About this Research Topic
Genome-wide association studies (GWAS) have been a powerful tool for genetic discovery in the last decade, leading to identification of numerous genetic variants underlying various characteristics of biological, agronomic, or ecological importance in plants, animals, and humans such as human diseases and crop yields. Challenges in GWAS include statistical modeling, computation, and multiplicity of testing. Typically, there exists population stratification or cryptic relatedness as well as other possibly influential factors in GWAS data. Statistical analysis needs to properly handle these factors to control false positives and/or improve power. The linear mixed-effect model, which has been widely used in GWAS, is a great success. A competitive alternative is the Bayesian approach. Computation in GWAS is challenging due to the big data issue, as we can have millions of SNP markers thanks to high-throughput genotyping technology and hundreds of thousands of samples in one dataset. Researchers have worked diligently to develop computational methodology and software packages for fast yet high-precision computing in GWAS. This remains an active research area.
This Research Topic serves as a forum for development of new methodology and novel applications of existing methods for genome-wide association studies. Scientific problems include modeling, computing, and testing. Given the vast volume of literature, reviews are usually very helpful for researchers to quickly learn what we have in a research field. Thus, comprehensive literature reviews regarding methodology, computation, and testing are highly desired, either general or with a focus on a specific population, species, or model organism (e.g., reviews of statistical models, software packages, and strains that are typically used in mouse/rat genetic studies, the issues that the research community has encountered, and significant findings that the community has achieved). Insightful Opinions and Perspectives will benefit the research community as well.
This collection welcomes, but is not limited to, the following subtopics:
• New development of statistical methodology, or novel application of existing statistical methodology, that potentially generates general interest in GWAS
• Reviews of statistical methodology, resources, and significant findings in genetic studies of humans, mice/rats, plants and crops
• New developments or reviews of bioinformatic tools that advance GWAS
• Inspiring perspectives or opinions
The above can extend to transcriptome-wide association studies (TWAS) or phenome-wide association studies (PheWAS).
This Research Topic serves as a forum for development of new methodology and novel applications of existing methods for genome-wide association studies. Scientific problems include modeling, computing, and testing. Given the vast volume of literature, reviews are usually very helpful for researchers to quickly learn what we have in a research field. Thus, comprehensive literature reviews regarding methodology, computation, and testing are highly desired, either general or with a focus on a specific population, species, or model organism (e.g., reviews of statistical models, software packages, and strains that are typically used in mouse/rat genetic studies, the issues that the research community has encountered, and significant findings that the community has achieved). Insightful Opinions and Perspectives will benefit the research community as well.
This collection welcomes, but is not limited to, the following subtopics:
• New development of statistical methodology, or novel application of existing statistical methodology, that potentially generates general interest in GWAS
• Reviews of statistical methodology, resources, and significant findings in genetic studies of humans, mice/rats, plants and crops
• New developments or reviews of bioinformatic tools that advance GWAS
• Inspiring perspectives or opinions
The above can extend to transcriptome-wide association studies (TWAS) or phenome-wide association studies (PheWAS).
Keywords: Bayesian analysis, genome-wide association studies, linear mixed models, population structure and relatedness, statistical computing
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