Scores ◽  
Genome Wide

RationaleGenome-wide association studies (GWASs) have identified numerous loci associated with lower pulmonary function. Pulmonary function is strongly related to smoking and has also been associated with asthma and dust endotoxin. At the individual SNP level, genome-wide analyses of pulmonary function have not identified appreciable evidence for gene by environment interactions. Genetic Risk Scores (GRSs) may enhance power to identify gene–environment interactions, but studies are few.MethodsWe analysed 2844 individuals of European ancestry with 1000 Genomes imputed GWAS data from a case–control study of adult asthma nested within a US agricultural cohort. Pulmonary function traits were FEV1, FVC and FEV1/FVC. Using data from a recent large meta-analysis of GWAS, we constructed a weighted GRS for each trait by combining the top (p value<5×10−9) genetic variants, after clumping based on distance (±250 kb) and linkage disequilibrium (r2=0.5). We used linear regression, adjusting for relevant covariates, to estimate associations of each trait with its GRS and to assess interactions.ResultsEach trait was highly significantly associated with its GRS (all three p values<8.9×10−8). The inverse association of the GRS with FEV1/FVC was stronger for current smokers (pinteraction=0.017) or former smokers (pinteraction=0.064) when compared with never smokers and among asthmatics compared with non-asthmatics (pinteraction=0.053). No significant interactions were observed between any GRS and house dust endotoxin.ConclusionsEvaluation of interactions using GRSs supports a greater impact of increased genetic susceptibility on reduced pulmonary function in the presence of smoking or asthma.

The Association Between Genetic Risk Factors and the Size of Intracranial Aneurysms at Time of Rupture

Neurosurgery ◽  
2013 ◽  
Vol 73 (4) ◽  
pp. 705-708 ◽  
Author(s):  
Rachel Kleinloog ◽  
Femke N.G. van 't Hof ◽  
Franciscus J. Wolters ◽  
Ingeborg Rasing ◽  
Irene C. van der Schaaf ◽  
...  
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Intracranial Aneurysms ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Association Studies ◽  
Genetic Risk Score ◽  
Genetic Risk Factors ◽  
Genome Wide Association Studies ◽  
Aneurysm Size ◽  
Genome Wide

Abstract BACKGROUND: Genetic risk factors for intracranial aneurysms may influence the size of aneurysms. OBJECTIVE: To assess the association between genetic risk factors and the size of aneurysms at the time of rupture. METHODS: Genotypes of 7 independent single-nucleotide polymorphisms (SNPs) of the 6 genetic risk loci identified in genome-wide association studies of patients with intracranial aneurysms were obtained from 700 Dutch patients with an aneurysmal subarachnoid hemorrhage (1997-2007) previously genotyped in the genome-wide association studies; 255 additional Dutch patients with an aneurysmal subarachnoid hemorrhage (2007-2011) were genotyped for these SNPs. Aneurysms were measured on computerized tomography angiography or digital subtraction angiography. The mean aneurysm size (with standard error) was compared between patients with and without a genetic risk factor by the use of linear regression. The association between SNPs and size was assessed for single SNPs and for the combined effect of SNPs by using a weighted genetic risk score. RESULTS: Single SNPs showed no association with aneurysm size, nor did the genetic risk score. CONCLUSION: The 6 genetic risk loci have no major influence on the size of aneurysms at the time of rupture. Because these risk loci explain no more than 5% of the genetic risk, other genetic factors for intracranial aneurysms may influence aneurysm size and thereby proneness to rupture.

scholarly journals The transferability of lipid loci across African, Asian and European cohorts

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Karoline Kuchenbaecker ◽  
◽  
Nikita Telkar ◽  
Theresa Reiker ◽  
Robin G. Walters ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Risk Score ◽  
Genetic Correlations ◽  
Serum Levels ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Gene Environment ◽  
Genome Wide ◽  
The Uk

