Model-based Cluster Analysis Research Articles

Association between betrayal trauma and typologies of anger and aggression

Anger and aggression are related forms of externalizing phenomena that are distinct in that anger is a form of emotion, whereas aggression is a behavior. Both anger and aggression (as well as their combination) function as responses to environmental conditions, one of the most common of which is trauma. Recent research has highlighted the association between trauma high in betrayal and externalizing problems, although this research has not been examined with respect to typologies of anger and aggression specifically. In this study, we examined the association between different forms of trauma and typologies of anger and aggression in a sample of women and men recruited online (N = 364) using model-based cluster analysis and multinomial logistic regression. Results suggest that trauma with a high degree of betrayal was associated with a typology of implicit anger characterized by intense anger but controlled aggression. Findings suggest the importance of understanding the impact of betrayal on anger and aggression.

Exploring Types of Parent Attachment via the Clustering Modules of a New Free Statistical Software, ROP-R

The aim of the paper is threefold: (1) to demonstrate the rich repertoire of clustering capabilities of a ROPstat and R-based new and free software, called ROP-R, by illustrating several analyses with real psychological data; (2) to show how well ROP-R works in tandem with ROPstat software in complex classification analyses; and (3) to explore some nontrivial types of parent attachment using the clustering modules of ROP-R. Four modules of ROP-R are available for performing cluster analyses (CAs), with several methods (e.g., divisive hierarchical CA, k-medoids CA, k-medians CA, model-based CA) not found in other user-friendly menu-driven software. In the paper, mother and father attachment data are used from a study with adolescents (Mirnics et al., 2021) to illustrate how the ROP-R software can be used to perform various CAs and evaluate the results using attractive graphs and useful tables. Comparing different clustering methods, it was found that both standard AHCA and k-means CA could discover a 7-type structure, which was also verified by the nonstandard k-medians CA. However, the nonstandard k-medoids CA and MBCA methods were not very effective in identifying a structure with an acceptable overall homogeneity. Nevertheless, they were able to identify some types through extremely homogeneous clusters.

Exploring Types of Parent Attachment via the Clustering Modules of a New Free Statistical Software, ROP-R.

The aim of the paper is threefold: (1) to demonstrate the rich repertoire of clustering capabilities of a ROPstat and R-based new and free software, called ROP-R, by illustrating several analyses with real psychological data; (2) to show how well ROP-R works in tandem with ROPstat software in complex classification analyses; and (3) to explore some nontrivial types of parent attachment using the clustering modules of ROP-R. Four modules of ROP-R are available for performing cluster analyses (CAs), with several methods (e.g., divisive hierarchical CA, k-medoids CA, k-medians CA, model-based CA) not found in other user-friendly menu-driven software. In the paper, mother and father attachment data are used from a study with adolescents (Mirnics et al., 2021) to illustrate how the ROP-R software can be used to perform various CAs and evaluate the results using attractive graphs and useful tables. Comparing different clustering methods, it was found that both standard AHCA and k-means CA could discover a 7-type structure, which was also verified by the nonstandard k-medians CA. However, the nonstandard k-medoids CA and MBCA methods were not very effective in identifying a structure with an acceptable overall homogeneity. Nevertheless, they were able to identify some types through extremely homogeneous clusters.

Utilizing SSR markers to examine the population structure and molecular genetic diversity of walnut (Juglans regia L.) genotypes in the Northwestern Himalayan region of Jammu and Kashmir

By using 16 simple sequence repeat (SSR) markers, the genetic relatedness of 21 exceptional walnut genotypes was assessed. A significant degree of genetic diversity was observed within a given population, as indicated by the number of alleles per locus ranging from 2 to 4. WGA-1, WGA-4, and WGA-79 contained the greatest number of alleles (4), followed by WGA-118, WGA-202, and WGA-42. Conversely, WGA-27, WGA-69, and WGA-32 contained the fewest alleles. The range of the PIC value was 0.11 to 0.38. Using model-based cluster analysis, all genotypes were categorized into two primary clusters according to the UPGMA dendrogram, with varying degrees of sub-clustering. All the genotypes were categorized into six genetically distant subpopulations. The genotypes were genetically distinct but had variable degrees of admixture. The anticipated heterozygosity at a specific locus ranged from 0.563 to 0.741. Additionally, population differentiation (Fst) ranged between 0.176 and 0.261. These findings highlight the importance of considering germplasm diversity in walnut breeding programs and conservation efforts aimed at enhancing walnut cultivation in the region. Overall, this study contributes to our understanding of walnut genetic diversity in the Northwestern Himalayan region of Jammu and Kashmir and informs future breeding and conservation strategies.

