Population structure of Phytophthora infestans collected from potatoes in Guatemala and Honduras

Population structure of Phytophthora infestans collected from potatoes in Guatemala and Honduras | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Confirmatory Results Population structure of Phytophthora infestans collected from potatoes in Guatemala and Honduras View ORCID Profile Myriam Izarra , Willmer Perez , Emil Vasquez , View ORCID Profile Glenda Perez , View ORCID Profile Soledad Gamboa , View ORCID Profile Jorge Andrade-Piedra , Breny Flores , Luz Montejo , View ORCID Profile Arie Sanders , View ORCID Profile Jan Kreuze doi: https://doi.org/10.1101/2025.05.28.656702 Myriam Izarra 1 International Potato Center (CIP) , 1895 La Molina Avenue, 15024, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Myriam Izarra Willmer Perez 1 International Potato Center (CIP) , 1895 La Molina Avenue, 15024, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emil Vasquez 2 Zamorano University , Honduras Find this author on Google Scholar Find this author on PubMed Search for this author on this site Glenda Perez 3 The Institute of Agricultural Science and Technology (ICTA) , Guatemala Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Glenda Perez Soledad Gamboa 1 International Potato Center (CIP) , 1895 La Molina Avenue, 15024, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Soledad Gamboa Jorge Andrade-Piedra 1 International Potato Center (CIP) , 1895 La Molina Avenue, 15024, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jorge Andrade-Piedra Breny Flores 4 Agricultural Science and Technology Directorate (DICTA) , Honduras Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luz Montejo 3 The Institute of Agricultural Science and Technology (ICTA) , Guatemala Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arie Sanders 2 Zamorano University , Honduras Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arie Sanders Jan Kreuze 1 International Potato Center (CIP) , 1895 La Molina Avenue, 15024, Peru Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jan Kreuze For correspondence: j.kreuze{at}cgiar.org Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Phytophthora infestans, the causal agent of late blight, remains a major constraint to potato production worldwide. In Central America, particularly in Guatemala and Honduras, information on the pathogen’s population structure is limited despite its economic importance and reliance on imported seed potatoes. This study characterized the population structure of P. infestans in potato fields in Guatemala and Honduras using microsatellite markers, mitochondrial haplotypes, and mating type analyses. Four SSR-defined groups were identified, three corresponding to the previously described clonal lineages EU13A2, NI_1, and US8A2, and one putative group designated CeA (Central America). The detection of EU13A2, a lineage first reported in Europe and subsequently in Asia, indicates its establishment in Central America. All successfully characterized isolates exhibited the Ia mitochondrial haplotype, indicating limited mitochondrial diversity despite substantial nuclear SSR variation. AMOVA revealed significant genetic differentiation between Honduras and Guatemala, accounting for 24.43% of the total SSR variation ( p = 0.022). Significant linkage disequilibrium was detected within the analyzed populations. The occurrence of genetically distinct P. infestans groups across the study area highlights the importance of monitoring transboundary pathogen movement in Central America. Introduction Phytophthora infestan s (Mont.) de Bary is an oomycete responsible for late blight in potatoes ( Solanum tuberosum L.), tomatoes ( Solanum lycopersicum L.), and other Solanaceae species ( Perez & Forbes, 2010 ). The disease is a well-known primary biotic limitation affecting potato cultivation on a global scale and has been the cause of devastating epidemics, such as the one leading to the Irish Potato Famine of 1845, and still poses a significant challenge to global food security ( Day & Shattock, 1997 ; Dong & Zhou, 2022 ). Management of the disease with fungicides continues to be the most common approach ( Abuley & Hansen, 2022 ), with a limited number of alternatives available to chemical control ( Kang & Dobinson, 2004 ). P. infestans is a heterothallic species that requires mating types A1 and A2 for sexual reproduction, resulting in oospore formation ( Drenth et al., 1995 ). This pathogen combines the advantages of sexual cycles, which generate genetic diversity, and an expansive population through asexual reproductive cycles ( McDonald & Linde, 2002 ). In sexual populations, both sexual and asexual propagules act as inoculum sources, whereas asexual populations rely entirely on inoculum generated through asexual reproduction ( Flier et al., 2002 ). Monitoring the A1/A2 ratio is crucial to understand the potential for sexual recombination, which supports their evolution ( Barton & Charlesworth, 1998 ). Sexual reproduction allows P. infestans to persist in the soil between seasons as thick-walled oospores ( Andersson, 2007 ). In addition, P. infestans strains derived from sexual recombination could potentially exhibit a greater capacity to adapt rapidly to environmental fluctuations, including fungicide selection pressure ( Goodwin, 1997 ). The emergence and global spread of aggressive, metalaxyl-resistant clonal lineages such as EU13A2 further illustrate this adaptive potential ( Cooke et al., 2012 ; Islam et al., 2023 ). Furthermore, in P. infestans, genetic diversity is fostered by adaptive evolution, which is driven by the pathogen’s unique genomic structure, particularly in regions enriched with rapidly evolving effector genes ( Haas et al., 2009 ; Leesutthiphonchai et al., 2018 ; Zhang et al., 2019 ; Coomber et al., 2024 ). The diversity of P. infestans is examined by using a combination of phenotypic and genotypic markers. Microsatellite markers, also referred to as simple sequence repeats (SSRs), are frequently used to determine multilocus genotypes (MLGs) and evaluate genetic diversity within populations ( Li et al., 2012 ; Martin et al., 2019 ). SSR markers are highly polymorphic, affordable, reproducible, neutral, and co-dominant ( Lees et al., 2006 ), and can differentiate between genotypes, even among clonal lineages ( Hansen et al., 2016 ). Mitochondrial haplotypes are used to trace maternal lineages and provide insights into population structure and migration patterns ( Griffith & Shaw, 1998 ; Widmark et al., 2007 ; Guo J et al., 2009 ). Mexico has been proposed as the geographic center of origin of late blight based on several lines of evidence, including the historical occurrence of both mating types (A1 and A2) and the remarkable diversity of P. infestans