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

2019 ◽  
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.

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

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.

scholarly journals Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank

Genetics ◽  
2020 ◽  
Vol 215 (4) ◽  
pp. 947-958 ◽  
Author(s):  
Chong Wu
Keyword(s):  
Association Studies ◽  
Error Rates ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Association Analyses ◽  
Trait Association ◽  
Type 1 Error ◽  
Genome Wide ◽  
Inflation Factor

Many 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 1 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 1 error rate was 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.

scholarly journals Statistical Learning Methods Applicable to Genome-Wide Association Studies on Unbalanced Case-Control Disease Data

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 736
Author(s):  
Xiaotian Dai ◽  
Guifang Fu ◽  
Shaofei Zhao ◽  
Yifei Zeng
Keyword(s):  
Type I Error ◽  
Association Studies ◽  
Case Control ◽  
Error Rates ◽  
Genome Wide Association ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Learning Approaches ◽  
Genome Wide ◽  
Control Disease

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.

scholarly journals A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets

2018 ◽  
Vol 20 (6) ◽  
pp. 2055-2065 ◽  
Author(s):  
Johannes Brägelmann ◽  
Justo Lorenzo Bermejo
Keyword(s):  
Statistical Power ◽  
Type I Error ◽  
Association Studies ◽  
Real Data ◽  
Error Rates ◽  
Data Sets ◽  
Type I ◽  
Cell Type ◽  
Type I Error Rates

Abstract Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS. In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics. While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.

scholarly journals Mendelian Randomization Analysis Using Multiple Biomarkers of an Underlying Common Exposure

2021 ◽  
Author(s):  
Jin Jin ◽  
Guanghao Qi ◽  
Zhi Yu ◽  
Nilanjan Chatterjee
Keyword(s):  
Structural Equation ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Error Rates ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Multiple Biomarkers

AbstractMendelian Randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers, or other types of traits, that are co-regulated by the exposure. We propose method MRLE, which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of summary association statistics, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies showed that MRLE has well-controlled type I error rates and increased power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α and MCP-1) provided evidence for potential causal effects of inflammation on increased risk of coronary artery disease, colorectal cancer and rheumatoid arthritis, while standard MR analysis for individual biomarkers often failed to detect consistent evidence for such effects.

scholarly journals An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations

BMC Genetics ◽  
2011 ◽  
Vol 12 (1) ◽  
pp. 10 ◽  
Author(s):  
Marcio AA Almeida ◽  
Paulo SL Oliveira ◽  
Tiago V Pereira ◽  
José E Krieger ◽  
Alexandre C Pereira
Keyword(s):  
Type I Error ◽  
Imputation Accuracy ◽  
Empirical Evaluation ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Genome Wide

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

2018 ◽  
Vol 43 (1) ◽  
pp. 102-111 ◽  
Author(s):  
Jeremy A. Sabourin ◽  
Cheryl D. Cropp ◽  
Heejong Sung ◽  
Lawrence C. Brody ◽  
Joan E. Bailey-Wilson ◽  
...  
Keyword(s):  
Type I Error ◽  
Association Studies ◽  
Genome Wide Association ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Multiple-Tissue Integrative Transcriptome-Wide Association Studies Discovered New Genes Associated With Amyotrophic Lateral Sclerosis

2020 ◽  
Vol 11 ◽  
Author(s):  
Lishun Xiao ◽  
Zhongshang Yuan ◽  
Siyi Jin ◽  
Ting Wang ◽  
Shuiping Huang ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Type I Error ◽  
Association Studies ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Combination Strategy ◽  
New Genes ◽  
Genome Wide ◽  
Causal Genes ◽  
Lateral Sclerosis

Genome-wide association studies (GWAS) have identified multiple causal genes associated with amyotrophic lateral sclerosis (ALS); however, the genetic architecture of ALS remains completely unknown and a large number of causal genes have yet been discovered. To full such gap in part, we implemented an integrative analysis of transcriptome-wide association study (TWAS) for ALS to prioritize causal genes with summary statistics from 80,610 European individuals and employed 13 GTEx brain tissues as reference transcriptome panels. The summary-level TWAS analysis with single brain tissue was first undertaken and then a flexible p-value combination strategy, called summary data-based Cauchy Aggregation TWAS (SCAT), was proposed to pool association signals from single-tissue TWAS analysis while protecting against highly positive correlation among tests. Extensive simulations demonstrated SCAT can produce well-calibrated p-value for the control of type I error and was often much more powerful to identify association signals across various scenarios compared with single-tissue TWAS analysis. Using SCAT, we replicated three ALS-associated genes (i.e., ATXN3, SCFD1, and C9orf72) identified in previous GWASs and discovered additional five genes (i.e., SLC9A8, FAM66D, TRIP11, JUP, and RP11-529H20.6) which were not reported before. Furthermore, we discovered the five associations were largely driven by genes themselves and thus might be new genes which were likely related to the risk of ALS. However, further investigations are warranted to verify these results and untangle the pathophysiological function of the genes in developing ALS.

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