Improving model choice in classification: an approach based on clustering of covariance matrices

This work introduces a refinement of the Parsimonious Model for fitting a Gaussian Mixture. The improvement is based on the consideration of clusters of the involved covariance matrices according to a criterion, such as sharing Principal Directions. This and other similarity criteria that arise from the spectral decomposition of a matrix are the bases of the Parsimonious Model. We show that such groupings of covariance matrices can be achieved through simple modifications of the CEM (Classification Expectation Maximization) algorithm. Our approach leads to propose Gaussian Mixture Models for model-based clustering and discriminant analysis, in which covariance matrices are clustered according to a parsimonious criterion, creating intermediate steps between the fourteen widely known parsimonious models. The added versatility not only allows us to obtain models with fewer parameters for fitting the data, but also provides greater interpretability. We show its usefulness for model-based clustering and discriminant analysis, providing algorithms to find approximate solutions verifying suitable size, shape and orientation constraints, and applying them to both simulation and real data examples.

Cluster analysis of school emotional engagement among Portuguese children in elementary school

The association between school emotional engagement and child mental health and school success highlights the importance of understanding how child–school relations are developed and what factors are related to this affective process during the initial years of schooling so that early intervention is possible. This study examined emotional engagement in Portuguese children attending elementary school and aimed to identify and characterize subgroups based on the child’s school liking and avoidance, using a model–based cluster analysis. In addition, differences between clusters in what concerns individual (self–efficacy, behavioral engagement, externalizing, and internalizing problems), family (parental emotional support), school (teacher-student closeness), and family-school relation (parental school involvement) variables were analyzed. The sample consisted of 394 elementary school children, 353 parents, and 35 teachers using a multimethod assessment (i.e. children, parents, and teachers’ reports). Cluster analysis identified three patterns of emotional school involvement: emotionally involved, emotionally involved with some avoidance, and emotionally ambivalent. Results showed that many children experience ambivalent feelings toward school in the first years of schooling. The emotionally ambivalent group stood out negatively from the other groups concerning self–efficacy, behavioral engagement, externalizing, and internalizing problems. Suggestions for future research and practical implications for schools are discussed.

Ambivalence and transnational intergenerational solidarity: the perspective of highly educated Portuguese women emigrant daughters

Existing literature has highlighted solidarity across generations as a crucial mechanism for transfers and maintaining cohesion within families. However, researching solidarity across generations among transnational families is still an underexplored area, especially from the viewpoint of highly skilled female migrants. This study explored the way highly educated Portuguese adult daughters, living transnationally, perceive the existing solidarity ties with their parents, in times of transition, such as during the process of migration. By using a cluster analysis approach to explore the statistical associations between ambivalence and intergenerational solidarity dimensions, this study provides insight into the migrant solidarity types incorporating the role of ambivalence in forging a typology of transnational intergenerational relationships. The sample comprised 248 daughters who volunteered to provide information in an online survey. Participants gave their full consent to partake in this study, and this study was approved by the ethical committee of the authors’ institution. Measures of intergenerational solidarity dimensions, perceived ambivalence, sociocultural adaptation, acculturation, and social well-being in the destination country were included in the survey. Model-based cluster analysis resulted in three clusters: low ambivalence with strong cohesion, autonomous with affection and low ambivalence, and ambivalent functional ties with low affection. These clusters differed significantly in terms of intergenerational solidarity dimensions, perceived ambivalence, and in several other measures addressed. Perceived high parent-daughter ambivalent ties were associated with high levels of functional contacts and financial exchange. That pattern was also associated with lower levels of affection and consensus, being most frequently found in older daughters, mostly when single, economically inactive, or unemployed. Low perceived ambivalent parent-daughter ties were the most frequently found (around 80%), with two different types of intergenerational relationships being revealed, namely the low ambivalence with strong cohesion type and the autonomous with affection and low ambivalence type. Half (50%) of the daughters that perceived low parent-daughter ambivalent ties have associated the highest intergenerational solidarity, being, in general, better adapted to the migration context and slightly younger on average. The other half, less well acculturated, living abroad for longer, in countries with stronger welfare state systems and less reliance on families as providers of care, are more associated with an autonomous relationship type with low solidarity, but high affection. These results suggest that there might be a spillover effect: those who fare well in the country of migration, have better relations with their families at home—either being very interdependent or very independent, but always with good affective quality and low ambivalence.

