Accelerating crop genetic gains with genomic selection

Presented by
VIOLINA BHARALI
Ph.D 1st yr
Dept. of GPBR
Credit Seminar : Accelerating crop genetic gains with genomic selection
Course no : GP – 691
Department : Department of Genetics and Plant Breeding
ACHARYA N. G. RANGAAGRICULTURAL UNIVERSITY
AGRICULTURAL COLLEGE, BAPATLA
1
Genetics and Plant Breeding Department, Agricultural College, Bapatla
DOCTORAL SEMINAR-I – GP 691 1(1+0)

Accelerating crop genetic gains with genomic
selection
2

Genetic improvement of crop
cultivars through plant
breeding for global food
security and need for speed
Climatic
fluctuation
Limited
cropland
area
Without
increasing
cropland
area
increase in
production
1961-877
million mT
2050- 4000
million mT
3

Current population
6.7 billion
9 billion by 2050
70% increase in the demand for agricultural
production is expected
The current rates of
yield improvements
are insufficient to
meet this goal.
4
Source: Tester and Langridge, 2010

Rapid genetic
gains in crop
yield is
required
Genetic gain is determined
by the formula
Traditional breeders equation
5

Genomic selection
Association mapping
Marker assisted selection
Reshaping of breeding program by molecular
marker technology
Plant breeding programmes take up two decades
until a new variety is released
6

Marker assisted selection
• 1980s- development of molecular markers.
• Selection of individuals based on integration of phenotypic selection
with markers linked to the QTLs governing the trait of interest - MAS
• Effective for the manipulation of large effect alleles with known
association to a marker.
• Linkage analysis for marker and trait done on biparental populations.
• Low power to detect marker-trait association due to low recombination
rate.
• QTLs with small-effects are missed entirely.
7

• Started in 2000s- mapping population consist of a diverse set of
individual/lines drawn from natural population.
• It exploits linkage disequilibrium.
• Linkage disequilibrium describes a nonrandom association between
alleles of two or more loci.
• Low power of detecting rare variants (QTLs with minor effects) that
may be associated with economically important trait.
Association mapping
8

Genomic selection
• Genomic selection scheme was proposed by Meuwissen et al. (2001) to rectify the
deficiency of MAS and MARS schemes.
• MAS- not suitable for improvement of quantitative traits, not designed to handle
QTLs with small effects.
• MARS- markers showing significant association with the traits is needed hence
some QTLs with minor effects may be missed.
• GWAS- a large number of markers are tested for association with various complex
trait. It requires significant association between marker and trait, this limits the
number of markers used.
• GS is fundamentally different from GWAS, as it involves the use of full-genome
information, regardless of its significance, in relation to a specific trait, rather than a
few markers as in GWAS. 9

Difference between GS
and MARS in intermated
B73 x Mo17 maize
recombinant inbreds
10
Source: Massman et al., 2013

• Genomic selection is a specialized form of MAS, in which information
from genotype data on marker alleles covering the entire genome forms
the basis for selection.
• GS- use all molecular markers for genomic enabled prediction of the
performance of test population.
• Novel approach for improving quantitative traits that uses whole
genome molecular markers (high density markers and high throughput
genotyping).
• Aim of GS: To predict breeding value.
• GS replaces phenotypic selection for a few cycle.
11

Milestones of selective breeding; improved agricultural productivity
12
Source: Jones and Koning, 2013

Training
population
1. Genotyping for a large
number of markers evenly
distributed over the entire
genome.
2. Replicated multilocation
phenotypic evaluation.
Genotype and phenotype
data used for training of
GS model
1. Estimation of GEBVs
for individuals from
the marker data.
2. Selection on the
basis of GEBV
values.
Breeding
population/
selection
candidates
1. Genotyping for the same
markers as in the training
population.
2. No phenotypic evaluation
Schematic representation of genomic selection
13

TRN: Training population
TST: Testing population
14
Crossa et al., 2017

Information flow within
genomic selection for the
example of maize.
15

• Determining the Genomic estimated breeding value(GEBV).
• Breeding value- reflects the true genetic potential of the individual.
• GS combines molecular and phenotypic data obtained from the training
population to obtain the prediction model on which the marker data
from test/ breeding population is fitted to derive GEBV of the
individuals.
• The derived GEBV is used for selection.
Concept of genomic selection
16

