STF's, STR's, and haplogroup frequencies. Geographic differentiation in affinities of Middle Eastern populations with Africa and Europe showed distinct contrasts between mtDNA and Y-chromosome data. Specifically, Lebanon's mtDNA shows a very strong association to Europe, while Yemen shows very strong affinity with Egypt and North and East Africa. Previous Y-chromosome results showed a Levantine coastal-inland contrast marked by J1 and J2, and a very strong North African component was evident throughout the Middle East. Neither of these patterns were observed in the mtDNA. While J2 has penetrated into Europe, the pattern of Y-chromosome diversity in Lebanon does not show the widespread affinities with Europe indicated by the mtDNA data. Lastly, while each population shows evidence of connections with expansions that now define the Middle East, Africa, and Europe, many of the populations in the Middle East show distinctive mtDNA and Y-haplogroup characteristics that indicate long standing settlement with relatively little impact from and movement into other populations.The Middle East was a funnel of human expansion out of Africa, a staging area for the Neolithic Agricultural Revolution, and the home to some of the earliest world empires. Post LGM expansions into the region and subsequent population movements created a striking genetic mosaic with distinct sex-based genetic differentiation. While prior studies have examined the mtDNA and Y-chromosome contrast in focal populations in the Middle East, none have undertaken a broad-spectrum survey including North and sub-Saharan Africa, Europe, and Middle Eastern populations. In this study 5,174 mtDNA and 4,658 Y-chromosome samples were investigated using PCA, MDS, mean-linkage clustering, AMOVA, and Fisher exact tests of  As a crossroad between Africa, Arabia, Asia, and Europe, the Levant has been a primary historical stepping stone in the first modern human expansions out of Africa and for later migrations into and out of Europe, Asia, and Africa While the out-of-Africa migrations have been major determining factors, other migratory events have strongly influenced genetic marker distributions throughout the Levant and the surrounding geographical areas. During the last glacial maximum , most of the Levant was an uninhabitable desert, with forested hills in Levantine Mediterranean coastal areas Most genetic studies of the Levant as a geographical area have focused exclusively on either Y-chromosome The samples were collected from donors after they had given their written informed consent to the project and to the data analysis, which was approved by the IRB of the Lebanese American University.3,663 mtDNA records collected from the literature represented populations from Burkina-Faso Given the broad cultural and genetic diversity in the region, terms such as “Middle East” may be problematical. Historically, the term evolved during the era of European Imperialism, and included all lands between Arabia and India, but came to include Turkey through Saudi Arabia, extending east through Afghanistan and Pakistan. In this report, “Middle Easterners” refers to Iraqis, Jordanians, Lebanese, Palestinians, Saudis, Syrians, and Yemenis. The Greek data represent the Southeastern Europe region for mtDNA analyses, and is labelled “Southeastern Europe” in the rest of this report. France represents Western Europe mtDNA, and is labelled “Western Europe” throughout the rest of this report. These are reported in www.hprg.com/hapest5/hapest5a/hapest5.htm. We have also computed STR-predicted Y haplogroups across populations that had been SNP defined to ascertain STR-based haplogroup assignment accuracy, and identify geographically correlated trends in assignment error rates that may impact our conclusions.1,774 previously published Y-chromosome records were obtained from the literature representing populations from the Balkans Total DNA was extracted from the peripheral leukocyte fraction of whole blood drawn in EDTA anticoagulant or cheek swab samples using a standard phenol/chloroform extraction procedure. The hypervariable region I (HVS-I) was amplified using primers designed by Maca-Meyer et al. Mutations in the HVS-I region were defined by aligning and comparing the sequences to the revised Cambridge Reference Sequence (rCRS) using the SeqScape software.mtDNA haplogroups were predicted using the Genographic Project's online haplogroup prediction tool: nnhgtool.nationalgeographic.com.Haplogroup affiliations were confirmed using the Taqman approach with customized primers and probe sets to identify the SNPs listed in MtDNA Haplogroups reported in the literature were updated and reconciled to the 2009 phylogeny reported by van Oven et al. Further, HVS-I regions reported by different sources varied. The range representing the largest common subset of HVS-I SNPs reported included 16090 through 16365. Identified SNPs are reported in Fisher exact tests were performed for haplogroup frequencies within populations. These tests were performed against a background of all populations , as wellNumbers of samples bearing mtDNA and Y-Chromosome reduced haplogroups within each population, and relative haplogroup frequencies within populations, were computed using R ST's ST distances in the same way that they were applied to the Euclidean distances as described in the PCA section. An identical MDS and clustering were applied to Slatkin's RST distances HVS-I SNPs were constructed against CRS ST and Y-chromosome RST distances were constructed using a heatmap based on the normalized ratio of the Y-chromosome RST distance with respect to the total distance (RST/(RST+FST)) for mtDNA HVS-1 FST and Y-chromosome RST distances  and Europe with clear distinctions between the Levant and the Arabian Peninsula with regards to Africa , Table 1Fisher exact tests were applied to determine when haplogroup frequency differences among populations over both Pan-Mediterranean tests , and regIn Lebanese, haplogroups H, HV, T, and K are over-represented, while Syrians are overrepresented in haplogroups T and K. Western Europeans show overrepresentation of haplogroups H, K.By contrast, haplogroups J, R0, and M are significantly overrepresented in Saudis, and underrepresented in Western Europeans. Haplogroup J was also significantly overrepresented in Iraqis, among Palestinians, and Yemenis.f≥8.29·10−4 would guarantee that at least one or more would be observed at least 95% of the time out of the 3614 samples collected across the data on which we are reporting. Assuming independent sampling following a binomial test, there is 5% or less chance of seeing zero L6 by chance with a relative frequency of f or higher, establishing 8.29·10−4 as an upper bound to the relative frequency of L6 with a 95% confidence.The African haplogroups L* and L3* are very rare (frequencies less than 1%) and underrepresented in Europe, noting that rarity reduces the power of these tests. In the Levant, Lebanese have the lowest frequency for these haplogroups with generally highly significant underrepresentation, The L haplogroups show rather broad penetration into Yemen, with most being significantly overrepresented, with Yemenis being the only population with an overrepresentation of L6. We have not found haplogroup L6 in our Lebanese (N = 980), Syrian (N = 234), and Jordanian samples (N = 290). Further, they are absent from Abu-Amero et al's samples from Saudi Arabia, as well as other published results included in his study. A lower bound on the relative frequency of L6 in non-Yemeni Middle East of We note that subtypes HV0, HV1 and HV2 are generally too weakly represented among our populations to yield tests with adequate power. HV0 and HV1 show sufficient power when pooled into regions. We note that HV0 appears primarily in Europe , HV1 is primarily non-European . HV2 were very rare, with no significant p-values.Some U subhaplogroups show regional localization, but none of them rose to sufficient frequency to make any significant contribution to the PCA. U3 appears most frequently in Jordan (Fisher's test: p = 2.39e-8), with representation throughout the Middle East. U4 (p = 1.49e-7) and U5 (p = 2.2e-16) appear to be more heavily European.The two leading principal components displayed in Mitochondrial DNA Haplogroups showing significant contributions to the principal components include H, L3, L2, L0, V, L1, M, J, U, T, K, HV, and R0. The principal vectors for HV, T, K, J, and U point almost directly at the Levantine cluster . H marksThe dendrogram based on mtDNA haplogroup frequencies  reveals UPGMA and PCA showed Yemenis and Saudis (two of the STR predicted Hg populations) closely associated, forming a clear outlier to clusters identifying more northerly Middle Eastern populations and Europe. Slovaks (the third predicted population) also