scholarly journals Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wenhan Chen ◽  
Yang Wu ◽  
Zhili Zheng ◽  
Ting Qi ◽  
Peter M. Visscher ◽  
...  
Keyword(s):  
False Positive ◽  
Rare Variants ◽  
Control Method ◽  
Association Studies ◽  
False Positive Rate ◽  
Reference Sample ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Positive Rate ◽  
Summary Data

AbstractSummary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data or LD reference or heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate in detecting secondary signals in the summary-data-based conditional and joint association analysis, especially for imputed rare variants (false-positive rate reduced from >28% to <2% in the presence of heterogeneity between GWAS and LD reference). We further show that DENTIST can improve other summary-data-based analyses such as fine-mapping analysis.

scholarly journals Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

2020 ◽  
Author(s):  
Wenhan Chen ◽  
Yang Wu ◽  
Zhili Zheng ◽  
Ting Qi ◽  
Peter M Visscher ◽  
...  
Keyword(s):  
Rare Variants ◽  
Control Method ◽  
Association Studies ◽  
False Positive Rate ◽  
Reference Sample ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Positive Rate ◽  
Trait Locus ◽  
Summary Data

AbstractSummary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data and heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate (FPR) in detecting secondary signals in the summary-data-based conditional and joint (COJO) association analysis, especially for imputed rare variants (FPR reduced from >28% to <2% in the presence of ancestral difference between GWAS and LD reference). We further show that DENTIST can improve other summary-data-based analyses such as LD score regression analysis, and integrative analysis of GWAS and expression quantitative trait locus data.

scholarly journals A fast mrMLM algorithm for multi-locus genome-wide association studies

2018 ◽  
Author(s):  
Cox Lwaka Tamba ◽  
Yuan-Ming Zhang
Keyword(s):  
False Positive ◽  
Statistical Power ◽  
Association Studies ◽  
False Positive Rate ◽  
Real Data ◽  
High Accuracy ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Positive Rate

AbstractBackgroundRecent developments in technology result in the generation of big data. In genome-wide association studies (GWAS), we can get tens of million SNPs that need to be tested for association with a trait of interest. Indeed, this poses a great computational challenge. There is a need for developing fast algorithms in GWAS methodologies. These algorithms must ensure high power in QTN detection, high accuracy in QTN estimation and low false positive rate.ResultsHere, we accelerated mrMLM algorithm by using GEMMA idea, matrix transformations and identities. The target functions and derivatives in vector/matrix forms for each marker scanning are transformed into some simple forms that are easy and efficient to evaluate during each optimization step. All potentially associated QTNs with P-values ≤ 0.01 are evaluated in a multi-locus model by LARS algorithm and/or EM-Empirical Bayes. We call the algorithm FASTmrMLM. Numerical simulation studies and real data analysis validated the FASTmrMLM. FASTmrMLM reduces the running time in mrMLM by more than 50%. FASTmrMLM also shows high statistical power in QTN detection, high accuracy in QTN estimation and low false positive rate as compared to GEMMA, FarmCPU and mrMLM. Real data analysis shows that FASTmrMLM was able to detect more previously reported genes than all the other methods: GEMMA/EMMA, FarmCPU and mrMLM.ConclusionsFASTmrMLM is a fast and reliable algorithm in multi-locus GWAS and ensures high statistical power, high accuracy of estimates and low false positive rate.Author SummaryThe current developments in technology result in the generation of a vast amount of data. In genome-wide association studies, we can get tens of million markers that need to be tested for association with a trait of interest. Due to the computational challenge faced, we developed a fast algorithm for genome-wide association studies. Our approach is a two stage method. In the first step, we used matrix transformations and identities to quicken the testing of each random marker effect. The target functions and derivatives which are in vector/matrix forms for each marker scanning are transformed into some simple forms that are easy and efficient to evaluate during each optimization step. In the second step, we selected all potentially associated SNPs and evaluated them in a multi-locus model. From simulation studies, our algorithm significantly reduces the computing time. The new method also shows high statistical power in detecting significant markers, high accuracy in marker effect estimation and low false positive rate. We also used the new method to identify relevant genes in real data analysis. We recommend our approach as a fast and reliable method for carrying out a multi-locus genome-wide association study.

scholarly journals Statistical testing and power analysis for brain-wide association study

