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Mapping of Simple & Complex Genetic Diseases Anne Haake Rhys Price Jones.

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1 Mapping of Simple & Complex Genetic Diseases Anne Haake Rhys Price Jones

2 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

3 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

4 Complex Diseases Examples: diabetes, asthma, cardiovascular disease, many cancers, high blood pressure, Alzheimers disease Many more..

5 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 URSE2000/Pdf/Drayna.pdf URSE2000/Pdf/Drayna.pdf

6 Positional Cloning Approach Isolate a disease gene based on its chromosomal position No prior knowledge of structure, function, or pathological mechanism

7 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

8 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) 1b.htm#ls

9 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 linkage/linkage6.htm linkage/linkage6.htm

10 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

11 Key Recombinants

12 Genes Mapped to 12q23-24.1 IGFInsulin-like Growth Factor NFYBNuclear Factor Binding to Y PAHPhenylalanine Hydroxlyase TSC3Tuberous Sclerosis ACADSacyl-coenzyme A dehydrogenase ATP2A2ATPase Ca ++ transporting SCA2Spinal Cerebellar Ataxia MYL2Myosin light polypeptide PMCHpro-melanin-concentration PLA2APhospholipase 2A IFNGInterferon gamma PPP1CCProtein phosphatase 1 ALDH2Aldehyde dehydrogenase NOS1Nitric Oxide Synthase TRA1Tumor Rejection Antigen ZNF26Zinc Finger Protein TCF1Transcription Factor 1 UBCUbiquitin C SPSMAScapuloperoneal spinal muscular atrophy

13 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

14 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

15 Complex traits dont 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

16 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)

17 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

18 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

19 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

20 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?

21 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

22 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

23 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

24 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

25 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.

26 Successful Use of Genome-Wide Screens Alzheimers disease –ApoE gene has 2 SNPs –3 alleles ApoE2, ApoE3, ApoE4 –Association of the ApoE4 allele with Alzheimers 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

27 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

28 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

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