Ancestry Estimation in Practice

The medicolegal system relies on forensic anthropologists to construct accurate biological profiles from skeletal remainsto narrow the pool of potential missing persons and provide support for positive identifications. The ancestry estimation component of theprofile offers physical descriptions of decedents through a combination of metric analysis and the interpretation of discrete traits believedto correlate with visible physical features. Forensic anthropologists employed in medical examiners’ offices in the United States regularlyconstruct these profiles in casework. However, ancestry estimation methods have been questioned in their ability to accurately describe theracial classification of the deceased. Although validation studies have documented the accuracy of ancestry estimation methods on skeletalcollections, it is unknown how well they operate in forensic casework and the assumption that methods mirror the results observed inacademic research studies remains unproven. In an effort to understand how well methods preform, this research was designed to evaluatethe accuracy ancestry estimation practices within three medical examiners’ offices in the United States. The results show an accuracy rateof 99% among 177 cases when both definitive and ambiguous ancestral and racial terminology was used to describe remains. Becauseunidentified cases lack antemortem information, it remains unknown if the ancestral assessments of the 280 unidentified individualsincluded in this study confer the same level of accuracy shown in resolved cases. The results presented here are informative not only forthe vital statistics obtained, but also for what this data reveals about the factors influencing ancestry estimation in practice.

A reappraisal of the species richness of Euneomys Coues 1874 (Rodentia, Cricetidae), with emendations of the type localities of Reithrodon fossor Thomas 1899 and Euneomys mordax Thomas 1912

The genus Euneomys is mostly distributed in the open environments of the central and southern Andes, adjacent Patagonian steppes of Argentina and Chile, and in several islands of the Tierra del Fuego Archipelago. This genus includes three living species: E. chinchilloides, E. fossor, and E. mordax. Euneomys fossor is a poorly known species, with an uncertain geographic provenance and known from a single specimen, whose distinction from the other species of the genus has not been accurately assessed. Here, using qualitative and quantitative morphological evidence, plus published information about karyotypes and genetic variation, we evaluate the taxonomic status of E. fossor and E. noei, a nominal form usually considered a synonym of E. mordax. Based on multivariate analysis of cranial measurements and morphological discrete traits, we recognize two main morphotypes within Euneomys, one referable to E. chinchilloides (with dabbenei, petersoni, and ultimus as synonyms), and another including E. fossor, E. mordax, and E. noei. The recognition of two major groups within Euneomys is also supported by molecular and chromosomal data. By the principle of the priority, the names of E. chinchilloides and E. fossor applies for each one of these morphotypes. In addition, after discussing the pros and cons of replacing the name mordax by fossor, we emended the type localities of both forms.

Evolutionary Sample Size and Consilience in Phylogenetic Comparative Analysis

Phylogenetic comparative methods (PCMs) are commonly used to study evolution and adaptation. However, frequently used PCMs for discrete traits mishandle single evolutionary transitions. They erroneously detect correlated evolution in these situations. For example, hair and mammary glands cannot be said to have evolved in a correlated fashion because each evolved only once in mammals, but a commonly used model (Pagel’s Discrete) statistically supports correlated (dependent) evolution. Using simulations, we find that rate parameter estimation, which is central for model selection, is poor in these scenarios due to small effective (evolutionary) sample sizes of independent character state change. Pagel’s Discrete model also tends to favor dependent evolution in these scenarios, in part, because it forces evolution through state combinations unobserved in the tip data. This model prohibits simultaneous dual transitions along branches. Models with underlying continuous data distributions (e.g., Threshold and GLMM) are less prone to favor correlated evolution but are still susceptible when evolutionary sample sizes are small. We provide three general recommendations for researchers who encounter these common situations: i) create study designs that evaluate a priori hypotheses and maximize evolutionary sample sizes; ii) assess the suitability of evolutionary models—for discrete traits, we introduce the phylogenetic imbalance ratio; and iii) evaluate evolutionary hypotheses with a consilience of evidence from disparate fields, like biogeography and developmental biology. Consilience plays a central role in hypothesis testing within the historical sciences where experiments are difficult or impossible to conduct, such as many hypotheses about correlated evolution. These recommendations are useful for investigations that employ any type of PCM. [Class imbalance; consilience; correlated evolution; evolutionary sample size; phylogenetic comparative methods.]

