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Published Genome-Wide Associations through 2011 1,617 published GWA at p≤5X10 -8 for 249 traits

CSCI2951-N: Advanced Algorithms in Computational Biology http ://www.cs.brown.edu/courses/ csci2951-n/ Prof. Sorin Istrail. Published Genome-Wide Associations through 2011 1,617 published GWA at p≤5X10 -8 for 249 traits. The GWAS Human Genome. Autism marker Multiple Sclerosis Marker.

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Published Genome-Wide Associations through 2011 1,617 published GWA at p≤5X10 -8 for 249 traits

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  1. CSCI2951-N: Advanced Algorithms in Computational Biology http://www.cs.brown.edu/courses/csci2951-n/Prof. Sorin Istrail Published Genome-Wide Associations through 2011 1,617 published GWA at p≤5X10-8 for 249 traits The GWAS Human Genome Autism marker Multiple Sclerosis Marker

  2. CSCI2951-N: Advanced Algorithms in Computational Biology http://www.cs.brown.edu/courses/csci2951-n/Prof. Sorin Istrail Published Genome-Wide Associations through 2011 1,617 published GWA at p≤5X10-8 for 249 traits Genetic Heterogeneity The GWAS Human Genome The Common Disease Common Variant (CDCV) hypothesis is dead. Long live the Common Disease Many Rare Variants hypothesis! The CDCV ‘s classical drawing metaphor as “Needles in the Haystack,” with few needles with a common look in a large haystack, needs to be replaced now with a van Gogh-like drawing, with many needles each differently looking and private to areas in the large haystack. Vincent

  3. CSCI2951-N: Advanced Algorithms in Computational Biology http://www.cs.brown.edu/courses/csci2951-n/Prof. Sorin Istrail Published Genome-Wide Associations through 2011 1,617 published GWA at p≤5X10-8 for 249 traits The Missing Heritability Puzzle The GWAS Human Genome Additivity of alleles? Just a convenient approximation, friendly to “heritability” measured as a correlation coefficient. Ronald

  4. CSCI2951-N: Advanced Algorithms in Computational Biology http://www.cs.brown.edu/courses/csci2951-n/Prof. Sorin Istrail Published Genome-Wide Associations through 2011 1,617 published GWA at p≤5X10-8 for 249 traits Application Topics include The GWAS Human Genome • haplotype phasing, linkage disequilibrium, tagging  SNPs, identical by descent (IBD), pedigrees, trios • coalescent theory, Polya urn game, Ewens sampling lemma, genome-wide graph theory algorithms • the genetic heterogeneity problem, the missing heritability problem • statistical models of disease, association tests and multiple hypothesis testing • autism, multiple sclerosis, type 2 diabetes

  5. Genomic Foundations • Modeling and Measuring Evolution: Linkage Disequilibrium (LD), Urn Models • Genome-Wide Association Studies (GWAS): Statistical associations, the missing heritabililty problem, genetic heterogeneity, genomic privacy

  6. Algorithms • Maximum Likelihood and Expectation-Maximization Algorithms Biological Problem: Inferring haplotype frequencies in populations. • Set-cover and Minimum Informative Subset Algorithms Biological Problem: Tagging SNPs selection, LD.

  7. Algorithms • Markov Chain Monte Carlo Algorithms Biological Problem: Population Substructure • Knapsack Algorithms and Statistical Hypothesis Testing, The Neyman-Pearson Lemma, Multiple Testing Biological Problem: Statistical Associations in GWAS

  8. Algorithms • Voting Theory Algorithms, von Neumann-Morgenstern Utility Theory & The Social Network of Protein Folds Biological Problem: The Protein Folding Problem - Individual Preferences of Amino Acids and the Thermodynamic "Social Choice" Hypothesis - The Protein Folding Energy Function Inference Problem

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