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Integrative Genomics Data G - genetic variation Concepts GF Mapping T - transcript levels Models: Networks P - protein concentrations Hidden Structures/ Processes M - metabolite concentrations Knowledge F – phenotype/phenome Evolution Analysis and Functional Explanation Single Data Analysis Molecular Dissection Single type + phenotype Analysis Detailed Dynamic Model Multiple Data Types/ Integrated Analysis Alan D Lopez, Colin D Mathers, Majid Ezzati, Dean T Jamison, Christopher J L Murray Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data Lancet 2006; 367: 1747–57 Cost of Disease •Most research in the bioscience is motivated by hope of disease intervention. • Major WHO projects have tried to tabulate the costs of different diseases • Genetic Diseases are diseases where there is genetic variation in the susceptibility. • Even small improvements would save many billions Central Dogma DNA RNA Protein Metabolism & Cell Structure Organism Prokaryote 1010 atoms Eukaryote 1013 atoms Human 1014 cells From wikipedia What is a bacteria? A human being? The Central Dogma & Data Protein-DNA binding Data Chip-chip protein arrays DNA Protei n mRNA Translation Transcription Genetic Data SNPs – Single Nucleotide Polymorphisms Re-sequencing CNV - Copy Number Variation Microsatellites Transcript Data Micro-array data Gene Expression Exon Splice Junction Metabolite Cellular processes Proteomic Data NMR Mass Spectrometry 2D-gel electrophoresis Embryology Organismal Biology Metabonomic Data NMR Mass Spectrometry 2D-Gel electrophoresis Metabonomics Genetical Genomics Proteomics Transcriptomics Genetic Mapping Phenotype Phenotypic Data Clinical Phenotypes Disease Status Quantitative Traits Blood Pressure Body Mass Index Structure of Integrative Genomics DNA Classes mRNA Protei n Metabolite Phenotype Parts Concepts GF Mapping Models: Networks Physical models: Phenomenological models: Unobservered/able Hidden Structures/ Processes Knowledge: Evolution: Externally Derived Constraints on which Models are acceptable Cells in Ontogeny Individuals/Sequences in a Population Analysis: Data + Models + Inference Functional Explanation Model Selection Species G: Genomes A diploid genome: Key challenge: Making a single molecule observable!! Classical Solution (70s): Many De Novo Sequencing: Halted extensions or degradation extension degradation 80s: From one to many: PCR – Polymerase Chain Reaction 00s: Re-sequencing: Hybridisation to complete genomes Future Solution: One is enough!! Observing the behavior of the polymerase Passing DNA through millipores registering changes in current G: Assembly and Hybridisation Target genome 3*109 bp (unobservable) Reads 3-400 bp (observable) Contigs Contigs and Contig Sizes as function of Genome Size (G), Read Size (L) and overlap (Ø): {A,C} Complementary or almost complementary strings allow interrogation. probe {T,G} Lander & Waterman, 1988 Statistical Analysis of Random Clone Fingerprinting Sufficient overlap allows concatenation T - Transcriptomics Classical Expression Experiment: The Gene is transcribed into pre-mRNA Pre-mRNA is processed into mRNA Probes are designed hybridizing to specific positions Measures transcript levels averaging of a set of cells. RNA-Seq Expression Experiment: Advantages - Discoveries More quantitative in evaluating expression levels More precise in positioning Much more is transcribed than expected. Transcription of genes very imprecise Wang, Gerstein and Snyder (2009) RNA-Seq: a revolutionary tool for Transcriptomics NATURE REVIEwS genetics VOLUME 10.57-64 T - Transcriptomics Cox and Mann (2007) Is Proteomics the New Genomics? Cell 130,39599 P – Proteomics P – Proteomics Hoog and Mann (2004) “Proteomics” Annu. Rev. Genomics Hum. Genet. 