Abstract Most genome-wide association studies are based on samples of European descent. We assess whether the genetic determinants of blood lipids, a major cardiovascular risk factor, are shared across populations. Genetic correlations for lipids between European-ancestry and Asian cohorts are not significantly different from 1. A genetic risk score based on LDL-cholesterol-associated loci has consistent effects on serum levels in samples from the UK, Uganda and Greece (r = 0.23–0.28, p < 1.9 × 10−14). Overall, there is evidence of reproducibility for ~75% of the major lipid loci from European discovery studies, except triglyceride loci in the Ugandan samples (10% of loci). Individual transferable loci are identified using trans-ethnic colocalization. Ten of fourteen loci not transferable to the Ugandan population have pleiotropic associations with BMI in Europeans; none of the transferable loci do. The non-transferable loci might affect lipids by modifying food intake in environments rich in certain nutrients, which suggests a potential role for gene-environment interactions.

Prostate cancer meta-analysis from more than 145,000 men to identify 65 novel prostate cancer susceptibility loci.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 1-1
Author(s):  
Rosalind Eeles ◽  
Ali Amin Al Olama ◽  
Sonja Berndt ◽  
Fredrik Wiklund ◽  
David V Conti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Relative Risk ◽  
Genetic Risk ◽  
Cancer Susceptibility ◽  
Association Studies ◽  
Genetic Risk Score ◽  
European Ancestry ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci

1 Background: Currently genome-wide association studies (GWAS) have identified over 100 prostate cancer (PrCa) susceptibility loci, capturing 33% of the PrCa familial relative risk (FRR) in Europeans. To identify further susceptibility variants, we conducted a PrCa GWAS, larger than previous studies, comprising ~49,000 cases and ~29,000 controls among individuals of European and Asian descent using the OncoArray, a platform consisting of a 260K GWAS backbone and 310K custom content selected from previous GWAS and fine-mapping studies of multiple cancers ( http://epi.grants.cancer.gov/oncoarray/ ). Methods: Genotypes from the OncoArray were used to impute genotypes from ~70M variants using the October 2014 release of the 1000 genomes project as a reference, and then combined with several previous PrCa GWAS of European ancestry: UK stage 1 (1,906 cases/1,934 controls) and stage 2 (3,888 cases/3,956 controls); CaPS 1 (498 cases/502 controls) and CaPS 2 (1,483 cases/519 controls); BPC3 (2,137 cases/3,101 controls); NCI PEGASUS (4,622 cases/2,954 controls); and iCOGS (21,209 cases/ 20,440 controls). Risk analyses for overall PrCa risk, aggressive PrCa (several definitions defined by PrCa clinical characteristics), and Gleason score were performed. Logistic and linear regression summary statistics were meta-analysed using an inverse variance fixed effect approach. Results: We identified novel loci significantly associated ( P < 5.0x10-8) with overall PrCa (N = 65). Our novel findings are comprised of several missense variants, including a SNP in the ATM gene - a key member of the DNA repair pathway. When combined multiplicatively, the 65 novel PrCa loci identified here increases the captured heritability of PrCa, explaining 38.5% of the FRR when combining novel and previously identified PrCa loci. Conclusions: In risk stratification, men in the top 1% of the genetic risk score group have a relative risk of 5.6 fold for developing PrCa compared with the median risk group. These results will improve the utility of genetic risk scores for targeted screening and prevention for prostate cancer.

scholarly journals Identification of two novel breast cancer loci through large-scale genome-wide association study in the Japanese population

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Siew-Kee Low ◽  
Yoon Ming Chin ◽  
Hidemi Ito ◽  
Keitaro Matsuo ◽  
Chizu Tanikawa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Risk ◽  
Japanese Population ◽  
Risk Group ◽  
Association Studies ◽  
Genetic Risk Score ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Weighted Genetic Risk Score

