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基因组数据对于预测未来气候不适应有多有用?

Brandon M Lind1, Rafael Candido-Ribeiro1, Pooja Singh2

  • 1Centre for Forest Conservation Genetics and Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada.

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基因组方法可以预测人口的不适应性,但验证是关键. 这项研究发现,渐变森林 (GF_offset) 和非适应性风险 (RONA) 方法在预测移植成功方面表现有希望,即使没有基因型-环境关联标志物.

关键词:
气候变化 气候变化 气候变化气候适应不良的情况基因组偏移的抵消机器学习是机器学习.

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科学领域:

  • 生态生态学 生态生态学
  • 遗传学 是一个遗传学.
  • 气候变化生物学 气候变化生物学

背景情况:

  • 基因组方法越来越多地用于预测人口对环境变化的不适应.
  • 缺乏模型验证阻碍了这些基因组方法在保护和政策中的应用.
  • 两个常见的方法,渐变森林 (GF_offset) 和不适应风险 (RONA),需要严格的验证.

研究的目的:

  • 为了比较来自GF_offset和RONA方法的适应不良估计的验证.
  • 评估这些算法对输入遗传标记源 (GEA候选人与随机标记) 的敏感性.
  • 评估这些方法的预测能力与实证移植实验数据对比.

主要方法:

  • 分析了35-39个道格拉斯和松种群的外生态捕获池-seq数据.
  • 使用梯度森林 (GF_offset) 和不适应风险 (RONA) 来生成不适应估计.
  • 模型的性能与独立的针叶树移植实验中的2年和5年生长和死亡数据进行了验证.

主要成果:

  • 无论是GF_offset还是RONA方法,都通常比气候或地理距离更好地预测移植性能.
  • 使用基因型-环境关联 (GEA) 候选标记器并没有改善与随机标记器相比,GF_offset或RONA模型的性能.
  • 尽管有希望的验证,但未来气候预测的变化使识别最不适应的种群变得复杂.

结论:

  • 这项研究促进了对基因组不适应性预测方法的敏感性和适用性的理解.
  • GF_offset和RONA显示出预测人口适应能力的潜力,但需要仔细验证.
  • 讨论了这些基因组方法在保护中的未来使用和发展的建议.