# Presentation: 070466

## Using Genomics To Create An Allele Index To NPGS

**Edward Buckler**
**USDA-ARS at Cornell University, Ithaca, NY**

## What do we want to do?

**We want to make more useful plants by conserving, finding and combining better alleles.**

**The National Germplasm conserves 464,000 accessions and may contain 100,000,000 distinct alleles, ****but there is no index.**

- Yield (CA)	     Disease Resistance	      Flavor

- Available Germplasm

- Good QTL

- Neutral QTL

- Bad QTL

- Although poor yielding, it has complementary yield QTL and good disease resistance and flavor

- Although good yielding, the current line already captures these QTL

**Absolute View**

- Yield (CA)	     Disease Resistance	      Flavor

- Current Variety
-       DGL 2343

- +3

- +0

- -2

- -1

- +2

- +3

- PI 265443

- +0

- +1

- +0

- PI 532443

- -1

- +2

- +1

- PI 783472

- +0

- +0

- -1

- PI 572811

- Available Germplasm

- Good QTL

- Neutral QTL

- Bad QTL

**Contrast View**

- Yield (CA)	     Disease Resistance	      Flavor

- +7

- +7

- +6

- Optimal Result

- Available Germplasm

- Good QTL

- Neutral QTL

- Bad QTL

**Absolute View**

## How do we build this interface?

**Map QTL controlling agronomic traits.**
**Genotype the NPGS germplasm for QTL**

## QTL mapping approaches now exist for virtually all types of populations.

**QTL mapping approaches now exist for virtually all types of populations.**

**Near gene level resolution achieved in multiple species**
**Identification of genes controlling flowering, starch, nutrients, wood quality**
**Positive Results in:**
**Maize**
**Rice**
**Arabidopsis**
**Conifers**

## Nested Association Mapping (NAM)

**25 nested mapping populations being created in maize**
**Permit simultaneous genome scan, allele mining, and high resolution mapping****	**
**Best results require high density of markers (near 100% LD with QTL)**
**Simulation **
**Marker data is real and at very high density**
**QTL effects simulated**
**Geometric distribution of 50 QTL with 60% *****h******2***

## NAM QTL detection power

**35 of 38 largest QTL (prop of variation >0.2%) are detected to the causative SNP or LD block**
**Best to sample across populations**
**3000-3500 RILs is enough to detect most of the QTL**
**This number is closely related to *****h******2***

**Pop#    0           5          10           15        20          25**

**Increase # of pops**

**Subsample across pops**

## What could genomics do?

**Discover gene space and SNPs **
**Focus on genera with 500+ accessions that have some intermating**
**Randomly sequence gene enriched space for 24 lines (~480Mb of total sequence)**
**Depending on diversity – 100K to 2M SNPs**
**Score 50,000-400,000 SNPs on each accession**
**Enough markers to map in most populations**
**Array or bead-chip based SNP discrimination techniques**

## What will this cost?

| $3 million | Gene space and SNP discovery in 60+ genera |
| --- | --- |
| $42 million | Genotype 414,944 accessions |
| $8 million | Bioinformatics and basic analysis |
| $53 million | ($18 million/year over 3 years) |

## Who should do this?

**USDA-ARS In-House**
**DNA Extraction**
**Phenotyping (ongoing)**
**Database design and warehousing**
**Analysis**
**Peer Reviewed Competitive Proposal (Private and/or Public consortia)**
**Gene space/SNP discovery and data curation**
**SNP genotyping and data curation**
**Implementation of analysis algorithms and software**

## When?

**The least proven technology is the gene space discovery, but discovery is already underway in genera that represent 60+% of the accessions**
**Mapping resolution will be affected by LD structure, but this should work for most species**
**Mapping shortcomings in some genera will still provide all the necessary information to design powerful follow-up studies**

| 2006 | Planning | Pilots |
| --- | --- | --- |
| 2007 |  |  |
| 2008 | DNA
Extraction |  |
| 2009 |  | SNP
Discovery |
| 2010 | SNP
Genotyping |  |
| 2011 |  | Phenotyping 
& Analysis |
| 2012 |  |  |

**Will it work?**

## What would this provide?

**Knowledge of relatedness**
**Improve curation of germplasm banks**
**Informed choice of germplasm core sets**
**Facilitate mapping**
**Integration with other mapping projects**
**Identification of common allele effects**
**Identification of lines carrying rare alleles of large effect**
**Encourage extensive phenotyping**
**Index for the library of alleles that can be applied to breeding**

## How would this change breeding?

**Informed choice of germplasm to initiate breeding**
**Knowledge of valuable new alleles with phenotypic effect estimates**
**Markers for any purpose**
**Marker assisted breeding becomes a reality for many species**
**Still need better methods for combining 10s and 100s of QTL**