Data from: Geographic cline analysis as a tool for studying genome-wide variation: a case study of pollinator-mediated divergence in a monkeyflower

A major goal of speciation research is to reveal the genomic signatures that accompany the speciation process. Genome scans are routinely used to explore genome-wide variation and identify highly differentiated loci that may contribute to ecological divergence, but they do not incorporate spatial, phenotypic or environmental data that might enhance outlier detection. Geographic cline analysis provides a potential framework for integrating diverse forms of data in a spatially explicit framework, but has not been used to study genome-wide patterns of divergence. Aided by a first-draft genome assembly, we combined an FCT scan and geographic cline analysis to characterize patterns of genome-wide divergence between divergent pollination ecotypes of Mimulus aurantiacus. FCT analysis of 58 872 SNPs generated via RAD-seq revealed little ecotypic differentiation (mean FCT = 0.041), although a small number of loci were moderately-to-highly diverged. Consistent with our previous results from the gene MaMyb2, which contributes to differences in flower colour, 130 loci have cline shapes that recapitulate the spatial pattern of trait divergence, suggesting that they may reside in or near the genomic regions that contribute to pollinator isolation. In the narrow hybrid zone between the ecotypes, extensive admixture among individuals and low linkage disequilibrium between markers indicate that most outlier loci are scattered throughout the genome, rather than being restricted to one or a few divergent regions. In addition to revealing the genomic consequences of ecological divergence in this system, we discuss how geographic cline analysis is a powerful but under-utilized framework for studying genome-wide patterns of divergence.

物种形成研究的一项核心目标，是揭示伴随物种形成过程产生的基因组特征（genomic signatures）。基因组扫描（genome scans）常被用于探究全基因组变异，并鉴定可能参与生态分化（ecological divergence）的高度分化位点，但此类方法并未纳入或可提升异常位点（outlier loci）检出效率的空间、表型或环境数据。地理梯度分析（geographic cline analysis）为在空间显性框架（spatially explicit framework）下整合多源数据提供了潜在路径，但目前该方法尚未被用于全基因组分化模式的研究。借助首份基因组草图组装（first-draft genome assembly），我们结合FCT扫描（FCT scan）与地理梯度分析，对橙黄猴面花（Mimulus aurantiacus）两个分化传粉生态型间的全基因组分化模式进行解析。对通过限制性位点相关DNA测序（Restriction-site Associated DNA sequencing，RAD-seq）获得的58872个单核苷酸多态性（Single Nucleotide Polymorphism，SNP）开展FCT分析后发现，尽管存在少量中度至高度分化的位点，但两类生态型间整体的生态分化水平较低（平均FCT值为0.041）。与我们此前针对参与花色差异调控的基因MaMyb2所得的研究结果一致，共有130个位点的地理梯度曲线能够重现性状分化的空间分布模式，这提示这些位点可能位于参与传粉者生殖隔离（pollinator isolation）的基因组区域内或其邻近区域。在这两个生态型之间的狭窄杂交带中，个体间广泛存在遗传混合（admixture），且标记间连锁不平衡（linkage disequilibrium）程度较低，这表明绝大多数异常位点分散于整个基因组中，而非局限于单个或少数几个分化区域。本研究不仅揭示了该系统中生态分化的基因组效应，同时还探讨了地理梯度分析作为一种功能强大却未被充分利用的研究框架，如何应用于全基因组分化模式的解析。