Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Robust trend tests for genetic association in case-control studies using family data.

ABSTRACT: We studied a trend test for genetic association between disease and the number of risk alleles using case-control data. When the data are sampled from families, this trend test can be adjusted to take into account the correlations among family members in complex pedigrees. However, the test depends on the scores based on the underlying genetic model and thus it may have substantial loss of power when the model is misspecified. Since the mode of inheritance will be unknown for complex diseases, we have developed two robust trend tests for case-control studies using family data. These robust tests have relatively good power for a class of possible genetic models. The trend tests and robust trend tests were applied to a dataset of Genetic Analysis Workshop 14 from the Collaborative Study on the Genetics of Alcoholism.

SUBMITTER: Tian X

PROVIDER: S-EPMC1866832 | biostudies-literature | 2005

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Robust trend tests for genetic association in case-control studies using family data.

Tian Xin X Joo Jungnam J Zheng Gang G Lin Jing-Ping JP

BMC genetics 20051230

We studied a trend test for genetic association between disease and the number of risk alleles using case-control data. When the data are sampled from families, this trend test can be adjusted to take into account the correlations among family members in complex pedigrees. However, the test depends on the scores based on the underlying genetic model and thus it may have substantial loss of power when the model is misspecified. Since the mode of inheritance will be unknown for complex diseases, w ...[more]

PMID: 16451563

Similar Datasets

Genetic association tests: a method for the joint analysis of family and case-control data.

Project description:With the trend in molecular epidemiology towards both genome-wide association studies and complex modelling, the need for large sample sizes to detect small effects and to allow for the estimation of many parameters within a model continues to increase. Unfortunately, most methods of association analysis have been restricted to either a family-based or a case-control design, resulting in the lack of synthesis of data from multiple studies. Transmission disequilibrium-type methods for detecting linkage disequilibrium from family data were developed as an effective way of preventing the detection of association due to population stratification. Because these methods condition on parental genotype, however, they have precluded the joint analysis of family and case-control data, although methods for case-control data may not protect against population stratification and do not allow for familial correlations. We present here an extension of a family-based association analysis method for continuous traits that will simultaneously test for, and if necessary control for, population stratification. We further extend this method to analyse binary traits (and therefore family and case-control data together) and accurately to estimate genetic effects in the population, even when using an ascertained family sample. Finally, we present the power of this binary extension for both family-only and joint family and case-control data, and demonstrate the accuracy of the association parameter and variance components in an ascertained family sample.

| S-EPMC2874328 | biostudies-literature

Optimal Trend Tests for Genetic Association Studies of Heterogeneous Diseases.

Project description:The Cochran-Armitage trend test is a standard procedure in genetic association studies. It is a directed test with high power to detect genetic effects that follow the gene-dosage model. In this paper, the author proposes optimal trend tests for genetic association studies of heterogeneous diseases. Monte-Carlo simulations show that the power gain of the optimal trend tests over the conventional Cochran-Armitage trend test is striking when the genetic effects are heterogeneous. The easy-to-use R 3.1.2 software (R Foundation for Statistical Computing, Vienna, Austria) code is provided. The optimal trend tests are recommended for routine use.

| S-EPMC4899796 | biostudies-literature

Robust analysis of secondary phenotypes in case-control genetic association studies.

Project description:The case-control study is a common design for assessing the association between genetic exposures and a disease phenotype. Though association with a given (case-control) phenotype is always of primary interest, there is often considerable interest in assessing relationships between genetic exposures and other (secondary) phenotypes. However, the case-control sample represents a biased sample from the general population. As a result, if this sampling framework is not correctly taken into account, analyses estimating the effect of exposures on secondary phenotypes can be biased leading to incorrect inference. In this paper, we address this problem and propose a general approach for estimating and testing the population effect of a genetic variant on a secondary phenotype. Our approach is based on inverse probability weighted estimating equations, where the weights depend on genotype and the secondary phenotype. We show that, though slightly less efficient than a full likelihood-based analysis when the likelihood is correctly specified, it is substantially more robust to model misspecification, and can out-perform likelihood-based analysis, both in terms of validity and power, when the model is misspecified. We illustrate our approach with an application to a case-control study extracted from the Framingham Heart Study. Copyright © 2016 John Wiley & Sons, Ltd.

| S-EPMC5870885 | biostudies-literature

Relative efficiency of trend tests with misspecified genetic models in stratified analyses of case-control or cohort data.

