BACKGROUND
admixture is a common form of gene flow between populations.
it refers to the process in which two or more genetically and phenotypically diverse populations with different allele frequencies mate and form a new, mixed or 'hybrid' population  <cit> .
a classic example of an admixed population in humans is the african-american population.
as a result of the genetic admixture, the african-american population contains stretches of dna as large as 20- <dig> cm that resemble mosaics of chromosomal segments, or ancestry blocks  <cit> .
these segments are derived from intermixing between european and african ancestry and have not had sufficient time to break up through recombination  <cit> .
as a result, in contrast to the million markers suggested to be necessary for genome-wide association studies   <cit> , modeling studies showed that between  <dig> and  <dig> well-distributed ancestry informative markers  distinguishing parental origins are sufficient for whole-genome scanning under the admixture mapping strategy  <cit> .
an ideal aim should have one allele that is fixed  in one ancestral population, and not present in the other  <cit> .
however, in the context of human genetics, most alleles are shared among populations  <cit> .
hence, it is important to identify and choose most ancestry informative markers across populations  <cit> ; the power of admixture mapping relies heavily on the ability of informative markers to infer ancestry along the chromosomes of admixed individuals.
several measures of marker informativeness for ancestry have been developed to select the most ancestry informative markers  from an ever-increasing wealth of genomic databases  <cit> .
these measures include: absolute allele frequency differences , shannon information content , fisher information content , f statistics , and the informativeness for assignment measure .
the cutoff value for δ is highly subjective and has steadily decreased over time from ≥  <dig>   <cit>  to  <dig>   <cit>  to  <dig>   <cit> .
cutoffs that have been used for other measures are fst ≥  <dig>   <cit> , fic ≥  <dig>   <cit> , sic ≥  <dig>   <cit> , and in ≥  <dig>   <cit> .
informative measures such as δ can be used for only two ancestral populations at a time  <cit> .
on the other hand, fst, fic, sic, and in can be applied to select informative markers for admixed populations formed from two or more ancestral populations.
for fic and sic indices, ancestral proportions in the admixed population need to be specified.
in spite of numerous studies with these measures of marker informativeness for ancestry, several questions are not systematically addressed, including how often are the same sets of snps selected by the different methods?
to what degree do they overlap and share common sets of snps?
how do aim panels selected by these different methods perform in estimating ancestry population contributions under different proportion of ancestral population in an admixed population?
with so many measures to choose from, it is very important to understand their common features as well as where they differ in terms of snp selection.
answering these questions with a systematic study would help users in choosing appropriate measures in a cost-effective manner.
in absence of a comprehensive comparative study on the performance of the different marker informativeness measures in marker selection, researchers selected markers using only the measure of their personal choice.
for example, the three major u.s. admixture mapping research groups led by david reich, michael seldin and mark shriver in their recent independent admixture mapping panels for latino populations used sic, fst and δ  <cit> , respectively.
it is not clear which measure-based panel is the most informative for admixture mapping.
in particular as more and more markers become available and as we study less differentiated populations, it is inevitable that prioritizing the most informative markers for ancestry inference or admixture mapping is critical.
with the availability of common sets of snps from hapmap populations, we are given the opportunity to compare these methods directly.
the objective of the present study is to compute and compare the commonly used measures of informativeness to select aim panels for admixture mapping and structured association testing for admixed populations.
to compare these methods, simulated as well as real data were used.
in the simulated data, the ancestral populations and their contributions for each individual are known, allowing comparison of accuracy of the different measures of marker informativeness for ancestry using true and estimated individual ancestry values.
RESULTS
snp allele frequencies and comparisons of informative marker selection measures
there are  <dig> , <dig> and  <dig> , <dig> autosomal snps in hapmap phase iii release # <dig> dataset for ceu and yri population, respectively.
table  <dig> shows, by chromosome and across the genome, the number of snps genotyped in each population and shared by both populations.
after removing snps that did not meet our criteria , we found  <dig> , <dig> snps shared by the ceu and the yri datasets.
furthermore, to avoid the possibility of choosing two redundant snps that are in strong ld , for each measure, we calculated the informativeness on all shared snps, then filtered them for the most informative ones such that the physical distance between consecutive selected snps must be at least  <dig> kb.
with this final filtering constraint and without using any cutoffs for any measures, all five measures gave aim panels of size ~ <dig>  k. figure  <dig> shows the distribution of the five measures of marker informativeness.
a predominantly right-skewed distribution was produced for each selection method.
summary statistics of the five measures of marker informativeness are shown in additional file  <dig>  table s <dig>  the means of δ, fst, fic, sic, and in were  <dig> ,  <dig> ,  <dig> ,  <dig> , and  <dig> , respectively.
the majority of the markers contained a small amount of ancestry information, suggesting a very high similarity in allele frequencies among common variants  in ceu and yri population.
a two inclusion criteria are in effective: 1) the snp is shared by both yri and ceu populations and 2) snps with missing frequency less than 10% of the samples.
b every snp is at least  <dig> kb from its nearest neighbor.
