首页> 美国卫生研究院文献>other >High-quality genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple peach and sweet cherry through a common workflow
【2h】

High-quality genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple peach and sweet cherry through a common workflow

机译:通过通用工作流程获得二倍体近交种苹果桃和甜樱桃的纯种质的高质量全基因组SNP基因型数据

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.
机译:高质量的基因型数据是许多遗传分析所必需的。对于任何作物,基因型调用,标记的定相,连锁图,谱系记录以及倍性水平的不明显变化中的错误都可能导致伪造的标记-基因座-性状关联以及对等位基因给个体的错误来源分配。高通量基因分型需要自动评分,因为手动检查成千上万个计分的基因座非常耗时。但是,自动SNP评分会导致错误,应予以纠正,以确保记录的基因型数据准确,从而确保对下游遗传分析的信心。为了能够快速识别大型基因型数据集中的错误,我们开发了一个全面的工作流程。此多步骤工作流基于继承原则,并且删除了不遵循这些原则的标记和个人,如此处针对苹果,桃子和甜樱桃所展示的。使用6-9K SNP阵列为每种作物获得了纯种质的基因型数据,并使用ASSIsT创建了性能良好的SNP的子集。使用正确(和更正)的谱系记录可以轻松识别出基因型数据中违反简单继承原则的情况,并通过FlexQTL软件进行了简化。保留的SNP被分组为单倍体,以增加单个等位基因的信息含量并降低下游遗传分析所需的计算能力。单倍体边界是由在祖先品种和选择中检测到的重组位置定义的。针对单倍型等位基因(即单倍型)进行了另一轮遗传检查。使用此工作流程创建了高质量的基因型数据集,用于代表RosBREED项目中评估的苹果,桃和甜樱桃美国种质的纯种收集。这些数据集分别包含3855、4005和1617个SNP,分别分布在苹果,桃子和甜樱桃的932、103和196个单元中。可通过酒渣鼻的基因组数据库获得苹果,桃和甜樱桃作物参考集种质资源的高度精选的分阶段SNP和单倍型数据集,以及原始iScan数据。

著录项

相似文献

  • 外文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号