Genetic Analysis in Human Disease. Learning Objectives Describe the differences between a linkage...
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Transcript of Genetic Analysis in Human Disease. Learning Objectives Describe the differences between a linkage...
Learning Objectives
Describe the differences between a linkage analysis and an association analysis
Identify potentially confounding factors in a genetic study
Define missing heritability
Question:
1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects?
A) Phenotype, gender and age B) Phenotype, gender and income C) Gender, age and income D) Age, income and education
Question:
2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong?
A) Recruited too many subjects B) Population was too homogeneous C) Not enough subjects D) Genotyped using only one platform
Question:
3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step?
A) End of story, move on to the next study B) Develop new drugs C) Replication/validation D) Patent the SNPs
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 Studies- all in the family Family based method to map location of disease
causing loci
Families Multiplex Trios Sib pairs
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
Validation
Independent replication set Same inclusion/exclusion subject criteria Sample size
Genotyping platform Same polymorphism
Analysis Different ethnic group (added bonus)
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
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
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
GWAS – what have we found?
3800 SNPs identified for 427 diseases and traits Only 7% in coding regions >50% in DNAse sensitive sites, presumed regulatory regions
Genome-Wide Association Studies The reality
Few causal variants have been identified Clinical heterogeneity and complexity of disease
Genetic results don’t account for all of disease risk
Genome-Wide Association Studies The potential clinical applications
Risk prediction Type 1 Diabetes (MHC and 50 loci)
Disease subtyping/classification MODY: HNF1A- C- reactive protein biomarker
Drug development Ribavirin- induced anemia: ITPA variants protective
Drug toxicity/ adverse effects MCR4 SNPs and extreme SGA-induced weight gain
(Manolio 2013)
Question:
1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects?
A) Phenotype, gender and age B) Phenotype, gender and income C) Gender, age and income D) Age, income and education
Answer:
1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects?
A) Phenotype, gender and age
Question:
2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong?
A) Recruited too many subjects B) Population was too homogeneous C) Not enough subjects D) Genotyped using only one platform
Answer:
2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong?
C) Not enough subjects
Question:
3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step?
A) End of story, move on to the next study B) Develop new drugs C) Replication/validation D) Patent the SNPs