AbstractGenome-wide association studies (GWAS) have successfully identified about 70 genomic loci associated with breast cancer. Owing to the complexity of linkage disequilibrium and environmental exposures in different populations, it is essential to perform regional GWAS for better risk prediction. This study aimed to investigate the genetic architecture and to assess common genetic risk model of breast cancer with 6,669 breast cancer patients and 21,930 female controls in the Japanese population. This GWAS identified 11 genomic loci that surpass genome-wide significance threshold of P < 5.0 × 10−8 with nine previously reported loci and two novel loci that include rs9862599 on 3q13.11 (ALCAM) and rs75286142 on 21q22.12 (CLIC6-RUNX1). Validation study was carried out with 981 breast cancer cases and 1,394 controls from the Aichi Cancer Center. Pathway analyses of GWAS signals identified association of dopamine receptor medicated signaling and protein amino acid deacetylation with breast cancer. Weighted genetic risk score showed that individuals who were categorized in the highest risk group are approximately 3.7 times more likely to develop breast cancer compared to individuals in the lowest risk group. This well-powered GWAS is a representative study to identify SNPs that are associated with breast cancer in the Japanese population.

scholarly journals Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans

2021 ◽  
Author(s):  
Segun Fatumo ◽  
Tinashe Chikowore ◽  
Robert Kalyesubula ◽  
Rebecca N Nsubuga ◽  
Gershim Asiki ◽  
...  
Keyword(s):  
Kidney Function ◽  
Fine Mapping ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genetic Risk Score ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Genome-wide association studies (GWAS) of kidney function have uncovered hundreds of loci, primarily in populations of European ancestry. We have undertaken the first continental African GWAS of estimated glomerular filtration rate (eGFR), a measure of kidney function used to define chronic kidney disease (CKD). We conducted GWAS of eGFR in 3288 East Africans from the Uganda General Population Cohort (GPC) and replicated in 8224 African Americans from the Women’s Health Initiative. Loci attaining genome-wide significant evidence for association (P &lt; 5 × 10−8) were followed up with Bayesian fine-mapping to localize potential causal variants. The predictive power of a genetic risk score (GRS) constructed from previously reported trans-ancestry eGFR lead single nucleotide polymorphism (SNPs) was evaluated in the Uganda GPC. We identified and validated two eGFR loci. At the glycine amidinotransferase (GATM) locus, the association signal (lead SNP rs2433603, P = 1.0 × 10−8) in the Uganda GPC GWAS was distinct from previously reported signals at this locus. At the haemoglobin beta (HBB) locus, the association signal (lead SNP rs141845179, P = 3.0 × 10−8) has been previously reported. The lead SNP at the HBB locus accounted for 88% of the posterior probability of causality after fine-mapping, but did not colocalise with kidney expression quantitative trait loci. The trans-ancestry GRS of eGFR was not significantly predictive into the Ugandan population. In the first GWAS of eGFR in continental Africa, we validated two previously reported loci at GATM and HBB. At the GATM locus, the association signal was distinct from that previously reported. These results demonstrate the value of performing GWAS in continental Africans, providing a rich genomic resource to larger consortia for further discovery and fine-mapping. The study emphasizes that additional large-scale efforts in Africa are warranted to gain further insight into the genetic architecture of CKD.

scholarly journals Stroke genetics informs drug discovery and risk prediction across ancestries

2022 ◽  
Author(s):  
Stéphanie Debette ◽  
Aniket Mishra ◽  
Rainer Malik ◽  
Tsuyoshi Hachiya ◽  
Tuuli Jürgenson ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Association Studies ◽  
European Ancestry ◽  
Risk Scores ◽  
Targeted Prevention ◽  
Genome Wide Association Studies ◽  
East Asians ◽  
Better Than

Abstract Previous genome-wide association studies (GWAS) of stroke, the second leading cause of death, have been conducted in populations of predominantly European ancestry.1,2 We undertook cross-ancestry GWAS meta-analyses of stroke and its subtypes in 110,182 stroke patients (33% non-European) and 1,503,898 control individuals of five ancestries from population- and clinic-based studies, nearly doubling the number of cases in previous stroke GWAS. We identified association signals at 89 independent loci, of which 61 were novel. Effect sizes were overall highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis using a novel machine-learning approach,3 transcriptome and proteome-wide association analyses revealed putative causal genes (e.g. SH3PXD2A and FURIN) and variants (e.g. at GRK5 and NOS3). Using a novel three-pronged approach,4 we provided genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWAS with vascular risk factor GWAS (iPGS) showed strong prediction of ischemic stroke risk in European and, for the first time, East-Asian populations.5,6 The iPGS performed better than stroke PGS alone and better than previous best iPGS, in Europeans and East-Asians. Transferability of European-specific iPGS to East-Asians was limited. Stroke genetic risk scores were predictive of ischemic stroke independent of clinical risk factors in 52,600 clinical trial participants with cardiometabolic disease and performed considerably better than previous scores, both in Europeans and East-Asians. Altogether our results provide critical insight to inform biology, reveal potential drug targets for intervention, and provide genetic risk prediction tools across ancestries for targeted prevention.

