Author
Bridget F. B. Algee-Hewitt
Other affiliations: University of Tennessee, Florida State University, Grand Valley State University
Bio: Bridget F. B. Algee-Hewitt is an academic researcher from Stanford University. The author has contributed to research in topics: Population & Forensic anthropology. The author has an hindex of 12, co-authored 34 publications receiving 505 citations. Previous affiliations of Bridget F. B. Algee-Hewitt include University of Tennessee & Florida State University.
Papers
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TL;DR: This analysis shows the extreme importance of an informative prior in any forensic application and shows that the sex of the individual can be reliably estimated using a small set of 11 craniometric variables.
Abstract: Forensic anthropology typically uses osteological and/or dental data either to estimate characteristics of unidentified individuals or to serve as evidence in cases where there is a putative identification. In the estimation context, the problem is to describe aspects of an individual that may lead to their eventual identification, whereas in the evidentiary context, the problem is to provide the relative support for the identification. In either context, individual characteristics such as sex and race may be useful. Using a previously published forensic case (Steadman et al. (2006) Am J Phys Anthropol 131:15-26) and a large (N = 3,167) reference sample, we show that the sex of the individual can be reliably estimated using a small set of 11 craniometric variables. The likelihood ratio from sex (assuming a 1:1 sex ratio for the "population at large") is, however, relatively uninformative in "making" the identification. Similarly, the known "race" of the individual is relatively uninformative in "making" the identification, because the individual was recovered from an area where the 2000 US census provides a very homogenous picture of (self-identified) race. Of interest in this analysis is the fact that the individual, who was recovered from Eastern Iowa, classifies very clearly with [Howells 1973. Cranial Variation in Man: A Study by Multivariate Analysis of Patterns of Difference Among Recent Human Populations. Cambridge, MA: Peabody Museum of Archaeology and Ethnology; 1989. Skull Shape and the Map: Craniometric Analyses in the Dispersion of Modern Homo. Cambridge, MA: Harvard University Press]. Easter Islander sample in an analysis with uninformative priors. When the Iowa 2000 Census data on self-reported race are used for informative priors, the individual is clearly identified as "American White." This analysis shows the extreme importance of an informative prior in any forensic application.
112 citations
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TL;DR: It is concluded that population identifiability regularly follows as a byproduct of the use of highly polymorphic forensic markers, and the design of new forensic marker sets is examined.
54 citations
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TL;DR: The method can link a dataset similar to those used in genomic studies with another dataset containing markers used for forensics, and it shows that records can be matched across genotype datasets that have no shared markers based on linkage disequilibrium between loci appearing in different datasets.
Abstract: Combining genotypes across datasets is central in facilitating advances in genetics Data aggregation efforts often face the challenge of record matching-the identification of dataset entries that represent the same individual We show that records can be matched across genotype datasets that have no shared markers based on linkage disequilibrium between loci appearing in different datasets Using two datasets for the same 872 people-one with 642,563 genome-wide SNPs and the other with 13 short tandem repeats (STRs) used in forensic applications-we find that 90-98% of forensic STR records can be connected to corresponding SNP records and vice versa Accuracy increases to 99-100% when ∼30 STRs are used Our method expands the potential of data aggregation, but it also suggests privacy risks intrinsic in maintenance of databases containing even small numbers of markers-including databases of forensic significance
44 citations
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TL;DR: This paper presents an objective, fully quantitative method for estimating age‐at‐death from the skeleton, which exploits a variance‐based score of surface complexity computed from vertices obtained from a scanner sampling the pubic symphysis.
Abstract: The pubic symphysis is widely used in age estimation for the adult skeleton. Standard practice requires the visual comparison of surface morphology against criteria representing predefined phases and the estimation of case-specific age from an age range associated with the chosen phase. Known problems of method and observer error necessitate alternative tools to quantify age-related change in pubic morphology. This paper presents an objective, fully quantitative method for estimating age-at-death from the skeleton, which exploits a variance-based score of surface complexity computed from vertices obtained from a scanner sampling the pubic symphysis. For laser scans from 41 modern American male skeletons, this method produces results that are significantly associated with known age-at-death (RMSE = 17.15 years). Chronological age is predicted, therefore, equally well, if not, better, with this robust, objective, and fully quantitative method than with prevailing phase-aging systems. This method contributes to forensic casework by responding to medico-legal expectations for evidence standards.
44 citations
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TL;DR: A more objective, quantitative method that analyzes three-dimensional surface scans of the pubic symphysis using a thin plate spline algorithm (TPS) that yields estimates comparable to established methods but offers a fully integrated, objective and quantitative framework of analysis and has potential for use in archaeological and forensic casework.
Abstract: Objectives:
The pubic symphysis is frequently used to estimate age-at-death from the adult skeleton. Assessment methods require the visual comparison of the bone morphology against age-informative characteristics that represent a series of phases. Age-at-death is then estimated from the age-range previously associated with the chosen phase. While easily executed, the "morphoscopic" process of feature-scoring and bone-to-phase-matching is known to be subjective. Studies of method and practitioner error demonstrate a need for alternative tools to quantify age-progressive change in the pubic symphysis. This article proposes a more objective, quantitative method that analyzes three-dimensional (3D) surface scans of the pubic symphysis using a thin plate spline algorithm (TPS).