Self-regulation profiles in addictive behaviors among adolescents: A transdiagnostic approach

Dual-pathway models suggest that poor self-regulation (immature regulatory combined with strong reactive processes) is an important factor underlying addictive behaviors among adolescents. This study examined whether there are different self-regulation profiles among community adolescents, and how these profiles are related to the presence, severity and comorbidity of different addictive behaviors. A community sample of 341 adolescents (54.5% female; 13–17 years) was recruited. Participants self-reported on regulatory (inhibitory control) and reactive (reward and punishment sensitivity) processes, as well as on different addictive behaviors (binge eating, tobacco-, cannabis- and alcohol use, gaming, gambling and pathological buying). A model-based clustering analysis found evidence for three meaningful profiles: ‘impulsive/under-controlled’, ‘anxious’ and ‘protective’. The ‘impulsive/under-controlled’ profile was characterized by the highest prevalence and severity of cannabis use and the most severe alcohol use. The ‘impulsive/under-controlled’ and ‘protective’ profiles demonstrated the highest prevalence and severity of tobacco use, whereas the ‘impulsive/under-controlled’ and ‘anxious’ profiles showed the highest binge eating scores. Adolescents who reported more than three types of addictive behaviors generally belonged to the ‘impulsive/under-controlled’ profile. The profiles did not differ for gaming, gambling and pathological buying. The ‘impulsive/under-controlled’ profile emerged as the most vulnerable profile in the context of addictive behaviors (especially for binge eating and substance use).

Statistical indicators based on mobile phone and street maps data for risk management in small urban areas

The use of new sources of big data collected at a high-frequency rate in conjunction with administrative data is critical to developing indicators of the exposure to risks of small urban areas. Correctly accounting for the crowding of people and for their movements is crucial to mitigate the effect of natural disasters, while guaranteeing the quality of life in a “smart city” approach. We use two different types of mobile phone data to estimate people crowding and traffic intensity. We analyze the temporal dynamics of crowding and traffic using a Model-Based Functional Cluster Analysis, and their spatial dynamics using the T-mode Principal Component Analysis. Then, we propose five indicators useful for risk management in small urban areas: two composite indicators based on cutting-edge mobile phone dynamic data and three indicators based on open-source street map static data. A case study for the flood-prone area of the Mandolossa (the western outskirts of the city of Brescia, Italy) is presented. We present a multi-dimensional description of the territory based on the proposed indicators at the level of small areas defined by the Italian National Statistical Institute as “Sezioni di Censimento” and “Aree di Censimento”.

Towards elucidating species diversity of European inlandStrigamia(Chilopoda: Geophilomorpha): a first reassessment integrating multiple lines of evidence

AbstractStrigamia centipedes are widespread in European forest soils. However, a complex and inconsistent taxonomy has developed over time. Based on a modern species concept, we evaluated multiple lines of evidence for speciation among inland populations of Strigamia from the Italian region to the Baltic region, across central Europe. Hypotheses of species delimitation were drawn independently from: (1) morphological differentiation, by means of model-based cluster analyses of 52 specimens, after controlling for allometry and sexual dimorphism; (2) syntopy of morphologically distinct individuals, assumed to be representatives of coexistent species, from > 700 sites; and (3) molecular differentiation, by various methods applied to cytochrome c subunit I sequences of the same 52 specimens. Diagnoses and geographical distributions were revised by re-examining > 2000 specimens and the entire literature. We found preliminary evidence for: a Strigamia acuminata species complex, widespread, including a candidate species from the Eastern Alps differing in the forcipules; a Strigamia carniolensis species complex, hitherto often called Strigamia crassipes, widespread south of the Alps but absent from Sicily; a Strigamia crassipes species complex, hitherto often called Strigamia transsilvanica, more widespread than previously known, including three candidate species co-occurring in the Western Alps and differing in body size and the number of legs despite little genetic divergence; and Strigamia engadina, exclusive to a narrow part of the Central Alps.

