scholarly journals A review of HLA and COVID-19 association studies

2020 ◽  
Vol 3 (2) ◽  
pp. 25-30
Author(s):  
Renata Zunec
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Human Leukocyte ◽  
Genetic System ◽  
Epidemiological Studies ◽  
Population Based ◽  
Case Control ◽  
Case Control Studies ◽  
First Case ◽  
Scale Population

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is reported to vary across different populations in the prevalence of infection, in the death rate of patients, in the severity of symptoms and in the drug response of patients. Among host genetic factors that can influence all these attributes human leukocyte antigen (HLA) genetic system stands out as one of the leading candidates. Case-control studies, large-scale population-based studies, as well as experimental bioinformatics studies are of utmost importance to confirm HLA susceptibility spectrum of COVID-19. This review presents the results of the first case-control and epidemiological studies performed in several populations, early after the pandemic breakout. The results are pointing to several susceptible and protective HLA alleles and haplotypes associations with COVID-19, some of which might be of interest for the future studies in Croatia, due to its common presence in the population. However, further multiple investigations from around the world, as numerous as possible, are needed to confirm or deteriorate these preliminary results.

scholarly journals Technical Note: Efficient and accurate estimation of genotype odds ratios in biobank-based unbalanced case-control studies

2019 ◽  
Author(s):  
Rounak Dey ◽  
Seunggeun Lee
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Case Control ◽  
Accurate Estimation ◽  
Genome Wide Association Studies ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale

AbstractIn genome-wide association studies (GWASs), genotype log-odds ratios (LORs) quantify the effects of the variants on the binary phenotypes, and calculating the genotype LORs for all of the markers is required for several downstream analyses. Calculating genotype LORs at a genome-wide scale is computationally challenging, especially when analyzing large-scale biobank data, which involves performing thousands of GWASs phenome-wide. Since most of the binary phenotypes in biobank-based studies have unbalanced (case : control = 1 : 10) or often extremely unbalanced (case : control = 1 : 100) case-control ratios, the existing methods cannot provide a scalable and accurate way to estimate the genotype LORs. The traditional logistic regression provides biased LOR estimates in such situations. Although the Firth bias correction method can provide unbiased LOR estimates, it is not scalable for genome-wide or phenome-wide scale association analyses typically used in biobank-based studies, especially when the number of non-genetic covariates is large. On the other hand, the saddlepoint approximation-based test (fastSPA), which can provide accurate p values and is scalable to analyse large-scale biobank data, does not provide the genotype LOR estimates as it is a score-based test. Here, we propose a scalable method based on score statistics, to accurately estimate the genotype LORs, adjusting for non-genetic covariates. Comparing to the Firth method, our proposed method reduces the computational complexity from O(nK2 + K3) to O(n), where n is the sample-size, and K is the number of non-genetic covariates. Our method is ~ 10x faster than the Firth method when 15 covariates are being adjusted for. Through extensive numerical simulations, we show that the proposed method is both scalable and accurate in estimating the genotype ORs in genome-wide or phenome-wide scale.

scholarly journals A Simple Approximation To The Bias Of Gene-Envinornment Interactions In Case/Control Studies With Silent Disease

2018 ◽  
Author(s):  
Iryna Lobach ◽  
Joshua Sampson ◽  
Siarhei Lobach ◽  
Alexander Alekseyenko ◽  
Alexandra Pryatinska ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Environmental Variable ◽  
Case Control ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Case Control Studies ◽  
Finite Samples ◽  
Research Areas ◽  
Gxe Interaction

AbstractOne of the most important research areas in case-control Genome-Wide Association Studies is to determine how the effect of a genotype varies across the environment or to measure the gene-environment interaction (GxE). We consider the scenario when some of the “healthy” controls actually have the disease and when the frequency of these latent cases varies by the environmental variable of interest. In this scenario, performing logistic regression of clinically defined case status on the genetic variant, environmental variable, and their interaction will result in biased estimates of GxE interaction. Here, we derive a general theoretical approximation to the bias in the estimates of the GxE interaction and show, through extensive simulation, that this approximation is accurate in finite samples. Moreover, we apply this approximation to evaluate the bias in the effect estimates of the genetic variants related to mitochondrial proteins a large-scale Prostate Cancer study.

The Epidemiology of Acute Stress Disorder and Other Early Responses to Trauma in Adults

2012 ◽  
pp. 68-83
Author(s):  
Quinn M. Biggs ◽  
Jennifer M. Guimond ◽  
Carol S. Fullerton ◽  
Robert J. Ursano ◽  
Christine Gray ◽  
...  
Keyword(s):  
Large Scale ◽  
Stress Disorder ◽  
Acute Stress ◽  
Traumatic Event ◽  
Female Gender ◽  
Epidemiological Studies ◽  
Acute Stress Disorder ◽  
Population Based ◽  
Scale Population ◽  
Assault Victims

Acute stress disorder (ASD) is an anxiety disorder characterized by exposure to a traumatic event followed by symptoms of re-experiencing, avoidance, hyper-arousal, peritraumatic dissociation, and impairment in functioning. ASD's time-limited duration (two days to one month) makes it distinct from but related to posttraumatic stress disorder (PTSD), which is diagnosed after one month. ASD's brief duration has contributed to a dearth of large-scale, population-based studies. Smaller studies have sought to determine rates of ASD after specific events in select populations; others have focused on ASD's role in predicting PTSD. Much can be learned from existing epidemiological studies. ASD's prevalence varies from 3% in a population of accident victims to 59% in female sexual assault victims. Female gender is a key risk factor; marital status, ethnicity, and socioeconomic status have also been associated with ASD in some studies. Comorbidities include depressive and anxiety disorders and substance use disorders.

