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PRESENTED BY,
SANDESH,G.M
2016610811
TNAU. MADURAI
 Generally single gene trait
 No environmental influence
 Presence or absence
• Growth habit: Tall vs. Dwarf
• Pigmenta...
 Generally more than one gene.
 Environmental effects.
 Quantity:
• Tillers
• Yield
Phenotype = Genotype + Environment ...
 Correlate segregation of the quantitative trait with that of
qualitative trait, i.e., markers
QTL = Quantitative Trait L...
 Locus, meaning region of the genome –
not necessarily a single gene, could be several linked genes.
 QTL is a region of...
 A variety may have some QTL that increase a trait (for example,
increase yield) and others that decrease the trait. Thes...
The key is identifying the “good” QTL – Those that affect the trait
in the direction you want, and then separating those f...
The process of constructing linkage maps and conducting QTL analysis–to
identify genomic regions associated with traits–is...
 Identify regions of the genome containing QTLs.
 Estimate the effects of the QTLs on the quantitative trait:
1.how much...
 Availability of a good linkage map (this can be done at the same time
the QTL mapping).
 A segregating population deriv...
Backcrosses
F2 intercrosses
Recombinant inbred (RI) lines
Double Haploids
• Co-segregation of QTL alleles and linked
marker alleles
Unobserved QTL alleles
q m
Q M
Observed marker alleles
pair of
c...
1. Select parents that differ for a trait.
2. Screen the two parents for polymorphic marker loci.
3. Generate recombinant ...
Population Features Example Species
Inbred lines
Backcross (BC) Simplest design; powerful if
dominance in ‘right’ directio...
 In large sample size, QTL with small effects can not be observed but
QTL with large effects can be observed.
 In small ...
 Number of markers used - estimation of both QTL position and effect.
 co-dominant marker shows three types of genetic d...
 QTL Data is typically pooled over locations and replications to obtain
a single quantitative trait for the line.
 It is...
 DNA markers can be used to map useful genes using recombination
frequencies of linked genes:
A
a
M
m
QTL Marker
• Marker...
1. Select parents that differ for a trait.
2. Screen the two parents for polymorphic marker loci.
3. Generate recombinant ...
 This method cannot determine whether the
markers are associated with one or more
QTLs.
 Chance of QTL detection decreas...
 Lander & Botstein (1989).
• Analyzes intervals between adjacent markers instead of single
markers.
• Statistically more ...
• Marker interval = the segment between 2 markers
• Interval mapping methods use information on values of 2 flanking
marke...
• LOD of 2 means that it is 100x more likely that a QTL exists in the interval
than that there is no QTL.
• LOD of 3 means...
 Analyzes intervals between adjacent markers+ additional markers
unlinked to the interval markers to focus on the interva...
 CIM evaluates the possibility of a target QTL at multiple analysis points across each
inter-marker interval (same as SIM...
 It is used to minimize effects of various linked QTLs.
 It is based on one QTL and other markers used as covariates.
 ...
 Mapping of multiple QTLs
 Increasing the precision of QTL mapping.
 By eliminating as much as the genetic variance pro...
 Intense computation.
 Rely on a genetic map with good quality.
 Difficult to incorporate covariate.
 Recent method of QTL Mapping.
 Multiple Interval Mapping (MIM) is the extension of interval
mapping to multiple QTLs, j...
 The introgression of QTLs into elite lines / germplasm
 Maker-aided selection (MAS) for QTLs in crop improvement has to...
 Number of genes controlling the target traits and their position.
 Heteritability of the genes segregating in a mapping...
The high quality phenotypic data is very important and useful for meaningful
genetic dissection and genomics-assisted bree...
TISSUE CULTURE
A) Isolation, selection and handling of mature somatic embryos
• Development of visualisation and handling ...
B) Identification and transfer of germinated plants to soil
• Development of visualisation technology for identification o...
‘‘Motoman takes a novel approach to
increasing the productivity of automated
assays. (This includes) a compact work
cell e...
