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Evaluation of oligonucleotide rice microarrays as a high-throughput, low-cost genotyping platform. Cheryl Dunham 1 , Steve Geislinger 1 and Dr. Megan Sweeney 2 1.Arcadia High School, Phoenix Arizona 2.University of Arizona, Tucson Arizona. Background

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Evaluation of oligonucleotide rice microarrays as a high-throughput, low-cost

genotyping platform

Cheryl Dunham1, Steve Geislinger1 and Dr. Megan Sweeney2

1.Arcadia High School, Phoenix Arizona 2.University of Arizona, Tucson Arizona


Traditional PCR methods for genotyping are time and labor-intensive and typically assess relatively few markers. While SNP genotyping can provide large amounts of data, the cost per sample makes genotyping whole populations prohibitive. Breeding programs would benefit from the ability to genotype large populations to map and track agronomic traits. Microarrays containing probes complementary to unique, polymorphic regions of the rice genome can be used to genotype large numbers of markers on a large number of individuals in a cost effective manner.


Genomic DNA extraction from cultivars of O. sativa

Shearing of genomic DNA by MseI restriction at 65C for two hours

DNA purification using 3.0 M sodium acetate and ethanol

Direct DNA Labeling reaction- dNTPs labeled with Alexa Fluor 3 and 5 dye incorporated with Klenow at 37C overnight

DNA purification using Invitrogen Purelink PCR purification system

DNA microarray hybridization

Microarray analysis using R with Limma and Marray.



Rice is the most consumed food on the planet, providing the primary caloric source for millions of people.

The microarray assay has been optimized to reduce cost

to about $40/ sample including replication. We have

developed and characterized a statistic to ascertain

the presence or absence of given loci based on the

hybridization signal intensities. We are using this data to

determine the sensitivity and specificity of the assay.

Eventually, we can use data from all 880 loci to infer the

ancestry of different chromosome segments.

Data was normalized to produce the plot to the left. ‘A’ represents the total intensity of fluorescence for both dye intensity channels. ‘M’ represents the ratio of AlexaFluor 5 intensity to AlexaFluor 3 intensity.


Genetic studies in cultivated rice are needed to help provide us with information for plant improvement as well as help us understand the more fundamental processes of adaptation and domestication.



Garris A, Tai T, Coburn J, Kresovich S, McCouch S. 2004. Genetic Structure and Diversity in Oryza sativa L. Genetics169; 1631-1638 (March 2005)

Salathia N, Lee H, Sangster T, Morneau K, Landry C, Schellenberg K, Behere AS, Gunderson K, Cavalieri D, Jander G, Queitsch C. 2007. Indel arrays; an affordable alternative for genotyping. The Plant Journal (2007) 51, 727-737.

Scharpf R., Iacobuzio-Donahue C., Sneddon J., Parmigiani G. 2006. When should one subtract background fluorescence in two color microarrays? Oxford University Press, 2006.

Smyth, G.K., Speed, T.P. (2003). Normalization of cDNA microarray data. Methods31, 265-273.

Sweeney M , McCouch S. 2007. The complex history of the domestication of rice. Annals of Botany 1-7, 2007

Sweeney M, Thomson M, Cho YG, Park YJ, Williamson S, Bustamante C, McCouch S. 2007. Global dissemination of a single mutation conferring white pericarp in rice. PLoS Genetics August 2007 3; Issue 8

Sweeney M, Thomson M, Pfeil B, McCouch S. 2006. Caught Red-handed: Rc encodes a basic helix-loop-helix protein conditioning red pericarp in rice. The Plant Cell, Vol. 18, 283-294.

Yang Y H, Dudoit S, 2009. Normalization: Bioconductor’s marray package. http://www.bioconductor.org/

Controlnorm <- maNorm(mraw[ , 1], norm = "loess", subset = controls[ , 1]) #single normalization for target 1

if (nrow([email protected]) >1) #loop to add normalizations for other targets, IF those targets exist


for (m in 2:nrow([email protected]))


temp <- maNorm(mraw[ , m], norm = "loess", subset = controls[ , m])

Controlnorm <- cbind(Controlnorm, temp)


Chr 3 RIL 172


The subarrays below represent one recombinant inbred line, RIL 175 (in green) against each of the control genomes (in red). When both genomes hybridize to a spot, it appears yellow. This plant is comprised of 50% indica and 50% japonica DNA.

The figures above both represent allele calls obtained for Chromosome 3 of RIL 172. The figure on the left is an early Locus Confidence plot from microarray data, while the graph on the right represents data obtained for specific loci using traditional PCR- based genotyping. Note that the distances between each marker are not uniform and the positions of the PCR based markers relative to the microarray markers is not known.


A DNA microarray comprised of 880 single feature polymorphism elements derived from known insertion/deletion sites within japonica and indica cultivarshas been established for rapid and cost-effective genetic mapping. The specific aims of this portion of the genotyping project involve:

1. Optimizing the direct DNA label method to increase the DNA yield while maintaining a sufficient dye to DNA ratio for accurate detection by the assay.

2. Writing script using R to interpret and output data. Specifically, this involves creating and characterizing a statistic to quantify the presence of specific loci in a given sample.

3. Identifying samples into one of the five subspecies of O.sativa.

The figure to the left is a thumbnail of an actual Locus Confidence plot, for RIL 172. The actual plot occupies an entire computer screen, and


Thank you Megan for mentoring us throughout this journey, it has been a pleasure working with you. Thanks to David for letting us play and to iPlant for providing this powerful learning opportunity.

prints onto a full sheet of paper. At that size, all of the individual locus indicators are fully visible, and easily comparable. Each separate plot represents a single chromosome of the rice cultivar.

RIL 175 v 9311 and RIL 175 v nipponbare