Mapping ofSimple & Complex Genetic Diseases

Anne Haake

Rhys Price Jones

Simple Diseases

• Follow Mendelian inheritance patterns – e.g. autosomal dominant, x-linked recessive

• Generally rare• Caused by changes in one gene• Examples: Cystic Fibrosis, Duchenne

Muscular Dystrophy

Complex Diseases

• aka Common Diseases• Tend to cluster in families but do not follow Mendelian

inheritance patterns• Result from action of multiple genes• Alleles of these genes are “susceptibility factors”• Most factors are neither necessary or sufficient for

disease• Complex interaction between environment and these

susceptibility alleles contributes to disease

Complex Diseases

• Examples: diabetes, asthma, cardiovascular disease, many cancers, high blood pressure, Alzheimer’s disease

• Many more..

How do we study these?

• Simple diseases: – Usually a complete correlation between genotype and

phenotype– “easy” to analyze

• A nice overview of strategies by Dennis Drayna at NHGRI

• http://www.nhgri.nih.gov/Pages/Hyperion/COURSE2000/Pdf/Drayna.pdf

Positional Cloning Approach

• Isolate a disease gene based on its chromosomal position

• No prior knowledge of structure, function, or pathological mechanism

Need some markers

• DNA polymorphisms “many forms”• Variation in population allows us to use them

as informative markers • Identified by common lab techniques such as

PCR• Examples:

– RFLP-restriction length polymorphisms– Microsatellites- tandem repeats, e.g (CA)n– SNPs-single nucleotide polymorphisms

Recombination Frequency

• RF (genetic distance), also called theta) between 2 loci is related to how far apart they are on the chromosome (physical distance)

• So..can estimate physical distances by measuring .

• 1% RF roughly equivalent to 1cM (1 Mb DNA)

http://www.abdn.ac.uk/~gen155/lectures/gn3801b.htm#ls

Strategy

• Look for co-inheritance of disease and some marker; known as linkage

• If a marker (polymorphism) is close to a disease gene then there is a low chance of meiotic recombination between them

• Family studies are required; study of individuals in generations allows us to figure out pattern of inheritance of disease relative to markers

• Generate LOD Scores

http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/linkage/linkage6.htm

An Example: Darier's Disease

Synonyms:

McKusick #12420

Darier-White Disease

Keratosis follicularis

Genetics:

autosomal dominant

high penetrance

1:100,000 Denmark

1:36,000 northeast England

Key Recombinants

D12S84 2 6 2 6 5 6 D12S105 3 4 3 4 3 4 D12S234 3 4 3 4 1 4 D12S129 - - 3 2 - - D12S354 1 1 1 1 1 1

6 3 2 4 - - 2 3 - - - - 2 3 - - 6 4 4 2 3 4 4 2 3 2 3 2 3 4 3 2 4 4 4 2 4 2 3 4 4 2 3 2 3 2 - - 3 2 4 4 4 2 2 2 3 3 3 2 3 2 3 2 - - 3 2 2 3 2 2 1 3 1 5 1 3 1 3 1 3 1 5 1 3 1 5 1 3

3 4 2 4 2 4 2 3 3 5

I

II

III

D12S84 D12S105 D12S234 D12S129 D12S354

Genes Mapped to 12q23-24.1IGFInsulin-like Growth FactorNFYB Nuclear Factor Binding to YPAH Phenylalanine HydroxlyaseTSC3 Tuberous SclerosisACADS acyl-coenzyme A dehydrogenaseATP2A2 ATPase Ca++ transportingSCA2 Spinal Cerebellar AtaxiaMYL2 Myosin light polypeptidePMCH pro-melanin-concentrationPLA2A Phospholipase 2AIFNG Interferon gammaPPP1CC Protein phosphatase 1ALDH2 Aldehyde dehydrogenaseNOS1 Nitric Oxide SynthaseTRA1 Tumor Rejection AntigenZNF26 Zinc Finger ProteinTCF1 Transcription Factor 1UBC Ubiquitin CSPSMA Scapuloperoneal spinal muscular atrophy

Burden of Proof

• Mendelian traits (1) Mapping the gene to a small genetic interval

(2) Study of candidate genes

(3) identification of sequence variants (often coding, but not always) in affected individuals

• More difficult for complex traits

Quantitative Trait Loci (QTL)