for chb and jpt population, the distribution of the five measures of marker informativeness is show in additional file  <dig>  figure s <dig> and summary statistics are shown in additional file  <dig>  table s <dig>  almost all the means of the five measures were about 10-fold less than those from the ceu and yri population, indicating that chb and jpt are much less differentiated than ceu and yri and the ancestry estimation for an admixed population from chb and jpt presents a much more difficult problem.
correlation, concordance, and overlapping analysis
spearman correlation
to assess the level of similarity of the estimates of genetic information contained in each snp marker across the different selection methods, spearman correlation coefficient was calculated for the estimates of informativeness from different selection methods for ceu and yri population.
figure  <dig> shows 3d scatterplots of ceu and yri allele frequencies and the five measures of informativeness.
extremely similar symmetric patterns were observed between fst and in, whereas fic and sic exhibited somewhat asymmetric patterns.
pairwise scatterplots of the five measures of informativeness showed that the measures had high levels of correlation , with spearman correlation coefficients ranging from  <dig>  between δ and fic to  <dig>  between fst and in.
fst and in had an almost perfect monotonically increasing relationship.
concordance by deciles
additional file  <dig>  figure s <dig> shows the scatter plot of allele frequencies of ceu and yri, with different colors indicating which decile group a snp fell into.
it is interesting to see how the ten groups partitioned the snps into symmetric patterns from the top-left and bottom-right corner representing the most informative snps to the center of the plot where the least informative snps resided.
it is evident that δ showed a partition pattern different from other four measures due to the fact that δ depends only on the difference between allele frequencies of the two populations.
fst and in exhibited very similar partition patterns, which is consistent with what we observed using the spearman correlation coefficient, mosaic plots, and kappa statistics.
it can also be recognized through the scatter plot that fic favors the selection of markers that are closer to fixation in one of the populations.
overlapping
discrimination analysis and estimation of ancestral contribution
discrimination analysis
estimation of ancestral contribution in admixed populations with top aims
additional file  <dig>  figure s <dig> shows an inferred population structure  for ceu, yri, and asw population with the top  <dig> aims selected by fic.
aim panels by other measures gave the same population structure.
furthermore, additional file  <dig>  figure s <dig> shows the estimates of ancestry contributions for the three populations as the number of aims increases.
the top informative snps  obtained using each measure yielded similar estimates of ancestry contribution.
with  <dig> aims, the estimate of ancestry in asw was 78% yri across all measures.
however, for individuals from ceu and yri population, where the estimated ancestry contribution from ceu and yri, respectively, is expected to be or close to  <dig>  aims selected by in performed slightly better than those selected by other measures of informativeness.
for the simulated admixed population from ceu and yri, a random sample of  <dig> individuals was extracted.
the true average ancestry contribution was 70: <dig>  additional file  <dig>  figure s <dig>  shows absolute errors in the estimation of the ancestry contribution for the simulated population with  <dig>   <dig>  and  <dig> top aims selected by different measures of informativeness.
the lowest errors were achieved at either  <dig> or  <dig> aims by the methods.
the bias in the estimation of the mean ancestry contribution was  <dig>  by in with  <dig> aims,  <dig>  by sic and fic with  <dig> aims, and  <dig>  by fst and delta with  <dig> aims.
using the top  <dig> aims, rmse's were  <dig> ,  <dig> ,  <dig> ,  <dig> , and  <dig>  for δ, fst, fic, sic, and in, respectively.
figure  <dig> shows the histogram of the individual true ancestry contributions and the scatter plot of individual estimated contributions vs. true contributions with top  <dig> aims selected by different methods.
for the simulated admixed population from chb and jpt, a random sample of  <dig> individuals was extracted.
the true average ancestry contribution was 72: <dig> for the simulated admixed population.
absolute errors in the estimation of the ancestry contribution for the simulated population with up to top  <dig> aims selected by different measures of informativeness are given in additional file  <dig>  figure s <dig> .
the bias in the estimation of mean ancestry contribution was generally higher than that for the simulated admixed population from ceu and yri as shown in additional file  <dig>  figure s <dig> .
specifically, the lowest bias of each method was  <dig>  by δ with  <dig> aims,  <dig>  by fst with  <dig> aims,  <dig>  by fic with  <dig> aims,  <dig>  by sic with  <dig> aims, and  <dig>  by in with  <dig> aims.
using top  <dig> aims, rmse's were  <dig> ,  <dig> ,  <dig> ,  <dig> , and  <dig>  for δ, fst, fic, sic, and in, respectively.
figure  <dig> shows the histogram of the individual true ancestry contributions and the scatter plot of individual estimated contributions vs. true contributions with  <dig> top aims selected by different methods.
the individual estimates became more accurate as more aims were used .
overall, relative large true ancestry contributions were more likely to be underestimated by all measures.
however, across the two simulation scenarios, in gave the lowest bias and rmse using only relatively small aim panels.
estimation of ancestral contribution in admixed populations with random subsets of top aim panels
results of simulation studies are shown in figure  <dig> and additional file  <dig>  table s <dig> for the admixed population from ceu and yri, where random subsets of  <dig> aims were used, and figure  <dig> and additional file  <dig>  table s <dig> for the admixed population from chb and jpt, where random subsets of  <dig> aims were used.
