GWAS、Linkage Mapping、GBLUP与Fine mapping在玉米株高遗传上研究应用

博主按：

这个故事有点意思。把株高这个老生常谈，但从来没研究透过的性状研究解释个大概了吧。更重要的是如题--研究策略的应用，把遗传学做到当前技术水平可达到的极致!

The Genetic Architecture Of Maize Height

Jason A. Peiffer*,1, Maria C. Romay†, Michael A. Gore‡, Sherry A. Flint-Garcia§,**, Zhiwu Zhang†, Mark J. Millard††‡‡, Candice A. C. Gardner††‡‡, Michael D. McMullen§,**, James B. Holland§§,***, Peter J. Bradbury††† and Edward S. Buckler†‡†††

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Supporting information is available online http://www.genetics.org/lookup/suppl/doi:10.1534/genetics.113.159152/-/DC1.

↵1Corresponding author: Department of Genetics, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695. E-mail: japeiffe@ncsu.edu

Abstract

Height is one of the most heritable and easily measured traits in maize (Zea mays L.). Given a pedigree or estimates of the genomic identity-by-state among related plants, height is also accurately predictable. But, mapping alleles explaining natural variation in maize height remains a formidable challenge. To address this challenge, we measured the plant height, ear height, flowering time, and node counts of plants grown in >64,500 plots across 13 environments. These plots contained >7300 inbreds representing most publically available maize inbreds in the United States and families of the maize Nested Association Mapping (NAM) panel. Joint-linkage mapping of quantitative trait loci (QTL), fine mapping in near isogenic lines (NILs), genome-wide association studies (GWAS), and genomic best linear unbiased prediction (GBLUP) were performed. The heritability of maize height was estimated to be >90%. Mapping NAM family-nested QTL revealed the largest explained 2.1 ± 0.9% of height variation. The effects of two tropical alleles at this QTL were independently validated by fine mapping in NIL families. Several significant associations found by GWAS colocalized with established height loci, including brassinosteroid-deficient dwarf1, dwarf plant1, and semi-dwarf2. GBLUP explained >80% of height variation in the panels and outperformed bootstrap aggregation of family-nested QTL models in evaluations of prediction accuracy. These results revealed maize height was under strong genetic control and had a highly polygenic genetic architecture. They also showed that multiple models of genetic architecture differing in polygenicity and effect sizes can plausibly explain a population’s variation in maize height, but they may vary in predictive efficacy.

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