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Update on the NSA SNP project
Dr. Venkatramana Pedagaraju – Molecular Breeding and Genomics Technology Manager
Dr. Brent Hulke -- Research Geneticist
NSA Sunflower Chip
CategoriesIdenifyed
SNPs/InDels # Single Bead Assay
Fixed variants 8313 6323
> 1SNP/contigs 5361 2072
RAD clustering to common EST 1167 430
Het variants 1557 1175
Total 16398 10000
Synthesis failure -1277Final set 8723
Sunflower Genotyping Panel
Advanta
USDA
Seeds 2000
Genosys
Mycogen
NuFlower
CHS
1134 samples
Diversity Panel Mapping Panel
HA89 x RHA464
F1
Self
141, F2 linesgenotyped
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 StudioSoftware
Call region
AA BBAB
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
#M
arke
r L
oci
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 16398 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
F1 plant
x
F3
Finished inbred
Testcross to tester lines, and evaluate in field
Analyze with OPA as seedlingsSelect top 30%
…
F2 plants (large number, >100)
Commercial hybriddevelopment
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 numberof 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 withEBVs 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, genotype5. Use markers to assess which are the most
likely lines to be release, and do field testing
Thanks for your support!