The relevance of epistasis in plant breeding by Abozar Rowshan

2. GP 591 (1+0)
03-03-2021
15:00-16:00
Relevance of Epistasis
in Plant Breeding
Abozar Rowshan
A-2019-30-035
Department of Genetics and Plant Breeding
SeminarIn-charge:
Dr.RKMittal
Dr.VKSood

3. Contents
1. INTRODUCTION
Historical perspectives,
Duality of meaning
2. EPISTASIS IN MENDELIAN GENETICS
Types, Molecular basis, Gene networks
3. EPISTASIS IN QUANTITATIVE
GENETICS
Effect on statistical parameters,
Role in evolution, Biometrical techniques,
QTL evidence
4. IMPLICATIONS IN PLANT BREEDING
Heterosis and inbreeding depression, Conversion to
additive variance, Selection strategy

5. • First observed by Bateson and Punnett (1905) in
comb type in chicken.
• Bateson coined the term “epistasis” for such gene
interactions which distort simple mendelian ratios.
• Later observed by Punnet in sweet peas.
• Weinberg (1910) noted the possibility of such
interactions calling them komplizierter polyhybridismus
or complicated polyhybridisms.
Historical Perspectives
Phillips, P.C., 1998, Genet. 149: 1167-1171.

6. Cont…
 Fisher (1918) brought mendelian and biometrical genetics
together and coined the term “epistacy” for all non additive
interactions. Later the “epistasis” replaced this.
 Fisher partitioned total genetic variance into additive,
dominance and epistatic
 Sewall Wright (1931-35) championed the importance of
gene interactions in evolutionary change.
 Cockerham and Kempthorne (1954) extended Fisher’s
partitioning by defining components of digenic epistasis –
AxA, AxD, DxD.
Phillips, P.C., 1998, Genet. 149: 1167-1171.

7. Duality of Meaning
MENDELIAN
GENETICS
QUANTITATIVE
GENETICS
 Describes a masking effect
whereby a variant or allele at
one locus prevents the
variant at another locus from
manifesting its effect.
 Any statistical deviation from
additive combination of two loci
in their effects on phenotype
Phillips, P.C., 1998, Genet. 149: 1167-1171.

9. Types
Interaction Type A_B_ A_bb aaB_ aabb
Classical Ratio 9 3 3 1
Dominant Epistasis 12 3 1
Recessive Epistasis 9 3 4
Duplicate genes with cumulative
effect
9 6 1
Duplicate dominant genes 15 1
Duplicate recessive genes 9 7
Dominance and recessive
interaction
13 3

10. Functional Classification
• Genetic suppression: - the double mutant has a less severe phenotype than either
single mutant.
• Genetic enhancement :- the double mutant has a more severe phenotype than one
predicted by the additive effects of the single mutants.
• Synthetic lethality or unlinked non-complementation :- two mutations fail to
complement and yet do not map to the same locus.
• Intragenic complementation or interallelic complementation :- two mutations map
to the same locus, yet the two alleles complement in the heteroallelic diploid.
Phillips, P.C., 1998, Genet. 149: 1167-1171.

11. Molecular Interactions
• Interactions can be at the level of enzymes encoded by the genes,
transcription or at any level between gene and phenotype.
Recessive Epistasis
Duplicate dominant Epistasis
Dominance and Recessive Epistasis
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

12. Gene Networks
• Gene regulatory networks - Set of genes which interact with each
other to regulate the transcription of each other.
• Interactions in metabolic pathways – enzymes competing for a
common substrate.
• Signaling cascades in host – pathogen interaction, developmental
regulation pathways etc.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

14. Additive and Non Additive Gene Action
• There can be additivity within and also between loci.
Additivity within loci : lack of dominance
Additivity between loci : lack of epistasis
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

15. Quantitative Genetic Models
 Such models incorporate gene action
effects of each locus plus the effects of
interaction between loci affecting a trait.
 They can help to define statistical
parameters that can be estimated from
phenotypic data.
 Explicitly defining components of a model
allows us to understand the relationship
between gene action effects and statistical
genetic parameters.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

16. Gene action parameters in a linear model
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

17. Gene action and Statistical Genetic Parameters
 Genetic component of variance are functions of the statistical effects of alleles and
allelic interactions and not the same as gene action of the same.
Even with the same
underlying gene action,
different populations can
have different statistical
genetic parameters

18. Cont…
 Additive genetic variance is a function of not only additive gene action (a)
but also dominance gene actions (d) even in absence of epistasis.
 Epistatic gene action effects influences the average effects of alleles and
dominance deviations and consequently additive and dominance genetic
variances.

19. Cont….

20. Cont….

21. Biometrical Evidence
Biometrical methods that use mean comparisons rather than
variance component estimation (GMA and TTC design)
regularly indicated epistatic effects.
Epistasis is detected experimentally more commonly in
autogamous than allogamous species.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

22. Biometrical Techniques for detection
 Most experimental designs assume epistatic interactions to be absent
even though a valid test to show this is not provided.
 TTC, GMA, Triallel, NCD etc can detect epistasis.
 GMA – utilizes different scales to detect epistasis.
 TTC – utilizes the contrast L1 + L2 – 2L3 for detecting epistasis.
Additive and dominance gene effects sum to zero in this contrast leaving
only epistatic gene effects.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

23. Role in Evolution
 Important in structure and evolution of complex genetic systems.
 Genetic divergence between species.
 Evolution of sexual reproduction.
 In the evolution of regulatory complexity.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

24. Molecular Marker Investigations
 Molecular markers made possible the analysis of genetic variance on a genome wide
basis.
 Initial QTL mapping studies ignored epistasis and failed to detect it.
 Openshaw and Frascaroli (1997) detected numerous epistatic QTLs with effects
similar to QTL main effects in maize.
 Yu et al. (1997) found significant epistasis for yield QTLs in rice and attributed
heterosis in an elite rice hybrid to epistasis.
 Li et al. (1997) also detected major epistatic effects for rice yield QTLs.
 Improved statistical methods for detecting and estimating QTL epistatic effects have
been developed by Kao et al. (1999) and Wang et al. (1999).
 These methods searches for epistatic interactions throughout the genome unlike
other methods which test interactions among significant main effect QTLs.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92. Carlborg, O., and Haley, C.S., 2004, Nat. Rev. Genet., 5: 618-625.

