1. Genome-wide Association Studies
A population-based survey to identify non-random associations between phenotypes and genetic markers across the genome
Does not rely on linkage analysis or trace the inheritance of traits and markers from a cross
Relies on historic linkage disequilibrium between genetic markers and QTL
Also called
Association mapping
Linkage disequilibrium mapping

2. Advantages of GWAS approach
More opportunity for recombination than in a biparental mapping population
Fine mapping of QTL
Validate candidate genes
Determine which polymorphisms within a gene determine different phenotypes
Surveys a broader gene pool
More than two individuals are represented
Identify multiple alleles for QTL
Evaluate effects of QTL in diverse genetic backgrounds
No need to create mapping populations for linkage analysis
New possibilities for QTL analysis
in species with a long generation time
where controlled crossing is difficult

3. Higher resolution maps with GWAS
Source: Conifer Genomics Learning Modules (Modified from Rafalski (2002), COPB 5: )

4. Disadvantages of GWAS approach
Not a controlled experiment!
Risk of false positives due to population structure
Results will be confounded by any background LD in the population that is not due to close linkage
It is critical to either confirm that the background LD is negligible or use statistical approaches to adjust for it
Need to know extent and structure of LD in order to identify best association mapping strategy
Power to detect QTL is unpredictable
Ideally…
LD has decayed to a large extent in the population as a whole and over fairly small map distances
Adequate LD still exists between marker loci and closely linked QTL

5. Steps in association mapping
Select an association panel
Measure phenotypes
Genotype the panel
Quantify extent of linkage disequilibrium
Assess population structure
Estimate kinship
Apply appropriate statistical model to detect associations between markers and traits

6. LD decay
Extent of LD “decays” as the distance between markers increases
Can also think of “decay” as distance along a chromosome
D is the covariance between alleles at different loci
Can consider r2 to be the square of the correlation coefficient r2 r2
0.2
10 kb
100 kb
distance between markers

7. Low LD requires high marker density
High LD Low density
Low LD Low density
Low LD High density
High power to detect QTL
High resolution of QTL

8. Extent of LD in barley Wild barley: LD decays within a gene
Landraces: ~ 90 kb
European germplasm - significant LD:
mean 3.9 cM, median 1.16 cM, maximum >60 cM
Modern European barley Landraces (ICARDA) Wild barley

9. Population Structure
Population structure may arise from various causes
geographic isolation
selection
breeding history
Population structure may cause false positive associations between genotypes and phenotypes
Methods to account for populations structure
Genomic control (GC)
Structured Association (SA)
Software: Structure 2.3.4
Principle Component Analysis (PCA)

10. Population Structure
Many individuals will not belong uniquely to one subpopulation, but will be the descendents of crosses between two or more ancestral populations
Estimates the proportion of ancestry attributable to each population for each individual

11. Slide courtesy of Alfonso Cuesta-Marcos
Marker
Distance
Line 1
Line 2
Line 3
Line 4
Line 5
Line 6
Line 7
Line 8
Line 9
Line 10
Line 11
Line 12
Line 13
Line 14
Line 15
Line 16
_3_0363_ A B
_1_1061_
0.8
_3_0703_
1.5
_1_1505_
_1_0498_
_2_1005_
3.8
_1_1054_
_2_0674_ 6
_1_0297_
8.8
_1_0638_
10.7
_1_1302_
11.4
_1_0422_
_2_0929_
15.3
_3_1474_
15.4
_1_1522_
17.3
_2_1388_
_3_0259_
18.1
_1_0325_
_2_0602_
20.8
_1_0733_
23.9
_2_0729
_1_1272_
_2_0891_
26.1
_2_0748_
26.6
_3_0251_
27.4
_1_0997_
35.5
_1_1133_
41.8
_2_0500_
42.5
_3_0634_
43.3 10
Desease severity 5
Slide courtesy of Alfonso Cuesta-Marcos

12. Q + K model Y = Xß + S + Qv + Zu + e random effects
Mixed Model – includes fixed and random effects
random effects
Y = Xß + S + Qv + Zu + e
Y is the individual observations of the phenotype
Xß includes fixed effects: population means, environments
S includes marker allele effects (fixed)
Q is a subpopulation incidence matrix (adjusts for structure)
v is a matrix of estimates of subpopulation mean effects (fixed)
Zu represents degree of relatedness not captured by population structure (adjusts for kinship)
u is the polygenic effect generated by other loci that are unlinked to the one being tested
Yu et al. (2006) Nature Genetics 38:

13. Linkage analysis + association mapping
Can we combine the benefits of both approaches?
Nested Association Mapping (NAM)
Method
Crossed 25 diverse inbreds to a common inbred B73
Derived recombinant inbred lines from each cross
Pros and Cons
Diverse and representative
High power to detect QTL
High resolution of QTL
A lot of work!!!
Yu, et al. (2008) Genetics McMullen, et al. (2009) Science

14. Linkage analysis + association mapping
Multi-parent Advanced Generation Intercrosses (MAGIC)
(A) Select founders (C) Intercross individuals across funnels
(B) Make defined crosses (funnels) (D) Self or create double haploids
Huang et al. (2015) Theor Appl Genet 128: