Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
Grantová podpora
268962
European Research Council - International
W 1225
Austrian Science Fund FWF - Austria
PubMed
23793030
PubMed Central
PMC3755268
DOI
10.1038/ng.2678
Knihovny.cz E-zdroje
- MeSH
- Arabidopsis genetika MeSH
- celogenomová asociační studie MeSH
- chromozomy rostlin MeSH
- genetická variace * MeSH
- genom rostlinný * MeSH
- jednonukleotidový polymorfismus MeSH
- mapování chromozomů MeSH
- molekulární evoluce MeSH
- mutace INDEL MeSH
- populační genetika MeSH
- selekce (genetika) * MeSH
- variabilita počtu kopií segmentů DNA MeSH
- vazebná nerovnováha MeSH
- vysoce účinné nukleotidové sekvenování MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Švédsko MeSH
Despite advances in sequencing, the goal of obtaining a comprehensive view of genetic variation in populations is still far from reached. We sequenced 180 lines of A. thaliana from Sweden to obtain as complete a picture as possible of variation in a single region. Whereas simple polymorphisms in the unique portion of the genome are readily identified, other polymorphisms are not. The massive variation in genome size identified by flow cytometry seems largely to be due to 45S rDNA copy number variation, with lines from northern Sweden having particularly large numbers of copies. Strong selection is evident in the form of long-range linkage disequilibrium (LD), as well as in LD between nearby compensatory mutations. Many footprints of selective sweeps were found in lines from northern Sweden, and a massive global sweep was shown to have involved a 700-kb transposition.
Central European Institute of Technology Masaryk University Brno Czech Republic
Gregor Mendel Institute Austrian Academy of Sciences Vienna Austria
Max F Perutz Laboratories University of Vienna Vienna Austria
Molecular and Computational Biology University of Southern California Los Angeles California USA
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