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Jiankang Wang. Principle of QTL mapping and inclusive composite interval mapping (ICIM)

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Jiankang Wang. Principle of QTL mapping and inclusive composite interval mapping (ICIM)

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Jiankang Wang. Principle of QTL mapping and inclusive composite interval mapping (ICIM)

  1. 1. 1 Principle of QTL mapping and Inclusive Composite Interval Mapping (ICIM) Jiankang Wang CIMMYT China and CAAS E-mail: wangjk@caas.net.cn or jkwang@cgiar.org The 9th Workshop on QTL Mapping and Breeding Simulation The University of Sydney, Cobbitty NSW, 7-9 March 2012
  2. 2. Outlines  Quantitative traits and QTL mapping  Inclusive composite interval mapping (ICIM) for additive and interacting QTL  Selected publications using ICIM  The BIP functionality in QTL IciMapping 2
  3. 3. 1. Quantitative traits and QTL mapping 3
  4. 4. Quantitative traits  Continuous phenotypic variation  Affected by many genes  Affected by environment  Epistasis  Polygene (or multi-factorial ) hypothesis  Classical quantitative genetics 4
  5. 5. What is QTL Mapping?  The procedure to map individual genetic factors with small effects on the quantitative traits, to specific chromosomal segments in the genome  The key questions in QTL mapping studies are:  How many QTL are there?  Where are they in the marker map?  How large an influence does each of them have on the trait of interest? 5
  6. 6. 6 Dataset of QTL mapping  Mapping population  Marker data of each individual in the mapping population  Linkage map  Phenotypic data
  7. 7. 7 Example: 10 RIL of Rice (linkage map of Chr. 5 ) Marker C263 R830 R3166 XNpb387 R569 R1553 C128 C1402 XNpb81 C246 R2953 C1447 Grain width (mm) Position (cM) 0.0 3.5 8.5 19.5 32.0 66.6 74.1 78.6 81.8 91.9 92.7 96.8 RIL1 0 0 0 0 0 0 0 0 0 0 0 0 2.33 RIL2 2 2 2 2 2 0 0 0 0 2 2 2 1.99 RIL3 0 2 2 2 2 2 2 2 2 2 2 2 2.24 RIL4 0 0 0 0 0 0 2 2 2 2 2 2 1.94 RIL5 0 0 0 0 0 2 2 0 0 0 0 0 2.76 RIL6 0 0 0 2 2 2 2 2 2 2 2 2 2.32 RIL7 0 0 0 0 0 0 0 0 0 0 0 0 2.32 RIL8 2 2 0 2 2 0 0 0 0 2 2 2 2.08 RIL9 0 0 0 0 2 2 0 0 0 0 0 0 2.24 RIL10 0 0 0 0 2 2 0 0 0 0 0 0 2.45
  8. 8. 8 Classification of mapping populations  Bi-parental mapping populations (linkage mapping)  Temporary population: F2 and BC  Permanent population: RIL, DH, CSSL  Secondary population  Association mapping  Natural populations: human and animals
  9. 9. 9 Overview on QTL mapping methods Single marker analysis (Sax 1923; Soller et al. 1976) The single marker analysis identifies QTLs based on the difference between the mean phenotypes for different marker groups, but cannot separate the estimates of recombination fraction and QTL effect. Interval mapping (IM) (Lander and Botstein 1989) IM is based on maximum likelihood parameter estimation and provides a likelihood ratio test for QTL position and effect. The major disadvantage of IM is that the estimates of locations and effects of QTLs may be biased when QTLs are linked. Regression interval mapping (RIM) (Haley and Knott 1992; Martinez and Curnow 1992 ) RIM was proposed to approximate maximum likelihood interval mapping to save computation time at one or multiple genomic positions.
  10. 10. 10 Composite interval mapping (CIM) (Zeng 1994) CIM combines IM with multiple marker regression analysis, which controls the effects of QTLs on other intervals or chromosomes onto the QTL that is being tested, and thus increases the precision of QTL detection. Multiple interval mapping (MIM) (Kao et al. 1999) MIM is a state-of-the-art gene mapping procedure. But implementation of the multiple-QTL model is difficult, since the number of QTL defines the dimension of the model which is also an unknown parameter of interest. Bayesian model (Sillanpää and Corander 2002) In any Bayesian model, a prior distribution has to be considered. Based on the prior, Bayesian statistics derives the posterior, and then conduct inference based on the posterior distribution. However, Bayesian models have not been widely used in practice, partially due to the complexity of computation and the lack of user-friendly software.
