Kardos Marty, Keller Lukas F, Funk W Chris
Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, USA.
Department of Evolutionary Biology and Environmental Studies & Natural History Museum, University of Zurich, Zurich, Switzerland.
Mol Ecol. 2024 Dec 16:e17608. doi: 10.1111/mec.17608.
Biologists have long sought to understand the impacts of deleterious genetic variation on fitness and population viability. However, our understanding of these effects in the wild is incomplete, in part due to the rarity of sufficient genetic and demographic data needed to measure their impact. The genomics revolution is promising a potential solution by predicting the effects of deleterious genetic variants (genetic load) bioinformatically from genome sequences alone bypassing the need for costly demographic data. After a historical perspective on the theoretical and empirical basis of our understanding of the dynamics and fitness effects of deleterious genetic variation, we evaluate the potential for these new genomic measures of genetic load to predict population viability. We argue that current genomic analyses alone cannot reliably predict the effects of deleterious genetic variation on population growth, because these depend on demographic, ecological and genetic parameters that need more than just genome sequence data to be measured. Thus, while purely genomic analyses of genetic load promise to improve our understanding of the composition of the genetic load, they are currently of little use for evaluating population viability. Demographic data and ecological context remain crucial to our understanding of the consequences of deleterious genetic variation for population fitness. However, when combined with such demographic and ecological data, genomic information can offer important insights into genetic variation and inbreeding that are crucial for conservation decision making.
长期以来，生物学家一直试图了解有害基因变异对适应性和种群生存能力的影响。然而，我们对这些在自然环境中的影响的理解并不完整，部分原因是难以获得衡量其影响所需的足够的遗传和人口数据。基因组学革命有望提供一个潜在的解决方案，即仅通过基因组序列，利用生物信息学方法预测有害基因变异（遗传负荷）的影响，从而无需昂贵的人口数据。在从历史角度审视我们对有害基因变异的动态和适应性影响的理解的理论和实证基础之后，我们评估了这些新的遗传负荷基因组测量方法预测种群生存能力的潜力。我们认为，仅靠当前的基因组分析无法可靠地预测有害基因变异对种群增长的影响，因为这些影响取决于人口统计学、生态学和遗传学参数，而这些参数的测量需要的不仅仅是基因组序列数据。因此，虽然对遗传负荷进行纯粹的基因组分析有望增进我们对遗传负荷组成的理解，但目前它们对于评估种群生存能力几乎没有用处。人口数据和生态背景对于我们理解有害基因变异对种群适应性的影响仍然至关重要。然而，当与这些人口统计学和生态数据相结合时，基因组信息可以提供有关遗传变异和近亲繁殖的重要见解，这对于保护决策至关重要。