formed a distinct outlier to all of these. Africans were partitioned into northern African populations and Sahel populations, and distinct from the other populations. Burkinabe formed a very distinct outlier to every other population.STF  Southeastern Europeans, Lebanese, Slovaks, and Western Europeans vs. (2) Cypriots, Jordanians, Palestinians, Saudis, and Syrians, vs. (3) Egyptians and Yemenis. The groups representing Y affinities were: (1) Southeast Europeans, Slovaks, and Western Europeans vs. (2) Cypriots, Lebanese, Syrians, and Yemenis, vs. (3) Egyptians, Jordanians, Palestinians, and Saudis. These two affinity groupings were applied to both the Y and the mtDNA data, yielding results presented in Y Chromosome haplogroup frequency analyses are limited by a relatively high misclassification rate, with more than half of the populations showing more than 10% misclassification, and Ethiopia showing nearly 50%. Since PCA is a non-linear computation which folds in all populations, the apparent locations of any two populations may shift relative to each other when a third population is added or distorted.Here we present mitochondrial characteristics of a large group of newly typed samples from five populations  and compare their geographical affinity, distribution, and frequency with those of Y-chromosome markers from populations across the broader region of Africa, Europe and the Arabian Peninsula.The Y-chromosome results of the current study are in agreement with previous studies, suggesting a Middle Eastern gene pool with greater affinity to Africa. Maternal lineages of the Levantine populations studied here, however, reveal stronger European genetic affinities, while not showing Arabian peninsular influences.Our results show a contrast of mtDNA affinities with previous Y-DNA results STF distances via MDS and mean-linkage cluster analysis. The mtDNA results are distinct from the Y-STR ST-Rbased mean-linkage cluster analysis that showed closer affinity of the Levant populations with Cypriots, North Africans, and Yemenis, than to Europeans.European mtDNA affinity with the Levant was established in haplogroup frequency data through Fisher exact tests, PCA, and mean-linkage clustering based on Euclidean distances, and in HVS-I derived This cluster analysis suggests that the position of Lebanese relative to European Y-chromosome genetics represented in STR haplotype data is also much more ambiguous than suggested entirely by frequency analysis, revealing otherwise cryptic relationships between Lebanese's Y-STR structure and that of Europeans. Cluster analysis of Y-chromosome frequency based data shows similar partitioning of Europe, Africa, and Middle East, with the Levant much more strongly associated with the Middle East than Europe. As with mtDNA, African Y-chromosome haplogroup data also shows a clear partition between Northern populations and Sahel populations. Due to uncertainties in haplogroup inference from STRs, affinities of Yemenis with Ethiopians vs. Egyptians are uncertain, as are the relationships of Saudi Arabian haplogroups both similar to Yemenis or differentiated from Yemenis in affinity with African populations.Beyond the associations noted above, Lebanese show affinity with Europeans for mtDNA haplogroups H, HV, T, K, J, and U, all of which have been identified as markers of agricultural expansions from the Fertile Crescent into Europe Colonization of West Eurasia by modern humans is believed to have been a consequence of the Out-of-Africa dispersal and to have occurred via the Levant th millennium B.C.E., empire expansions and trade, including the slave trade, heavily influenced genetic migration between Yemen and East Africa. Alternatively, known trade networks linking Egypt with Yemen included those for obsidian, and later through Aksum, spices, incense and other precious materials, as well as slaves STF distances also suggest that Yemen appears more similar to Egypt than Ethiopia.From the 7Two haplogroups in this region show significant evidence of relative isolation. First, mtDNA patterns for haplogroup J reflect relatively moderate genetic outflow from Saudi Arabia, and haplogroup L6 is strongly localized within Yemen. Haplogroup J is evenly distributed throughout the Middle East, except in Saudi Arabia where it is significantly overrepresented.It is likely the pattern of Hg J's significant penetration, and the shared underrepresentation of Hg H, tips the balance for Yemenis' mtDNA affinity with Egyptians. Given the significant underrepresentation of Hg J in East Africa, while not being significantly uncommon in Egypt, it is therefore plausible that Arabian female gene flow followed well established trade routes on the Red Sea with Egypt and North Africa while avoiding assimilation of Yemeni L6's on the way.The most striking feature of the heat map  is the rThe expansion of trade through the Red Sea and into the Indian Ocean basin starting in Classical times has provided the largest opportunities for genetic transfers from Africa into Yemen, being dominated by the Red Sea superpower: Egypt. The distribution of mtDNA haplogroup L6 provides a measure of the limited impact of genetic outflow from Yemen, and this flow seems to have been primarily unidirectional. This establishes the upper limits for Yemeni female-mediated gene flow during the Muslim Expansions, as well as identifying possible routes for the expansions.STF based MDS analysis of HVS-I data, mtDNA shows a much stronger affinity between Levantine populations and Europeans compared with the rest of the Middle Eastern populations, or with North Africans. While Lebanese and Yemeni mtDNA epitomize very distinct affinities to different populations and regions well outside of the Middle East, Saudi Arabia seems to display strong local over-representation haplogroup J, while Yemen is even more localized in its L6. Further, these large-scale differences in affinity between mtDNA genetics appear in sharp contrast to regional affinities seen in their Y-chromosomal counterparts. While the mtDNA signal is sharp and clear in its affinities, the Y-chromosome results show somewhat more ambiguous associations in STR based analyses, with Lebanese showing less within-group variation when organized consistently with mtDNA and demonstrating associations closer to Europeans than Africans. This would suggest that while male migrants accompanied female migrants, especially to Europe, females did not always accompany male migrants, especially into North Africa. This leaves a more ambiguous signal for male compared to female migrations.Whether considering haplogroup composition revealed in Fisher tests, PCA, or The historical and archaeological record reveals how trade and labour, colonization and settlement events, and military expansions all contributed to the immigration and displacement of individuals throughout these regions. As a distinct crossroad between geographic regions and civilizations, the Levant and the Near East harbour unique genetic affinities which are revealed most clearly through the comparison of Y-chromosome and mtDNA data.Due to uncertainties in haplogroup inference from STRs, specific questions regarding affinities of Yemen with Ethiopia vs. Egypt are inaccessible, as are questions regarding the relationship of Saudi Arabian haplogroups both similar to Yemenis or differentiated from Yemenis in affinity with African populations.Figure S1Geographic distribution of Y haplogroups. Frequencies from published data as reported in (TIF)Click here for additional data file.Figure S2Populations comparison based on Y haplogroups a) Principal Component Analysis of relative frequencies of Y haplogroups within populations, b) with mean-linkage (UPGMA) dendrogram determined from Euclidean distances.(TIF)Click here for additional data file.Table S1mtDNA haplotypes analyzed in this study.(XLS)Click here for additional data file.Table S2Y chromosome STR haplotypes and haplogroups employed in this study.(XLS)Click here for additional data file.Table S3Y chromosome Haplogroup frequencies of populations used in this study.(XLS)Click here for additional data file.Table S4List of the mtDNA Haplogroup marker SNPs typed for this study.(XLS)Click here for additional data file.Table S5List of HVS-I mtDNA SNPs identified across populations used in this study.(XLS)Click here for additional data file.Table S6Fisher exact tests for haplogroup frequencies vs. population across all study populations.(XLS)Click here for additional data file.Table S7Fisher exact tests for haplogroup frequencies vs. population within the Middle East.(XLS)Click here for additional data file.Table S8mtDNA STF distances between populations.(XLS)Click here for additional data file.Table S9Y STR STR distances between populations.(XLS)Click here for additional data file.