  11. 11. 11 Principle of QTL mapping  Three marker types at one marker locus A. 很可能存 在QTL和标 记的连锁 性状平均数 mm MMMm B. 不一定存 在QTL和标 记的连锁 性状平均数 mm MMMm
  12. 12. 12 Backcrosses (P1BC1 and P2BC1) of P1: MMQQ and P2: mmqq BC1 BC2 Genotype Frequency Genotypic value Genotype Frequency Genotypic value MMQQ )1(2 1 r− m+a MmQq )1(2 1 r− m+d MMQq r2 1 m+d Mmqq r2 1 m-a MmQQ r2 1 m+a mmQq r2 1 m+d MmQq )1(2 1 r− m+d mmqq )1(2 1 r− m-a
  13. 13. 13 Principle of single marker analysis (P1BC1 as example)  Two marker types:  Difference in phenotype between the two types MMQqMMQQMM )1( µµµ rr +−= rdarmdmramr +−+=+++−= )1()())(1( MmQqMmQQMm )1( µµµ rr −+= drramdmramr )1())(1()( −++=+−++= ))(21(MmMM dar −−=− µµ
  14. 14. 14 Interval mapping (IM) (Lander and Botstein 1989)  Linear model (j=1,2,…,n ) b* represent QTL effect, is the indicator variable (0 or 1) for QTL genotype  Likelihood profile  Support interval: One-LOD interval * jx jji exbby ++= ** 0
  15. 15. 15 QTL genotypes under each marker type in P1BC1 (double crossover not considered) P1: P2: F1: P1: 区间标记类型1 区间标记类型2 区间标记类型3 区间标记类型4 Mi Q Mi +1 Mi Q Mi +1 mi q mi +1 mi q mi +1 × Mi Q Mi +1 Mi Q Mi +1 Mi Q Mi +1 × Mi Q Mi +1 Mi Q Mi +1 Mi Q Mi +1 Mi Q Mi +1 Mi Q Mi +1 Mi Q mi +1 mi q mi q mi +1 Mi Q Mi +1 Mi q mi +1 mi q Mi +1 Mi Q Mi +1 mi Q Mi +1 mi q mi +1 Marker class I Marker class II Marker class III Marker class IV
  16. 16. 2. Inclusive Composite Interval Mapping (ICIM) 16
  17. 17. 17 Problems with IM  Assumption: No more than one QTL per chromosome or linkage group  “Ghost QTL” for linked QTL  Large confidence interval  Biased effect estimation  Composite interval mapping (CIM) (Zeng 1994)
  18. 18. 18 Problems with CIM In the algorithm of CIM, both QTL effect at the current testing position and regression coefficients of the marker variables used to control genetic background were estimated simultaneously in an expectation and maximization (EM) algorithm. Thus, this algorithm could not completely ensure that the effect of QTL at current testing interval was not absorbed by the background marker variables and therefore may result in biased estimation of the QTL effect.
  19. 19. 19 Theoretical basis of ICIM ∑∑ <= += kj kjjk m j jj ggaagaG 1 1)|( ++= jjjjj xxgE ρλX 1111)|( ++++ +++= kjkjkjkjkjkjkjkjkj xxxxxxxxggE ρρλρρλλλX i kj ikijjk m j ijji exxbxbby +++= ∑∑ < + = 1 1 0
  20. 20. 20 Genomic scanning for additive and interacting QTL  Two-dimensional scanning (interval mapping)  One-dimensional scanning (interval mapping) ∑+≠ −=∆ 1, ˆ kkj ijjii xbyy ∑∑ +≠ +≠++≠ −−=∆ 1, 1,1,,1, ˆˆ kks jjr isirrs kkjjr irrii xxbxbyy
  21. 21. 0 10 20 30 40 11111111111222222222233333333334444444444 LODscore Scanning posoition along the genome -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 11111111111222222222233333333334444444444 Effect Scanning posoition along the genome 0 20 40 60 80 11111111111222222222233333333334444444444 LODscore Scanning posoition along the genome -4 -3 -2 -1 0 1 2 3 11111111111222222222233333333334444444444 Effect Scanning posoition along the genome 0 10 20 30 40 50 60 70 11111111111222222222233333333334444444444 LODscore Scanning posoition along the genome -1.5 -1 -0.5 0 0.5 1 1.5 11111111111222222222233333333334444444444 Effect Scanning posoition along the genome 21 IM CIM ICIM
  22. 22. 22 Detecting epistasis where the interacting QTL don’t have significant additive effects