Different prediction models in genomic selection
17

Accuracy of the Genomic enabled prediction model depends on:
• The size and genetic diversity of training population and its relationship with testing
population.
• Heritability of the trait- complex traits with low heritability and small marker effects
are suitable for GS and GP with large training population.
• Markers should be in linkage disequilibrium with the QTLs.
Genomic enabled prediction model
Phenotypic response = Summation of genetic values + Summation of residual values
Predicts effect of each
marker on the trait
simultaneously
18

• The accuracy of GEBV with a training population of size N, heritability of phenotypes h2 and Me
independent loci affecting the trait is
• Me corresponds to the effective number of independent chromosome segments in the population
• Me = 2NeL, where Ne is the effective population size and L is the length of the genome in Morgans.
• If narrow-sense heritability is h2 =0.2 and accuracy of GEBV of 0.5 is desired (to enable rapid genetic gains),
the above equation says that 5000 individuals are required in the training population to achieve this level of
accuracy of GEBV.
• If phenotyping has been done poorly and heritability is low (e.g. h2 =0.1), then the training population needs
to include 10,000 genotyped and phenotyped individuals.
Equation to determine the size of the training population
19
Source: Fels et al., 2019

Characteristics of training population
• The training population must be representative of the breeding population.
• It should maximize the proportion of trait variance-associated with the marker.
• The individuals/lines included in the training population should have undergone
several rounds of recombination.
• The training population should adequately represent the genetic diversity present
in the breeding population.
• To ensure the accuracy of GEBV is maintained over time, the reference
population must be frequently updated with new genotyped and phenotypes
individual.
20

How genetic gain is achieved by Genomic Selection?
• GS enhances genetic gain by shortening the breeding cycle or enhancing testing
efficiency in field evaluation.
• When GEBV accuracy is high enough, GS can reduce breeding time by increasing
the proportion of high-performing offspring in a breeding population, thus
accelerating gain from selection.
• Proper selection of prediction models depending on complexity of the trait.
• Recent availability of high throughput genotyping and phenotyping and cost
effective sequencing allows large scale availability of genomic resources.
21

vgggg
GS: a potential breeding technology that accelerates the genetic gain
‘i’ = selection differential
‘y’ = time to complete one
cycle of breeding cycle.
‘r’ = predictive accuracy
(correlation between estimated
genotypic or breeding values in
the training sets and their
corresponding true values in
target population of
population.)
GS increases ‘i’
and ‘r’ and
decrease ‘y’
22

Advantage of GS over phenotype based selection
• Reduces the cost per cycle and the time required for variety development.
• Reduces the cost of test cross formation and evaluation at each stage of multi location
environment.
• GS predicts breeding value of the candidates for selection.
• GS takes advantage of dense markers for sampling thus avoiding the need to extensively
phenotype the progeny.
• This saves time by reducing the cycle length and enhances genetic gain and selection
response per unit time.
• It has the potential to improve complex traits with low heritability.
23

• GS has been most successfully implemented in dairy cattle breeding, where its efficiency is
proven as GS reduced generation interval from 7 years to 12 months.
• In temperate crops GS can accelerate gain from selection per unit time through the use of
off-season nurseries, while in tropical crops like rice, GS can be used with one or more
cycles of rapid generation advance for a similar gain.
• GS was deployed first in soyabean among other legume crops to improve yield and
agronomic traits using GBS.
• Genetic gain from rapid cycle GS in CIMMYT for improving yield in drought stress
conditions.
• Use of GS in private sector in developing hybrids that gives higher yield under both
favorable and drought stress condition in USA. These varieties were referred as AQUA
max maize hybrids and was developed by DuPont Pioneer Optimum.
Use of GS for plant and animal breeding
24

Disadvantages of GS
• High throughput genotyping and phenotyping of the training population is
required increasing the cost of variety development.
• Unclear guidance as to where GS can be efficiently applied in a breeding
programme. As less complex traits with high heritability can be predicted by
few markers and improvement can be carried out by MAS, MARS or
association mapping.
25

• Sequencing –based-genotyping approaches like genotyping-by-sequencing (GBS)
have become very popular for genotyping large plant populations at moderate
cost.
• Recent GBS strategy - skim-based GBS (skimGBS), enables high-resolution
genotyping via low-coverage sequencing (Bayer et al., 2015).
• Powerful when populations of homozygous individuals, such as recombinant
inbred lines (RIL) or doubled-haploid (DH) lines, are used and a high-quality
reference sequence of the parents is available.
• Buckler et al. (2016) proposed a different genotyping platform specifcally
tailored to GS approaches called “rAmpSeq” in maize.
Genotyping for genomic selection
26