from the results we make the following observations.
first, even with less aims, the estimates for the admixed population from ceu and yri were more accurate than those for the admixed population from chb and jpt, which is expected since ceu and yri are more divergent genetically than chb and jpt.
secondly, for all the five measures, as the aim panels included more markers that were less ancestry informative, the mean and standard deviation of estimation errors increased.
finally, aim panels chosen by in gave the smallest mean error, whereas those chosen by fic gave the largest mean error.
the superiority of in was evident in the simulation study using chb and jpt .
however, different from what we observed in the asw population, fic and sic underestimated ancestry contribution.
a possible interpretation for the observed discrepancy between the asw data and the simulated data is that fic and sic are more sensitive to the underlying distribution of the ancestry contribution of individuals in the admixed population.
the other possibility could be that the assumption we made about the genetic structure of asw population is wrong.
although we tried to mimic the formulation of an african-american population in our simulation , the simulated data was more than likely to be different from the true african-american population.
simulated and empirical data differ in that the simulations used fully-differentiated populations, which is not the case for the empirical data.
the 3d scatter plot of ceu and yri allele frequencies and the five measures of informativeness in figure  <dig> also showed the asymmetry of fic and sic, which may contribute to their sensitivity to the underlying distribution of individual ancestry contribution.
overall, the aim panels chosen by in performed the best, giving the lowest bias and rmse, whereas those by fic performed the worst across the real dataset and the simulated datasets.
for the real dataset , the combined method by using the average ranking of the five measures outperformed all the five measures .
however, for the simulated datasets, the average ranking and minimal ranking methods were outperformed by in and fst, but their performance were either better or very similar to the most commonly used method delta, fic and sic .
discussion
admixture mapping is a powerful gene mapping approach  <cit> .
however, the power of this method relies on the ability of ancestry informative markers  to infer ancestry along the chromosomes of previously separated but recently admixed individuals.
in a recent paper from ashg, royal et al.
<cit>  outlined the challenges, opportunities and implications of genetic ancestry inference.
over  <dig> companies provide genetic ancestry testing to the public.
however, these companies differ in their approaches, the types of ancestry markers used and tests they offer.
the promise of utilizing genetic ancestry information to advance medical genomics depends on our ability to correctly and precisely infer/measure ancestry using informative markers.
several methods have been proposed to measure ancestry informativeness of markers and to choose a panel of markers to be genotyped while maintaining the power of detecting ancestral chromosome segments in each genomic location.
the choice of which of these measures to use should depend on the efficiency of each measure in selecting most ancestry informative markers.
however, there is no consensus as to which criteria to use to select markers for ancestry inference or admixture mapping, and the performance of these methods has not been carefully evaluated and compared.
the rule of thumb is to select markers with large allele frequency differences between ancestry populations.
however, the number of markers required for population assignment will depend on the populations under consideration, their respective level of genetic differentiation and the desired stringency of assignment  <cit> .
for instance, in humans, the level of genetic variation between populations is only 5%-10% whereas genetic variation within dogs is about 27%  <cit> .
as a result, the number and types of markers required for individual assignment and discrimination amongst populations is different between populations/species under consideration  <cit> .
previous studies selected markers based on different datasets and marker types using only one of the methods at a time, and there has not yet been a formal comparison of the performance of these methods.
therefore there is a need to compare all methods using the same data sets and evaluate their efficiencies and accuracies in estimating ancestral proportions for admixed populations.
in this study, we applied five different analytic tools to evaluate the concordance of selected informative snps using the same dataset.
our investigation using  <dig> top ranked markers for each measure and accounting for the physical distance between consecutive aims to be at least  <dig> kb, showed the following overlap between the different measures: δ vs fst , δ vs fic , δ vs sic , δ vs in , fst vs fic , fst vs sic , fst vs in , fic vs sic , fic vs in , and sic vs in  .
on average, the overlap of each measure with the other four was  <dig>   <dig>   <dig>   <dig>  and  <dig> for δ, fst, fic, sic, and in, respectively.
fic had the least overlap with other measures.
however, based on current cutoff values used for each measure, the δ measure included a number of loci that were not selected by the remaining four methods.
fst, fic, and in gave relatively smaller and similar aim panels, whereas sic gave a very small panel of aims .
analyses based on deciles showed that sets of snps at the highest or lowest snp information content selected for admixture mapping were highly similar across the different measures of informativeness.
towards the middle of the informativeness scales, the agreement among the sets of snps selected by different methods to discriminate between populations decreased .
furthermore, fic and sic were more likely to pick the same set of snps, δ, fst, and in were more likely to pick the same set of snps, and fic was more likely to choose snps that were not chosen by the other measure.
analytically, the fic and sic measures require pre-defined ancestral proportions in an admixed population, whereas fst, δ, and in do not.
we ran sensitivity analysis to study the impact of ancestral proportion in choosing informative markers using arbitrary values using ceu and yri population.
compared with fic, sic was less sensitive to the proportion of ancestry contribution in the selection of aims .
proportion of ancestry had virtually no effect on the selection of top 1% aims for sic.
