|XIAO, YINGJIE - Huazhong Agricultural University|
|LIU, HAIJUN - Huazhong Agricultural University|
|WU, LIUJI - Henan Agricultural University|
|YAN, JIANBING - Huazhong Agricultural University|
Submitted to: Molecular Plant
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/20/2016
Publication Date: 3/1/2017
Citation: Xiao, Y., Liu, H., Wu, L., Warburton, M.L., Yan, J. 2017. Genome-wide association studies in maize: praise and stargaze. Molecular Plant. 10:358-374.
Interpretive Summary: New genetic analysis methods have allowed scientists to more quickly find the genes responsible for important traits in maize breeding, and knowing these genes aids maize breeders to more quickly create improved varieties for farmers to grow and consumers to buy. These new varieties may be higher yielding, more healthful for consumption, more hardy during growth, and need fewer chemical inputs such as fertilizers, pesticides, or irrigation water. One mapping method, genome wide association mapping (GWAS), is reviewed here, including strengths and proper usage, and weaknesses with suggestions on new methods to correct these weaknesses. Just as GWAS became possible due to advances in high throughput sequencing, new methods are becoming available due to advances in other high throughput analyses of proteins, RNA and gene expression, metabolites, and DNA chemical modification. These are discussed in this review as well.
Technical Abstract: Genome-wide association study (GWAS) has appeared as a widespread strategy in decoding genotype-phenotype associations in many species thanks to technical advances in next-generation sequencing (NGS) applications. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics and discusses future prospects in the omics era. Perspectives on the following aspects of a typical GWAS are given: novel genetic and over-genetic variation; benefits to answering more complex biological questions at a systematic level; new statistical models and methods; improvement in mapping power and computational efficiency for large-scale studies; and new population designs to retrieve missing heritability underlying causal variants with confounding effects and low frequencies. The joint and continuous efforts of the whole community will enhance the understanding of maize quantitative traits and crop molecular breeding design.