AgeEst: An open access web application for skeletal age-at-death estimation employing machine learning

The present study tests the accuracy of commonly adopted age-at-death estimation markers based on the morphology of the pubic symphysis, iliac auricular surface and cranial sutures on a contemporary documented skeletal collection from Greece (81 males and 59 females). Machine learning techniques are used to assess whether a) machine learning classification models can correctly classify skeletons into their correct age group and b) machine learning regression models can predict the correct age to a satisfactory degree. The constructed models are used in a web application (AgeEst), where users can easily employ them to make predictions for their own skeletal assemblages. The results show that the use of machine learning improves age predictions in terms of bias and inaccuracy compared to the direct application of the original methods. However, there is a strong misclassification of middle-aged individuals, stressing the inherent biases both of the skeletal markers traditionally used in age-at-death prediction and of machine learning methods that, in our case, tend to classify most individuals to one of the two extremes (young or old). We would like to invite colleagues to share with us raw data from other skeletal collections to expand the training dataset to address to some extent issues of age mimicry, while the notebook used for the analysis as well as the code used to construct the web application are openly available to promote the further development of this or similar applications by other scholars

Position effect, cryptic complexity, and direct gene disruption as disease mechanisms in de novo apparently balanced translocation cases.

The majority of apparently balanced translocation (ABT) carriers are phenotypically normal. However, several mechanisms were proposed to underlie phenotypes in affected ABT cases. In the current study, whole-genome mate-pair sequencing (WG-MPS) followed by Sanger sequencing was applied to further characterize de novo ABTs in three affected individuals. WG-MPS precisely mapped all ABT breakpoints and revealed three possible underlying molecular mechanisms. Firstly, in a t(X;1) carrier with hearing loss, a highly skewed X-inactivation pattern was observed and the der(X) breakpoint mapped ~87kb upstream an X-linked deafness gene namely POU3F4, thus suggesting an underlying long-range position effect mechanism. Secondly, cryptic complexity and a chromothripsis rearrangement was identified in a t(6;7;8;12) carrier with intellectual disability. Two translocations and a heterozygous deletion disrupted SOX5; a dominant nervous system development gene previously reported in similar patients. Finally, a direct gene disruption mechanism was proposed in a t(4;9) carrier with dysmorphic facial features and speech delay. In this case, the der(9) breakpoint directly disrupted NFIB, a gene involved in lung maturation and development of the pons with important functions in main speech processes. To conclude, in contrast to familial ABT cases with identical rearrangements and discordant phenotypes, where translocations are considered coincidental, translocations seem to be associated with phenotype presentation in affected de novo ABT cases. In addition, this study highlights the importance of investigating both coding and non-coding regions to decipher the underlying pathogenic mechanisms in these patients, and supports the potential introduction of low coverage WG-MPS in the clinical investigation of de novo ABTs