The geographical position of Maharashtra state makes it rather essential to study the dispersal of modern humans in South Asia. Several hypotheses have been proposed to explain the cultural, linguistic and geographical affinity of the populations living in Maharashtra state with other South Asian populations. The genetic origin of populations living in this state is poorly understood and hitherto been described at low molecular resolution level.To address this issue, we have analyzed the mitochondrial DNA (mtDNA) of 185 individuals and NRY (non-recombining region of Y chromosome) of 98 individuals belonging to two major tribal populations of Maharashtra, and compared their molecular variations with that of 54 South Asian contemporary populations of adjacent states. Inter and intra population comparisons reveal that the maternal gene pool of Maharashtra state populations is composed of mainly South Asian haplogroups with traces of east and west Eurasian haplogroups, while the paternal haplogroups comprise the South Asian as well as signature of near eastern specific haplogroup J2a.Our analysis suggests that Indian populations, including Maharashtra state, are largely derived from Paleolithic ancient settlers; however, a more recent (∼10 Ky older) detectable paternal gene flow from west Asia is well reflected in the present study. These findings reveal movement of populations to Maharashtra through the western coast rather than mainland where Western Ghats-Vindhya Mountains and Narmada-Tapti rivers might have acted as a natural barrier. Comparing the Maharastrian populations with other South Asian populations reveals that they have a closer affinity with the South Indian than with the Central Indian populations. Genetic diversity in India can be understood as a result of long term large effective population size, a number of dispersal events and its unique social structure. Maharashtra is the western most state of India inhabited by several caste and tribal populations. It is politically bordered by Gujarat, Madhya Pradesh, Chhattisgarh, Karnataka and Andhra Pradesh in the northwest, northeast, east, south and southeast, respectively. The Arabian Sea makes up Maharashtra's west coast. There are three mountain ranges in this state viz. Sahyadri in west, Satpuda in north and Gondwan in the east. More importantly, the Western Ghats mountain ranges of India is considered as the most densely populated global biodiversity hotspot which has a mosaic of natural, semi-natural and agroecosystems in close proximity to one another The Indian subcontinent is legendary for the cultural, linguistic and genetic diversity of its inhabitants. The contemporary populations of India offer a platform to study the wide range of disciplines In India, the Maharashtra state ranks second in population and third in area. It constitutes 9.33 percent of India's population (Census 2001). The tribal populations accounts to about 9.3 percent of the total population of the state (Census 2001). There are 47 scheduled tribal population groups in the state and the majority of them are inhabitants of these geographically difficult topography e.g., inadequate surveys or thick vegetation not allowing proper surveys, lithics being transported after use instead of being discarded) Mitochondrial DNA (mtDNA) evidence has been argued to support the model according to which populations of South Asians can trace their origin back to the Out-of-Africa (OoA) dispersal along the southern coastal route approximately 60 KYA vs locally differentiated genes appear to be dependent on the particular locus and populations being examined. The geographical location of Maharashtra state makes it quite interesting to study the dispersal of modern humans in South Asia. Besides harboring such an important geographical position in Indian subcontinent, the origin and migration of several populations living in various regions in this state of India and their affinity with other contemporary South Asian population has not been explored, so far, at the high resolution level. The available genetic source of this region is poorly known due to less sample sizes and low resolution Previous genetic studies on South Asian populations have identified their genepool as a composite of lineages that have evolved locally since the OoA dispersal and those that have been introduced by a number of more recent dispersal events About 8–10 ml of blood was collected from 185 healthy unrelated individuals belonging to two tribal populations  residing in Thane district of Maharashtra . This prPolymerase Chain Reactions were carried out with 10 ng DNA in a 10 ul reaction volume with 1U of Taq DNA polymerase enzyme. Cycling conditions used were 94°C for 5 min, 35 cycles at 95°C for 30 s, 58°C for 30 s, and 72°C for 2 min, then 72°C for 7 min. Sequencing of the PCR products were directly carried out by using Big Dye™ Terminator cycle sequencing Kit  in 3730 DNA Analyzer, following manufacture's protocol. To minimize errors, both strands were double-sequenced. The individual mtDNA sequences were compared against the rCRS 2, 250 µm dNTPs, 3.0 µm of each primer. The conditions for the polymerase chain reaction are (1) 95°C for 10 min, (2) 28 cycles; 94°C for 1 min, 55°C for 1 min, 72°C for 1 min, (3) 60°C for 45 min, and (4) 25°C hold. ABI 3730 DNA Analyser  and GeneMapper V4.0 software program  was used for the analysis of raw data.Twenty Y chromosome biallelic polymorphic markers M89, YAP, M216, M130, M9, M45, M74, M52, M304, M172, M410, M67, M11, M20, M27, M175, M95, M173, M17, and M124 were typed. The PCR cycles were set-up with an initial denaturation of 5 min at 95°C, followed by 30–35 cycles of 30 sec. at 94°C, 30 sec at the primer-specific annealing temperature (52–60°C), and 45 sec. at 72°C, and final extension of 7 min at 72°C. Length variation at 17 Y-STR loci: DYS456, DYS3891, DYS390, DYS389II, DYS458, DYS19, DYS385a/b, DYS393, DYS391, DYS439, DYS635, DYS392, DYS437, DYS438, DYS448 and Y GATA H4 were genotyped using AmpFℓSTR® Y-filer™ PCR amplification Kit (Applied Biosystems). Multiplex polymerase chain reaction was carried out with 0.5ng template with 1 U AmpliTaq Gold DNA polymerase enzyme  with total reaction volume of 6.25 µl which includes 10mM Tris-HCl (PH 8.3), 50mM KCl, 1.5mM Mgclwww.fluxus-engineering.com). Reduced median and median-joining procedures were applied sequentially. Coalescence time has been calculated between nucleotide positions 16090–16365 (HVS-I) considering one transition equals to 20,180 years Fst analysis were calculated using ARLEQUIN 3.01 software Principal component analysis (PCA) was performed using POPSTR, kindly provided by H. Harpending. Median-joining and reduced median joining networks were reconstructed with NETWORK program (version 4.1)  . The hapThe high resolution analysis at haplogroup and sub-haplogroup level identified a monophylacity of previously classified M2a and M2b subclades with a single coding as well as control region substitution and named as M2a'b . This fiThe Y chromosome analysis identified nine major haplogroups in Maharashtra populations , of whicNear Eastern specific hg J2 is also significantly present in both of the studied populations . This haBy using the Y-STR data from both of the populations, we have calculated the variance and coalescent ages for different haplogroups . The ageIn conclusion, our results on Maharashtra populations are consistent with other Indian populations suggest that the tribal as well as caste populations of Indian subcontinent practice a strict endogamy even though they live in a close proximity and share the ritual and social customs. The mtDNA results dissected and increased the clarity of South Asian mtDNA phylogeny. The colonization of western part of Western Ghat is facilitated mainly through migration of populations via western coast rather than mainland where Western Ghat-Vindhya mountains and Narmada-Tapti rivers worked as a natural barrier. Our data is in congruent with the other observations that Indian populations including Maharashtra state are largely derived from Paleolithic ancient settlers, however, a more recent (∼10 Ky older) detectable paternal gene flow from west Asia is well reflected in present genetic study.Figure S1The sampling location of Mahadeo Koli and Thakar populations.(TIF)Click here for additional data file.Figure S2Unrooted phylogenetic network of haplogroup R1a Y-STR haplotypes among different Indian populations showing the haplotype sharing of Thakur and Mahadeo-Koli. The network was constructed using a median-joining algorithm as implemented in the Network 4.5.0.2 program. The size of the circles is proportional to the number of samples.(TIF)Click here for additional data file.Table S1The complete mtDNA data from Mahadeo-Koli and Thakur populations.(XLS)Click here for additional data file.Table S2The Y-SNP and Y-STR complete data from Mahadeo-Koli and Thakur populations.(XLS)Click here for additional data file.


