1. Genetic Analysis in Human Disease

2. Power of Genetic Analysis
Success stories
Age-related Macular Degeneration
Crohn’s Disease
Allopecia Areata
Type1 Diabetes
Not so successful
Ovarian Cancer
Obesity

3. Getting Started Question to be answered
Which gene(s) are responsible for genetic susceptibility for Disease A?
What is the measurable difference
Clinical phenotype
biomarkers, drug response, outcome
Who is affected
Demographics
male/female, ethnic/racial background, age

4. Study Design
Linkage (single gene diseases: cystic fibrosis, Huntington’s disease, Duchene's Muscular Dystrophy)
Families
Association (complex diseases: RA, SLE, breast cancer, autism, allopecia, AMD, Alzheimer’s)
Case - control

5. Linkage vs. Association Analysis5M

6. Linkage Studies- all in the family
Family based method to map location of disease causing loci
Families
Multiplex
Trios
Sib pairs

7. Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

8. Association Studies – numbers game
Genome-Wide Association Studies (GWAS)
Tests the whole genome for a statistical association between a marker and a trait in unrelated cases and controls
Affecteds
Controls

9. Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

10. So you have a hit: p< 5 x10-7
Validation/ replication
Dense mapping/Sequencing
Functional Analysis

11. Validation Independent replication set Genotyping platform Analysis
Same inclusion/exclusion subject criteria
Sample size
Genotyping platform
Same polymorphism
Analysis
Different ethnic group (added bonus)

12. Staged Genetic Analysis - RA Linkage/Association/Candidate Gene

13. Dense Mapping/Sequencing
Identifies the boundaries of your signal
close in on the target gene/ causal variant
find other (common or rare) variants

14. Functional Analysis Does your gene make sense? pathway function
cell type
expression
animal models
PTPN22: first non-MHC gene associated with RA (TCR signaling)

15. Perfect vs Imperfect Worlds
Linkage and/or GWAS – identify causative gene polymorphism for your disease Publish
Imperfect world
nothing significant
identify genes that have no apparent influence in your disease of interest
Now what?

16. What Happened? Disease has no genetic component.
Viral, bacterial, environmental
Genetic effect is small and your sample size wasn’t big enough to detect it.
CDCV vs CDRV
Phenotype /or demographics too heterogeneous
Too many outliers
Wrong controls.
Population stratification; admixture
Not asking the right question.
wrong statistics, wrong model

17. Meta-Analysis – Bigger is better
Meta-analysis - combines genetic data from multiple studies; allows identification of new loci
Rheumatoid Arthritis
Lupus
Crohn’s disease
Alzheimer’s
Schizophrenia
Autism

19. Influence of Admixture
Not all Subjects are the same

20. Missing heritability
Except for a few diseases (AMD, T1D) genetics explains less than 50% of risk.
Large number of genes with small effects
Other influences?

21. Other Contributors Environmental Epigenetic MicroRNA Microbiome
Any change in gene expression can influence disease state- not always related directly to DNA sequence
Environmental
Epigenetic
MicroRNA
Microbiome
Copy Number Variation
Gene-Gene Interactions
Alternative splice sites/transcription start sites

22. GWAS- What have we found?
3,800 SNPs identified for 427 diseases and traits

23. Genome-Wide Association Studies
The promise
Better understanding of biological processes leading to disease pathogenesis
Development of new treatments
Identify non-genetic influences of disease
Better predictive models of risk
and the reality
Few causal variants have been identified
Clinical heterogeneity and complexity of disease
Genetic results don’t account for all of disease risk

24. Pathway Analysis – Crohn’s disease

25. Personalized Medicine "5P" Health Care
Personalized medicine is:
Predictive: Uses state-of-the-art molecular and diagnostic tools to precisely predict individual health risks and outcomes
Personalized: Is informed by each person’s unique clinical, social, genetic, genomic, and environmental profile
Preventive: Emphasizes wellness and prevention to stop disease before it progresses
Preemptive: Incorporates action-oriented, individualized health planning
Participatory: Empowers each patient to participate in their own care, with coordinated support from their health care team

26. Things to remember You can never have too many samples
You can never collect too much information on a subject
The more you know about the disease and your subjects, the more homogeneous your study will be and the less interference from “population” noise you will have.

27. Questions
True/ False
Association studies are comprised of many multiplex families
With 100 randomly chosen polymorphisms and 10,000 diverse human subjects you have a high probability of finding the causative polymorphism for your disease of interest
It’s better to ascertain all of your case subjects in one small town and all of your control subjects in a distant small town so there is no overlap in genetic composition.
The ability to combine data from different large studies to perform a meta-analysis can result in identifying new loci which were not significant in the original studies
If it weren’t for admixture we would not be able to study complex genetics.