BACKGROUND
the south african coloured population  is a so-called admixed population that derived its origins from the diverse population groups that settled in the early cape colony, including the indigenous san, early european settlers, slaves that were imported from indonesia, india and other parts of africa, and south african bantu-speakers who later migrated to the area.
previous genetic research has shown that the sac received ancestry contributions from click-speaking africans , bantu-speaking africans, europeans and south and east asians, which is consistent with the historical records .
a high degree of heterogeneity in ancestral contributions between sac individuals has also been illustrated previously  <cit> .
the admixture that occurred in the sac is therefore complex, constituting a number of different source ancestries, with dissimilar genetic distances between them.
our study group of sac individuals was recruited from metropolitan areas in cape town that have some of the highest reported incidences of tuberculosis  worldwide, despite extensive bcg vaccination and low prevalence of hiv  <cit> .
as the group received contributions from diverse source populations that may differ in their genetic susceptibility to tb, the group is ideally suited to the discovery of tb susceptibility genetic variants and their probable ethnic origins.
our previous work has shown that african ancestry in this group is associated with higher risk of tb infection, whereas european and asian ancestries are protective  <cit> .
areas of the genome with african ancestry that is much higher than the norm in a group of tb cases may therefore harbour genetic variants that increase the risk of developing tb.
the process of finding such areas is known as admixture mapping, and this technique relies on the accurate inference of what is known as local ancestry per individual across their genome  <cit> .
when admixture occurs between two or more population groups that were previously isolated, recombination events result in chromosomes that are a mosaic of blocks of ancestry deriving from different source populations.
given genetic data of an admixed individual and their source populations, statistical techniques can be used to determine the bounds of these segments and to assign the most probable source ancestries to them.
these techniques rely on the probability of recombination events to distinguish the bounds of segments, and differences in allele and haplotype frequencies between source populations for classification of the ancestry of segments.
the process is known as local ancestry inference .
in a previous study, chimusa et al.
concluded that accurate multi-way lai was not feasible in the sac using the lai algorithms available at the time  <cit> .
in this study, we re-evaluate this position using the novel lai algorithm implemented in the rfmix software package, focusing on the classification of san and bantu ancestry.
these ancestries are of particular interest as southern african populations were not exposed to modern strains of mycobacterium tuberculosis , the most prevalent in our sac study group  <cit> , until the recent past  <cit> .
the relative lack of exposure of the sac and bantu populations to modern strains of m. tuberculosis could possibly have resulted in decreased resistance to developing the disease, especially in densely populated areas with low socio-economic conditions.
supporting this argument, a significant positive association between san ancestry and tb susceptibility in the sac was found by chimusa et al.
<cit> .
the association was confirmed in an independent sample in a later study, which also found a positive association with bantu ancestry, although it was relatively weak  <cit> .
although each of the non-african ancestry components of the sac  were negatively associated with tb susceptibility when tested in individual models, these associations were no longer significant when all five ancestry components were tested together.
in this study, we first explore the accuracy of lai in the sac using rfmix and compare its performance to other algorithms.
after quantifying this using simulated data, we proceed to identify regions with excess san or bantu ancestry found in tb cases but not in controls, and hypothesize that these regions may contain genes that affect tb susceptibility.
RESULTS
evaluating multi-way lai accuracy using simulated data
chimusa et al.
previously evaluated the accuracy of lai in the sac using various software programs and found that lamp-ld performed best  <cit> .
as rfmix was not available at that time, our first step was to compare the accuracies of lamp-ld and rfmix using a simulated data set of  <dig> sac chromosomes .
lai was run five-way, but since only san and bantu genome-wide ancestry is independently associated with tb susceptibility  <cit> , ancestry of snps that were called as european, south asian or east asian were labelled as non-african.
the percentage of snps for which the called ancestry matched the known ancestry was  <dig> % and  <dig> % for lamp-ld and rfmix respectively.
the percentage of snps per type of miss-called ancestry is summarized in table  <dig>  which shows that rfmix offers a significant improvement in the calling of local ancestry, especially when distinguishing san and bantu ancestry.table  <dig>
percentage of miss-called ancestry
this table reports the interquartile range  of the percentage of snps that were miss-called by lamp-ld and rfmix per each of the six possible miss-call categories.
the known ancestry of a simulated data set of  <dig> sac chromosomes was compared to the ancestry called by the software program .
the median percentage of miss-called snps across all snps as well as the median percentage of miss-called snps across snps of that source ancestry are shown.
san ancestry can for example be miss-called as either bantu or non-african ancestry.
the median percentage of all snps that were miss-called as such are shown in the second and third columns of the first two rows, and the median percentage of san snps that were miss-called as such are shown in the fourth and fifth columns of the first two rows.
