Details

Autor(en) / Beteiligte

• Anagnostou, Paolo
• Dominici, Valentina
• Battaggia, Cinzia
• Pagani, Luca
• Vilar, Miguel
• Wells, R Spencer
• Pettener, Davide
• Sarno, Stefania
• Boattini, Alessio
• Francalacci, Paolo
• Colonna, Vincenza
• Vona, Giuseppe
• Calò, Carla
• Destro Bisol, Giovanni
• Tofanelli, Sergio

Titel

Overcoming the dichotomy between open and isolated populations using genomic data from a large European dataset

Ist Teil von

• Scientific reports, 2017-02, Vol.7 (1), p.41614-41614, Article 41614

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2017

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Human populations are often dichotomized into "isolated" and "open" categories using cultural and/or geographical barriers to gene flow as differential criteria. Although widespread, the use of these alternative categories could obscure further heterogeneity due to inter-population differences in effective size, growth rate, and timing or amount of gene flow. We compared intra and inter-population variation measures combining novel and literature data relative to 87,818 autosomal SNPs in 14 open populations and 10 geographic and/or linguistic European isolates. Patterns of intra-population diversity were found to vary considerably more among isolates, probably due to differential levels of drift and inbreeding. The relatively large effective size estimated for some population isolates challenges the generalized view that they originate from small founding groups. Principal component scores based on measures of intra-population variation of isolated and open populations were found to be distributed along a continuum, with an area of intersection between the two groups. Patterns of inter-population diversity were even closer, as we were able to detect some differences between population groups only for a few multidimensional scaling dimensions. Therefore, different lines of evidence suggest that dichotomizing human populations into open and isolated groups fails to capture the actual relations among their genomic features.