Systems Genetics for Evolutionary Studies.
Systems Genetics for Evolutionary Studies.
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DOI：
10.1007/978-1-4939-9074-0_21
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发表时间：
2019
影响因子：
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通讯作者：
P. Prins;G. Smant;D. Arends;Megan K. Mulligan;Rob Williams;R. Jansen
中科院分区：
文献类型：
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作者：
P. Prins;G. Smant;D. Arends;Megan K. Mulligan;Rob Williams;R. Jansen
系统遗传学将高通量基因组数据与遗传分析相结合。在本章中，我们将回顾和讨论系统遗传学在进化研究中的应用，在进化研究中，高通量分子技术正在与分离群体中的数量性状基因座（QTL）分析相结合。最近高通量数据的爆炸-测量数千种RNA，蛋白质和代谢物，使用深度测序，质谱，染色质，甲基DNA免疫沉淀等。允许解剖所有类型的数量表型的遗传变异的原因。为了处理大量的数据，需要强大的统计工具来分析多维关系，并提取有价值的信息以及物种内部和物种之间变化的新模式和机制。在进化计算生物学的背景下，一个精心设计的实验和正确的群体可以帮助解剖复杂的性状，这些性状可能正在使用已被证明的统计方法进行选择，将表型变异与染色体位置相关联。最近的进化表达QTL（eQTL）研究集中在基因表达适应，绘制基因表达景观，并尝试性地，定义转录本和蛋白质的网络，这些网络是QTL网络的共同调节集。在这里，我们讨论了引入一个进化的“先验”的形式的基因家族显示的正选择的证据，并使用该先验的背景下，一个QTL实验，阐明宿主-病原体蛋白质-蛋白质interactions.Here，我们回顾了一个典型的evolutionary eQTL实验，并讨论了实验设计，平台的选择，分析方法，范围和结果的解释。简而言之，我们强调了QTL是如何定义的;它们如何用于组装RNA，蛋白质和代谢物的相互作用和因果连接网络;以及一些QTL如何有效地转化为合理定义的序列变体。
Systems genetics combines high-throughput genomic data with genetic analysis. In this chapter, we review and discuss application of systems genetics in the context of evolutionary studies, in which high-throughput molecular technologies are being combined with quantitative trait locus (QTL) analysis in segregating populations.The recent explosion of high-throughput data—measuring thousands of RNAs, proteins, and metabolites, using deep sequencing, mass spectrometry, chromatin, methyl-DNA immunoprecipitation, etc.—allows the dissection of causes of genetic variation underlying quantitative phenotypes of all types. To deal with the sheer amount of data, powerful statistical tools are needed to analyze multidimensional relationships and to extract valuable information and new modes and mechanisms of changes both within and between species. In the context of evolutionary computational biology, a well-designed experiment and the right population can help dissect complex traits likely to be under selection using proven statistical methods for associating phenotypic variation with chromosomal locations.Recent evolutionary expression QTL (eQTL) studies focus on gene expression adaptations, mapping the gene expression landscape, and, tentatively, define networks of transcripts and proteins that are jointly modulated sets ofeQTL networks. Here, we discuss the possibility of introducing an evolutionary “prior” in the form of gene families displaying evidence of positive selection, and using that prior in the context of aneQTL experiment for elucidating host-pathogen protein-protein interactions.Here we review one exemplar evolutionairy eQTL experiment and discuss experimental design, choice of platforms, analysis methods, scope, and interpretation of results. In brief we highlight howeQTL are defined; how they are used to assemble interacting and causally connected networks of RNAs, proteins, and metabolites; and how some QTLs can be efficiently converted to reasonably well-defined sequence variants.