2 Power of Genetic Analysis Success storiesAge-related Macular DegenerationCrohn’s DiseaseAllopecia AreataType1 DiabetesNot so successfulOvarian CancerObesity
3 Getting Started Question to be answered Which gene(s) are responsible for genetic susceptibility for Disease A?What is the measurable differenceClinical phenotypebiomarkers, drug response, outcomeWho is affectedDemographicsmale/female, ethnic/racial background, age
4 Study DesignLinkage (single gene diseases: cystic fibrosis, Huntington’s disease, Duchene's Muscular Dystrophy)FamiliesAssociation (complex diseases: RA, SLE, breast cancer, autism, allopecia, AMD, Alzheimer’s)Case - control
6 Linkage Studies- all in the family Family based method to map location of disease causing lociFamiliesMultiplexTriosSib 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 controlsAffectedsControls
9 Staged Genetic Analysis - RA Linkage/Association/Candidate Gene
10 So you have a hit: p< 5 x10 -7Validation/ replicationDense mapping/SequencingFunctional Analysis
11 Validation Independent replication set Genotyping platform Analysis Same inclusion/exclusion subject criteriaSample sizeGenotyping platformSame polymorphismAnalysisDifferent ethnic group (added bonus)
12 Staged Genetic Analysis - RA Linkage/Association/Candidate Gene
13 Dense Mapping/Sequencing Identifies the boundaries of your signalclose in on the target gene/ causal variantfind other (common or rare) variants
14 Functional Analysis Does your gene make sense? pathway function cell typeexpressionanimal modelsPTPN22: 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 PublishImperfect worldnothing significantidentify genes that have no apparent influence in your disease of interestNow what?
16 What Happened? Disease has no genetic component. Viral, bacterial, environmentalGenetic effect is small and your sample size wasn’t big enough to detect it.CDCV vs CDRVPhenotype /or demographics too heterogeneousToo many outliersWrong controls.Population stratification; admixtureNot 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 lociRheumatoid ArthritisLupusCrohn’s diseaseAlzheimer’sSchizophreniaAutism
19 Influence of Admixture Not all Subjects are the same
20 Missing heritabilityExcept for a few diseases (AMD, T1D) genetics explains less than 50% of risk.Large number of genes with small effectsOther influences?
21 Other Contributors Environmental Epigenetic MicroRNA Microbiome Any change in gene expression can influence disease state- not always related directly to DNA sequenceEnvironmentalEpigeneticMicroRNAMicrobiomeCopy Number VariationGene-Gene InteractionsAlternative 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 promiseBetter understanding of biological processes leading to disease pathogenesisDevelopment of new treatmentsIdentify non-genetic influences of diseaseBetter predictive models of riskand the realityFew causal variants have been identifiedClinical heterogeneity and complexity of diseaseGenetic results don’t account for all of disease risk
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 outcomesPersonalized: Is informed by each person’s unique clinical, social, genetic, genomic, and environmental profilePreventive: Emphasizes wellness and prevention to stop disease before it progressesPreemptive: Incorporates action-oriented, individualized health planningParticipatory: 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 subjectThe 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 QuestionsTrue/ FalseAssociation studies are comprised of many multiplex familiesWith 100 randomly chosen polymorphisms and 10,000 diverse human subjects you have a high probability of finding the causative polymorphism for your disease of interestIt’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 studiesIf it weren’t for admixture we would not be able to study complex genetics.