Sex and ancestry patterning of residual correlations in human dental development: Cooperative genetic interaction and phenotypic plasticity.

OBJECTIVES: Most research in human dental age estimation has focused on point estimates of age, and most research on dental development theories has focused on morphology or eruption. Correlations between developing teeth using ordinal staging have received less attention. The effect of demographic variables on these correlations is unknown. I tested the effect of reference sample demographic variables on the residual correlation matrix using the lens of cooperative genetic interaction (CGI). MATERIALS AND METHODS: The sample consisted of Moorrees et al., Journal of Dental Research, 1963, 42, 1490-1502, scores of left mandibular permanent teeth from panoramic radiographs of 880 London children 3-22.99 years of age stratified by year of age, sex, and Bangladeshi or European ancestry. A multivariate cumulative probit model was fit to each sex/ancestry group (n = 220), each sex or ancestry (n = 440), and all individuals (n = 880). Residual correlation matrices from nine reference sample configurations were compared using Bartlett's tests of between-sample difference matrices against the identity matrix, hierarchical cluster analysis, and dendrogram cophenetic correlations. RESULTS: Bartlett's test results were inconclusive. Cluster analysis showed clustering by tooth class, position within class, and developmental timing. Clustering patterns and dendrogram correlations showed similarity by sex but not ancestry. DISCUSSION: Expectations of CGI were supported for developmental staging. This supports using CGI as a model for explaining patterns of variation within the dentition. Sex was found to produce consistent patterns of dental correlations, whereas ancestry did not. Clustering by timing of development supports phenotypic plasticity in the dentition and suggests shared environment over genetic ancestry to explain population differences.

The effect of reference sample composition and size on dental age interval estimates.

OBJECTIVES: Validation studies in juvenile dental age estimation primarily focus on point estimates while interval performance for reference samples of different ancestry group compositions has received minimal attention. We tested the effect of reference sample size and composition by sex and ancestry group on age interval estimates. MATERIALS AND METHODS: The dataset consisted of Moorrees et al. dental scores from panoramic radiographs of 3334 London children of Bangladeshi and European ancestry and 2-23 years of age. Model stability was assessed using standard error of mean age-at-transition for univariate cumulative probit and sample size, group mixing (sex or ancestry), and staging system as factors. Age estimation performance was tested using molar reference samples of four sizes, stratified by year of age, sex, and ancestry. Age estimates were performed using Bayesian multivariate cumulative probit with 5-fold cross-validation. RESULTS: Standard error increased with decreasing sample size but showed no effect from mixing by sex or ancestry. Estimating ages using a reference and target sample of different sex reduced success rate significantly. The same test by ancestry groups had a lesser effect. Small sample size (n < 20/year of age) negatively affected most performance metrics. DISCUSSION: We found that reference sample size, followed by sex, primarily drove age estimation performance. Combining reference samples by ancestry produced equivalent or better estimates of age by all metrics than using a single-demographic reference of smaller size. We further proposed that population specificity is an alternative hypothesis of intergroup difference that has been erroneously treated as a null.

Reference and target sample age distribution impacts between model types in dental developmental age estimation.

The performance of age estimation methods may vary due to a combination of method- and sample-related factors. Method development and evaluation necessitates understanding what influences these factors have on age estimation outcomes. In the specific context of juvenile dental age estimation, we used a single dataset and complete factorial design to systematically test four potential sources of difference: age distributions of reference and target sample (uniform, unimodal, U-shaped), Bayesian (multivariate Bayesian cumulative probit) vs. classical regression modeling (multivariate adaptive regression splines i.e. MARS), and model selection bias. The dataset consisted of 850 sets of left mandibular molar scores from London children 5-18 years old. True age and estimated age intervals in target samples were compared for bias, root-mean-squared error, precision, and accuracy using locally weighted smoothing of performance measures across the age range and means of performance metrics between factor-level combinations. We found interactions of model type, reference distribution, and target distribution. MARS models showed consistent evidence of age mimicry. Central tendency of the reference sample corresponded with increased bias while central tendency of the target sample corresponded with reduced RMSE and reduced precision for both model types. We found evidence of model selection bias, mitigated through averaging model metrics. We conclude that reference and target sample distribution influences and model selection bias are sufficient to cause difference in model performance within a single population. We suggest using Bayesian modeling, drawing uniform reference and target samples, and calculating test error on a hold-out sample to mitigate these challenges in method development.