PPPT3B and the height associated SUPT3H-RUNX2. We hypothesize that skeletal remodeling could be a key biological process underlying the Pygmy phenotype. In addition, we showed that these regions have most likely evolved under positive selection. These results constitute the first genetic hint of adaptive evolution in the African Pygmy phenotype, which is consistent with the independent emergence of the Pygmy height in other continents with similar environments.African Pygmies are hunter-gatherer populations from the equatorial rainforest that present the lowest height averages among humans. The biological basis and the putative adaptive role of the short stature of Pygmy populations has been one of the most intriguing topics for human biologists in the last century, which still remains elusive. Worldwide convergent evolution of the Pygmy size suggests the presence of strong selective pressures on the phenotype. We developed a novel approach to survey the genetic architecture of phenotypes and applied it to study the genomic covariation between allele frequencies and height measurements among Pygmy and non-Pygmy populations. Among the regions that were most associated with the phenotype, we identified a significant excess of genes with pivotal roles in bone homeostasis, such as The online version of this article (doi:10.1007/s00439-012-1157-3) contains supplementary material, which is available to authorized users. African Pygmy groups are defined by phenotypic, geographic and cultural characteristics  in African Pygmy populations  contains to infer the geographic origin of one individual (Q) after removing the information that is provided by knowing its genotype (J) . For instance, knowing an individual is 140 cm tall increases the probability that this individual belongs to a Pygmy population rather than to the Maasai. With the aim of identifying the causal genetic variants of the phenotype, one would seek in the genome for SNPs that provide as much information as that contained in the phenotype to assign that individual to each of the populations. Specifically, the conditional informativeness (CIn) of the phenotype over genotype isWe aimed to identify markers that covary with height at a population level among the different populations. We derived a new statistic based on n is to 0 the more similar the amount of information gathered from allele frequencies is compared to that of considering both allelic frequencies and height information. Therefore, markers showing the lowest CIn values are those that covary most strongly with the phenotypic variation in the considered populations. Using the genomic distribution of this statistic, we can identify the loci that compared with the rest of the genome covary most with the phenotype and thus are most likely to have phenotypic effects on the trait. Therefore, the power to identify trait-associated markers will depend on two factors: (1) how well the genomic background associates with the trait (i.e. decreased power if the demographic history follows the same cline as the phenotype) and (2) the amount of genetic differentiation at the causal loci as compared to the rest of the genome. For instance, our statistic should perform well under a scenario of positive selection, as adaptive pressures on a phenotype would create large differences in allele frequencies in causal genes, standing out from the average genomic differentiation. We studied the association of allele frequencies of each SNP with the population distributions of height in five populations: Eastern and Western Pygmies  as well as in three non-Pygmy Africans  . We specifically excluded non-African populations to avoid the stronger secular trends in height in regions with post-industrial economies . We observed that the most common order in the genome follows the phenotypic trait . However, it is also noticeable that the demographic history of these five populations has been complex, since only 15% of the markers follow the cited pattern, whereas the remaining 85% is subdivided in other clines. The inclusion of several populations with different demographic histories is expected to reduce the number of markers that covary with the phenotype due to random processes, thus restricting the effect of population substructure on the rate of false positives.We explored the phenotypic outcome of 192 different genetic architectures, ranging from 3 to 1,000 causal SNPs with phenotypic effects ranging from 10 to 0.01 cm per allele . By choosing random SNPs from the genome and considering them as causal, we used their allele frequencies to create phenotypic distributions for each population under an additive model. To obtain realistic phenotypes, we considered that the causal SNPs followed this minor allele frequency order: Mbuti ≤ Biaka < Yoruba/Luhya < Maasai. At this step, we considered two different cases . For the first scenario we chose random SNPs from the genome with the single condition of the cited minor allele frequency order. For the second scenario, we additionally forced that the causal SNPs would show a minimum difference of 0.37 (95% genomic quantile) in allele frequency between Maasai and Mbuti. Then, we calculated the phenotypic distributions for each architecture and chose the four most realistic ones regarding the ~30 cm height difference between Maasai and Mbuti populations. The reason why we focused on the difference between these two groups was because we aimed to find the genetic basis of the differences in height between Pygmy and non-Pygmy populations, instead of simulating the human height itself .n with them. To compute the power of the statistic, we averaged the number of times the real causal variants were in the 5% extreme of the empirical distribution over the replicates. Owing to computational reasons, we ran these simulations with a random subset of 20,000 SNPs from the genome which did not statistically differ from the values of the statistic in the whole dataset.We also explored two additional cases considering that the phenotype does not follow a common demographic cline (scenario 3) and that the causal SNPs show high differentiation (scenario 4). When computing the statistic for the third and forth scenarios, we used the same causal SNPs as in scenarios 1 and 2, respectively but changed the population labels so that the allele frequency order was a rare one in the genome . For each selected architecture, we ran 10 independent replicates taking the causal SNPs from the genome, generating the phenotypic distributions and computing CIn statistic is that it assumes that the genetic loci responsible for height are shared in the five African populations. This assumption would not hold if the causal loci were Pygmy specific and therefore absent in other African non-Pygmy populations. To identify the genomic regions that show highest genetic differentiation between Pygmy and non-Pygmy populations, we computed the informativeness for assignment (In) for each marker  Pygmy  (2) Yoruba and (3) French. Subsequently, we subtracted the In computed using only French and Yoruba. Hence, PIn = In  − In . By means of this subtraction, we were able to distinguish between Pygmy versus non-Pygmy differentiation  ~0.63  could bias the PIn results, we repeated the analysis with a different set of populations covering Africa and Eurasia .The main limitation of the CIulations  Pygmy  by means of local Moran’s I statistic  indicate that the core SNP is located in a region in which nearby markers have similar values of the statistic in comparison to the genomic background value. Thus, we aimed to detect clusters of highly differentiated (PIn) or height covarying (CIn) SNPs. In the case of PIn, we subsequently excluded those markers showing negative PIn values since these represent differentiation in French and/or Yorubans. After the normalization of the local Moran’s I values, we obtained the local Moran’s I distributions, which under the null hypothesis of no spatial autocorrelation are normal distributions with μ = 0 and σ = 1  and correcting for multiple testing by Bonferroni . Whenever two significant markers were located within the same 200 Kb window, we considered they belonged to the same genomic regions. We checked the overlap of these regions with previous selection scans based on extended haplotype tests performed on both African Pygmies  . Subsequently, we drew 1,000 values from the posterior distributions of each parameter  and used them to simulate 1,000 different genome stretches of 5 Mb with Cosi simulator  SNPs versus the nongenic ones in the tails of the spatial autocorrelation in PIhttp://www.ingenuity.com). The analysis is based on the Ingenuity Pathways Knowledge Base repository collected from the full text of the peer-reviewed literature in life sciences. The functional relationships considered in the database were only those experimentally confirmed. Fisher’s exact test was used to calculate the p value determining the probability that the association between the genes in the dataset and the canonical pathway was explained by chance alone.We subjected the candidate genes detected in our study to the Ingenuity pathway analysis .We first validated the CIn for the Pygmy phenotype. Specifically, we analyzed four different scenarios  were among the strongest genomic signals regarding statistical significance and number of significant SNPs.We identified the outlier SNPs for the spatial autocorrelation values of CIPIn Fig. . These 1n and PIn) observed in the candidate regions was a genomic feature  or if this overlap was only characteristic of the regions showing extreme height covariation (expected if the phenotype were moderately polygenic but highly differentiated). The genomic correlation between the values of spatial autocorrelation of the two statistics was low , and increased little at the top 99.9% quantile of the distributions . This weak correlation showed that the signals captured by the two statistics were independent at a genome-wide level and that genetic differentiation was not sufficient to reach significance in the height covariation analysis.We checked if the overlap between the two analyses  cause acute, rapid and severe bone loss, resulting in up to 65% incidence of fractures  that are