tl iii_genetic gains_icrisat

Rajeev K. Varshney

Research Program Director

- Genetic Gains

Comparison of woo-gen (right) and dee-geo-woo-gen

strains, the latter containing the sd1 mutation The effects of different Rht alleles on plant height in

wheat (cv. April Bearded). The wild-type contains Rht-

B1a and Rht-D1a, which are homoeologous

(corresponding) genes on the B and D genomes. Rht-

B1c is a more severe allele at the Rht-B1 locus

Green Revolution in Wheat & Rice (1968)

Transformational genes and breeding

• First-generation Green Revolution varieties “sold themselves” on the basis of large, visible differences induced by dwarfing genes

The “stalled” Green Revolution

• Second-generation Green Revolution varieties “sold themselves” as a result of quality and disease resistance improvements

• Second-generation GR varieties got “stuck” in farmers’ fields because of:

(i) Lack of yield advantage in non-stress conditions

(ii) Inability of public crop improvement systems to drive varietal turnover

Source: Gary Atlin, BMGF

© 2012 Bill & Melinda Gates Foundation |

4

September 7, 2016

Variety name Year of release Total area (x 1000

ha)

Proportion of total

area under rice (%)

Swarna 1980 3,808 27.7

Pooja 1999 998 7.3

Lalat 1989 898 6.5

Moti 1989 277 2

Mahsuri 1975 1,208 8.8

Swarna-Sub1 2009 367 2.7

Sambha Mahsuri 1989 220 1.6

ARIZE 6444 2010 681 4.9

Sarju-52 1982 350 2.5

MTU1001 1997 523 3.8

MTU1010 2000 346 2.5

Sahbhagi Dhan 2012 35 0.3

Samba-Sub1 2012 30 0.2

Other hybrid 232 1.7

Other improved 1,358 9.9

Other traditional 622 4.5

Unknown 1,80 13.1

Total 13,758 100

Area and age of rice varieties grown in rainfed eastern India: 2014 wet season (T. Yamano, IRRI)

Area-weighted avg age of varieties = 28 yr



ESTIMATES OF RATES OF GENETIC GAIN IN STAPLE

GRAIN CROPS: RARELY MEASURED, AND TOO LOW

TO DRIVE ADOPTION

5

September 7, 2016

Species

Region/

environment Period

Rate of genetic

gain (kg ha-1 yr-1) Reference

Maize

(Pioneer)

Corn Belt 1930-2010 89 (1.2%) Duvick (2005)

Maize

(CIMMYT)

Optimal

environments

2000-2010 109 (1.4%) B. Masuka (unpublished

data)

Wheat

(CIMMYT)

High-yield

envs

1977-2008 64 (0.9%) Lopes et al. (2012)

Wheat

(CIMMYT)

Drought envs 1977-2008 10 (0.6%) Lopes et al. (2012)

Maize

(CIMMYT)

Low-N 2000-2010 21 (0.6%) B. Masuka (unpublished

data)

Rice (IRRI) Wet season 1966-2013 22 (0.7%) IRRI (unpublished data)

Rice (IRRI) Dry season 1966-2013 15 (0.2%) IRRI (unpublished data)

Note that these are genetic gain measured in research plots. Genetic gains in farmers’ fields are almost certainly lower


THE GENETIC GAINS INITIATIVE AIMS TO: (I) INCREASE THE RATE OF GAINS GENERATED

THROUGH BREEDING AND (II) INCREASE THE RATE OF VARIETAL REPLACEMENT IN

FARMERS’ FIELDS

September 7, 2016

Good systems generate and deliver genetic gains of >1.5% annually, most now <0.5%

Rapid-cycle improvement of source population drives the rate of genetic gain (by changing gene frequencies

Candidate cultivars that fit the product profile

Continuously deliver new varieties (via foundation seed) to companies/GOs/NGOs

NARES identify and release superior replacements for current varieties (data!!)

Continuous delivery of new varieties and replacement of old via the seed system (climate change adaptation)

Selection of the product: for dissemination: a weak link in the public system

Trait introgression

Discovery and Gene/Trait Mobilization

Genomic prediction Intermate best

lines

Select superior lines

Global public goods/CGIAR

Company 1

Company 2

Company 3

Farmers

• Genetic gains initiative aims to shorten breeding cycle from ~15 to 5 years while selecting more accurately

• Breeding-to-seed system handoff needs to be managed to provide rapid varietal turnover (average age of varieties in farmers’ fields should be <10 years (now 15-30)

Foundation seed

© Bill & Melinda Gates Foundation | 9 Confidential

Gates Foundation’s priorities



What are the routes to increased genetic gains?

