在基因型-环境关联研究中整合高分辨率环境代用指标。

IF 3.5 2区 生物学 Q1 EVOLUTIONARY BIOLOGY
Annie S. Guillaume, Kevin Leempoel, Aude Rogivue, Felix Gugerli, Christian Parisod, Stéphane Joost
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引用次数: 0

摘要

将遗传变异与环境变量联系起来的景观基因组分析是研究物种局部适应性分子特征和检测选择下候选基因的有力工具。在过去十年中,基因组和环境数据集分辨率的提高推动了景观基因组学的发展,据称这种方法提高了识别非模式生物局部适应性潜在基因的能力。尽管这些关联已成功应用于多种分类群中的众多物种,但环境预测变量的空间尺度在很大程度上被忽视了,这可能会限制这些方法得出的结论。为了填补这一知识空白,我们系统地评估了使用多种空间分辨率预测变量的基因型-环境关联(GEA)模型的性能。具体来说,我们使用多变量冗余分析,将从瑞士阿尔卑斯山西部四个相邻山谷中收集到的植物 Arabis alpina L. 的全基因组序列数据与从粒度为 0.5 米至 16 米的数字高程模型中获得的高分辨率地形变量联系起来。这些比较凸显了景观基因组模型对空间分辨率的敏感性,其中最佳粒度取决于变量类型、地形特征和研究范围。为了帮助在适当的空间分辨率下选择变量,我们展示了一种实用的方法来生成、选择多尺度变量并将其整合到 GEA 模型中。在将细粒度变量归纳为多个空间分辨率后,我们采用了前向选择程序,只保留与特定环境最相关的变量。根据空间分辨率的不同,地形变量在 GEA 研究中的相关性要求将多个空间尺度整合到景观基因组模型中。通过仔细考虑空间分辨率,可以为下游分析检测出受到更现实压力选择的候选基因,这对自然种群的实验研究和保护管理具有重要的应用意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrating very high resolution environmental proxies in genotype–environment association studies

Integrating very high resolution environmental proxies in genotype–environment association studies

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype–environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

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来源期刊
Evolutionary Applications
Evolutionary Applications 生物-进化生物学
CiteScore
8.50
自引率
7.30%
发文量
175
审稿时长
6 months
期刊介绍: Evolutionary Applications is a fully peer reviewed open access journal. It publishes papers that utilize concepts from evolutionary biology to address biological questions of health, social and economic relevance. Papers are expected to employ evolutionary concepts or methods to make contributions to areas such as (but not limited to): medicine, agriculture, forestry, exploitation and management (fisheries and wildlife), aquaculture, conservation biology, environmental sciences (including climate change and invasion biology), microbiology, and toxicology. All taxonomic groups are covered from microbes, fungi, plants and animals. In order to better serve the community, we also now strongly encourage submissions of papers making use of modern molecular and genetic methods (population and functional genomics, transcriptomics, proteomics, epigenetics, quantitative genetics, association and linkage mapping) to address important questions in any of these disciplines and in an applied evolutionary framework. Theoretical, empirical, synthesis or perspective papers are welcome.
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