Resequencing of 200 human exomes identifies an excess of low-frequency non-synonymous coding variants

Research output: Contribution to journalJournal articleResearchpeer-review

  • Yingrui Li
  • Nicolas Vinckenbosch
  • Geng Tian
  • Emilia Huerta-Sanchez
  • Tao Jiang
  • Hui Jiang
  • Gitte Andersen
  • Hongzhi Cao
  • Yiran Guo
  • Ines Hellman
  • Xin Jin
  • Qibin Li
  • Jiangtao Liu
  • Xiao Liu
  • Thomas Sparsø
  • Meifang Tang
  • Honglong Wu
  • Renhua Wu
  • Chang Yu
  • Hancheng Zheng
  • Arne Astrup
  • Lars Bolund
  • Johan Holmkvist
  • Torben Jørgensen
  • Ole Schmitz
  • Xiuqing Zhang
  • Ruiqiang Li
  • Huanming Yang
  • Jian Wang
  • Jun Wang
Targeted capture combined with massively parallel exome sequencing is a promising approach to identify genetic variants implicated in human traits. We report exome sequencing of 200 individuals from Denmark with targeted capture of 18,654 coding genes and sequence coverage of each individual exome at an average depth of 12-fold. On average, about 95% of the target regions were covered by at least one read. We identified 121,870 SNPs in the sample population, including 53,081 coding SNPs (cSNPs). Using a statistical method for SNP calling and an estimation of allelic frequencies based on our population data, we derived the allele frequency spectrum of cSNPs with a minor allele frequency greater than 0.02. We identified a 1.8-fold excess of deleterious, non-syonomyous cSNPs over synonymous cSNPs in the low-frequency range (minor allele frequencies between 2% and 5%). This excess was more pronounced for X-linked SNPs, suggesting that deleterious substitutions are primarily recessive.
Original languageEnglish
JournalNature Genetics
Issue number11
Pages (from-to)969-72
Number of pages4
Publication statusPublished - 2010

    Research areas

  • Base Sequence, Chromosomes, Human, X, Exons, Gene Conversion, Gene Frequency, Genes, Recessive, Genetic Variation, Genetics, Population, Human Genome Project, Humans, Introns, Polymorphism, Single Nucleotide, Untranslated Regions

ID: 33498076