for two proportions of ancestry contribution within a distance of  <dig> , fic selected 56%-71% common sets of aims and sic selected 75%-100% common sets of aims.
therefore, it is important, when using fic, to have a good a priori estimate of proportion of ancestry contribution.
there are some limitations for some of these measures, for example, fic favors selection of markers that are closer to fixation in one parental population and may not be appropriate to assess the level of informative markers when ancestral populations are more than two  <cit> .
compared with other methods such as fst, δ is easy to calculate and independent of mutation and model assumptions, however, δ has a major limitation of being only useful for admixed populations from two parental populations and it doesn't account for multiallelic situations at a locus.
fst may not be appropriate to assess the level of genetic information in snp markers when the number of populations is >  <dig>  as the method could result in the selection of snp markers which are specific for a single most genetically distinct population.
the selected snp markers that were specific for only the most distinct population are expected to have low heterozygosity.
genetic markers with high expected heterozygosity are informative and therefore useful in individual assignment analysis  <cit> .
although most researchers traditionally focus on global axes of variation in a dataset, substantial information about population ancestry exists locally- across chromosomes.
adjustment of global ancestry between study subjects may lead to false positives when chromosomal  population ancestry is an important confounding factor  <cit> .
in a recent chromosome-based study by baye   <cit> , fine-scale substructure was detectable beyond the broad population level classifications that previously have been explored using genome-wide average estimates.
the study of population ancestry in terms of local ancestry has broader practical relevance because genetic diversity is directly related to recombination rate , which differs among regions of the genome, and genes are not randomly distributed along chromosomes.
the current analytical approach using genome-wide average estimates will control for confounding due to global ancestry but will not control for confounding due to the local ancestry effect because the global ancestry information is obtained from all markers across the genome and may not accurately reflect local ancestry variation.
it is becoming increasingly important to recognize local ancestry variation, especially when populations have been recently admixed  <cit> .
future studies should focus on the applications of these measures to important genomic regions.
many factors impact the accuracy of the estimation of ancestry contributions, which include but are not limited to sample size, the panel of aims used, the number of aims used, and the underlying distribution of ancestry contribution of the individuals in the sample.
the use of a phased hapmap dataset allowed us to simulate individuals that share common founding populations.
moreover, the ancestry proportion for each individual is known, allowing for the comparison of true and estimated individual admixture values, thus enabling the comparison of different methods by estimation accuracy.
our findings indicate that, the different measures of marker informativeness  performed well and as few as the top  <dig> ranked informative markers were adequate for accurate classification of ancestral populations.
this is in agreement with the commonly made claim in the literature on marker selection for population assignment that 'classification accuracy can be substantially improved if only a subset of loci is used in the assignment test'  <cit> .
for instance, lao et al.,   <cit>  found that  <dig> snp markers from a  <dig> k snp array contained enough genetic information to differentiate individuals from africa, europe, asia and america and no further gain in power of assignment was achieved by including more snp markers.
indeed, it is generally considered that uninformative markers  may add variability and noise to the results and compromise the power of population genetic studies  <cit> .
although the marker selection methods explored in this study agreed to a large extent in identifying the most informative snps, there were differences in their performance in ancestry estimation.
the simulation study revealed that in was the best in selecting the set of aims giving the smallest bias and mean square error in ancestry estimation.
analysis based on random subsets of top 1% to 10% ranked aims indicated that, compared to other methods, aim panels selected by in behaved consistently and reasonably well for both the asw population and simulated admixed populations.
these results illustrate that effective exploration of all these methods can help to not only identify the most informative markers but also produce an optimal minimum set of markers that can accurately and efficiently differentiate among populations.
we suggest that the different measures may provide unique insights into a marker's informativeness under different scenarios, including varying ancestral proportion and when more than two ancestral populations are present.
to identify all potentially informative snps, results from all measures could be considered.
for example, the union of the top  <dig> snps for all five measures could be considered as the best aims panel.
researchers need to be aware of the differences between the various methods for evaluating ancestry informativeness of snp markers.
furthermore, as we attempted in our simulation studies using either average rank or minimal rank of all five measures, combined information from more than one method may provide a reliable means, although may not be the best, in selecting markers for ancestry inference.
further research on this topic may shed light on how to best integrate different measures to obtain a set of aims most effective for the populations under consideration.
we believe that the information that a set of markers provides for assigning or discriminating individuals to their source populations or different relationships must be critically evaluated before investing millions of dollars on an admixture or ancestry related project.
we anticipate identification of more complex patterns of ancestry will require explorations of these and newer methods yet to be defined, to identify an optimal set of markers to use, however this should become increasingly feasible as genotyping costs decrease and available data grow on different populations.
this in turn will allow the development of higher resolution of genogeographic and ethnic maps and help investigators designing genetic association studies in stratified homogeneous groups.
CONCLUSIONS
although millions of snp markers with varying levels of information content for ancestry inference have been identified, only small subsets of highly informative markers need to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner.
in this article, we compared various methods for selecting informative snps and showed that the in measure estimated ancestry proportion  with lower bias and mean square error.
in summary, we showed the utility of several measures of informativeness using simulations and real snp genotype data from samples of admixed populations.
the use of several available methods to prioritize informative markers for ancestry inference can reduce genotyping costs and avoid false positive genotype-phenotype associations while retaining most of the power found in much larger sets of published aim panels.