in central Mexico in terms of virulence characteristics and molecular markers ( Gallegly, 1958 ; Fry et al., 1993 ). However, an alternate theory proposes Peru (or the Andean region) as the center of origin, based on phylogenetic, genomic, and population genetic evidence, including the global prevalence of the US-1 clonal lineage, which is notably absent in Mexico ( Abad & Abad, 1997 ; Gomez-Alpizar et al., 2007 ; Saville et al., 2016 ; Martin et al., 2016 ; Coomber et al., 2024 ). A recent study conducted by Patarroyo et al . (2024) , based on approximate Bayesian computation of alternative migration scenarios, identified a Peruvian origin of P. infestans as the most probable scenario among those evaluated, with an initial migration toward Colombia and Mexico, followed by subsequent migration from Mexico to the United States and later to Europe and Asia, with no return to northern South America. The A1 mating type dispersed worldwide during the Irish potato famine, whereas the A2 mating type was first reported in Europe, the Middle East, and South America in 1981 ( Spielman et al., 1991 ; Goodwin, 1995a ). Genomic studies of historical herbarium samples have shown that the lineage responsible for the nineteenth-century famine pandemic, HERB-1, was genetically distinct from current populations ( Yoshida et al., 2013 ; Saville et al., 2016 ). The related FAM-1 lineage was widely distributed across multiple continents during the nineteenth and early twentieth centuries before being displaced by later clonal lineages, including US-1, which became globally dominant during the mid-twentieth century ( Saville & Ristaino, 2021 ). In the United States, US-1 was subsequently displaced by newly emerged A2 clonal lineages, including US-7 and US-8, during the early 1990s, increasing the risk of late blight epidemics ( Goodwin, 1995b ; Goodwin et al., 1998 ). In Central America, the NI_1 clonal lineage, characterized by the A2 mating type and Ia mitochondrial haplotype, was reported as a predominant lineage infecting both potato and tomato in Nicaragua ( Blandón-Díaz et al., 2012 ). Guatemala produces over 540,000 metric tons of potatoes per year, which are primarily exported to the neighboring El Salvador, and less than 1% of the crop is planted with certified seed potatoes. Late blight is the most important disease because of its cool and wet weather conditions ( Joyce, 2020 ), and there is high genetic variability among isolates ( Ruiz-Chutan et al., 2018 ). The United States is a significant supplier of seed potatoes to Guatemala, and few quantities have been imported from Peru ( World Bank, 2023a ; b). In contrast, potato cultivation in Honduras was concentrated in Intibucá, Ocotepeque, La Paz, and Francisco Morazán, where 39,633 metric tons of potatoes were produced between 2011 and 2012. The Honduran potato industry also relies on imports; in 2014 and 2015 6,000 tons were used annually for industrial processing, while seed potato imports reached 1,000 tons. Similar to Guatemala, late blight is recognized as the most significant disease affecting potato cultivation in Honduras ( Toledo, 2016 ). According to the USDA, in 2021, the value of potato imports to Honduras grew by 1.9%, increasing from US$7.8 million in 2016 to US$8.4 million in 2020. Most potatoes for fresh consumption were imported from the United States, whereas most seed potatoes were imported from the Netherlands, France ( SAG & UPEG, 2021 ; World Bank, 2023c ), Guatemala, and Chile ( Sanders et al., 2024 ). This research focused on examining the population structure of Phytophthora infestans that impacts potato crops in Guatemala and Honduras. We specifically investigated the composition of clonal lineages, the distribution of mating types, mitochondrial haplotypes, and multilocus genetic diversity to create a regional baseline for epidemiological monitoring and informed disease management strategies. Materials and Methods Sampling and sample processing The sampling strategy used in this study was designed to identify dominant pathogen genotypes in Honduras and Guatemala. Between 2021-2022, potato leaflet samples exhibiting late blight symptoms were collected from the main potato-growing areas at altitudes ranging from 1170 to 2088 m above sea level (masl) in Honduras and 1750–3699 masl in Guatemala, with approximately 94 fields studied based on latitude and longitude (Table S1). Samples were collected on FTA cards (Whatman FTA Classic Card, catalog number WB120055; GE Healthcare UK Ltd.), following the manufacturer’s instructions ( Cooke, 2020a ). Each sample had its sporulating side facing down, crushed individually in the field, and then FTA cards were stored in zip-lock bags and sent by post for processing in the pathology laboratory at the International Potato Center (CIP), Lima, Peru. DNA extraction from FTA was performed following the manufacturer’s instructions, with some modifications using two discs of 3 mm, and two rinses with modified Tris(hydroxymethyl) aminomethane-ethylenediaminetetraacetic acid (TE -1 ) buffer (10 mM Tris and 0.1 mM EDTA), and then resuspended in 50 µL TE buffer (10mM Tris and 1mM EDTA) and incubated for 5 min at 95 °C as the last step before being stored at -20 °C until use. Mating type determination The mating types were determined using the W16 CAPS assay to distinguish A1 and A2 mating types of P. infestans isolates using PCR primers W16-1 (5’-AACACGCACAGGCATATAAATGTA-3’) and W16-2 (5’-GCGTAATGTAGCGTAACAGCTCTC-3’) following the protocol outlined by Judelson et al. (1995) . The PCR reaction was carried out in 20 μL mixture, which included 4 μL of 5x buffer (Gotaq®, Promega), 6 μl MgCl 2 (25 mM), 0.2 μl dNTPs (100 μM), 0.4 μM of each primer, 1 U of Taq DNA polymerase (Gotaq®, Promega) and 10ng of template DNA. The thermal cycling conditions were as follows: initial denaturation at 94 °C for 5 min, followed by 30 cycles of denaturation at 94 °C for 1 min, primer annealing at 53 °C for 1 min, and primer elongation at 72 °C for 1 min. The PCR process was concluded with a final elongation step at 72 °C for 10 minutes. Subsequently, the PCR products were subjected to a three-hour digestion at 37 °C using the restriction enzyme (recognition site 5X-GG/CC-3X from New England Biolabs (NEB) ( https://www.neb.com ). The digestion mixture for each sample included 7.7 μL of H 2 O, 2 μL of CutSmart Buffer 10X, 0.3 μL Hae III (NEB, #R0108) and 10 μl of PCR product ( Mazáková et al., 2006 ; Brylińska et al., 2018 ). Mating type determination was based primarily on the presence or absence of the undigested 557 bp fragment following Hae III digestion. The mating type A2 was confirmed using the PHYB-1 (5’-GAT CGG ATT AGT CAG ACG AG-3X) and PHYB-2 (5’-GCG TCT GCA AGG CGC ATT TT-3X) primers ( Kim & Lee ). The PCR was performed in 20 