Population genetic analysis of shotgun assemblies of genomic sequences from multiple individuals

Ines Hellmann, Yuan Mang, Zhiping Gu, Peter Li, Francisco M. De La Vega, Andrew G. Clark, Rasmus Nielsen

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

We introduce a simple, broadly applicable method for obtaining estimates of nucleotide diversity θ from genomic shotgun sequencing data. The method takes into account the special nature of these data: random sampling of genomic segments from one or more individuals and a relatively high error rate for individual reads. Applying this method to data from the Celera human genome sequencing and SNP discovery project, we obtain estimates of nucleotide diversity in windows spanning the human genome and show that the diversity to divergence ratio is reduced in regions of low recombination. Furthermore, we show that the elevated diversity in telomeric regions is mainly due to elevated mutation rates and not due to decreased levels of background selection. However, we find indications that telomeres as well as centromeres experience greater impact from natural selection than intrachromosomal regions. Finally, we identify a number of genomic regions with increased or reduced diversity compared with the local level of human-chimpanzee divergence and the local recombination rate.

Original languageEnglish (US)
Pages (from-to)1020-1029
Number of pages10
JournalGenome Research
Volume18
Issue number7
DOIs
StatePublished - Jul 2008

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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