 admixture is a common form of gene flow between populations. it refers to the process in which two or more genetically and phenotypically diverse populations with different allele frequencies mate and form a new, mixed or'hybrid' population. a classic example of an admixed population in humans is the african-american population. as a result of the genetic admixture, the african-american population contains stretches of dna as large as 20- cm that resemble mosaics of chromosomal segments, or ancestry blocks. these segments are derived from intermixing between european and african ancestry and have not had sufficient time to break up through recombination. as a result, in contrast to the million markers suggested to be necessary for genome-wide association studies, modeling studies showed that between and well-distributed ancestry informative markers distinguishing parental origins are sufficient for whole-genome scanning under the admixture mapping strategy. an ideal aim should have one allele that is fixed in one ancestral population, and not present in the other. however, in the context of human genetics, most alleles are shared among populations. hence, it is important to identify and choose most ancestry informative markers across populations; the power of admixture mapping relies heavily on the ability of informative markers to infer ancestry along the chromosomes of admixed individuals. several measures of marker informativeness for ancestry have been developed to select the most ancestry informative markers from an ever-increasing wealth of genomic databases. these measures include: absolute allele frequency differences, shannon information content, fisher information content, f statistics, and the informativeness for assignment measure. the cutoff value for δ is highly subjective and has steadily decreased over time from ≥ to to. cutoffs that have been used for other measures are fst ≥, fic ≥, sic ≥, and in ≥. informative measures such as δ can be used for only two ancestral populations at a time. on the other hand, fst, fic, sic, and in can be applied to select informative markers for admixed populations formed from two or more ancestral populations. for fic and sic indices, ancestral proportions in the admixed population need to be specified. in spite of numerous studies with these measures of marker informativeness for ancestry, several questions are not systematically addressed, including how often are the same sets of snps selected by the different methods? to what degree do they overlap and share common sets of snps? how do aim panels selected by these different methods perform in estimating ancestry population contributions under different proportion of ancestral population in an admixed population? with so many measures to choose from, it is very important to understand their common features as well as where they differ in terms of snp selection. answering these questions with a systematic study would help users in choosing appropriate measures in a cost-effective manner. in absence of a comprehensive comparative study on the performance of the different marker informativeness measures in marker selection, researchers selected markers using only the measure of their personal choice. for example, the three major u.s. admixture mapping research groups led by david reich, michael seldin and mark shriver in their recent independent admixture mapping panels for latino populations used sic, fst and δ, respectively. it is not clear which measure-based panel is the most informative for admixture mapping. in particular as more and more markers become available and as we study less differentiated populations, it is inevitable that prioritizing the most informative markers for ancestry inference or admixture mapping is critical. with the availability of common sets of snps from hapmap populations, we are given the opportunity to compare these methods directly. the objective of the present study is to compute and compare the commonly used measures of informativeness to select aim panels for admixture mapping and structured association testing for admixed populations. to compare these methods, simulated as well as real data were used. in the simulated data, the ancestral populations and their contributions for each individual are known, allowing comparison of accuracy of the different measures of marker informativeness for ancestry using true and estimated individual ancestry values.  snp allele frequencies and comparisons of informative marker selection measures there are, and, autosomal snps in hapmap phase iii release # dataset for ceu and yri population, respectively. table shows, by chromosome and across the genome, the number of snps genotyped in each population and shared by both populations. after removing snps that did not meet our criteria, we found, snps shared by the ceu and the yri datasets. furthermore, to avoid the possibility of choosing two redundant snps that are in strong ld, for each measure, we calculated the informativeness on all shared snps, then filtered them for the most informative ones such that the physical distance between consecutive selected snps must be at least kb. with this final filtering constraint and without using any cutoffs for any measures, all five measures gave aim panels of size ~ k. figure shows the distribution of the five measures of marker informativeness. a predominantly right-skewed distribution was produced for each selection method. summary statistics of the five measures of marker informativeness are shown in additional file table s the means of δ, fst, fic, sic, and in were,,,, and, respectively. the majority of the markers contained a small amount of ancestry information, suggesting a very high similarity in allele frequencies among common variants in ceu and yri population. a two inclusion criteria are in effective: 1) the snp is shared by both yri and ceu populations and 2) snps with missing frequency less than 10% of the samples. b every snp is at least kb from its nearest neighbor. for chb and jpt population, the distribution of the five measures of marker informativeness is show in additional file figure s and summary statistics are shown in additional file table s almost all the means of the five measures were about 10-fold less than those from the ceu and yri population, indicating that chb and jpt are much less differentiated than ceu and yri and the ancestry estimation for an admixed population from chb and jpt presents a much more difficult problem. correlation, concordance, and overlapping analysis spearman correlation to assess the level of similarity of the estimates of genetic information contained in each snp marker across the different selection methods, spearman correlation coefficient was calculated for the estimates of informativeness from different selection methods for ceu and yri population. figure shows 3d scatterplots of ceu and yri allele frequencies and the five measures of informativeness. extremely similar symmetric patterns were observed between fst and in, whereas fic and sic exhibited somewhat asymmetric patterns. pairwise scatterplots of the five measures of informativeness showed that the measures had high levels of