Data from: Fast and cost-effective genetic mapping in apple using next-generation sequencing

Next-generation DNA sequencing (NGS) produces vast amounts of DNA sequence data, but it is not specifically designed to generate data suitable for genetic mapping. Recently developed DNA library preparation methods for NGS have helped solve this problem, however, by combining the use of reduced representation libraries with DNA sample barcoding to generate genome-wide genotype data from a common set of genetic markers across a large number of samples. Here we use such a method, called genotyping-by-sequencing (GBS), to produce a data set for genetic mapping in an F1 population of apples (Malus x domestica) segregating for skin color. We show that GBS produces a relatively large, but extremely sparse, genotype matrix: over 270,000 SNPs were discovered, but most SNPs have too much missing data across samples to be useful for genetic mapping. After filtering for genotype quality and missing data, only 6% of the 85 million DNA sequence reads contributed to useful genotype calls. Despite this limitation, using existing software and a set of simple heuristics, we generated a final genotype matrix containing 3967 SNPs from 89 DNA samples from a single lane of Illumina HiSeq and used it to create a saturated genetic linkage map and to identify a known QTL underlying apple skin color. We therefore demonstrate that GBS is a cost effective method for generating genome-wide SNP data suitable for genetic mapping in a highly diverse and heterozygous agricultural species. We anticipate future improvements to the GBS analysis pipeline presented here that will enhance the utility of next-generation DNA sequence data for the purposes of genetic mapping across diverse species.

下一代DNA测序（Next-generation DNA sequencing, NGS）可产生海量DNA序列数据，但其并非专为生成适用于遗传作图的数据集而设计。不过，近年来针对NGS开发的DNA文库制备方法，通过将简化基因组文库与DNA样本条形码标记相结合，能够从大量样本的共有遗传标记中获取全基因组基因型数据，从而解决了这一难题。本研究采用一种名为测序分型（genotyping-by-sequencing, GBS）的此类方法，针对一个果皮颜色性状分离的苹果（Malus × domestica）F1群体构建了用于遗传作图的数据集。研究结果显示，GBS可生成规模较大但极为稀疏的基因型矩阵：共挖掘到超过27万个单核苷酸多态性（Single Nucleotide Polymorphism, SNP），但多数SNP在样本中存在大量缺失数据，无法用于遗传作图。在经过基因型质量与缺失数据过滤后，8500万条DNA测序读段中仅有6%可生成有效的基因型分型结果。尽管存在这一局限，我们借助现有软件与一系列简单启发式算法，基于单个Illumina HiSeq测序通道产出的89个DNA样本，最终构建了包含3967个SNP的基因型矩阵，并利用该矩阵构建了饱和遗传连锁图谱，同时定位到一个控制苹果果皮颜色的已知数量性状位点（Quantitative Trait Locus, QTL）。综上，本研究证明，对于高度杂合且具有丰富多样性的农业物种而言，GBS是一种可生成适用于遗传作图的全基因组SNP数据的成本效益较高的方法。我们预计，后续对本研究中所用的GBS分析流程的改进，将进一步提升下一代DNA测序数据在不同物种遗传作图研究中的应用价值。