the mean proportion of san, bantu and non-african ancestry in the simulated data set was  <dig> ,  <dig>  and  <dig>  respectively.
the difference in number of snps miss-called by rfmix, compared to the corresponding number of snps miss-called by lamp-ld, were significant with p-values <2×10- <dig> for each of the six possible miss-call categories.
histograms of the difference between the mean ancestry called by rfmix per chromosome and the known mean ancestry in the simulated data set are shown in additional file 1: figure s <dig>  ancestry called by rfmix was on average  <dig> % lower than the known san ancestry, whilst bantu ancestry was on average  <dig> % higher.
this discrepancy can be ascribed to the relatively large proportion of san snps that were miss-called as bantu.
non-african ancestry calls compared well to known values .
could chromosomal segments with large deviations from the mean ancestry be the result of lai errors?
to answer this question, the simulated chromosome  <dig> data set was divided into segments by determining the positions where ancestry switches occur , yielding  <dig> segments.
deviation from the overall rfmix mean ancestry was calculated for each of the ancestries, for each of the segments.
table  <dig> summarizes the correlation between the number of miss-called ancestry segments and deviation in ancestry.
segments with lack of san ancestry are associated with san segments that are miss-called as bantu or non-african, whereas segments with excess bantu ancestry are associated with bantu segments miss-called as san.
the relationship between number of errors in segments and deviation in ancestry are further depicted in additional file 1: figures s <dig>  s <dig> and s <dig>  although errors appear to be distributed fairly evenly across the entire chromosome, segments with lack of san ancestry generally have more errors.
the exception is a large number of errors that occurred around the centromere , likely due to the dearth of snps in this region.table  <dig>
correlation between the number of miss-called ancestry segments and deviation in ancestry
this table summarizes the correlation between the number of ancestry miss-calls that occurred at a segment of ancestry, per each of the six possible miss-call categories, and the deviation in local ancestry of the segment.
miss-called ancestry was identified by comparing the known ancestry of a simulated data set of  <dig> sac chromosomes to the ancestry called by rfmix .
deviations in ancestry were calculated by subtracting the overall mean rfmix ancestry from the local ancestry of each segment, for each of the three source ancestries .
the negative effect that short tracts of ancestry and a large degree of admixture could have on lai accuracy was explored next.
additional file 1: figure s <dig> shows the distribution of the length of tracts of ancestry in the simulated data and the proportion of snps with miss-called ancestry per tract.
tracts that were completely miss-called  occurred far more frequently in very short tracts of ancestry, and longer tracts of ancestry correlated with a smaller proportion of miss-called snps .
additional file 1: figure s <dig> shows that there is a positive correlation between the number of tracts of ancestry on a chromosome and the number of miss-called snps for that chromosome , indicating that inferring local ancestry may be more error-prone for chromosomes with a large degree of admixture.
local ancestry across the genome
we proceeded to run lai on our study group of  <dig> unrelated sac individuals using rfmix .
figure  <dig> depicts local ancestry across the genome for cases and controls.
the mean genome-wide san ancestry calculated from the local ancestry estimates was  <dig>  and  <dig>  for cases and controls respectively, the mean bantu ancestry was  <dig>   and  <dig>  , and the mean non-african ancestry was  <dig>   and  <dig>  .
mean san ancestry was on average  <dig> % lower than corresponding admixture estimates and bantu ancestry was on average  <dig> % higher, whilst the mean non-african ancestry was comparable .
the differences in ancestry estimates are illustrated in additional file 1: figure s <dig>  we speculate that the large discrepancy between san and bantu estimates can in part be ascribed to some of the san ancestry in the sac being contributed by southern african bantu populations such as the xhosa  <cit> .
admixture between the san and these populations likely occured during the bantu expansion  <cit> .
the relative older age of these admixture events would result in short tracts of san ancestry, which are harder to distinguish with genotype array data  <cit> , also evident from our simulated data .
the distributions of called san and european tract lengths are comparable, supporting our conclusion that very short tracts of san ancestry may not have been identified, whereas the bantu tract lengths are longer .
xhosa admixture into the nascent sac population likely occurred later than admixture between the san and europeans  <cit> , helping to explain the longer bantu tract lengths.figure  <dig>
mean local ancestry across the genome.
the mean local ancestry estimates of tb cases and controls are shown per genomic position, for each of the source ancestries.
each panel represents a separate chromosome.
rfmix output was divided into  <dig>  <dig> segments of local ancestry and local ancestry deviations were calculated.
histograms of the local ancestry deviations in cases and controls are depicted in additional file 1: figure s <dig> and boxplots of the deviations are shown in additional file 1: figure s <dig>  similar to our findings in simulated data, the figures suggest that the tails of the deviation distributions are biased towards lack of san ancestry and excess bantu and non-african ancestry.
