AUTHOR=Boudeau Samantha , Ramakodi Meganathan P. , Zhou Yan , Liu Jeffrey C. , Ragin Camille , Kulathinal Rob J. 

TITLE=Extensive set of African ancestry-informative markers (AIMs) to study ancestry and population health

JOURNAL=Frontiers in Genetics

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1061781

DOI=10.3389/fgene.2023.1061781

ISSN=1664-8021

ABSTRACT=Populations are often highly structured due to differences in genetic ancestry among groups, posing difficulties in associating genes with diseases. Ancestry informative markers (AIMs) can aid in the detection of population stratification and provide an alternative approach to map population-specific alleles to disease. Here, we identify and characterize a comprehensive and novel set of African AIMs that separate populations of African ancestry from other global populations including those of European ancestry. Using data from the 1000 Genomes Project, highly informative SNP markers from five African subpopulations were selected based on estimates of informativeness (In) and compared against the European population to generate a final set of 46,737 African ancestry informative markers (AIMs). This set of African AIMs effectively separates populations of African ancestry from other global populations and further identifies substructure between populations of African ancestry. When a subset of these AIMs was applied to an independent dataset, they differentiated people who self-identify as Black or African American from those who identify as White. While some functional annotations on both coding and non-coding African AIMs are supported by literature, linking these high-frequency African alleles to diseases in African populations, more effort is needed to map genes to diseases in these genetically diverse subpopulations. The relative dearth of these African AIMs on current genotyping platforms (largest fraction is 23.59% on Illumina’s Omni 5 array) also demonstrates a greater need to better represent historically understudied populations.