correlation, with spearman correlation coefficients ranging from between δ and fic to between fst and in. fst and in had an almost perfect monotonically increasing relationship. concordance by deciles additional file figure s shows the scatter plot of allele frequencies of ceu and yri, with different colors indicating which decile group a snp fell into. it is interesting to see how the ten groups partitioned the snps into symmetric patterns from the top-left and bottom-right corner representing the most informative snps to the center of the plot where the least informative snps resided. it is evident that δ showed a partition pattern different from other four measures due to the fact that δ depends only on the difference between allele frequencies of the two populations. fst and in exhibited very similar partition patterns, which is consistent with what we observed using the spearman correlation coefficient, mosaic plots, and kappa statistics. it can also be recognized through the scatter plot that fic favors the selection of markers that are closer to fixation in one of the populations. overlapping discrimination analysis and estimation of ancestral contribution discrimination analysis estimation of ancestral contribution in admixed populations with top aims additional file figure s shows an inferred population structure for ceu, yri, and asw population with the top aims selected by fic. aim panels by other measures gave the same population structure. furthermore, additional file figure s shows the estimates of ancestry contributions for the three populations as the number of aims increases. the top informative snps obtained using each measure yielded similar estimates of ancestry contribution. with aims, the estimate of ancestry in asw was 78% yri across all measures. however, for individuals from ceu and yri population, where the estimated ancestry contribution from ceu and yri, respectively, is expected to be or close to aims selected by in performed slightly better than those selected by other measures of informativeness. for the simulated admixed population from ceu and yri, a random sample of individuals was extracted. the true average ancestry contribution was 70: additional file figure s shows absolute errors in the estimation of the ancestry contribution for the simulated population with and top aims selected by different measures of informativeness. the lowest errors were achieved at either or aims by the methods. the bias in the estimation of the mean ancestry contribution was by in with aims, by sic and fic with aims, and by fst and delta with aims. using the top aims, rmse's were,,,, and for δ, fst, fic, sic, and in, respectively. figure shows the histogram of the individual true ancestry contributions and the scatter plot of individual estimated contributions vs. true contributions with top aims selected by different methods. for the simulated admixed population from chb and jpt, a random sample of individuals was extracted. the true average ancestry contribution was 72: for the simulated admixed population. absolute errors in the estimation of the ancestry contribution for the simulated population with up to top aims selected by different measures of informativeness are given in additional file figure s. the bias in the estimation of mean ancestry contribution was generally higher than that for the simulated admixed population from ceu and yri as shown in additional file figure s. specifically, the lowest bias of each method was by δ with aims, by fst with aims, by fic with aims, by sic with aims, and by in with aims. using top aims, rmse's were,,,, and for δ, fst, fic, sic, and in, respectively. figure shows the histogram of the individual true ancestry contributions and the scatter plot of individual estimated contributions vs. true contributions with top aims selected by different methods. the individual estimates became more accurate as more aims were used. overall, relative large true ancestry contributions were more likely to be underestimated by all measures. however, across the two simulation scenarios, in gave the lowest bias and rmse using only relatively small aim panels. estimation of ancestral contribution in admixed populations with random subsets of top aim panels results of simulation studies are shown in figure and additional file table s for the admixed population from ceu and yri, where random subsets of aims were used, and figure and additional file table s for the admixed population from chb and jpt, where random subsets of aims were used. from the results we make the following observations. first, even with less aims, the estimates for the admixed population from ceu and yri were more accurate than those for the admixed population from chb and jpt, which is expected since ceu and yri are more divergent genetically than chb and jpt. secondly, for all the five measures, as the aim panels included more markers that were less ancestry informative, the mean and standard deviation of estimation errors increased. finally, aim panels chosen by in gave the smallest mean error, whereas those chosen by fic gave the largest mean error. the superiority of in was evident in the simulation study using chb and jpt. however, different from what we observed in the asw population, fic and sic underestimated ancestry contribution. a possible interpretation for the observed discrepancy between the asw data and the simulated data is that fic and sic are more sensitive to the underlying distribution of the ancestry contribution of individuals in the admixed population. the other possibility could be that the assumption we made about the genetic structure of asw population is wrong. although we tried to mimic the formulation of an african-american population in our simulation, the simulated data was more than likely to be different from the true african-american population. simulated and empirical data differ in that the simulations used fully-differentiated populations, which is not the case for the empirical data. the 3d scatter plot of ceu and yri allele frequencies and the five measures of informativeness in figure also showed the asymmetry of fic and sic, which may contribute to their sensitivity to the underlying distribution of individual ancestry contribution. overall, the aim panels chosen by in performed the best, giving the lowest bias and rmse, whereas those by fic performed the worst across the real dataset and the simulated datasets. for the real dataset, the combined method by using the average ranking of the five measures outperformed all the five measures. however, for the simulated datasets, the average ranking and minimal ranking methods were outperformed by in and fst, but their performance were either better or very similar to the most commonly used method delta, fic and sic. discussion admixture mapping is a powerful gene mapping approach. however, the power of this method relies on the ability of ancestry informative markers to infer ancestry along the chromosomes of previously separated but recently admixed individuals. in a recent paper from ashg, royal et al. outlined the challenges, opportunities and implications of genetic ancestry inference. over companies provide genetic