scholarly journals Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories

2020 ◽  
Author(s):  
Ricky Lali ◽  
Michael Chong ◽  
Arghavan Omidi ◽  
Pedrum Mohammadi-Shemirani ◽  
Ann Le ◽  
...  
Keyword(s):  
Rare Variant ◽  
Genetic Risk ◽  
Complex Disease ◽  
Rare Variants ◽  
Disease Risk ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Considerable Proportion ◽  
Asian Populations ◽  
Artery Disease

ABSTRACTRare variants are collectively numerous and may underlie a considerable proportion of complex disease risk. However, identifying genuine rare variant associations is challenging due to small effect sizes, presence of technical artefacts, and heterogeneity in population structure. We hypothesized that rare variant burden over a large number of genes can be combined into predictive rare variant genetic risk score (RVGRS). We propose a novel method (RV-EXCALIBER) that leverages summary-level data from a large public exome sequencing database (gnomAD) as controls and robustly calibrates rare variant burden to account for the aforementioned biases. A RVGRS was found to strongly associate with coronary artery disease (CAD) in European and South Asian populations. Calibrated RVGRS capture the aggregate effect of rare variants through a polygenic model of inheritance, identifies 1.5% of the population with substantial risk of early CAD, and confers risk even when adjusting for known Mendelian CAD genes, clinical risk factors, and common variant gene scores.

scholarly journals Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ricky Lali ◽  
Michael Chong ◽  
Arghavan Omidi ◽  
Pedrum Mohammadi-Shemirani ◽  
Ann Le ◽  
...  
Keyword(s):  
Rare Variant ◽  
Risk Score ◽  
Genetic Risk ◽  
Complex Disease ◽  
Rare Variants ◽  
Disease Risk ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Considerable Proportion ◽  
Asian Populations

AbstractRare variants are collectively numerous and may underlie a considerable proportion of complex disease risk. However, identifying genuine rare variant associations is challenging due to small effect sizes, presence of technical artefacts, and heterogeneity in population structure. We hypothesize that rare variant burden over a large number of genes can be combined into a predictive rare variant genetic risk score (RVGRS). We propose a method (RV-EXCALIBER) that leverages summary-level data from a large public exome sequencing database (gnomAD) as controls and robustly calibrates rare variant burden to account for the aforementioned biases. A calibrated RVGRS strongly associates with coronary artery disease (CAD) in European and South Asian populations by capturing the aggregate effect of rare variants through a polygenic model of inheritance. The RVGRS identifies 1.5% of the population with substantial risk of early CAD and confers risk even when adjusting for known Mendelian CAD genes, clinical risk factors, and a common variant genetic risk score.

scholarly journals Prediction of Inflammatory Bowel Diseases using Genetic Risk Score in Asian  populations

2021 ◽  
Author(s):  
Shifteh Abedian ◽  
Sunny H Wong ◽  
Suzanne Van Sommeren ◽  
Atsushi Takahashi ◽  
Jae Hee Cheon ◽  
...  
Keyword(s):  
Genetic Risk ◽  
Rare Variants ◽  
Genetic Risk Score ◽  
East Asian ◽  
Risk Scores ◽  
Total Load ◽  
Asian Populations ◽  
Central Asian ◽  
Disease Occurrence ◽  
Inflammatory Bowel

Abstract Background and Aims: The incidence of Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and Ulcerative colitis (UC), is rising in Asian populations. We undertook a cross-population study to explore whether genetic risk scores (GRS) of IBD, CD and UC could explain their occurrence, and whether they can be used to predict disease occurrence in general populations from East Asia (EA) and Central Asia (CA). Methods We studied 9,698 subjects – 4,733 IBD patients (2,003 CD; 2,730 UC) and 4,965 matched controls – who had been genotyped using Immunochip. The subjects were from three East Asian (Japan, South Korea and China) and two Central Asian populations (India and Iran). We generated GRS for each population by combining information from up to 201 genome-wide significant IBD-associated variants to summarize the total load of genetic risk for each phenotype. We then estimated the explained variance and predictability of IBD using the GRS. Results IBD GRS could explain up to 4.40% and 4.14% of IBD variance at a significant level in East Asian and Central Asian populations, respectively, but, given a prevalence of 0.01% and 0.04% for IBD, these yield limited predictive probability. GRS for CD and UC separately proved less significant than GRS for IBD. Conclusion GRS alone can explain only a limited percentage of disease occurrence (< 5% of disease susceptibility) and cannot be used to predict IBD in Asian populations at this time. Our results highlight the significant missing heritability, which may be due to genetic epistasis, gene-environment interactions, or rare variants.

