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MATERIALS AND METHODS
Selection of isolates. A set of 409 isolates was selected to represent 11 geographical regions on six continents (Africa, Asia, Australia, Europe, North America and South America) from a collection of more than 4,000 isolates available at Institut National de la Recherche Agronomique (INRA), France and Aarhus University, Denmark. The selection was made to maximize the representation of each population (partially assessed previously by AFLP, microsatellites and virulence profiles (Ali et al., 2010; Bahri et al., 2009; Bahri et al., 2011; de Vallavieille-Pope et al., 2012; Enjalbert et al., 2005; Hovmøller et al., 2008; Mboup et al., 2009)) such that isolates from different genotypic groups were present in any given geographical region. Isolates representative of aggressive strains were selected from the two recently emerged aggressive strains, PstS1 (associated with the post-2000 epidemics in the USA and Australia), and the European strain, PstS2, as well as a set of aggressive isolates frequently reported in Europe, PstS3, which were lesser aggressive than PstS1 and PstS2 (Milus et al., 2009). Details regarding the number of isolates are shown in Table. 1.
Molecular genotyping. For most isolates, DNA was already available, having been previously extracted through modified CTAB protocols (Enjalbert et al., 2002; Justesen et al., 2002). For isolates received from Pakistan and Nepal in 2008 and China in 2005, DNA was extracted from 5 mg of spores following Ali et al.(2011; ANNEX II of thesis). All of the isolates were multiplied from single pustule lesions to avoid a mixture of genotypes. Molecular genotyping was carried out using a set of 20 microsatellite loci in three multiplex reactions, with subsequent separation of the PCR products using a Beckman Coulter CEQ-8000 DNA Analyzer. Electrophorograms were processed using the CEQ-8000 Genetic Analysis System Software (Beckman Coulter) (ANNEX II of thesis; Ali et al., 2011).
Analyses of population subdivision. The level of population subdivision among different geographical regions was assessed using both model-based Bayesian and non-parametric, multivariate clustering approaches. We used the model-based Bayesian method implemented in STRUCTURE 2.2 (Pritchard et al., 2000). The rationale of this method is to assign multilocus genotypes to different clusters while minimizing the Hardy-Weinberg disequilibrium and the gametic phase disequilibrium between loci within clusters (where the number of clusters may be unknown). The Monte Carlo Markov Chain (MCMC) sampling scheme was run for 200,000 iterations with a 100,000 burn-in period, with K ranging from 1 to 10 and 20 independent replications for each K. The STRUCTURE outputs were processed with CLUMPP (Jakobsson and Rosenberg, 2007); a G’-statistic greater than 80% was used to assign groups of runs to a common clustering pattern. Because STRUCTURE can overestimate the number of clusters when there is relatedness among some genotypes (e.g., due to asexual reproduction; Gao et al., 2007), we also analyzed the level of population subdivision using a non-parametric approach that does not rely on a particular population model. We used discriminate analyses of principal components (DAPC), implemented in the ADEGENET package in the R environment (Jombart et al., 2010). The number of clusters was identified based on the Bayesian Information Criterion (BIC), as suggested by Jombart et al. (2010). The relatedness among populations was plotted using a neighbor-joining population tree based on the genetic distance DA (Nei et al., 1983), as implemented in the POPULATION program (Langella,
2008). Significance was assessed using 1000 bootstraps. The level of population differentiation was assessed using pairwise FST statistics among pairs of populations (GENETIX 4.05.2 (Belkhir et al.,
2004)).
Analyses for genetic variability and recombination. The quality of the set of markers for inferring population structure was tested by assessing the ability of the set of microsatellite loci to detect multilocus genotypes (MLGs) under panmixia, using GENCLONE (Arnaud-Haond and Belkhir, 2007). The redundancy of the set of loci was tested by estimating the linkage disequilibrium among different loci and generating 1000 random permutations with GENETIX 4.05.2 (Belkhir et al., 2004). Within-population variability was assessed using allele richness and gene diversity, calculated with FSTAT 2.9.3 (Goudet, 2001). Private allelic richness was estimated using a rarefaction approach, implemented in ADZE (Szpiech et al., 2008). Observed (Ho) and unbiased expected heterozygosity (He) were computed using GENETIX 4.05.2 (Belkhir et al., 2004). The null hypothesis of Hardy-Weinberg equilibrium within each population was tested using the exact test implemented in GENEPOP 4.0 (Raymond and Rousset, 1995). Calculations were performed both on the whole dataset and on the clone-corrected data (i.e., a dataset in which only one representative of each repeated MLG was kept). Only the clone-corrected data are reported in cases where the two datasets yielded different results because the sampling during epidemics would result in over-representation of certain clones due to the recent/epidemic clonality resulting from epidemic clonal structure (Maynard-Smith et al., 1993).
Ancestral relationship and migration patterns among populations: Different competing scenarios were tested to infer the ancestral relationship among populations through Approximate Bayesian Computations (ABC) analyses implemented in DIYABC (Cornuet et al., 2010; Cornuet et al., 2008). The method has been reported to be appropriate for complex population genetic models (Cornille et al., 2012; Dilmaghani et al., 2012), as instead of exact likelihood estimation, the method estimates the posterior probabilities of given scenarios based on the posterior distributions of demographic parameters from observed and simulated datasets.
