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Rapid growth of the human population has caused the accumulation of rare genetic variants that may play a role in the origin of genetic diseases. However, it is challenging to identify those rare variants responsible for specific diseases without genetic data from an extraordinarily large population sample. Here we focused on the accumulated data from the human mitochondrial (mt) genome sequences because this data provided 7,098 whole genomes for analysis. In this dataset we identified 6,110 single nucleotide variants (SNVs) and their frequency and determined that the best-fit demographic model for the 7,098 genomes included severe population bottlenecks and exponential expansions of the non-African population. Using this model, we simulated the evolution of mt genomes in order to ascertain the behavior of deleterious mutations. We found that such deleterious mutations barely survived during population expansion. We derived the threshold frequency of a deleterious mutation in separate African, Asian, and European populations and used it to identify pathogenic mutations in our dataset. Although threshold frequency was very low, the proportion of variants showing a lower frequency than that threshold was 82, 83, and 91% of the total variants for the African, Asian, and European populations, respectively. Within these variants, only 18 known pathogenic mutations were detected in the 7,098 genomes. This result showed the difficulty of detecting a pathogenic mutation within an abundance of rare variants in the human population, even with a large number of genomes available for study.

Poor Man’s 1000 Genome Project: Recent Human Population Expansion Confounds the Detection of Disease Alleles in 7,098 Complete Mitochondrial Genomes