Accurately predicting phenotypes from genotypes holds great promise to improve health management in humans and animals, and breeding efficiency in animals and plants. Although many prediction methods have been developed, the optimal method differs across datasets due to multiple factors, including species, environments, populations and traits of interest. Studies have demonstrated that the number of genes underlying a trait and its heritability are the two key factors that determine which method fits the trait the best. In many cases, however, these two factors are unknown for the traits of interest. We developed a cloud computing platform for Mining the Maximum Accuracy of Predicting phenotypes from genotypes (MMAP) using unsupervised learning on publicly available real data and simulated data. MMAP provides a user interface to upload input data, manage projects and analyses and download the output results. The platform is free for the public to conduct computations for predicting phenotypes and genetic merit using the best prediction method optimized from many available ones, including Ridge Regression, gBLUP, compressed BLUP, Bayesian LASSO, Bayes A, B, Cpi and many more. Users can also use the platform to conduct data analyses with any methods of their choice. It is expected that extensive usage of MMAP would enrich the training data, which in turn results in continual improvement of the identification of the best method for use with particular traits.

中文翻译：

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MMAP：一个云计算平台，用于挖掘从基因型预测表型的最大准确性

从基因型准确预测表型对于改善人类和动物的健康管理以及动植物的育种效率具有很大的前景。尽管已经开发了许多预测方法，但由于多种因素，包括物种、环境、种群和感兴趣的特征，最佳方法在数据集之间有所不同。研究表明，性状背后的基因数量及其遗传力是决定哪种方法最适合该性状的两个关键因素。然而，在许多情况下，这两个因素对于感兴趣的性状是未知的。我们开发了一个云计算平台，用于使用对公开可用的真实数据和模拟数据的无监督学习来挖掘基因型预测表型的最大准确性 (MMAP)。MMAP 提供了一个用户界面来上传输入数据，管理项目并分析和下载输出结果。该平台免费供公众使用从许多可用方法中优化的最佳预测方法进行预测表型和遗传价值的计算，包括岭回归、gBLUP、压缩 BLUP、贝叶斯 LASSO、贝叶斯 A、B、Cpi 等等。用户还可以使用该平台以他们选择的任何方法进行数据分析。预计 MMAP 的广泛使用将丰富训练数据，这反过来会导致持续改进识别用于特定特征的最佳方法。包括岭回归、gBLUP、压缩 BLUP、贝叶斯 LASSO、贝叶斯 A、B、Cpi 等等。用户还可以使用该平台以他们选择的任何方法进行数据分析。预计 MMAP 的广泛使用将丰富训练数据，这反过来会导致持续改进识别用于特定特征的最佳方法。包括岭回归、gBLUP、压缩 BLUP、贝叶斯 LASSO、贝叶斯 A、B、Cpi 等等。用户还可以使用该平台以他们选择的任何方法进行数据分析。预计 MMAP 的广泛使用将丰富训练数据，这反过来会导致持续改进识别用于特定特征的最佳方法。