regions with excess san or bantu ancestry in cases relative to controls
previous work has shown that san and bantu ancestry is associated with increased susceptibility to tb in the sac, and that the non-african ancestry components  are protective.
the non-african ancestry components were however not associated with tb susceptibility after adjustment for the other ancestry components, whereas the san and bantu components remained significant.
the positive association between san ancestry and tb susceptibility was highly significant, whereas association with bantu ancestry was relatively weak   <cit> .
we were therefore interested in finding regions of the genome with excess san or bantu ancestry in cases, but not in controls.
from our simulations and analysis of the distribution of local ancestry deviations in our study group, it is evident that excess bantu ancestry may be enriched with miss-called ancestry.
as this is not the case for excess san ancestry, or excess san or bantu ancestry , we used two joint models to test for differences in ancestry between cases and controls per ancestry segment.
one model tested for difference in san ancestry, and the other tested for differences in african  ancestry.
only segments with ancestry two standard deviations above the genome-wide mean in cases were included in the models , and age, gender and genome-wide ancestry were adjusted for, yielding p-values of  <dig> ×10- <dig>  and < <dig> ×10- <dig> .
regions that differ significantly between cases and controls are summarized in tables  <dig> and  <dig>  the regions are comprised of a number of contiguous ancestry segments, and individual p-values for these sub-regions are summarized in additional file 1: tables s <dig> and s <dig>  ranging between  <dig>  and  <dig> .
some of the segments in the african analysis also have large differences in san ancestry .
these segments were however not detected in the san analysis as the mean proportion of san ancestry in tb cases fell just short of the mean plus two standard deviation cut-off for inclusion in the san ancestry model .
on chromosome  <dig>   <dig> segments with the smallest p-values gave estimated tb case-control odds ratios of having african ancestry versus any other ancestry ranging from  <dig>   to  <dig>  .
table  <dig>
regions of the genome with excess san ancestry in tb cases relative to controls
this table summarizes regions of the genome with excess san ancestry, found in tb cases relative to controls, after adjusting for age, gender and genome-wide san ancestry.
ancestry segments that are associated with increased san ancestry in cases compared to controls were identified and contiguous segments were merged.
p-values for each of the individual ancestry segments are available in additional file 1: table s <dig>  the mean rfmix genome-wide san ancestry estimates are  <dig>  and  <dig>  for cases and controls respectively, and the standard deviation of san local ancestry deviations is  <dig>  and  <dig>  in cases and controls respectively.
only regions of  <dig>  <dig> base pairs or longer are shown .
regions of the genome with excess african ancestry in tb cases relative to controls
this table summarizes regions of the genome with excess african ancestry , found in tb cases relative to controls, after adjusting for age, gender and genome-wide african ancestry.
ancestry segments that are associated with increased african ancestry in cases compared to controls were identified and contiguous segments were merged.
p-values for each of the individual ancestry segments are available in additional file 1: table s <dig>  the mean rfmix genome-wide african ancestry estimates are  <dig>  and  <dig>  for cases and controls respectively, and the standard deviation of local ancestry deviations is  <dig>  and  <dig>  in cases and controls respectively.
only regions of  <dig>  <dig> base pairs or longer and that contain protein coding genes are shown .
discussion
previously it was thought that admixture mapping in the sac was not feasible using available lai methods, due to the complex five-way admixture that occurred in this population  <cit> .
using the novel lai algorithm implemented in rfmix, and by focusing on finding regions with excess san or excess san or bantu ancestry in tb cases relative to controls, we have demonstrated that this technique can be applied to our sac tb susceptibility study group.
by limiting our tests for associations to only these directional ancestry components, and by using only two models to test for excess ancestry in tb cases relative to controls, we also reduced our study’s vulnerability to false positive association signals that can arise as a result of multiple testing.
based on their putative role in tb pathogenesis, we identified a number of genes in regions of excess ancestry that are convincing candidates for future studies.
gadd45a  is located on chromosome 1p <dig>  a region with excess san ancestry in tb cases, and encodes a stress sensor protein that is involved in the regulation of myeloid cell innate immune function.
salerno et al.
demonstrated that mice lacking gadd45a are not able to recruit granulocytes and macrophages to the intraperitoneal cavity after administration of lipopolysaccharide   <cit> .
the recruitment of immune cells to the site of mycobacterial infection is crucial during tb.
the oncostatin m gene  is also located in a region with excess san ancestry in tb cases .
friendland et al.
found that m. tuberculosis infection of monocytes resulted in prostaglandin-dependent osm secretion, which synergized with tumour necrosis factor- α to drive fibroblast matrix metalloproteinases-1/- <dig> secretion  <cit> .
