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CS273A Lecture 18: Human Disease Genomics http://cs273a.stanford.edu [Bejerano Fall16/17] 1 Announcements • Hope my vocal chords last till 2:50 :) http://cs273a.stanford.edu [Bejerano Fall16/17] 2 START WITH HYPERBOLE http://cs273a.stanford.edu [Bejerano Fall16/17] 3 The Human Genome Genome 1013 cells ≈ Human Genome = The Operating System that runs every cell in our body 3*109 letters long, over the DNA alphabet = {A,C,G,T} http://cs273a.stanford.edu [Bejerano Fall16/17] 4 The Biggest Challenge in Genomics… … is computational: How does this Program encode this Output This “coding” question has profound implications for our lives http://cs273a.stanford.edu [Bejerano Fall16/17] 5 Match to the Next Three Slides… Time Negative Selection Neutral Drift http://cs273a.stanford.edu [Bejerano Fall16/17] Positive Selection 6 The Biggest Challenge in Genomics… … is computational: How does this encode this Program Forks & re-merges Where did we come from? How are we different from each other? http://cs273a.stanford.edu [Bejerano Fall16/17] 7 The Biggest Challenge in Genomics… … is computational: How does this encode this Program Suite of related products What in our genomes make us different from other species? http://cs273a.stanford.edu [Bejerano Fall16/17] 8 The Biggest Challenge in Genomics… … is computational: How does this Program encode this Bugs Output What genomic mutations predispose us to disease? http://cs273a.stanford.edu [Bejerano Fall16/17] 9 The Biggest Challenge in Genomics… … is computational: How does this Program encode this Bugs Patching What genomic mutations determine our drug response? http://cs273a.stanford.edu [Bejerano Fall16/17] 10 The Biggest Challenge in Genomics… … is computational: How does this Program encode this Bugs Verification We can eliminate suffering by not “booting” “buggy” embryos http://cs273a.stanford.edu [Bejerano Fall16/17] 11 The Biggest Challenge in Genomics… … is computational: How does this Program encode this Bugs Debugging We can eliminate suffering by fixing people’s “buggy” genomes http://cs273a.stanford.edu [Bejerano Fall16/17] 12 Literally Save Lives From Your Keyboard! Read Understand Fix http://cs273a.stanford.edu [Bejerano Fall16/17] 13 Gene Therapy: We Design the Cure Life saving “code injection” http://cs273a.stanford.edu [Bejerano Fall16/17] 14 Biomedicine is facing a phase transition From an obsession with the interpreter, Code Interpreter Output To an obsession with the code. (When your code has a bug what do you fix?..) http://cs273a.stanford.edu [Bejerano Fall16/17] 15 Gene Therapy 3.0: Precise, Hereditary http://cs273a.stanford.edu [Bejerano Fall16/17] 16 Every New Technology Raises Ethical Issues http://cs273a.stanford.edu [Bejerano Fall16/17] 17 Human disease http://cs273a.stanford.edu [Bejerano Fall16/17] 18 Even single basepair mutations can be devastating (to the individual) The Species Tree S S Sampled Genomes S Speciation 20 Time Fixation, Positive & Negative Selection Time Negative Selection Neutral Drift http://cs273a.stanford.edu [Bejerano Fall16/17] Positive Selection 21 Mosaicism germ cells offsprings Mosaicism can be: • somatic (ie in most body cells) or • gonadal (confined solely to the gonads). http://cs273a.stanford.edu [Bejerano Fall16/17] 22 Fundamental Changes in Cancer Cell Physiology Exploitation of natural pathways for cellular growth • Growth Signals (e.g. TGF family) • Angiogenesis • Tissue Invasion & Metastasis Evasion of anti-cancer control mechanisms • Apoptosis (e.g. p53) • Antigrowth signals (e.g. pRb) • Cell Senescence Acceleration of Cellular Evolution Via Genome Instability • DNA Repair • DNA Polymerase Hanahan and Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70. Many Paths Lead to Cancer Self-Sufficiency Hanahan, Douglas, and Ra Weinberg. 