|Mccouch, Susan - Cornell University - New York|
|Zhao, Keyan - Cornell University - New York|
|Wright, Mark - Cornell University - New York|
|Tung, Chih-wei - Cornell University - New York|
|Ebana, Kaworu - National Institute Of Agrobiological Sciences (NIAS)|
|Thomson, Michael - International Rice Research Institute|
|Reynolds, Andy - Cornell University - New York|
|Wang, Diane - Cornell University - New York|
|Declerck, Genevieve - Cornell University - New York|
|Ali, M. Liakat - University Of Arkansas|
|Bustamante, Carlos - Cornell University - New York|
Submitted to: Journal of Breeding Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/23/2010
Publication Date: 12/28/2010
Citation: McCouch, S.R., Zhao, K., Wright, M., Tung, C., Ebana, K., Thomson, M., Reynolds, A., Wang, D., DeClerck, G., Ali, M., McClung, A.M., Eizenga, G.C., Bustamante, C. 2010. Development of genome-wide SNP assays for rice. Journal of Breeding Science. 60:524-535. doi:10.1270/jsbbs.60.524.
Interpretive Summary: Recent advancements in DNA technology make it possible to more quickly dissect DNA from crop plants like rice, into the four nucleic acids composing DNA: A (adenine), T (thymine), G (guanine) and C (cytosine). Because of these developments, it is now possible to identify single nucleotide polymorphisms (SNPs) in the DNA when different rice cultivars are compared at the DNA level. Based on these differences it is possible to design DNA markers from these SNPs and utilize the markers in rice improvement. To develop SNP markers requires the creation of new genotyping and statistical tools. These “tools” will make it possible to utilize SNP markers to introduce traits of interest, determine seed purity, identify rice varieties, and a multitude of other applications. As a “proof of concept”, several different genotyping platforms were developed based on 96, 384, 1,536, 44,100 and 600,000 SNP markers. In addition, a statistical method was developed which allowed the SNP markers to be more accurately identified in self-pollinating crop plants like rice. Development of these SNP platforms and the statistical method will allow genotypic data to be obtained in a more timely manner because there is less personnel time required to obtain the genotypic data and the genotypic data is more accurate. When in place, SNP technology will reduce the time to obtain genotypic data for rice by about 25%.
Technical Abstract: With the introduction of new sequencing technologies, single nucleotide polymorphisms (SNPs) are rapidly replacing simple sequence repeats (SSRs) as the DNA marker of choice for applications in plant breeding and genetics because they are more abundant, stable, amenable to automation, efficient, and increasingly cost-effective. SNPs are being used in breeding programs for marker-assisted and genomic selection, association and QTL mapping, positional cloning, haplotype and pedigree analysis, developing near-isogenic lines, pyramiding useful alleles, capturing positive transgressive variation through backcross breeding, seed purity testing and variety identification. SNPs are also extensively used to determine population substructure, understand the history of domestication, and explore the ancestry of specific alleles. Major efforts to identify SNPs in rice include sequencing the rice cultivar, Nipponbare, developing BAC end-sequence for 12 wild rice forms, and resequencing 20 diverse rice cultivars. This pool of SNPs is currently being expanded through the efforts of several rice programs world-wide, identified as the Rice SNP Consortium. Subsets of this data have been used to develop low, medium and high-resolution SNP assays for many of the aforementioned purposes utilizing 96, 384, 1,536, 44,100 and 600,000 SNP assays. Resources for designing allele specific primers based on the genotypic data are discussed and the newly developed statistical method, Alchemy, to reliably perform allele calling, is introduced. Alchemy, was developed for highly inbred species, like rice, where one of the genotypic classes, i.e. the heterozygous class, is often missing and works well even if only a few samples are processed. The limitations of resequencing data and the importance of exploring the genomic diversity in the world-wide collections of wild and cultivated rice accessions are discussed.