Multiple Locus Linkage Analysis of Genomewide Expression in Yeast

With the ability to measure thousands of related phenotypes from a single biological sample, it is now feasible to genetically dissect systems-level biological phenomena. The genetics of transcriptional regulation and protein abundance are likely to be complex, meaning that genetic variation at multiple loci will influence these phenotypes. Several recent studies have investigated the role of genetic variation in transcription by applying traditional linkage analysis methods to genomewide expression data, where each gene expression level was treated as a quantitative trait and analyzed separately from one another. Here, we develop a new, computationally efficient method for simultaneously mapping multiple gene expression quantitative trait loci that directly uses all of the available data. Information shared across gene expression traits is captured in a way that makes minimal assumptions about the statistical properties of the data. The method produces easy-to-interpret measures of statistical significance for both individual loci and the overall joint significance of multiple loci selected for a given expression trait. We apply the new method to a cross between two strains of the budding yeast Saccharomyces cerevisiae, and estimate that at least 37% of all gene expression traits show two simultaneous linkages, where we have allowed for epistatic interactions. Pairs of jointly linking quantitative trait loci are identified with high confidence for 170 gene expression traits, where it is expected that both loci are true positives for at least 153 traits. In addition, we are able to show that epistatic interactions contribute to gene expression variation for at least 14% of all traits. We compare the proposed approach to an exhaustive two-dimensional scan over all pairs of loci. Surprisingly, we demonstrate that an exhaustive two-dimensional scan is less powerful than the sequential search used here. In addition, we show that a two-dimensional scan does not truly allow one to test for simultaneous linkage, and the statistical significance measured from this existing method cannot be interpreted among many traits.

凭借单次生物样本即可检测数千种相关表型的技术能力，如今我们已能够从遗传学角度解析系统级的生物学现象。转录调控与蛋白质丰度的遗传学调控机制大概率较为复杂，即多个基因座（locus，复数形式为loci）上的遗传变异会影响这些表型。近期多项研究将传统连锁分析方法应用于全基因组表达数据，以探究遗传变异在转录过程中的作用：该方法将每个基因的表达水平视为一个数量性状，并单独开展分析。本研究提出一种全新且计算高效的方法，可直接利用全部可用数据，同时定位多个基因表达数量性状基因座（gene expression quantitative trait loci）。该方法以对数据统计特性做出最少假设的方式，整合利用了不同基因表达性状间共享的信息。本方法可生成易于解读的统计显著性度量结果，既能够针对单个基因座，也能够针对某一表达性状所选定的多个基因座的整体联合显著性进行评估。我们将该新方法应用于两株酿酒酵母（Saccharomyces cerevisiae）的杂交实验，并估算得到：在考虑上位性相互作用的前提下，至少37%的基因表达性状存在两处同时发生的连锁关联。我们以高置信度为170个基因表达性状鉴定出了成对的联合连锁数量性状基因座，且预计其中至少153个性状的两处基因座均为真阳性结果。此外，我们证实至少14%的基因表达性状的变异受到上位性相互作用的调控。我们将所提出的方法与针对所有基因座对的全量二维扫描方法进行了对比。令人意外的是，我们证实全量二维扫描的检验效能弱于本研究采用的序贯搜索方法。此外，我们发现二维扫描方法无法真正实现同时连锁的检验，且该现有方法所得到的统计显著性结果难以在多个性状间进行统一解读。