Project description:Background/aimsGenetic single-nucleotide polymorphism (SNP) data are often analyzed using trend tests that rely on a specific assumption about the way that disease frequency varies across genotypes, but the validity of this assumption is not typically known. We explore the relative efficiency of trend tests in which the assumed model may or may not correspond to the true genetic model.MethodsWe derive formulae for the asymptotic relative efficiencies (AREs) comparing tests that assume different genetic models. We consider both unstratified and stratified tests, using both case-control and cohort data. We illustrate these formulae using realistic parameters and compare the calculated AREs to simulated relative efficiencies in finite samples.ResultsThe AREs are identical for unstratified tests using case-control and cohort data, but differ for stratified tests. Loss of efficiency can be substantial, given specific combinations of high-risk allele frequencies, disease frequencies, and assumed versus actual genetic models. Given reasonably large sample sizes, asymptotic calculations align well with finite sample simulations of relative efficiency.ConclusionsARE is a useful estimate of the relative efficiency of statistics using different underlying genetic models. ARE calculations reveal that additive gene doses, which are most commonly used, lead to large losses in power in some settings.

| S-EPMC3190174 | biostudies-literature

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

Project description:Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package.

| S-EPMC5811375 | biostudies-literature

On multi-marker tests for association in case-control studies.

Project description:Genome-wide association studies (GWAs) have identified thousands of DNA loci associated with a variety of traits. Statistical inference is almost always based on single marker hypothesis tests of association and the respective p-values with Bonferroni correction. Since commercially available genomic arrays interrogate hundreds of thousands or even millions of loci simultaneously, many causal yet undetected loci are believed to exist because the conditional power to achieve a genome-wide significance level can be low, in particular for markers with small effect sizes and low minor allele frequencies and in studies with modest sample size. However, the correlation between neighboring markers in the human genome due to linkage disequilibrium (LD) resulting in correlated marker test statistics can be incorporated into multi-marker hypothesis tests, thereby increasing power to detect association. Herein, we establish a theoretical benchmark by quantifying the maximum power achievable for multi-marker tests of association in case-control studies, achievable only when the causal marker is known. Using that genotype correlations within an LD block translate into an asymptotically multivariate normal distribution for score test statistics, we develop a set of weights for the markers that maximize the non-centrality parameter, and assess the relative loss of power for other approaches. We find that the method of Conneely and Boehnke (2007) based on the maximum absolute test statistic observed in an LD block is a practical and powerful method in a variety of settings. We also explore the effect on the power that prior biological or functional knowledge used to narrow down the locus of the causal marker can have, and conclude that this prior knowledge has to be very strong and specific for the power to approach the maximum achievable level, or even beat the power observed for methods such as the one proposed by Conneely and Boehnke (2007).

| S-EPMC3863805 | biostudies-other

Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.

Project description:There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

| S-EPMC5860584 | biostudies-literature

Marker selection for genetic case-control association studies.

Project description:Association studies can focus on candidate gene(s), a particular genomic region, or adopt a genome-wide association approach, each of which has implications for marker selection. The strategy for marker selection will affect the statistical power of the study to detect a disease association and is a crucial element of study design. The abundant single nucleotide polymorphisms (SNPs) are the markers of choice in genetic case-control association studies. The genotypes of neighboring SNPs are often highly correlated ('in linkage disequilibrium', LD) within a population, which is utilized for selecting specific 'tagSNPs' to serve as proxies for other nearby SNPs in high LD. General guidelines for SNP selection in candidate genes/regions and genome-wide studies are provided in this protocol, along with illustrative examples. Publicly available web-based resources are utilized to browse and retrieve data, and software, such as Haploview and Goldsurfer2, is applied to investigate LD and to select tagSNPs.

| S-EPMC3025519 | biostudies-literature

Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies.

Project description:MotivationIn the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution.ResultsWe propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case-control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant-phenotype associations.Availability and implementationThe summary statistics and code generated in this study are available at https://github.com/styvon/TAPE.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9477535 | biostudies-literature

Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data.

Project description:BackgroundIn human genetic association studies with high-dimensional gene expression data, it has been well known that statistical selection methods utilizing prior biological network knowledge such as genetic pathways and signaling pathways can outperform other methods that ignore genetic network structures in terms of true positive selection. In recent epigenetic research on case-control association studies, relatively many statistical methods have been proposed to identify cancer-related CpG sites and their corresponding genes from high-dimensional DNA methylation array data. However, most of existing methods are not designed to utilize genetic network information although methylation levels between linked genes in the genetic networks tend to be highly correlated with each other.ResultsWe propose new approach that combines data dimension reduction techniques with network-based regularization to identify outcome-related genes for analysis of high-dimensional DNA methylation data. In simulation studies, we demonstrated that the proposed approach overwhelms other statistical methods that do not utilize genetic network information in terms of true positive selection. We also applied it to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project.ConclusionsThe proposed variable selection approach can utilize prior biological network information for analysis of high-dimensional DNA methylation array data. It first captures gene level signals from multiple CpG sites using data a dimension reduction technique and then performs network-based regularization based on biological network graph information. It can select potentially cancer-related genes and genetic pathways that were missed by the existing methods.

| S-EPMC6805595 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data