Digital image analysis is a practical, non-invasive, and relatively low-cost tool that may assist in the evaluation of body traits in Nile tilapia, being particularly useful for assessing difficult-to-measure variables, such as body areas. In this study, we aimed to estimate variance components and genetic parameters for body areas of Nile tilapia obtained by digital images. The data set comprised body weight (BW) records of 1,917 pond-reared fish at 366 days of age. Of this total, 656 animals were photographed and subjected to image analysis of trunk area (TA), head area (HA), caudal fin area (CFA) and fillet area (FA). Heritabilities and genetic correlations were estimated through multiple-trait models based on Bayesian inference. Heritability estimates for BW, TA, HA, CFA and FA were 0.25, 0.23, 0.26, 0.21 and 0.25, respectively. Genetic correlations between the traits were high and positive, ranging from 0.70 to 0.98. We highlight the genetic correlation between BW and TA (r_G = 0.98) and FA (r_G = 0.97). In view of the observed results, it can be concluded that trunk and fillet areas obtained by digital image analysis can lead to indirect genetic gains in weight and other body areas. In addition, the areas studied have potential as a selection criterion and may assist in studies on changes in the body shape in Nile tilapia.

中文翻译：

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通过数字图像分析估计尼罗罗非鱼身体区域的遗传参数

数字图像分析是一种实用、非侵入性且成本相对较低的工具，可帮助评估尼罗罗非鱼的身体特征，尤其适用于评估难以测量的变量，例如身体部位。在这项研究中，我们旨在估计通过数字图像获得的尼罗罗非鱼身体区域的方差分量和遗传参数。数据集包括 366 天龄的 1,917 条池塘养殖鱼的体重 (BW) 记录。其中 656 只动物被拍照并进行躯干面积 (TA)、头部面积 (HA)、尾鳍面积 (CFA) 和鱼片面积 (FA) 的图像分析。通过基于贝叶斯推理的多性状模型估计遗传力和遗传相关性。BW、TA、HA、CFA 和 FA 的遗传力估计值分别为 0.25、0.23、0.26、0.21 和 0.25。性状之间的遗传相关性高且呈正相关，范围从 0.70 到 0.98。我们强调了 BW 和 TA 之间的遗传相关性（r _G  = 0.98) 和 FA ( r _G  = 0.97)。鉴于观察到的结果，可以得出结论，通过数字图像分析获得的躯干和鱼片区域可以导致体重和其他身体部位的间接遗传增加。此外，所研究的区域具有作为选择标准的潜力，可能有助于尼罗罗非鱼体型变化的研究。