Using Quantitative Genetic Methods to Understand Evolutionary Processes from Archival Anatomical Data: A Case Study of Arikara Groups

Abstract

Anatomical data are often used to infer genetic similarities within and between human populations. Intergroup similarities may result from multiple processes, including common ancestry, shared environment during ontogeny, or gene flow. In many cases, researchers have used pattern‐based comparisons, such as least squares distances between groups, to suggest these processes, even though pattern‐based methods inadequately measure such evolutionary and developmental processes. Methods that utilize the genetic covariance matrix (or phenotypic covariance matrix as a proxy) to understand the apportionment of variance are better suited to the analysis of how evolutionary processes shaped morphology.A goal for researchers is to apply analytical methods that better measure processes to reevaluate studies that used pattern‐based approaches. Because these studies were often conducted using skeletons that are no longer available, an additional challenge is that only archival data are available. In this study, we demonstrate this research approach to validate its effectiveness and explore its limitations.We use fifteen craniometric distances recorded by Dr. Richard Jantz in the 1970s from ten Arikara village burial sites in South Dakota (c. 1550–1832 AD). Jantz used pattern‐based approaches to suggest increased gene flow over time with the Mandan and Europeans. The conclusion from this study was that gene flow increased as pressure from colonizing groups forced intermarriage between previously discrete groups. Our study tests this conclusion using a quantitative genetic model developed by Relethford & Blangero. Their method uses population variance based on metric traits to model the amount of variance that should be expected in each group versus what is actually observed.Our results show that the evidence for gene flow varied more between sites than over time, and that there is no clear temporal trend. For example, two early sites (early Mobridge and Rygh, c. 1550–1600) have contrasting evidence for gene flow. Rygh has less‐than‐expected variance, an indicator of a lack of gene flow, while higher‐than‐expected variance in the earliest component of Mobridge suggests possible increased gene flow that is not present in the site's later component (c. 1625–1750). Two later period sites, Larson and Cheyenne River, also show contrasting evidence for gene flow. The most recent site, Leavenworth (dating to the early 1800s) has greater than expected variance, which is congruent with historical documentation that indicates the occurrence of gene flow. We conclude that the patterns of gene flow during the period of European colonization and population decimation in the Northern Plains is more nuanced than previous methods were able to detect.This project provides new insight into questions that have been of interest to researchers for decades by using a process‐based approach instead of a pattern‐based one. Moreover, it highlights the importance of archival data in pursuing research questions, as (with a few exceptions) archival data are often an underused resource. With newer methods these data still provide further understanding of evolutionary processes and human variation.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.