1. Bigger programs (= higher selection intensity)

− Mechanization, automation, digitization

2. Adequate genetic variability

− Donors, elite but exotic materials

3. More accurate selection (=higher heritability)

− Higher-quality phenotyping, better experimental designs, more reps, MAS

4. Faster breeding cycles

− State of the art program design, genomic prediction

5. Management that is empowered and accountable for product delivery

− Research managers lead product development, planning, monitor progress, provide

supportive environment, and ensure effective coordination among teams

6. Well-trained staff who understand product development

− Training of plant breeders needs to be modeled on engineering training, with a focus on

quantitative analysis, mechanization, internships in commercial and high-quality public

sector programs


Farmers fields

Research Program

Genetic Gains

Research Program

Innovation System

for the Drylands

Research Programs

Asia, ESA, WCA Crop Improvement

Int. Crop Management

Breeding Population

Selfing and selection

Advanced

breeding

lines

Parental

lines

Varieties Hybrids

NARS/ Pvt Sector

Genebank

Pre-breeding

Genomics &

Trait discovery

Forward

Breeding

Marker

Rt =irsAy

Cell, Molecular

Biology & Genetic

Engineering

Seed Systems Agribusiness and

Innovation

Platform

Systems analysis

for Climate Smart

Agriculture

Integrating RP-Genetic Gains in Regional Programs

Phenotyping

Diversity

Trait specific

lines

Strategies

Lessons

Mapping

homologous

environments to

target varieties/

quantity of seed

MIND analysis to

maximize

outcomes

From: DG’s Dialogue, 11th May, 2016

New Global RP- Genetic Gains @ ICRISAT

Pre-breeding

Genomics and

Trait Discovery

Forward

Breeding

Cell, Molecular

Biology & Genetic

Engineering

Genebank

Seed Systems Optimizes and strengthens seed delivery system

Provide seed adoption road maps in developing countries

Molecular biology and genetic engineering research

Induced variants for breeding programs e.g. developing

transgenics for desirable traits

Deals with usage of markers in breeding programs

Provides tools and technologies for molecular breeding

Develops genomic resources and uses comparative and

functional genomics approaches for allele discovery

Identifies markers/ genes for traits of interest

Characterizes germplasm collection for making them

suitable for breeding programs

Develops novel genetic populations

Conserves, characterizes and distributes germplasm

Research on germplasm to identify trait specific lines

Seq

uen

cin

g an

d in

form

atic

s Se

rvic

es

10

Predict

Phenotypes

Inbreeding

Multi-location, Multi-year

testing

Seed Increase

Rt =irsAy

genetic gain over time

years per cycle

selection intensity selection accuracy

genetic variance

cheaper to genotype =

larger populations for

same $$

make selections in

‘off target’ years

maintain favorable

rare alleles

Select years

earlier on single

plant basis

Composition of genetic gains

Large F2 populations

Large screening nurseries

Large number of crosses

Replicated testing

Marker based genotyping

High throughput phenotyping

Barcode reader

Selection intensity (i) ()

Selection accuracy (r) ()

Generation of 500 lines

Bring new

genes (not

present in a

current

breeding

program)

Genetic variance (σA)

()

Years per cycle (y) ()

What we need to do?

Screening genes for better breeding

By Patterson Clark April 16, 2014

Forward Breeding

Many lines having undesirable alleles are discarded

Opportunity to the evaluation of fewer lines in later generations

Provides tools, technologies and platforms to deploy markers in

breeding programs for developing improved lines in cost- and time-

effective manner

© 2012 Bill & Melinda Gates Foundation | 14 September 7, 2016

Centralized support: high-density profiling

The Integrated Genotyping Service and Support (IGSS) is a collaboration between DArT and BecA to provide GBS profiling services to African breeding programs

IGSS will provide both profiles and support on how to apply them.

Centralized support: shared Industrial-Scale High-Throughput Genotyping Facility (led by ICRISAT)

The Shared High-Throughput Genotyping Service will provide uniplex SNP assays through a commercial service provider

Will deliver SNP genotyping for $.05 per data point, with DNA extraction at $0.50. Target is to deliver a 5-10 SNP genotype for $1

Allows selection for diagnostic markers at very low cost, with profiling restricted to a small subset.

Will permit large increases in selection intensity

How should pipelines be re-designed to maximize genetic gain based on low-cost diagnostic genotyping?

New Breeding Funnel for public sector

F2 F3 F4-6 F7 NARS

• 100,000 F2s (1000 x 100 crosses) • MAS for disease,

plant habit, quality, yield etc.

• Sowing of selected ~10,000 single plant selection (100/1,000 per crosses)

• SPS continues for each cross

• ~100-200 (ca. 1-2 best entries/ cross), reconfirm for homo & alleles

Advanced breeding lines ready for NARS partners/ station trials

• ~5-10 superior breeding lines identified for release as superior variety

10

0-F

1 C

ross

es

(10

00

F2 e

ach

)

Disease Plant habit Quality Yield Positive alleles

Sample collection

Early generation screening for

Forward Breeding

Validation of upto 100 markers free

Include 10 markers in the screening panel

Generate MORE and BIG populations

Collect leaf samples in cassettes (PlantTrak system)

Ship cassettes to service provider (Hyderabad, Sweden)

Genotyping (including DNA extraction) costs:

US$ 2 per sample for 1536 samples for CG centres/ partners

ICRISAT can provide subsidy US$ 0.50 per sample (

Target US$ 1 per sample

In summary…

Thank you

tl iii_genetic gains_icrisat

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