A robot system for phenotyping large tomato plants in the
greenhouse using a 3D light field camera
Angaji S.A., QTL Mapping: A Few Key points. International Journal of Applied
Research in Natural Products, 2(2), 1-3 (2009...
THANK YOU..,
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QTL

Quantitative trait loci analysis in plant breeding

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QTL

  1. 1. PRESENTED BY, SANDESH,G.M 2016610811 TNAU. MADURAI
  2. 2.  Generally single gene trait  No environmental influence  Presence or absence • Growth habit: Tall vs. Dwarf • Pigmentation: Pigmented vs. Non-pigmented • Disease reaction: Resistant vs. susceptible
  3. 3.  Generally more than one gene.  Environmental effects.  Quantity: • Tillers • Yield Phenotype = Genotype + Environment + Management
  4. 4.  Correlate segregation of the quantitative trait with that of qualitative trait, i.e., markers QTL = Quantitative Trait Locus = GENE
  5. 5.  Locus, meaning region of the genome – not necessarily a single gene, could be several linked genes.  QTL is a region of the genome that contain gene(s)associated with a quantitative trait.  Allelic variation at a QTL region causes phenotypic variation in a quantitative trait.  It is coined by Gelderman .
  6. 6.  A variety may have some QTL that increase a trait (for example, increase yield) and others that decrease the trait. These work together to create the phenotype of the plant. In this example genome have 3 loci, one associated with decreased yield, and one associated with higher yield. The phenotype, depending on the size of the effect of each QTL and how they work together, may be low yield.
  7. 7. The key is identifying the “good” QTL – Those that affect the trait in the direction you want, and then separating those from the negative ones. This is where QTL identification techniques are important. e.g. Positive QTL: Grain Yield, Disease resistance, Oil content, Protein or Mineral linked. Negative QTL: Plant Height, Environment effected traits. Note that these techniques are simply statistical correlations, just like genetic mapping and any marker-trait correlations; however, because we are looking for many markers that correlate with a single trait, it is somewhat more complex statistically.
  8. 8. The process of constructing linkage maps and conducting QTL analysis–to identify genomic regions associated with traits–is known as QTL mapping (McCouch & Doerge, 1995). A QTL map is the correlation of genotypic data of individuals from mapping population with phenotypic information Phenotype data + linkage map = QTLs
  9. 9.  Identify regions of the genome containing QTLs.  Estimate the effects of the QTLs on the quantitative trait: 1.how much of the variation for the trait is caused by a specific region? 2.what is the gene action associated with the QTL – additive effect? 3.Dominant effect? 4.which allele is associated with the favourable effect?
  10. 10.  Availability of a good linkage map (this can be done at the same time the QTL mapping).  A segregating population derived from parents that differ for the trait(s) of interest, and which allow for replication of each segregant, so that phenotype can be measured with precision (such as RILs or DHs).  A good assay for the trait(s) of interest.  Software available for analyses.