involved in histone modification, a functional category significantly enriched among height associated genes in genome-wide association studies (GWAS)  [MIM 119600]. Intriguingly, overexpression of calcineurin in osteoblasts (gene product of PPPT3B gene from region 12 above) increases dramatically the expression levels of RUNX2 protein and enhances bone formation  suggests that our regions could be enriched for genes under positive selection. In this study, the authors suggested that TRIP4 (in region 14 in our study) gathers strong functional evidence for a role in the Pygmy phenotype  were previously reported to have evolved under positive selection (based on extended haplotype homozygosity tests) in African Pygmies . However, neither neutral evolution nor purifying selection can explain the presence of clusters of highly differentiated or height covarying SNPs in the genome . Therefore, the genomic regions located in the upper extreme of the spatial autocorrelation distributions of both PIn and CIn are strong candidates to have evolved under positive selection in Pygmies.To confirm the role of selective pressures in the evolution of the ascertained regions, we performed simulations and obtained null distributions of the statistics under plausible demographic scenarios. We observed a high overlap between the simulated and real PIons Fig. . In contp value cutoffs considered  SNPs versus the nongenic ones in the tails of the distributions as in Hancock et al. . Nongenin) and the 24 genes that strongly covaried with height (CIn) . Conversely, in the case of the height covarying genes the strongest pathway identified was “role of osteoblasts, osteoclasts and chondrocytes in rheumatoid arthritis” , whereas the other two significant signals were shared with the highly differentiated gene set (“O-glycan biosynthesis” and “protein ubiquitination”). The finding of significant enrichment of genes involved in bone remodeling in the regions that strongly covary with the phenotypic trait constitutes a strong hint for a putative role of this pathway in the African Pygmy phenotype.To unravel the biological pathways responsible for the putative adaptive evolution of the candidate regions, we subjected the 33 genes strongly differentiated in Pygmies  statistic would be enriched for true causal variants. Nevertheless, the statistic has several limitations. First, it assumes that the genetic basis of the phenotype is, at least partially, shared by all the populations studied and neglects the effect of genetic interactions. Second, spurious signals in CIn analysis could occur under (1) the presence of genetic variants associated with other phenotypes with a geographic distribution similar to height in these populations or (2) allele frequency clines mimicking the distribution of the phenotype due to genetic drift. Nonetheless, the inclusion of several African populations with very different demographic histories (counteracting drift effects), and the study of spatial autocorrelation with extremely conservative significance cutoffs could be expected to reduce these spurious signals.We observed that in the case where the genetic architecture of Pygmy height was similar to that of normal height variation, with thousands of causal markers  to identify markers that are highly differentiated in African Pygmies when compared with other worldwide populations. In addition, we studied the genomic distribution of the two statistics by means of spatial autocorrelation to identify clusters of markers showing strong differentiation and/or association with the phenotype.Because the assumption of shared genetic basis among African populations could be violated in the case of the Pygmy phenotype, we pursued a second analysis with no premises regarding the phenotypic model. We computed the n and CIn from simulations differed from real SNP data, and since purifying selection cannot explain these patterns, these suggests an enrichment of loci that have undergone positive selection. In addition, the substantial functional excess found in the extreme of the distributions of the statistics (particularly of non-synonymous SNPs) also points at positive selection as the most likely force for explaining this disproportion.Some of the ascertained regions harbored genes that were previously described as being under positive selection in African Pygmies, suggesting these loci could be enriched in adaptive genes. To confirm whether our statistics would be empowered to detect non-neutral evolving markers, we performed simulations to learn about the distribution expected under the most plausible demographic scenarios. Even though demography can be a confounding factor when assessing the adaptive value of highly differentiated or height covarying markers, we showed that it is very unlikely that demographic processes alone could create haplotypes like the ones observed at the extremes of the spatial autocorrelation distributions. Because neither the LD pattern nor the genome-wide distribution of PIThe selective scenario fits well within the finding that the regions that most strongly covaried with height distributions showed extreme differentiation. This suggests that the genetic architecture of the phenotype could be constituted by a limited set of adaptive genes with moderate to large phenotypic effect. The list of candidate regions includes well-known height associated loci in other human populations and an excess of genes involved in skeletal remodeling. The functional pathway enrichment analysis supported our approach, as the incorporation of population–phenotypic information allowed to distinguish regions more likely to be associated with the trait of interest from adaptive signals corresponding to other traits (such as immunity).GH-IGF pathway, which could be due to an incomplete coverage in those genes or that genetic variants in the GH-IGF pathway are not relevant in the phenotype. Not being able to completely discard the role of the growth hormone pathway, our results suggest a predominant role of bone remodeling in the etiology of the Pygmy phenotype. Interestingly, recent research has shown functional links between bone remodeling, energy metabolism and fertility . We found no evidence for a role of genes within the During the last few years, most scans for human adaptations have focused on genetic data alone, ignoring the information that population-based phenotypes may provide. Instead, our approach goes along the lines of recent efforts that have focused in incorporating environmental variables in the genetic analysis to understand local adaptation Below is the link to the electronic supplementary material.


Genetic diversity across different human populations can enhance understanding of the genetic basis of disease. We calculated the genetic risk of 102 diseases in 1,043 unrelated individuals across 51 populations of the Human Genome Diversity Panel. We found that genetic risk for type 2 diabetes and pancreatic cancer decreased as humans migrated toward East Asia. In addition, biliary liver cirrhosis, alopecia areata, bladder cancer, inflammatory bowel disease, membranous nephropathy, systemic lupus erythematosus, systemic sclerosis, ulcerative colitis, and vitiligo have undergone genetic risk differentiation. This analysis represents a large-scale attempt to characterize genetic risk differentiation in the context of migration. We anticipate that our findings will enable detailed analysis pertaining to the driving forces behind genetic risk differentiation. The environment humans inhabit has changed many times in the last 100,000 years. Migration and dynamic local environments can lead to genetic adaptations favoring beneficial traits. Many genes responsible for these adaptations can alter disease susceptibility. Genes can also affect disease susceptibility by varying randomly across different populations. We have studied genetic variants that are known to modify disease susceptibility in the context of worldwide migration. We found that variants associated with 11 diseases have been affected to an extent that is not explained by random variation. We also found that the genetic risk of type 2 diabetes has steadily decreased along the worldwide human migration trajectory from Africa to America. Analyzing the impact of human migration on genetic disease susceptibility is critical to the understanding of complex disease. Moving to new environments causes adaptation, which can also affect disease susceptibility. The availability of methods to measure genomic differences between worldwide populations has increased our understanding of human migration. For example, a worldwide human relationships phylogenetic tree was constructed after genotyping over 50 worldwide populations The human genome has been subjected to many selective pressures in recent history. They include changes brought about by the domestication of crops and animals, and the rise of urbanization Studies have shown that unlinked single nucleotide polymorphisms (SNPs) associated with a single phenotype may be affected as a group if the phenotype undergoes differentiation Adaptation to new environments may have caused genetic risk differences across many human populations. Despite the recent explosion of knowledge regarding disease-associated loci and the genetic structure of different world populations It is also plausible that allele differentiation may have occurred in unexamined combinations of diseases and populations. One study probed 2,186 disease-associated SNPs in the HapMap CEU cohort and concluded that they were enriched for low-frequency alleles In a previous study, we surveyed 8,377 SNPs representing 437 diseases in 11 subpopulations. We found differences in risk distribution and protective alleles in different diseases across many subpopulations Importantly, our approach controls for genetic substructure within and among diverse populations These patterns identified worldwide trends of increases or decreases in genetic risk and show a high-level view of variation in genetic risk for different diseases. An interactive version of Placing genetic risk on a map does not reveal close relationships between populations. de novo in a population rather than via inheritance from an ancestral population. “Dependent risk differentiation” is a genetic risk inherited from an ancestor. 