Managing With Atrial Fibrillation: An Exploratory Model-Based Cluster Analysis of Clinical and Personal Patient Characteristics

BackgroundExamining characteristics of patients with atrial fibrillation (AF) has the potential to help in identifying groups of patients who might benefit from different management approaches. MethodsSecondary analysis of online survey data was combined with clinic referral data abstraction from 196 patients with AF attending an AF specialty clinic. Cluster analyses were performed to identify distinct, homogeneous clusters of AF patients defined by 11 relevant variables: CHA2DS2-VASc score, age, AF symptoms, overall health, mental health, AF knowledge, perceived stress, household and recreation activity, overall AF quality of life, and AF symptom treatment satisfaction. Follow-up analyses examined differences between the cluster groups in additional clinical variables. ResultsEvidence emerged for both 2- and 4-cluster solutions. The 2-cluster solution involved a contrast between patients who were doing well on all variables (n = 129; 66%) vs those doing less well (n = 67; 34%). The 4-cluster solution provided a closer-up view of the data, showing that the group doing less well was split into 3 meaningfully different subgroups of patients who were managing in different ways. The final 4 clusters produced were as follows: (i) doing well; (ii) stressed and discontented; (iii) struggling and dissatisfied; and (iv) satisfied and complacent. ConclusionsPatients with AF can be accurately classified into distinct, natural groupings that vary in clinically important ways. Among the patients who were not managing well with AF, we found 3 distinct subgroups of patients who may benefit from tailored approaches to AF management and support. The tailoring of treatment approaches to specific personal and/or behavioural patterns, alongside clinical patterns, holds potential to improve patient outcomes (eg, treatment satisfaction).

Estimation of a clustering model for non Gaussian functional data

Model-based clustering analysis of functional data often has normality assumption. This article considers clustering non Gaussian functional data. We propose a novel non Gaussian functional mixed-effects model without the prior information and clustering number. We use transformation functions to accommodate non Gaussian functional data. Smoothing spline ANOVA and cubic B-spline approximate unknown fixed effects and random effects, respectively. A penalized likelihood is used to estimate unknown parameters, and the consistency and asymptotic normality is provided after that. We take simulations for different measurement error distribution assumptions and adopt the air quality of Italian city data. Both simulation and actual data analysis show that the proposed method performs well and has a better clustering effect.

Male diversity matters: genetic structuring of insular male date palm (Phoenix dactylifera L.) revealed valuable breeding and conservation resources

ABSTRACT Most male date palms available for pollinating different female cultivars have mainly originated from seed propagation resulting in many different local males that represent a source of genetic diversity. Favourable fruit production is related to the quality of pollen and its compatibility with a certain female variety. Therefore, the genetic characterisation of the male progenitors should be the first step to establish a selection programme to produce superior males through different procedures. In this study, the genetic diversity and population structure of 72 male date palm accessions were investigated using 15 microsatellite loci. The highest mean number of alleles per locus was obtained for Tozeur group. Bayesian model-based clustering analysis indicated the presence of two differentiated endemic male date palm genetic clusters, continental and insular, with the presence of introduced accessions originating from the Middle East. The diversity analysis in the insular region of Tunisia, which had never been performed before, revealed that this germplasm enclosed valuable endemic resources supporting the hypothesis of the presence of wild material. These findings are crucial for identifying interesting genotypes that can be integrated into international coordinated actions of Phoenix dactylifera L. breeding programmes and the protection and conservation of valuable resources.

Genetic Diversity and Population Structure in Türkiye Bread Wheat Genotypes Revealed by Simple Sequence Repeats (SSR) Markers.

Wheat genotypes should be improved through available germplasm genetic diversity to ensure food security. This study investigated the molecular diversity and population structure of a set of Türkiye bread wheat genotypes using 120 microsatellite markers. Based on the results, 651 polymorphic alleles were evaluated to determine genetic diversity and population structure. The number of alleles ranged from 2 to 19, with an average of 5.44 alleles per locus. Polymorphic information content (PIC) ranged from 0.031 to 0.915 with a mean of 0.43. In addition, the gene diversity index ranged from 0.03 to 0.92 with an average of 0.46. The expected heterozygosity ranged from 0.00 to 0.359 with a mean of 0.124. The unbiased expected heterozygosity ranged from 0.00 to 0.319 with an average of 0.112. The mean values of the number of effective alleles (Ne), genetic diversity of Nei (H) and Shannon's information index (I) were estimated at 1.190, 1.049 and 0.168, respectively. The highest genetic diversity (GD) was estimated between genotypes G1 and G27. In the UPGMA dendrogram, the 63 genotypes were grouped into three clusters. The three main coordinates were able to explain 12.64, 6.38 and 4.90% of genetic diversity, respectively. AMOVA revealed diversity within populations at 78% and between populations at 22%. The current populations were found to be highly structured. Model-based cluster analyses classified the 63 genotypes studied into three subpopulations. The values of F-statistic (Fst) for the identified subpopulations were 0.253, 0.330 and 0.244, respectively. In addition, the expected values of heterozygosity (He) for these sub-populations were recorded as 0.45, 0.46 and 0.44, respectively. Therefore, SSR markers can be useful not only in genetic diversity and association analysis of wheat but also in its germplasm for various agronomic traits or mechanisms of tolerance to environmental stresses.