μL mixture, which included 4 μL of 5x buffer (Gotaq®, Promega), 1.2 μL MgCl 2 (25 mM), 0.2 μl dNTPs (2.5 mM), 0.4 μM of each primer, 1 U of Taq DNA polymerase (Gotaq®, Promega) and 10ng of template DNA. The thermal cycling conditions were as follows: initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 1 min, primer annealing at 62 °C for 1 min, and primer elongation at 72 °C for 1 min. The PCR process was concluded with a final elongation step at 72 °C for 7 minutes. Mating type assignments were based on concordant results obtained from both the W16 CAPS and the A2-specific PHYB marker. Mitochondrial haplotype determination Mitochondrial DNA (mtDNA) haplotypes were determined following the profiles of Griffith and Shaw (1998) . This process included amplifying the P2 and P3 mitochondrial regions which were then digested with MspI for P2 and EcoR I for P3. The combined restriction DNA patterns from P2/ MspI and P3/ EcoRI enabled differentiation among haplotypes Ia, Ib, IIa, and IIb. The resulting restriction fragment length polymorphisms (RFLPs) were separated through gel electrophoresis and compared to reference profiles - for precise haplotype classification. Microsatellite analysis The genomic DNA of the P. infestans isolates was amplified using a predetermined set of microsatellite markers following a modified version of the protocol for the 12-plex method (Table S1, Li et al., 2013 ). The Type-It Microsatellite PCR kit (QIAGEN, https://www.qiagen.com ) was used for PCR reactions according to a modified protocol described by Saville et al . (2016) . The PCR amplicons were then sent to Arizona State University for post-PCR processing in 96-well plates. To prepare the samples for fragment analysis on the 3730 instrument, 3 μL of the LIZ500 size standard ladder was mixed with 1 mL of formamide in a 96-sample plate. Each well of the plate contained 9 μL of the ladder/formamide mixture and 2 μL of 1:50 diluted sample. The total sample volume in each well was 11 μL when loaded onto 3730 instrument for fragment analysis. SSR allele scoring and genotype structure Microsatellite genotypes were obtained using a 12-plex SSR system for Phytophthora infestans , following standard protocols ( Cooke, et al., 2012 ; Li et al., 2013 ). SSR allele sizing was conducted using GeneMarker v.1.9 software (SoftGenetics, State College, PA, USA), applying allele binning criteria described by Li et al. ( 2013). To ensure consistent allele scoring across different runs, previously characterized reference isolates representing the clonal lineages EU13A2, US8A2 and NI_1, genotyped at The James Hutton Institute (Dundee, UK) and from TBAS ( Coomber et al., 2023 ), were included during allele scoring. These reference isolates were used to standardize allele binning and facilitate lineage interpretation. Allele calls were manually reviewed following the EuroBlight SSR allele-scoring guidelines ( Cooke, 2020b ), taking into account locus-specific stutter patterns, relative peak intensity, amplification quality, and minor size-calling variation. For each isolate, up to three alleles per locus were retained when present, reflecting the complex allelic profiles reported for clonal lineage EU13A2, including variation in ploidy levels described in previous studies ( Li et al., 2017 ). Loci with fewer than three observed alleles were retained as recorded, without imputation or artificial completion of allele states. Missing or unobserved alleles positions were coded as 0 in the SSR dataset and treated as missing information rather than true allelic states. No allele dosage was inferred for loci with fewer than three observed alleles. During downstream analyses, these coded values were treated as missing data for the calculation of Bruvo’s genetic distance, following the implementation in the poppr package ( Kamvar et al., 2014 ). Allelic profiles were treated as multilocus genotypes without assigning alleles to specific chromosomal copies or haplotypes. This approach avoids assumptions regarding allele dosage, phase, segregation, or strict ploidy, which cannot be reliably inferred from SSR peak profiles. MLGs were defined as unique combinations of observed alleles across all loci using mlg.id() in the poppr package ( Kamvar et al., 2014 ). No genetic-distance or similarity threshold was applied to collapse closely related profiles for the calculation of MLG-based population diversity statistics. Pairwise genetic distances among isolates were computed between individual isolates using Bruvo’s genetic distance, based on their multilocus SSR profiles. Bruvo’s distance is based on a stepwise mutation model for microsatellite evolution and facilitates comparisons among genotypes with varying ploidy, without requiring a fixed ploidy level throughout the dataset ( Bruvo et al., 2004 ). Clonal lineage designations were based on comparison of the 12-locus SSR multilocus profiles of field isolates with those of reference isolates representing the previously characterized lineages EU13A2, NI_1, and US8A2. Reference isolates were included in the clustering analyses to provide an internal basis for comparison. Lineage interpretation considered multilocus SSR similarity and the genetic relationships observed in analyses based on Bruvo’s genetic distance, including the PCoA and Neighbor-Joining tree. Isolates showing SSR profiles and genetic clustering consistent with a reference lineage were assigned to the corresponding lineage. Clonal lineage designations were therefore derived from the SSR dataset and were used for genotype classification and comparison but not to define populations in the primary geographic AMOVA. Accordingly, the term “clonal lineage” is used here as the established genotype designation for these reference lineages and does not imply that strict clonality or the absence of recombination was demonstrated for all field isolates assigned to them. Data Analysis Statistical analyses were performed using R statistical software version 4.3.1 ( R Core Team, 2019 ). The R package poppr was used to calculate locus and population summary statistics, including the number of multilocus genotypes (MLGs), diversity indices, and linkage disequilibrium ( Kamvar et al., 2014 ). Locus-level allelic diversity statistics, including Simpson’s diversity (1-D), expected heterozygosity (Hexp), and evenness, were calculated using locus_table() in the poppr package from the polyploid-recoded dataset of 237 field isolates. Prior to calculation of allele-frequency-based statistics, zero-coded unobserved allele positions were removed using recode_polyploids(). Hexp was estimated from allele frequencies at each locus; because allele dosage cannot be unambiguously inferred from the SSR profiles, Hexp was interpreted as a descriptive measure of allelic diversity. Population