Presentation on potential approaches for landrace gap analysis for the Annual Genebanks Meeting in Melbourne (Australia) in 2016.

1. Julian Ramirez-Villegas
Nora Castañeda
Colin Khoury
Peter Wenzl
Andy Jarvis
Gap analysis
for CGIAR
crops

2. A gap analysis approach for CGIAR crops
1. Brief history of gap analysis
2. Targets and outputs in the Genebank platform
3. A simple method to assess the geography of
collections and their representativeness
4. An opportunity to develop more
understanding of drivers of diversity at
taxonomic, trait and genetic levels

3. Brief history of gap analysis
• Term first coined for in situ wildlife conservation
• Various authors have adapted the concept for ex situ conservation
analyses, most notably:
• Maxted et al. (2008) –Vigna spp.
• Ramirez-Villegas et al. (2010) –Phaseolus spp.
• Shehadeh et al. (2013) –Lathyrus spp.
• 2008: Development of gap analysis methodology for crop wild relatives
• 2008–2009: GPG2 analyses on key genepools (CWR + cultivated
materials)
• 2011–2016: Global assessment of CWR conservation gaps
Gap analysis is a tool used in wildlife conservation to identify gaps in conservation lands (e.g.,
protected areas and nature reserves) or other wildlands where significant plant and animal
species and their habitat or important ecological features occur (Scott and Schipper, 2006)

4. Gather
occurrence data
Make collecting
recommendations
Model
distributions
Process data
Determine gaps
in collections
Taxonomic
Geographic
Ecological
Choose species
or area
81 crop genepools
1076 crop wild relative
taxa (close relatives)
CWR gap analysis method

5. Lettuce Sunflower Sweet potato
PigeonpeaPotato Beans
81 crop genepools

6. Castañeda-Alvarez et al. (2016), Nat. Plants
Main findings – CWR gap analysis

7. Methodology for cultivated species
CIAT & IRRI (unpublished) –Sorghum bicolor – data from the ICRISAT genebank (through SINGER)
• Gather data from SINGER (Genesys),
and GBIF
• Assess variation in density of
accessions
• Assess environmental similarity
between accessions and ref. dist.
1 2
3 • Mash out [2] and [3] to produce gap
maps4

8. Genebank platform targets and outputs
Targets
• Representation of crop genepools in ex situ conservation quantified
• Gaps in at least 5 crop genepools addressed
Outputs
• Representation of genetic (G), taxonomic (T), geographical (Ge) and
environmental (E) diversity and traits improved
Key strategic points
• Identification of duplicates using genomic information at accession level
• Global analysis of diversity of collections and assess G, T, and Ge gaps
• Use of GIS-based tools with focus on threatened germplasm

9. Part 1: simple method to assess geography of collections
and their representativeness
Address major limitations in GPG2 approach
by
• Including other drivers of germplasm
distribution, e.g. language and culture,
land-use change, seed and delivery systems
• Use of more robust estimators of species
distributions through niche-based models
• Enhance taxonomic resolution when
possible, e.g. look at races individually
instead of at the entire species
Lasky et al. (2015) Sci. Adv.

10. Part 2: An opportunity to develop our understanding of
drivers of diversity at taxonomic, trait and genetic levels
• One key objective of ex situ conservation is to be able to have
material at hand for breeders to look for adaptive traits.
• Hence, our approach should seek to address
some key questions:
• Which traits are priorities?
• Which alleles control such traits?
• What conditions the presence these alleles
(geography, culture, environment)?
• What is the ”global” (likely) and “conserved”
distribution of the trait?
• How is trait expression affected by environment
(and management)? (i.e. GxExM)
• How important?
• Where is it distributed?

11. Drivers of landrace genetic diversity
Sorghum in Africa
• Evidence of cultural factors
shaping crop diversity.
• Close association of linguistic
families and crop diversity.
Westengen et al 2014, PNAS
What conditions the presence these alleles (geography, culture, environment)?

12. Drivers of landrace genetic diversity
Maize in Oaxaca (México)
• Traits vs genetics.
• Culture and environment as
drivers of morphological and
agronomic differences.
• Genetic distance small.
• Low population structure.
Perales et al 2005, PNAS
What conditions the presence these alleles (geography, culture, environment)?

13. • Analyzed 104 wild and 297 cultivated
accessions
• Drought stress indices and population
structure are useful for genome-wide
genetic-environmental associations
Population structure and environment
What conditions the presence these alleles (geography,
culture, environment)?

14. What is the global distribution of a trait and how is its
expression affected by environment?
• Germplasm characterization
provides “G” component of trait,
with little “E” impact.
• To predict distribution, we need “E”
impact on trait, too.
• Analogues may help find areas with
characteristics which are similar to
evaluation sites/years
• Some traits may have small “E”
component
• Breeding trials may help assessing
GxE for a sub-set of accessions
Sorghum 100-seed weight (CIAT & ICRISAT, unpublished)
100-seed weight
Climate analogues
of Patancheru

15. Final remarks on approach
• Take stock on methods and current thinking on genetic
diversity of CGIAR crops (e.g. a systematic literature review
paper)
• Need heavy input from genebank managers and their
groups, perhaps through visits (1-2 month) and joint work
at their stations –key to tap into existing knowledge
• Input from breeders for trait prioritization
• Data organization will be key –a long-term investment
• Too much we could do, and limited time, so need to
prioritize (e.g. at trait level)

16. Thanks!
Julian Ramirez-Villegas
j.r.villegas@cgiar.org