Predicting Archaic Hominin Phenotypes from Genomic Data.

IF 4.4 2区 化学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Colin M Brand, Laura L Colbran, John A Capra
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引用次数: 7

Abstract

Ancient DNA provides a powerful window into the biology of extant and extinct species, including humans' closest relatives: Denisovans and Neanderthals. Here, we review what is known about archaic hominin phenotypes from genomic data and how those inferences have been made. We contend that understanding the influence of variants on lower-level molecular phenotypes-such as gene expression and protein function-is a promising approach to using ancient DNA to learn about archaic hominin traits. Molecular phenotypes have simpler genetic architectures than organism-level complex phenotypes, and this approach enables moving beyond association studies by proposing hypotheses about the effects of archaic variants that are testable in model systems. The major challenge to understanding archaic hominin phenotypes is broadening our ability to accurately map genotypes to phenotypes, but ongoing advances ensure that there will be much more to learn about archaic hominin phenotypes from their genomes.

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从基因组数据预测古人类表型。
古代DNA为了解现存和灭绝物种的生物学提供了一个强大的窗口,包括人类的近亲:丹尼索瓦人和尼安德特人。在这里,我们回顾了从基因组数据中已知的古人类表型,以及这些推断是如何做出的。我们认为,了解变异对低水平分子表型(如基因表达和蛋白质功能)的影响,是利用古代DNA了解古人类特征的一种很有前途的方法。分子表型比生物体水平的复杂表型具有更简单的遗传结构,并且这种方法可以通过提出关于在模型系统中可测试的古老变异的影响的假设来超越关联研究。了解古人类表型的主要挑战是扩大我们准确地将基因型映射到表型的能力,但持续的进步确保了从古人类基因组中了解更多的古人类表型。
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来源期刊
CiteScore
7.20
自引率
6.00%
发文量
810
期刊介绍: ACS Applied Polymer Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics, and biology relevant to applications of polymers. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates fundamental knowledge in the areas of materials, engineering, physics, bioscience, polymer science and chemistry into important polymer applications. The journal is specifically interested in work that addresses relationships among structure, processing, morphology, chemistry, properties, and function as well as work that provide insights into mechanisms critical to the performance of the polymer for applications.
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