Scripts and data of the genetic analysis of Syrah x Grenache progeny

R-scripts and data associated with the publication "Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine". Article abstract: Viticulture has to cope with climate change and decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction is a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and allowing the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping as well as univariate and multivariate penalized regression, in a bi-parental progeny. We used a new denser genetic map, simulated two traits under four QTL configurations, and re-analyzed 14 traits measured in semi-controlled conditions under different watering conditions. According to our simulations, we recommend the penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than interval mapping for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using experimental data, penalized regression methods proved as very efficient for intra-population prediction whatever the genetic architecture of the trait, with accuracies reaching 0.68. These methods applied on the denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding. Scripts and data associated with the SNP genetic map of Syrah x Grenache progeny are available at: https://doi.org/10.15454/QEDX2V. Raw phenotypic data of Syrah x Grenache progeny in the phenotypic platform PhenoArch are available at: https://doi.org/10.15454/YTRKV6.

本数据集关联的R脚本与数据源自论文《利用多变量惩罚回归方法开展葡萄干旱相关性状的基因组预测（genomic prediction）与数量性状基因座（Quantitative Trait Locus, QTL）检测》。
论文摘要如下：葡萄栽培业需在维持产量与葡萄酒品质的同时，应对气候变化并减少农药投入。育种是应对这一挑战的关键手段，而基因组预测则是加速育种进程的极具潜力的工具。多变量方法的预测精度理论上优于单变量方法。此外，部分预测方法可实现标记筛选，进而支持QTL检测与位置候选基因的鉴定。
为探究葡萄干旱相关性状的基因组预测与QTL检测方法，本研究在一双亲本杂交后代群体中应用了多种方法，包括区间作图法（interval mapping）、单变量及多变量惩罚回归（penalized regression）模型。研究中采用了全新的高密度遗传图谱，针对四种QTL构型模拟了两个性状，并对半控制环境下不同浇水条件下测得的14个性状进行了重新分析。
基于模拟实验结果，我们推荐使用弹性网（Elastic Net, EN）惩罚回归模型开展基因组预测，并通过对弹性网筛选得到的标记控制边际错误发现率（False Discovery Rate, FDR），以优先筛选候选QTL。事实上，针对不同遗传架构的性状，惩罚回归模型在QTL检测中的效力均优于区间作图法。
尽管性状间存在较强的遗传相关性，但多变量预测的表现并未优于单变量预测。
利用实验数据开展分析时，无论性状的遗传架构如何，惩罚回归模型在群体内预测中均展现出极高的效率，预测精度可达0.68。基于高密度遗传图谱应用该类方法，可鉴定出调控耐旱相关性状的全新QTL，并得到相关候选基因。
综上，本研究结果为在葡萄育种中应用基因组预测技术提供了坚实的证据支撑。
与西拉（Syrah）×歌海娜（Grenache）杂交后代的单核苷酸多态性（Single Nucleotide Polymorphism, SNP）遗传图谱相关的脚本与数据可通过以下链接获取：https://doi.org/10.15454/QEDX2V。西拉×歌海娜杂交后代在表型平台PhenoArch中的原始表型数据可通过以下链接获取：https://doi.org/10.15454/YTRKV6。