ITMO Génétique, génomique et bioinformatique

1. ITMO Génétique, génomique et bioinformatique From population genetics to cellular genomics: a path towards personalised medicine Lluis Quintana-Murci, Institut Pasteur-CNRS CEA CHRU CNRS CPU INRA INRIA INSERM INSTITUT PASTEUR IRD ARIIS CIRAD EFS FONDATION MERIEUX INERIS INSTITUT CURIE INSTITUT MINES-TELECOM IRBA IRSN UNICANCER

2. Genetic variation in health and disease Genetic Diversity Phenotypic Diversity Phenotypic Diversity ACGTGTACAGAAGGGCCATGAACACTGTTATTACTCTTACACAATTGTGAGGCAGCCCTCGAGCCACAGGCGGGTCCAGCTGTTGGCTATAAACGGATAGCCTACCGGTCTCTGATCGGAGATCACCATGTTTCTGGGTCCCTTGTACCCTGGATGCAGTACCTCGCCCCAGTTTCGCATACAAATTCGCATACAAAACGTGTACAGAAGGGCCATGAACACTGTTATTACTCTTACACAATTGTGAGGCAGCCCTCGAGCCACAGGCGGGTCCAGCTGTTGGCTATAAACGGATAGCCTACCGGTCTCTGATCGGAGATCACCATGTTTCTGGGTCCCTTGTACCCTGGATGCAGTACCTCGCCCCAGTTTCGCATACAAATTTCGCATACAAAAA Inter-Individual Variation Inter-Population Variation Personal genomics Personalised medicine

3. The last decade in human genomics 1. The complete sequence of the human genome 2. SNP Discovery and the HapMap Project 3. The 1,000 Genomes Project

4. Lessons from genome diversity tttctccatttgtcgtgacacctttgttgacaccttcatttctgcattctcaattctatttcactggtctatggcagagaacacaaaatatggccagtggcctaaatccagcctactaccttttttttttttttgtaacattttactaacatagccattcccatgtgtttccatgtgtctgggctgcttttgcactctaatggcagagttaagaaattgtagcagagaccacaatgcctcaaatatttactctacagccctttataaaaacagtgtgccaactcctgatttatgaacttatcattatgtcaataccatactgtctttattactgtagttttataagtcatgacatcagataatgtaaatcctccaactttgtttttaatcaaaagtgttttggccatcctagatatactttgtattgccacataaatttgaagatcagcctgtcagtgtctacaaaatagcatgctaggattttgatagggattgtgtagaatctatagattaattagaggagaatgactatcttgacaatactgctgcccctctgtattcgtgggggattggttccacaacaacacccaccccccactcggcaacccctgaaacccccacatcccccagcttttttcccctgctaccaaaatccatggatgctcaagtccatataaaatgccatactatttgcatataacctctgcaatcctcccctatagtttagatcatctctagattacttataatactaataaaatctaaatgctatgtaaatagttgctatactgtgttgagggttttttgttttgttttgttttatttgtttgtttgtttgtattttaagagatggtgtcttgctttgttgcccaggctggagtgcagtggtgagatcatagcttactgcagcctcaaactcctggactcaaacagtcctcccacctcagcctcccaaagtgctgggatacaggtgtgacccactgtgcccagttattattttttatttgtattattttactgttgtattatttttaattattttttctgaatattttccatctatagttggttgaatcatggatgtggaacaggcaaatatggagggctaactgtattgcatcttccagttcatgagtatgcagtctctctgtttatttaaagttttagtttttctcaaccatgtttacttttcagtatacaagactttgacgttttttgttaaatgtatttgtaagtattttattatttgtgatgttatttaaaaagaaattgttgactgggcacagtggctcacgcctgtaatcccagcactttgggaggctgaggcgggcagatcacgaggtcaggagatcaagaccatcctggctaacatggtaaaaccccgtctctactaaaaatagaaaaaaattagccaggcg 3 million differences between individuals

