Inference of Haplotype Effects in Case-Control Studies Using Unphased Genotype and Environmental Data

2008 ◽  
Vol 50 (2) ◽  
pp. 270-282 ◽  
Author(s):  
Xiaowu Chen ◽  
Zhaohai Li
Keyword(s):  
Case Control ◽  
Environmental Data ◽  
Case Control Studies ◽  
Unphased Genotype

A Simple Loglinear Model for Haplotype Effects in a Case-Control Study Involving Two Unphased Genotypes

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Stuart G. Baker
Keyword(s):  
Interactive Effect ◽  
Case Control ◽  
Nuisance Parameters ◽  
Design Matrix ◽  
Case Control Studies ◽  
Weinberg Equilibrium ◽  
Hardy Weinberg Equilibrium ◽  
Unphased Genotype ◽  
Unphased Genotype Data ◽  
Control Study

Because haplotypes may parsimoniously summarize the effect of genes on disease, there is great interest in using haplotypes in case-control studies of unphased genotype data. Previous methods for investigating haplotypes effects in case-control studies have not allowed for both of the following two scenarios that could have a large impact on results (i) departures from Hardy-Weinberg equilibrium in controls as well as cases, and (ii) an interactive effect of haplotypes and environmental covariates on the probability of disease. A new method is proposed that generalizes the model of Epstein and Satten to incorporate both (i) and (ii). Computations are relatively simple involving a single loglinear design matrix for parameters modeling the distribution of haplotype frequencies in controls, parameters modeling the effect of haplotypes and covariate-haplotype interactions on disease, and nuisance parameters required for correct inference. Based on simulations with realistic sample sizes, the method is recommended with data from two genotypes, a recessive or dominant model linking haplotypes to disease, and estimates of haplotype effects among haplotypes with a frequency greater than 10%. The methodology is most useful with candidate genotype pairs or for searching through pairs of genotypes when scenarios (i) and (ii) are likely. An example without a covariate illustrates the importance of modeling a departure from Hardy-Weinberg equilibrium in controls.

Case-Control Studies

2009 ◽  
Author(s):  
Ruth H. Keogh ◽  
D. R. Cox
Keyword(s):  
Case Control ◽  
Case Control Studies

Clinical Trials in Thrombosis: Secondary Prevention of Myocardial Infarction

1976 ◽  
Vol 35 (01) ◽  
pp. 049-056 ◽  
Author(s):  
Christian R Klimt ◽  
P. H Doub ◽  
Nancy H Doub
Keyword(s):  
Myocardial Infarction ◽  
Secondary Prevention ◽  
Case Control ◽  
Therapeutic Effects ◽  
Case Control Studies ◽  
Definitive Answer ◽  
Inhibition Of Platelet Aggregation ◽  
Prospective Trials

SummaryNumerous in vivo and in vitro experiments, investigating the inhibition of platelet aggregation and the prevention of experimentally-induced thrombosis, suggest that anti-platelet drugs, such as aspirin or the combination of aspirin and dipyridamole or sulfinpyrazone, may be effective anti-thrombotic agents in man. Since 1971, seven randomized prospective trials and two case-control studies have been referenced in the literature or are currently being conducted, which evaluate the effects of aspirin, sulfinpyrazone, or dipyridamole in combination with aspirin in the secondary prevention of myocardial infarction. A critical review of these trials indicates a range of evidence from no difference to a favorable trend that antiplatelet drugs may serve as anti-thrombotic agents in man. To date, a definitive answer concerning the therapeutic effects of these drugs in the secondary prevention of coronary heart disease is not available.

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.

scholarly journals A systematic assessment of the association between frequently prescribed medicines and the risk of common cancers: a series of nested case-control studies

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
R. D. McDowell ◽  
C. Hughes ◽  
P. Murchie ◽  
C. Cardwell
Keyword(s):  
Cancer Risk ◽  
Conditional Logistic Regression ◽  
Exposure Dose ◽  
Case Control ◽  
Case Control Studies ◽  
Systematic Assessment ◽  
Exposure Response ◽  
Prescribed Medicines ◽  
Nested Case Control ◽  
Novel Associations

Abstract Background Studies systematically screening medications have successfully identified prescription medicines associated with cancer risk. However, adjustment for confounding factors in these studies has been limited. We therefore investigated the association between frequently prescribed medicines and the risk of common cancers adjusting for a range of confounders. Methods A series of nested case-control studies were undertaken using the Primary Care Clinical Informatics Unit Research (PCCIUR) database containing general practice (GP) records from Scotland. Cancer cases at 22 cancer sites, diagnosed between 1999 and 2011, were identified from GP records and matched with up to five controls (based on age, gender, GP practice and date of registration). Odds ratios (OR) and 95% confidence intervals (CI) comparing any versus no prescriptions for each of the most commonly prescribed medicines, identified from prescription records, were calculated using conditional logistic regression, adjusting for comorbidities. Additional analyses adjusted for smoking use. An association was considered a signal based upon the magnitude of its adjusted OR, p-value and evidence of an exposure-response relationship. Supplementary analyses were undertaken comparing 6 or more prescriptions versus less than 6 for each medicine. Results Overall, 62,109 cases and 276,580 controls were included in the analyses and a total of 5622 medication-cancer associations were studied across the 22 cancer sites. After adjusting for comorbidities 2060 medicine-cancer associations for any prescription had adjusted ORs greater than 1.25 (or less than 0.8), 214 had a corresponding p-value less than or equal to 0.01 and 118 had evidence of an exposure-dose relationship hence meeting the criteria for a signal. Seventy-seven signals were identified after additionally adjusting for smoking. Based upon an exposure of 6 or more prescriptions, there were 118 signals after adjusting for comorbidities and 82 after additionally adjusting for smoking. Conclusions In this study a number of novel associations between medicine and cancer were identified which require further clinical and epidemiological investigation. The majority of medicines were not associated with an altered cancer risk and many identified signals reflected known associations between medicine and cancer.

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