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New Methods for Analyzing Complex Traits

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New Methods for Analyzing Complex Traits Jun Zhu Institute of Bioinformatics Department of Agronomy Zhejiang University Most Important Traits are Complex Trait New ... – PowerPoint PPT presentation

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Title: New Methods for Analyzing Complex Traits


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 are Complex 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(ij)
Pi
Pj
Bi
Bi
Bj
Bj
Bi
Bj
?
Ci
Ci
Cj
Cj
Cj
Ci
5
P1 P2
?
F1
DH
Haploid
P1F1
P2F1
BC1
BC2
?
?
F2
????
?
IF2
RIL
6
Interval Mapping (Lander Botstein,1989) Genetic
Model
Advantages Can Detect Position Effect of
QTL Between Markers Mi- 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
Mi
Qi
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 (AD)

Relationship Between Two Estimates
10
IM
CIM
11
Mixed-model Based Composite Interval Mapping
(MCIM)(Zhu, 1998)
y ? GQ GM ?
MCIM??
CIM??
y ? GQ GM ?
12
IM
CIM
MCIM
13
Genetic Model Construction
Mixed-model Based CIM for QTL Mapping (Wang et
al. 1999, TAG, 991255-1264)
Mapping QTL with AAA 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
? All regression effects are fixed ? Cannot
including random effects E QE ? Cannot handling
complex effects by regression model
Advantages for MCIM Methods
? Mixed linear model having both fixed and
random effects ? Fixed Q effects and random QE
effects estimated/predicted with no biase
? Can handling complex effects
18
New Approache of Mapping QTL
  • Full Model

19
  • Estimations of effects in mixed linear model can
    be given by Hendersons 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 Effects MCMC Method can be applied for
making inference via Gibbs sampling.
21
QTLNetwork version 2.0
22
QTLNetwork 2.0
23
(No Transcript)
24
QTL?????????
25
Estimate Parameter
26
????A,D,AA,AD,DA,DD ?AE,DE,AAE,ADE,DAE,DDE
IF2 ??
27
Summarized statistics of simulation study with
200 replicates for SLE QTL
28
Summarized statistics of simulation study with
200 replicates for epistasis
29
Mapping QTLs for Yield in Barley
  • Map Data Files

30
QTL Detection by Two Methods
Mean of Yield 1577.6
31
Heritability Estimated by Two Methods
32
Predicting Total Genotype Value and Potential
Breeding Merit
Mean of Yield 1577.6
33
Mapping Developmental QTL 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(tt-1) ?(tt-1) GQ(tt-1) E(tt-1)
GQE(tt-1) GM(tt-1) GME(tt-1) ?(tt-1)
Analyzing Net Q QE Effects From Time t -1 ? t
34
Table 2. Chromosomal regions and estimated
genetic effects of QTLs for plant height (cm) at
different stages in two environments.
35
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(RC) ?(RC) GQ(RC) E(RC)
GQE(RC) GM(RC) GME(RC) ?(RC )
Analyzing Net Q QE Effects on Result Trait
When Influence of Cause Trait Is Excluded
36
Zhao et al, 2006, TAG, 11333-38
8QTL 422
7QTL 205
37
Acknowledgments
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