Diaz-Garcia Luis, Covarrubias-Pazaran Giovanny, Schlautman Brandon, Grygleski Edward, Zalapa Juan
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, Pabellon de Arteaga, Aguascalientes, Mexico.
University of Wisconsin-Madison, Madison, WI, USA.
PeerJ. 2018 Aug 15;6:e5461. doi: 10.7717/peerj.5461. eCollection 2018.
Image-based phenotyping methodologies are powerful tools to determine quality parameters for fruit breeders and processors. The fruit size and shape of American cranberry ( L.) are particularly important characteristics that determine the harvests' processing value and potential end-use products (e.g., juice vs. sweetened dried cranberries). However, cranberry fruit size and shape attributes can be difficult and time consuming for breeders and processors to measure, especially when relying on manual measurements and visual ratings. Therefore, in this study, we implemented image-based phenotyping techniques for gathering data regarding basic cranberry fruit parameters such as length, width, length-to-width ratio, and eccentricity. Additionally, we applied a persistent homology algorithm to better characterize complex shape parameters. Using this high-throughput artificial vision approach, we characterized fruit from 351 progeny from a full-sib cranberry population over three field seasons. Using a covariate analysis to maximize the identification of well-supported quantitative trait loci (QTL), we found 252 single QTL in a 3-year period for cranberry fruit size and shape descriptors from which 20% were consistently found in all years. The present study highlights the potential for the identified QTL and the image-based methods to serve as a basis for future explorations of the genetic architecture of fruit size and shape in cranberry and other fruit crops.
基于图像的表型分析方法是为水果育种者和加工者确定质量参数的强大工具。美国蔓越莓（Vaccinium macrocarpon Ait.）的果实大小和形状是特别重要的特征，它们决定了收获果实的加工价值和潜在的最终用途产品（例如，果汁与甜蔓越莓干）。然而，对于育种者和加工者来说，蔓越莓果实的大小和形状属性测量起来可能既困难又耗时，尤其是依靠人工测量和视觉评级时。因此，在本研究中，我们采用基于图像的表型分析技术来收集有关蔓越莓果实基本参数的数据，如长度、宽度、长宽比和偏心率。此外，我们应用了持久同调算法来更好地表征复杂的形状参数。使用这种高通量人工视觉方法，我们在三个田间季节对来自一个全同胞蔓越莓群体的351个后代的果实进行了表征。通过协变量分析以最大限度地识别得到充分支持的数量性状位点（QTL），我们在三年时间内发现了252个与蔓越莓果实大小和形状描述符相关的单个QTL，其中20%在所有年份中都能持续发现。本研究突出了所鉴定的QTL和基于图像的方法作为未来探索蔓越莓及其他水果作物果实大小和形状遗传结构基础的潜力。
