An Integrated Phylogeographic Analysis of the Bantu Migration

• Main Author: Ford, Colby Tyler
• Language: EN
• Published: The University of North Carolina at Charlotte 2018
• Subjects: Applied mathematics|Genetics|Systematic biology|Sub Saharan Africa studies|Computer science
• Online Access: http://pqdtopen.proquest.com/#viewpdf?dispub=10748780

<p> "Bantu" is a term used to describe lineages of people in around 600 different ethnic groups on the African continent ranging from modern-day Cameroon to South Africa. The migration of the Bantu people, which occurred around 3,000 years ago, was influential in spreading culture, language, and genetic traits and helped to shape human diversity on the continent. Research in the 1970s was completed to geographically divide the Bantu languages into 16 zones now known as "Guthrie zones" (Guthrie, 1971). </p><p> Researchers have postulated the migratory pattern of the Bantu people by examining cultural information, linguistic traits, or small genetic datasets. These studies offer differing results due to variations in the data type used. Here, an assessment of the Bantu migration is made using a large dataset of combined cultural data and genetic (Y-chromosomal and mitochondrial) data. </p><p> One working hypothesis is that the Bantu expansion can be characterized by a primary split in lineages, which occurred early on and prior to the population spreading south through what is now called the Congolese forest (i.e. "early split"). A competing hypothesis is that the split occurred south of the forest (i.e. "late split"). </p><p> Using the comprehensive dataset, a phylogenetic tree was developed on which to reconstruct the relationships of the Bantu lineages. With an understanding of these lineages in hand, the changes between Guthrie zones were traced geospatially. </p><p> Evidence supporting the "early split" hypothesis was found, however, evidence for several complex and convoluted paths across the continent were also shown. These findings were then analyzed using dimensionality reduction and machine learning techniques to further understand the confidence of the model.</p><p>