  23. 23. 105 120 40 80 140 1, - 10, - 155 4, - 9, - 4, + 90 1, + 0 60 9, - 5, - 180 2, - 5, + 200 1, - 9, + 95 1, - 2, - 305 2, + 65 1, + 7, + 15 5, + 30 9, + 5, - 10, + 166 2, - 1 TL03BWW 2 TL03AIS 4 TL04BWW 5 TL04AIS 7 ZW03BIS 8 ZW03BSS 9 ZW04AWW 10 ZW04BSS qFFLW1-1 qFFLW1-2 qFFLW1-3 qFFLW2 qFFLW3-1 qFFLW3-2 qFFLW4-1 qFFLW4-2qFFLW4-3qFFLW5-1 qFFLW5-2 qFFLW7-1 qFFLW7-2 qFFLW8-1 qFFLW8-2 qFFLW8-3 qFFLW10-1 qFFLW10-2 23 Digenic epistatic networks of FFLW in maize using a RIL population
  24. 24. 3. Selected publications using ICIM 24
  25. 25. In rice  Crop Science (2008) 48: 1799-1806; Tiller angle  Hereditas (2009) 146: 67-73; Brown planthopper resistance  Mol. Breeding (2010) 25: 287-298; Heading date  Scientia Agricultura Sinica 2010,43(21): 4331-4340; Nitrogen efficiency 25
  26. 26. In wheat Euphytica (2009) 165: 435-444; flour and noodle color components and yellow pigment content 26
  27. 27. More in wheat  Acta Agronomica Sinica (2011) 37 (2): 294-301; Coleoptile Length and Radicle Length  Crop & Pasture Science (2009) 60: 587-597; White salted noodle quality  Crop & Pasture Science (2011) 62: 625-638; Kernel morphology traits  Mol. Breeding (2010) 25: 615-622; Adult-plant resistance to powdery mildew  Theor. Appl. Genet. (2009) 119: 1349-1359; Adult-plant resistance to stripe rust  Mol. Breeding (2011) on line published; Grain protein content and grain yield component  Scientia Agricultura Sinica 2011,44(14):2857-2867; Grain yield per plant and plant height 27
  28. 28. In soybean  Breeding Science (2008) 58: 355-359 ; Salt tolerance  ACTA AGRONOMICA SINICA 2009, 35(12): 2139−2149; Protein Related Traits 28
  29. 29. In Maize  Theor. Appl. Genet. (2011) 123: 327-338; Partial restoration of male fertility of C-type cytoplasmic male sterility  Plant Mol. Biol. Rep. (2011) on line published; Nitrogen Use Efficiency  HEREDITAS 32(6): 625-631; The area of leaves 29
  30. 30. In melon 30  Mol Breeding (2011) 27: 181-192; Powdery mildew
  31. 31. Publications using RSTEP-LRT  In Rice: Theor Appl Genet (2006) 112: 1258-1270; Grain length Plant Cell Report (2009) 28: 247-256; Mature seed culturability Mol. Breeding (2010) 25: 287-298; Heading date  Plant Cell Rep. (2009) 28: 247-256; Mature seed culturability  In Maize:  Scientia Agricultura Sinica (2011),44(17):3508-3519; Yield 31
  32. 32. 4. The BIP functionality in QTL IciMapping 32
  33. 33. Six methods in BIP  SMA: single marker analysis (Soller et al., 1976. Theor. Appl. Genet. 47: 35-39)  IM-ADD: the conventional simple interval mapping (Lander and Botstein, 1989. Genetics 121: 185-199)  ICIM-ADD: inclusive composite interval mapping of additive (and dominant) QTL (Li et al., 2007. Genetics 175: 361-374. Zhang et al., 2008. Genetics 180: 1177- 1190)  IM-EPI: interval mapping of digenic epistatic QTL  ICIM-EPI: inclusive composite interval mapping of digenic epistatic QTL (Li et al., 2008. Theor. Appl. Genet. 116: 243-260)  SGM: selective genotyping mapping (Lebowitz et al., 1987. Theor. Appl. Genet. 73: 556–562)
  34. 34. Interface of the BIP functionality Menu Bar Tool Bar Project Window Message Button Task List Button Display Window Parameter Setting Window
  35. 35. LOD profile of ICIM additive mapping (ICIM-ADD)
  36. 36. Figures of interacting QTL from ICIM epistatic mapping (ICIM-EPI)

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