25. How evidence from QTL experiments is different?
 Estimates effects in specific chromosomal regions rather than
testing the average or total gene effects of the entire genome.
 QTL studies estimate gene action effects (a & d) rather than
statistical genetic effects.
 Statistical genetic effects are generally not reliable indicators of
underlying gene action.

27. Bias in Estimates of Genetic Parameters
• Epistatic gene action can strongly affect statistical parameters such as
additive and dominance variances that are estimated in biometric
techniques leading to a bias.
• In turn leads to erogenous estimates of genetic parameters such as
heritability and expected gain under selection
• Hampers the efficiency of plant breeding programmes.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

28. Inbreeding Depression
• ad, da and dd gene action effects can affect inbreeding
depression.

29. Heterosis
 In terms of gene action effects.
 In terms of statistical genetic effects.
 So hybrid cultivars can exploit all forms of epistasis.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

30. Hybrid evaluation
 Epistatic gene complexes play an important role in single cross hybrid
performance.
 3-way and double crosses provides an opportunity for disruption of these
gene complexes.
 In case of cross combinations strongly influenced by epistasis, evaluation
procedure should include testing of such higher order crosses.
Sofi, P.A., Rather, A.G., and Warsi M.Z.K., 2007, J. Plant Breed. and Genet., 1(1):1-11.

31. Recycling of inbred lines
 Recycling of elite inbred lines (crossing the best lines with each other)
done to avoid inbreeding depression.
 Occasionally recycling doesn't yield promising lines.
 This may be due to dissipation of favorable epistatic combinations on
segregation and recombination.
 Back crosses with better parent can help to maintain these favourable
combinations
Sofi, P.A., Rather, A.G., and Warsi M.Z.K., 2007, J. Plant Breed. and Genet., 1(1):1-11.

32. Transformation into Additive Variance
 Epistatic variance can be
transformed into additive
variance after drift.
 Sometimes following a
population bottleneck, the
genetic variance within a
subpopulation may increase.
Holland, J.B., 2001, Plant Breed. Rev. 21: 27-92.

33. Selection Strategy
 Selection is fruitful when gene action is entirely additive.
 If dominance is present selection strategy depends on degree of
dominance.
 Epistasis makes the picture even less clear.
 Mass and family selection techniques can exploit AxA interaction and not
AxD and DxD type.
 Significant epistasis leads to lower response to selection in early
generations with improved results later when substantial homozygosity is
achieved.
Sofi, P.A., Rather, A.G., and Warsi M.Z.K., 2007, J. Plant Breed. and Genet., 1(1):1-11.

36.  The high resolution MAGIC wheat population WM-800, consisting of 910 F4:6
lines derived from intercrossing eight recently released European winter wheat
cultivars. Were used in the study.
 The present study demonstrates that plant height in the MAGIC-WHEAT
population WM-800 is mainly determined by large-effect QTL and di-genic
epistatic interactions. As a proof of concept, the study confirms that WM-800 is
a valuable tool to dissect the genetic architecture of important agronomic traits.

38.  Estimated effectively four types of epistatic components among dual QTLs
on heading date based on eight single segment substitution lines (SSSLs) in
rice. The results confirmed that they carried truly with heading date QTLs.
Eleven pairs of QTLs were with 50.0% of significant epistatic effects, of
which additive-additive, additive-dominance or dominance-additive, and
dominance-dominance interaction components occupied 40.9%, 50.0% and
59.1%, respectively.
 Several characteristics of epistasis on heading date were found that
1) different epistatic components had almost consistent directions;
2) dominance-dominance epistasis was perhaps most important in the four
epistatic components;
3) epistasis was mostly positive, delaying rice heading; and
4) all epistatic components were seasonal sensitive.

40. Association mapping was performed to detect markers associated with (boll
number, boll weight and lint percentage) traits using 651 simple sequence repeats
(SSRs). A mixed linear model including epistasis and environmental interaction
was used to screen the loci associated with these three yield traits by 323
accessions of Gossypium hirsutum L. evaluated in nine different environments.
251 significant loci were detected to be associated with lint yield and its three
components, including 69 loci with individual effects and all involved in epistasis
interactions. These significant loci explain , 62.05% of the phenotypic variance
(ranging from 49.06% , 72.29% for these four traits).
There was one locus and 6 pairs of epistasis for lint yield, 4 loci and 10 epistasis
for boll number, 15 loci and 2 epistasis for boll weight, and 2 loci and 5 epistasis
for lint percentage, respectively.

41. Issues:
 Complex nature
 Unpredictable
 Lack of adequate theoretical understanding
 Procedural inaccuracies

42. Conclusion
 Epistasis is important in both Mendelian and quantitative
genetics.
 It is an integral part of genetic architecture of quantitative
traits and hence has several implications in plant breeding.
 Growing evidence from QTL studies.
 Genetic models to test and estimate it precisely are elusive.
 Need to search for epistasis and optimize its use in crop
improvement rather than attributing it to left over variance.