4.6 Article

Genome-wide association study of grain shape variation among Oryza sativa L. germplasms based on elliptic Fourier analysis

期刊

MOLECULAR BREEDING
卷 25, 期 2, 页码 203-215

出版社

SPRINGER
DOI: 10.1007/s11032-009-9319-2

关键词

Association mapping; Rice grain; Image analysis; Principal component; Multiple QTL; Bayesian approach

资金

  1. Ministry of Agriculture, Forestry and Fisheries of Japan [QT1001]
  2. Ministry of Education, Culture, Sports, Science and Technology of Japan [15780007]
  3. Grants-in-Aid for Scientific Research [15780007] Funding Source: KAKEN

向作者/读者索取更多资源

Although grain shape is an important cereal breeding target, it has only been evaluated using simple measurements, e.g., the length:width ratio. We used elliptic Fourier analysis to evaluate grain shape variation and conducted whole genome association mapping of grain shape using a germplasm collection of Asian cultivated rice. The first principal component of elliptic Fourier descriptors, accounting for over 90% of the total variation, was associated with the length:width ratio of brown rice. That component was the most significant among the first ten components: the length:width ratio is the major genetic variation of rice grain shape. Bayesian multilocus association mapping detected five significant markers for this component; three might be linked to previously reported quantitative trait loci. Allelic effects of significant markers were visualized using inverse Fourier transformation, showing that the allele of a Japonica variety Nipponbare produced plumper grains in four of five significant markers. For the second to tenth principal components, varietal effects were significant (P < 0.001), although most accounted for less than 1% of the total variation in elliptic Fourier descriptors. Association mapping detected at least one quantitative trait locus in six of these nine components: breeding programs can improve the heritable shape characteristics associated with these components. For association mapping, elliptic Fourier analysis' accuracy and high throughput are suitable; it is readily applicable to cereal crops because it is not based on rice-specific morphological characteristics but rather on universal shape descriptors that can delineate any closed contour.

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