Genetic structure correlates with ethnolinguistic diversity in eastern and southern Africa

Abstract

African populations are the most diverse in the world yet are sorely underrepresented in medical genetics research. Here, we examine the structure of African populations using genetic and comprehensive multigenerational ethnolinguistic data from the Neuropsychiatric Genetics of African Populations-Psychosis study (NeuroGAP-Psychosis) consisting of 900 individuals from Ethiopia, Kenya, South Africa, and Uganda. We find that self-reported language classifications meaningfully tag underlying genetic variation that would be missed with consideration of geography alone, highlighting the importance of culture in shaping genetic diversity. Leveraging our uniquely rich multi-generational ethnolinguistic metadata, we track language transmission through the pedigree, observing the disappearance of several languages in our cohort as well as notable shifts in frequency over three generations. We further find significantly higher language transmission rates for matrilineal groups as compared to patrilineal. We highlight both the diversity of variation within the African continent, as well as how within-Africa variation can be informative for broader variant interpretation; many variants appearing rare elsewhere are common in parts of Africa. The work presented here improves the understanding of the spectrum of genetic variation in African populations and highlights the enormous and complex genetic and ethnolinguistic diversity within Africa.

Fine-scale cultural variation reinforces genetic structure in England

“…This correspondence between genetic boundaries – which are associated with both historical migrations and distributions of ethnicities – and linguistic boundaries – associated with dialect and other cultural differences – demonstrate that language and genetics have similar geographic patterns in England. These gene-language patterns at the the English-Welsh and the English-Scottish borders support previous studies that identify the covariation of culture and genetics at ethnic boundaries (22, 24, 26–29). We find that, in addition to more prominent cultural borders, this covariation extends to less dramatic distinctions, including those between South East and South West England and between North and South England.…”

“…One such trait is the specific set of languages or dialects a person speaks, since languages contain clues about the history of populations (6,(18)(19)(20)(21) and have been found to co-vary with genetic variation on a worldwide scale (5,6), though this pattern of covariation is not found everywhere (7,22). Finer-scale analyses have found linguistic-genetic covariation in smaller regions as well, including the Caucasus (23,24), the Levant (25), the Amazon (26), and Africa (27,28). Other studies have directly compared the rates of linguistic and genetic change, as these are expected to be highest near geographic or behavioral barriers to the interaction of people (29).…”

GWAS in the southern African context

“…Differential assortment of ancestral allele frequencies across a genome results from selection, mutation or genetic drift, when previously isolated populations interbreed (1,2). Taking advantage of these differences in allele frequencies can help identify population-specific disease risk alleles associated with disease phenotypes due to various ancestries being exposed to distinct environments and pathogens (3).…”

“…GWAS is further complicated when individuals have admixture-induced linkage disequilibrium (admixture LD) blocks. Admixture mapping uses the admixture LD blocks inherited from a specific ancestral population to test for an association with the trait of interest (2). Hence, the ancestry rather than the genotypes are traced in the association process (7) and the SNPs that affect the trait of interest can only be localised to their respective ancestral blocks (presented as an admixture peak on a Manhattan plot).…”

GWAS in the southern African context

“…Admixed populations present unique opportunities to identify ancestry-specific disease risk alleles for various populations simultaneously. African ancestries harbour the most genetic diversity and contain more complex linkage disequilibrium (LD) blocks than other continental populations and the precise evolutionary events that shaped their genomes are mostly unknown [ 4 , 5 ]. When the historical events are unknown, it is often difficult to know beforehand which effect (allele, ancestry or the interaction between them) has the most significant effect on the disease phenotype under study.…”