5. t c g a g a t c g a t c t c g a g a t c g a g c g c g a t c g c Lessons from genome diversity tttctccatttgtcgtgacacctttgttgacaccttcatttctgcattctcaattctatttcactggtctatggcagagaacacaaaatatggccagtggcctaaatccagcctactaccttttttttttttttgtaacattttactaacatagccattcccatgtgtttccatgtgtctgggctgcttttgcactctaatggcagagttaagaaattgtagcagagaccacaatgcctcaaatatttactctacagccctttataaaaacagtgtgccaactcctgatttatgaacttatcattatgtcaataccatactgtctttattactgtagttttataagtcatgacatcagataatgtaaatcctccaactttgtttttaatcaaaagtgttttggccatcctagatatactttgtattgccacataaatttgaagatcagcctgtcagtgtctacaaaatagcatgctaggattttgatagggattgtgtagaatctatagattaattagaggagaatgactatcttgacaatactgctgcccctctgtattcgtgggggattggttccacaacaacacccaccccccactcggcaacccctgaaacccccacatcccccagcttttttcccctgctaccaaaatccatggatgctcaagtccatataaaatgccatactatttgcatataacctctgcaatcctcccctatagtttagatcatctctagattacttataatactaataaaatctaaatgctatgtaaatagttgctatactgtgttgagggttttttgttttgttttgttttatttgtttgtttgtttgtattttaagagatggtgtcttgctttgttgcccaggctggagtgcagtggtgagatcatagcttactgcagcctcaaactcctggactcaaacagtcctcccacctcagcctcccaaagtgctgggatacaggtgtgacccactgtgcccagttattattttttatttgtattattttactgttgtattatttttaattattttttctgaatattttccatctatagttggttgaatcatggatgtggaacaggcaaatatggagggctaactgtattgcatcttccagttcatgagtatgcagtctctctgtttatttaaagttttagtttttctcaaccatgtttacttttcagtatacaagactttgacgttttttgttaaatgtatttgtaagtattttattatttgtgatgttatttaaaaagaaattgttgactgggcacagtggctcacgcctgtaatcccagcactttgggaggctgaggcgggcagatcacgaggtcaggagatcaagaccatcctggctaacatggtaaaaccccgtctctactaaaaatagaaaaaaattagccaggcg Most mutations have no (known) phenotypic effects

6. t c g a g a t c g a t c t c g a g a t c g a g c g c g a t c g c Lessons from genome diversity tttctccatttgtcgtgacacctttgttgacaccttcatttctgcattctcaattctatttcactggtctatggcagagaacacaaaatatggccagtggcctaaatccagcctactaccttttttttttttttgtaacattttactaacatagccattcccatgtgtttccatgtgtctgggctgcttttgcactctaatggcagagttaagaaattgtagcagagaccacaatgcctcaaatatttactctacagccctttataaaaacagtgtgccaactcctgatttatgaacttatcattatgtcaataccatactgtctttattactgtagttttataagtcatgacatcagataatgtaaatcctccaactttgtttttaatcaaaagtgttttggccatcctagatatactttgtattgccacataaatttgaagatcagcctgtcagtgtctacaaaatagcatgctaggattttgatagggattgtgtagaatctatagattaattagaggagaatgactatcttgacaatactgctgcccctctgtattcgtgggggattggttccacaacaacacccaccccccactcggcaacccctgaaacccccacatcccccagcttttttcccctgctaccaaaatccatggatgctcaagtccatataaaatgccatactatttgcatataacctctgcaatcctcccctatagtttagatcatctctagattacttataatactaataaaatctaaatgctatgtaaatagttgctatactgtgttgagggttttttgttttgttttgttttatttgtttgtttgtttgtattttaagagatggtgtcttgctttgttgcccaggctggagtgcagtggtgagatcatagcttactgcagcctcaaactcctggactcaaacagtcctcccacctcagcctcccaaagtgctgggatacaggtgtgacccactgtgcccagttattattttttatttgtattattttactgttgtattatttttaattattttttctgaatattttccatctatagttggttgaatcatggatgtggaacaggcaaatatggagggctaactgtattgcatcttccagttcatgagtatgcagtctctctgtttatttaaagttttagtttttctcaaccatgtttacttttcagtatacaagactttgacgttttttgttaaatgtatttgtaagtattttattatttgtgatgttatttaaaaagaaattgttgactgggcacagtggctcacgcctgtaatcccagcactttgggaggctgaggcgggcagatcacgaggtcaggagatcaagaccatcctggctaacatggtaaaaccccgtctctactaaaaatagaaaaaaattagccaggcg Some variants can explain phenotypic variation (in health and disease)

