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Whole Genome Selection ;
Theoretical Consideration

Raghavendra N.R

Ph.D Scholar
Plant Breeding & Genetics
Presentation Overview
1.
2.
3.
4.
5.
6.

Introduction (GS)
Why Genomic Selection ?
Steps involved in GS
Factors contribute...
Method of Selection ; Where we come
from..??
Genetic gain /GA;
Selection was played important role in
Human-plant co evolu...
Method of Selection ; Where we come
from..??
Genetic gain

selection

QTL/gene
Phenotype
Breeder
Genotype
Find markers
Fin...
Traditional Selection
Traits with low heritability
Traits that are expressed late in individual’s life
Traits that can not...
PS
Limitations of MAS
“Picking the low hanging fruit”

The genes with big QTL effects

The major success is only achieved wit...
The term ‘GS’ was first introduced by Haley and Visscher at the
6th World Congress on Genetics Applied to Livestock Produc...
Whole Genome Selection

Genomic Selection; an emerging breeding methodology designed to
exploit high-throughput, inexpensi...
Genome Selection
Trace all segments of the genome with markers
-Capture all QTL = all genetic variance

Predict genomic br...
How to estimate Breeding value?

X

10 litre

What is the Breeding value of this
cow for milk production?

0.5 litre

8 li...
GS
GS : genome-wide panel of dense
markers so that all QTls are in LD
with at least one marker

MAS
MAS concentrates on a ...
Nakaya et al 2012
Why Genomic selection
important to turn on now..??
Relatively slow progress via phenotypic
selection
Large cost of phenoty...
Pre-requisite for the introduction of GS
The need for adequate and affordable genotyping
platforms.
Relatively simple bree...
Cont..

Genomic Information
How can we do that..?
Crops are Concerned

Prerequisite
Training Population (genotypes + phenotypes)
Selection Candidates ...
Training Population
Biparental vs. Multi-Family
Biparental
1.
2.
3.
4.
5.
6.

Population specific
Reduced epistasis
Reduce...
Genomic Selection
Cardinal points for success of GS
1. Population type & size of training population
2. Genotyping Platforms & marker densit...
Marker types & Marker density
SNP

DArT

GBS
SNP chip in Genomic selection
Single markers (gene) predict in very small
differences.
Abundant in nature. 1kb-2SNP.
Predi...
What sequences we can call as haplotypes?

The similar haplotypes will make haplotype block where there will be high LD an...
Is GBS a suitable marker platform for genomic
selection?

Obviously ..!!!
GBS
Elshire et al (2010)

GBS accesses regulatory regions and sequence tag mapping.
Flexibility and low cost.
GBS markers ...
Poland et al (2011)
Statistical model used
i.

RF

ii.

MVN EM

GBS markers are more uniformly distributed across the geno...
GS Prediction Accuracies
Number and size of QTLs.
LD between marker and QTLs.
Marker density, marker type, and training po...
GS Prediction Accuracies
Heritability of the trait.
Genetic structure of the trait.
Simulation study results.
Cross-valida...
Genomic selection prediction models

Meuwissen et al (2001) Prediction of total genetic value using genome-wide dense
mark...
Stepwise Regression (SR)
Select most significant markers on the basis of arbitrary
significant thresholds and non signific...
Ridge Regression BLUP (RR-BLUP)
Simultaneously select all marker effects rather than categorizing
into significant or havi...
Bayesian Regression (BR)
Marker variance treated more realistically by assuming
specified prior distribution.

BayesA: use...
Other potential Genomic selection
prediction models
i.

Least absolute shrinkage and selection operator (LASSO)

ii. Repro...
A genome of 1000 cM was simulated with a marker spacing of 1 cM
Modeling epistasis and dominance
Accurate prediction of dominance and epistatic effects fetch
advantageous.
Lorenza et al ...
GS in relation to strong subpopulation
structure
GWAS studies, SPS potentially cause spurious long distance /
unlinked ass...
Long-term selection
Improving gain in the long-term necessarily requires a trade-off
with short-term gain.