our analysis also found that chromosome 15q <dig> may contain an african tb susceptibility locus.
chromosome 15q was previously identified as containing tb susceptibility genes in a linkage study using african families  <cit>  and fine-mapping localised the region to 15q <dig>  <cit> .
chromosome 17q <dig> also showed evidence of having excess african ancestry in tb cases.
it is known that chromosome 17q11-q <dig> may contain a cluster of susceptibility genes for diseases caused by intramacrophage pathogens, such as m. tuberculosis <cit> .
another gene with excess african tb ancestry was b7-h <dig> , located on chromosome 10q <dig>  its protein product plays a key regulatory role in t cell growth and cytokine production  <cit>  and is expressed in macrophages.
two of the  <dig> snps that were found to be moderately associated with having tb in a previous genome-wide association study  are located in or proximal to regions identified in this study  <cit>  .
considering that the gwas used the same sac data set, we note that a number of new putative susceptibility regions were identified by our admixture mapping study, which would not have been evident based solely on single snp association statistics.
study limitations
due to the correlation between segments of ancestry, especially contiguous segments of ancestry, p-values that accurately quantify the statistical significance of our results could not be estimated in this study.
regions of excess ancestry in tb cases, having ancestry more than  <dig> standard deviations from the mean, were first identified.
p-values were not estimated in this step due to the correlation between segments of ancestry.
the identified regions would be the most probable regions to harbour ancestry-related tb susceptibility variants, and a statistical model was then used to determine which of the regions had excess ancestry in tb cases relative to controls.
the estimated p-values from this model do not account for the fact that the variables were selected by first performing a case-only analysis.
the statistical model also contained correlated variables, due to contiguous segments with similar ancestry.
correlation between a model’s independent variables may result in inflated variance, and estimated p-values may therefore be inflated .
we therefore note that the reported p-values should not be interpreted as a strict quantification of the statistical significance of the results, but that the p-values rather indicate which regions are most likely to harbour ancestry related tb susceptibility variants.
rfmix estimates of san ancestry were much lower than admixture estimates.
this may be explained by the small group size of our san source population as well as short tracts of san ancestry inherited via southern african bantu populations, and results in bias towards local ancestry deviations with lack of san ancestry.
we speculate that roughly  <dig> san individuals would be required to alleviate the small group size problem .
we have worked around these limitations by focusing our attention on finding regions of excess san or excess san or bantu ancestry, which are less likely to be affected by spurious deviation in local ancestry, and this approach also supports our hypothesis regarding the directionality of these ancestries and tb susceptibility.
the possibility of short tracts of san ancestry harbouring tb susceptibility genes are also less likely compared to longer tracts, as the former are less likely to overlap in a group of individuals.
miss-identification of these short tracts as bantu ancestry has therefore probably not resulted in loss of information regarding tb susceptibility.
the small size of the san source population also limited the number of ancestral chromosomes that were used in the generation of our simulated data set.
as a result, the bias seen in the simulated local ancestry deviation distributions may have been exacerbated.
due to lack of accurate historical records, our simulation also did not take into account such complexities as the timing of admixture events, and potential inaccuracies of the source  populations.
pasanuic et al.
<cit>  recently evaluated the accuracy of multi-way lai in a group of nuclear latino families by determining whether the local ancestry of offspring is congruous with mendelian inheritance .
multi-way lai accuracy of several algorithms was shown to be much lower compared to reported accuracies calculated from simplified simulation data sets.
despite these issues, we have been able to use our simulated data set to demonstrate the relatively superior lai accuracy of rfmix, and to explore the direction of potential local ancestry deviation bias.
our sac study group samples were genotyped with the aim of performing a case-only admixture mapping study, and as a result only a small number of controls were genotyped.
as case-only admixture mapping has subsequently  been described as inappropriate for multi-way admixed populations due to artefactual ancestry deviations arising from inaccuracies in lai <cit> , we first identified regions of excess ancestry found in cases only, and then validated these findings by testing for excess ancestry in cases relative to controls.
despite the small number of controls that were available, bias in local ancestry inferences was still controlled, and a number of novel regions that contain highly plausible candidate tb susceptibility genes were uncovered by our study.
CONCLUSIONS
the genetics of the south african coloured population is arguably one of the most challenging and interesting examples found in present day multi-way admixed human populations.
this is the first study to apply genome-wide admixture mapping to this highly complex group.
we have demonstrated that admixture mapping is feasible in the south african coloured population, a result which may be useful for other researchers that either study this population, or other populations with complex admixture.
we have identified a number of novel candidate tb susceptibility genomic regions, as well as providing evidence to validate genetic loci previously implicated.