Phenotyping for genomic selection
• High-throughput phenotyping (HTP) technologies and remote sensing platforms - provides
the opportunity to generate useful trait information at an unprecedented scale.
• Canopy temperature and vegetation index from aerial measurements in multi-variate
models - predicts grain yield in wheat.
• Near-infrared (NIR) and nuclear magnetic resonance (NMR) spectra - to accurately predict
end use quality traits for wheat from very small quantities of flour.
• Envirotyping - involves collecting environmental factors through multi-environment trials,
geographic and soil information systems and empirical evaluations and has various
applications, including environmental characterisation, genotype × environment interaction
analysis and phenotypic prediction.
27

A conventional
crop breeding
programme
A modern crop
breeding
programme
28
Fels et al., 2019

Challenges to the success of GS in crops
1. Genotype and environment interaction (GEI):
• GEI limits gain by reducing the heritability of the trait. As the GEI variance increases
relative to magnitude of genetic variance the heritability of the trait will decrease and the
breeders equation will predict reduced gain.
h2
GEI
29

2. Non additive genetic variance
• Due to non additive genetic variance allelic substitution cause change in the ranking of the
alleles.
• This decrease the genotypic value of the trait.
3. Tackling the combined effect of epistasis and GEI
• Epistasis along with environmental effect decrease the genotypic value which limits GS.
• Decrease in the genotypic value decreases the individual QTL effects due to the interaction
with other QTLs and make it difficult to obtain the desired phenotype.
30

Application of genomic selection in breeding programmes
1. Implementing GS in modern plant breeding programmes
• GS in maize led to significant increase in genetic gain (Gaffney et al., 2015; Garcia-Ruiz et
al., 2016).
• In wheat classical phenotypic selection has led to significant improvements of modern
varieties, however implementation of GS holds the potential to increase the rate of gain
further.
31

2. Increasing gain through rapid generation advancement and GS
• GS reduce the length of breeding cycle as compared to classical phenotypic selection by
allowing the breeders to select genotypes earlier in the cycle.
• Selection candidates have to go through line development via selfing or double haploid
technology until tested in MET.
• DH is widely used and has led to significant reduction in generation time.
• Speed breeding is gaining popularity as they allow the breeders to turn over many
generations under glasshouse condition reducing the generation time.
• Wheat, barley and rapeseed – F6 lines obtained in 1-1.5 years with speed breeding.
• CIMMYT- combining GS and speed breeding increase genetic gain significantly.
• Facilitates the introgression of novel diversity.
• La Fuente et al. (2013) – extended the idea of rapid generation advance by introducing the
idea of in vitro nurseries. In vitro production and fusion of gametes eliminates the entire
process of plant growth. Genotyping is done on gametes or new cell lines to estimate the
GEBV and accelerates the process of genetic gain.
32

3. Leveraging GS to harness useful diversity from gene banks.
• Crop improvement- introgression of novel allelic diversity absent in elite germplasm pools.
• Longin and Reif (2014)- proposed a new strategy for exploitation of wheat genetic
resources in modern breeding programme by using hybrid technology in combination with
GS.
• Yu et al. (2016) – GS strategy to predict performance of gene bank germplasm using
sorghum dataset which were taken as the training population to determine GEBV values for
selection of superior agronomic traits.
33

4. Combining genomic selection with genome editing.
• Yang et al., (2017) – information about deleterious alleles by the use of genome editing
tools can improve prediction accuracies for grain yield and plant height in maize by using
GS.
• Ramu et al. (2017) – potential in combining GS and GE for improving cassava.
• Bernardo (2017) – used CRISPR technology to induce targeted recombination breakpoints.
Here, genomic prediction can be used to predict marker effects, which are used to target
optimal recombination points.
34

Case studies
35

Case study: 1
• Beyene et al., 2015 studied the Genetic Gains in Grain Yield Through Genomic
Selection in Eight Bi-parental Maize Populations under Drought Stress.
• The objective of this study was to estimate genetic gains in grain yield in eight bi-
parental maize populations evaluated in four managed drought stress environment.
• 5 populations from Water Efficient Maize for Africa (WEMA) projects and 3
populations from Drought Tolerant Maize for Africa (DTMA) were used.
36

Schematic illustration of the various steps followed in the present study
37

The average gain per selection cycle
38

Comparison of the overall genetic gain in grain yield across the eight populations, the three Drought
Tolerant Maize for Africa (DTMA) and five Water Efficient Maize for Africa (WEMA) populations
evaluated in four managed drought-stress environments.
39

