Common genetic variants identify targets for COVID-19 and individuals at high risk of severe disease.
Ontology highlight
ABSTRACT:
SUBMITTER: Horowitz JE
PROVIDER: S-EPMC7899471 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Publications
Genome-wide analysis in 756,646 individuals provides first genetic evidence that <i>ACE2</i> expression influences COVID-19 risk and yields genetic risk scores predictive of severe disease.
Horowitz J E JE Kosmicki J A JA Damask A A Sharma D D Roberts G H L GHL Justice A E AE Banerjee N N Coignet M V MV Yadav A A Leader J B JB Marcketta A A Park D S DS Lanche R R Maxwell E E Knight S C SC Bai X X Guturu H H Sun D D Baltzell A A Kury F S P FSP Backman J D JD Girshick A R AR O'Dushlaine C C McCurdy S R SR Partha R R Mansfield A J AJ Turissini D A DA Li A H AH Zhang M M Mbatchou J J Watanabe K K Gurski L L McCarthy S E SE Kang H M HM Dobbyn L L Stahl E E Verma A A Sirugo G G Ritchie M D MD Jones M M Balasubramanian S S Siminovitch K K Salerno W J WJ Shuldiner A R AR Rader D J DJ Mirshahi T T Locke A E AE Marchini J J Overton J D JD Carey D J DJ Habegger L L Cantor M N MN Rand K A KA Hong E L EL Reid J G JG Ball C A CA Baras A A Abecasis G R GR Ferreira M A MA
medRxiv : the preprint server for health sciences 20210610
SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, <i>P</i>=4.5×10<sup>-13</sup>) that down-regulates <i>ACE2</i> expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases. ...[more]
Similar Datasets
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human host cells via angiotensin-converting enzyme 2 (ACE2) and causes coronavirus disease 2019 (COVID-19). Here, through a genome-wide association study, we identify a variant (rs190509934, minor allele frequency 0.2-2%) that downregulates ACE2 expression by 37% (P = 2.7 × 10-8) and reduces the risk of SARS-CoV-2 infection by 40% (odds ratio = 0.60, P = 4.5 × 10-13), providing human genetic evidence that ACE2 expression levels influence COVID-19 risk. We also replicate the associations of six previously reported risk variants, of which four were further associated with worse outcomes in individuals infected with the virus (in/near LZTFL1, MHC, DPP9 and IFNAR2). Lastly, we show that common variants define a risk score that is strongly associated with severe disease among cases and modestly improves the prediction of disease severity relative to demographic and clinical factors alone.
| S-EPMC9005345 | biostudies-literature
Project description:The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk. We derive common measures of predictive accuracy and improvement as functions of the training sample size, chip heritabilities of disease and environmental score, and genetic correlation between disease and environmental risk factors. We consider simple addition of the two scores and a weighted sum that accounts for their correlation. Using examples from studies of cardiovascular disease and breast cancer, we show that improvements in discrimination are generally small but reasonable degrees of reclassification could be obtained with current sample sizes. Correlation between genetic and environmental scores has only minor effects on numerical results in realistic scenarios. In the longer term, as the accuracy of polygenic scores improves they will come to dominate the predictive accuracy compared to environmental scores.
| S-EPMC5847122 | biostudies-literature
Project description:Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well-established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome-wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.
| S-EPMC6941594 | biostudies-literature
Project description:The interpretation of genetic information in clinical settings raises moral issues about adequate risk communication and individual responsibility about one's health behavior. However, it is not well-known what role numeric probabilities and/or the conception of disease and genetics play in the lay understanding of predictive genetic diagnostics. This is an important question because lay understanding of genetic risk information might have particular implications for self-responsibility of the patients. Aim: Analysis of lay attitudes and risk perceptions of German lay people on genetic testing with a special focus on how they deal with the numerical information. Methods: We conducted and analyzed seven focus group discussions (FG) with lay people (n = 43). Results: Our participants showed a positive attitude toward predictive genetic testing. We identified four main topics: (1) Anumeric risk instead of statistical information; (2) Treatment options as a factor for risk evaluation; (3) Epistemic and aleatory uncertainty as moral criticism; (4) Ambivalence as a sign of uncertainty. Conclusion: For lay people, risk information, including the statistical numeric part, is perceived as highly normatively charged, often as an emotionally significant threat. It seems necessary to provide lay people with a deeper understanding of risk information and of the limitations of genetic knowledge with respect to one's own health responsibility.
| S-EPMC6497735 | biostudies-literature
Project description:Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.
| S-EPMC3605113 | biostudies-literature
Project description:Genome-wide association studies (GWASs) show that genetic factors contribute to the risk of severe coronavirus disease 2019 (COVID-19) and blood analyte levels. Here, we utilize GWAS summary statistics to study the shared genetic influences (pleiotropy) between severe COVID-19 and 344 blood analytes at the genome, gene, and single-nucleotide polymorphism (SNP) levels. Our pleiotropy analyses genetically link blood levels of 71 analytes to severe COVID-19 in at least one of the three levels of investigation-suggesting shared biological mechanisms or causal relationships. Six analytes (alanine aminotransferase, alkaline phosphatase, apolipoprotein B, C-reactive protein, triglycerides, and urate) display evidence of pleiotropy with severe COVID-19 at all three levels. Causality analyses indicate that higher triglycerides levels causally increase the risk of severe COVID-19, thereby providing important support for the use of lipid-lowering drugs such as statins and fibrates to prevent severe COVID-19.