grouping varied according to the objective of each analysis. For the principal coordinates analysis (PCoA), isolates were grouped according to their sampling locations, while reference isolates representing known clonal lineages were retained as independent groups to facilitate lineage assignment. For the hierarchical analysis of molecular variance (AMOVA), populations were defined geographically, with departments nested within country. This hierarchical structure was used to partition SSR genetic variation among countries, among departments within countries, and within departments. To minimize the effect of repeated sampling of identical multilocus genotypes on variance partitioning, clone correction was applied to the hierarchical AMOVA, retaining a single representative of each multilocus genotype within each department. Statistical significance of the variance components was evaluated using 999 permutations. A complementary AMOVA partitioning genetic variation according to clonal lineage and sampling location was retained as a supplementary descriptive analysis. Because clonal lineage assignments were based on SSR profiles, this analysis was not interpreted as an independent test of population genetic structure. Reference control isolates were excluded from analyses of population genetic diversity and from the MLG-based minimum spanning network. Reference isolates were retained in the PCoA and Neighbor-Joining analyses to facilitate comparison with previously characterized clonal lineages. Linkage disequilibrium was assessed for the pooled dataset of 237 field isolates using the index of association (IA) and the standardized index of association (rbarD), with statistical significance evaluated using 999 permutations. Population clustering was evaluated by principal coordinates analysis (PCoA) based on Bruvo’s genetic distance ( Bruvo et al., 2004 ) using the polysat package ( Clark & Jasieniuk, 2011 ). A Neighbor-Joining tree was reconstructed using the BioNJ algorithm from the matrix of Bruvo’s genetic distances calculated for the complete dataset, including field isolates and reference controls ( Saitou & Nei, 1987 ; Gascuel O, 1997 ). Isolates were not manually ordered by country, department, or host; tip order was determined automatically by the BioNJ algorithm. Reference isolates were included solely to facilitate assignment of field isolates to previously described clonal lineages. The resulting tree was visualized and annotated using the Interactive Tree of Life (iTOL)( Letunic & Bork, 2024 ). Genotypic relationships were further explored using a minimum spanning network (MSN), constructed with the poppr package ( Kamvar et al., 2014 ), based on Bruvo’s genetic distance. Each original MLG was represented as a node in the network, with node size reflecting the number of isolates sharing that MLG. No genetic-distance threshold was applied to collapse closely related MLGs in the MSN. Edge width and shading represent Bruvo’s genetic distance between connected MLGs. Genotypic diversity based on MLG frequencies was evaluated according to sampling location. Simpson’s and Shannon–Wiener diversity indices were used to characterize MLG diversity, while genotypic richness was estimated as the expected number of multilocus genotypes (eMLG), standardized to the smallest sample size using rarefaction ( Simpson, 1949 ; Shannon, 2001 ). These diversity estimates were calculated using the original MLG definition without genetic-distance-based contraction. The analytical scripts and the data are available at https://github.com/myriamiz/Pinf_G_H . Results Samples collected In Honduras, 192 potato samples were collected from four departments (equivalent to the state): Intibucá, Francisco Morazán, Ocotepeque, and Lempira. In Guatemala, 45 potato samples were collected from five departments: Huehuetenango, Quetzaltenango, San Marcos, Chimaltenango, and Sololá ( Tables 1 and S2). View this table: View inline View popup Download powerpoint Table 1. Number of Phytophthora infestans isolates collected in Honduras and Guatemala. Mating type test The primer set W16–1 and W16–2 generated a 557-bp product. Following digestion with HaeIII , A2 isolates were identified by the absence of the undigested 557-bp fragment and the presence of the expected 457-bp fragment; the complementary 100-bp fragment was observed in several representative samples (Fig. S1A). Using this assay, 226 of the 237 isolates showed the molecular pattern associated with A2, including 181 isolates from Honduras and 45 from Guatemala, while the remaining 11 isolates were undetermined by this assay. For confirmation, amplification with the PHYB-1/PHYB-2 primers produced a 347-bp fragment in isolates from Honduras (n=166) and Guatemala (n=10), supporting their assignment to the A2 mating type (Fig. S1B). A total of 176 isolates were concordantly identified as A2 by both marker systems (Table S1). Because only isolates with concordant results between the W16 CASPS assay and the A2-specific PHYB marker were considered confidently assigned, all isolates meeting these criteria were molecularly assigned to the A2 mating type. Genotypic characterization Mitochondrial haplotype analysis identified 209 of 237 isolates as haplotype Ia, while the remaining 28 isolates were undetermined (Table S2). Digestion of the P2 region with MspI produced fragments of 720 and 350 bp, a restriction pattern consistent with the haplotype Ia or IIb (Fig. S2A). To distinghish between these haplotypes, the P3 region was digested with EcoRI . Successful amplification of the P3 region was obtained for 209 isolates (169 from Honduras and 40 from Guatemala), yielding fragments of 1064 and 228 bp, corresponding to group I haplotypes (Fig. S2B). The combined P2/ MspI and P3/ EcoRI restriction patterns identified all 209 successfully analyzed isolates as haplotype Ia; the remaining 28 isolates could not be conclusively assigned because the P3 region was not successfully amplified. Allelic diversity varied among the 12 SSR loci ( Table 2 ). D13 was the most polymorphic marker, exhibiting the highest number of alleles and the highest values of Simpson’s diversity (1-D) and expected heterozygosity (Hexp), whereas Pi70 showed the lowest level of genetic diversity. Overall, the mean values (1-D=0.496; Hexp=0.497; Evenness=0.713) indicated a moderate level of allelic diversity across the studied population ( Table 2 ). View this table: View inline View popup Download powerpoint Table 2. Genetic diversity statistics for the 12 microsatellite loci analyzed in Phytophthora infestans populations from Honduras and Guatemala The hierarchical AMOVA based on geographic structure indicated that the greatest proportion of genetic variation occurred within departments (75.143%, p=0.001). Differences