GS tend to increase grain yield
40

Conclusion
• Higher gains in grain yield under drought stress were achieved using genomic
selection compared to conventional pedigree selection.
• GS offered the advantage of significant time-savings over conventional breeding
methods, since up to three cycles of GS could be conducted within a year.
41

Case study: 2
• Zhang et al., 2017 studied the Rapid Cycling Genomic Selection in a Multiparental
Tropical Maize Population
• The objective of this study was to
1. To report the realized genetic gains of four cycles (C1, C2, C3, and C4),
plus the original training population (C0) in multi-environmental field trials
of RCGS-assisted breeding evaluated together with four benchmark checks
in two Mexican environments (locations),
2. To investigate the genetic diversity of the families within each RCGS
selection cycle to assess the level of genetic diversity after three cycles of
rapid cycling GS.
42

Breeding schemes used in the formation of multiparent population
43

Heritability and prediction accuracy of grain yield in the training
population.
• The combined grain yield heritability in both the location was 0.34 and the grain
yield heritability at individual location viz., Agua Fria and Tlaltizapan were 0.48 and
0.19 respectively.
• Low to intermediate grain yield heritability was observed.
44

Mean of GY (ton ha-1 ) for each genomic cycle C0, C1, C2, C3, and C4, broad-sense
heritability (H2), and mean of the four testers at each location (Agua Fria and
Tlaltizapan), and combined across the two locations
45

Means of entry and checks for traits anthesis days (AD, days), silking days (SD,
days), plant height (PH, centimeter), ear height (EH, centimeter), and moisture
content (MOI, %) in each cycle across the two locations
46

Distribution of parents,
cycle C0 entries (A) and
the selected parents, and
cycle C1 entries (B) and
the selected parents based
on rapid
cycling genomic selection-
assisted recombination.
47

Bar-plot of the Shannon
Diversity Index (blue) and
heterozygosity (brown and light
pink) for the original parents,
individuals from cycles C0–C3
and the selected entries in light
blue and light pink.
48

Distribution of parents,
cycle C2 entries (A), cycle
C3 entries (B) and the
selected parents based on
rapid cycling genomic
selection assisted
recombination.
49

Response to selection for grain
yield from the families of (A)
rapid cycling recombination
genomic selection for cycles C0,
C1, C2, C3, and C4 and of (B)
rapid cycling recombination
genomic selection for cycles C1,
C2, C3, and C4. Mean of the
checks (red), and mean of the
entries (blue).
50

Conclusion
• Realized grain yield from C1 to C4 reached 0.225 tons/ha per cycle which was
equivalents to 0.100 ton ha-1yr-1 over 4.5 year of breeding period from the initial
cross the the last cycle.
• Comparing 18 parents used from C0, genetic diversity narrowed only slightly during
the last GS cycle.
• So from the study we can conclude that RCGS can be used as an effective breeding
strategy for simultaneously conserving genetic diversity and achieving high genetic
gains in a short period of time.
51

Case study: 3
• Rutkoski et al., 2015 studied the Genetic Gain from Phenotypic and Genomic
Selection for Quantitative Resistance to Stem Rust of Wheat
1. Compare realized genetic gain per unit time from GS based on markers only
with that of PS for QSRR in spring wheat,
2. Determine if realized gains are consistent with expected gains based on
selection theory, and
3. Compare GS with PS in terms of impact on inbreeding and genetic variance
and correlated response for pseudo-black chaff (PBC).
52

53

Mean stem rust severity, mean pseudo-black chaff (PBC), mean level of inbreeding, and genetic variance for each
population and for each selection method.
54

• Over the years both due to PS and GS
stem rust severity decreased and it was
on par with the expected gain.
• However, PBC increased both due to
PS and GS.
• This showed that both the traits are
highly linked.
55

• Inbreeding levels between PS and GS
were not significantly different per
cycle or per unit time.
• Two selection cycles were completed
for GS, leading to a significant
reduction in genetic variance on a per
unit time basis compared with PS.
• This shows that though GS and PS can
lead to equal rates of short term gains,
GS can reduce genetic variance more
rapidly.
56

Conclusion
• Study showed that on a per unit time basis and using equal selection intensities, GS
could perform as well as PS for improving Quantitative Stem Rust Resistance
(QSRR) in Wheat.
• GS resulted in significantly less genetic variance.
• GS scheme used in this experiment may be considered less favourable than PS from
practical stand point as it was more costly and did not lead to increase in the genetic
gain.
57

