Fracture in teeth—a diagnostic for inferring bite force and tooth function

Teeth are brittle and highly susceptible to cracking. We propose that observations of such cracking can be used as a diagnostic tool for predicting bite force and inferring tooth function in living and fossil mammals. Laboratory tests on model tooth structures and extracted human teeth in simulated biting identify the principal fracture modes in enamel. Examination of museum specimens reveals the presence of similar fractures in a wide range of vertebrates, suggesting that cracks extended during ingestion or mastication. The use of ‘fracture mechanics’ from materials engineering provides elegant relations for quantifying critical bite forces in terms of characteristic tooth size and enamel thickness. The role of enamel microstructure in determining how cracks initiate and propagate within the enamel (and beyond) is discussed. The picture emerges of teeth as damage-tolerant structures, full of internal weaknesses and defects and yet able to contain the expansion of seemingly precarious cracks and fissures within the enamel shell. How the findings impact on dietary pressures forms an undercurrent of the study.

USING THE R PACKAGE crlmm FOR GENOTYPING AND COPY NUMBER ESTIMATION

Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number, develops markerand study-level summaries of batch effects, and demonstrates how the marker-level estimates can be integrated with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R. A compendium for reproducing the analysis is available from the author’s website (http://www.biostat.jhsph.edu/~rscharpf/crlmmCompendium/index.html).