Login / Signup

Genetic landscape of human mitochondrial genome using whole-genome sequencing.

Yijing WangGuihu ZhaoZhenghuan FangHongxu PanYuwen ZhaoYige WangXun ZhouXiaomeng WangTengfei LuoYi ZhangZheng WangQian ChenLijie DongYuanfeng HuangQiao ZhouLu XiaBin LiJi-Feng GuoKun XiaBei-Sha TangJin-Chen Li
Published in: Human molecular genetics (2022)
Increasing evidences suggest that mitochondrial dysfunction is implicated in diseases and aging, and whole-genome sequencing (WGS) is the most unbiased method in analyzing the mitochondrial genome (mtDNA). However, the genetic landscape of mtDNA in the Chinese population has not been fully examined. Here, we described the genetic landscape of mtDNA using WGS data from Chinese individuals (n = 3241). We identified 3892 mtDNA variants, of which 3349 (86%) were rare variants. Interestingly, we observed a trend toward extreme heterogeneity of mtDNA variants. Our study observed a distinct purifying selection on mtDNA, which inhibits the accumulation of harmful heteroplasmies at the individual level: (1) mitochondrial dN/dS ratios were much <1; (2) the dN/dS ratio of heteroplasmies was higher than homoplasmies; (3) heteroplasmies had more indels and predicted deleterious variants than homoplasmies. Furthermore, we found that haplogroup M (20.27%) and D (20.15%) had the highest frequencies in the Chinese population, followed by B (18.51%) and F (16.45%). The number of variants per individual differed across haplogroup groups, with a higher number of homoplasmies for the M lineage. Meanwhile, mtDNA copy number was negatively correlated with age but positively correlated with the female sex. Finally, we developed an mtDNA variation database of Chinese populations called MTCards (http://genemed.tech/mtcards/) to facilitate the query of mtDNA variants in this study. In summary, these findings contribute to different aspects of understanding mtDNA, providing a better understanding of the genetic basis of mitochondrial-related diseases.
Keyphrases
  • copy number
  • mitochondrial dna
  • genome wide
  • dna methylation
  • oxidative stress
  • single cell
  • emergency department
  • gene expression
  • climate change
  • machine learning
  • big data
  • electronic health record