%0 Journal Article %1 10.1371/journal.pcbi.1003480 %A Lao, Oscar %A Liu, Fan %A Wollstein, Andreas %A Kayser, Manfred %D 2014 %I Public Library of Science %J PLoS Comput Biol %K F_ST eurogenetics genetic_distance popgen statistics %N 2 %P e1003480 %R 10.1371/journal.pcbi.1003480 %T GAGA: A New Algorithm for Genomic Inference of Geographic Ancestry Reveals Fine Level Population Substructure in Europeans %U http://dx.doi.org/10.1371%2Fjournal.pcbi.1003480 %V 10 %X <title>Author Summary</title><p>Understanding genetic population substructure is important in evolutionary biology, behavioral ecology, medical genetics and forensic genetics, among others. Several algorithms have recently been developed for investigating genetic population substructure. However, detecting genetic population substructure can be cumbersome in humans since most of the genetic diversity present in that species exists among individuals from the same population rather than between populations. We developed a Genetic Algorithm for Genetic Ancestry (GAGA) to overcome current limitations in reliably detecting population substructure from genetic and genomic data in humans, which can also be applied to any other species. The method was validated by means of extensive demographic simulations. When applied to a real, human genome-wide SNP microarray dataset covering a reasonable proportion of the European continent, we identified previously undetected fine-scale genetic population substructure. Overall, our study thus not only introduces a new method for investigating genetic population substructure in humans and other species, but also highlights that fine population substructure can be detected among European humans.</p>

@article{10.1371/journal.pcbi.1003480, abstract = {<title>Author Summary</title><p>Understanding genetic population substructure is important in evolutionary biology, behavioral ecology, medical genetics and forensic genetics, among others. Several algorithms have recently been developed for investigating genetic population substructure. However, detecting genetic population substructure can be cumbersome in humans since most of the genetic diversity present in that species exists among individuals from the same population rather than between populations. We developed a Genetic Algorithm for Genetic Ancestry (GAGA) to overcome current limitations in reliably detecting population substructure from genetic and genomic data in humans, which can also be applied to any other species. The method was validated by means of extensive demographic simulations. When applied to a real, human genome-wide SNP microarray dataset covering a reasonable proportion of the European continent, we identified previously undetected fine-scale genetic population substructure. Overall, our study thus not only introduces a new method for investigating genetic population substructure in humans and other species, but also highlights that fine population substructure can be detected among European humans.</p>}, added-at = {2014-04-03T18:44:35.000+0200}, author = {Lao, Oscar and Liu, Fan and Wollstein, Andreas and Kayser, Manfred}, biburl = {https://www.bibsonomy.org/bibtex/2a802d12b58ac042545e5486b0acf4263/peter.ralph}, doi = {10.1371/journal.pcbi.1003480}, interhash = {9c50c13d53be46b5e68857fb7a7e6820}, intrahash = {a802d12b58ac042545e5486b0acf4263}, journal = {PLoS Comput Biol}, keywords = {F_ST eurogenetics genetic_distance popgen statistics}, month = {02}, number = 2, pages = {e1003480}, publisher = {Public Library of Science}, timestamp = {2014-04-03T18:44:35.000+0200}, title = {GAGA: A New Algorithm for Genomic Inference of Geographic Ancestry Reveals Fine Level Population Substructure in Europeans}, url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1003480}, volume = 10, year = 2014 }