2016 ◽  
Author(s):  
Weikang Gong ◽  
Lin Wan ◽  
Wenlian Lu ◽  
Liang Ma ◽  
Fan Cheng ◽  
...  
Keyword(s):  
False Positive ◽  
Power Analysis ◽  
Statistical Power ◽  
Spatial Information ◽  
Association Studies ◽  
False Positive Rate ◽  
Gaussian Random Field ◽  
Resting State Fmri ◽  
Statistical Testing ◽  
Positive Rate

AbstractThe identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

scholarly journals DISEASES: Text mining and data integration of disease–gene associations

2014 ◽  
Author(s):  
Sune Pletscher-Frankild ◽  
Albert Pallejà ◽  
Kalliopi Tsafou ◽  
Janos X Binder ◽  
Lars Juhl Jensen
Keyword(s):  
Text Mining ◽  
Disease Gene ◽  
Association Studies ◽  
False Positive Rate ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Genome Wide Association Studies ◽  
Human Genes ◽  
Gene Associations ◽  
Positive Rate

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease–gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease–gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a user-friendly web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.

scholarly journals A parsimonious model for mass-univariate vertex-wise analysis

2021 ◽  
Author(s):  
Baptiste Couvy-Duchesne ◽  
Futao Zhang ◽  
Kathryn E. Kemper ◽  
Julia Sidorenko ◽  
Naomi R. Wray ◽  
...  
Keyword(s):  
False Positive ◽  
Grey Matter ◽  
Fluid Intelligence ◽  
Smoking Status ◽  
Association Studies ◽  
False Positive Rate ◽  
Joint Effect ◽  
Association Analyses ◽  
Functional Brain Networks ◽  
Positive Rate

2.AbstractCovariance between grey-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g. age, head size, scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertex-wise analyses. We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise grey matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. We showed that when performed on a large sample (N=8,662, UK Biobank), GLMs yielded large spatial clusters of significant vertices and greatly inflated false positive rate (Family Wise Error Rate: FWER=1, cluster false discovery rate: FDR>0.6). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate (yet FWER>5% after Bonferroni correction) but at a cost of increased computation. In practice, the parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence and smoking status) and LMM yielded fewer and more localised associations. We identified 19 significant clusters displaying small associations with age, sex and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes.

scholarly journals PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects

2019 ◽  
Vol 35 (17) ◽  
pp. 3046-3054 ◽  
Author(s):  
Anastasia Gurinovich ◽  
Harold Bae ◽  
John J Farrell ◽  
Stacy L Andersen ◽  
Stefano Monti ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
False Positive Rate ◽  
Principal Component ◽  
True Positive Rate ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Positive Rate

Abstract Motivation Over the last decade, more diverse populations have been included in genome-wide association studies. If a genetic variant has a varying effect on a phenotype in different populations, genome-wide association studies applied to a dataset as a whole may not pinpoint such differences. It is especially important to be able to identify population-specific effects of genetic variants in studies that would eventually lead to development of diagnostic tests or drug discovery. Results In this paper, we propose PopCluster: an algorithm to automatically discover subsets of individuals in which the genetic effects of a variant are statistically different. PopCluster provides a simple framework to directly analyze genotype data without prior knowledge of subjects’ ethnicities. PopCluster combines logistic regression modeling, principal component analysis, hierarchical clustering and a recursive bottom-up tree parsing procedure. The evaluation of PopCluster suggests that the algorithm has a stable low false positive rate (∼4%) and high true positive rate (>80%) in simulations with large differences in allele frequencies between cases and controls. Application of PopCluster to data from genetic studies of longevity discovers ethnicity-dependent heterogeneity in the association of rs3764814 (USP42) with the phenotype. Availability and implementation PopCluster was implemented using the R programming language, PLINK and Eigensoft software, and can be found at the following GitHub repository: https://github.com/gurinovich/PopCluster with instructions on its installation and usage. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identification of Influential Variants in Significant Aggregate Rare Variant Tests

2021 ◽  
pp. 1-13
Author(s):  
Rachel Z. Blumhagen ◽  
David A. Schwartz ◽  
Carl D. Langefeld ◽  
Tasha E. Fingerlin
Keyword(s):  
Outlier Detection ◽  
Rare Variant ◽  
False Positive ◽  
Rare Variants ◽  
False Positive Rate ◽  
Experimental Studies ◽  
Detection Methods ◽  
True Positive ◽  
Adaptive Combination ◽  
Positive Rate

