APMEN Vivax Working Group Genotyping Workshop Kota Kinabalu Malaysia

Transcription

1 APMEN Vivax Working Group Genotyping Workshop Kota Kinabalu Malaysia May 8, 2011

2 APMEN III Vivax Working Group Genomic epidemiology of Plasmodium: lessons from falciparum, expectations for vivax Olivo Miotto 8 May 2011

3 What anti-malarial drug-resistant and insecticide-resistant mutations will emerge? Can we rapidly identify these mutations before they spread globally and make the drug useless? Can we develop more effective strategies for drug and insecticide deployment based on global monitoring? What questions are we trying to answer?

4 Project Aims and Strategies Create an open community of scientific research on malaria genomics Support collaborating investigators with capacity for sequencing and analysis of genome data Understand and describe molecular-level epidemiology of natural populations P falciparum, P vivax, Anopheles spp., Homo Sapiens Characterize drug resistance and monitor its emergence to inform public policy Build the most complete resource of malaria genomics

5 Solexa Sequencing Cheap Sequencing Output of Solexa sequencing Pair of Reads Not known (estimation) 35 to 76 bases long

6 Genomic Alignment If there are multiple allele at a SNP in the same sample, it s either an error, or a mixed infection! SNP Identification Reads aligned with the reference Pileup Reference genome

7 Our Pf Paper Country Sequenced samples Typable samples Sampling method Lead investigators Burkina Faso Direct Jean-Bosco Ouedraogo Mali Direct Abdoulaye Djimde Kenya Culture Alexis Nzila, Steffen Borrmann Thailand Culture Francois Nosten, Tim Anderson Cambodia Culture Xinzhuan Su, Rick Fairhurst Papua New Guinea Direct Pascal Michon, Ivo Mueller Reference Genome: 3D7 Annotation: GeneDB

8 SNP discovery and genotyping Discovery of ~10 6 potential SNPs Illumina read alignments (MAQ > BWA) Combine Illumina data with previous capillary data Identify credible SNPs Stringent alignment against potential SNPs (SNP-o-matic) Define well-covered regions of the genome Define minimum read requirement Exclude false heterozygotes 86,000 typable SNPs in 227 typable samples After removing SNPs and samples with high levels of missingness

9 Well-covered Regions Non-coding Coding

10 Problematic Surface Proteins

11 Typable SNPs and Samples

12 Our curated data 227 typable samples in 3 or 4 populations [AFR, SEA, PNG] or [WAF, EAF, SEA, PNG] 86 K typable SNPs Small number of manually curated important SNPs Read counts from alignments Computed single-allele Genotypes The most common allele, if >= 5 reads Semi-random selection when both alleles have the same number of reads (selection depends on position) Outgroup Genotypes (P reichenowi, chimp malaria)

13 PCA Analysis

14 PCA Analysis

15 Neighbour-Joining Tree Mali & Burkina Faso Kenya Thailand Cambodia Papua New Guinea

16 For each SNP, we compute Allele Frequencies Reference/Non-reference alleles frequencies in each population Minor alleles frequency (MAF) in each population Whether the SNP is private to a population Whether the SNP is universal (present in all populations) Ancestral/Derived alleles, based on outgroup alleles (P reichenowi), corroborated by private allele analysis Derived alleles frequency (DAF) in each population To reduce sample size effect, we perform simulations with random subsamples

17 Derived Alleles P falciparum P falciparum P falciparum AFR SEA P reichenowi AFR SEA P reichenowi AFR SEA P reichenowi T T T T T T T T A AAAA T T T T A AAAA AAAT A Universal Derived Allele Population-private Derived Allele Rare Derived Allele

18 Anopheles adaptation? Private Alleles

19 Highly Differentiated Alleles Fertility of female gametocytes (Anopheles adaptation?)

20 Heterozygosity Revisited Common definition is for diploid organisms Two parental chromosome copies have different alleles But Plasmodium is haploid! Use population genetics definition Heterozygosity is the probability that two chromosomes (i.e. two parasites) picked at random from a population have different alleles Statistical relationship with allele frequencies If one allele is uncommon -> low heterozygosity If both alleles are common -> high heterozygosity We can measure it, by counting sequence reads!