 the south african coloured population is a so-called admixed population that derived its origins from the diverse population groups that settled in the early cape colony, including the indigenous san, early european settlers, slaves that were imported from indonesia, india and other parts of africa, and south african bantu-speakers who later migrated to the area. previous genetic research has shown that the sac received ancestry contributions from click-speaking africans, bantu-speaking africans, europeans and south and east asians, which is consistent with the historical records. a high degree of heterogeneity in ancestral contributions between sac individuals has also been illustrated previously. the admixture that occurred in the sac is therefore complex, constituting a number of different source ancestries, with dissimilar genetic distances between them. our study group of sac individuals was recruited from metropolitan areas in cape town that have some of the highest reported incidences of tuberculosis worldwide, despite extensive bcg vaccination and low prevalence of hiv. as the group received contributions from diverse source populations that may differ in their genetic susceptibility to tb, the group is ideally suited to the discovery of tb susceptibility genetic variants and their probable ethnic origins. our previous work has shown that african ancestry in this group is associated with higher risk of tb infection, whereas european and asian ancestries are protective. areas of the genome with african ancestry that is much higher than the norm in a group of tb cases may therefore harbour genetic variants that increase the risk of developing tb. the process of finding such areas is known as admixture mapping, and this technique relies on the accurate inference of what is known as local ancestry per individual across their genome. when admixture occurs between two or more population groups that were previously isolated, recombination events result in chromosomes that are a mosaic of blocks of ancestry deriving from different source populations. given genetic data of an admixed individual and their source populations, statistical techniques can be used to determine the bounds of these segments and to assign the most probable source ancestries to them. these techniques rely on the probability of recombination events to distinguish the bounds of segments, and differences in allele and haplotype frequencies between source populations for classification of the ancestry of segments. the process is known as local ancestry inference. in a previous study, chimusa et al. concluded that accurate multi-way lai was not feasible in the sac using the lai algorithms available at the time. in this study, we re-evaluate this position using the novel lai algorithm implemented in the rfmix software package, focusing on the classification of san and bantu ancestry. these ancestries are of particular interest as southern african populations were not exposed to modern strains of mycobacterium tuberculosis, the most prevalent in our sac study group, until the recent past. the relative lack of exposure of the sac and bantu populations to modern strains of m. tuberculosis could possibly have resulted in decreased resistance to developing the disease, especially in densely populated areas with low socio-economic conditions. supporting this argument, a significant positive association between san ancestry and tb susceptibility in the sac was found by chimusa et al.. the association was confirmed in an independent sample in a later study, which also found a positive association with bantu ancestry, although it was relatively weak. although each of the non-african ancestry components of the sac were negatively associated with tb susceptibility when tested in individual models, these associations were no longer significant when all five ancestry components were tested together. in this study, we first explore the accuracy of lai in the sac using rfmix and compare its performance to other algorithms. after quantifying this using simulated data, we proceed to identify regions with excess san or bantu ancestry found in tb cases but not in controls, and hypothesize that these regions may contain genes that affect tb susceptibility.  evaluating multi-way lai accuracy using simulated data chimusa et al. previously evaluated the accuracy of lai in the sac using various software programs and found that lamp-ld performed best. as rfmix was not available at that time, our first step was to compare the accuracies of lamp-ld and rfmix using a simulated data set of sac chromosomes. lai was run five-way, but since only san and bantu genome-wide ancestry is independently associated with tb susceptibility, ancestry of snps that were called as european, south asian or east asian were labelled as non-african. the percentage of snps for which the called ancestry matched the known ancestry was % and % for lamp-ld and rfmix respectively. the percentage of snps per type of miss-called ancestry is summarized in table which shows that rfmix offers a significant improvement in the calling of local ancestry, especially when distinguishing san and bantu ancestry.table percentage of miss-called ancestry this table reports the interquartile range of the percentage of snps that were miss-called by lamp-ld and rfmix per each of the six possible miss-call categories. the known ancestry of a simulated data set of sac chromosomes was compared to the ancestry called by the software program. the median percentage of miss-called snps across all snps as well as the median percentage of miss-called snps across snps of that source ancestry are shown. san ancestry can for example be miss-called as either bantu or non-african ancestry. the median percentage of all snps that were miss-called as such are shown in the second and third columns of the first two rows, and the median percentage of san snps that were miss-called as such are shown in the fourth and fifth columns of the first two rows. the mean proportion of san, bantu and non-african ancestry in the simulated data set was, and respectively. the difference in number of snps miss-called by rfmix, compared to the corresponding number of snps miss-called by lamp-ld, were significant with p-values <2×10- for each of the six possible miss-call categories. histograms of the difference between