7. Lessons from genome diversity • The burden of deleterious mutations in the human genome • 10,000 amino-acid altering mutations • 300-400 stop/splice/indels disrupting 200-300 genes • heterozygous at 50-100 mutations associated with an inherited disorder

8. Impact of genetic variation on phenotypes Genetic Diversity Phenotypic Diversity Phenotypic Diversity ACGTGTACAGAAGGGCCATGAACACTGTTATTACTCTTACACAATTGTGAGGCAGCCCTCGAGCCACAGGCGGGTCCAGCTGTTGGCTATAAACGGATAGCCTACCGGTCTCTGATCGGAGATCACCATGTTTCTGGGTCCCTTGTACCCTGGATGCAGTACCTCGCCCCAGTTTCGCATACAAATTCGCATACAAAACGTGTACAGAAGGGCCATGAACACTGTTATTACTCTTACACAATTGTGAGGCAGCCCTCGAGCCACAGGCGGGTCCAGCTGTTGGCTATAAACGGATAGCCTACCGGTCTCTGATCGGAGATCACCATGTTTCTGGGTCCCTTGTACCCTGGATGCAGTACCTCGCCCCAGTTTCGCATACAAATTTCGCATACAAAAA Inter-Individual Variation Inter-Population Variation Personal genomics Personalised medicine

9. Genome-wide association studies - GWAS By September 2013: 11,304 SNPs associated with 891 traits/diseases (1,701 studies)

10. GWAS results in personalised medicine Thomas et al. Nature. 2009 • High p-values do not translate to clinically useful diagnostic assays • Strong population variation (only useful in European populations)

11. Healthy-based population approaches • Understanding patterns of population variation of our genomes • Searching for the signatures of selection in the genome

12. Population and evolutionary genetics Innate immunity to infection Type-III IFNs and hepatitis C Manry et al., J Exp Med, 2011 Quintana-Murci & Clark. Nat Rev Immunol, 2013 Variation in biological relevance in host defence of immune receptors Positive selection targeting protective mutations in Europeans

13. Principles of cellular genomics Complexphenotypes Lack of mechanisticunderstanding Simpler phenotypes Focus on mechanisms Infection Drugs Radiation Untreated cells Treated cells Variation in gene expression Genetic/epigenetic markers Gene-environment interactions

14. Cellular genomics complementing GWAS • GWAS of TB have mostly failed in identifying susceptibility genes • A recent cellular genomics study has identified >100 mutations that affect expression only after infection (response eQTLs) • These mutations may contribute in an additive manner to susceptibility to develop TB - > added value of cellular genomics

15. Integrative genomics • Importance of taking first basic science approaches • Need for understanding how genotypes affect phenotypes in BOTH health and disease • Need for complementary approaches based on model systems • Opportunities to tackle these questions at the highest resolution (e.g., NGS, rare variants, epigenetics) • Improve methods in statistical genetics and computational biology (e.g., epistasis)

16. A path towards personalised medicine LIFESTYLE ENVIRONMENTS Model systems PHENOTYPES Molecular phenotypes (expression) Cellular phenotypes Organismal phenotypes (traits or diseases) GENETICS complete sequences EPIGENETICS Methylation, Histone modification HEALTH and DISEASE Responses to treatment BIOMARKERS Vaccine efficiency, doses

18. Population and evolutionary genetics Barreiro & Quintana-Murci, Nat Rev Genet, 2010 180 immunity-related genes targeted by positive selection

19. Disease-based approaches Rare mutations Strong individual effect Common polymorphisms Modest individual effect Little public health impact Strong public health impact

20. Limitations of “disease-only”-based approaches • Associations based on strict statistic thresholds • Disease is a complex “phenotype” • Odds ratios remain rather low • Assumption of genes acting “alone” • Causal mutations remain mostly unidentified -> Need for complementary approaches

21. Using “simpler” phenotypes (molecular) • Expression is a molecular phenotype that varies between individuals and populations • Levels of expression can be under genetic control (eQTL) Environment • 1/3 of the world’s population infected with Mycobacterium tuberculosis (MTB) • Only 10% of infected individuals will develop the active form of the disease Genotype mRNA M. tuberculosis