Integrative Genomics

1. Integrative Genomics Data Concepts G - genetic variation GF 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

2. 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 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 • Genetic Diseases are diseases where there is genetic variation in the susceptibility. • Even small improvements would save many billions

3. What is a bacteria? A human being? From wikipedia Central Dogma DNA RNA Protein Metabolism & Cell Structure Organism Prokaryote 1010 atoms Eukaryote 1013 atoms Human 1014 cells

4. The Central Dogma & Data Protein-DNA binding Data Chip-chip protein arrays Phenotype Protein Metabolite DNA mRNA Embryology Organismal Biology Translation Cellular processes Transcription Genetic Data SNPs – Single Nucleotide Polymorphisms Re-sequencing CNV - Copy Number Variation Microsatellites Transcript Data Micro-array data Gene Expression Exon Splice Junction Proteomic Data NMR Mass Spectrometry 2D-gel electrophoresis Metabonomic Data NMR Mass Spectrometry 2D-Gel electrophoresis Phenotypic Data Clinical Phenotypes Disease Status Quantitative Traits Blood Pressure Body Mass Index Metabonomics Genetical Genomics Proteomics Transcriptomics Genetic Mapping

5. Structure of Integrative Genomics Classes Protein Metabolite DNA mRNA Phenotype Concepts Parts GF Mapping Physical models: Models: Networks Phenomenological models: Hidden Structures/ Processes Unobservered/able Knowledge: Externally Derived Constraints on which Models are acceptable Evolution: Cells in Ontogeny Individuals/Sequences in a Population Species Model Selection Data + Models + Inference Analysis: Functional Explanation

6. 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

7. G: Assembly and Hybridisation Target genome 3*109 bp (unobservable) Reads 3-400 bp (observable) Contigs Sufficient overlap allows concatenation Contigs and Contig Sizes as function of Genome Size (G), Read Size (L) and overlap (Ø): Lander & Waterman, 1988 Statistical Analysis of Random Clone Fingerprinting {A,C} Complementary or almost complementary strings allow interrogation. probe {T,G}

8. E - Epigenomics

9. 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.

10. T - Transcriptomics RNA-Seq Expression Experiment: Advantages - Discoveries More quantitative in evaluating expression levels More precise in positioning Much more is transcribed than expected. Wang, Gerstein and Snyder (2009) RNA-Seq: a revolutionary tool for Transcriptomics NATURE REVIEwS genetics VOLUME 10.57-64 Transcription of genes very imprecise

11. M - Metabonomics

12. Concepts Physical models: GF Mapping Models: Networks Phenomenological models: Unobservered/able Hidden Structures/ Processes Knowledge: Externally Derived Constraints on which Models are acceptable Cells in Ontogeny Individuals/Sequences in a Population Species Evolution:

13. 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. Phenotype Protein Metabolite DNA mRNA “Zero”-knowledge mapping: dominance, recessive, interactions, penetrance, QTL,. Mapping with knowledge: weighting interactions according to co-occurence in pathways. Model based mapping: genomesystemphenotype Height Weight Disease status Intelligence ………. Environment

14. 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 Dominance Recessive Additive Heterotic • Multiple Loci 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.

15. Genotype and Phenotype Covariation: Gene Mapping Decay of local dependency Time Reich et al. (2001) Genetype -->Phenotype Function Dominant/Recessive Penetrance Spurious Occurrence Heterogeneity genotype phenotype Genotype  Phenotype Sampling Genotypes and Phenotypes Result:The Mapping Function A set of characters. Binary decision (0,1). Quantitative Character.

16. D r r M D M Pedigree Analysis & Association Mapping Association Mapping: Pedigree Analysis: 2N generations Pedigree known Few meiosis (max 100s) Resolution: cMorgans (Mbases) Pedigree unknown Many meiosis (>104) Resolution: 10-5 Morgans (Kbases) Adapted from McVean and others

17. 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 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: Parents: Siblings: Visscher, Hill and Wray (2008) Heritability in the genomics era — concepts and misconceptions nATurE rEvIEWS | genetics volumE 9.255-66

18. 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

19. Protein Interaction Network based model of Interactions PHENOTYPE NETWORK GENOME 2 n 1 The path from genotype to genotype could go through a network and this knowledge can be exploited Rhzetsky et al. (2008) Network Properties of genes harboring inherited disease mutations PNAS. 105.11.4323-28 Groups of connected genes can be grouped in a supergene and disease dominance assumed: a mutation in any allele will cause the disease.

20. PIN based model of Interactions Emily et al, 2009 Gene 1 Gene 2 Single marker association Protein Interaction Network PIN gene pairs are allowed to interact Phenotype i 3*3 table SNP 1 Interactions creates non-independence in combinations SNP 2

21. Summary of this lecture Data Concepts G - genetic variation GF Mapping T - transcript levels Models: Networks P - protein concentrations Hidden Structures/ Processes M - metabolite concentrations Knowledge F – phenotype/phenome Evolution GF Mapping General Function Enormous Used for Disease Gene Finding Can Include Biological Knowledge

22. P – Proteomics Cox and Mann (2007) Is Proteomics the New Genomics? Cell 130,395-99

23. 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/