the mean ancestry called by rfmix per chromosome and the known mean ancestry in the simulated data set are shown in additional file 1: figure s ancestry called by rfmix was on average % lower than the known san ancestry, whilst bantu ancestry was on average % higher. this discrepancy can be ascribed to the relatively large proportion of san snps that were miss-called as bantu. non-african ancestry calls compared well to known values. could chromosomal segments with large deviations from the mean ancestry be the result of lai errors? to answer this question, the simulated chromosome data set was divided into segments by determining the positions where ancestry switches occur, yielding segments. deviation from the overall rfmix mean ancestry was calculated for each of the ancestries, for each of the segments. table summarizes the correlation between the number of miss-called ancestry segments and deviation in ancestry. segments with lack of san ancestry are associated with san segments that are miss-called as bantu or non-african, whereas segments with excess bantu ancestry are associated with bantu segments miss-called as san. the relationship between number of errors in segments and deviation in ancestry are further depicted in additional file 1: figures s s and s although errors appear to be distributed fairly evenly across the entire chromosome, segments with lack of san ancestry generally have more errors. the exception is a large number of errors that occurred around the centromere, likely due to the dearth of snps in this region.table correlation between the number of miss-called ancestry segments and deviation in ancestry this table summarizes the correlation between the number of ancestry miss-calls that occurred at a segment of ancestry, per each of the six possible miss-call categories, and the deviation in local ancestry of the segment. miss-called ancestry was identified by comparing the known ancestry of a simulated data set of sac chromosomes to the ancestry called by rfmix. deviations in ancestry were calculated by subtracting the overall mean rfmix ancestry from the local ancestry of each segment, for each of the three source ancestries. the negative effect that short tracts of ancestry and a large degree of admixture could have on lai accuracy was explored next. additional file 1: figure s shows the distribution of the length of tracts of ancestry in the simulated data and the proportion of snps with miss-called ancestry per tract. tracts that were completely miss-called occurred far more frequently in very short tracts of ancestry, and longer tracts of ancestry correlated with a smaller proportion of miss-called snps. additional file 1: figure s shows that there is a positive correlation between the number of tracts of ancestry on a chromosome and the number of miss-called snps for that chromosome, indicating that inferring local ancestry may be more error-prone for chromosomes with a large degree of admixture. local ancestry across the genome we proceeded to run lai on our study group of unrelated sac individuals using rfmix. figure depicts local ancestry across the genome for cases and controls. the mean genome-wide san ancestry calculated from the local ancestry estimates was and for cases and controls respectively, the mean bantu ancestry was and, and the mean non-african ancestry was and. mean san ancestry was on average % lower than corresponding admixture estimates and bantu ancestry was on average % higher, whilst the mean non-african ancestry was comparable. the differences in ancestry estimates are illustrated in additional file 1: figure s we speculate that the large discrepancy between san and bantu estimates can in part be ascribed to some of the san ancestry in the sac being contributed by southern african bantu populations such as the xhosa. admixture between the san and these populations likely occured during the bantu expansion. the relative older age of these admixture events would result in short tracts of san ancestry, which are harder to distinguish with genotype array data, also evident from our simulated data. the distributions of called san and european tract lengths are comparable, supporting our conclusion that very short tracts of san ancestry may not have been identified, whereas the bantu tract lengths are longer. xhosa admixture into the nascent sac population likely occurred later than admixture between the san and europeans, helping to explain the longer bantu tract lengths.figure mean local ancestry across the genome. the mean local ancestry estimates of tb cases and controls are shown per genomic position, for each of the source ancestries. each panel represents a separate chromosome. rfmix output was divided into segments of local ancestry and local ancestry deviations were calculated. histograms of the local ancestry deviations in cases and controls are depicted in additional file 1: figure s and boxplots of the deviations are shown in additional file 1: figure s similar to our findings in simulated data, the figures suggest that the tails of the deviation distributions are biased towards lack of san ancestry and excess bantu and non-african ancestry. regions with excess san or bantu ancestry in cases relative to controls previous work has shown that san and bantu ancestry is associated with increased susceptibility to tb in the sac, and that the non-african ancestry components are protective. the non-african ancestry components were however not associated with tb susceptibility after adjustment for the other ancestry components, whereas the san and bantu components remained significant. the positive association between san ancestry and tb susceptibility was highly significant, whereas association with bantu ancestry was relatively weak. we were therefore interested in finding regions of the genome with excess san or bantu ancestry in cases, but not in controls. from our simulations and analysis of the distribution of local ancestry deviations in our study group, it is evident that excess bantu ancestry may be enriched with miss-called ancestry. as this is not the case for excess san ancestry, or excess san or bantu ancestry, we used two joint models to test for differences in ancestry between cases and controls per ancestry segment. one model tested for difference in san ancestry, and the other tested for differences in african