Genetic association between EPHX1 and Crohn's disease: population stratification, genotyping error, or random chance?
Ontology highlight
ABSTRACT:
SUBMITTER: Cuthbert AP
PROVIDER: S-EPMC1774173 | biostudies-literature | 2004 Sep
REPOSITORIES: biostudies-literature
Publications
Genetic association between EPHX1 and Crohn's disease: population stratification, genotyping error, or random chance?
Cuthbert A P AP Fisher S A SA Lewis C M CM Mathew C G CG Sanderson J J Forbes A A
Gut 20040901 9
Similar Datasets
Project description:In population-based case-control association studies, the regular chi (2) test is often used to investigate association between a candidate locus and disease. However, it is well known that this test may be biased in the presence of population stratification and/or genotyping error. Unlike some other biases, this bias will not go away with increasing sample size. On the contrary, the false-positive rate will be much larger when the sample size is increased. The usual family-based designs are robust against population stratification, but they are sensitive to genotype error. In this article, we propose a novel method of simultaneously correcting for the bias arising from population stratification and/or for the genotyping error in case-control studies. The appropriate corrections depend on sample odds ratios of the standard 2x3 tables of genotype by case and control from null loci. Therefore, the test is simple to apply. The corrected test is robust against misspecification of the genetic model. If the null hypothesis of no association is rejected, the corrections can be further used to estimate the effect of the genetic factor. We considered a simulation study to investigate the performance of the new method, using parameter values similar to those found in real-data examples. The results show that the corrected test approximately maintains the expected type I error rate under various simulation conditions. It also improves the power of the association test in the presence of population stratification and/or genotyping error. The discrepancy in power between the tests with correction and those without correction tends to be more extreme as the magnitude of the bias becomes larger. Therefore, the bias-correction method proposed in this article should be useful for the genetic analysis of complex traits.
| S-EPMC2227923 | biostudies-other
Project description:Population stratification (PS) is a primary consideration in studies of genetic determinants of human traits. Failure to control for PS may lead to confounding, causing a study to fail for lack of significant results, or resources to be wasted following false-positive signals. Here, historical and current approaches for addressing PS when performing genetic association studies in human populations are reviewed. Methods for detecting the presence of PS, including global and local ancestry methods, are described. Also described are approaches for accounting for PS when calculating association statistics, such that measures of association are not confounded. Many traits are being examined for the first time in minority populations, which may inherently feature PS. © 2017 by John Wiley & Sons, Inc.
| S-EPMC6007879 | biostudies-literature
Project description:BackgroundPopulation structure (PS), including population stratification and admixture, is a significant confounder in genome-wide association studies (GWAS), as it may produce spurious associations. Random forest (RF) has been increasingly applied in GWAS data analysis because of its advantage in analysing high dimensional genetic data. RF creates importance measures for single nucleotide polymorphisms (SNPs), which are helpful for feature selections. However, if PS is not appropriately corrected, RF tends to give high importance to disease-unrelated SNPs with different frequencies of allele or genotype among subpopulations, leading to inaccurate results.MethodsIn this study, the authors propose to correct for the confounding effect of PS by including the information of PS in RF analysis. The correction procedure starts by extracting the information of PS using EIGENSTRAT or multi-dimensional scaling clustering procedure from a large number of structure inference SNPs. Phenotype and genotypes adjusted by the information of PS are then used as the outcome and predictors in RF analysis.ResultsExtensive simulations indicate that the importance measure of the causal SNP is increased following the PS correction. By analysing a real dataset, the proposed correction removes the spurious association between the lactase gene and height.ConclusionThe authors propose a simple method to correct for PS in RF analysis on GWAS data. Further studies in real GWAS datasets are required to validate the robustness of the proposed approach.
| S-EPMC3535752 | biostudies-literature
Project description:IntroductionPopulation stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity.Materials and methodsTo address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs.ResultsSimulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses.DiscussionThe automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.