The influence of human leukocyte antigen-DRB1*15:01 and its interaction with smoking in MS development is dependent on DQA1*01:01 status

2019 ◽  
Vol 26 (13) ◽  
pp. 1638-1646 ◽  
Author(s):  
Anna Karin Hedström ◽  
Ola Hössjer ◽  
Jan Hillert ◽  
Pernilla Stridh ◽  
Ingrid Kockum ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Human Leukocyte Antigen ◽  
Human Leukocyte ◽  
Population Based ◽  
Case Control ◽  
Case Control Studies ◽  
Swedish Population ◽  
Leukocyte Antigen ◽  
Smoking Interaction ◽  
Additive Scale

Background: HLA-DRB1*15:01, absence of HLA-A*02:01, and smoking interact to increase multiple sclerosis (MS) risk. Objective: To analyze whether MS-associated human leukocyte antigen (HLA) alleles, apart from DRB1*15:01 and absence of A*02:01, interact with smoking in MS development, and to explore whether the established HLA-smoking interaction is affected by the DQA1*01:01 allele, which confers a protective effect only in the presence of DRB1*15:01. Methods: In two Swedish population-based case–control studies (5838 cases, 5412 controls), subjects with different genotypes and smoking habits were compared regarding MS risk, by calculating odds ratios with 95% confidence intervals employing logistic regression. Interaction on the additive scale between different genotypes and smoking was evaluated. Results: The DRB1*08:01 allele interacted with smoking to increase MS risk. The interaction between DRB1*15:01 and both the absence of A*02:01 and smoking was confined to DQA1*01:01 negative subjects, whereas no interactions occurred among DQA1*01:01 positive subjects. Conclusion: Multifaceted interactions take place between different class II alleles and smoking in MS development. The influence of DRB1*15:01 and its interaction with the absence of A*02:01 and smoking is dependent on DQA1*01:01 status which may be due to differences in the responding T-cell repertoires.

Large-scale, Population-based Epidemiological Studies with Record Linkage can be done in Germany

2004 ◽  
Vol 19 (12) ◽  
pp. 1073-1074 ◽  
Author(s):  
Ulrich Ronellenfitsch ◽  
Catherine Kyobutungi ◽  
Heiko Becher ◽  
Oliver Razum
Keyword(s):  
Record Linkage ◽  
Large Scale ◽  
Epidemiological Studies ◽  
Population Based ◽  
Scale Population

scholarly journals Green tea drinking and risk of pancreatic cancer: A large-scale, population-based case–control study in urban Shanghai

2012 ◽  
Vol 36 (6) ◽  
pp. e354-e358 ◽  
Author(s):  
Jing Wang ◽  
Wei Zhang ◽  
Lu Sun ◽  
Herbert Yu ◽  
Quan-Xing Ni ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Green Tea ◽  
Large Scale ◽  
Case Control Study ◽  
Population Based ◽  
Case Control ◽  
Scale Population ◽  
Tea Drinking ◽  
Control Study

Association between ALDH2 Gene Polymorphism and Late-onset Alzheimer Disease: An Up-to-date Meta-analysis

2020 ◽  
Vol 17 (2) ◽  
pp. 105-111
Author(s):  
Haitao Liu ◽  
Wei Ge ◽  
Wei Chen ◽  
Xue Kong ◽  
Weiming Jian ◽  
...  
Keyword(s):  
Gene Polymorphism ◽  
Large Scale ◽  
Late Onset ◽  
Meta Analysis ◽  
Case Control ◽  
Cochrane Library ◽  
Positive Trend ◽  
Case Control Studies ◽  
China National Knowledge Infrastructure ◽  
Aldh2 Gene

Objectives: Previous case-control studies have focused on the relationship between ALDH2 gene polymorphism and late-onset Alzheimer's Disease (LOAD), but no definite unified conclusion has been reached. Therefore, the correlation between ALDH2 Glu504Lys polymorphism and LOAD remains controversial. To analyze the correlation between ALDH2 polymorphism and the risk of LOAD, we implemented this up-to-date meta-analysis to assess the probable association. Methods: Studies were searched through China National Knowledge Infrastructure (CNKI), VIP Database for Chinese Technical Periodicals, China Biology Medicine, PubMed, Cochrane Library, Clinical- Trials.gov, Embase, and MEDLINE from January 1, 1994 to December 31, 2018, without any restrictions on language and ethnicity. Results: Five studies of 1057 LOAD patients and 1136 healthy controls met our criteria for the analysis. Statistically, the ALDH2 GA/AA genotype was not linked with raising LOAD risk (odds ratio (OR) = 1.48, 95% confidence interval (CI) = 0.96-2.28, p = 0.07). In subgroup analysis, the phenomenon that men with ALDH2*2 had higher risk for LOAD (OR = 1.72, 95%CI = 1.10-2.67, p = 0.02) was observed. Conclusions: This study comprehends only five existing case-control studies and the result is negative. The positive trend might appear when the sample size is enlarged. In the future, more large-scale casecontrol or cohort studies should be done to enhance the association between ALDH2 polymorphism and AD or other neurodegenerative diseases.

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