ancestry. only segments with ancestry two standard deviations above the genome-wide mean in cases were included in the models, and age, gender and genome-wide ancestry were adjusted for, yielding p-values of ×10- and < ×10-. regions that differ significantly between cases and controls are summarized in tables and the regions are comprised of a number of contiguous ancestry segments, and individual p-values for these sub-regions are summarized in additional file 1: tables s and s ranging between and. some of the segments in the african analysis also have large differences in san ancestry. these segments were however not detected in the san analysis as the mean proportion of san ancestry in tb cases fell just short of the mean plus two standard deviation cut-off for inclusion in the san ancestry model. on chromosome segments with the smallest p-values gave estimated tb case-control odds ratios of having african ancestry versus any other ancestry ranging from to. table regions of the genome with excess san ancestry in tb cases relative to controls this table summarizes regions of the genome with excess san ancestry, found in tb cases relative to controls, after adjusting for age, gender and genome-wide san ancestry. ancestry segments that are associated with increased san ancestry in cases compared to controls were identified and contiguous segments were merged. p-values for each of the individual ancestry segments are available in additional file 1: table s the mean rfmix genome-wide san ancestry estimates are and for cases and controls respectively, and the standard deviation of san local ancestry deviations is and in cases and controls respectively. only regions of base pairs or longer are shown. regions of the genome with excess african ancestry in tb cases relative to controls this table summarizes regions of the genome with excess african ancestry, found in tb cases relative to controls, after adjusting for age, gender and genome-wide african ancestry. ancestry segments that are associated with increased african ancestry in cases compared to controls were identified and contiguous segments were merged. p-values for each of the individual ancestry segments are available in additional file 1: table s the mean rfmix genome-wide african ancestry estimates are and for cases and controls respectively, and the standard deviation of local ancestry deviations is and in cases and controls respectively. only regions of base pairs or longer and that contain protein coding genes are shown. discussion previously it was thought that admixture mapping in the sac was not feasible using available lai methods, due to the complex five-way admixture that occurred in this population. using the novel lai algorithm implemented in rfmix, and by focusing on finding regions with excess san or excess san or bantu ancestry in tb cases relative to controls, we have demonstrated that this technique can be applied to our sac tb susceptibility study group. by limiting our tests for associations to only these directional ancestry components, and by using only two models to test for excess ancestry in tb cases relative to controls, we also reduced our study’s vulnerability to false positive association signals that can arise as a result of multiple testing. based on their putative role in tb pathogenesis, we identified a number of genes in regions of excess ancestry that are convincing candidates for future studies. gadd45a is located on chromosome 1p a region with excess san ancestry in tb cases, and encodes a stress sensor protein that is involved in the regulation of myeloid cell innate immune function. salerno et al. demonstrated that mice lacking gadd45a are not able to recruit granulocytes and macrophages to the intraperitoneal cavity after administration of lipopolysaccharide. the recruitment of immune cells to the site of mycobacterial infection is crucial during tb. the oncostatin m gene is also located in a region with excess san ancestry in tb cases. friendland et al. found that m. tuberculosis infection of monocytes resulted in prostaglandin-dependent osm secretion, which synergized with tumour necrosis factor- α to drive fibroblast matrix metalloproteinases-1/- secretion. our analysis also found that chromosome 15q may contain an african tb susceptibility locus. chromosome 15q was previously identified as containing tb susceptibility genes in a linkage study using african families and fine-mapping localised the region to 15q. chromosome 17q also showed evidence of having excess african ancestry in tb cases. it is known that chromosome 17q11-q may contain a cluster of susceptibility genes for diseases caused by intramacrophage pathogens, such as m. tuberculosis. another gene with excess african tb ancestry was b7-h, located on chromosome 10q its protein product plays a key regulatory role in t cell growth and cytokine production and is expressed in macrophages. two of the snps that were found to be moderately associated with having tb in a previous genome-wide association study are located in or proximal to regions identified in this study. considering that the gwas used the same sac data set, we note that a number of new putative susceptibility regions were identified by our admixture mapping study, which would not have been evident based solely on single snp association statistics. study limitations due to the correlation between segments of ancestry, especially contiguous segments of ancestry, p-values that accurately quantify the statistical significance of our results could not be estimated in this study. regions of excess ancestry in tb cases, having ancestry more than standard deviations from the mean, were first identified. p-values were not estimated in this step due to the correlation between segments of ancestry. the identified regions would be the most probable regions to harbour ancestry-related tb susceptibility variants, and a statistical model was then used to determine which of the regions had excess ancestry in tb cases relative to controls. the estimated p-values from this model do not account for the fact that the variables were selected by first performing a case-only analysis. the statistical model also contained correlated variables, due to contiguous segments with similar ancestry. correlation between a model’s independent variables may result in inflated variance, and estimated