Mapping of Simple & Complex Genetic Diseases: Understanding Inheritance Patterns and Study Methods

1. Mapping ofSimple & 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, Alzheimer’s 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 • http://www.nhgri.nih.gov/Pages/Hyperion/COURSE2000/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 q (theta) between 2 loci is related to how far apart they are on the chromosome (physical distance) • So..can estimate physical distances by measuring q. • 1% RF roughly equivalent to 1cM (1 Mb DNA) http://www.abdn.ac.uk/~gen155/lectures/gn3801b.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 http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/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 IGF Insulin-like Growth Factor NFYB Nuclear Factor Binding to Y PAH Phenylalanine Hydroxlyase TSC3 Tuberous Sclerosis ACADS acyl-coenzyme A dehydrogenase ATP2A2 ATPase Ca++ transporting SCA2 Spinal Cerebellar Ataxia MYL2 Myosin light polypeptide PMCH pro-melanin-concentration PLA2A Phospholipase 2A IFNG Interferon gamma PPP1CC Protein phosphatase 1 ALDH2 Aldehyde dehydrogenase NOS1 Nitric Oxide Synthase TRA1 Tumor Rejection Antigen ZNF26 Zinc Finger Protein TCF1 Transcription Factor 1 UBC Ubiquitin C SPSMA Scapuloperoneal 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 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

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 http://www.genome.gov/10001665

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 • 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

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