1 = −108.848. The log-likelihoods for 2,3,4,5, and 6 events were l2 = −66.6083, l3 = −40.7165, l4 = −33.7324, l5 = −26.8371, l6 = −23.3415. We used the likelihood ratio test to determine the number of branches undergoing genetic risk differentiation independently that exceeded what would be expected under genetic drift. This test allows for the calculation of a p-value for n independent branches by converting two log-likelihood scores to a χ2 variable as follows: 2(ln−ln−1)∼χ21. The p-value for 2 branches versus 1 branch undergoing independent genetic risk differentiation for type 2 diabetes was less than 1.00×10−16, meaning there was evidence for more than one independent genetic risk differentiation event. The p-values for 3, 4, 5, and 6 branches were 6.30×10−13, 1.86×10−4, 8.19×10−3, and 5.64×10−2, respectively. There was evidence for 5 distinct genetic risk differentiation events  had the highest genetic risk (LLR:2.81), and an individual in the Han population (HGDP1291) had the lowest risk (LLR: −2.21).The effect size of each individual variant is not necessarily the same in each population We examined genetic risk in individuals to expose potential outliers  and to vWe observed genetic risk trends associated with migration in other diseases, including prostate cancer, alopecia areata, melanoma, asthma, neuroblastoma, polycystic ovary syndrome, and pancreatic cancer. One of the most extreme examples of genetic risk differentiation was observed in ulcerative colitis. We found genetic risk differentiation in multiple worldwide populations for pancreatic cancer and other diseases . DetaileIndividual SNPs may have a large impact on overall levels of genetic risk differentiation . One notOur method can detect genetic-risk differentiation that is not captured by Fst calculations. Individuals may be outliers with respect to genetic risk, leading to false detection of genetic risk differentiation in a population. We defined outliers as individuals deviating >1.5 times the interquartile genetic risk range from the median genetic risk and removed them from each population-disease sets showing genetic risk differentiation . Each diDisease-associated SNPs are likely to be biased towards genomic regions with higher LD levels where they are tagged more efficiently. We addressed this bias by resampling from SNPs reported to be associated with any phenotype (disease or not) as opposed to resampling from all SNPs. We discarded 614,563 and retained 46,192 of the genotyped SNPs in the HGDP cohort. Genetic risk differentiation was still detected after resampling exclusively from SNPs associated with a phenotype .Our findings place the genetic basis of disease susceptibility in the context of human migration and increase understanding of the role of population differentiation in complex disease. The HGDP-CEPH cohort represents 51 populations from around the world, with >650,000 genotyped SNPs per person. We combined this cohort with SNPs from our disease association database and were able to assess the genetic risk of disease of all populations in the HGDP-CEPH cohort. We demonstrated that differences in genetic risk for multiple diseases go well beyond what is expected by genetic drift. In addition, using a human population phylogenetic tree allowed us to incorporate a substructure of worldwide relationships.A certain amount of variation can be expected due to random frequency variation in disease-associated genotypes. Genetic risk differentiation not caused by genetic drift is more likely to be caused by environmental adaptation, and provides insight into factors affecting disease susceptibility. Our method controls for random genetic risk variation by randomly sampling SNPs across the genome. When testing for significance in our model, the baseline was the expected amount of risk differentiation caused by genetic drift between a population of interest and all others. The low q-values in Genetic risk differentiation occurred in multiple populations in type 2 diabetes. In contrast, genetic risk differentiation in biliary liver cirrhosis was found in only two populations. Ulcerative colitis exhibits the single most extreme example of genetic risk differentiation . These tWhile the HGDP is an important sampling of the worldwide distribution of genotypes, it is possible that some populations are admixed. Detecting genetic risk differentiation in admixed populations may be more difficult, as the levels of genetic risk would most likely be less extreme. The genetic risk for any two populations in which admixed individuals were erroneously sampled is expected to be somewhere between the population with the least and most genetic risk. This explains why controlling for admixture is unlikely to decrease the significance of our findings.Multiple independent genetic risk differentiation events have occurred in various worldwide populations. There were 44 SNPs associated with biliary liver cirrhosis used in this study. We found genetic risk differentiation in the Japanese and Druze populations . Risk waLike all the diseases discussed in this study, ulcerative colitis has genetic and environmental components Specific environmental differences inducing genetic risk differentiation in type 2 diabetes and other diseases have not been found. However, there is evidence that climate, diet, and living conditions have led to them The concepts discussed here could be used to link diseases that may share pathophysiology and environmental triggers. It is possible that modulation of environmental features in different global regions changed the genetic risk of certain diseases. If an environmental feature affects multiple diseases, risk estimates for the diseases should correlate across the same populations, even if no common genetic basis is apparent. If the environment increases risk for one disease and decreases it for another, a negative correlation is expected. Finding diseases with genetic risk estimates correlated across worldwide populations would represent a novel and potentially highly informative approach to uncover shared pathophysiologies. This type of analysis would benefit from the largest possible catalog of genetic variants. For example, in the HGDP, East Asia is biased to detect more genetic risk differentiation than the Americas, due to the increased sensitivity that comes from 17 East Asian versus 5 American populations. In addition, full genome sequence analysis enables the inclusion of copy number polymorphisms and rare variants that may be found to contribute to complex disease susceptibility. As sequencing costs decrease, analyses expanding the scope of this study will occur.We investigated >650,000 SNPs from the HGDP-CEPH, which has DNA from 1043 individuals in 51 populations on 8 continents −6. SNPs were excluded if information about them was insufficient to compute likelihood ratios for the genotypes of associated SNPs. VARIMED was used to compute genetic risk estimates of the resulting diseases across all HGDP populations.For this study, we used only GWAS SNPs that had been detected across ≥2 populations with p-values<10The HapMap Phase 3 cohort includes 11 populations with 1.6 million SNPs genotyped per person sample genetic risk in a population is referred to as genetic risk. Any computation of a population's genetic risk is inaccurate, due to the inability to genotype all individuals in a population and the existence of many undiscovered disease-associated variants.In this paper, the 2≥0.2) in a population were excluded.The likelihood ratio (LR) represents the effect size of a particular genotype on genetic disease risk. SNPs in linkage disequilibrium (RL(g) maps the genotype g to the estimated likelihood ratio. The LR used in our calculations represents the weighted mean LR reported across all studies For a given bi-allelic SNP, there are three possible genotypes: homozygous for the major allele, homozygous for the minor allele, or heterozygous. The function A is the vector of all sample sizes in each study. P(g | disease is present)i is the estimated probability that a genotype g is in the disease population of the ith GWAS. P(g | disease is not present)i is the estimated probability that a genotype g is present in the non-disease population of the ith GWAS.The following is our method for combining multiple LRs from multiple GWASs. G is the vector of all genotypes in disease-associated SNPs in person m.Once computed, the combined LR was used to compute the genetic disease risk for each individual, as follows. r for person m is the log of the combined likelihood ratios for all disease-associated variants present in that person.The predicted genetic risk S is the vector of a population.The genetic risk for a population was the mean risk of all its members. Derived populations created by combining multiple populations in the HGDP were treated as single populations, and the following computations applied equally. R for an entire population P is the mean predicted risk for everyone in the population:The predicted genetic risk D) between two populations A and B, we subtracted the genetic risk of one population from the other's:In order to compare genetic risk . Each randomly drawn SNP also matched the functional category of the SNP it replaced in vector H.Our goal was to assess the significance of a difference in risk H had been replaced, the genetic risk of all populations was recomputed, effectively assigning a randomly generated genetic risk score to each population. Since each population was assigned a genetic risk score from the same randomly drawn set of SNPs, the expected amount of correlation between genetic risk values among all populations was preserved. We created phylogenetic trees of our results with each branch representing a migration event. We computed the genetic risk difference of each migration event by subtracting the genetic risk of all descendant populations from the risk of all ancestral populations (those above the branch). Branches on the human phylogenetic tree created from the HGDP populations were tested for genetic risk differences. We computed the difference in risk between all ancestral and descendant populations. A phylogenetic