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Medical variations

Medical variations. Gabor T. Marth Boston College Biology Department BI543 Fall 2013 February 5, 2013. Medical variations. Phenotypic effects are often caused by genetic variants. Many SNPs have phenotypic effects. Some notable genetic diseases: cystic fibrosis

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Medical variations

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  1. Medical variations Gabor T. Marth Boston College Biology Department BI543 Fall 2013 February 5, 2013

  2. Medical variations

  3. Phenotypic effects are often caused by genetic variants

  4. Many SNPs have phenotypic effects Some notable genetic diseases: cystic fibrosis (Mendelian recessive) sickle-cell anemia (Mendelian recessive) Badano and Katsanis, NRG 2002

  5. Genetic variants may affect drug metabolism: Pharmacogenetics Evans and Relling, Science 1999

  6. Genetic variants in Pharmacogenetics Evans and Rellig, Science 1999

  7. Finding variants that cause genetic disease

  8. TAACAAT • mutations are propagated down through generations MRCA TAAAAAT TAAAAAT TAACAAT TAAAAAT TAAAAAT TAACAAT TAACAAT TAACAAT • and determine present-day variation patterns Population genetics 101 • sequence variations are the result of mutation events TAAAAAT

  9. Mendelian diseases have simple inheritance genotype inheritance Mendelian diseases have simple relationship between genotype + phenotype inheritance

  10. Linkage analysis compares the transmission of marker genotype and phenotype in families Sequence regions of the genome to determine which loci are linked with the trait. Works well for Mendelian diseases

  11. However, some diseases have complex inheritance • Multiple genes may influence the trait. • E.g. retinitis pigmentosa requires heterozygosity for two genes. Badano and Katsanis, NRG 2002

  12. acggttatgtaga acggttatgtaga acggttatgtaga accgttatgtaga acggttatgtaga acggttatgtaga accgttatgtaga acggttatgtaga acggttatgtaga accgttatgtaga Population genetics continued… accgttatgtaga accgttatgtaga acggttatgtaga • because of recombination, DNA sequences may not have a unique common ancestor, hence phylogenetic analysis may not apply

  13. Genetic mapping

  14. Allelic association (linkage disequilibrium, LD) • allelic association is the non-random assortment between alleles i.e. it measures how well knowledge of the allele state at one site permits prediction at another functional site marker site • significant allelic association between a marker and a functional site permits localization (mapping) even without having the functional site in our collection • allelic association, and the use of genetic markers is the basis for mapping functional alleles

  15. genotyping cases and controls at various polymorphisms clinical cases clinical controls AF(controls) • searching for markers with “significant” marker allele frequency differences between cases and controls; these marker signify regions of possible causative alleles AF(cases) Case-control association testing

  16. Genome-wide scans for human diseases SNPs in Complement Factor H (CFH) gene are associated with Age-related Macular Degeneration (AMD) Klein et al, Science 2005

  17. Where is the missing heritability of disease? Manolioet al. Nature 2009

  18. Variant discovery in population sequencing data

  19. Intro • International project to construct a foundational data set for human genetics • Discover virtually all common human variations by investigating many genomes at the base pair level • Consortium with multiple centers, platforms, funders • Aims • Discover population level human genetic variations of all types (95% of variation > 1% frequency) • Define haplotype structure in the human genome • Develop sequence analysis methods, tools, and other reagents that can be transferred to other sequencing projects

  20. 1000 Genomes Project Populations EUROPE TSI FIN IBS GBR CEU AMERICAS EAST ASIA CHB Finland ASW Great Britain Utah, USA JPT MXL Colorado, USA Beijing, China Italy Tokyo, Japan Los Angeles, USA Spain Southwest, USA PUR CHS Yunnan, China Pakistan Hunan & Fujian, China Houston, USA The Gambia Puerto Rico Bangladesh Sierra, Leone ACB CDX Vietnam Barbados Nigeria Medellín, Colombia Kenya CLM KHV Lima, Peru PEL BEB PJL GIH STU ITU LWK YRI GWD MSL ESN SOUTH ASIA AFRICA ~2,500 samples representing all continents HapMap3 Population International HapMap Population New 1000 Genomes Population International HapMap Population* New 1000 Genomes Population HapMap3 Population *- International HapMap CEU samples are in NIGMS Human Genetic Cell Repository; 1KGP – included in 1000 Genomes Project

  21. Sequencing strategies Deep-coverage whole-exome data Low-coverage whole-genome data

  22. 1000 Genome Project variants

  23. We know 99% of SNP variants in any individual 38M SNPs are known as of Phase 1 of the 1000 Genomes Project Ryan Poplin, David Altshuler

  24. Newly discovered SNPs are mostly rare 12M 10M 8M number of sites 6M 4M 2M (Ryan Poplin) 0 0.001 0.01 0.1 1.0 frequency of alternate allele

  25. Deep exome vs. low-cov. WG sequencing

  26. Properties of low-frequency variation

  27. Rare SNPs enriched for functional variants

  28. Challenges for finding rare disease variants Bansal et al. NRG 2010

  29. Concepts for method development Bansal et al. NRG 2010

  30. Concepts for method development Bansal et al. NRG 2010

  31. A rare variant predictor (VAAST) Yandell et al. GR 2011 • Instead of individual variants, use a larger unit for comparison e.g. a gene • Weight predicted impact of variant (e.g. non-synonymous change, large allele frequency difference etc.)

  32. Systems bringing high-res genetic knowledge to the “bedside”

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