between Honduras and Guatemala accounted for 24.428% of the total genetic variation and were statistically significant (p=0.022). In contrast, variation among departments within countries accounted for only 0.429% of the total genetic variation and was not statistically significant (p=0.510) ( Table 3 ). A complementary analysis partitioning genetic variation according to clonal lineage and sampling location showed that 63.250% of the variation was associated with clonal lineage, whereas 38.537% occurred within sampling locations; the component among sampling locations within clonal lineages was not significant (-1.786%, p=0.486) (Table S4). This analysis is presented for descriptive purposes only. View this table: View inline View popup Download powerpoint Table 3. Hierarchical analysis of molecular variance (AMOVA) based on SSR data, with departments nested within countries, for Phytophthora infestans populations from Honduras and Guatemala Genetic relationships among isolates based on microsatellite data and Bruvo genetic distance were explored using PCoA. The analysis revealed four main groups, with most isolates from Honduras (n=180) and Guatemala (n=14) clustering with the EU13A2 reference isolates. A second group was associated with the NI_1 reference isolates, and a third group with US8A2 reference isolates, and a fourth distinct group lacked a corresponding reference genotype ( Figure 1 ). Download figure Open in new tab Figure 1. Principal coordinates analysis (PCoA) based on Bruvo’s genetic distance of Phytophthora infestans isolates from Honduras and Guatemala. The prefix H denotes Honduras and G denotes Guatemala. Triangles represent isolates from Honduras and squares represent isolates from Guatemala. Isolates are colored according to their sampling location: H.Int (Intibucá, light lilac), H.FrMz (Francisco Morazán, sky blue), H.Oco (Ocotepeque, light green), H.Lemp (Lempira, pale blue), G.Sol (Sololá, dark gray), G.Qtz (Quetzaltenango, dark red), G.Htg (Huehuetenango, yellow), G.Sm (San Marcos, dark green), and G.Cht (Chimaltenango, pink). Reference isolates representing clonal lineages EU13A2 (dark blue), US8A2 (orange), and NI_1 (magenta) are indicated. Ellipses highlight genetic groups among isolates. The Neighbor-Joining tree based on separated the isolates into four main clusters associated with the identified clonal lineages ( Figure 2 ). Using a maximum distance threshold of 0.01 for cluster delineation, the cluster associated with EU13A2 comprised n=194 isolates distributed across Guatemala [G. Quetzaltenango (6), G. San Marcos (2), G. Chimaltenango (2), G. Sololá (2), G. Huehuetenango (2)] and Honduras [H. Intibucá (169), H. Lempira (1), H. Ocotepeque (5), H. Francisco Morazán (5)]. Notably, reference isolates from Honduras previously designated as HN_1 also clustered within the EU13A2 group, together with the EU13A2 reference isolates from multiple European countries (Figure2). The NI_1 cluster included n=31 isolates from G. Quetzaltenango (6), G. San Marcos (6), G. Chimaltenango (3), G. Sololá (1), G. Huehuetenango (6), and H. Intibucá (9). A distinct cluster, designated CeA (Central America), comprised n=9 isolates from G. Quetzaltenango (4), G. San Marcos (1), G. Sololá (1), G. Huehuetenango (1), H. Intibucá (2). The US8A2 cluster included n= 2 isolates from G. San Marcos (1) and G. Huehuetenango (1). One isolate from Intibucá (H55) could not be confidently assigned to any of these clonal lineages because of incomplete SSR data and was therefore designated as not determined (ND). The geographic distribution of these clonal lineages is shown in Figure 3 . Download figure Open in new tab Figure 2. Neighbor-Joining (BioNJ) tree based on Bruvo genetic distance showing the relationships among Phytophthora infestans isolates from Honduras and Guatemala and reference genotypes. Reference isolates representing previously characterized clonal lineages from Europe and the Americas were included for comparison and lineage assignment. The EU13A2, NI_1 and US8A2 clonal lineages are shown in dark blue, magenta, and orange, respectively, while the CeA cluster is shown in cyan. Download figure Open in new tab Figure 3. Geographical distribution of Phytophthora infestans clonal lineages EU13A2, NI_1, CeA, and US8A2 among isolates (n = 237) collected in Honduras and Guatemala in 2022. Pie charts represent the proportion of isolates assigned to each clonal lineage per department, with pie-chart size proportional to the total number of isolates sampled. Number within pie-chart sectors indicate the number of isolates assigned to each clonal lineage. Background shading represents elevation in meters above sea level (m.a.s.l.), and departmental boundaries are shown in gray. The international boundary line between Guatemala and Honduras (right) is indicated by a black line. The inset shows the location of Guatemala and Honduras within Central America and highlights the geographic extent the study area. A total of 148 multilocus genotypes (MLGs) were identified among the 237 isolates analyzed. The allele profiles of the 148 MLGs across the 12 SSR loci are provided in Table S3. Diversity analyses were conducted excluding reference control isolates to avoid artificial inflation of genotypic richness. Populations with a high number of isolates, such as Intibucá, comprised 75.3% of the MLGs, with EU13A2, CeA, and NI_1 clonal lineages identified. Intibucá and San Marcos showed the highest eMLG diversity, indicating elevated genotypic richness after rarefaction ( Table 4 ). View this table: View inline View popup Download powerpoint Table 4. Population genetic diversity statistics based on 12 microsatellite (SSR) loci for Phytophthora infestans isolates grouped according to sampling location in Honduras and Guatemala. *H:Honduras, G:Guatemala, Int: Intibucá, Lemp: Lempira, Oco: Ocotepeque, FrMz: Francisco Morazán, Qtz: Quetzaltenango, Sm: San Marcos, Cht: Chimaltenango, Sol: Sololá, Htg: Huehuetenango. Overall diversity indices were high (H=4.67; λ=0.985), indicating a high probability that two randomly selected isolates belong to different genotypes. However, evenness values (E.5 = 0.619) suggest partial dominance of specific MLGs within the population ( Table 4 ). Significant multilocus linkage disequilibrium was detected (IA = 2.829, p=0.001; rbarD = 0.299, p=0.001), indicating non-random association among loci. No strong geographic structuring of MLGs was detected, as major clonal lineages were distributed across multiple departments ( Table 4 ). The most frequent MLGs were MLG94 (n=14), MLG123 (n=11), MLG16 (n=9). MLG94 was detected exclusively in Intibucá, Honduras. Of the 148 MLGs identified, 116 were represented by a single isolate. Nine MLGs (MLG123, MLG16, MLG133, MLG138, MLG80, MLG101, MLG104, MLG46, MLG60) were