Agronomy and Horticulture Department
Date of this Version
3-2018
Citation
The Quarterly Review of Biology Mar 2018, Volume 93, Issue 1, p. 50
Abstract
Genome-wide association studies (GWAS) has become a powerful tool in the area of quantitative genetics to map the relationship between trait and genomic variations. This volume provides a great resource for beginners to learn about the recent advances in GWAS and for domain experts to identify the gaps in the area. The first part of the volume lays out the statistical background of GWAS. I really liked the article by Yang et al., Introduction to Statistical Methods in Genome-Wide Association Studies. In this chapter, the authors talked about the missing heritability issue and introduced ways to calculate heritability using the traditional pedigree-based method and GWAS method that was developed by J. Yang et al. (2011. American Journal of Human Genetics 88:76–82). They discussed the linear mixed model (LMM) approach in conducting GWAS and also introduced how to predict the disease risk using the obtained information. At the end, they pointed to future directions by providing some thoughts on the challenges for mapping traits with highly polygenic genetic architecture.
Included in
Agricultural Science Commons, Agriculture Commons, Agronomy and Crop Sciences Commons, Botany Commons, Horticulture Commons, Other Plant Sciences Commons, Plant Biology Commons
Comments
Published by University of Chicago Press. Used by permission.