scholarly journals Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study

2009 ◽  
Vol 3 (Suppl 7) ◽  
pp. S46 ◽  
Author(s):  
Stephen R Piccolo ◽  
Ryan P Abo ◽  
Kristina Allen-Brady ◽  
Nicola J Camp ◽  
Stacey Knight ◽  
...  
Lipids ◽  
2019 ◽  
Vol 54 (10) ◽  
pp. 641-650
Author(s):  
Caitlin J. Smith ◽  
Elizabeth A. Jasper ◽  
Rebecca J. Baer ◽  
Patrick J. Breheny ◽  
Randi A. Paynter ◽  
...  

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 222-223
Author(s):  
L. Liu ◽  
A. Amar ◽  
J. Robinson ◽  
I. N. Bruce ◽  
D. Morris ◽  
...  

Background:The biologic drug Rituximab (anti-CD20) is used therapeutically in SLE, however the clinical response to the therapy, which is expensive, is quite variable. Factors influencing the efficacy have been challenging to determine. The MRC funded MASTERPLANS consortium has investigated prognostic factors that determine the therapeutic response to biologic therapy in SLE. Genetics has not been studied on a large scale in this context. SLE is a complex clinical phenotype, it is likewise a complex genetic trait, although it has recently been shown that polygenic risk scores do have a relationship to the severity of the disease (1). In addition, genetic risk factors for SLE, coded at the IgG Fc gamma receptor locus, have the potential to influence antibody-dependent cell-mediated cytotoxicity.Objectives:To determine whether the genetics influences the clinical outcome of therapy with Rituximab. The study used both genome-wide data in the form of genetic risk scores as well as specific genetic data at a candidate locus, namely the IgG Fc gamma receptor locusMethods:Samples from the BILAG Biologics Register (BILAG BR) of individuals treated with Rituximab were subject to genome-wide genotyping with Illumina GSA V2 chip. Genetic risk scores (GRS) were calculated through a weighted risk sum. Genetic variation at the IgG Fc gamma receptor locus is not captured well on genotyping chips and hence common coding and copy number variation was studied using Multiplex Ligation-dependent Probe Amplification (MLPA) and sequencing.Results:BILAG-BR samples for SLE part of receiving Rituximab therapy were genotyped on GSA chip, 573 samples passed QC and were used in principal components analysis (PCA), among them, 310 samples both have RTX treatment information and GRS calculated. Examining the population using PCA in the informative samples revealed that the largest distinction, European versus African ancestry did not correlate with Rituximab response. When GRS was determined in the Responders versus the Non-responders there was a weak correlation with those with a higher risk score showing a tendency to be in the responder group (Fig. 1). We also examined variation at the IgG Fc gamma receptor locus, polymorphisms of which are associated with SLE and have been correlated with therapeutic outcome in lymphoma (2). In a subset of the BILAG-BR cohort, we show that carriage of the SLE risk allele atFCGR3A(158F) was enriched in the ‘responder at some point’ group compared to the non-responder group (P=0.03, Chi-square).Conclusion:We present preliminary data indicating that genetics at both the genome wide level and at theFCGRlocus show some influence on the outcome of therapy with Rituximab in SLE; more data are required in order to draw firm conclusions.References:[1]Reid S et al. High genetic risk score is associated with early disease onset, damage accrual and decreased survival in systemic lupus erythematosus.Ann Rheum Dis.2019 Dec 11. [Epub ahead of print][2]Weng WK, Levy R. Two immunoglobulin G fragment C receptor polymorphisms independently predict response to rituximab in patients with follicular lymphoma. J Clin Oncol. 2003;21(21):3940–3947.Acknowledgments:King’s and GSTT Biomedical Research Centre and M01665X/1MRC Stratified Medicine grantDisclosure of Interests:Lu Liu: None declared, Ariella Amar: None declared, James Robinson: None declared, Ian N. Bruce Grant/research support from: Genzyme Sanofi, GSK, and UCB, Consultant of: Eli Lilly, AstraZeneca, UCB, Iltoo, and Merck Serono, Speakers bureau: UCB, David Morris: None declared, Tim Vyse: None declared


Thorax ◽  
2021 ◽  
pp. thoraxjnl-2020-215624
Author(s):  
Sinjini Sikdar ◽  
Annah B Wyss ◽  
Mi Kyeong Lee ◽  
Thanh T Hoang ◽  
Marie Richards ◽  
...  

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.


2018 ◽  
Vol 179 (6) ◽  
pp. 363-372 ◽  
Author(s):  
Gunn-Helen Moen ◽  
Marissa LeBlanc ◽  
Christine Sommer ◽  
Rashmi B Prasad ◽  
Tove Lekva ◽  
...  

Objective Hyperglycaemia during pregnancy increases the risk of adverse health outcomes in mother and child, but the genetic aetiology is scarcely studied. Our aims were to (1) assess the overlapping genetic aetiology between the pregnant and non-pregnant population and (2) assess the importance of genome-wide polygenic contributions to glucose traits during pregnancy, by exploring whether genetic risk scores (GRSs) for fasting glucose (FG), 2-h glucose (2hG), type 2 diabetes (T2D) and BMI in non-pregnant individuals were associated with glucose measures in pregnant women. Methods We genotyped 529 Norwegian pregnant women and constructed GRS from known genome-wide significant variants and SNPs weakly associated (p > 5 × 10−8) with FG, 2hG, BMI and T2D from external genome-wide association studies (GWAS) and examined the association between these scores and glucose measures at gestational weeks 14–16 and 30–32. We also performed GWAS of FG, 2hG and shape information from the glucose curve during an oral glucose tolerance test (OGTT). Results GRSFG explained similar variance during pregnancy as in the non-pregnant population (~5%). GRSBMI and GRST2D explained up to 1.3% of the variation in the glucose traits in pregnancy. If we included variants more weakly associated with these traits, GRS2hG and GRST2D explained up to 2.4% of the variation in the glucose traits in pregnancy, highlighting the importance of polygenic contributions. Conclusions Our results suggest overlap in the genetic aetiology of FG in pregnant and non-pregnant individuals. This was less apparent with 2hG, suggesting potential differences in postprandial glucose metabolism inside and outside of pregnancy.


