Update on the NSA SNP project Dr. Venkatramana Pedagaraju – Molecular Breeding and Genomics Technology Manager Dr. Brent Hulke -- Research Geneticist

NSA Sunflower Chip Categories Idenifyed SNPs/InDels# Single Bead Assay Fixed variants > 1SNP/contigs RAD clustering to common EST Het variants Total Synthesis failure Final set 8723

Sunflower Genotyping Panel Advanta USDA Seeds 2000 Genosys Mycogen NuFlower CHS 1134 samples Diversity PanelMapping Panel HA89 x RHA464 F1 Self 141, F2 lines genotyped

24x1 HD NSA Bead Chip A1 well: Reproducibility controls(3 unique lines selected from sequencing panel) A12 well: heterozygous controls (F1 hybrids)

Infinium Work Flow

Data Analysis using Genome Studio Software Call region AABB AB Polar PlotCartesian Plot Example of a good SNP

Challenges Posed due to Deletions, Nearby Polymorphisms & Paraolog sequences

Creating Project Specific Cluster Files Improved call rates All Samples Specific project

Performance of SNP markers across various Diversity Panels # Marker Loci Projects Combined Specific Projects

Reproducibility Reproducibility is based on the replicate pairs identified in sample manifest. The metric for reproducibility is calculated based on number of matching allele calls. Marker displayed 99.54% reproducibility.

Mendelian Consistency Mendelian Consistency is based on the trios identified in sample manifest. The metric is calculated based on the number of matching genotypes (Mendelian Inheritance) between a child and each of its parents

Summary

Conclusions Out of total SNP identified, a subset of 8723 SNP were successfully validated across wide range of sunflower breeding lines. Deletions, nearby polymorphism and presences of paralog sequences cause the locus success rate to vary among different breeding lines. About 91% of SNPs were successfully scored in the sunflower diversity panel and linkage mapping population. Approximately 5500 polymorphic loci were identify in the USDA bi-parental mapping population

Future Directions —Develop a SNP based genetic map using genotypic data derived from USDA mapping population (HA89 x RHA464). —Constitute a standard panel of 384 sunflower SNP markers for routine usage across range of breeding projects(diversity analysis, genome selection, qtl mapping, trait introgression programs), based on below criteria: Highly polymorphic & informative in any panel of sunflower germplasm(MAF>0.05) Uniformly distributed on sunflower genome Easily scorable on genome studio and produce automatic genotypic calls

Future Prospects with SNPs 1.Mapping of SNPs to linkage groups defined by the SSR map 2.Development of a 384 marker suite for background selection in trait capture and genomic selection 3.Development of a suite of trait specific markers (may be included in the 384) 4.Genomic selection concept and practice

Trait specific markers Obtained two ways: –Association mapping with Phase II germplasm from all companies and USDA Use existing inbred lines to find markers for traits Strong possibility for IMISUN, SURES, HO, Pl6, Pl8, R-gene, recessive branching, and confection traits –Two parent mapping Will happen for RHA 464 rust gene and Plarg gene as part of Lili’s mapping Other traits, like other rust, vert resistance will need to be started new or translated from existing populations with prior SSR data

Trait specific markers Markers from any type of discovery method can be put together on a Bead Express assay, which is either part of the 384 Bead with random markers for genomic and background selection, or will stand on its own (48 Bead?)

Genomic Selection Using a moderate set of markers (384) to statistically associate with previous breeding data, to provide a way to make early selections before you have field info Instead of just field measurement of traits, you can preselect lines based on marker data, and put only the “best” to field testing

Genomic Selection What is the ideal use of this to a breeder? –Take information from your own yield trials and apply it to new breeding lines –Standard set of random markers (like a 384 SNP bead) that are equally distributed over genome (divides genome into “blocks” or “bins”) –Only marker-assisted system with “pipeline” characteristics like a breeding program Conceptual bins for a chromosome, vertical bars as SNPs

Genomic Selection – “training” Breeder has a population that has good potential to produce exceptional lines Data is collected on existing breeding lines for a quantitative trait over many locations (yield, oil) A moderately sized marker set (384) is regressed statistically against the data Markers are random effects –Marker significance is not determined individually, but as the full set of markers together –All markers are included in the selection model, however, each has a different weighting (importance) for selection (called Estimated Breeding Values)

Genomic Selection – “selection” Elite x Elite cross F 1 plant x F3F3 Finished inbred Testcross to tester lines, and evaluate in field Analyze with OPA as seedlings Select top 30% … F 2 plants (large number, >100) Commercial hybrid development Very narrow based population for short term improvement and rapid inbred extraction Pick the most likely plants to have the phenotype of interest by selecting the plants with the best marker profile Simple and straightforward Alternatively, advance large number of lines by SSD to F4 or F5 and analyze with SNPs to fix genes and improve predictions. x x x Data from YT used to “tweak” model for next gen. Data from previous YT with EBVs calculated for SNPs

Where is GS best used? Excellent technique if you want to maximize selection accuracy and rate of genetic gain on a pop. by pop. basis. –Inference space is the population(s) of interest –Different populations have different gene structure, thus different EBVs for each bin in each population will improve gain from selection Excellent technique if data is routinely generated for the trait of interest (e.g. yield data will always be generated in plant breeding)

Time course for Genomic Selection 1.Assemble prior information – yield trials, special trait trials, on all lines tested the last few years 2.Get these same lines genotyped with 384 markers of equal genome distribution 3.“Train” your model and find the value of each marker 4.Take your newest germplasm, genotype 5.Use markers to assess which are the most likely lines to be release, and do field testing

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