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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Sunflower and Plant Biology Research » Research » Publications at this Location » Publication #325325

Research Project: Sunflower Genetic Improvement with Genes from Wild Crop Relatives and Domesticated Sunflower

Location: Sunflower and Plant Biology Research

Title: An innovative SNP genotyping method adapting to multiple platforms and throughputs

Author
item Long, Yunming - North Dakota State University
item Chao, Wun
item Ma, Gjojia - North Dakota State University
item Xu, Steven
item Qi, Lili

Submitted to: Theoretical and Applied Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/26/2016
Publication Date: 3/1/2017
Citation: Long, Y.M., Chao, W.S., Ma, G.J., Xu, S.S., Qi, L.L. 2017. An innovative SNP genotyping method adapting to multiple platforms and throughputs. Theoretical and Applied Genetics. 130(3):597-607.

Interpretive Summary: Single nucleotide polymorphisms (SNPs) are highly abundant, distributed throughout the genome in various species, and therefore they are widely used as genetic markers. Recent advances in DNA sequencing technology have accelerated the discovery of SNPs. However, the usefulness of this genetic tool relies heavily on the availability of user-friendly SNP genotyping methods. Although several SNP genotyping methods were previously developed, they all have certain drawbacks such as sophisticated procedures, low throughput, low allelic specificity and/or high operational costs. Moreover, the requirement for a particular instrument for bi-allelic discrimination in the previous methods prevents the sharing of SNP data among researchers who use different instruments. Therefore, it is highly desirable to develop a new SNP genotyping method, which incorporates all the major advantages of previous methods and is compatible with all genotyping platforms that are commonly used in most laboratories. To meet this need, we employed a state-of-the-art design approach to develop and optimize the thermal valve PCR (TV-PCR) method by integrating all the major advantages (e.g., highly allelic-specific, high-throughput, easy-to-use, cost-effective, etc.) of the previous SNP genotyping methods. In addition, TV-PCR is the first SNP genotyping method that is compatible with most genotyping platforms, and therefore, it has great potential for wide use in genotyping SNPs by various laboratories.

Technical Abstract: Single nucleotide polymorphisms (SNPs) are highly abundant, distributed throughout the genome in various species, and therefore they are widely used as genetic markers. However, the usefulness of this genetic tool relies heavily on the availability of user-friendly SNP genotyping methods. We have developed a state-of-the-art SNP genotyping method designated as thermal valve PCR (TV-PCR), which integrates all major advantages (easy to use, low operation cost, high allele specificity, and high throughput) of the previous SNP genotyping approaches. TV-PCR involves competitive amplification of two alleles followed by equal scaling amplification as a function of temperature. Competitive amplification is carried out at low temperature (from 56 °C to 51 °C) using two forward allele-specific primers (AS-primers) in combination with a common reverse primer. These two AS-primers are designed with novel principle for high-specificity amplification of both alleles as well as similar allelic PCR efficiency and tailed with two distinct sequences, which provide priming sites for two universal priming-element-adjustable primers (PEA-primers) for equal scaling amplification at higher temperature (62 °C). In addition, the structure of PEA-primer can effectively reduce primer-dimer formation and spurious amplification. Bi-allelic distinction is achieved by fluorescence signals at PCR endpoint or size separation of post-PCR. TV-PCR is performed under standard assay conditions for most SNPs and indels and has been successfully applied in rice, wheat, and sunflower. The first SNP genotyping method is compatible with most genotyping platforms, and therefore, it will have wide applications.