Data from: Phylogenetic ANOVA: the Expression Variance and Evolution model for quantitative trait evolution

A number of methods have been developed for modeling the evolution of a quantitative trait on a phylogeny. These methods have received renewed interest in the context of genome-wide studies of gene expression, in which the expression levels of many genes can be modeled as quantitative traits. We here develop a new method for joint analyses of quantitative traits within- and between species, the Expression Variance and Evolution (EVE) model. The model parameterizes the ratio of population to evolutionary expression variance, facilitating a wide variety of analyses, including a test for lineage-specific shifts in expression level, and a phylogenetic ANOVA that can detect genes with increased or decreased ratios of expression divergence to diversity, analogous to the famous Hudson Kreitman Aguadé (HKA) test used to detect selection at the DNA level. We use simulations to explore the properties of these tests under a variety of circumstances and show that the phylogenetic ANOVA is more accurate than the standard ANOVA (no accounting for phylogeny) sometimes used in transcriptomics. We then apply the EVE model to a mammalian phylogeny of 15 species typed for expression levels in liver tissue. We identify genes with high expression divergence between species as candidates for expression level adaptation, and genes with high expression diversity within species as candidates for expression level conservation and/or plasticity. Using the test for lineage-specific expression shifts, we identify several candidate genes for expression level adaptation on the catarrhine and human lineages, including genes putatively related to dietary changes in humans. We compare these results to those reported previously using a model which ignores expression variance within species, uncovering important differences in performance. We demonstrate the necessity for a phylogenetic model in comparative expression studies and show the utility of the EVE model to detect expression divergence, diversity, and branch-specific shifts.

学界已开发出诸多可在系统发育（phylogeny）树上对数量性状的演化进行建模的方法。此类方法在全基因组基因表达研究的背景下重新获得关注——在此类研究中，众多基因的表达水平可被视作数量性状开展建模分析。本文提出了一种可用于物种内与物种间数量性状联合分析的新方法：表达方差与演化模型（Expression Variance and Evolution，简称EVE模型）。该模型对种群表达方差与演化表达方差的比值进行参数化，可支撑多样化分析场景：包括针对谱系特异性表达水平偏移的检验，以及可检测表达分化与多样性比值升高或降低基因的系统发育方差分析（Analysis of Variance，简称ANOVA）——该方法类比于用于检测DNA层面选择的经典哈德森-克里特曼-阿加德（Hudson Kreitman Aguadé，简称HKA）检验。本文通过模拟实验探究了上述检验在多种场景下的性能表现，并证实系统发育方差分析的准确性优于转录组学研究中偶尔采用的未考虑系统发育因素的标准方差分析。随后，本文将EVE模型应用于包含15个物种的哺乳动物系统发育数据集，这些物种的肝脏组织表达水平均已完成分型检测。本文筛选出物种间表达分化程度较高的基因作为表达水平适应性演化的候选基因，同时筛选出物种内表达多样性较高的基因作为表达水平保守性及/或可塑性的候选基因。借助谱系特异性表达偏移检验，本文在狭鼻类（catarrhine）与人类谱系中筛选出若干与表达水平适应性演化相关的候选基因，其中包括推测与人类饮食变化相关的基因。本文将上述结果与此前采用忽略物种内表达方差的模型所得到的研究结果进行对比，揭示了二者在性能上的显著差异。本文证实了系统发育模型在比较表达研究中的必要性，并展示了EVE模型在检测表达分化、表达多样性以及分支特异性偏移方面的应用价值。