  11. 11. Backcrosses F2 intercrosses Recombinant inbred (RI) lines Double Haploids
  12. 12. • Co-segregation of QTL alleles and linked marker alleles Unobserved QTL alleles q m Q M Observed marker alleles pair of chromoso mes
  13. 13. 1. Select parents that differ for a trait. 2. Screen the two parents for polymorphic marker loci. 3. Generate recombinant inbred lines (can use F2-derived lines). 4. Phenotype (screen in field). 5. Contrast the mean of the MM and mm lines at every marker locus. 6. Declare QTL where (MM-mm) is greatest
  14. 14. Population Features Example Species Inbred lines Backcross (BC) Simplest design; powerful if dominance in ‘right’ direction mice, plants F2 Estimation of additive and dominance effects; more powerful than BC for additive effects mice, rats Advanced intercross line (AIL) As for F2 but with increased resolution of map location mice Recombinant inbred lines (RIL) F1 followed by inbreeding; homozygous comparisons only; powerful for additive effects; less environmental noise mice, plants Congenic lines (= Nearly isogenic lines) Backcrossing followed by inbreeding; homozygous comparisons only after inbreeding. Lines contain ~1% of donor genome mice, rats, plants Double haploid lines (DHL) Instant homozygosity through doubling of F1 gametes; homozygous comparisons only; powerful for additive effects and QTLxE interactions plants F2:3 Inbred progeny of F2; increased precision through progeny means plants Structured outbred populations BC / F2 / AIL As for inbred lines; mapping variation between lines livestock, outbreeding trees/plants Large fullsib families Estimating contrasts between parental alleles. Allows for dominance estimation. trees, fish, poultry Halfsib families Estimating contrasts between common parent alleles cattle, pigs, poultry, trees Nuclear families, including sibpairs Detection of variance explained by markers humans, livestock Unstructured outbred populations Complex pedigrees Detection of variance explained by markers humans, livestock
  15. 15.  In large sample size, QTL with small effects can not be observed but QTL with large effects can be observed.  In small sample size also, QTL with small effects can not be observed but QTL with major effects can be observed.
  16. 16.  Number of markers used - estimation of both QTL position and effect.  co-dominant marker shows three types of genetic difference while dominant marker shows two types of genetic difference.
  17. 17.  QTL Data is typically pooled over locations and replications to obtain a single quantitative trait for the line.  It is also preferred to measure the target trait(s) in experiments conducted in multiple (and appropriate) locations to have a better understanding of the QTL x environment interaction, if any.
  18. 18.  DNA markers can be used to map useful genes using recombination frequencies of linked genes: A a M m QTL Marker • Markers near QTLs co-segregate with them. • Markers tightly linked to QTL detected by ANOVA. • Most gametes from this F1 = AM or am. If crossover between marker & QTL, Am & aM gametes will be produced.
  19. 19. 1. Select parents that differ for a trait. 2. Screen the two parents for polymorphic marker loci. 3. Generate recombinant inbred lines (can use F2-derived lines). 4. Phenotype (screen in field). 5. Do a separate ANOVA on the effect of each marker. 6. Declare QTL where F-test is significant. This technique is good choice when the goal is simple detection of a QTL linked to a marker, rather than estimation of its position and effects.
  20. 20.  This method cannot determine whether the markers are associated with one or more QTLs.  Chance of QTL detection decreases with distance between marker and QTL.  Its accuracy is less .
  21. 21.  Lander & Botstein (1989). • Analyzes intervals between adjacent markers instead of single markers. • Statistically more powerful. • Takes proper account of missing data. • Gives improved estimates of QTL effects. • Provides pretty graphs. • Assume a single QTL model. M1 A m1 a M2 m2
  22. 22. • Marker interval = the segment between 2 markers • Interval mapping methods use information on values of 2 flanking markers to estimate QTL position • The probability that the data could be obtained assuming a QTL at several positions between the markers is calculated. • QTL = declared where the probability of obtaining the observed data is highest.
  23. 23. • LOD of 2 means that it is 100x more likely that a QTL exists in the interval than that there is no QTL. • LOD of 3 means that it is 1000x more likely. Significance test:  Logarithm of the odds ratio (LOD score): Probability of the data occurring with a QTL Odds ratio = Probability of the data occurring with no QTL
  24. 24.  Analyzes intervals between adjacent markers+ additional markers unlinked to the interval markers to focus on the interval and eliminate confounding effects from other QTLs  Jansen and Stam (1994) M1 A m1 a M2 m2 M3 B m3 b M4 m4
  25. 25.  CIM evaluates the possibility of a target QTL at multiple analysis points across each inter-marker interval (same as SIM). However at each point it also includes the effect of one or more background markers.  Background markers: That have been shown to be associated with the trait and therefore lie close to other QTLs (background QTLs) affecting the trait.  The inclusion of a background marker in the analysis helps in one of two ways, Based upon the linkage of Background marker and the target interval 1) If they are linked, inclusion of the background marker may help to separate the target QTL from other linked QTLs. 2) If they are not linked, inclusion of the background marker makes the analysis more sensitive to the presence of a QTL in the target interval.
