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从过去预测未来:棘鱼反复快速进化的基因组基础。

Predicting future from past: The genomic basis of recurrent and rapid stickleback evolution.

作者信息

Roberts Kingman Garrett A, Vyas Deven N, Jones Felicity C, Brady Shannon D, Chen Heidi I, Reid Kerry, Milhaven Mark, Bertino Thomas S, Aguirre Windsor E, Heins David C, von Hippel Frank A, Park Peter J, Kirch Melanie, Absher Devin M, Myers Richard M, Di Palma Federica, Bell Michael A, Kingsley David M, Veeramah Krishna R

机构信息

Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305-5329, USA.

Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY 11794-5245, USA.

出版信息

Sci Adv. 2021 Jun 18;7(25). doi: 10.1126/sciadv.abg5285. Print 2021 Jun.

Abstract

Similar forms often evolve repeatedly in nature, raising long-standing questions about the underlying mechanisms. Here, we use repeated evolution in stickleback to identify a large set of genomic loci that change recurrently during colonization of freshwater habitats by marine fish. The same loci used repeatedly in extant populations also show rapid allele frequency changes when new freshwater populations are experimentally established from marine ancestors. Marked genotypic and phenotypic changes arise within 5 years, facilitated by standing genetic variation and linkage between adaptive regions. Both the speed and location of changes can be predicted using empirical observations of recurrence in natural populations or fundamental genomic features like allelic age, recombination rates, density of divergent loci, and overlap with mapped traits. A composite model trained on these stickleback features can also predict the location of key evolutionary loci in Darwin's finches, suggesting that similar features are important for evolution across diverse taxa.

摘要

相似的形态在自然界中常常反复演化,这引发了关于其潜在机制的长期问题。在这里,我们利用棘鱼的反复演化来识别大量基因组位点,这些位点在海水鱼类向淡水栖息地定殖的过程中会反复发生变化。在现存种群中反复使用的相同位点,当从海洋祖先实验性地建立新的淡水种群时,也会显示出快速的等位基因频率变化。在5年内就出现了显著的基因型和表型变化,这得益于现存的遗传变异以及适应性区域之间的连锁。变化的速度和位置可以通过对自然种群中反复出现的情况的实证观察,或诸如等位基因年龄、重组率、分歧位点密度以及与定位性状的重叠等基本基因组特征来预测。基于这些棘鱼特征训练的复合模型还可以预测达尔文雀关键进化位点的位置,这表明相似的特征对不同分类群的进化很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11b0/8213234/a6128e16af7f/abg5285-F1.jpg

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