| S-EPMC11624906 | biostudies-literature
Project description:MotivationThe rapid development of genotyping technology and extensive cataloguing of single nucleotide polymorphisms (SNPs) across the human genome have made genetic association studies the mainstream for gene mapping of complex human diseases. For many diseases, the most practical approach is the population-based design with unrelated individuals. Although having the advantages of easier sample collection and greater power than family-based designs, unrecognized population stratification in the study samples can lead to both false-positive and false-negative findings and might obscure the true association signals if not appropriately corrected.MethodsWe report PHYLOSTRAT, a new method that corrects for population stratification by combining phylogeny constructed from SNP genotypes and principal coordinates from multi-dimensional scaling (MDS) analysis. This hybrid approach efficiently captures both discrete and admixed population structures.ResultsBy extensive simulations, the analysis of a synthetic genome-wide association dataset created using data from the Human Genome Diversity Project, and the analysis of a lactase-height dataset, we show that our method can correct for population stratification more efficiently than several existing population stratification correction methods, including EIGENSTRAT, a hybrid approach based on MDS and clustering, and STRATSCORE , in terms of requiring fewer random SNPs for inference of population structure. By combining the flexibility and hierarchical nature of phylogenetic trees with the advantage of representing admixture using MDS, our hybrid approach can capture the complex population structures in human populations effectively.Software availabilityCodes can be downloaded from http://people.pcbi.upenn.edu/ approximately lswang/phylostrat/Contactmingyao@upenn.edu; iswang@upenn.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2832820 | biostudies-literature
Project description:Complex human diseases commonly differ in their phenotypic characteristics, e.g., Crohn's disease (CD) patients are heterogeneous with regard to disease location and disease extent. The genetic susceptibility to Crohn's disease is widely acknowledged and has been demonstrated by identification of over 100 CD associated genetic loci. However, relating CD subphenotypes to disease susceptible loci has proven to be a difficult task. In this paper we discuss the use of cluster analysis on genetic markers to identify genetic-based subgroups while taking into account possible confounding by population stratification. We show that it is highly relevant to consider the confounding nature of population stratification in order to avoid that detected clusters are strongly related to population groups instead of disease-specific groups. Therefore, we explain the use of principal components to correct for population stratification while clustering affected individuals into genetic-based subgroups. The principal components are obtained using 30 ancestry informative markers (AIM), and the first two PCs are determined to discriminate between continental origins of the affected individuals. Genotypes on 51 CD associated single nucleotide polymorphisms (SNPs) are used to perform latent class analysis, hierarchical and Partitioning Around Medoids (PAM) cluster analysis within a sample of affected individuals with and without the use of principal components to adjust for population stratification. It is seen that without correction for population stratification clusters seem to be influenced by population stratification while with correction clusters are unrelated to continental origin of individuals.
| S-EPMC3798408 | biostudies-literature
Project description:BackgroundA usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure.ResultsWe assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer's disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach.ConclusionsMatching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.
| S-EPMC4367953 | biostudies-literature
Project description:For genome-wide association studies (GWAS) using case-control data with stratification, a commonly used association test is the generalized Armitage (GA) trend test implemented in the software EIGENSTRAT. The GA trend test uses principal component analysis to correct for population stratification. It usually assumes an additive disease model and can have high power when the underlying disease model is additive or multiplicative, but may have relatively low power when the underlying disease model is recessive or dominant. The purpose of this paper is to provide a test procedure for GWAS with increased power over the GA trend test under the recessive and dominant models, while maintaining the power of the GA trend test under the additive and multiplicative models.We extend a Hardy-Weinberg disequilibrium (HWD) trend test for a homogeneous population to account for population stratification, and then propose a robust association test procedure for GWAS that incorporates information from the extended HWD trend test into the GA trend test.Our simulation studies and application of our method to a GWAS data set indicate that our proposed method can achieve the purpose described above.
| S-EPMC3786013 | biostudies-literature
Project description:Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.
| S-EPMC10862757 | biostudies-literature
Project description:Population stratification can be a serious obstacle in the analysis of genomewide association studies. We propose a method for evaluating the significance of association scores in whole-genome cohorts with stratification. Our approach is a randomization test akin to a standard permutation test. It conditions on the genotype matrix and thus takes into account not only the population structure but also the complex linkage disequilibrium structure of the genome. As we show in simulation experiments, our method achieves higher power and significantly better control over false-positive rates than do existing methods. In addition, it can be easily applied to whole-genome association studies.
| S-EPMC2265648 | biostudies-other