scholarly journals Application of the Optimal Discovery Procedure to Genetic Case-Control Studies: Comparison with p Values and Asymptotic Bayes Factors

2011 ◽  
Vol 71 (1) ◽  
pp. 37-49
Author(s):  
Ioanna Tachmazidou ◽  
Maria De Iorio ◽  
Frank Dudbridge
Keyword(s):  
Case Control ◽  
Bayes Factors ◽  
Case Control Studies ◽  
P Values ◽  
Optimal Discovery Procedure

scholarly journals Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1160
Author(s):  
Atsuko Okazaki ◽  
Sukanya Horpaopan ◽  
Qingrun Zhang ◽  
Matthew Randesi ◽  
Jurg Ott
Keyword(s):  
Null Hypothesis ◽  
Pattern Mining ◽  
Genetic Diseases ◽  
Frequent Pattern Mining ◽  
Case Control ◽  
Frequent Pattern ◽  
Permutation Testing ◽  
Case Control Studies ◽  
P Values ◽  
Dna Variants

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

scholarly journals Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

2021 ◽  
Author(s):  
Atsuko Okazaki ◽  
Sukanya Horpaopan ◽  
Qingrun Zhang ◽  
Matthew Randesi ◽  
Jurg Ott
Keyword(s):  
Null Hypothesis ◽  
Pattern Mining ◽  
Genetic Diseases ◽  
Frequent Pattern Mining ◽  
Case Control ◽  
Frequent Pattern ◽  
Permutation Testing ◽  
Case Control Studies ◽  
P Values ◽  
Dna Variants

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent Pattern Mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

scholarly journals An efficient resampling method for calibrating single and gene-based rare variant association analysis in case–control studies

Biostatistics ◽  
2015 ◽  
Vol 17 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Seunggeun Lee ◽  
Christian Fuchsberger ◽  
Sehee Kim ◽  
Laura Scott
Keyword(s):  
Computation Time ◽  
Case Control ◽  
P Value ◽  
Case Control Studies ◽  
P Values ◽  
Resampling Methods ◽  
Score Statistics ◽  
Resampling Method ◽  
Asymptotic Tests ◽  
Discrete Nature

AbstractFor aggregation tests of genes or regions, the set of included variants often have small total minor allele counts (MACs), and this is particularly true when the most deleterious sets of variants are considered. When MAC is low, commonly used asymptotic tests are not well calibrated for binary phenotypes and can have conservative or anti-conservative results and potential power loss. Empirical $p$-values obtained via resampling methods are computationally costly for highly significant $p$-values and the results can be conservative due to the discrete nature of resampling tests. Based on the observation that only the individuals containing minor alleles contribute to the score statistics, we develop an efficient resampling method for single and multiple variant score-based tests that can adjust for covariates. Our method can improve computational efficiency $>$1000-fold over conventional resampling for low MAC variant sets. We ameliorate the conservativeness of results through the use of mid-$p$-values. Using the estimated minimum achievable $p$-value for each test, we calibrate QQ plots and provide an effective number of tests. In analysis of a case–control study with deep exome sequence, we demonstrate that our methods are both well calibrated and also reduce computation time significantly compared with resampling methods.

scholarly journals Correcting for batch effects in case-control microbiome studies

2017 ◽  
Author(s):  
Sean M. Gibbons ◽  
Claire Duvallet ◽  
Eric J. Alm
Keyword(s):  
Statistical Tests ◽  
Meta Analysis ◽  
Case Control ◽  
Data Generation ◽  
Batch Effects ◽  
Case Control Studies ◽  
P Values ◽  
Batch Correction ◽  
Model Free ◽  
Second Generation Sequencing

AbstractHigh-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.Author SummaryBatch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fisher’s method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets.

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.

Sign in / Sign up

Export Citation Format

Share Document