Association mapping identifies signals of marker-trait relationship that can be attributed to the strength of linkage disequilibrium between marker polymorphisms and functional variants across a set of diverse germplasm. General understanding of association mapping has increased significantly since its debut in plants. We have seen a more concerted effort in assembling various association-mapping populations and initiating experiments through either candidate-gene studies or genome-wide association stuides (GWAS).
Much of the basics in assocaition mapping has been reviewed thoroughly in a set of previous review papers and here are several of them for your reference:
Genetic dissection of complex traits (Science, 1994, 265:2037-2048)
The future of genetic studies of complex human diseases (Science, 1996, 273:1516–1517)
The genetic architecture of quantitative traits (Annu Rev Genet, 2001, 35:303–339)
Structure of linkage disequilibrium in plants (Annu Rev Plant Biol, 2003, 54:357–374)
The lowdown on linkage disequilibrium (Plant Cell, 2003, 15:1502-1506)
Genetic association mapping and genome organization of maize (Curr Opin Biotechnol, 2006, 17:155-160)
Status and prospects of association mapping in plants (Plant Genome, 2008, 1:5-20)
Applications of linkage disequilibrium and association mapping in crop plants (Bookchapter in Genomics-Assisted Crop Improvement: Vol 1, pp197-120, 2007)
Association mapping of genetic resources: Achievements and future perspectives (In R. Tuberosa et al. (ed.) Genomics of Plant Genetic Resources, 207-235, 2014)
From association to prediction: Statistical methods for the dissection and selection of complex traits in plants (Current Opinion in Plant Biology, 2015, 24:110-118)
A recent review from Peter Visscher and colleagues on GWAS, 10 Years of GWAS Discovery: Biology, Function, and Translation (American Journal of Human Genetics, 2017, 101:5-22). Here is the earlier one: Five years of GWAS discovery (American Journal of Human Genetics, 2012, 90:7–24).
Mixed Model GWAS
We are trying to understand this new one at this time: Quantitative Trait Cluster Association Test (QTCAT) for GWAS, A multi-marker association method for genome-wide association studies without the need for population structure correction