genetic analysis in human disease. learning objectives describe the differences between a linkage...

Genetic Analysis in Human Disease

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

The spectrum of genetic effects in complex diseases

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

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)

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

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:


A) Phenotype, gender and age B) Phenotype, gender and income C) Gender, age and income D) Age, income and education

Answer:


A) Phenotype, gender and age

Question:


A) Recruited too many subjects B) Population was too homogeneous C) Not enough subjects D) Genotyped using only one platform

Answer:


C) Not enough subjects

Question:


A) End of story, move on to the next study B) Develop new drugs C) Replication/validation D) Patent the SNPs

Answer:


C) Replication/validation

genetic analysis in human disease. learning objectives describe the differences between a linkage...

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