Case study: 4
• Das et al., 2020 studied the Genetic Gains with rapid-cycle genomic selection for
combined drought and waterlogging tolerance in tropical maize (Zea mays L.)
1. To assess the realized genetic gains under different moisture regimes after
two cycles of rapid cycling of multi-parent population using GS.
2. To investigates the changes in genetic diversity of the populations subjected
to two cycles of RC-GS.
58

Inbred lines involved in the constitution of two heterotic multi-parent, yellow synthetic(MYS) populations
59

Steps in the breeding scheme used for the constitution of base populations and their advancement using
rapid-cycle genomic selection (RC-GS)
60

Phenotyping locations in different stress-prone ago-ecologies
61

Across-location grain yield (t ha−1) of S2 family test crosses under optimal moisture (GY-
Opt), managed drought (GY-DT) and waterlogging stress (GY-WL), and performance of
selected families relative to trial means in each environment
62

Regression of mean grain yield of
selection cycles over number of
selection cycles (C1, C2 and C3)
(indicating genetic gains under (a)
drought stress, (b) waterlogging stress
and (c) optimal moisture conditions
with genomic selection in two
multiparent populations (MYS-1 and
MYS-2). Open dots indicate the
performance of check hybrids evaluated
along with selection cycles under
different moisture regimes
63

Changes in population structure with selections in MYS-1 (Pa = parents, C1, C2 and C3 = cycle-1, 2 and 3,
respectively). Pairwise Phi statistics: Pa-C1: 0, Pa-C2: 0, Pa-C3: 0.019, C1-C2: 0.009, C1-C3: 0.034, and C2-C3:
0.019
64

Changes in population structure with selections in MYS-2. (Pa = parents, C1, C2 and C3 = cycle-1, 2 and 3,
respectively). Pairwise Phi statistics: Pa-C1: 0, Pa-C2: 0.003, Pa-C3: 0.034, C1-C2: 0.011, C1-C3: 0.043, and C2-
C3: 0.017
65

Expected and observed heterozygosity
and Shannon’s Diversity Index in
parents, C1, C2 and C3 of the two
multi-parent yellow synthetics (MYS)
populations
66

Conclusion
• The realized genetic gain under drought stress was 0.110 t ha−1 yr−1 and 0.135 t ha−1
yr−1, respectively, for MYS-1 and MYS-2.
• The gain was less under waterlogging stress, where MYS-1 showed 0.038 t ha−1 yr−1
and MYS-2 reached 0.113 t ha−1 yr−1.
• Genomic selection for drought and waterlogging tolerance resulted in no yield
penalty under optimal moisture conditions.
• The genetic diversity of the two populations did not change significantly after two
cycles of GS, suggesting that RC-GS can be an effective breeding strategy to achieve
high genetic gains without losing genetic diversity.
67

Case study: 5
• Cui et al., 2020 studied the Hybrid breeding of rice via genomic selection.
• How to select parents?
• Only small proportion of crosses can be evaluated in the field and hence many
potential superior crosses mat not have opportunity to be tested.
• MARS has shown weakness of a long selection cycle and inability to capture effects
of minor genes.
• To identify parents for hybrid breeding genomic selection was implemented.
68

1495 hybrid rice (100k SNP+10 Traits)
Develop statistical model
BLUP method
44,636 hybrids from 27 maintainer lines cross
with 1720 japonica and 839 indica (100 SNP)
Select the top 200 and
bottom 200 hybrids
Further validate in field
100 hybrid rice from a half diallel cross of 21
varieties (100k SNP+6 Traits)
Genotype of 44,636 hybrids
were inferred from their
parents.
Training population
(Pop 1)
Predicted population
(Pop 3)
Validation population
(Pop2)
predict
predict
Multi-trait selection index
69

Plots of predicted phenotypes against observed phenotypes
70

Conclusion
• Increasing the rate of crop genetic improvement is essential for future food security.
• Novel technologies are required to boost genetic gain.
• Although implementation of GS in past two decades have shown immense opportunity for
accelerating crop improvement, the lacuna lies on its dependency on species and breeding
programme and requires continued research.
• Improvements to genotyping and phenotyping technologies will increase the adoption of
GS in plant breeding.
• Extension of GS based approach to tackle problems associated with GEI and other sources
of GEI.
• GS can be used to enhance the utilization of elite and exotic source of genetic diversity.
• To reduce the gap between current production trend and projected future demand strategies
combining GS, gene editing, high throughput phenotyping, rapid generation advancement
and technologies from other disciplines are crucial.
71

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Accelerating crop genetic gains with genomic selection

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