Dynamical Modularity of the Genotype-Phenotype Map

An organism’s phenotype can be thought of as consisting of a set of discrete traits, able to evolve relatively independently of each other. This implies that the developmental processes generating these traits—the underlying genotype-phenotype map—must also be functionally organised in a modular manner. The genotype-phenotype map lies at the heart of evolutionary systems biology. Recently, it has become popular to define developmental modules in terms of the structure of gene regulatory networks. This approach is inherently limited: gene networks often do not have structural modularity. More generally, the connection between structure and function is quite loose. In this chapter, we discuss an alternative approach based on the concept of dynamical modularity, which overcomes many of the limitations of structural modules. A dynamical module consists of the activities of a set of genes and their interactions that generate a specific dynamic behaviour. These modules can be identified and characterised by phase-space analysis of data-driven models. We showcase the power and the promise of this new approach using several case studies. Dynamical modularity forms an important component of a general theory of the evolution of regulatory systems and the genotype-phenotype map they define.

A consensus Covid-19 immune signature combines immuno-protection with discrete sepsis-like traits associated with poor prognosis

Person-to-person transmission of SARS-CoV-2 virus has triggered a global emergency because of its potential to cause life-threatening Covid-19 disease. By comparison to paucisymptomatic virus clearance by most individuals, Covid-19 has been proposed to reflect insufficient and/or pathologically exaggerated immune responses. Here we identify a consensus peripheral blood immune signature across 63 hospital-treated Covid-19 patients who were otherwise highly heterogeneous. The core signature conspicuously blended adaptive B cell responses typical of virus infection or vaccination with discrete traits hitherto associated with sepsis, including monocyte and dendritic cell dampening, and hyperactivation and depletion of discrete T cell subsets. This blending of immuno-protective and immuno-pathogenic potentials was exemplified by near-universal CXCL10/IP10 upregulation, as occurred in SARS1 and MERS. Moreover, specific parameters including CXCL10/IP10 over-expression, T cell proliferation, and basophil and plasmacytoid dendritic cell depletion correlated, often prognostically, with Covid-19 progression, collectively composing a resource to inform SARS-CoV-2 pathobiology and risk-based patient stratification.

Macroevolutionary analysis of discrete traits with rate heterogeneity

Organismal traits show dramatic variation in phylogenetic patterns of origin and loss across the Tree of Life. Understanding the causes and consequences of this variation depends critically on accounting for heterogeneity in rates of trait evolution among lineages. Here, we describe a method for modeling among-lineage evolutionary rate heterogeneity in a trait with two discrete states. The method assumes that the present-day distribution of a binary trait is shaped by a mixture of stochastic processes in which the rate of evolution varies among lineages in a phylogeny. The number and location of rate changes, which we refer to as rate-shift events, are inferred automatically from the data. Simulations reveal that the method accurately reconstructs rates of trait evolution and ancestral character states even when simulated data violate model assumptions. We apply the method to an empirical dataset of mimetic coloration in snakes and find elevated rates of trait evolution in two clades of harmless snakes that are broadly sympatric with dangerously venomous New World coral snakes, recapitulating an earlier analysis of the same dataset. Although the method performed well on many simulated data sets, we caution that overall power for inferring heterogeneous dynamics of single binary traits is low.

PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping

Comparative phylogenetics has been largely lacking a method for reconstructing the evolution of phenotypic entities that consist of ensembles of multiple discrete traits—entire organismal anatomies or organismal body regions. In this study, we provide a new approach named PARAMO (PhylogeneticAncestralReconstruction ofAnatomy byMappingOntologies) that appropriately models anatomical dependencies and uses ontology-informed amalgamation of stochastic maps to reconstruct phenotypic evolution at different levels of anatomical hierarchy including entire phenotypes. This approach provides new opportunities for tracking phenotypic radiations and evolution of organismal anatomies.

A multivariate phylogenetic comparative method incorporating a flexible function between discrete and continuous traits

One major challenge of using the phylogenetic comparative method (PCM) is the analysis of the evolution of interrelated continuous and discrete traits in a single multivariate statistical framework. In addition, more intricate parameters such as branch-specific directional selection have rarely been integrated into such multivariate PCM frameworks. Here, originally motivated to analyze the complex evolutionary trajectories of group size (continuous variable) and social systems (discrete variable) in African subterranean rodents, we develop a flexible approach using approximate Bayesian computation (ABC). Specifically, our multivariate ABC-PCM method allows the user to flexibly model an underlying latent evolutionary function between continuous and discrete traits. The ABC-PCM also simultaneously incorporates complex evolutionary parameters such as branch-specific selection. This study highlights the flexibility of ABC-PCMs in analyzing the evolution of phenotypic traits interrelated in a complex manner.