Conditional independence assumption and appropriate number of stages in dental developmental age estimation.

When estimating the age of an individual it is critical that 1) age ranges are as narrow as possible while still capturing the true age of the individual with an acceptable frequency, and 2) this frequency is known. When multiple traits are used to produce a single age estimate, the simplest practice is to assume that the traits are conditionally independent from one another given age. Unfortunately, if the traits are correlated once the effect of age is accounted for, the resulting age intervals will be too narrow. The frequency at which the age interval captures the true age of the individual will be decreased below the expected value to some unknown degree. It is therefore critical that age estimation methods that include multiple traits incorporate the possible correlations between them. Moorrees et al. (1963) [1] scores of the permanent mandibular dentition from 2607 individuals between 2 and 23 years were used to produce and cross-validate a cumulative probit model for age estimation with an optimal number of stages for each tooth. Two correction methods for covariance of development between teeth were tested: the variance-covariance matrix for a multivariate normal, and the Boldsen et al. (2002) [2] ad-hoc method. Both correction methods successfully decreased age interval error rates from 21% to 23% in the uncorrected model to the expected value of 5%. These results demonstrate both the efficacy of these correction methods and the need to move away from assuming conditional independence in multi-trait age estimation.

Prior Probabilities and the Age Threshold Problem: First and Second Molar Development.

Dental development has been used to assess whether an individual may be below or above an age that serves as a legal threshold. This study used development of the first and second mandibular molars from a large sample of individuals (N = 2,676) to examine the age threshold for minimum age of criminal responsibility. A bivariate ordered probit model was applied to dental scores following the Moorrees et al. (1963) system, with the addition of a crypt-absent/present stage. Then a 10-fold cross-validation within each of the sexes showed that the bivariate models produce unbiased estimates of age but are heteroskedastic (with increasing spread of the estimates against actual age). To address the age threshold problem, a normal prior centered on the threshold is assumed, and the product of the prior and the likelihood is integrated up to the age threshold and again starting at the age threshold. The ratio of these two integrals is a Bayes factor, which because the prior is symmetric around the threshold, can also be interpreted as the posterior odds that an individual is over versus under the age threshold. It was necessary to assume an unreasonably high standard deviation of age in the prior to achieve posterior odds that were well above "evens." These results indicate that dental developmental evidence from the first and second molars is of limited use in examining the question of whether an individual is below or over the minimum age of criminal responsibility. As the third molar is more variable in its development than the first two molars, the question of dental evidence regarding the age of majority (generally 18 years) remains problematic.

The Use of Roche, Wainer, and Thissen's Skeletal Maturity of the Knee.

The Roche, Wainer, and Thissen (RWT) knee method was designed to assess maturity in living individuals, but is infrequently cited in studies of skeletal measures for legal age thresholds or estimation of age-at-death for forensic cases. We implement the RWT method using R scripts and analyze its utility in establishing an age threshold of 18 years. We fit the RWT model and verify its accuracy in estimating skeletal age against results and known age data provided by RWT. We also found the transition distribution for full skeletal maturity. Agreement between the RWT method and our implementation was good, with a maximum deviation of 0.03 years. The modes of transition to full skeletal maturity are 18.37 and 17.16 years for males and females. The corresponding Bayes factors are 5.73 and 1.77, indicating that complete skeletal maturity of the knee does not provide conclusive evidence of attainment of 18 years of age.

Covalent bonding of heme to protein prevents heme capture by nontypeable Haemophilus influenzae.

Nontypeable Haemophilus influenzae (NTHi) are Gram-negative pathogens that contribute to a variety of diseases, including acute otitis media and chronic obstructive pulmonary disease. As NTHi have an absolute requirement for heme during aerobic growth, these bacteria have to scavenge heme from their human hosts. These heme sources can range from free heme to heme bound to proteins, such as hemoglobin. To test the impact of heme structural factors on heme acquisition by NTHi, we prepared a series of heme sources that systematically vary in heme exposure and covalent binding of heme to peptide/protein and tested the ability of NTHi to use these sources to support growth. Results from this study suggest that NTHi can utilize protein-associated heme only if it is noncovalently attached to the protein.