ComPaSS-GWAS: A method to reduce type I error in genome-wide association studies when replication data are not available

Despite the fact that imbalance between case and control groups is prevalent in genome-wide association studies (GWAS), it is often overlooked. This imbalance is getting more significant and urgent as the rapid growth of biobanks and electronic health records have enabled the collection of thousands of phenotypes from large cohorts, in particular for diseases with low prevalence. The unbalanced binary traits pose serious challenges to traditional statistical methods in terms of both genomic selection and disease prediction. For example, the well-established linear mixed models (LMM) yield inflated type I error rates in the presence of unbalanced case-control ratios. In this article, we review multiple statistical approaches that have been developed to overcome the inaccuracy caused by the unbalanced case-control ratio, with the advantages and limitations of each approach commented. In addition, we also explore the potential for applying several powerful and popular state-of-the-art machine-learning approaches, which have not been applied to the GWAS field yet. This review paves the way for better analysis and understanding of the unbalanced case-control disease data in GWAS.

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Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes

10.1101/2020.10.09.333146 ◽

2020 ◽

Author(s):

Wenjian Bi ◽

Wei Zhou ◽

Rounak Dey ◽

Bhramar Mukherjee ◽

Joshua N Sampson ◽

...

Keyword(s):

Mixed Model ◽

Type I Error ◽

Association Studies ◽

Error Rates ◽

Genome Wide Association ◽

Alternative Methods ◽

Type I ◽

Genome Wide Association Studies ◽

Type I Error Rates ◽

Genome Wide

AbstractIn genome-wide association studies (GWAS), ordinal categorical phenotypes are widely used to measure human behaviors, satisfaction, and preferences. However, due to the lack of analysis tools, methods designed for binary and quantitative traits have often been used inappropriately to analyze categorical phenotypes, which produces inflated type I error rates or is less powerful. To accurately model the dependence of an ordinal categorical phenotype on covariates, we propose an efficient mixed model association test, Proportional Odds Logistic Mixed Model (POLMM). POLMM is demonstrated to be computationally efficient to analyze large datasets with hundreds of thousands of genetic related samples, can control type I error rates at a stringent significance level regardless of the phenotypic distribution, and is more powerful than other alternative methods. We applied POLMM to 258 ordinal categorical phenotypes on array-genotypes and imputed samples from 408,961 individuals in UK Biobank. In total, we identified 5,885 genome-wide significant variants, of which 424 variants (7.2%) are rare variants with MAF < 0.01.

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Operating Characteristics of the Rank-Based Inverse Normal Transformation for Quantitative Trait Analysis in Genome-Wide Association Studies

10.1101/635706 ◽

2019 ◽

Cited By ~ 2

Author(s):

Zachary R. McCaw ◽

Jacqueline M. Lane ◽

Richa Saxena ◽

Susan Redline ◽

Xihong Lin

Keyword(s):

Type I Error ◽

Association Studies ◽

Genome Wide Association ◽

Type I ◽

Genome Wide Association Studies ◽

Operating Characteristics ◽

Association Tests ◽

Genome Wide ◽

Normal Transformation ◽

Normally Distributed

SummaryQuantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often non-normally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based Inverse Normal Transformation (INT) to non-normally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of non-normally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for non-normally distributed traits, INT-based tests outperform the standard untransformed association test (UAT), both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN.

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Collapsing SNP Genotypes in Case-Control Genome-Wide Association Studies Increases the Type I Error Rate and Power

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1325 ◽

2008 ◽

Vol 7 (1) ◽

Cited By ~ 6

Author(s):

Abigail G Matthews ◽

Chad Haynes ◽

Chang Liu ◽

Jurg Ott

Keyword(s):

Error Rate ◽

Type I Error ◽

Association Studies ◽

Case Control ◽

Genome Wide Association ◽

Type I ◽

Genome Wide Association Studies ◽

Type I Error Rate ◽

Genome Wide

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Efficient identification of context dependent subgroups of risk from genome-wide association studies

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2013-0062 ◽

2014 ◽

Vol 13 (2) ◽

Cited By ~ 1

Author(s):

Greg Dyson ◽

Charles F. Sing

Keyword(s):

Prediction Models ◽

Disease Risk ◽

Hypothesis Test ◽

Association Studies ◽

Genome Wide Association ◽

Computational Time ◽

Type I ◽

Genome Wide Association Studies ◽

Genomic Variations ◽

Genome Wide

AbstractWe have developed a modified Patient Rule-Induction Method (PRIM) as an alternative strategy for analyzing representative samples of non-experimental human data to estimate and test the role of genomic variations as predictors of disease risk in etiologically heterogeneous sub-samples. A computational limit of the proposed strategy is encountered when the number of genomic variations (predictor variables) under study is large (>500) because permutations are used to generate a null distribution to test the significance of a term (defined by values of particular variables) that characterizes a sub-sample of individuals through the peeling and pasting processes. As an alternative, in this paper we introduce a theoretical strategy that facilitates the quick calculation of Type I and Type II errors in the evaluation of terms in the peeling and pasting processes carried out in the execution of a PRIM analysis that are under-estimated and non-existent, respectively, when a permutation-based hypothesis test is employed. The resultant savings in computational time makes possible the consideration of larger numbers of genomic variations (an example genome-wide association study is given) in the selection of statistically significant terms in the formulation of PRIM prediction models.

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Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank

10.1101/758326 ◽

2019 ◽

Cited By ~ 1

Author(s):

Chong Wu

Keyword(s):

Type I Error ◽

Association Studies ◽

Error Rates ◽

Type I ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Type I Error Rates ◽

Trait Association ◽

Genome Wide ◽

Inflation Factor

AbstractMany genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits and may yield inflated Type I error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type I error rates were well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

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Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/2564531 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Baolin Wu ◽

James S. Pankow

Keyword(s):

Quantitative Traits ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Type I ◽

Genome Wide Association Studies ◽

Competitive Performance ◽

Multiple Traits ◽

Atherosclerosis Risk In Communities ◽

Genome Wide

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.

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