Summary of genetic variation
We performed multilocus genotyping of 409 PST isolates, representatives of a worldwide collection, using a set of 20 microsatellite markers. Plotting the multilocus genotypes detected against the number of loci re-sampled showed that the full set of SSRs was sufficient for discriminating clonal lineages (supplementary files; Fig. S1). No significant linkage disequilibrium was found among SSR loci (data not shown), suggesting a lack of redundancy among markers. Some of the loci were monomorphic in certain geographical areas, except that China had no fixed loci and Pakistan had only one monomorphic locus (RJN-12; supplementary files; Table S1).
Population subdivision
Genotypes clearly clustered according to their geographical origin in the analyses with the model-based clustering method implemented in STRUCTURE, with an optimal number of clusters (K) equal to 6, based on the rate of change in the log probability of data across successive K values (Evanno et al., 2005). At K = 2, Middle Eastern, Mediterranean and Central Asian populations were assigned to one group; the Chinese population was assigned to the other group; and Nepalese, Pakistani and NW European populations had a mixed assignment of the two groups (Fig. S2). Increasing K to 3 individualized a Pakistan-specific group, while increasing K to 4 split the cluster of Middle East, Central Asia and Mediterranean region into two groups, one specific to the Middle East and East Africa and the other specific to the Central-Asia and Mediterranean region, with substantial admixture from the Middle East. The Middle Eastern and East African populations had no differentiation from each other and are termed as Middle East-Red Sea Area, onward. At K = 5, the NW European populations were separated from the Chinese population, and at K = 6, the Nepalese group individualized (Fig. S2). Increasing K above 6 did not reveal any further subdivisions. We confirmed that the presence of some of the clonal populations would not result in strong deviation from the STRUCTURE results, as the existence of six genetic groups was further supported by the non-parametric DAPC analysis (Fig. 1 and Fig.2). The BIC curve in the DAPC analyses also supported K=6 with a clear discrimination of genotypes from China, Pakistan, Nepal, Middle East-Red Sea Area, NW Europe and Central Asia-Mediterranean region (Fig. 2).
Geographical patterns of genotypic variability
Populations from NW Europe, USA, South America, Australia, South Africa, Eritrea, the Middle East and the Mediterranean region displayed low genotypic diversity as well as an excess of heterozygosity compared to expectations under HWE, confirming their long-term clonality. Samples from Pakistan, Nepal and China did not depart from HWE, suggesting the occurrence of recombination within the populations (Fig. 3). Himalayan (Nepalese and Pakistani) and near Himalayan (Chinese) populations had a higher genotypic diversity, higher number of alleles and higher allele richness (Fig. 4) than the populations from Middle East-Red Sea area. The latter two were themselves more diverse than the European and Mediterranean populations, where the maximum clonal resampling was observed. Thus, Asian populations appeared as the zone of the highest diversity of the pathogen. A similar pattern was observed for private allele richness, with Pakistan possessing the highest number of private alleles (Fig. 4). Isolates representing NW Europe also had high private allele richness, probably due to their strict clonality (Enjalbert et al., 2005; Justesen et al., 2002) and isolation from other populations.
Source of recently emerged populations
We detected only a few recent migrants, admixed and unassigned isolates in each geographical region, in the clustering analyses (Fig. 2 and Fig. S2). Clear migration footprints were only found when focusing on recently colonized areas. Analyses confirmed NW Europe as the source of the North American and Australian populations, and the Mediterranean region and Central Asia appeared to be the source of the South African population (Fig. 2 and Fig. S2; non-significant FST, Table 2). Additionally, the South American isolates were assigned to NW European isolates and displayed very low diversity, revealing another incursion from NW Europe.
Ancestral relationship and migration patterns of populations
The results from the ABC analyses carried on “tripl ets” of different populations were combined to give a preliminary vision about the ancestral relationship among populations, summarised in Fig. 5 (detailed in Supporting information_on_ABC_analyses). The “triplet” results revealed that the subdivision between Pakistani and Chinese populations is the most ancestral split among the three Himalayan recombinant populations. Nepal was confirmed to be the result of an admixture between Pakistan and China. A similar pattern was observed for Middle-East, East Africa and Central Asia, when compared with Pakistan and China individually. Analyses also revealed that the Mediterranean population was an admixture between Middle East and Central Asia, whereas the Central Asian population itself was a divergence from Middle Eastern population. The comparison of NW European population in “triplet” with other populations reve aled that this population resulted from an admixture from Pakistan and China. These results, however, need to be confirmed through the analyses of more complex models including more than three populations i) to confirm the above mentioned historical relationships and ii) to identify the relative positioning of different divergence and admixture events in time.
Table of contents :
CHAPTER I. Origin, migration routes and genetic structure of worldwide populations of the wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici
Introduction
Materials and Methods
Results
Disucussion
References
Supporting information
CHAPTER II. Reduction in the sex ability of worldwide clonal populations of Puccinia striiformis f.sp.tritici
Introduction
Materials and Methods
Results
Disucussion
References
Supporting information
CHAPTER III. The role of sexual recombination and off-season survival in temporal maintenance of Puccinia striiformis f.sp. tritici at its centre of diversity; the Himalayan region of Pakistan
Introduction
Materials and Methods
Results
Disucussion
References
Supporting information
CHAPTER IV. Recapturing clones to estimate the sexuality and population size of pathogens
Introduction
Theoretical Model
Performance in simulations
Application to a fungal pathogen
Disucussion
Materials and Methods
References