scholarly journals 959Coronary artery disease risk according to genetic risk score deciles, Age and cardiovascular risk factors

2017 ◽  
Vol 38 (suppl_1) ◽  
Author(s):  
A. Pereira ◽  
M. Neto ◽  
R. Rodrigues ◽  
J. Monteiro ◽  
A.C. Sousa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Risk Score ◽  
Genetic Risk ◽  
Disease Risk ◽  
Genetic Risk Score ◽  
Artery Disease

scholarly journals Sexual Differences in Genetic Predisposition of Coronary Artery Disease

2020 ◽  
Author(s):  
Yunfeng Huang ◽  
Qin Hui ◽  
Marta Gwinn ◽  
Yi-Juan Hu ◽  
Arshed A. Quyyumi ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Men And Women ◽  
High Genetic Risk ◽  
Genome Wide ◽  
Artery Disease

Background - The genomic structure that contributes to the risk of coronary artery disease (CAD) can be evaluated as a risk score of multiple variants. However, sex differences have not been fully examined in applications of genetic risk score of CAD. Methods - Using data from the UK Biobank, we constructed a CAD genetic risk score based on all known loci, three mediating trait-based (blood pressure, lipids, body mass index) sub-scores, and a genome-wide polygenic risk score based on 1.1 million variants. The differences in genetic associations with prevalent and incident CAD between men and women were investigated among 317,509 unrelated individuals of European ancestry. We also assessed interactions with sex for 161 individual loci included in the comprehensive genetic risk score. Results - For both prevalent and incident CAD, the associations of comprehensive and genome-wide genetic risk scores were stronger among men than women. Using a score of 161 loci, we observed a 2.4 times higher risk for incident CAD comparing men with high genetic risk to men with low genetic risk, but an 80 percent greater risk comparing women with high genetic risk to women with low genetic risk. (interaction p=0.002). Of the three sub-scores, the blood pressure-associated sub-score exhibited sex differences (interaction p=0.0004 per SD increase in sub-score). Analysis of individual variants identified a novel gene-sex interaction at locus 21q22.11 . Conclusions - Sexual differences in genetic predisposition should be considered in future studies of coronary artery disease, and genetic risk scores should not be assumed to perform equally well in men and women.

scholarly journals Genetic architecture of type 1 diabetes with low genetic risk score informed by 41 unreported loci

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Hui-Qi Qu ◽  
Jingchun Qu ◽  
Jonathan Bradfield ◽  
Luc Marchand ◽  
Joseph Glessner ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Genetic Risk ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Rare Variants ◽  
Genetic Risk Score ◽  
Autism Spectrum ◽  
Risk Scores ◽  
Interferon Α

AbstractType 1 diabetes (T1D) patients with low genetic risk scores (GRS) may be non-autoimmune or autoimmune mediated by other genetic loci. The T1D-GRS2 provides us an opportunity to look into the genetic architecture of these patients. A total of 18,949 European individuals were included in this study, including 6599 T1D cases and 12,323 controls. 957 (14.5%) T1D patients were identified with low GRS (GRS < 8.43). The genome-wide association study on these patients identified 41 unreported loci. Two loci with common variants and 39 loci with rare variants were identified in this study. This study identified common SNPs associated with both low GRS T1D and expression levels of the interferon-α-induced MNDA gene, indicating the role of viral infection in T1D. Interestingly, 16 of the 41 unreported loci have been linked to autism spectrum disorder (ASD) by previous studies, suggesting that genes residing at these loci may underlie both T1D and autism.

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.

Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

2021 ◽  
pp. 1-10
Author(s):  
Zoe Guan ◽  
Ronglai Shen ◽  
Colin B. Begg
Keyword(s):  
Rare Variant ◽  
Rare Variants ◽  
Association Studies ◽  
The Cancer Genome Atlas ◽  
Considerable Proportion ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Risk Variants ◽  
Cancer Types ◽  
Pan Cancer

<b><i>Background:</i></b> Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The “rare variant hypothesis” proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. <b><i>Objectives:</i></b> In this study, we investigated associations between rare variants and 14 cancer types. <b><i>Methods:</i></b> We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). <b><i>Results:</i></b> We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). <b><i>Conclusions:</i></b> Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

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