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 Badano and Katsanis, NRG 2002 Some notable genetic diseases: cystic fibrosis (Mendelian recessive) sickle-cell anemia (Mendelian recessive)

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

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 Badano and Katsanis, NRG 2002 A)Multiple genes may influence the trait. B)E.g. retinitis pigmentosa requires heterozygosity for two genes.

12. Population genetics continued… acggttatgtaga accgttatgtaga acggttatgtaga accgttatgtaga 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 marker site functional 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. Case-control association testing searching for markers with “significant” marker allele frequency differences between cases and controls; these marker signify regions of possible causative alleles AF(cases) AF(controls) clinical cases clinical controls genotyping cases and controls at various polymorphisms

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

17. Where is the missing heritability of disease? Manolio et 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. New 1000 Genomes Population HapMap 3 Population International HapMap Population * * - International HapMap CEU samples are in NIGMS Human Genetic Cell Repository; 1KGP – included in 1000 Genomes Project EUROPE AMERICAS AFRICA SOUTH ASIA LWK MSLESN YRI GIH BEB STUITU PJL ASW MXL PUR CLM PEL EAST ASIA CHB JPT CHS CDX KHV TSI IBSGBR FIN Spain Finland Beijing, China Tokyo, Japan Yunnan, China Vietnam Hunan & Fujian, China Los Angeles, USA Puerto Rico Medellín, Colombia Lima, Peru Pakistan Bangladesh Great Britain Italy ACB Barbados Colorado, USA Southwest, USA Houston, USA The Gambia Sierra, Leone Kenya Nigeria GWD New 1000 Genomes Population HapMap 3 Population International HapMap Population 1000 Genomes Project Populations CEU Utah, USA ~2,500 samples representing all continents

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

22. 1000 Genome Project variants

23. We know 99% of SNP variants in any individual DateFraction not in dbSNP February, 200098% February, 200180% April, 200810% February, 20112% May 20111% Ryan Poplin, David Altshuler 38M SNPs are known as of Phase 1 of the 1000 Genomes Project

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

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) 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.) Yandell et al. GR 2011

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