21 Inbreeding by population

22 Inbreeding by population F IS West Africa East Africa SE Asia Papua New Guinea

23 Signatures of Selection (SOS)

24 Recombination event Recombination

25 Generation 1: 1/2 of ancestor genome (12.5M) Generation 2: 1/4 of ancestor genome (6.25M) Generation 3: 1/8 of ancestor genome (3.1M) Generation 10: 1/1000 of ancestor genome (25k) Generation 20: 1/10 6 of ancestor genome (25bp) Clearly much smaller than a recombination segment! This is a statistical average (i.e. probability that a parasite will contain a segment descended from the original individual diminishes at every generation) Original haplotype inherited from ancestor but only in some parasites

26 An advantageous mutation will increase chance of survival. The probability of observing parasites with the recombination segment containing the mutation increases far beyond normal expectations. Selection Original haplotype inherited from ancestor in most parasites

27 Selection: Long Haplotypes Normal diversity

28 Selection: Long Haplotypes New mutation arises

29 Selection: Long Haplotypes New mutation rises rapidly in frequency: we see loss of diversity We can detect this statistically.

30 Selection: Long Haplotypes Over time, recombination restores diversity

31 Characteristics of a SoS Caused by successful adaptive mutations Survival advantage -> rapid spread in the population Relatively large recombination segment Recent introduction, limited breakdown Many contiguous linked SNPs Muck higher frequency than expected in the population (Hopefully) Differentiated from other populations Widespread Long Haplotypes containing multiple SNPs, including one or more causal mutation

32 Signatures of Selection Example: MAL7 at PfCRT locus WAF EAF SEA PNG PfCRT locus ~460K

33 MLHF Results WAF EAF SEA PNG DHFR locus ~755K (MAL4) DHPS locus ~550k (MAL8) MDR1 locus ~960K (MAL5) MAL6 MAL9 MAL13

34 Combining Methods PfCRT Chloroquine Resistance PfDHFR SP Resistance PfDHP SP Resistance PfMDR1 Multi-drug Resistance

35 Population Differentiation Transporter Gene! Nonsynonymous Derived Allele Differential in Allele Frequency High F ST

36 What about P vivax?

37 Where are we? 21 samples of P vivax sequenced at Sanger Preliminary alignments against existing reference sequence Initial SNP candidate list: ~100k candidate SNPs. Many fewer than P falciparum Quality filtering process not yet developed

38 Coverage P falciparum P vivax

39 Normal -coverage coding SNPs 12 good samples!

40 Where can we go from here? Worldwide Genomic Epidemiology Allele frequencies, Differentiation, MOI Drug resistant loci (e.g. chloroquine) Signatures of Selection, Associations Design of specialized assays Distinguish between geographical origins Recrudescence vs. re-infection Pv-Specific studies E.g. dormancy and recrudescence

41 Leadership Dominic Kwiatkowski Coordination Vikki Cornelius Kim Johnson Bronwyn MacInnis Jo Rodford Lab Daniel Alcock Elisa Anastasi Sarah Auburn Susana Campino Rachel Craik Kate Fitzpatrick Anna Jeffreys Christina Hubbart Katja Kivinen Kathrin Schuldt Kirk Rockett Kate Rowlands Analysis & informatics Jacob Almagro Garcia Taane Clark Dushyanth Jyothi Magnus Manske Gareth Maslen Olivo Miotto Jack O Brien Valentin Ruano Rubio Kerrin Small YY Teo Sanger Collaborators Matt Berriman Chris Newbold Julian Rayner Mike Quail Alison Coffey Mike Quail Mandy Sanders Dan Turner Collaborators Abdoulaye Djimde, Ogobara Doumbo, Issaka Zongo, Jean-Bosco Oudreago, Pascal Michon, Ivo Mueller, Peter Siba, Alexis Nzila, Steffen Borrmann, Kevin Marsh, Honying Yiang, Xin-Suan Su, Rick Fairhurst, Chanaki Amaratunga, Duong Socheat, Francois Nosten, Tim Anderson, Mallika Imwong, Nick White, Colin Sutherland, Cally Roper, Valentina Mangano, David Modiano, Alfred Ngwa, David Conway, Shannon Takala, Chris Plowe