1. GENOME WIDE ASSOCIATION STUDIES (GWAS)
Presented by: Oyeyemi Ajayi

2. DEFINITION OF GWAS
A genome-wide association study is an approach that involves rapidly scanning markers across the complete sets of DNA, or genomes, to find genetic variations associated with a particular trait of interest (e.g disease).

3. POTENTIALS OF GWAS
McCarthy, M. et al. 2017

4. Xu, 2007

5. Other modeling methods like the Bayesian (BayesA, BayesB etc, can be used for genomic selection on GWAS data

7. C
Body size association results. Manhattan and quantile–quantile plots of (a) breed-average male weight (n=1,873) and (b) breed-average male height (n=1,873), showing the 17 significant loci, four of which are novel (shown in bold). Inflation factors are shown on the quantile–quantile plots. (C) Proportion of variance explained by the 17 size loci in a linear model for weight (blue bars) and height (green bars), with sex and inbreeding corrections.

8. Epistasis plots for fur phenotypes
Epistasis plots for fur phenotypes. (a) Breed average shedding showing the interaction between RSPO2 and MC5R alleles, (b) breed fur length showing the interaction between MC5R and RSPO2 alleles, FGF5 and
RSPO2 alleles, and MC5R and FGF5 alleles. a=ancestral allele, d=derived allele.

9. REFERENCES
1. McCarty et al Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genetics, 9(5):
2. Xu, Applying the Generalized partitioning principle to control the Generalized family =-wise error rate. Biometrical Journal 49:52-57 3. 4.
5. Zhao et al Genomewide association analysis for awn length linked to the seed shattering gene qSH1 in rice. J Genet, 95(3):

10. THANK YOU! The more we find, the more we see,
the more we come to learn.
The more that we explore, the more we shall return.”
Sir Tim Rice, Aida, 2000
THANK YOU!