Self-Reported Ancestry and Craniofacial SNPs

Genotype-phenotype studies increasingly link single nucleotide polymorphism (SNPs) to the dimensions of the face for presumed homogeneous populations. To appreciate the significance of these findings, it is essential to investigate how these results differ between the genetic and phenotypic profiles of individuals. In prior work, we investigated the connection between SNPs previously identified as informative of soft tissue expression and measurements of the craniofacial skeleton. Using matched genetic and skeletal information on 17 individuals who self-identified as White with presumed common continental ancestry (European), we obtained significant Spearman correlations for 11 SNPs. In the present study, we looked at self-identified ancestry to understand the intersectional background of the individual’s phenotype and genotype. We integrated our samples within a diverse dataset of 2,242 modern Americans and applied an unsupervised model-based clustering routine to 13 craniometrics. We generated a mean estimate of 69.65% (±SD = 18%) European ancestry for the White sample under an unsupervised cluster model. We estimated higher quantities of European ancestry, 88.5%–93%, for our subset of 17 individuals. These elevated estimates were of interest with respect to the distribution of population-informative SNPs; we found, for example, that one of our sampled self-identified White individuals displayed SNPs commonly associated with Latin American populations. These results underscore the complex interrelationship between environment and genetics, and the need for continued research into connections between population affinity, social identity, and morphogenetic expression.

Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences

Drought frequently spreads across large spatial and time scales and is more complicated than other natural disasters that can damage economic and other natural resources worldwide. However, improved drought monitoring and forecasting techniques can help to minimize the vulnerability of society to drought and its consequent influences. This emphasizes the need for improved drought monitoring tools and assessment techniques that provide information more precisely about drought occurrences. Therefore, this study developed a new method, Model-Based Clustering for Spatio-Temporal Categorical Sequences (MBCSTCS), that uses state selection procedures through finite mixture modeling and model-based clustering. The MBCSTCS uses the functional structure of first-order Markov model components for modeling each data group. In MBCSTCS, the suitable order K of the components is selected by Bayesian information criterion (BIC). In MBCSTCS, the estimated mixing proportions and the posterior probabilities are used to compute probability distribution associated with the future steps of transitions. Furthermore, MBCSTCS predicts drought occurrences in future time using spatiotemporal categorical sequences of various drought classes. The MBCSTCS is applied to the six meteorological stations in the northern area of Pakistan. Moreover, it is found that MBCSTCS provides expeditious information for the long-term spatiotemporal categorical sequences. These findings may be helpful to make plans for early warning systems, water resource management, and drought mitigation policies to decrease the severe effects of drought.

Constrained parsimonious model-based clustering

A new methodology for constrained parsimonious model-based clustering is introduced, where some tuning parameter allows to control the strength of these constraints. The methodology includes the 14 parsimonious models that are often applied in model-based clustering when assuming normal components as limit cases. This is done in a natural way by filling the gap among models and providing a smooth transition among them. The methodology provides mathematically well-defined problems and is also useful to prevent us from obtaining spurious solutions. Novel information criteria are proposed to help the user in choosing parameters. The interest of the proposed methodology is illustrated through simulation studies and a real-data application on COVID data.

Summarizing Finite Mixture Model with Overlapping Quantification

Finite mixture models are widely used for modeling and clustering data. When they are used for clustering, they are often interpreted by regarding each component as one cluster. However, this assumption may be invalid when the components overlap. It leads to the issue of analyzing such overlaps to correctly understand the models. The primary purpose of this paper is to establish a theoretical framework for interpreting the overlapping mixture models by estimating how they overlap, using measures of information such as entropy and mutual information. This is achieved by merging components to regard multiple components as one cluster and summarizing the merging results. First, we propose three conditions that any merging criterion should satisfy. Then, we investigate whether several existing merging criteria satisfy the conditions and modify them to fulfill more conditions. Second, we propose a novel concept named clustering summarization to evaluate the merging results. In it, we can quantify how overlapped and biased the clusters are, using mutual information-based criteria. Using artificial and real datasets, we empirically demonstrate that our methods of modifying criteria and summarizing results are effective for understanding the cluster structures. We therefore give a new view of interpretability/explainability for model-based clustering.