Long-term gain...
Has proved its value in animal breeding particularly dairy cattle
(Hayes and Goddard, 2010)
Still to prove its value over ...
Future Directions..???
GS has been seldom implemented in the field

Where to apply GS in the breeding cycle
(which generat...
Future Directions..???

How many markers are required, determined by the
extent of LD.
How can we implement non additive e...
Outstanding questions that remain
unanswered..??

How much gain do we expect when using GS?
how much potential loss ??
can...
GS future perspectives
Training population design.

Epistatic modelling in GS.
Strength of different statistical methods.
...
Further Interest..??
Visit….
Lorenz Lab
Department of Agronomy & Horticulture
University of Nebraska-Lincoln
http://www.lo...
Ongoing projects on GS
Crop

Trait

Markers

FUNDING
AGENCY

PROJECT
DURATION

Tomato

Quality, shape,
shelf life

SNP

Ba...
Conclusion

“Nothing In Science Has Any Value To
Society If It Is Not Communicated”-Anne
Roe
Whole Genome Selection
Whole Genome Selection
Whole Genome Selection
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Whole Genome Selection

  1. 1. Whole Genome Selection ; Theoretical Consideration Raghavendra N.R Ph.D Scholar Plant Breeding & Genetics
  2. 2. Presentation Overview 1. 2. 3. 4. 5. 6. Introduction (GS) Why Genomic Selection ? Steps involved in GS Factors contribute to success of GS Future directions of GS Conclusion
  3. 3. Method of Selection ; Where we come from..?? Genetic gain /GA; Selection was played important role in Human-plant co evolution ΔG= Accuracy of selection X intensity of selection X genetic standard deviation Generation interval Selection in GS is usually based on Genomic estimated of breeding values. Selections can take place in laboratory
  4. 4. Method of Selection ; Where we come from..?? Genetic gain selection QTL/gene Phenotype Breeder Genotype Find markers Find population
  5. 5. Traditional Selection Traits with low heritability Traits that are expressed late in individual’s life Traits that can not be measured easily (ex: disease resistance & quality traits) Time consuming and the rate of breeding is slow
  6. 6. PS
  7. 7. Limitations of MAS “Picking the low hanging fruit” The genes with big QTL effects The major success is only achieved with the qualitative traits The biparental mapping populations used in most QTL studies do not readily translate to breeding applications
  8. 8. The term ‘GS’ was first introduced by Haley and Visscher at the 6th World Congress on Genetics Applied to Livestock Production at Armidale, Australia in 1998. Dr. Theo Meuwissen GS was first propounded by Meuwissen et al (2001) : Seminal paper ‘Meuwissen et al (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819-29.”
  9. 9. Whole Genome Selection Genomic Selection; an emerging breeding methodology designed to exploit high-throughput, inexpensive DNA marker information to accurately predict the genetic value of breeding candidates for complex traits. EBV; An estimate of the additive genetic merit for a particular trait that an individual will pass on to its descendant's.” GEBVs; Prediction of the genetic merit of an individual based on its genome.
  10. 10. Genome Selection Trace all segments of the genome with markers -Capture all QTL = all genetic variance Predict genomic breeding values as sum of effects over all segments Genomic selection exploits LD. Genomic selection avoids bias in estimation of effects due to multiple testing, as all effects fitted simultaneously.
  11. 11. How to estimate Breeding value? X 10 litre What is the Breeding value of this cow for milk production? 0.5 litre 8 litre 10 litre 12 litre Breeding value =h2(milk production-average) = (12-7.625)*h2 = 4.35 litres
  12. 12. GS GS : genome-wide panel of dense markers so that all QTls are in LD with at least one marker MAS MAS concentrates on a small number of QTLs that are tagged by markers with well verified associations. 120 cms 15cms
  13. 13. Nakaya et al 2012