Materials and Methods:
This algorithm models the bending of a flat plane to approximately match the surface of the bone and minimizes the bending energy required for this transformation. Known age-at-death and bending energy were used to construct a linear model to predict age from observed bending energy. This approach is tested with scans from 44 documented white male skeletons and 12 casts.
Results:
The results of the surface analysis show a significant association (regression p-value = 0.0002 and coefficient of determination = 0.2270) between the minimum bending energy and age-at-death, with a root mean square error of ≈19 years.
Discussion:
This TPS method yields estimates comparable to established methods but offers a fully integrated, objective and quantitative framework of analysis and has potential for use in archaeological and forensic casework. Am J Phys Anthropol 158:431–440, 2015. © 2015 Wiley Periodicals, Inc.
39 citations
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TL;DR: This updated version of mclust adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
Abstract: Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
1,841 citations
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TL;DR: Development of newer and better methodologies for sex estimation as well as re-evaluation of the existing ones will continue in the endeavour of forensic researchers for more accurate results.
197 citations
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TL;DR: This book can be used not only as an advanced textbook for graduate students in statistics and mathematical modeling but also as a superb synthesis of recent statistical-genetics developments that any practitioner in this field will find immensely useful.
Abstract: Ever since Gregor Mendel deduced the probabilistic “laws” of heredity, mathematical and statistical methods have proved to be essential for genetic analyses. To keep pace both with the flood of genetic information pouring into journals and public databases and with our evolving knowledge of the complexity of genetic mechanisms, new models of genetic analysis are continually needed, particularly for traits measured in observational studies of humans. Mathematical and Statistical Methods for Genetic Analysis provides a solid bridge between modern genetics and advanced statistical methods, and it is filled with mathematical and statistical insights into the development of new genetic models. This book can be used not only as an advanced textbook for graduate students in statistics and mathematical modeling but also as a superb synthesis of recent statistical-genetics developments that any practitioner in this field will find immensely useful.As implied by the title and explicitly stated in the preface, this book is not intended as a cookbook for performing a genetic study; rather, the anticipated audience of this book is students who are already sophisticated in theoretical statistics, as well as in calculus and linear algebra. Readers with a suitable background are guided through fundamental statistical and mathematical concepts and then through a keen demonstration of how these methods can be adapted for genetic research.The book is composed of 13 chapters, of which the first describes the basic principles of population genetics; a brief appendix gives a summary of molecular genetics. Although these are nice summaries, I suspect that they would be too brief for mathematicians who have no background in biology; serious students of statistical genetics should look elsewhere for a fuller introduction to basic genetics. Chapter 2 covers the EM algorithm, with applications to ascertainment correction for segregation analysis. Chapter 3 discusses Newton's Method and Scoring, which are indispensable methods for obtaining maximum-likelihood estimates, and it illustrates their application for empirical Bayes estimation of allele frequencies. Some topics related to categorical data analysis, such as the transmission/disequilibrium test and tests for clustering of multinomial data, are covered in chapter 4. Kinship coefficients and their generalizations to more than two people are clearly presented in chapter 5, along with efficient recursive computational methods. These ideas of probabilistic descriptions of genetic relationships are key to genetic applications and are further expanded in chapter 6—for example, by partitioning the covariances of a pedigree into their genetic contributions.Chapter 7 covers computational methods for pedigree analyses, a topic on which Lange has published extensively. Although this chapter is full of key ideas that will prove useful for both segregation and linkage analyses, it was disappointing to not see more discussion of the hidden Markov methods that are the basis of the Lander-and-Green algorithm used in popular genetic-analysis software. Nonetheless, the breadth of topics covered is impressive, and generous insights appear throughout. Chapters 8–13 cover the polygenic model (with novel approximations), Markov-Chain Monte Carlo methods, evolutionary trees, radiation-hybrid mapping, models of recombination, and Poisson approximation. The explanations offered for the Markov-Chain Monte Carlo approach and for Poisson approximation, two highly active areas of research, are self-contained and worth reading for any statistician interested in these topics. The general steps needed to derive a genetic model are provided in each chapter, but readers who wish to get the most out of this book should plan to sit down with pencil and paper to fill in the details. Each chapter includes 10–12 problems to work through that reinforce the mathematical concepts.Mathematical and Statistical Methods for Genetic Analysis synthesizes many of the key statistical and mathematical topics that are used for genetic analyses. Lange indicates that this book represents his polished notes from years of teaching at UCLA and the University of Michigan, as well as a synthesis of his own published work. This bias toward Lange's own work is perhaps the book's greatest strength, because it allows him to offer deeper statistical and mathematical explanations for genetic analyses than are found in most other sources. This book is a must-read for students wishing to move into the field of statistical genetics, as well as for practicing statisticians who wish to adapt familiar statistical methods for genetic analyses.
176 citations