High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis
High-density haplotyping with microarray-based expression and single feature polymorphism markers in Arabidopsis
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DOI：
10.1101/gr.5011206
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发表时间：
2006-06-01
期刊：
影响因子：
7
通讯作者：
Michelmore, Richard W.
中科院分区：
文献类型：
--
作者：
West, Marilyn A. L.;van Leeuwen, Hans;Michelmore, Richard W.
与RNA杂交的表达微阵列可以同时提供表型（基因表达）和基因型（标记）数据。我们开发了两种类型的遗传标记，从Affyssin基因芯片表达数据生成详细的单倍型148重组自交系（RILs）来自拟南芥种质拜罗伊特和Shahdara。基因表达标记（GEM）基于在分离后代中表现出双峰分布的转录水平的差异，而单特征多态性（SFP）标记依赖于与单个寡核苷酸探针杂交的差异。与SFP不同，GEM可以来自任何类型的基于DNA的表达微阵列。我们的方法识别独立于基因表达水平的SFPs。每个GEM和SFP标记的等位基因确定与基因芯片数据从父母的加入，以及RIL;等位基因测定的一种新的算法，使用RIL分布利用高水平的遗传复制每个位点。GEM和SFP标记分别在187和968个基因中提供了稳健的标记，这允许估计与从Col-O基因组序列预测的基因顺序一致的基因顺序。使用微阵列对人口，同时测量基因表达变异，并获得基因型数据的连锁图谱将有助于表达QTL分析，而不需要单独的基因分型。我们已经证明，可以利用微阵列的基因表达测量来识别整个基因组的多态性，并可以有效地开发成遗传标记，在一个大的分离RIL人口是可验证的。这两种标记类型也为未测序和研究较少的物种提供了大规模并行作图的机会。
Expression microarrays hybridized with RNA can simultaneously provide both phenotypic (gene expression) and genotypic (marker) data. We developed two types of genetic markers from Affymetrix GeneChip expression data to generate detailed haplotypes for 148 recombinant inbred lines (RILs) derived from Arabidopsis thaliana accessions Bayreuth and Shahdara. Gene expression markers (GEMs) are based on differences in transcript levels that exhibit bimodal distributions in segregating progeny, while single feature polymorphism (SFP) markers rely on differences in hybridization to individual oligonucleotide probes. Unlike SFPs, GEMs can be derived from any type of DNA-based expression microarray. Our method identifies SFPs independent of a gene's expression level. Alleles for each GEM and SFP marker were ascertained with GeneChip data from parental accessions as well as RILs; a novel algorithm for allele determination using RIL distributions capitalized on the high level of genetic replication per locus. GEMs and SFP markers provided robust markers in 187 and 968 genes, respectively, which allowed estimation of gene order consistent with that predicted from the Col-O genomic sequence. Using microarrays on a population to simultaneously measure gene expression variation and obtain genotypic data for a linkage map will facilitate expression QTL analyses without the need for separate genotyping. We have demonstrated that gene expression measurements from microarrays can be leveraged to identify polymorphisms across the genome and can be efficiently developed into genetic markers that are verifiable in a large segregating RIL population. Both marker types also offer opportunities for massively parallel mapping in unsequenced and less studied species.