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QTL Mapping

QTL Mapping. R. M. Sundaram. 2. 0. 1. 5. 1. 0. 5. 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. QTL (Quantitative Trait Loci)

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QTL Mapping

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  1. QTL Mapping R. M. Sundaram

  2. 2 0 1 5 1 0 5 0 1 2 3 4 5 6 7 8 9 • QTL (Quantitative Trait Loci) • Polygenic inheritance, also known as quantitative or multifactorial inheritance refers to inheritance of a phenotypic characteristic (trait) that is attributable to two or more genes and their interaction with the environment • Unlike monogenic traits, polygenic traits do not follow patterns of Mendelian inheritance (qualitative traits). • Instead, their phenotypes typically vary along a continuous gradient depicted by a bell curve

  3. QTL individually follow Mendelian rules • Many genes control any given trait • Individual gene effects are small • The genes involved can be dominant, or co-dominant with respect allelic interaction at a single QTL • In addition the different QTLs can interact with each other (additive or epistatic interactions)

  4. What does QTL mean for trait/phenotype ? Salt tolerance For eg., A salt tolerant genotype may have many QTLs controlling salt tolerance like 1 • Survival of seedlings (20%) • Na concentration (root and shoot) (15%) • K concentration (root and shoot) (25%) • Na uptake (10%) • K uptake (6%) • Na and K ratio (4%) • Dry mass (20%) 2 3 4 5 6 7

  5. Individual component QTLs: Spread across the genome IR4630 / IR15324 (2001) 2 4 1 5 3 7 6

  6. Combinations of QTL for salt tolerance High tolerance Score Low tolerance Score • Survival of seedlings (20%) • Na concentration (root and shoot) (15%) • K concentration (root and shoot) (25%) • Na uptake (10%) • K uptake (6%) • Na and K ratio (4%) • Dry mass (20%)

  7. How to identify these QTL?

  8. Steps in QTL mapping • Selection of target trait • Identification of parents differing in the trait of interest, development of appropriate mapping population and parental polymorphism survey using markers • Screening the population for the target trait (Phenotyping) • Genotyping of the mapping population and development of linkage maps • Identification of major QTLs controlling the trait • Validation of the major QTLs (with > 15-20% influence on trait phenotype) across environments/populations • Utilization of the major QTLs in breeding programs

  9. Local Linkage maps

  10. Mapping analysis Based on recombination frequency i.e. regression between marker genotype and trait phenotype Single point analysis – uses one marker at a time Flanking marker analysis – uses a pair of markers simultaneously Composite multipoint mapping – uses multiple markers simultaneously

  11. Single point analysis – uses one marker at a time Simple T-test analysis ANOVA Linear regression Multiple regression

  12. Flanking marker analysis – uses a pair of markers simultaneously Maximum likelihood Maximum likelihood estimation through regression In each method of estimation, a likelihood profile of the region between two flanking markers is produced. The log of each likelihood is then mapped against chromosome position to produce a likelihood map,

  13. Composite multipoint mapping – uses multiple markers simultaneously Marker regression Composite interval mapping Multiple interval mapping Standard marker-trait regressions considered for specific marker intervals

  14. Gene/QTL mapping software Mapmaker/QTL Map Manager QTL cartographer Qgene PlabQTL R QTL Joinmap • MAP MANAGER QTX • Windows based • Simpler

  15. Detection of QTL using Mapmanager QTX • Detection of a QTL depends on a statistical test • To detect QTLs, trait values are tested for statistical association with genotypes of marker loci in the progeny of a cross • QTX fits a regression equation for the effect of a hypothetical QTL at the position of each marker locus and at regular intervals between the marker loci

  16. Detection of QTL using Mapmanager QTX Regression Free model  Additive Recessive Interval mapping QTL presence and estimates position in a map Simple interval mapping – a single QTL without effects Composite interval mapping – a single QTL with effects on other QTL

  17. Detection of QTL using Mapmanager QTX • Likelihood Ratio Statistic (LRS) is a measure of the significance of a possible QTL like LOD values calculated by other QTL mapping software. • LOD score compares the likelihood of obtaining the test data if the two loci are indeed linked, to the likelihood of observing the same data purely by chance • Positive LOD/LRS scores favor the presence of linkage, whereas negative LOD scores indicate that linkage is less likely • By convention, a LOD score greater than 3.0 is considered evidence for linkage • A LOD score of +3 indicates 1000 to 1 odds that the linkage being observed did not occur by chance Likelihood ratio statistic (LRS) and LOD LRS/4.6 = LOD 73.9/4.6 = 16.1

  18. Detection of QTL using Mapmanager QTX (Additive/Dominant allelic interaction) • Additive genetic effects consist of the effects of the two alleles located at a single QTL combined in such a way that the sum of their effects in unison is equal to the sum of their effects individually. • Such phenomena are only possible when the alleles involved do not interact with one another in such a way that would modify, hinder, or amplify the effects of any one gene involved. • If the average trait value of a heterozygote is midway between the average trait values of the homozygotes, the QTL alleles are additive • If the heterozygotic value is the same as one of the homozygotic values, one allele is recessive and the other is dominant (Dominant interaction)

  19. Detection of QTL using Mapmanager QTX (Interaction between different QTLs- Epistasis) • Like Mendelian genes, QTLs do interact with each other and such interactions can be classified as - Additive QTL interaction - Epistatic QTL interaction (masking effect of one QTL on another) • Additive QTLs are always better than epistatic QTLs since additive QTLs can easily be combined together

  20. Detection of QTL using Mapmanager QTX (Mapping population sizes and marker spacing) • Mapping population size of 250 is enough for unravelling a major QTL; • But several hundreds are needed for discovering a minor QTL • Ideal spacing of markers for better QTL detection – 20 cM apart across the genome • Boot strap analysis

  21. Detection of QTL using Mapmanager QTX (Mapping population sizes and marker spacing) The p-value that QTX calculates for QTL mapping is the probability of a ‘false positive’ Recommended p-value = < 0.001 For F2 – minimum LRS is 20 (LOD 4.3) For BC – minimum LRS is 15 (LOD 3.3) Interactions between/among the QTLs are also measured with a model that measures the main effects of each locus and interactions involving both loci -Morgan = complete interference -Haldane = no interference -Kosambi = intermediate interference (Ideal)

  22. Stat (LRS/LOD statistic) The likelihood ratio statistic (LRS) for the association of the trait with this locus (LRS/4.6 = LOD) % (Percentage of trait variance explained by a particular QTL) The amount of the total trait variance which would be explained by a QTL at this locus, as a percent. For simple regression (no background loci), this is the difference between the total trait variance and the residual variance, expressed as a percent of the total variance. P The probability of an association this strong happening by chance. CI (Confidence interval) An estimate of the size of a 95% confidence interval for a QTL of this strength, using the estimate of Darvasi and Soller (1997. Add The additive regression coefficient for the association. Dom (intercross only) The dominance regression coefficient for the association. Marker Regression Window of Mapmanager QTX

  23. Thank you

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