detected in both Honduras and Guatemala (Table S2). Within EU13A2, 194 isolates represented 120 MLGs. NI_1 comprised 31 isolates representing 21 MLGs, with MLG16 being the most frequent (n=9). In contrast, the nine CeA isolates comprised four MLGs, predominantly MLG138 (n=5), followed by MLG140 (n=2). The two US8A2 isolates represented two different MLGs (Table S2). The MSN illustrated the genetic relationships among the 148 original MLGs, with closely related MLGs occurring within the assigned clonal lineages (Figure S3). Discussion The primary finding of this study is the predominance of the EU13A2 clonal lineage in Honduras and Guatemala. EU13A2 is a highly aggressive clonal lineage that has been associated with severe late blight outbreaks and reduced sensitivity or resistance to fungicides in Europe and Asia ( Cooke et al., 2012 ; Chowdappa et al., 2013 , 2015 ). The assignment to EU13A2 was supported by clustering with multiple EU13A2 reference isolates included in the SSR-based analyses. Interestingly, reference isolates from Honduras previously designated as HN_1, collected in 2010 and 2014 ( Coomber et al., 2023 ), also clustered within the EU13A2 group. This finding indicates that HN_1 and EU13A2 share closely related 12-locus SSR profiles and suggests that EU13A2-related genotypes may have been present in Honduras prior to the present sampling under the HN_1 designation. In addition to EU13A2, the previously described clonal lineages NI_1 and, US8A2, and the putative CeA group were identified, indicating the coexistence of multiple lineages in the sampled populations. NI_1 was detected in both Honduras and Guatemala, and its clustering with reference isolates from Nicaragua indicates the occurrence of NI_1-related genotypes beyond Nicaragua. US8A2 represented a minor component of the population analyzed, with two isolates detected in Guatemala, one from San Marcos and one from Huehuetenango. Genotypes showing genetic similarity to US8A2 have previously been reported in Central America, including Costa Rica ( Gomez-Alpizar, 2005 ; Lucca, Florencia et al., 2023 ). The detection of US8A2 in Guatemala therefore provides additional evidence of the occurrence of this genetic group in Central America. However, these similarities alone do not demonstrate direct ancestry or an epidemiological relationship between the previously reported Costa Rican populations and the isolates identified in the present study. In Honduras, Forbes (2004) reported only A1, in contrast to our results, in which all isolates confidently characterized using both molecular marker systems were assigned to A2. The emergence of A2 mating-type isolates outside their center of origin has previously been associated with major population restructuring events. For example, the detection of A2 isolates in Europe in the early 1980s was linked to the introduction and subsequent expansion of genetically distinct lineages ( Drenth et al., 1993 ). Similar changes in mating-type composition have been documented in other regions; for example, A2 isolates were reported in Taiwan in populations previously dominated by A1, illustrating how lineage introduction and expansion can alter local population structure ( Deahl et al., 2008 ). Nonetheless, other explanations, such as low-frequency persistence or selective expansion, are also possible. Consequently, the predominance of A2 in our samples may reflect the expansion of A2 lineages, temporal changes in population structure, or differences in sampling intensity rather than the complete disappearance of A1. Furthermore, the presence of both mating types does not necessarily indicate sexual reproduction, as reported in Costa Rica by Gomez-Alpizar (2005) , where the population showed a predominantly clonal genetic structure. However, because conventional pairing assays with known A1 and A2 tester isolates were not performed, the potential occurrence of self-fertile isolates cannot be excluded based solely on the molecular assays used in this study. With respect to the mitochondrial haplotype, our results were consistent with the reference isolates previously characterized by Martin et al . (2019) and with the findings of Blandón-Díaz et al . (2012) , who reported that mtDNA haplotype Ia replaced IIb in Nicaraguan potato fields near the border with Honduras. All successfully characterized isolates in our study carried the Ia mitochondrial haplotype, despite the substantial diversity observed in their nuclear SSR profiles. This contrast indicates lower mitochondrial than nuclear genetic diversity in the sampled populations. Importantly, mitochondrial monomorphism should not be interpreted as evidence that a single clone colonized the sampled region, because genetically differentiated genotypes and lineages may share the same mitochondrial haplotype. Thus, the uniform Ia haplotype observed here does not exclude multiple introductions of genotypes carrying the same mitochondrial background or subsequent genetic exchange among populations. Significant multilocus linkage disequilibrium was detected in the pooled dataset of 237 field isolates, indicating non-random association among loci. However, the magnitude of the standardized index of association (rbarD) was moderate, and significant linkage disequilibrium alone should not be interpreted as conclusive evidence of predominant clonality. Other processes, including population subdivision, genetic drift, selection, demographic history, and small or unequal subpopulation sizes, may also contribute to non-random association among loci. This consideration is particularly relevant given the geographic differentiation between Honduras and Guatemala detected by the revised AMOVA. We therefore interpret the significant rbarD as evidence of non-random multilocus association in the pooled dataset rather than as direct evidence of a predominantly clonal reproductive mode. The pronounced nuclear SSR diversity observed in this study may reflect several evolutionary and demographic processes. Genetic variation can accumulate within clonal lineages of P. infestans through processes such as mutation and mitotic recombination ( Goodwin et al., 1994 ), and substantial subclonal variation has been documented within major contemporary P. infestans clonal lineages ( Stellingwerf et al., 2018 ). More broadly, genetic diversity in P. infestans populations may be shaped by mutation, selection, migration and, where sexual reproduction occurs, recombination ( Tian et al., 2016 ; Stellingwerf et al., 2018 ; Leesutthiphonchai et al., 2018 ; Tang et al., 2023 ). Environmental pressures, including fungicide use and host resistance deployment, may further shape local population structure; however, these factors were not specifically evaluated in the present study. Moreover, mitochondrial