Addressing Provenance Issues in Big Data Genome Wide Association Studies (GWAS)

2016 ◽  
Author(s):  
David Lauzon ◽  
Beatriz Kanzki ◽  
Victor Dupuy ◽  
Alain April ◽  
Michael S. Phillips ◽  
...  
Keyword(s):  
Big Data ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals An Academic Clinician’s Road Map to Hypertension Genomics

Hypertension ◽  
2021 ◽  
Author(s):  
Emma F. Magavern ◽  
Helen R. Warren ◽  
Fu L. Ng ◽  
Claudia P. Cabrera ◽  
Patricia B. Munroe ◽  
...  
Keyword(s):  
Big Data ◽  
Essential Hypertension ◽  
Clinical Benefit ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Polygenic Risk ◽  
Road Map ◽  
Genome Wide ◽  
Cardiovascular Morbidity And Mortality

At the dawn of the new decade, it is judicious to reflect on the boom of knowledge about polygenic risk for essential hypertension supplied by the wealth of genome-wide association studies. Hypertension continues to account for significant cardiovascular morbidity and mortality, with increasing prevalence anticipated. Here, we overview recent advances in the use of big data to understand polygenic hypertension, as well as opportunities for future innovation to translate this windfall of knowledge into clinical benefit.

scholarly journals BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in Millions

2017 ◽  
Author(s):  
Meng Huang ◽  
Xiaolei Liu ◽  
Yao Zhou ◽  
Ryan M. Summers ◽  
Zhiwu Zhang
Keyword(s):  
Big Data ◽  
Linkage Disequilibrium ◽  
Association Studies ◽  
Information Criteria ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Effect Model ◽  
Computationally Expensive ◽  
Bin Method

Big data, accumulated from biomedical and agronomic studies, provides the potential to identify genes controlling complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big data also leads to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed Fixed and random model Circulating Probability Unification (FarmCPU) method uses a bin method under the assumption that Quantitative Trait Nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to a remarkable improvement in computing time. Now, a dataset with half million markers and one million individuals can be analyzed within five hours, compared with one week using FarmCPU.

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