%0 Journal Article %1 lawson2012inference %A Lawson, Daniel John %A Hellenthal, Garrett %A Myers, Simon %A Falush, Daniel %D 2012 %I Public Library of Science %J PLoS Genet %K human_genome population_genomics structure %N 1 %P e1002453 %R 10.1371/journal.pgen.1002453 %T Inference of Population Structure using Dense Haplotype Data %U http://dx.doi.org/10.1371%2Fjournal.pgen.1002453 %V 8 %X The first step in almost every genetic analysis is to establish how sample members are related to each other. High relatedness between individuals can arise if they share a small number of recent ancestors, e.g. if they are distant cousins or a larger number of more distant ones, e.g. if their ancestors come from the same region. The most popular methods for investigating these relationships analyse successive markers independently, simply adding the information they provide. This works well for studies involving hundreds of markers scattered around the genome but is less appropriate now that entire genomes can be sequenced. We describe a “chromosome painting” approach to characterising shared ancestry that takes into account the fact that DNA is transmitted from generation to generation as a linear molecule in chromosomes. We show that the approach increases resolution relative to previous techniques, allowing differences in ancestry profiles among individuals to be resolved at the finest scales yet. We provide mathematical, statistical, and graphical machinery to exploit this new information and to characterize relationships at continental, regional, local, and family scales.

@article{lawson2012inference, abstract = {The first step in almost every genetic analysis is to establish how sample members are related to each other. High relatedness between individuals can arise if they share a small number of recent ancestors, e.g. if they are distant cousins or a larger number of more distant ones, e.g. if their ancestors come from the same region. The most popular methods for investigating these relationships analyse successive markers independently, simply adding the information they provide. This works well for studies involving hundreds of markers scattered around the genome but is less appropriate now that entire genomes can be sequenced. We describe a “chromosome painting” approach to characterising shared ancestry that takes into account the fact that DNA is transmitted from generation to generation as a linear molecule in chromosomes. We show that the approach increases resolution relative to previous techniques, allowing differences in ancestry profiles among individuals to be resolved at the finest scales yet. We provide mathematical, statistical, and graphical machinery to exploit this new information and to characterize relationships at continental, regional, local, and family scales.}, added-at = {2012-01-30T07:01:15.000+0100}, author = {Lawson, Daniel John and Hellenthal, Garrett and Myers, Simon and Falush, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/2539d734769c56b742b4a281c381f1522/peter.ralph}, description = {PLoS Genetics: Inference of Population Structure using Dense Haplotype Data}, doi = {10.1371/journal.pgen.1002453}, interhash = {2b68126d8ac06a182313ae6b3ecef3ba}, intrahash = {539d734769c56b742b4a281c381f1522}, journal = {PLoS Genet}, keywords = {human_genome population_genomics structure}, month = {01}, number = 1, pages = {e1002453}, publisher = {Public Library of Science}, timestamp = {2012-01-30T07:01:15.000+0100}, title = {Inference of Population Structure using Dense Haplotype Data}, url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1002453}, volume = 8, year = 2012 }