haplotypes and nuclear SSR markers provide complementary information on population history and lineage differentiation ( Martin et al., 2019 ). Overall, the uniform mitochondrial haplotype alongside substantial nuclear SSR diversity indicates contrasting levels of mitochondrial and nuclear variation, but does not by itself distinguish among multiple introductions, genetic exchange, or diversification following introduction. Higher diversity is typically found in areas closer to the center of origin of the pathogen ( Wang et al., 2017 ; Shakya et al., 2018 ). In addition, sexual reproduction can occur in regions where both A1 and A2 mating types coexist, potentially leading to progeny with diverse genetic compositions ( Oliva et al., 2002 ). The geographically structured AMOVA showed that most of the SSR genetic variation occurred within departments (75.14%), whereas differences between Honduras and Guatemala accounted for 24.43% of the total variation and were statistically significant. In contrast, variation among departments within countries was low (0.43%) and not significant. These results indicate substantial genetic variation at the local scale, together with a broader component of differentiation between the two countries. Previous studies have shown that substantial variation can occur among established clonal lineages of P. infestans ; for example, Goodwin (1995) reported that 63% of the pathogenic variation among isolates from the United States and Canada occurred among lineages, compared with 37% within lineages. However, the present geographic AMOVA does not directly test whether lineage composition accounts for the differentiation observed between Honduras and Guatemala. Differences in lineage composition, together with the movement of infected seed potatoes or other propagules, could nevertheless contribute to the observed regional genetic patterns. The introduction and dissemination of genetically distinct P. infestans populations through planting material have been proposed in several regions. For instance, in France, cluster analyses identified two differentiated genetic groups of P. infestans isolates, consistent with multiple introductions through either a mixed population or successive introductions of isolates with diverse genetic backgrounds ( Montarry et al., 2010 ). Similarly, in Algeria, three main lineages (EU13A2, EU2A1, and EU23A1) were identified, and their distribution was influenced more by seed dispersal than by cropping regions ( Beninal et al., 2022 ). Evidence also suggests an important role for EU13A2 in the 2013–14 late blight epidemic in eastern and northeastern India, where it replaced previously established populations ( Dey et al., 2018 ), consistent with its introduction and subsequent expansion in the region. Although EU13A2 has not previously been reported under this designation in Honduras, the clustering of historical HN_1 reference isolates with EU13A2 suggests that EU13A2-related genotypes may have been present in the country since at least 2010. In our study, the allele sizes of the 12 SSR markers were consistent with those documented in multiple European reference isolates of the EU13A2 clonal lineage, supporting a close genetic relationship with these reference genotypes. In contrast, a European origin was not established in the results of Blandón-Díaz et al. (2012) for the Nicaraguan isolates. Because seed potatoes in Honduras are largely imported from the Netherlands and France ( SAG & UPEG, 2021 ; World Bank, 2023c ), where EU13A2 has previously been reported ( Cooke, et al., 2012 ; Mariette et al., 2015 ), the movement of infected seed potatoes represents a plausible pathway for the historical introduction of EU13A2-related genotypes into Honduras. However, the present data cannot determine whether its occurrence in Guatemala resulted from subsequent regional movement from Honduras or from an independent introduction. However, the detection of multiple multilocus genotypes (MLGs) among isolates asigned to EU13A2 indicates genetic heterogeneity within this group. Subclonal SSR variation has also been reported within EU13A2 populations elsewhere ( Göre et al., 2021 ). However, the present SSR data do not allow us to determine whether this variation arose before or after introduction into Central America or to distinguish among mutation, recombination, selection, and multiple introductions of related genotypes. In addition, complete SSR profiles could not be obtained for all isolates despite repeated amplification attempts, and missing allele data may have reduced the resolution of some multilocus genotypes and genetic relationships. Future studies using genome-wide markers could provide greater resolution of genetic relationships and population structure. In this study, MLG94, which was the most abundant, belonged to EU13A2 and was detected exclusively in Intibucá, suggesting a more geographically restricted distribution in the present sampling. In contrast, MLG123 and MLG16, the second and third most abundant genotypes, were detected across different departments in both countries. This broader distribution likely reflects greater spatial spread of certain multilocus genotypes within the region rather than differences in time of origin. Such patterns are consistent with the expansion and diversification of clonal lineages following introduction, as reported for P. infestans ( Dey et al., 2018 ). The proposed CeA clonal lineage formed a distinct genetic group and remained differentiated from the reference genotypes included in the SSR-based analyses. Although CeA shares several SSR allele sizes with EC-1, it did not cluster with the EC-1 reference isolates and also differed in mitochondrial haplotype, with CeA carrying haplotype Ia rather than the IIa reported for EC-1 ( Martin et al., 2019 ). These results support maintaining CeA as a distinct putative clonal lineage. Given the movement of seed potatoes from South America into the region, its occurrence may nevertheless be associated with introductions from South America (World Bank, 2023b ). Therefore, these importation issues pose a significant risk because infected tubers can introduce inoculum early in the growing season, thereby facilitating rapid disease establishment. Even a few infected seed tubers that may go unnoticed can initiate epidemics. Moreover, tubers infected immediately before planting are more likely to produce viable infected shoots, which increases the risk of disease transmission ( Johnson, 2010 ). These factors highlight the importance of quarantine measures to prevent the introduction of new, aggressive clonal lineages through imported seeds potatoes and the need to strengthen local seed production, starting with the introduction of advanced germplasm to be tested and