• Complex traits are also known as QTLs• Term used most in agricultural, horticultural

genetics• Why quantitative?• Consider Mendelian traits

– Cross short pea plant vs. tall pea plant– F2 generation: you know the genotype of the short plants

and you can generalize the genotype of the tall & can predict phenotype from genotype

– Phenotypes are called discontinuous traits

• Complex traits don’t fall into discrete classes

• Consider ear length in corn– Cross short ears with long ears

– F1 generation: intermediate ears

– F2: ranges from short to tall with intermediate lengths in a normal distribution

• Called continuous traits

• Often given a quantitative value

• Loci controlling these traits are QTL

Complex Diseases

• Difficult to study• Conflicting theories of the genetics underlying

these diseases• 2 major theories: very controversial!• Common Disease/Common Variant (CD/CV)• Common Disease/Rare Allele (CD/RA)

• CD/CV– Alleles that existed prior to the global dispersal of humans or

those subject to positive selection represent a significant proportion of the susceptibility alleles for common disease

• CD/RA– Most mutations underlying common disease have occurred

after the divergence of populations– Expect heterogeneity in genes in common diseases

CD/CV

• Susceptibility alleles confer moderate risk and occur at relatively high rates in the population (>= 1%).

• Suggests that association studies in large cohort populations (e.g. unrelated individuals sharing the common disease) will be fruitful

• SNPs have facilitated this type of study– easy to measure, stable in population

SNPs

• Single Nucleotide Polymorphisms (SNPs) “snips”• SNP Facts:

– Humans share about 99.9% sequence identity– The other 0.1% (about 3 million bases) are mostly SNPs– SNPs occur about every 1000 bases– There are “hot-spots”– Most SNPs have only 2 alleles– Most SNPs not in coding regions (99% not in genes)– SNPs can cause silent, harmless, harmful, or latent changes

• Current estimates only about 2000 of the 2.3 million change an amino acid

– Haplotype: a set of SNPs along a chromosome

http://www.genome.gov/10001665

SNPs

• Where does SNP data come from?• Lots of sources:

– Parallel sequencing on a genome-wide scale– EST data mining– BAC clone sequencing– Sequencing within suspected disease genes – Sequencing of individual chromosomes

• Questions for validation– Are they sequencing errors? Is a suspected SNP simply a

splice variant? Duplicated regions?

Association Studies

• SNPs usually serve as biological markers rather than underlying cause of disease

• SNP is located near a gene associated with a disease

• Allelic association aka linkage disequilibrium• Compare genome wide SNP profiles from individuals

with the disease to those without the disease. • Difference identifies a putative disease profile that

may eventually be used in diagnosis

Haplotype Mapping

• Definition of a complete HapMap one of the goals of the SNP Consortium

• Questions remain in the community about the degree of linkage disequilibrium in the human population

• Estimates vary from 3kb-400 kb• Not very useful for disease mapping at either

end

Burden of Proof

• Complex Diseases-what are the steps to gene discovery? (1) Linkage or Association

-challenges in testing numerous genetic markers for linkage and correlating inheritance patterns

-minimal intervals of QTLs are usually no less than 10-30 cM (typically 100-300 genes in that interval)

-makes candidate gene studies difficult

Burden of Proof for Complex Diseases

• (2) Fine-mapping– Genetic crosses, family-based studies of linkage

disequilibrium using dense markers– Are SNPs the optimal markers?

• (3) Sequence analysis to identify candidate variants

• (4) Functional tests such as replacement of variant to swap phenotypes

• (5) Additional evidence at cellular and tissue levels

Model Organisms

• One of most promising approaches is to extend the human mapping studies to animal models

• Take advantage of highly inbred strains • Take advantage of genome synteny to relate

mouse results back to human genes.

Successful Use of Genome-Wide Screens

• Alzheimer’s disease– ApoE gene has 2 SNPs– 3 alleles ApoE2, ApoE3, ApoE4– Association of the ApoE4 allele with Alzheimer’s disease &

APOE4 protein in brain lesions

• Mouse: mutations in tubby gene – Cause obesity, retinal degeneration, hearing loss– More evidence of multi-gene interactions

• Modifier gene (moth1) protects tubby mice from hearing loss • Mtap1a cDNA rescues hearing loss

High-throughput SNP analysis

• Genotyping via oligonucleotide arrays• e.g. Affymetrix has 10K and 100K arrays• Analysis with DNA isolated from only a few

drops of blood

Data Analysis?

• Shares some problems with gene expression arrays– e.g. get measurements across many, many genes

• Some use of clustering/classification approaches to discover patterns in the data