Population genetic analysis of shotgun assemblies of genomic sequences from multiple individuals
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Population genetic analysis of shotgun assemblies of genomic sequences from multiple individuals. / Hellmann, Ines; Mang, Yuan; Gu, Zhiping; Li, Peter; de la Vega, Francisco M; Clark, Andrew G; Nielsen, Rasmus.
I: Genome Research, Bind 18, Nr. 7, 2008, s. 1020-9.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Population genetic analysis of shotgun assemblies of genomic sequences from multiple individuals
AU - Hellmann, Ines
AU - Mang, Yuan
AU - Gu, Zhiping
AU - Li, Peter
AU - de la Vega, Francisco M
AU - Clark, Andrew G
AU - Nielsen, Rasmus
N1 - Keywords: Animals; Genetic Variation; Genetics, Population; Genome, Human; Humans; Likelihood Functions; Models, Genetic; Pan troglodytes; Polymorphism, Single Nucleotide; Sequence Analysis, DNA
PY - 2008
Y1 - 2008
N2 - We introduce a simple, broadly applicable method for obtaining estimates of nucleotide diversity 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.
AB - We introduce a simple, broadly applicable method for obtaining estimates of nucleotide diversity 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.
U2 - 10.1101/gr.074187.107
DO - 10.1101/gr.074187.107
M3 - Journal article
C2 - 18411405
VL - 18
SP - 1020
EP - 1029
JO - Genome Research
JF - Genome Research
SN - 1088-9051
IS - 7
ER -
ID: 9855418