2022 ◽  
Vol 20 (8) ◽  
pp. 3045
Author(s):  
E. A. Sotnikova ◽  
A. V. Kiseleva ◽  
A. N. Meshkov ◽  
A. I. Ershova ◽  
A. A. Ivanova ◽  
...  

Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.


2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Carly A. Conran ◽  
Zhuqing Shi ◽  
William Kyle Resurreccion ◽  
Rong Na ◽  
Brian T. Helfand ◽  
...  

Abstract Background Genome-wide association studies have identified thousands of disease-associated single nucleotide polymorphisms (SNPs). A subset of these SNPs may be additively combined to generate genetic risk scores (GRSs) that confer risk for a specific disease. Although the clinical validity of GRSs to predict risk of specific diseases has been well established, there is still a great need to determine their clinical utility by applying GRSs in primary care for cancer risk assessment and targeted intervention. Methods This clinical study involved 281 primary care patients without a personal history of breast, prostate or colorectal cancer who were 40–70 years old. DNA was obtained from a pre-existing biobank at NorthShore University HealthSystem. GRSs for colorectal cancer and breast or prostate cancer were calculated and shared with participants through their primary care provider. Additional data was gathered using questionnaires as well as electronic medical record information. A t-test or Chi-square test was applied for comparison of demographic and key clinical variables among different groups. Results The median age of the 281 participants was 58 years and the majority were female (66.6%). One hundred one (36.9%) participants received 2 low risk scores, 99 (35.2%) received 1 low risk and 1 average risk score, 37 (13.2%) received 1 low risk and 1 high risk score, 23 (8.2%) received 2 average risk scores, 21 (7.5%) received 1 average risk and 1 high risk score, and no one received 2 high risk scores. Before receiving GRSs, younger patients and women reported significantly more worry about risk of developing cancer. After receiving GRSs, those who received at least one high GRS reported significantly more worry about developing cancer. There were no significant differences found between gender, age, or GRS with regards to participants’ reported optimism about their future health neither before nor after receiving GRS results. Conclusions Genetic risk scores that quantify an individual’s risk of developing breast, prostate and colorectal cancers as compared with a race-defined population average risk have potential clinical utility as a tool for risk stratification and to guide cancer screening in a primary care setting.


2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
F.V Moniz Mendonca ◽  
M.I Mendonca ◽  
A Pereira ◽  
J Monteiro ◽  
J Sousa ◽  
...  

Abstract Background The risk for Coronary Artery Disease (CAD) is determined by both genetic and environmental factors, as well as by the interaction between them. It is estimated that genetic factors could account for 40% to 55% of the existing variability among the population (inheritability). Therefore, some authors have advised that it is time we integrated genetic risk scores into clinical practice. Aim The aim of this study was to evaluate the magnitude of the association between an additive genetic risk score (aGRS) and CAD based on the cumulative number of risk alleles in these variants, and to estimate whether their use is valuable in clinical practice. Methods A case-control study was performed in a Portuguese population. We enrolled 3120 participants, of whom 1687 were CAD patients and 1433 were normal controls. Controls were paired to cases with respect to gender and age. 33 genetic variants known to be associated with CAD were selected, and an aGRS was calculated for each individual. The aGRS was further subdivided into deciles groups, in order to estimate the CAD risk in each decile, defined by the number of risk alleles. The magnitude of the risk (odds ratio) was calculated for each group by multiple logistic regression using the 5th decile as the reference group (median). In order to evaluate the ability of the aGRS to discriminate susceptibility to CAD, two genetic models were performed, the first with traditional risk factors (TRF) and second with TRF plus aGRS. The AUC of the two ROC curves was calculated. Results A higher prevalence of cases over controls became apparent from the 6th decile of the aGRS, reflecting the higher number of risk alleles present (see figure). The difference in CAD risk was only significant from the 6th decile, increasing gradually until the 10th decile. The odds ratio (OR) for the last decile related to 5th decile (median) was 1.87 (95% CI:1.36–2.56; p&lt;0.0001). The first model yielded an AUC=0.738 (95% CI:0.720–0.755) and the second model was slightly more discriminative for CAD risk (AUC=0.748; 95% CI:0.730–0.765). The DeLong test was significant (p=0.0002). Conclusion Adding an aGRS to the non-genetic risk factors resulted in a modest improvement in the ability to discriminate the risk of CAD. Such improvement, even if statistically significant, does not appear to be of real value in clinical practice yet. We anticipate that with the development of further knowledge about different SNPs and their complex interactions, and with the inclusion of rare genetic variants, genetic risk scores will be better suited for use in a clinical setting. Funding Acknowledgement Type of funding source: None


Sign in / Sign up

Export Citation Format

Share Document