Data from: Deciphering the genomic architecture of the stickleback brain with a novel multi-locus gene-mapping approach

Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small-effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine-spined sticklebacks (Pungitius pungitius) with the aid of novel multi-locus QTL mapping approaches based on a de-biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single-locus analyses of an F2-interpopulation cross with 239 individuals and 15 198 fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multi-locus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multi-locus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1-6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small-effect loci. Apart from suggesting moderately heritable (h2 ≈ 0.15-0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL mapping approach developed here has distinctive advantages over the traditional QTL mapping methods in analyses of dense marker panels.

与机体功能及适应度紧密相关的数量性状（如脑容量），推测由诸多小效应位点调控。采用传统数量性状位点（QTL）定位方法解析这类性状的遗传结构颇具挑战。本研究以九棘刺鱼（Pungitius pungitius）为研究材料，借助基于去偏最小绝对收缩和选择算子（LASSO）的新型多位点QTL定位方法，解析其脑容量（以及5种不同脑区体积）的遗传结构。相较于传统QTL定位方法，该方法不仅拥有更高的QTL检测统计效力与更低的假阳性率，还可处理大型标记面板数据，并能估算基因组遗传力。
研究团队对包含239个个体、15198个完全信息单核苷酸多态性（SNPs）的种群间F2杂交群体开展单位点分析，共检出79个与刺鱼脑容量性状变异相关的QTL。其中诸多位点间存在强烈的连锁不平衡（LD），因此，考虑数据中LD结构的单SNP多位点定位仅识别出4个显著QTL。不过，基于连锁群（LG）对SNP进行分组后的多位点定位，则鉴定出14个影响脑性状变异的连锁群（依性状不同，覆盖1至6个连锁群）。例如，分布于6个连锁群的SNP累积效应可解释17.6%的相对脑容量变异，而全部21个连锁群的累积效应则可解释42%的性状变异。
综上，研究结果表明刺鱼脑性状变异受众多小效应位点调控。本研究不仅证实脑性状存在遗传力中等（h²≈0.15-0.42）的多因素遗传结构，还凸显了识别数量性状变异相关位点所面临的挑战。尽管如此，本研究证实，相较于传统QTL定位方法，本文开发的新型QTL定位方法在高密度标记面板分析中具备显著优势。