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Accelerating crop genetic gains with genomic selection

This document discusses genomic selection (GS), a plant and animal breeding technique that uses genome-wide molecular markers to predict and select for an individual's genetic merit or breeding value. GS can accelerate genetic gain compared to traditional breeding by increasing selection intensity and accuracy. Key points covered include: how GS works, factors affecting its accuracy, challenges like genotype-environment interaction, and examples of its successful application in maize and wheat breeding programs.

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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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Source: Tester and Langridge, 2010
Rapid genetic gains in crop yield is required Genetic gain is determined by the formula Traditional breeders equation 5 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
• 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Difference between GS and MARS in intermated B73 x Mo17 maize recombinant inbreds 10 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Milestones of selective breeding; improved agricultural productivity 12 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
TRN: Training population TST: Testing population 14 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Crossa et al., 2017
Information flow within genomic selection for the example of maize. 15 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
• 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Different prediction models in genomic selection 17 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
• 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
• 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
• 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
A conventional crop breeding programme A modern crop breeding programme 28 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Case studies 35 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Schematic illustration of the various steps followed in the present study 37 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
The average gain per selection cycle 38 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
GS tend to increase grain yield 40 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Breeding schemes used in the formation of multiparent population 43 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Distribution of parents, cycle C2 entries (A), cycle C3 entries (B) and the selected parents based on rapid cycling genomic selection assisted recombination. 49 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Case study: 3 • Rutkoski et al., 2015 studied the Genetic Gain from Phenotypic and Genomic Selection for Quantitative Resistance to Stem Rust of Wheat • The objective of this study was to 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
53 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
• 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
• 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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.) • The objective of this study was to 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Inbred lines involved in the constitution of two heterotic multi-parent, yellow synthetic(MYS) populations 59 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Steps in the breeding scheme used for the constitution of base populations and their advancement using rapid-cycle genomic selection (RC-GS) 60 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Phenotyping locations in different stress-prone ago-ecologies 61 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
Plots of predicted phenotypes against observed phenotypes 70 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
72 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)

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Accelerating crop genetic gains with genomic selection

  • 1.
    Presented by VIOLINA BHARALI Ph.D1st 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)
  • 2.
    Accelerating crop geneticgains with genomic selection 2 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 3.
    Genetic improvement ofcrop 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 4.
    Current population 6.7 billion 9billion 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Source: Tester and Langridge, 2010
  • 5.
    Rapid genetic gains incrop yield is required Genetic gain is determined by the formula Traditional breeders equation 5 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 6.
    Genomic selection Association mapping Markerassisted selection Reshaping of breeding program by molecular marker technology Plant breeding programmes take up two decades until a new variety is released 6 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 7.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 8.
    • Started in2000s- 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 9.
    Genomic selection • Genomicselection 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 10.
    Difference between GS andMARS in intermated B73 x Mo17 maize recombinant inbreds 10 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Source: Massman et al., 2013
  • 11.
    • Genomic selectionis 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 12.
    Milestones of selectivebreeding; improved agricultural productivity 12 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Source: Jones and Koning, 2013
  • 13.
    Training population 1. Genotyping fora 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 14.
    TRN: Training population TST:Testing population 14 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Crossa et al., 2017
  • 15.
    Information flow within genomicselection for the example of maize. 15 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 16.
    • Determining theGenomic 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 17.
    Different prediction modelsin genomic selection 17 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 18.
    Accuracy of theGenomic 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 19.
    • The accuracyof 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Source: Fels et al., 2019
  • 20.
    Characteristics of trainingpopulation • 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 21.
    How genetic gainis 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 22.
    vgggg GS: a potentialbreeding 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 23.
    Advantage of GSover 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 24.
    • GS hasbeen 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 25.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 26.
    • Sequencing –based-genotypingapproaches 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 27.
    Phenotyping for genomicselection • 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 28.
    A conventional crop breeding programme Amodern crop breeding programme 28 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0) Fels et al., 2019
  • 29.
    Challenges to thesuccess 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 30.
    2. Non additivegenetic 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 31.
    Application of genomicselection 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 32.
    2. Increasing gainthrough 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 33.
    3. Leveraging GSto 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 34.
    4. Combining genomicselection 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 35.
    Case studies 35 Genetics andPlant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 36.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 37.
    Schematic illustration ofthe various steps followed in the present study 37 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 38.
    The average gainper selection cycle 38 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 39.
    Comparison of theoverall 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 40.
    GS tend toincrease grain yield 40 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 41.
    Conclusion • Higher gainsin 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 42.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 43.
    Breeding schemes usedin the formation of multiparent population 43 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 44.
    Heritability and predictionaccuracy 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 45.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 46.
    Means of entryand 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 47.
    Distribution of parents, cycleC0 entries (A) and the selected parents, and cycle C1 entries (B) and the selected parents based on rapid cycling genomic selection- assisted recombination. 47 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 48.
    Bar-plot of theShannon 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 49.
    Distribution of parents, cycleC2 entries (A), cycle C3 entries (B) and the selected parents based on rapid cycling genomic selection assisted recombination. 49 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 50.
    Response to selectionfor 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 51.
    Conclusion • Realized grainyield 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 52.
    Case study: 3 •Rutkoski et al., 2015 studied the Genetic Gain from Phenotypic and Genomic Selection for Quantitative Resistance to Stem Rust of Wheat • The objective of this study was to 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 53.
    53 Genetics and PlantBreeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 54.
    Mean stem rustseverity, mean pseudo-black chaff (PBC), mean level of inbreeding, and genetic variance for each population and for each selection method. 54 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 55.
    • Over theyears 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 56.
    • Inbreeding levelsbetween 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 57.
    Conclusion • Study showedthat 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 58.
    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.) • The objective of this study was to 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 59.
    Inbred lines involvedin the constitution of two heterotic multi-parent, yellow synthetic(MYS) populations 59 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 60.
    Steps in thebreeding scheme used for the constitution of base populations and their advancement using rapid-cycle genomic selection (RC-GS) 60 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 61.
    Phenotyping locations indifferent stress-prone ago-ecologies 61 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 62.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 63.
    Regression of meangrain 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 64.
    Changes in populationstructure 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 65.
    Changes in populationstructure 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 66.
    Expected and observedheterozygosity and Shannon’s Diversity Index in parents, C1, C2 and C3 of the two multi-parent yellow synthetics (MYS) populations 66 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 67.
    Conclusion • The realizedgenetic 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 68.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 69.
    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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 70.
    Plots of predictedphenotypes against observed phenotypes 70 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 71.
    Conclusion • Increasing therate 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 Genetics and Plant Breeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)
  • 72.
    72 Genetics and PlantBreeding Department, Agricultural College, Bapatla DOCTORAL SEMINAR-I – GP 691 1(1+0)