首页> 外文期刊>BMC Plant Biology >Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies
【24h】

Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

机译:通过测序和SNP阵列进行基因分型和SNP阵列,用于通过在关联研究中标记不同的单倍型来检测定量性状基因座

获取原文
           

摘要

Single Nucleotide Polymorphism (SNP) array and re-sequencing technologies have different properties (e.g. calling rate, minor allele frequency profile) and drawbacks (e.g. ascertainment bias). This lead us to study their complementarity and the consequences of using them separately or combined in diversity analyses and Genome-Wide Association Studies (GWAS). We performed GWAS on three traits (grain yield, plant height and male flowering time) measured in 22 environments on a panel of 247 F1 hybrids obtained by crossing 247 diverse dent maize inbred lines with a same flint line. The 247 lines were genotyped using three genotyping technologies (Genotyping-By-Sequencing, Illumina Infinium 50?K and Affymetrix Axiom 600?K arrays). The effects of ascertainment bias of the 50?K and 600?K arrays were negligible for deciphering global genetic trends of diversity and for estimating relatedness in this panel. We developed an original approach based on linkage disequilibrium (LD) extent in order to determine whether SNPs significantly associated with a trait and that are physically linked should be considered as a single Quantitative Trait Locus (QTL) or several independent QTLs. Using this approach, we showed that the combination of the three technologies, which have different SNP distributions and densities, allowed us to detect more QTLs (gain in power) and potentially refine the localization of the causal polymorphisms (gain in resolution). Conceptually different technologies are complementary for detecting QTLs by tagging different haplotypes in association studies. Considering LD, marker density and the combination of different technologies (SNP-arrays and re-sequencing), the genotypic data available were most likely enough to well represent polymorphisms in the centromeric regions, whereas using more markers would be beneficial for telomeric regions.
机译:单核苷酸多态性(SNP)阵列和重新排序技术具有不同的性质(例如,呼叫率,次要等位基因频率分布)和缺点(例如,确定偏置)。这导致我们研究他们的互补性和单独使用它们的后果或组合在多样性分析和基因组 - 范围内的协会研究中(GWAS)。我们在22个环境中测量的三个特征(谷物产量,植物高度和雄性开花时间)进行了Gwas,在247 F1混合动力板上测量了247个F1杂种,其具有相同的燧石型自交线。使用三种基因分型技术(基因分型逐次测序,Illumina infinium 50〜K和Affymetrix Axiom 600?K阵列)进行247条线。为50?K和600?K阵列的确定偏差的影响可以忽略不计,以解密全球多样性的遗传趋势和估算本面板中的相关性。我们开发了一种基于联系不平衡(LD)范围的原始方法,以确定是否与特征有显着相关的SNP,并且物理相关的SNP应被视为单个数量特质基因座(QTL)或几个独立的QTL。使用这种方法,我们认为三种技术的组合具有不同的SNP分布和密度,使我们能够检测更多的QTL(功率增益)并可能细化因果多态性的定位(分辨率的增益)。概念上的不同技术是通过在关联研究中标记不同单倍型来检测QTL的互补性。考虑到LD,标记密度和不同技术的组合(SNP-阵列和重新排序),可用的基因型数据最可能在很好地代表焦化区域中的多态性,而使用更多标记将是有益的直角区域。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

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