A latent class analysis of the socio-demographic factors and associations with mental and behavioral disorders among Australian children and adolescents.

Previous studies have shown a relationship between socio-demographic variables and the mental health of children and adolescents. However, no research has been found on a model-based cluster analysis of socio-demographic characteristics with mental health. This study aimed to identify the cluster of the items representing the socio-demographic characteristics of Australian children and adolescents aged 11-17 years by using latent class analysis (LCA) and examining the associations with their mental health. Children and adolescents aged 11-17 years (n = 3152) were considered from the 2013-2014 Young Minds Matter: The Second Australian Child and Adolescent Survey of Mental Health and Wellbeing. LCA was performed based on relevant socio-demographic factors from three levels. Due to the high prevalence of mental and behavioral disorders, the generalized linear model with log-link binomial family (log-binomial regression model) was used to examine the associations between identified classes, and the mental and behavioral disorders of children and adolescents. This study identified five classes based on various model selection criteria. Classes 1 and 4 presented the vulnerable class carrying the characteristics of "lowest socio-economic status and non-intact family structure" and "good socio-economic status and non-intact family structure" respectively. By contrast, class 5 indicated the most privileged class carrying the characteristics of "highest socio-economic status and intact family structure". Results from the log-binomial regression model (unadjusted and adjusted models) showed that children and adolescents belonging to classes 1 and 4 were about 1.60 and 1.35 times more prevalent to be suffering from mental and behavioral disorders compared to their class 5 counterparts (95% CI of PR [prevalence ratio]: 1.41-1.82 for class 1; 95% CI of PR [prevalence ratio]: 1.16-1.57 for class 4). Although children and adolescents from class 4 belong to a socio-economically advantaged group and shared the lowest class membership (only 12.7%), the class had a greater prevalence (44.1%) of mental and behavioral disorders than did class 2 ("worst education and occupational attainment and intact family structure") (35.2%) and class 3 ("average socio-economic status and intact family structure") (32.9%). Among the five latent classes, children and adolescents from classes 1 and 4 have a higher risk of developing mental and behavioral disorders. The findings suggest that health promotion and prevention as well as combating poverty are needed to improve mental health in particular among children and adolescents living in non-intact families and in families with a low socio-economic status.

Genomic variability and population structure of six Colombian cattle breeds

Analyses of the genetic diversity of indigenous cattle are essential for implementing conservation programs, promoting their sustainable use and maintaining productive advantages offered by these breeds in local conditions. The aim of this study was to investigate the genetic diversity and population structure of six Colombian cattle breeds: Blanco Orejinegro (BON), Costeño con Cuernos (CCC), Romosinuano (ROM), Sanmartinero (SAM), Casanareño (CAS), and Hartón del Valle (HDV). Two additional breed groups were included for comparison: Zebu (CEB) and a crossbreed of Colombian cattle breeds × Zebu. Genetic diversity within breeds was analyzed using expected heterozygosity (He), inbreeding coefficient (f), and runs of homozygosity (ROH). Population structure was assessed using model-based clustering (ADMIXTURE) and principal components analysis (PCA). Zebu cattle showed the lowest genetic diversity (He = 0.240). Breeds with the highest genetic diversity level were HDV and BON (He = 0.350 and 0.340, respectively). Inbreeding was lower for Colombian cattle breeds ranging between 0.005 and 0.045. Overall, the largest average genetic distance was found among the group of Colombian cattle breeds and Zebu, while the smallest was found between ROM and CCC. Model-based clustering revealed some level of admixture among HDV and CAS cattle which is consistent with their recent history. The results of the present study provide a useful insight on the genetic structure of Colombian cattle breeds.