5:267– 9 P uses Mass Spectrometry and 2D gel electrophoresis of degraded peptides and Protein Arrays using immuno-recognition of complete proteins http://www.hupo.org/ Concepts GF Mapping Physical models: Models: Networks Phenomenological models: Hidden Structures/ Processes Knowledge: Evolution: Unobservered/able Externally Derived Constraints on which Models are acceptable Cells in Ontogeny Individuals/Sequences in a Population Species G F • Mechanistically predicting relationships between different data types is very difficult • Empirical mappings are important • Functions from Genome to Phenotype stands out in importance G is the most abundant data form - heritable and precise. F is of greatest interest. DNA mRNA Protei n Metabolite Phenotype “Zero”-knowledge mapping: dominance, recessive, interactions, penetrance, QTL,. Mapping with knowledge: weighting interactions according to co-occurence in pathways. Model based mapping: genomesystemphenotype Height Weight Disease status Intelligence ………. Environment The General Problem is Enormous Set of Genotypes: 1 3* 107 • Diploid Genome • In 1 individual, 3* 107 positions could segregate. • In the complete human population 5*108 might segregate. • Thus there could be 2500.000.00 possible genotypes Partial Solution: Only consider functions dependent on few positions • Causative for the trait Classical Definitions: • Single Locus • Multiple Loci Dominance Recessive Additive Heterotic Epistasis: The effect of one locus depends on the state of another Quantitative Trait Loci (QTL). For instance sum of functions for positions plus error term. X (G ) i i i causative positions Genotype and Phenotype Covariation: Gene Mapping Sampling Genotypes and Phenotypes Decay of local dependency Time Reich et al. (2001) Genetype -->Phenotype Function Result:The Mapping Function Dominant/Recessive Penetrance A set of characters. Binary decision (0,1). Spurious Occurrence Quantitative Character. Heterogeneity genotype Genotype Phenotype phenotype Pedigree Analysis & Association Mapping Association Mapping: Pedigree Analysis: M r D Pedigree known D 2N generations M r Few meiosis (max 100s) Resolution: cMorgans (Mbases) Pedigree unknown Many meiosis (>104) Resolution: 10-5 Morgans (Kbases) Adapted from McVean and others Heritability: Inheritance in bags, not strings. The Phenotype is the sum of a series of factors, simplest independently genetic and environmental factors: F= G + E Parents: Relatives share a calculatable fraction of factors, the rest is drawn from the background population. This allows calculation of relative effect of genetics and environment Heritability is defined as the relative contribution to the variance of the genetic factors: G2 / F2 Siblings: Visscher, Hill and Wray (2008) Heritability in the genomics era — concepts and misconceptions nATurE rEvIEWS | genetics volumE 9.255-66 Heritability Examples of heritability Heritability of multiple characters: Rzhetsky et al. (2006) Probing genetic overlap among complex human phenotypes PNAS vol. 104 no. 28 11694–11699 Visscher, Hill and Wray (2008) Heritability in the genomics era — concepts and misconceptions nATurE rEvIEWS | genetics volumE 9.255-66 Protein Interaction Network based model of Interactions The path from genotype to genotype could go through a network and this knowledge can be exploited NETWORK GENOME 1 Groups of connected genes can be grouped in a supergene and disease dominance assumed: a mutation in any allele will cause the disease. 2 n Rhzetsky et al. (2008) Network Properties of genes harboring inherited disease mutations PNAS. 105.11.4323-28 PHENOTYPE PIN based model of Interactions Emily et al, 2009 Single marker association Protein Interaction Network PIN gene pairs are allowed to interact Interactions creates nonindependence in combinations Phenotype i SNP 1 Gene 1 Gene 2 3*3 table SNP 2 Summary of this lecture Data G - genetic variation Concepts GF Mapping T - transcript levels Models: Networks P - protein concentrations Hidden Structures/ Processes M - metabolite concentrations Knowledge F – phenotype/phenome Evolution GF Mapping General Function Enormous Used for Disease Gene Finding Can Include Biological Knowledge