Genetic Analysis in Human Disease

Power of Genetic Analysis

Success stories Age-related Macular Degeneration Crohn’s Disease Allopecia Areata Type1 Diabetes

Not so successful Ovarian Cancer Obesity

Getting StartedQuestion to be answeredWhich 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

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

Linkage vs. Association Analysis

5M

Linkage Studies- all in the family Family based method to map location of disease

causing loci

Families Multiplex Trios Sib pairs

Staged Genetic Analysis - RALinkage/Association/Candidate Gene

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

Staged Genetic Analysis - RALinkage/Association/Candidate Gene

So you have a hit: p< 5 x10

Validation/ replication

Dense mapping/Sequencing

Functional Analysis

-7

Validation

Independent replication set Same inclusion/exclusion subject criteria Sample size

Genotyping platform Same polymorphism

Analysis Different ethnic group (added bonus)

Staged Genetic Analysis - RALinkage/Association/Candidate Gene

Dense Mapping/Sequencing

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

Functional Analysis

Does your gene make sense? pathway function cell type expression animal models

PTPN22: first non-MHC gene associated with RA (TCR signaling)

Perfect vs Imperfect Worlds

Perfect world 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?

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

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

Influence of Admixture Not all Subjects are the same

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?

Other ContributorsAny 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

GWAS- What have we found?

3,800 SNPs identified for 427 diseases and traits

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

Pathway Analysis – Crohn’s disease

Personalized Medicine "5P" Health CarePersonalized 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

http://www.dukepersonalizedmedicine.org/what_is_personalized_medicine

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.

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.