Recombination networks as genetic markers in a human variation study of the Old World View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-04

AUTHORS

Asif Javed, Marta Melé, Marc Pybus, Pierre Zalloua, Marc Haber, David Comas, Mihai G. Netea, Oleg Balanovsky, Elena Balanovska, Li Jin, Yajun Yang, GaneshPrasad ArunKumar, Ramasamy Pitchappan, Jaume Bertranpetit, Francesc Calafell, Laxmi Parida, The Genographic Consortium

ABSTRACT

We have analyzed human genetic diversity in 33 Old World populations including 23 populations obtained through Genographic Project studies. A set of 1,536 SNPs in five X chromosome regions were genotyped in 1,288 individuals (mostly males). We use a novel analysis employing subARG network construction with recombining chromosomal segments. Here, a subARG is constructed independently for each of five gene-free regions across the X chromosome, and the results are aggregated across them. For PCA, MDS and ancestry inference with STRUCTURE, the subARG is processed to obtain feature vectors of samples and pairwise distances between samples. The observed population structure, estimated from the five short X chromosomal segments, supports genome-wide frequency-based analyses: African populations show higher genetic diversity, and the general trend of shared variation is seen across the globe from Africa through Middle East, Europe, Central Asia, Southeast Asia, and East Asia in broad patterns. The recombinational analysis was also compared with established methods based on SNPs and haplotypes. For haplotypes, we also employed a fixed-length approach based on information-content optimization. Our recombinational analysis suggested a southern migration route out of Africa, and it also supports a single, rapid human expansion from Africa to East Asia through South Asia. More... »

PAGES

601-613

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00439-011-1104-8

DOI

http://dx.doi.org/10.1007/s00439-011-1104-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039751591

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22006219


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