Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach.
Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach.
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DOI：
10.1371/journal.pone.0032253
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Jannink JL
中科院分区：
文献类型：
--
作者：
Poland JA;Brown PJ;Sorrells ME;Jannink JL
Advancements in next-generation sequencing technology have enabled whole genome re-sequencing in many species providing unprecedented discovery and characterization of molecular polymorphisms. There are limitations, however, to next-generation sequencing approaches for species with large complex genomes such as barley and wheat. Genotyping-by-sequencing (GBS) has been developed as a tool for association studies and genomics-assisted breeding in a range of species including those with complex genomes. GBS uses restriction enzymes for targeted complexity reduction followed by multiplex sequencing to produce high-quality polymorphism data at a relatively low per sample cost. Here we present a GBS approach for species that currently lack a reference genome sequence. We developed a novel two-enzyme GBS protocol and genotyped bi-parental barley and wheat populations to develop a genetically anchored reference map of identified SNPs and tags. We were able to map over 34,000 SNPs and 240,000 tags onto the Oregon Wolfe Barley reference map, and 20,000 SNPs and 367,000 tags on the Synthetic W9784×Opata85 (SynOpDH) wheat reference map. To further evaluate GBS in wheat, we also constructed a de novo genetic map using only SNP markers from the GBS data. The GBS approach presented here provides a powerful method of developing high-density markers in species without a sequenced genome while providing valuable tools for anchoring and ordering physical maps and whole-genome shotgun sequence. Development of the sequenced reference genome(s) will in turn increase the utility of GBS data enabling physical mapping of genes and haplotype imputation of missing data. Finally, as a result of low per-sample costs, GBS will have broad application in genomics-assisted plant breeding programs.
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影响因子：
3.7
作者：
Baird NA;Etter PD;Atwood TS;Currey MC;Shiver AL;Lewis ZA;Selker EU;Cresko WA;Johnson EA
通讯作者：
Johnson EA
影响因子：
4.4
作者：
Chutimanitsakun Y;Nipper RW;Cuesta-Marcos A;Cistué L;Corey A;Filichkina T;Johnson EA;Hayes PM
通讯作者：
Hayes PM
影响因子：
7
作者：
Miller, Michael R.;Dunham, Joseph P.;Johnson, Eric A.
通讯作者：
Johnson, Eric A.
影响因子：
3.1
作者：
Sorrells, Mark E.;Gustafson, J. Perry;Qualset, Calvin O.
通讯作者：
Qualset, Calvin O.
DOI：
10.1073/pnas.0401076101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Wenzl, P;Carling, J;Kilian, A
通讯作者：
Kilian, A