1. Association Modeling With iPlant

2. Goals of this Section
Familiarize with the basic concepts of quantitative genetics:
Traits, phenotypes, genotypes
Understand the basics of trait mapping
Understand the conceptual foundations of association studies
Lear how to perform a genome wide association study in the iPlant Discovery Environment
Obtain genotypes
Run a Mixed Linear Model

3. Phenotype Observable (measurable) trait (character) of an organism
Trait: eye color
Phenotype: wild type (red), white eyed, orange eyed

4. Qualitative Traits
Campbell, 8e

5. Controlled by One Locus

6. Co-segregation in Pedigree
Donahue, R. P., et al., Probable assignment of the Duffy blood group locus to chromosome 1 in man, Proceedings of the National Academy of Sciences 61, (1968).

7. Quantitative Trait
Carlos Harjes

8. Trait Varies on a Continuous Scale
Frequency
Trait Value

9. Quantitative Traits Probably caused by multiple loci
Interaction effects
Environment
If the mean trait value for individuals with marker state MM is different from the mean trait value of individuals with marker state mm (i.e. the marker is associated with the phenotype), then the marker is linked to a quantitative trait locus.

10. Individuals
Trait value
Markers
Marker #6
Mean Trait Value
Present
110 ± 10
Absent
115 ± 13
Marker #3
Mean Trait Value
Present
99 ± 5
Absent
118 ± 8

11. Quantitative Genetics
Exploring the Genetic Architecture* Underlying Quantitative Traits
*Genetic Architecture
How many loci?
Which location?
How strong?

12. Tools for Statistical Genetics in the DE
Purpose
Genotype by Sequencing Workflow
Automatic pipeline for extracting SNPs from GBS data (with genome from user or from iPlant database)
UNEAK pipeline
Automatic pipeline for extracting SNPs from GBS data without reference genomes
MLM workflow
Automatic workflow for fitting Mixed Linear Model
GLM workflow
Automatic workflow for fitting General Linear Model
QTLC workflow
Automatic workflow for composite interval mapping
QTL simulation workflow
Automatic workflow for simulating trait data with given linkage map
PLINK
PLINK implementation of various association models
Zmapqtl
Interval mapping and composite interval mapping with the options to perform a permutation test
LRmapqtl
Linear regression modeling
SRmapqtl
Stepwise regression modeling
AntEpiSeeker
Epistatic interaction modeling
Random Jungle
Random Forest implementation for GWAS
FaST-LMM
Factored Spectrally Transformed Linear Mixed Modeling
Qxpak
Versatile mixed modeling
gluH2P
Convert Hapmap format to Ped format LD
Linkage Disequilibrium plot
Structure
Estimation of population structure
PGDSpider
Data conversion tool
GLMstrucutre
GLM with population structure as fixed effect

13. A Model for Quantitative Traits
Phenotype
Genotype
Environment
P = G + E + GG + GE
P = G + e
P=Phenotype
G=Genotype
E=Environment
GG=Interaction between genotypes
GE=Interaction between genotype and environment

14. A Statistical Model for QTLs
P=G + e
yij trait value in individual j with genotype i
β0 population average of trait value
β1 effect of marker i on trait value
xi marker genotype i
εij error term
General Linear Model (in matrix notation): Y=Xb + e
Note: If errors are not normally distributed, use generalized linear models

16. Linkage Mapping (QTL Mapping)
Designed population F2
Recombinant inbred (RIL)
Double-Haploid (DH)
Back-cross (B2)

18. Limitation of Linkage Mapping
Needs large number of related individuals
Resolution limited (interval contains 100s of genes)
QTL position and effect are confounded

19. Association Mapping
Use random collection of individuals from natural population
Very dense marker map = very high resolution

20. Linkage & Recombination
Recombination causes linkage decay
Other factors affecting LD:
Selection (artificial or natural)
Drift
Mutations
Population structure
Demography

21. Linkage Disequilibrium

22. Pitfalls: Population Structure
Difference in allele frequencies between subpopulations
Due to neutral or adaptive processes
Can create spurious association

23. No association within groups

24. Similar effect due to presence of related individuals (esp. in plants)
Can be accounted for using the data:
Estimate number of subpopulations
Assign individuals to subpopulation
Estimate kinship

25. Accounting for Random Effects: Mixed Linear Models
"Cost" associated with estimating a parameter
We are not interested in the value of the parameter, only the variance
Q-K method (structured association)
y=Xβ+Sα+Qv+Zu+e
Fixed effects:
β Vector of fixed effects
α Vector of SNPs effects
v Vector of subpopulation effects
Random effects:
u Vector of kinship effects
e Residuals
Q Matrix of population association (STRUCTURE)
X, S, Z Incidence Matrices

26. Traits
MLM
Markers
Population Structure
STRUCTURE
Kinship
TASSEL

27. Obtain Markers
Genome Resequencing Workflow
Genotyping By Sequencing

28. MLM Pipeline for GWAS Ed Buckler (Cornell University) TASSEL
marker
trait
filter
convert
impute K
GLM
MLM
Zhang et al. Nature Genetics. 2010; doi: /ng.546

29. MLM Input Files Hapmap file Phenotype data Kinship matrix*
traits
strain
Hapmap file
Phenotype data
Kinship matrix*
Population structure*
Population structure
3 populations sum to 1
strain
* Kinship matrix & population structure data can be generated using TASSEL or with “MLM Workflow” App in DE

30. Origin Hapmap file: Phenotype data Kinship matrix Population structure
Download (e.g.
Convert from PLINK (.map/.ped) using Tassel 3 Conversion
Impute with NPUTE
Transform to numerical format with NumericalTransform
Phenotype data
Kinship matrix
Generate from hapmap marker data with Kinship
Population structure
Generate using ParallelStructure
Convert to matrix with Structure2Tassel

31. MLM Output MLM1.txt MLM2.txt MLM3.txt See TASSEL manual for details:
Marker
“df” degrees of freedom
“F” F distribution for test of marker
“p” p-value
“errordf” df used for denominator of F-test
etc.
MLM2.txt
Estimated effect for each allele for each marker
MLM3.txt
The compression results shows the likelihood, genetic variance, and error variance for each compression level tested during the optimization process.
See TASSEL manual for details:

32. THANKS!