tree of all the HGDP populations was used as described previously H 100,000 times.Once all elements of i,kr represent the genetic risk difference between ancestral and descendant populations in branch i computed from the thk randomly generated H.We computed a matrix representing 100,000 randomly generated phylogenetic trees and compared it with the observed phylogenetic tree in the context of genetic risk. Let iO is the observed genetic risk difference between ancestral and descendant populations in the thi branch of the phylogenetic tree.This matrix of 100,000 randomly generated phylogenetic trees was then compared to the observed tree. thi branch is as follows:The probability of observing a genetic risk difference between ancestral and descendant populations in the i. However, we tested multiple strongly correlated populations simultaneously. In addition, the genetic risk differences of nearby branches in the phylogenetic tree were strongly correlated due to genetic similarity between populations. One row in matrix R represented the genetic risk difference of all branches in the tree between ancestral and descendant populations in a single random draw. The genetic risk difference was calculated with the same set of SNPs in every branch in the phylogenetic tree in each row. This preserved the correlation of genetic risk among closely related populations and enabled us to detect independent genetic risk deviations.This equation gave a probability for a specific genetic risk difference observed on branch α in the context of the entire tree, we first computed the number of branches having equal or greater significance for genetic risk differentiation.To assess the significance of a p-value N(α) is the observed number of branches with a p-value for genetic risk differentiation at or below α.Significant genetic risk differentiation events were calculated follows. α from the matrix representing 100,000 randomly generated phylogenetic trees.Q(α)  for a branch with p-value α for genetic risk differentiation was:We next computed the expected number of branches with genetic risk differentiation at significance Q can inform on the significance for a particular p-value, it does not estimate the total number of distinct genetic risk differentiation events in a phylogenetic tree. We estimated the most likely number of migration events independently contributing to observed genetic risk deviation in a phylogenetic tree using a maximum likelihood approach.While P is the vector containing all probabilities of the observed genetic risk difference between ancestral and descendant populations of each branch of tree t.k is not a descendant of branch z:We defined a function that returns true if branch z represents the branch in which an independent genetic risk differentiation event occurred. Applying the principle of maximum likelihood, the branch z that maximizes the likelihood function in tree t is shown below. The number of events refers to the number of independent genetic risk differentiation events.Branch true if one or more of the two operands is true. The branch that maximized likelihood was the one most likely to have caused the risk differentiation. However, there may have been multiple genetic risk differentiation events. This maximum likelihood method can be generalized to detect an arbitrary number of branches that underwent genetic risk differentiation.The log likelihood function produces the branch most likely to have undergone genetic risk differentiation, given that a single genetic risk differentiation event occurred. The ‘1, z2, …,zyz for branches that maximize the following likelihood function:Find the indices de novo genetic risk differentiation (as opposed to an arbitrary combination of branches that have undergone de novo genetic risk differentiation) can pass a significantly modified genetic risk for a disease to a descendant branch. This led us to the method used for this study. The following calculation was used to find all branches with sufficient evidence of having undergone independent genetic risk differentiation.Due to the constraints this equation imposed, a very large number of random draws was required to produce an accurate estimate. All indicator functions in the numerator must return a non-zero value for the count to be incremented by one; this was computationally infeasible. In order to make the computation tractable, we made a simplifying assumption that only a single ancestral branch which has undergone 1,z2,…,zyz that maximize the following likelihood function:Find the indices Figure S1Impact of individual SNPs on genetic risk differentiation. The relative deviation from expected genetic risk attributable to each SNP is shown in each pie chart. The branch in the human phylogeny tree with the most significance for genetic risk differentiation was used to assess the impact of individual SNPs for each disease. The branch selected for alopecia areata includes European, Central South Asian, East Asian, Oceaniac, American, Palestinian, and Druze populations. The branch selected for inflammatory bowel disease includes the Brahui and Makrani populations. The branch selected for pancreatic cancer includes Central South Asian, East Asian, Oceaniac, and American populations. The branch selected for systemic lupus erythematosus includes the Mayan and the Pima populations. The branch selected for type 2 diabetes includes East Asian, Oceaniac, and American populations. The branch selected for ulcerative colitis includes the Brahui and Makrani populations. The branches selected for biliary liver cirrhosis, bladder cancer, and membranous nephropathy are the Druze, Tu, and French Basque populations, respectively. Each branch is compared to all other worldwide populations. Some SNPs have a disproportionate impact on genetic risk. An example is rs13151961, associated with inflammatory bowel disease.(TIF)Click here for additional data file.Figure S2Fst analysis for biliary liver cirrhosis. Global Fst values using the HGDP cohort were calculated for the 44 high confidence biliary liver cirrhosis SNPs used in this study. The Fst score for each SNP was compared to all other SNPs with the same minor allele frequency. The p-value represents the fraction of SNPs with a lower Fst value. No individual lung cancer SNP appeared to be significantly differentiated. Fst analysis failed to capture the localized genetic-risk differentiation that has occurred in the Japanese and Druze populations. Combining the p-values revealed no signs that these SNPs have collectively undergone differentiation .(TIF)Click here for additional data file.Figure S3Fst analysis for type 2 diabetes. Global Fst values were calculated for the 16 type 2 diabetes-associated SNPs used in this study. The distribution of p-values did not reveal elevated type 2 diabetes genetic risk differentiation across worldwide populations compared to non-disease associated SNPs. The Fst score for each SNP was compared to all other SNPs with the same minor allele frequency. The combined p-value for these SNPs failed to capture the extreme extent to which genetic-risk differentiation has occurred at a global scale .(TIF)Click here for additional data file.Table S1Type 2 diabetes-associated SNPs replication demographics. The number of times any publication has found each type 2 diabetes SNP used in this study to be associated with the disease is shown. European populations have the most replications across the majority of SNPs associated with type 2 diabetes. Arabic and American Indian populations have the lowest number of replications.(DOCX)Click here for additional data file.Table S2Asian-specific type 2 diabetes effect size. The table compares the overall likelihood ratio for each SNP associated with type 2 diabetes against the Asian-specific likelihood ratio. The Asian-specific likelihood ratio was computed by including only GWASs based in Asian populations. While many of the observed effect sizes did not differ, some were significantly different and may lead to a modified risk estimate for Asian populations.(DOCX)Click here for additional data file.Table S3Genetic risk of type 2 diabetes using Asian GWASs. Most GWASs are based on European cohorts. The effect size of an associated variant may differ across distinct populations. This table compares the combined likelihood ratio for type 2 diabetes  with the combined likelihood ratio using only GWASs in Asian populations. This approach allowed us to base the effect size of each variant on Asian populations as opposed to mainly European derived populations. Genetic risk for type 2 diabetes is still significantly lower in Asian populations when the effect size for each associated variant is taken from GWASs exclusively based in Asian populations .(DOCX)Click here for additional data file.Table S4Replication after removal of outliers. Outliers carry a disproportionate number of risk alleles compared to the rest of their population. For each population, we calculated the interquartile range (IQR) of genetic risk for each individual and removed persons with a genetic risk deviating more than 1.5 IQRs from the population median. The table compares original p-values to the p-value after removal of outliers. A total of 21 out of the original 24 genetic risk differentiation events had p-values<0.006 after removing outliers.(DOCX)Click here for additional data file.Table S5Replication after resampling from known associations. A resampling procedure is used to model the expected genetic risk difference between a population and the rest of the worldwide set of populations combined. However, a GWAS is more likely to detect associations in regions with higher linkage disequilibrium, which is a potential source for bias in detecting genetic risk differentiation. We address this by reproducing the results after randomly drawing only from SNPs previously reported to show association to any phenotype during the resampling step. Genetic risk differentiation is still detected after randomly drawing from other known associations during the resampling step.(DOCX)Click here for additional data file.Text S1Analysis of the genetic basis of disease in the context of worldwide human relationships and migration.(DOC)Click here for additional data file.


