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Kiraly, A.

Publications and source records attributed to Kiraly, A..

3 recordsLinked to original sources

An integrative skeletal and paleogenomic analysis of prehistoric stature variation suggests relatively reduced health for early European farmers

Human culture, biology, and health were shaped dramatically by the onset of agriculture ~12,000 years before present (BP). Subsistence shifts from hunting and gathering to agriculture are hypothesized to have resulted in increased individual fitness and population growth as evidenced by archaeological and population genomic data alongside a simultaneous decline in physiological health as inferred from paleopathological analyses and stature reconstructions of skeletal remains. A key component of the health decline inference is that relatively shorter statures observed for early farmers may (at least partly) reflect higher childhood disease burdens and poorer nutrition. However, while such stresses can indeed result in growth stunting, height is also highly heritable, and substantial inter-individual variation in the height genetic component within a population is typical. Moreover, extensive migration and gene flow were characteristics of multiple agricultural transitions worldwide. Here, we consider both osteological and ancient DNA data from the same prehistoric individuals to comprehensively study the trajectory of human stature variation as a proxy for health across a transition to agriculture. Specifically, we compared predicted genetic contributions to height from paleogenomic data and achieved adult osteological height estimated from long bone measurements on a per-individual basis for n=160 ancient Europeans from sites spanning the Upper Paleolithic to the Iron Age (~38,000-2,400 BP). We found that individuals from the Neolithic were shorter than expected (given their individual polygenic height scores) by an average of -4.47 cm relative to individuals from the Upper Paleolithic and Mesolithic (P=0.016). The average osteological vs. expected stature then increased relative to the Neolithic over the Copper (+2.67 cm, P=0.052), Bronze (+3.33 cm, P=0.032), and Iron Ages (+3.95 cm, P=0.094). These results were partly attenuated when we accounted for genome-wide genetic ancestry variation in our sample (which we note is partly duplicative with the individual polygenic score information). For example, in this secondary analysis Neolithic individuals were -3.48 cm shorter than expected on average relative to individuals from the Upper Paleolithic and Mesolithic (P=0.056). We also incorporated observations of paleopathological indicators of non-specific stress that can persist from childhood to adulthood in skeletal remains (linear enamel hypoplasia, cribra orbitalia, and porotic hyperostosis) into our model. Overall, our work highlights the potential of integrating disparate datasets to explore proxies of health in prehistory.

genomics↗

A Minimally Destructive Protocol for DNA Extraction from Ancient Teeth

Ancient DNA sampling methods--although optimized for efficient DNA extraction--are destructive, relying on drilling or cutting and powdering (parts of) bones and teeth. As the field of ancient DNA has grown, so have concerns about the impact of destructive sampling of the skeletal remains from which ancient DNA is obtained. Due to a particularly high concentration of endogenous DNA, the cementum of tooth roots is often targeted for ancient DNA sampling, but standard destructive sampling methods often result in the loss of at least one entire root. Here, we present a minimally destructive method for extracting ancient DNA from dental cementum present on the surface of tooth roots. This method does not require destructive drilling or grinding, and, following extraction, the tooth remains safe to handle and suitable for most morphological studies, as well as other biochemical studies, such as radiocarbon dating. We extracted and sequenced ancient DNA from 30 teeth (and 9 corresponding petrous bones) using this minimally destructive extraction method in addition to a typical tooth sampling method. We find that the minimally destructive method can provide ancient DNA that is of comparable quality to extracts produced from teeth that have undergone destructive sampling processes. Further, we find that a rigorous cleaning of the tooth surface combining diluted bleach and UV light irradiation seems sufficient to minimize external contaminants usually removed through the physical removal of a superficial layer when sampling through regular powdering methods.

evolutionary biology↗

Predicting Brain Age Using Structural Neuroimaging and Deep Learning

Early detection of age-related diseases will greatly benefit from a model of the underlying biological aging process. In this paper, we develop a brain-age predictor by using structural magnetic resonance imaging (SMRI) and deep learning and evaluate the predicted brain age as a marker of brain-aging in Alzheimer's disease. Our approach does not require any domain knowledge in that it trains end-to-end on the SMRI image itself, and has been validated on real SMRI data collected from elderly subjects. We developed two different models by using convolutional neural network (CNN) based regression and bucket classification to predict brain ages from SMRI images. Our models achieved root mean squared errors (RMSE) of 5.54 and 6.44 years in predicting brain ages of healthy subjects. Further analysis showed that there is a substantial difference between the predicted brain ages of cognitively impaired and healthy subjects with similar chronological ages.

bioengineering↗