AI Article Synopsis
- Researchers have been enhancing genome-wide association studies (GWAS) for examining quantitative traits for the past 25 years, starting with mixed linear models (MLM).
- Recent advancements in high throughput next-generation sequencing (NGS) have shifted the focus to using pangenomes, structural variations (SVs), and k-mers in GWAS research.
- These new approaches have improved the identification of marker-trait associations (MTAs), benefiting fields like human healthcare and agricultural crop improvement.
Article Abstract
The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.
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| http://dx.doi.org/10.1002/bies.202100109 | DOI Listing |
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