ancestry testing to the public. however, these companies differ in their approaches, the types of ancestry markers used and tests they offer. the promise of utilizing genetic ancestry information to advance medical genomics depends on our ability to correctly and precisely infer/measure ancestry using informative markers. several methods have been proposed to measure ancestry informativeness of markers and to choose a panel of markers to be genotyped while maintaining the power of detecting ancestral chromosome segments in each genomic location. the choice of which of these measures to use should depend on the efficiency of each measure in selecting most ancestry informative markers. however, there is no consensus as to which criteria to use to select markers for ancestry inference or admixture mapping, and the performance of these methods has not been carefully evaluated and compared. the rule of thumb is to select markers with large allele frequency differences between ancestry populations. however, the number of markers required for population assignment will depend on the populations under consideration, their respective level of genetic differentiation and the desired stringency of assignment. for instance, in humans, the level of genetic variation between populations is only 5%-10% whereas genetic variation within dogs is about 27%. as a result, the number and types of markers required for individual assignment and discrimination amongst populations is different between populations/species under consideration. previous studies selected markers based on different datasets and marker types using only one of the methods at a time, and there has not yet been a formal comparison of the performance of these methods. therefore there is a need to compare all methods using the same data sets and evaluate their efficiencies and accuracies in estimating ancestral proportions for admixed populations. in this study, we applied five different analytic tools to evaluate the concordance of selected informative snps using the same dataset. our investigation using top ranked markers for each measure and accounting for the physical distance between consecutive aims to be at least kb, showed the following overlap between the different measures: δ vs fst, δ vs fic, δ vs sic, δ vs in, fst vs fic, fst vs sic, fst vs in, fic vs sic, fic vs in, and sic vs in. on average, the overlap of each measure with the other four was and for δ, fst, fic, sic, and in, respectively. fic had the least overlap with other measures. however, based on current cutoff values used for each measure, the δ measure included a number of loci that were not selected by the remaining four methods. fst, fic, and in gave relatively smaller and similar aim panels, whereas sic gave a very small panel of aims. analyses based on deciles showed that sets of snps at the highest or lowest snp information content selected for admixture mapping were highly similar across the different measures of informativeness. towards the middle of the informativeness scales, the agreement among the sets of snps selected by different methods to discriminate between populations decreased. furthermore, fic and sic were more likely to pick the same set of snps, δ, fst, and in were more likely to pick the same set of snps, and fic was more likely to choose snps that were not chosen by the other measure. analytically, the fic and sic measures require pre-defined ancestral proportions in an admixed population, whereas fst, δ, and in do not. we ran sensitivity analysis to study the impact of ancestral proportion in choosing informative markers using arbitrary values using ceu and yri population. compared with fic, sic was less sensitive to the proportion of ancestry contribution in the selection of aims. proportion of ancestry had virtually no effect on the selection of top 1% aims for sic. for two proportions of ancestry contribution within a distance of, fic selected 56%-71% common sets of aims and sic selected 75%-100% common sets of aims. therefore, it is important, when using fic, to have a good a priori estimate of proportion of ancestry contribution. there are some limitations for some of these measures, for example, fic favors selection of markers that are closer to fixation in one parental population and may not be appropriate to assess the level of informative markers when ancestral populations are more than two. compared with other methods such as fst, δ is easy to calculate and independent of mutation and model assumptions, however, δ has a major limitation of being only useful for admixed populations from two parental populations and it doesn't account for multiallelic situations at a locus. fst may not be appropriate to assess the level of genetic information in snp markers when the number of populations is > as the method could result in the selection of snp markers which are specific for a single most genetically distinct population. the selected snp markers that were specific for only the most distinct population are expected to have low heterozygosity. genetic markers with high expected heterozygosity are informative and therefore useful in individual assignment analysis. although most researchers traditionally focus on global axes of variation in a dataset, substantial information about population ancestry exists locally- across chromosomes. adjustment of global ancestry between study subjects may lead to false positives when chromosomal population ancestry is an important confounding factor. in a recent chromosome-based study by baye, fine-scale substructure was detectable beyond the broad population level classifications that previously have been explored using genome-wide average estimates. the study of population ancestry in terms of local ancestry has broader practical relevance because genetic diversity is directly related to recombination rate, which differs among regions of the genome, and genes are not randomly distributed along chromosomes. the current analytical approach using genome-wide average estimates will control for confounding due to global ancestry but will not control for confounding due to the local ancestry effect because the global ancestry information is obtained from all markers across the genome and may not accurately reflect local ancestry variation. it is becoming increasingly important to recognize local ancestry variation, especially when populations have been recently admixed. future studies should focus on the applications of these measures to important genomic regions. many factors impact the accuracy of the estimation of ancestry contributions, which include but are not limited to sample size, the panel of aims used, the number of aims used, and the underlying distribution of ancestry contribution of the individuals in the sample. the use of a phased hapmap dataset allowed us to simulate individuals that share common founding populations. moreover, the ancestry proportion for each individual is known, allowing for the comparison of true and estimated individual admixture values, thus enabling the comparison of different methods by estimation accuracy. our findings indicate that, the different measures of marker informativeness performed