Maximum genetic proportion of metric traits from different regions of the skull in ancient humanpopulations of Northwestern Argentina

In order to explain the evolutionary process of ancient human populations that inhabited a specific geographical region from quantitative skull traits, it is advisable to know the evolutionary potential of metric characters. For this reason, the proportion of the maximum genetic variance or maximum heritability (h2m) of the variables studied was estimated. In addition, it was evaluated whether h2m changes between regions of the skull (face, base and vault) and the degree of association between the phenotypic variance and the maximum genetic variance. Twenty-one symmetrical variables on the left and right sides of the skull were measured in 245 skulls from five prehistoric samples from northwestern Argentina. The upper limit of heritability was estimated using the repeated measurement method. To test whether there are differences between the h2m of each group, the Kruskal-Wallis test was used. The maximum genetic values of each variable were obtained through a regression analysis (right measure on left measure). The relationship between phenotypic and maxi-mum genetic values was evaluated by correlation analysis. Significant bilateral difference is demon-strated in six of 21 characters. The average h2m is 0.77 and ranges between 0.58 and 0.93. The aver-age correlation between phenotypic values and maximum genotypic values was 0.8 (R2=0.65), suggesting that it is possible to make inferences of the genetic structure of the population from phenotypic information. The high proportion of maximum observed genetic variance indicates an important evolutionary potential of the craniofacial complex in ancient populations of northwestern Argentina

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How to Group Genes according to Expression Profiles?

The most commonly applied strategies for identifying genes with a common response profile are based on clustering algorithms. These methods have no explicit rules to define the appropriate number of groups of genes. Usually the number of clusters is decided on heuristic criteria or through the application of different methods proposed to assess the number of clusters in a data set. The purpose of this paper is to compare the performance of seven of these techniques, including traditional ones, and some recently proposed. All of them produce underestimations of the true number of clusters. However, within this limitation, the gDGC algorithm appears to be the best. It is the only one that explicitly states a rule for cutting a dendrogram on the basis of a testing hypothesis framework, allowing the user to calibrate the sensitivity, adjusting the significance level.