  14. 14. Why Genomic selection important to turn on now..?? Relatively slow progress via phenotypic selection Large cost of phenotyping Limited throughput (plot area, time, people) QTs + small effects Decreasing cost of genotyping Promising results from simulation and cross validation of GS. Meet the challenge of feeding 9.5 billion @ 2050.
  15. 15. Pre-requisite for the introduction of GS The need for adequate and affordable genotyping platforms. Relatively simple breeding schemes in which selection of additive genetic effects will generate useful results. Statistical methods.
  16. 16. Cont.. Genomic Information
  17. 17. How can we do that..? Crops are Concerned Prerequisite Training Population (genotypes + phenotypes) Selection Candidates (genotypes) Accurate phenotypes Inexpensive, high-density genotypes Heffner et al (2009)
  18. 18. Training Population Biparental vs. Multi-Family Biparental 1. 2. 3. 4. 5. 6. Population specific Reduced epistasis Reduced number of markers required Smaller training populations required Balanced allele frequencies Best for introgression of exotic Multi-Family 1. 2. 3. 4. 5. Allows prediction across a broader range of adapted germplasm Allows sampling of more E Cycle duration is reduced because retraining model is on-going. Allows larger training populations Greater genetic diversity
  19. 19. Genomic Selection
  20. 20. Cardinal points for success of GS 1. Population type & size of training population 2. Genotyping Platforms & marker densities. 3. Availability of HD genome wide markers. 4. Appropriate statistical methods for accurate GEBVs. 5. Epistasis & G x E. 6. Linkage disequilibrium 7. Long term selection
  21. 21. Marker types & Marker density SNP DArT GBS
  22. 22. SNP chip in Genomic selection Single markers (gene) predict in very small differences. Abundant in nature. 1kb-2SNP. Predicting differences in BVs.
  23. 23. What sequences we can call as haplotypes? The similar haplotypes will make haplotype block where there will be high LD and less recombination's.
  24. 24. Is GBS a suitable marker platform for genomic selection? Obviously ..!!!
  25. 25. GBS Elshire et al (2010) GBS accesses regulatory regions and sequence tag mapping. Flexibility and low cost. GBS markers led to higher genomic prediction accuracies. Impute missing data. Highly multiplexed Even for a species with a genome as challenging as wheat (Absence of a reference genome)
  26. 26. Poland et al (2011) Statistical model used i. RF ii. MVN EM GBS markers are more uniformly distributed across the genome than the DArT markers
  27. 27. GS Prediction Accuracies Number and size of QTLs. LD between marker and QTLs. Marker density, marker type, and training population size. Number of lines increases (accuracy GEBVs ↑)
  28. 28. GS Prediction Accuracies Heritability of the trait. Genetic structure of the trait. Simulation study results. Cross-validation; How close is the simulated data to real data?
  29. 29. Genomic selection prediction models Meuwissen et al (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819-29.
  30. 30. Stepwise Regression (SR) Select most significant markers on the basis of arbitrary significant thresholds and non significant markers effect equals to zero. (Lande and Thompson, 1990) Estimate the effect of significant markers using multiple regression Since, only a portion of the genetic variance will be captured. Limitations : Detects only large effects and that cause overestimation of significant effects. (Goddard and Hayes, 2007; Beavis, 1998 ) SR resulted in low GEBVs accuracy due to limited detection of QTLs. (Meuwissen et al 2001)
  31. 31. Ridge Regression BLUP (RR-BLUP) Simultaneously select all marker effects rather than categorizing into significant or having no effect Ridge regression shrinks all marker effects towards zero. The method makes the assumption that markers are random effects with a equal variance. (Meuwissen et al 2001) Limitations : RR-BLUP incorrectly treats all effects equally which is unrealistic. (Xu et al 2003) RR-BLUP Superior to SR
  32. 32. Bayesian Regression (BR) Marker variance treated more realistically by assuming specified prior distribution. BayesA: uses an inverted chi-square to regress the marker variance towards zero. All marker effects are > 0 (Bayes A) BayesB: assume a prior mass at zero, thereby allowing for markers with no effects. Some marker effects can be = 0 (Bayes B) (Meuwissen et al 2001)