  26. 26.  It is used to minimize effects of various linked QTLs.  It is based on one QTL and other markers used as covariates.  This technique gives more precise results and used to exclude bias due to another QTLs (non-target QTLs) linked to target QTL.  The partial regression coefficient is used to determine genetic variance due to non-target QTLs.
  27. 27.  Mapping of multiple QTLs  Increasing the precision of QTL mapping.  By eliminating as much as the genetic variance produced by other QTL - residual variance is reduced - efficiency of determination of QTL is increased.  CIM is more efficient than SIM, but not widely used in QTL mapping as in SIM.
  28. 28.  Intense computation.  Rely on a genetic map with good quality.  Difficult to incorporate covariate.
  29. 29.  Recent method of QTL Mapping.  Multiple Interval Mapping (MIM) is the extension of interval mapping to multiple QTLs, just as multiple regression extends analysis of variance.  It is used to map multiple QTLs.  This method is potential tool for detection of QTL X QTL interaction.
  30. 30.  The introgression of QTLs into elite lines / germplasm  Maker-aided selection (MAS) for QTLs in crop improvement has to be undertaken in some of the crop like  Maize (Li et al.,2008),  Tomato (Stevens et al., 2007)  Wheat (Naz et al.,2008).  QTLs so identified for diverse traits in different crops have been met in crop improvement especially to enhance the yield and to develop disease resistance elite lines.
  31. 31.  Number of genes controlling the target traits and their position.  Heteritability of the genes segregating in a mapping population.  Statistical tools.
  32. 32. The high quality phenotypic data is very important and useful for meaningful genetic dissection and genomics-assisted breeding applications, including:  QTL interval mapping  Candidate-gene based association mapping  Genome-wide association studies (GWAS)  QTL cloning  QTL meta-analysis  Marker-assisted selection (MAS)  Marker-assisted recurrent selection (MARS)  TILLING (Targeting Induced Local Lesions in Genomes)and  Genomic selection (GS) or genome-wide selection (GWS) (Welcker, 2011; Tuberosa et al., 2012; Cobb et al., 2013;).
  33. 33. TISSUE CULTURE A) Isolation, selection and handling of mature somatic embryos • Development of visualisation and handling technology for quality assessment, sorting and orientation of 3-5 mm long mature embryos. • Development of technologies for transfer of embryos to germination medium.
  34. 34. B) Identification and transfer of germinated plants to soil • Development of visualisation technology for identification of germinated plants with root. • Development of handling technologies for transfer of plants from sterile growth medium to non sterile growth plugs.
  35. 35. ‘‘Motoman takes a novel approach to increasing the productivity of automated assays. (This includes) a compact work cell equipped with washers, dispensers, readers and incubators, serviced by a multitude of plate handlers
  36. 36. A robot system for phenotyping large tomato plants in the greenhouse using a 3D light field camera
  37. 37. Angaji S.A., QTL Mapping: A Few Key points. International Journal of Applied Research in Natural Products, 2(2), 1-3 (2009) Basten, C., B. Weir and Z.-B. Zeng, 2001. QTL cartographer. Department of Statistics, North Carolina State University, Raleigh, NC. Bernardo, R. 2002. Breeding for quantitative traits in plants. Chapters 13 and 14 Collard B.C.Y., Jahufer M.Z.Z., Brouwer J.B. and Pang E.C.K., An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts, Euphytica 142, 169–196 (2005) Davierwala A., Chowdari K., Kumar S., Reddy A., Ranjekar P. and Gupta V., Use of three different marker systems to estimate genetic diversity of Indian elite rice varieties, Genetica 108, 269–284 (2000) Kearsey, M.J. and Pooni, H.S. 1996. The genetical analysis of quantitative traits. Chapter 7
  38. 38. THANK YOU..,

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