selected under local conditions or the implementation of a local breeding program. Other actions for strengthening local seed production include the production of early generation seeds, decentralized seed multiplication, and the adaptation of quality assurance mechanisms, among others ( Forbes et al., 2020 ). Collectively, this diversity underscores the importance of the continuous monitoring and characterization of P. infestans populations to inform effective disease management strategies ( Dey et al., 2018 ). Our findings highlight the dynamic nature of P. infestans populations in Central America and emphasize the need for sustained genetic surveillance to detect emerging lineages and guide evidence-based, region-specific disease management strategies. Author contributions MI: coordination, sample processing, molecular laboratory work, marker scoring, analysis, interpretation of data, and drafting of the manuscript. WP: coordination. SG: sample processing. JA: coordination. EV: sampling. BF: sampling. AS: coordination. GP: sampling. LM: sampling. JK: interpretation of data and drafting of the manuscript. Supplementary Information Supplementary Table Legend Table S1. Details of the SSR markers of the 12-plex microsatellite assay utilized for the characterization of Phytophthora infestans isolates Table S2. Phenotypic, genotypic, geographic, and sampling information for Phytophthora infestans isolates from Honduras and Guatemala, including multilocus genotype (MLG) assignments and allele profiles across the 12 SSR loci Table S3. Multilocus genotypes (MLGs) identified among Phytophthora infestans isolates from Honduras and Guatemala and their allele profiles across 12 SSR loci. Table S4. Complementary partitioning of SSR genetic variation according to clonal lineage and sampling location in Phytophthora infestans isolates from Honduras and Guatemala. This analysis is presented for descriptive purposes only because clonal lineage assignments were based on SSR profiles. Supplementary Figure Legend Figure S1. Mating type compatibility assay of Honduran and Guatemalan Phytophthora infestans isolates. (A) HaeIII -digested W16 amplicons; (B) PCR products generated with PHYB primers from Honduran (H7, H120) and Guatemalan (G53, G44) isolates. 1 kb Plus DNA ladder. Figure S2. RFLP-based mitochondrial haplotypes of Honduran and Guatemalan Phytophthora infestans isolates. (A) MspI digestion of the P2 region; (B) EcoRI digestion of the P3 region from Honduran (H7, H120) and Guatemalan (G53, G44) isolates. 1 kb Plus DNA ladder. Figure S3. Minimum spanning network (MSN) of the 148 original multilocus genotypes (MLGs) identified among 237 Phytophthora infestans field isolates from Honduras and Guatemala, based on Bruvo’s genetic distance. Each node represents an original MLG, with node size proportional to the number of isolates sharing that MLG. Node colors indicate assigned clonal lineages, and labels identify individual MLGs. Edge width and shading represent Bruvo’s genetic distance between connected MLGs. Connections represent genetic relationships among MLGs and should not be interpreted as ancestral or genealogical relationships. Funder Information Declared This study was funded by the CGIAR Plant Health Initiative (PHI), funded by the CGIAR Trust Fund Donors ( https://www.cgiar.org/funders/ ) and the Feed the Future Innovation Lab for Current and Emerging Threats to Crops provided by the United States Agency for International Development (USAID) cooperative agreement No: 7200AA21LE00005 Data Availability Statement The generated data are available on the Supplementary Table S2, available at https://www.biorxiv.org/content/10.1101/2025.05.28.656702v4 Footnotes Conflict of Interest declaration: The authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript. Acknowledgements We would like to thank Freddy Ventura for technical support during sample processing. We also thank Dr. David Cooke and Dr. Ristaino for providing the raw data used as controls for the scoring process. Field collections in Honduras and Guatemala were conducted in collaboration with the DICTA and ICTA, respectively. We would like to thank both institutes for their excellent support and contributions. Funder Information Declared CGIAR Plant Health Initiative (PHI), funded by the CGIAR Trust Fund Donors the Feed the Future Innovation Lab for Current and Emerging Threats to Crops provided by the United States Agency for International Development (USAID) cooperative agreement , 7200AA21LE00005 Footnotes The minimum spanning network (MSN) and its corresponding description have been revised to improve the visualization and interpretation of the genetic relationships among the identified multilocus genotypes (MLGs). Bibliography ↵ Abad ZG , Abad JA ( 1997 ) Another Look at the Origin of Late Blight of Potatoes, Tomatoes, and Pear Melon in the Andes of South America . 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Share Population structure of Phytophthora infestans collected from potatoes in Guatemala and Honduras Myriam Izarra , Willmer Perez , Emil Vasquez , Glenda Perez , Soledad Gamboa , Jorge Andrade-Piedra , Breny Flores , Luz Montejo , Arie Sanders , Jan Kreuze bioRxiv 2025.05.28.656702; doi: https://doi.org/10.1101/2025.05.28.656702 Share This Article: Copy Citation Tools Population structure of Phytophthora infestans collected from potatoes in Guatemala and Honduras Myriam Izarra , Willmer Perez , Emil Vasquez , Glenda Perez , Soledad Gamboa , Jorge Andrade-Piedra , Breny Flores , Luz Montejo , Arie Sanders , Jan Kreuze bioRxiv 2025.05.28.656702; doi: https://doi.org/10.1101/2025.05.28.656702 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Pathology Subject Areas All Articles Animal Behavior and Cognition (8013) Biochemistry (18739) Bioengineering (14888) Bioinformatics (44418) Biophysics (22599) Cancer Biology (19723) Cell Biology (26899) Clinical Trials (138) Developmental Biology (13965) Ecology (21005) Epidemiology (2067) Evolutionary Biology (25455) Genetics (16166) Genomics (23507) Immunology (18705) Microbiology (42503) Molecular Biology (18059) Neuroscience (93451) Paleontology (700) Pathology (2977) Pharmacology and Toxicology (5095) Physiology (8114) Plant Biology (15999) Scientific Communication and Education (2095) Synthetic Biology (4560) Systems Biology (10235) Zoology (2391) window.__CF$cv$params={r:'a3e08b3c5baf750b',t:'MTc4OTkwNDMwNg==',u:'01a0be9c494c772d8c5cab0735681545',ut:'uh3iu7LfZjMueCsV8edCuBRoTq5ZZQtH0YPj7pRXKis-1789904308-1.2.1.1-SXF5CY.AHCs.G3GV1RVkV_ulD6lU2jwYNideC379hG6cWVlW5EBchPlgAjRK4PyLVW7gWtGTXWgJbxF.MGf1WF6LCYEQycYa1QaoBO2et9s',i:60};(function(){if(!document.body)return;var s=document.createElement('script');s.src='/cdn-cgi/challenge-platform/scripts/precursor/main.js';document.head.appendChild(s);})();