Amato, Roberto, Pinelli, Michele, Miele, Gennaro and Cocozza, Sergio (2009) Genome-wide scan for signatures of human population differentiation and their relationship with natural selection, functional pathways and diseases. [Pubblicazione in rivista scientifica]

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Item Type: Pubblicazione in rivista scientifica
Resource language: English
Title: Genome-wide scan for signatures of human population differentiation and their relationship with natural selection, functional pathways and diseases.
Creators:
Creators
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Amato, Roberto
UNSPECIFIED
Pinelli, Michele
UNSPECIFIED
Miele, Gennaro
UNSPECIFIED
Cocozza, Sergio
UNSPECIFIED
Autore/i: Amato R., Pinelli M., Monticelli A., Marino D., Miele G., Cocozza S.
Date: 2009
Number of Pages: 8
Department: Scienze fisiche
Identification Number: 10.1371/journal.pone.0007927
Journal or Publication Title: PLOS ONE
Date: 2009
Volume: 4
Page Range: pp. 1-8
Number of Pages: 8
Identification Number: 10.1371/journal.pone.0007927
Date Deposited: 21 Oct 2010 06:57
Last Modified: 30 Apr 2014 19:43
URI: http://www.fedoa.unina.it/id/eprint/7531

Collection description

Genetic differences both between individuals and populations are studied for their evolutionary relevance and for their potential medical applications. Most of the genetic differentiation among populations are caused by random drift that should affect all loci across the genome in a similar manner. When a locus shows extraordinary high or low levels of population differentiation, this may be interpreted as evidence for natural selection. The most used measure of population differentiation was devised by Wright and is known as fixation index, or FST. We performed a genome-wide estimation of FST on about 4 millions of SNPs from HapMap project data. We demonstrated a heterogeneous distribution of FST values between autosomes and heterochromosomes. When we compared the FST values obtained in this study with another evolutionary measure obtained by comparative interspecific approach, we found that genes under positive selection appeared to show low levels of population differentiation. We applied a gene set approach, widely used for microarray data analysis, to detect functional pathways under selection. We found that one pathway related to antigen processing and presentation showed low levels of FST, while several pathways related to cell signalling, growth and morphogenesis showed high FST values. Finally, we detected a signature of selection within genes associated with human complex diseases. These results can help to identify which process occurred during human evolution and adaptation to different environments. They also support the hypothesis that common diseases could have a genetic background shaped by human evolution.

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