2000. The hallmarks of cancer. Cell 100: 57-70. Cancer Mutations The relation between sporadic and inherited forms of the same tumour. The target tissue contains n cells and the chance of one cell suffering a loss of function mutation in the tumour suppressor gene is µ. http://cs273a.stanford.edu [Bejerano Fall16/17] 25 Cancer: A Disease of the Genome Cancer genomes have a really messed up genome. Challenge in Treating Cancer: Every tumor is different Every cancer patient is different http://cs273a.stanford.edu [Bejerano Fall16/17] 26 Cancer Genomics Goal http://cs273a.stanford.edu [Bejerano Fall16/17] 27 Statistical Genetics http://cs273a.stanford.edu [Bejerano Fall16/17] 28 Genome Wide Association Studies (GWAS) Control A/G A/G G/G G/G A/G G/G G/G Disease A/A A/G A/A A/G A/G A/A A/A AA 0 4 AG 3 3 GG 4 0 p-value Genome Semantics • The Genome is ultimately a Programming Language • Statistical approaches ignore genome semantics • Population Genetics (neutral drift) • Selective sweeps (positive selection) • GWAS (negative selection) • All ask questions about allele transmission • Not allele meaning http://cs273a.stanford.edu [Bejerano Fall16/17] 30 Consumer Genomics 1 Collect scientific literature about all structural variant correlations with human disease & traits. 2 Genotype customers for as many informative loci as is commercially viable. 3 Offer counseling for your findings, and their meaning. 4 Ask customers to phenotype themselves. 5 Discover new associations! http://cs273a.stanford.edu [Bejerano Fall16/17] Knowledge = technology + pre-conceptions Our knowledge is strongly influenced by what our technology allows us to see. What it does not, we will with pre-conceptions. And those may often be wrong. Even after half the human genome was sequenced, people thought there will be over 100,000 coding genes in it. There are in fact ~20,000 (in par with the gene repertoire of much simpler animals). Before the next generation sequencing revolution people thought the majority (99%) of human disease mutations will be in the coding regions. 80-90% of human disease GWAS associated SNPs are in fact non-coding. The majority is likely gene regulatory. http://cs273a.stanford.edu [Bejerano Fall16/17] 32 Developmental Defects gene genome protein Limb Malformation Over 300 genes already implicated in limb malformations. http://cs273a.stanford.edu [Bejerano Fall16/17] 33 Genetic Causes gene genome NO protein made Limb Malformation More and more cases are being discovered. http://cs273a.stanford.edu [Bejerano Fall16/17] 34 Modularity: Disease Implications http://cs273a.stanford.edu [Bejerano Fall16/17] 35 Critical regulatory sequences http://cs273a.stanford.edu [Bejerano Fall16/17] 36 Cis-regulatory contribution to Human Disease SHH 1Mb LMBR1 Limb Lettice et al. HMG 2003 12: 1725-35 http://cs273a.stanford.edu [Bejerano Fall16/17] 37 Mendelian Diseases • Caused by 1 or 2 mutations in a patient’s gene • Because of “extensive code reuse” in our genome these few mutations result in complicated phenotypes http://cs273a.stanford.edu [Bejerano Fall16/17] 38 Mendelian Diseases • There are 8,000 known rare Mendelian diseases • Each can cause over a dozen different phenotypes of 10,000 known disease phenotypes • Together rare Mendelian diseases affect 1 in 33 babies • There are over 20,000 genes in the human genome • Sequencing all genes is cheap, and getting cheaper • We now know of thousands of genes that when mutated cause thousands of different Mendelian diseases. • Diagnosing a case is vital for patient & their family • Because doctors rarely see each rare disease twice, and because there are so many genes and so many rare diseases, MANUAL DIAGNOSIS IS UNSUSTAINABLY SLOW, INACCURATE AND EXPENSIVE. http://cs273a.stanford.edu [Bejerano Fall16/17] 39 Diagnosis is theoretically straightforward affected variants unaffected gene-phenotypes associations variants causal mutation http://cs273a.stanford.edu [Bejerano Fall16/17] 40 WHAT NEXT? http://cs273a.stanford.edu [Bejerano Fall16/17] 41 Beyond Coding CODING SNV 1-50bp mutations Exome http://cs273a.stanford.edu [Bejerano Fall16/17] 42 Targeted Sequencing, or looking under the lamp Exome Library Shotgun Library Exon 1 Exon 2 Genomic DNA Capture Methods vs. Shotgun • Targeted sequencing allows for much higher coverage at less cost • Will only capture known sites • These methods also introduce significant captures bias, including 0.1% SNP array $100 failure to capture sites that differ significantly from the reference 2% Exome $1000 genome. (analogous to microarrays) 100%* Genome $2000 http://cs273a.stanford.edu [Bejerano Modified from Meyerson et al. . 2010. Advances in understanding cancer genomes through Fall16/17] second-generation sequencing. Nature Reviews Genetics 11, no. 10 (October): 685-696 Beyond Coding CODING SNV 1-50bp mutations SV rest of spectrum NONCODING Exome http://cs273a.stanford.edu [Bejerano Fall16/17] 44 Beyond Coding SNV 1-50bp mutations SV rest of spectrum CODING NONCODING Exome Genome Genome Genome http://cs273a.stanford.edu [Bejerano Fall16/17] 45 http://cs273a.stanford.edu [Bejerano Fall16/17] 46