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New Methods for Analyzing Complex Traits

New Methods for Analyzing Complex Traits. Jun Zhu Institute of Bioinformatics Department of Agronomy Zhejiang University. Phenotype Property of Complex Trait. y =  + E + G + G E + e. Genome. Genetic Effects. QTL Position & Effects. Genetic Effect: A 、 D 、 I. G. GE: AE 、 DE 、 IE.

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New Methods for Analyzing Complex Traits

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  1. New Methods for Analyzing Complex Traits Jun Zhu Institute of Bioinformatics Department of Agronomy Zhejiang University

  2. Phenotype Property of Complex Trait y=+E + G+ GE+ e Genome Genetic Effects QTL Position & Effects Genetic Effect: A、D、I G GE: AE、DE、IE GE Macro Env., Micro Env. E Phenotype

  3. Most Important Traits areComplex Trait Complex traits: • Phenotypes controlled by multiple genes • Epistasis (gene-gene interaction) • Gene-environment interaction • Genetic heterogeneity • Low heritability • Limited statistical power

  4. Genetic Definition of Gene Effects y=+E + G+ GE+ e F1(i×j) Pj Pi Bi Bi Bj Bj Bi Bj  Ci Ci Cj Cj Cj Ci

  5. P1× P2 ? F1 DH Haploid P1×F1 P2×F1 BC1 BC2   F2 连续自交  IF2 RIL

  6. Interval Mapping(Lander & Botstein,1989) Genetic Model: Advantages: Can Detect Position & Effect of QTL Between MarkersMi- & Mi+ Disadvantages： Can Be Affected by Other QTLs

  7. IM Method for Mapping QTL Matrix form for QTL Mapping Model Test H0: No QTLs vs H1: Having QTLs by The Likelihood Ratio Statistic, LR Test H0: No QTLs by The LOD Statistic For df = 1, LR = 4.6052 × LOD, or LOD = 0.2171 × LR Estimation of QTL Effects

  8. Composite Interval Mapping (Zeng, 1994) Genetic Model: Mi– Qi Mi+ Advantages: Eliminate Inference of Other QTLs Disadvantages： QTL Effect Is Determined Also by Other Marker Effects in the Model

  9. CIM Method for Mapping QTL： Matrix Form for QTL Mapping Model Test H0: No QTLs by The Likelihood Ratio Statistic, LR Estimation of QTL Effects (A+D) Relationship Between Two Estimates

  10. IM CIM

  11. Mixed-model Based Composite Interval Mapping （MCIM）（Zhu, 1998） y=+GQ+ GM+ MCIM方法 CIM方法 y= +GQ+ GM+

  12. IM CIM MCIM

  13. Genetic Model Construction Mixed-model Based CIM for QTL Mapping (Wang et al. 1999, TAG, 99:1255-1264) Mapping QTL with A+AA and QE Interaction (DH, RIL) Ai AAij Aj

  14. MCIM Method (Zhu, 1998,1999) Test H0: No QTLs vs H1: Having QTLs by The Likelihood Ratio Statistic, LR

  15. Estimation of QTL Main Effects Test of QTL Main Effects df = n – rank(X)

  16. Prediction of QTL by Environment Interaction Effects Test of QE Interaction Effects

  17. Disadvantages for IM & CIM Methods: ⑴ All regression effects are fixed ⑵ Cannot including random effects E & QE ⑶ Cannot handling complex effects by regression model Advantages for MCIM Methods: ⑴ Mixed linear model having both fixed and random effects ⑵ Fixed Q effects and random QE effects estimated/predicted with no biase ⑶ Can handling complex effects

  18. New Approache of Mapping QTL • Full Model:

  19. Estimations of effects in mixed linear model can be given by Henderson’s Method

  20. One-dimensional (1D) Search for QTLs with Single-locus Effects • Henderson Method III (Searle, 1971) Partial Two-dimensional Search for QTLs with Episrasis EffectsMCMC Method can be applied for making inference via Gibbs sampling.

  21. QTLNetwork version 2.0

  22. QTLNetwork 2.0

  23. QTL位置和效应分析结果

  24. Estimate [Parameter]

  25. 无偏估算A,D,AA,AD,DA,DD 及AE,DE,AAE,ADE,DAE,DDE IF2 群体

  26. Summarized statistics of simulation study with 200 replicates for SLE QTL

  27. Summarized statistics of simulation study with 200 replicates for epistasis

  28. Mapping QTLs for Yield in Barley • Map & Data Files

  29. QTL Detection by Two Methods Mean of Yield = 1577.6

  30. Heritability Estimated by Two Methods

  31. Predicting Total Genotype Value and Potential Breeding Merit Mean of Yield = 1577.6

  32. Mapping DevelopmentalQTL for Quantitative Traits Time: 0 1 2  …t -1tt +1 …f Unconditional Model for Phenotypic Value at Time t y(t) = (t) +GQ(t) + E(t) +GQE(t) +GM(t) + GME(t)+(t) Analyzing Q & QE Effects From Time 0t Conditional Model for Phenotypic Value at Time t y(t|t-1) = (t|t-1) +GQ(t|t-1) + E(t|t-1) +GQE(t|t-1)+GM(t|t-1) + GME(t|t-1)+(t|t-1) Analyzing Net Q & QE Effects From Time t -1 t

  33. Table 2. Chromosomal regions and estimated genetic effects of QTLs for plant height (cm) at different stages in two environments.

  34. Mapping QTL for Cause & Result Traits Cause CResult R Unconditional Model for Phenotypic Value of Result Trait y(R) = (R) +GQ(R) + E(R) +GQE(R)+GM(R) + GME(R)+(R ) Conditional Model for Phenotypic Value of Result Trait Given Cause Trait y(R|C) = (R|C) +GQ(R|C) + E(R|C) +GQE(R|C)+GM(R|C) + GME(R|C)+(R|C ) Analyzing Net Q & QE Effects on Result Trait When Influence of Cause Trait Is Excluded

  35. Zhao et al, 2006, TAG, 113:33-38 8QTL 4+2+2 7QTL 2+0+5

  36. Acknowledgments

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