Abstract 13702: A Novel Genetic Risk Score Predicts Ischemic Stroke in Patients With Cardiometabolic Disease

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Parth N Patel ◽  
Nicholas A Marston ◽  
Frederick K Kamanu ◽  
Lu Chen Weng ◽  
Marc P Bonaca ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Trials ◽  
Ischemic Stroke ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
European Ancestry ◽  
Cardiometabolic Disease ◽  
Genome Wide Association Studies ◽  
Increased Risk

Introduction: Recent genome-wide association studies have identified single nucleotide polymorphisms (SNPs) that are associated with an increased risk of stroke. We sought to determine whether a genetic risk score (GRS) could identify subjects at higher risk for first ischemic stroke after accounting for traditional risk factors in four clinical trials across the spectrum of cardiometabolic disease. Methods: Subjects who had consented for genetic testing, were of European ancestry, and had no prior history of stroke from the SOLID-TIMI 52, SAVOR-TIMI 53, PEGASUS-TIMI 54, and FOURIER trials were included in this analysis. A recently validated GRS composed of 36 SNPs associated with ischemic stroke was calculated in each patient. A Cox model was used to calculate hazard ratios for ischemic stroke across genetic risk groups, adjusted for age, sex, ancestry, hypertension, hyperlipidemia, smoking, diabetes mellitus, atrial fibrillation, coronary artery disease, and congestive heart failure. Results: In 23,089 subjects across the four trials, a total of 313 ischemic strokes occurred over a median follow-up of 3 years. Those with higher genetic risk were at significantly increased risk for ischemic stroke with an adjusted HR per 1-SD GRS of 1.12 (1.004-1.25; p=0.043). Individuals in the top 10% of genetic risk had a 42% greater hazard for ischemic stroke than those in the lower 90% of genetic risk (adjusted HR 1.42 [1.03-1.97]; p=0.034). The magnitude of risk conferred by high genetic risk was similar or greater than the risk provided by well-established clinical risk factors (Figure). Conclusions: Across four large clinical trials of subjects with cardiometabolic disease, a 36-SNP GRS was a strong, independent predictor of first ischemic stroke. The risk of stroke was particularly high in patients in the top 10% of genetic risk.

scholarly journals Reconstructing Genotypes in Private Genomic Databases from Genetic Risk Scores

2020 ◽  
Author(s):  
Brooks Paige ◽  
James Bell ◽  
Aurélien Bellet ◽  
Adrià Gascón ◽  
Daphne Ezer
Keyword(s):  
Genetic Risk ◽  
Association Studies ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Genome Database ◽  
Genomic Databases ◽  
Aggregate Information ◽  
Genome Wide ◽  
The Uk

AbstractSome organisations like 23andMe and the UK Biobank have large genomic databases that they re-use for multiple different genome-wide association studies (GWAS). Even research studies that compile smaller genomic databases often utilise these databases to investigate many related traits. It is common for the study to report a genetic risk score (GRS) model for each trait within the publication. Here we show that under some circumstances, these GRS models can be used to recover the genetic variants of individuals in these genomic databases—a reconstruction attack. In particular, if two GRS models are trained using a largely overlapping set of participants, then it is often possible to determine the genotype for each of the individuals who were used to train one GRS model, but not the other. We demonstrate this theoretically and experimentally by analysing the Cornell Dog Genome database. The accuracy of our reconstruction attack depends on how accurately we can estimate the rate of co-occurrence of pairs of SNPs within the private database, so if this aggregate information is ever released, it would drastically reduce the security of a private genomic database. Caution should be applied when using the same database for multiple analysis, especially when a small number of individuals are included or excluded from one part of the study.

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