Modern humans have populated Europe for more than 45,000 years1,2. Our knowledge of the genetic relatedness and structure of ancient hunter-gatherers is however limited, owing to the scarceness and poor molecular preservation of human remains from that period3. Here we analyse 356 ancient hunter-gatherer genomes, including new genomic data for 116 individuals from 14 countries in western and central Eurasia, spanning between 35,000 and 5,000 years ago. We identify a genetic ancestry profile in individuals associated with Upper Palaeolithic Gravettian assemblages from western Europe that is distinct from contemporaneous groups related to this archaeological culture in central and southern Europe4, but resembles that of preceding individuals associated with the Aurignacian culture. This ancestry profile survived during the Last Glacial Maximum (25,000 to 19,000 years ago) in human populations from southwestern Europe associated with the Solutrean culture, and with the following Magdalenian culture that re-expanded northeastward after the Last Glacial Maximum. Conversely, we reveal a genetic turnover in southern Europe suggesting a local replacement of human groups around the time of the Last Glacial Maximum, accompanied by a north-to-south dispersal of populations associated with the Epigravettian culture. From at least 14,000 years ago, an ancestry related to this culture spread from the south across the rest of Europe, largely replacing the Magdalenian-associated gene pool. After a period of limited admixture that spanned the beginning of the Mesolithic, we find genetic interactions between western and eastern European hunter-gatherers, who were also characterized by marked differences in phenotypically relevant variants.

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Objectives We studied the sex differences in the distribution of entheseal changes (EC) in an archeological population through a Bayesian approach that allows incorporating existing knowledge while controlling for confounder factors that may affect EC development. Materials and methods We performed a meta‐analysis of published research on sex differences in EC frequencies from archeological populations. Also, EC were assessed for fibrocartilaginous entheses following the “New Coimbra Method” in a Spanish population that dates from the 15th to the 18th century. Data were analyzed with multivariate generalized linear mixed models (MGLMM). Results Meta‐analysis showed a consistent but small effect of males usually manifesting higher EC frequencies. Similarly, our MGLMM analysis showed that bone formation and erosion is unequally distributed in the archeological population we studied, with bone formation more present in male lower limbs and erosion more frequent in male upper limbs. Discussion Bayesian inference makes it possible to assess more complex models than traditional frequentist methods, and can be informed by meta‐analysis to reflect the current state of knowledge on any given topic. MGLMM are an appropriate technique for the study of EC as they can accommodate several response variables in a single model, controlling for well‐known confounders of EC formation to infer sex differences that could be attributed to daily behavior.

Objectives: In this paper, we introduce the use of generalized linear mixed models (GLMM) as a better alternative to traditional statistical methods for studying factors associated to the prevalence of degenerative joint disease (DJD) in bioarchaeological contexts.Materials and Methods: DJD prevalence was assessed for the appendicular joints and the spine of a Spanish population dated from the 15th to the 18th century. Data were analyzed using contingency tables, logistic regression models, and logistic GLMM.Results: In general, results from GLMMs find agreement in other methods. However, by being able to analyze the data at the level of individual bones instead of aggregated joints or limbs, GLMMs are capable of revealing associations that are not evident in other frameworks.Discussion: Currently widely available in statistical analysis software, GLMMs can accommodate a wide array of data distributions, account for hierarchical correlations, and return estimates of DJD prevalence within individuals and skeletal locations that are unbiased by the effect of covariates. This gives clear advantages for the analysis of bioarchaeological datasets which can lead to more robust and comparable analyses across populations.

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