TY - JOUR
T1 - On the apportionment of population structure
AU - Granot, Yaron
AU - Tal, Omri
AU - Rosset, Saharon
AU - Skorecki, Karl
N1 - Publisher Copyright:
© 2016 Granot et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2016/8
Y1 - 2016/8
N2 - Measures of population differentiation, such as FST, are traditionally derived from the partition of diversity within and between populations. However, the emergence of population clusters from multilocus analysis is a function of genetic structure (departures from panmixia) rather than of diversity. If the populations are close to panmixia, slight differences between the mean pairwise distance within and between populations (low FST) can manifest as strong separation between the populations, thus population clusters are often evident even when the vast majority of diversity is partitioned within populations rather than between them. For any given FST value, clusters can be tighter (more panmictic) or looser (more stratified), and in this respect higher FST does not always imply stronger differentiation. In this study we propose a measure for the partition of structure, denoted EST, which is more consistent with results from clustering schemes. Crucially, our measure is based on a statistic of the data that is a good measure of internal structure, mimicking the information extracted by unsupervised clustering or dimensionality reduction schemes. To assess the utility of our metric, we ranked various human (HGDP) population pairs based on FST and EST and found substantial differences in ranking order. EST ranking seems more consistent with population clustering and classification and possibly with geographic distance between populations. Thus, EST may at times outperform FST in identifying evolutionary significant differentiation.
AB - Measures of population differentiation, such as FST, are traditionally derived from the partition of diversity within and between populations. However, the emergence of population clusters from multilocus analysis is a function of genetic structure (departures from panmixia) rather than of diversity. If the populations are close to panmixia, slight differences between the mean pairwise distance within and between populations (low FST) can manifest as strong separation between the populations, thus population clusters are often evident even when the vast majority of diversity is partitioned within populations rather than between them. For any given FST value, clusters can be tighter (more panmictic) or looser (more stratified), and in this respect higher FST does not always imply stronger differentiation. In this study we propose a measure for the partition of structure, denoted EST, which is more consistent with results from clustering schemes. Crucially, our measure is based on a statistic of the data that is a good measure of internal structure, mimicking the information extracted by unsupervised clustering or dimensionality reduction schemes. To assess the utility of our metric, we ranked various human (HGDP) population pairs based on FST and EST and found substantial differences in ranking order. EST ranking seems more consistent with population clustering and classification and possibly with geographic distance between populations. Thus, EST may at times outperform FST in identifying evolutionary significant differentiation.
UR - http://www.scopus.com/inward/record.url?scp=84983427304&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0160413
DO - 10.1371/journal.pone.0160413
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AN - SCOPUS:84983427304
SN - 1932-6203
VL - 11
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e0160413
ER -