  33. 33. Other potential Genomic selection prediction models i. Least absolute shrinkage and selection operator (LASSO) ii. Reproducing Kernel Hilbert spaces and support vector machine regression. (RKHS) Gianola et al (2006) iii. Partial Least Squares regression & principle component regression. iv. RF (R package random forest) v. MVN EM Algorithm R-Package for GS http://www.r-project.org
  34. 34. A genome of 1000 cM was simulated with a marker spacing of 1 cM
  35. 35. Modeling epistasis and dominance Accurate prediction of dominance and epistatic effects fetch advantageous. Lorenza et al pointed out inclusion of epistatic effects in prediction models will give improve accuracy with condition as;  Epistasis is present & can be modelled accurately. Blanc et al (2006) reported that epistasis will contribute to marker effects. Empirical studies harnessing data are illuminating for this topic.
  36. 36. GS in relation to strong subpopulation structure GWAS studies, SPS potentially cause spurious long distance / unlinked association b/w marker allele & phenotype. GS, shifts to being able to maintain predictive ability despite a structure training data set & spurious association will not be an important cause for loss of predictive ability. LD is not consistent, allelic effects estimated in one subpopulation will not be predictive for another subpopulation.
  37. 37. Long-term selection Improving gain in the long-term necessarily requires a trade-off with short-term gain. Long-term gain is often explicit, as in quantitative genetic models that maximize immediate predicted gain subject to a constraint on the rate of inbreeding. Meuwissen (1997). Two approaches: 1. Select individuals or groups 2. Analytical prediction, deterministic simulation using Numerical approaches to optimization, and stochastic simulation
  38. 38. Has proved its value in animal breeding particularly dairy cattle (Hayes and Goddard, 2010) Still to prove its value over generations in crop plants Simulation studies in plants suggest potential for improved gain per unit time. (Jannink et al 2010)
  39. 39. Future Directions..??? GS has been seldom implemented in the field Where to apply GS in the breeding cycle (which generations) How many lines to select for genotyping. Where and how do we place our training population in comparison to the selection candidates?
  40. 40. Future Directions..??? How many markers are required, determined by the extent of LD. How can we implement non additive effects into our models to allow predictions across multiple generations? How do non-additive effects affect the accuracy of genomic selection. How often to re-estimate the chromosome segment effects?
  41. 41. Outstanding questions that remain unanswered..?? How much gain do we expect when using GS? how much potential loss ?? can a breeding program absorb?
  42. 42. GS future perspectives Training population design. Epistatic modelling in GS. Strength of different statistical methods. Managing short & long term gain.
  43. 43. Further Interest..?? Visit…. Lorenz Lab Department of Agronomy & Horticulture University of Nebraska-Lincoln http://www.lorenzlab.net Rex Bernardo Department of Agronomy and Plant Genetics University of Minnesota
  44. 44. Ongoing projects on GS Crop Trait Markers FUNDING AGENCY PROJECT DURATION Tomato Quality, shape, shelf life SNP Barley FHB resistance SNP Univ. of Minnesota 2013 Trifolium Yield SNP Danish plant research and for Aarhus University 2010-2015 Wheat Winter wheat genotype-bysequencing Wheat Breeding Presidential Chair 2014 Maize Drought SNP CIMMYT 2014 Maize Total biomass yield and silage quality SNP USDA-AFRI 2014 Sugar beet White sugar yield, sugar content SNP State Plant Breeding Institute, University of Hohenheim 2013 2009-2013 USDA/AFRI
  45. 45. Conclusion “Nothing In Science Has Any Value To Society If It Is Not Communicated”-Anne Roe

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