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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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适应GS:基于自适应堆叠组合机器学习的可解释的基因组选择框架.

Zhen Yang1, Mei Song1, Xianggeng Huang1

  • 1School of Mathematics and Statistics, Ludong University, Yantai, 264025, Shandong, China.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
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概括
此摘要是机器生成的。

我们开发了adaptiveGS,这是一种新的基因组选择框架,通过自适应地选择机器学习模型来提高预测准确性. 这种方法增强了特征预测,并确定了分子育种的重要SNP.

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 机器学习在育种中的应用

背景情况:

  • 基因组选择 (GS) 对现代分子育种至关重要.
  • 堆叠集体学习 (SEL) 通过结合多个模型来增强GS.
  • 目前还没有统一的框架来选择SEL的基础学习者 (BLs).

研究的目的:

  • 在堆叠基因组选择中开发适应性和统一的基础学习者选择框架.
  • 为了提高基因组选择中的预测准确性和模型解释性.
  • 识别与目标特征相关的显著单核酸多态 (SNP).

主要方法:

  • 开发了自适应的GS,这是一种基于数据的策略,用于根据PR指数 (PCC和NRMSE) 选择最佳的BL.
  • 实施了SHapley添加式解释 (SHAP) 用于模型解释和SNP识别.
  • 将自适应GS与13个其他GS算法进行比较,涉及4个物种的21个特征.

主要成果:

  • 适应式GS表现出卓越的预测准确性和稳定性,在大多数特征上优于其他13种GS模型.
  • 实现了0.703的平均预测准确度 (PCC),平均改善率为14.4%.
  • 通过SHAP分析成功识别了通过SHAP分析影响特征变异的显著SNP和潜在相互作用效应.

结论:

  • 适应GS为堆叠GS提供了可操作和统一的解决方案,提高了植物和动物育种中的预测准确性.
  • 该框架为复杂特征的遗传结构提供了有价值的见解.
  • 适应性GS包是公开可用的,用于在育种领域的更广泛应用.