well and as few as the top ranked informative markers were adequate for accurate classification of ancestral populations. this is in agreement with the commonly made claim in the literature on marker selection for population assignment that'classification accuracy can be substantially improved if only a subset of loci is used in the assignment test'. for instance, lao et al., found that snp markers from a k snp array contained enough genetic information to differentiate individuals from africa, europe, asia and america and no further gain in power of assignment was achieved by including more snp markers. indeed, it is generally considered that uninformative markers may add variability and noise to the results and compromise the power of population genetic studies. although the marker selection methods explored in this study agreed to a large extent in identifying the most informative snps, there were differences in their performance in ancestry estimation. the simulation study revealed that in was the best in selecting the set of aims giving the smallest bias and mean square error in ancestry estimation. analysis based on random subsets of top 1% to 10% ranked aims indicated that, compared to other methods, aim panels selected by in behaved consistently and reasonably well for both the asw population and simulated admixed populations. these results illustrate that effective exploration of all these methods can help to not only identify the most informative markers but also produce an optimal minimum set of markers that can accurately and efficiently differentiate among populations. we suggest that the different measures may provide unique insights into a marker's informativeness under different scenarios, including varying ancestral proportion and when more than two ancestral populations are present. to identify all potentially informative snps, results from all measures could be considered. for example, the union of the top snps for all five measures could be considered as the best aims panel. researchers need to be aware of the differences between the various methods for evaluating ancestry informativeness of snp markers. furthermore, as we attempted in our simulation studies using either average rank or minimal rank of all five measures, combined information from more than one method may provide a reliable means, although may not be the best, in selecting markers for ancestry inference. further research on this topic may shed light on how to best integrate different measures to obtain a set of aims most effective for the populations under consideration. we believe that the information that a set of markers provides for assigning or discriminating individuals to their source populations or different relationships must be critically evaluated before investing millions of dollars on an admixture or ancestry related project. we anticipate identification of more complex patterns of ancestry will require explorations of these and newer methods yet to be defined, to identify an optimal set of markers to use, however this should become increasingly feasible as genotyping costs decrease and available data grow on different populations. this in turn will allow the development of higher resolution of genogeographic and ethnic maps and help investigators designing genetic association studies in stratified homogeneous groups.  although millions of snp markers with varying levels of information content for ancestry inference have been identified, only small subsets of highly informative markers need to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner. in this article, we compared various methods for selecting informative snps and showed that the in measure estimated ancestry proportion with lower bias and mean square error. in summary, we showed the utility of several measures of informativeness using simulations and real snp genotype data from samples of admixed populations. the use of several available methods to prioritize informative markers for ancestry inference can reduce genotyping costs and avoid false positive genotype-phenotype associations while retaining most of the power found in much larger sets of published aim panels. 
in this study, more than one million snps from hapmap databases and simulated data have been interrogated in admixed populations using various measures of ancestry informativeness: fisher information content, shannon information content, f statistics, informativeness for assignment measure, and the absolute allele frequency differences. the power of this method relies on the ability of ancestry informative markers to infer ancestry along the chromosomes of admixed individuals. although the different measures of marker informativeness performed comparably well, analyses based on the top to 10% ranked informative markers of simulated data showed that in was better in estimating ancestry for an admixed population. admixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies. in this article, we compared various methods for selecting ancestry informative snps using simulations as well as snp genotype data from samples of admixed populations and showed that the in measure estimates ancestry proportion with lower bias and mean square error. the objectives are to compare these measures of informativeness to select snp markers for ancestry inference, and to determine the accuracy of aim panels selected by each measure in estimating the contributions of the ancestors to the admixed population. fst and in had the highest spearman correlation and the best agreement as measured by kappa statistics based on deciles. although millions of snps have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner.
 admixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies. the power of this method relies on the ability of ancestry informative markers to infer ancestry along the chromosomes of admixed individuals. in this study, more than one million snps from hapmap databases and simulated data have been interrogated in admixed populations using various measures of ancestry informativeness: fisher information content, shannon information content, f statistics, informativeness for assignment measure, and the absolute allele frequency differences. the objectives are to compare these measures of informativeness to select snp markers for ancestry inference, and to determine the accuracy of aim panels selected by each measure in estimating the contributions of the ancestors to the admixed population.  fst and in had the highest spearman correlation and the best agreement as measured by kappa statistics based on deciles. although the different measures of marker informativeness performed comparably well, analyses based on the top to 10% ranked informative markers of simulated data showed that in was better in estimating ancestry for an admixed population.  although millions of snps have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner. in this article, we compared various methods for selecting ancestry informative snps using simulations as well as snp genotype data from samples of admixed populations and showed that the in measure estimates ancestry proportion with lower bias and mean square error. 