p-values may therefore be inflated. we therefore note that the reported p-values should not be interpreted as a strict quantification of the statistical significance of the results, but that the p-values rather indicate which regions are most likely to harbour ancestry related tb susceptibility variants. rfmix estimates of san ancestry were much lower than admixture estimates. this may be explained by the small group size of our san source population as well as short tracts of san ancestry inherited via southern african bantu populations, and results in bias towards local ancestry deviations with lack of san ancestry. we speculate that roughly san individuals would be required to alleviate the small group size problem. we have worked around these limitations by focusing our attention on finding regions of excess san or excess san or bantu ancestry, which are less likely to be affected by spurious deviation in local ancestry, and this approach also supports our hypothesis regarding the directionality of these ancestries and tb susceptibility. the possibility of short tracts of san ancestry harbouring tb susceptibility genes are also less likely compared to longer tracts, as the former are less likely to overlap in a group of individuals. miss-identification of these short tracts as bantu ancestry has therefore probably not resulted in loss of information regarding tb susceptibility. the small size of the san source population also limited the number of ancestral chromosomes that were used in the generation of our simulated data set. as a result, the bias seen in the simulated local ancestry deviation distributions may have been exacerbated. due to lack of accurate historical records, our simulation also did not take into account such complexities as the timing of admixture events, and potential inaccuracies of the source populations. pasanuic et al. recently evaluated the accuracy of multi-way lai in a group of nuclear latino families by determining whether the local ancestry of offspring is congruous with mendelian inheritance. multi-way lai accuracy of several algorithms was shown to be much lower compared to reported accuracies calculated from simplified simulation data sets. despite these issues, we have been able to use our simulated data set to demonstrate the relatively superior lai accuracy of rfmix, and to explore the direction of potential local ancestry deviation bias. our sac study group samples were genotyped with the aim of performing a case-only admixture mapping study, and as a result only a small number of controls were genotyped. as case-only admixture mapping has subsequently been described as inappropriate for multi-way admixed populations due to artefactual ancestry deviations arising from inaccuracies in lai, we first identified regions of excess ancestry found in cases only, and then validated these findings by testing for excess ancestry in cases relative to controls. despite the small number of controls that were available, bias in local ancestry inferences was still controlled, and a number of novel regions that contain highly plausible candidate tb susceptibility genes were uncovered by our study.  the genetics of the south african coloured population is arguably one of the most challenging and interesting examples found in present day multi-way admixed human populations. this is the first study to apply genome-wide admixture mapping to this highly complex group. we have demonstrated that admixture mapping is feasible in the south african coloured population, a result which may be useful for other researchers that either study this population, or other populations with complex admixture. we have identified a number of novel candidate tb susceptibility genomic regions, as well as providing evidence to validate genetic loci previously implicated. 
using simulated data, we demonstrate reasonable accuracy of local ancestry inference by rfmix, with a tendency towards miss-calling san ancestry as bantu. regions with either excess san ancestry or excess african ancestry are less likely to be affected by this bias, and we therefore proceeded to identify such regions, found in cases but not in controls. the admixed south african coloured population is ideally suited to the discovery of tuberculosis susceptibility genetic variants and their probable ethnic origins, but previous attempts at finding such variants using genome-wide admixture mapping were hampered by the inaccuracy of local ancestry inference. local ancestry inference15q1517q22gadd45aosmb7-h5issue-copyright-statement© the author 2014 in this study, we infer local ancestry using the novel algorithm implemented in rfmix, with the emphasis on identifying regions of excess san or bantu ancestry, which we hypothesize may harbour tb susceptibility genes. promising immune-related susceptibility genes such as the gadd45a, osm and b7-h genes are also harboured in the identified regions.
 the admixed south african coloured population is ideally suited to the discovery of tuberculosis susceptibility genetic variants and their probable ethnic origins, but previous attempts at finding such variants using genome-wide admixture mapping were hampered by the inaccuracy of local ancestry inference. in this study, we infer local ancestry using the novel algorithm implemented in rfmix, with the emphasis on identifying regions of excess san or bantu ancestry, which we hypothesize may harbour tb susceptibility genes.  using simulated data, we demonstrate reasonable accuracy of local ancestry inference by rfmix, with a tendency towards miss-calling san ancestry as bantu. regions with either excess san ancestry or excess african ancestry are less likely to be affected by this bias, and we therefore proceeded to identify such regions, found in cases but not in controls. a number of promising regions were found, including chromosomes 15q and 17q which are close to genomic regions previously implicated in tb. promising immune-related susceptibility genes such as the gadd45a, osm and b7-h genes are also harboured in the identified regions.  admixture mapping is feasible in the south african coloured population and a number of novel tb susceptibility genomic regions were uncovered. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords local ancestry inference15q1517q22gadd45aosmb7-h5issue-copyright-statement© the author 2014 