<b><i>Introduction:</i></b> Studies that examine the role of rare variants in both simple and complex disease are increasingly common. Though the usual approach of testing rare variants in aggregate sets is more powerful than testing individual variants, it is of interest to identify the variants that are plausible drivers of the association. We present a novel method for prioritization of rare variants after a significant aggregate test by quantifying the influence of the variant on the aggregate test of association. <b><i>Methods:</i></b> In addition to providing a measure used to rank variants, we use outlier detection methods to present the computationally efficient Rare Variant Influential Filtering Tool (RIFT) to identify a subset of variants that influence the disease association. We evaluated several outlier detection methods that vary based on the underlying variance measure: interquartile range (Tukey fences), median absolute deviation, and SD. We performed 1,000 simulations for 50 regions of size 3 kb and compared the true and false positive rates. We compared RIFT using the Inner Tukey to 2 existing methods: adaptive combination of <i>p</i> values (ADA) and a Bayesian hierarchical model (BeviMed). Finally, we applied this method to data from our targeted resequencing study in idiopathic pulmonary fibrosis (IPF). <b><i>Results:</i></b> All outlier detection methods observed higher sensitivity to detect uncommon variants (0.001 &#x3c; minor allele frequency, MAF &#x3e; 0.03) compared to very rare variants (MAF &#x3c;0.001). For uncommon variants, RIFT had a lower median false positive rate compared to the ADA. ADA and RIFT had significantly higher true positive rates than that observed for BeviMed. When applied to 2 regions found previously associated with IPF including 100 rare variants, we identified 6 polymorphisms with the greatest evidence for influencing the association with IPF. <b><i>Discussion:</i></b> In summary, RIFT has a high true positive rate while maintaining a low false positive rate for identifying polymorphisms influencing rare variant association tests. This work provides an approach to obtain greater resolution of the rare variant signals within significant aggregate sets; this information can provide an objective measure to prioritize variants for follow-up experimental studies and insight into the biological pathways involved.

Leveraging pleiotropy in genome-wide association studies in multiple traits with per trait interpretations

2020 ◽  
Author(s):  
Kodi Taraszka ◽  
Noah Zaitlen ◽  
Eleazar Eskin
Keyword(s):  
False Positive ◽  
Statistical Power ◽  
Association Studies ◽  
False Positive Rate ◽  
Joint Analysis ◽  
P Value ◽  
Ratio Test ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Test Statistic

AbstractWe introduce pleiotropic association test (PAT) for joint analysis of multiple traits using GWAS summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect.Additionally, simulations comparing PAT to two multi-trait methods, HIPO and MTAG show PAT having a 43.0% increase in the number of omnibus associations over the other methods. When these associations are interpreted on a per trait level using m-values, PAT has 52.2% more per trait interpretations with a 0.57% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT identifies 22,095 novel associated variants. Through the m-values interpretation framework, the number of total per trait associations for two traits are almost tripled and are nearly doubled for another trait relative to the original single trait GWAS.

scholarly journals Applying meta-analysis to Genotype-Tissue Expression data from multiple tissues to identify eQTLs and increase the number of eGenes

2017 ◽  
Author(s):  
Dat Duong ◽  
Lisa Gai ◽  
Sagi Snir ◽  
Eun Yong Kang ◽  
Buhm Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Tissue Expression ◽  
Great Effort ◽  
Genome Wide Association Studies ◽  
Biological Insight ◽  
Positive Rate ◽  
Computationally Intensive

AbstractDuring the last decade, with the advent of inexpensive microarray and RNA-seq technologies, there have been many expression quantitative trait loci (eQTL) studies for identifying genetic variants called eQTLs that regulate gene expression. Discovering eQTLs has been increasingly important as they may elucidate the functional consequence of non-coding variants identified from genome-wide association studies. Recently, several eQTL studies such as the Genotype-Tissue Expression (GTEx) consortium have made a great effort to obtain gene expression from multiple tissues. One advantage of these multi-tissue eQTL datasets is that they may allow one to identify more eQTLs by combining information across multiple tissues. Although a few methods have been proposed for multi-tissue eQTL studies, they are often computationally intensive and may not achieve optimal power because they do not consider a biological insight that a genetic variant regulates gene expression similarly in related tissues. In this paper, we propose an efficient meta-analysis approach for identifying eQTLs from large multi-tissue eQTL datasets. We name our method RECOV because it uses a random effects (RE) meta-analysis with an explicit covariance (COV) term to model the correlation of effect that eQTLs have across tissues. Our approach is faster than the previous approaches and properly controls the false-positive rate. We apply our approach to the real multi-tissue eQTL dataset from GTEx that contains 44 tissues, and show that our approach detects more eQTLs and eGenes than previous approaches.

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