1. Post-GWAS and Mechanistic Analyses
Session 1, Day 4, Liu
Post-GWAS and Mechanistic Analyses

2. I identified a SNP, now what?
Congratulations! But…

3. More Questions
A genotype-phenotype association does not mean a cause-effect relationship

4. Are You Sure?
If you are dedicated to do some real science, every step down the road means time, money, labor, value, reputation and life!
Validate the result carefully before you jump into any mechanistic study
If you can’t do it, ask other people and collaborate.

5. You Only Identified a Marker!
Is the top SNP what I should choose to go?
Should I ignore the SNPs that do not reach the significance?
P value is not a smart judgement!
Did I miss any other SNP?
Fine mapping
Narrow down
Top SNP
Genotyped SNPs
Untyped SNPs
Unidentified SNPs
Other type of variants

6. “Balloon”
“Skyscraper”

7. Things To Do
Imputation
Sequencing LD
Haplotype

8. Genotype Imputation
Predicting or imputing genotypes that are not directly assayed in a sample of individuals
“in silico genotyping”
Missing data
Untyped
No cost
Increase the possibility for identification

9. Application Before GWAS: increase chance After GWAS: fine mapping
Alternative GWAS: using existing control data
Meta-analysis: combining data

10. SNPs
Patients ?

11. Process
Marchini et al., 2011

12. Accuracy
Howie, et al., Nature Genetics 44, 955–959 (2012)

13. Strategies SNP tagging-based approaches
PLINK
SNPMSTAT
UNPHASED
TUNA
Simple multinomial model of haplotype frequencies
Low accuracy but quick

14. Strategies Hidden markov model (HMM)-based approaches High accuracy
Rare SNPs
IMPUTE v1
IMPUTE v2
fastPHASE/BIMBAM
MACH
BEAGLE

15. IMPUTE v1, IMPUTE v2, SNPTEST
fastPHASE, BIMBAM
MACH, MACH2DAT, MACH2QTL
csg/abecasis/MACH/BEAGLE
SNPMSTAT
GEDI
PLINK
UNPHASED
unphased/TUNA
ProbABEL

16. Other Variants Known or unknown CNVs/nucleotide repeats/indel
1000 Genome (1KG)
Other shared NGS data
Re-sequencing

17. Other Factors
Common disease:
gene X environment
Multiple genes

18. Other Factors Gene X Environment (GXE)
Age, gender, diet, smoke, drink, etc

19. Other Factors
Gene X Gene (GXG)
Epistasis
SNP1
SNP2
Disease X +

20. Other GWAS Approaches Prior Knowledge-based grouping
PWAS
Gene-based grouping
PORCE

21. You Only Determined a “Locus”!
Direction
Transcription Translation Catalysis
DNA RNA Protein Metabolites
Genome Transcriptome Proteome Metabolome/Lipidome Clinical endpoint
dysregulation
Genetic effect
Environmental effect

22. Which Gene? The closest? Gene desert mRNA vs other?
Candidate variants
Top SNP
Genotyped SNPs
Untyped SNPs
Unidentified SNPs
Other type of variants
The closest?
Gene desert
mRNA vs other?
Knowledge integration
Candidate gene

23. How?—Functional Pathway
How the SNP change the gene function?
Quantitative regulation
Regulatory region: TF binding, stability, miRNA targeting, methylation, etc
Gene/protein expression
eQTL, p-QTL, mQTL
Qualitative regulation
Gene function: AA change, frame shift, splicing, CNV gene deletion, etc
enzyme activity
substrate binding
phosphorylation

24. Integration of Different Omics
Transcription Translation Catalysis
DNA RNA Protein Metabolites
Genome Transcriptome Proteome Metabolome/Lipidome Clinical endpoint
dysregulation
Genetic effect
Environmental effect
QTL mapping

25. Why?—Signaling Pathway
Proof-of-concept
Cells: Gene knock-down, overexpression, CRISPR/CAS9
Model species: KO, TG, mice, yeast, etc.
Signaling pathway: dependency test
Gene 1 X
Gene 2
Knockdown X
Knockdown
Omics
Gene 3
Knockdown ….
disease
Gene 4
Knockdown

26. Tools SNPnexus SNAP GTExPortal http://snp-nexus.org/
GTExPortal