Prins Pjotr, Smant Geert, Arends Danny, Mulligan Megan K, Williams Rob W, Jansen Ritsert C
Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, CG, Utrecht, The Netherlands.
Department of Genetics, Genomics and Informatics, The University of Tennessee Health Science Center, Memphis, TN, USA.
Methods Mol Biol. 2019;1910:635-652. doi: 10.1007/978-1-4939-9074-0_21.
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 of eQTL 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 an eQTL 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 how eQTL 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.
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，我们回顾并讨论系统遗传学在进化研究背景下的应用，其中高通量分子技术正与分离群体中的数量性状基因座（QTL）分析相结合。最近高通量数据呈爆炸式增长——使用深度测序、质谱、染色质、甲基化DNA免疫沉淀等技术测量数千种RNA、蛋白质和代谢物——使得剖析各类数量表型潜在的遗传变异原因成为可能。为了处理如此海量的数据，需要强大的统计工具来分析多维关系，并提取物种内部和物种之间变化的有价值信息以及新模式和机制。在进化计算生物学背景下，精心设计的实验和合适的群体有助于使用已证实的统计方法剖析可能受到选择的复杂性状，这些方法可将表型变异与染色体位置相关联。最近的进化表达QTL（eQTL）研究聚焦于基因表达适应性、绘制基因表达图谱，并初步定义由eQTL网络共同调控的转录本和蛋白质网络。在此，我们讨论以显示正选择证据的基因家族形式引入进化“先验”的可能性，并在eQTL实验背景下使用该先验来阐明宿主-病原体蛋白质-蛋白质相互作用。在此我们回顾一个典型的进化eQTL实验，并讨论实验设计、平台选择、分析方法、范围及结果解释。简而言之，我们重点介绍eQTL是如何定义的；它们如何用于构建相互作用且因果相连的RNA、蛋白质和代谢物网络；以及一些QTL如何能有效地转化为定义合理明确的序列变异。