Finite mixture of semiparametric multivariate skew-normal distributions

Although the normal distribution is the most frequently used distribution for modeling a given data in many applications due to its desirable theoretical properties and computational convenience, it is often not suitable to model the data having asymmetric or heavy tailed distributions. The skew-normal distribution is an important alternative to the normal distribution as it can cover not only the normal distribution but also some asymmetric distributions. However, it cannot well approximate heavy tailed distributions. In this paper, we propose a semiparametric skew-normal distribution which contains skew-normal distributions using the nonparametric scale mixture of skew-normal distributions and apply the proposed model to finite mixture models so that we can obtain more efficient and insightful knowledge in the model-based cluster analysis. We provide a feasible algorithm to compute all parametric and nonparametric components in the proposed model. Numerical examples to show the applicability of the proposed model are also presented.

The shift towards an eco-welfare state: growing stronger together

AbstractPoverty, inequality and climate change are profoundly interconnected issues and represent grave threats to the future of our planet and civilization. Failure in one will result in failure in the other; thus, government responses to such threats must be meticulously coordinated, especially across environmental and welfare state programs. In recent years, a growing body of research has examined the links between these two domains, lauding the eco-welfare state as a viable path forward. As the literature on the eco-welfare state is at an early stage, this study proposes two essential theoretical and empirical contributions. First, it examines the most prominent theoretical interpretations of the concept of eco-welfare state and proposes a refined understanding. Second, using model-based cluster analysis for 42 countries, this study empirically unveils a global shift towards and the existence of an eco-welfare state.

Whole-genome resequencing data support a single introduction of the invasive white pine sawfly, Diprion similis.

Biological introductions are unintended "natural experiments" that provide unique insights into evolutionary processes. Invasive phytophagous insects are of particular interest to evolutionary biologists studying adaptation, as introductions often require rapid adaptation to novel host plants. However, adaptive potential of invasive populations may be limited by reduced genetic diversity-a problem known as the "genetic paradox of invasions." One potential solution to this paradox is if there are multiple invasive waves that bolster genetic variation in invasive populations. Evaluating this hypothesis requires characterizing genetic variation and population structure in the invaded range. To this end, we assemble a reference genome and describe patterns of genetic variation in the introduced white pine sawfly, Diprion similis. This species was introduced to North America in 1914, where it has rapidly colonized the thin-needled eastern white pine (Pinus strobus), making it an ideal invasion system for studying adaptation to novel environments. To evaluate evidence of multiple introductions, we generated whole-genome resequencing data for 64 D. similis females sampled across the North American range. Both model-based and model-free clustering analyses supported a single population for North American D. similis. Within this population, we found evidence of isolation-by-distance and a pattern of declining heterozygosity with distance from the hypothesized introduction site. Together, these results support a single-introduction event. We consider implications of these findings for the genetic paradox of invasion and discuss priorities for future research in D. similis, a promising model system for invasion biology.

Genetic Diversity and Population Structure of Common Bean (Phaseolus vulgaris L.) Landraces in the Lazio Region of Italy.

Common bean cultivation has historically been a typical component of rural economies in Italy, particularly in mountainous and hilly zones along the Apennine ridge of the central and southern regions, where the production is focused on local landraces cultivated by small-scale farmers using low-input production systems. Such landraces are at risk of genetic erosion because of the recent socioeconomic changes in rural communities. One hundred fourteen accessions belonging to 66 landraces still being grown in the Lazio region were characterized using a multidisciplinary approach. This approach included morphological (seed traits), biochemical (phaseolin and phytohemagglutinin patterns), and molecular (microsatellite loci) analyses to investigate their genetic variation, structure, and distinctiveness, which will be essential for the implementation of adequate ex situ and in situ conservation strategies. Another objective of this study was to determine the original gene pool (Andean and Mesoamerican) of the investigated landraces and to evaluate the cross-hybridization events between the two ancestral gene pools in the P. vulgaris germplasm in the Lazio region. Molecular analyses on 456 samples (four for each of the 114 accessions) revealed that the P. vulgaris germplasm in the Lazio region exhibited a high level of genetic diversity (He = 0.622) and that the Mesoamerican and Andean gene pools were clearly differentiated, with the Andean gene pool prevailing (77%) and 12% of landraces representing putative hybrids between the two gene pools. A model-based cluster analysis based on the molecular markers highlighted three main groups in agreement with the phaseolin patterns and growth habit of landraces. The combined utilisation of morphological, biochemical, and molecular data allowed for the differentiation of all landraces and the resolution of certain instances of homonymy and synonymy. Furthermore, although a high level of homozygosity was found across all landraces, 32 of the 66 examined (49%) exhibited genetic variability, indicating that the analysis based on a single or few plants per landrace, as usually carried out, may provide incomplete information.