| S-EPMC9637519 | biostudies-literature
Project description:Recent studies have revealed the polygenic nature of bipolar disorder (BP), and identified common risk variants associated with illness. However, the role of common polygenic risk in multiplex families has not previously been examined. The present study examined 249 European-ancestry families from the NIMH Genetics Initiative sample, comparing subjects with narrowly defined BP (excluding bipolar II and recurrent unipolar depression; n = 601) and their adult relatives without BP (n = 695). Unrelated adult controls (n = 266) were from the NIMH TGEN control dataset. We also examined a prospective cohort of young (12-30 years) offspring and siblings of individuals with BPI and BPII disorder (at risk; n = 367) and psychiatrically screened controls (n = 229), ascertained from five sites in the US and Australia and assessed with standardized clinical protocols. Thirty-two disease-associated SNPs from the PGC-BP Working Group report (2011) were genotyped and additive polygenic risk scores (PRS) derived. We show increased PRS in adult cases compared to unrelated controls (P = 3.4 × 10(-5) , AUC = 0.60). In families with a high-polygenic load (PRS score ≥32 in two or more subjects), PRS distinguished cases with BPI/SAB from other relatives (P = 0.014, RR = 1.32). Secondly, a higher PRS was observed in at-risk youth, regardless of affected status, compared to unrelated controls (GEE-χ(2) = 5.15, P = 0.012). This report is the first to explore common polygenic risk in multiplex families, albeit using only a small number of robustly associated risk variants. We show that individuals with BP have a higher load of common disease-associated variants than unrelated controls and first-degree relatives, and illustrate the potential utility of PRS assessment in a family context.
| S-EPMC5054905 | biostudies-literature
Project description:Polygenic 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.
| S-EPMC8302739 | biostudies-literature
Project description:BackgroundGenetics plays an important role in venous thromboembolism (VTE). Factor V Leiden (FVL or rs6025) and prothrombin gene G20210A (PT or rs1799963) are the genetic variants currently tested for VTE risk assessment. We hypothesized that primary VTE risk assessment can be improved by using genetic risk scores with more genetic markers than just FVL-rs6025 and prothrombin gene PT-rs1799963. To this end, we have designed a new genetic risk score called Thrombo inCode (TiC).Methods and resultsTiC was evaluated in terms of discrimination (Δ of the area under the receiver operating characteristic curve) and reclassification (integrated discrimination improvement and net reclassification improvement). This evaluation was performed using 2 age- and sex-matched case-control populations: SANTPAU (248 cases, 249 controls) and the Marseille Thrombosis Association study (MARTHA; 477 cases, 477 controls). TiC was compared with other literature-based genetic risk scores. TiC including F5 rs6025/rs118203906/rs118203905, F2 rs1799963, F12 rs1801020, F13 rs5985, SERPINC1 rs121909548, and SERPINA10 rs2232698 plus the A1 blood group (rs8176719, rs7853989, rs8176743, rs8176750) improved the area under the curve compared with a model based only on F5-rs6025 and F2-rs1799963 in SANTPAU (0.677 versus 0.575, P<0.001) and MARTHA (0.605 versus 0.576, P=0.008). TiC showed good integrated discrimination improvement of 5.49 (P<0.001) for SANTPAU and 0.96 (P=0.045) for MARTHA. Among the genetic risk scores evaluated, the proportion of VTE risk variance explained by TiC was the highest.ConclusionsWe conclude that TiC greatly improves prediction of VTE risk compared with other genetic risk scores. TiC should improve prevention, diagnosis, and treatment of VTE.
| S-EPMC4323784 | biostudies-literature
Project description:BackgroundAsthma is a major public health problem that affects millions of children worldwide, and exacerbations account for most of its morbidity and costs. Primary-care providers lack efficient tools to identify children at high risk for exacerbations. We aimed to construct a clinical score to help providers to identify such children.MethodsOur main outcome was severe asthma exacerbation, which was defined as any hospitalization, urgent visit, or systemic steroid course for asthma in the previous year, in children. A clinical score, consisting of a checklist questionnaire made up of 17 yes-no questions regarding asthma symptoms, use of medications and health-care services, and history, was built and validated in a cross-sectional study of Costa Rican children with asthma. It was then evaluated using data from the Childhood Asthma Management Program (CAMP), a longitudinal trial cohort of North American children.ResultsCompared with children at average risk for an exacerbation in the Costa Rican validation set, the odds of an exacerbation among children in the low-risk (OR, 0.2; 95% CI, 0.1-0.4) and high-risk (OR, 5.4; 95% CI, 1.5-19.2) score categories were significantly reduced and increased, respectively. In CAMP, the hazard ratios for an exacerbation after 1-year follow-up in the low-risk and high-risk groups were 0.6 (95% CI, 0.5-0.7) and 1.9 (95% CI, 1.4-2.4), respectively, with similar results at 2 years.ConclusionsThe proposed Asthma Exacerbation Clinical Score is simple to use and effective at identifying children at high and low risk for asthma exacerbations. The tool can easily be used in primary-care settings.
| S-EPMC2972623 | biostudies-literature