# Distance from Sub-Saharan Africa Predicts Mutational Load in Diverse Human Genomes

## TL;DR

This study finds that human populations farther from Sub-Saharan Africa have more harmful genetic mutations, likely due to historical population expansions.

## Contribution

The study provides empirical evidence linking geographic distance from Africa to increased mutational load during human dispersal.

## Key findings

- Genomes from populations farther from Africa have more predicted deleterious alleles.
- Simulations support the Out-of-Africa dispersal model with recessive deleterious mutations.
- Purifying selection is strong in Africa, but nearly neutral mutations increased during expansion.

## Abstract

The Out-of-Africa (OOA) dispersal ∼50,000 years ago is characterized by a series of founder events as modern humans expanded into multiple continents. Population genetics theory predicts an increase of mutational load in populations undergoing serial founder effects during range expansions. To test this hypothesis, we have sequenced full genomes and high-coverage exomes from 7 geographically divergent human populations from Namibia, Congo, Algeria, Pakistan, Cambodia, Siberia and Mexico. We find that individual genomes vary modestly in the overall number of predicted deleterious alleles. We show via spatially explicit simulations that the observed distribution of deleterious allele frequencies is consistent with the OOA dispersal, particularly under a model where deleterious mutations are recessive. We conclude that there is a strong signal of purifying selection at conserved genomic positions within Africa, but that many predicted deleterious mutations have evolved as if they were neutral during the expansion out of Africa. Under a model where selection is inversely related to dominance, we show that OOA populations are likely to have a higher mutation load due to increased allele frequencies of nearly neutral variants that are recessive or partially recessive.

Data have been deposited in the SRA [SRP036155] and in dbSNP. VCF files are freely available on the Henn Lab website: http://ecoevo.stonybrook.edu/hennlab/data/.