EDA, Na/K ATPase). In estuarine sites, both marine and freshwater allelic variants were commonly detected. We also found typically marine alleles present in a few freshwater lakes, especially those with completely plated morphology. These results provide a general model for postglacial colonization of freshwater habitat by sticklebacks and illustrate the tremendous potential of genome-wide SNP data sets hold for resolving patterns and processes underlying recent adaptive divergences.Threespine stickleback populations are model systems for studying adaptive evolution and the underlying genetics. In lakes on the Haida Gwaii archipelago (off western Canada), stickleback have undergone a remarkable local radiation and show phenotypic diversity matching that seen throughout the species distribution. To provide a historical context for this radiation, we surveyed genetic variation at >1000 single nucleotide polymorphism (SNP) loci in stickleback from over 100 populations. SNPs included markers evenly distributed throughout genome and candidate SNPs tagging adaptive genomic regions. Based on evenly distributed SNPs, the phylogeographic pattern differs substantially from the disjunct pattern previously observed between two highly divergent mtDNA lineages. The SNP tree instead shows extensive within watershed population clustering and different watersheds separated by short branches deep in the tree. These data are consistent with separate colonizations of most watersheds, despite underlying genetic connections between some independent drainages. This supports previous suppositions that morphological diversity observed between watersheds has been shaped independently, with populations exhibiting complete loss of lateral plates and giant size each occurring in several distinct clades. Throughout the archipelago, we see repeated selection of SNPs tagging candidate freshwater adaptive variants at several genomic regions differentiated between marine–freshwater populations on a global scale (e.g.  Anolis lizards  has been the focus of a diverse range of studies examining evolutionary processes    and is aDA)  markers  from the mid-Pacific Ocean  were also genotyped as archetypal Pacific marine fish. We maximized the number of populations sampled to obtain a broad survey comparable in scope to previous morphological analyses (n = 77), stream (n = 28) or marine/estuarine (n = 10). Morphological variation between sampled populations encompassed the extremes seen within the species. Here, we have highlighted (i) ‘unarmoured’ populations with extensive loss of bony lateral plates [12 populations with a mean of less than one lateral plate on left side of fish (n = 6) and 2009 (n = 6) were genotyped to confirm samples were comparable.From the Haida Gwaii archipelago, a total of 462 stickleback from 115 localities representing 54 watersheds were genotyped  was used to visualize intensity signals. Genotypes were initially called automatically, then position of all intensity clusters were visually inspected and adjusted manually. SNPs with poorly separated loose clusters or exhibiting low signals were excluded from further analysis. SNPs missing >10% of genotypes calls and any individuals with >5% missing data were excluded. Repeatability of calls was >99% for individual DNA samples genotyped multiple times. The final data set included 1170 SNPs  following n = 760, hereafter referred to as the evenly spaced SNP data subset). Given the large number of loci, individual heterozygosity estimates are precise  of 5.0%). Variance between individuals within a population was also small . This suggests estimates of relative population heterozygosities are robust even with SNP data from only a few individuals.Population heterozygosity was calculated as mean of individual observed multilocus heterozygosities based on all evenly distributed SNPs . For lake populations, we also assessed correlations between heterozygosity and three physical parameters  by fitting linear models. The relative importance of the independent variables  was determined using the relaimpo .mega version 5 . These trees were constructed in ersion 5  calculatprcomp in r statistical software (v 2.9.0) to perform the PCA .Principal component analysis (PCA) is an effective approach for dimension reduction of multivariate data sets and has been widely adopted in the analysis of SNP data sets as an unsupervised method to identify underlying structure .The Haida Gwaii data set included 10 mtDNA SNPs, including two SNPs known to differentiate the Japan Sea and ENA lineages (cytochrome b gene position 564 and 690 from EDA (chr4—12.8 Mb) and Na/K ATPase , both loci are highly differentiated between marine and freshwater stickleback in several populations . To examine the association between allelic forms of EDA and lateral plate phenotype, we scored all stickleback for lateral plate number (left side). We also looked for potentially novel genomic regions that are highly differentiated between marine and freshwater localities in our data set. To do this, we calculated the difference in allele frequency between a pool of all freshwater populations (n = 104) vs. a pool of all the marine/estuarine populations (n = 10) and compared the most divergent SNPs (>50% difference) to previously identified outlier regions . Instead, we focused on allelic variants tagged by multiple SNPs on adaptive haplotypes documented across several populations in previous studies. These include SNPs tagging Finally, we consider SNPs identified by R2: 0.47). Percentage of variation in heterozygosity explained by each variable model (averaged over orderings) was as follows: lake elevation 33.2% (95% CI = 21.5–45.5%); lake area 8.7% (95% CI = 2.2–20.6%); distance from ocean 5.4% (95% CI = 3.7–8.8%).Heterozygosity of Haida Gwaii populations for evenly spaced SNPs ranged from 0.002 to 0.343 (mean = 0.206 ± 0.078 SD) and was higher in marine localities compared to streams and lakes a. The loA distance-based tree constructed using two fish per collection site reveals several levels of genetic structuring in Haida Gwaii populations . GeneticThe next group of well-supported clusters primarily joins fish collected from common watersheds . For exaThe basal region of the tree is characterized by a large number of short branches that are poorly supported by bootstrap values. Despite being poorly supported, these branches deep within the tree still tend to group populations by geographic region. When a condensed tree is generated , the basal region of the tree collapses into an unresolved node with 80 independent branches . This is consistent with armour loss occurring independently in separate watersheds. These populations include unarmoured stickleback in two groups of headwater lakes in adjacent watersheds that are geographically close (<750 m apart) but genetically distant , the first cluster contains marine localities as well as freshwater populations from the entire western and southern regions of the archipelago. The second cluster is limited to localities on the north-east tip of Graham Island, including watersheds that flow both north and east into the ocean. The final cluster consists of a large number of Graham Island populations distributed from the Sangan watershed in the north through the central area and to the east coast  separates out three closely linked lakes in the headwaters of the Oeanda River .Principal component analysis partitioned genetic variation into three broadly congruent clusters regardless of whether individual genotypes or population level allele frequencies are considered (population level data presented here). Based on population allele frequencies, the first two PCs account for 8.2% and 5.5% of the variation respectively (st coast . Furtherr2 > 0.5) for any of the 10 SNP data subsets created from the 200 top-weighted SNPs on either of the first two principal components. This analysis suggests that a broad range of SNPs rather than a few selected SNPs are driving the observed PCA clustering.To investigate the number of SNPs that are driving separation of the three PCA clusters, we considered small groups of SNPs categorized according to their informativeness (relative weightings on eigenvector); by examining these SNPs, in turn, we evaluated how quickly the ability to approximate the observed clustering declined . All low plated fish we genotyped were homozygous for the L allele  was also highly divergent between these habitats in our data set (EDA locus pattern) and the freshwater haplotype was generally fixed in lakes and streams  produces a gradient of marine-like to freshwater-like fish along PC1 , but otherwise contained many alleles usually found in marine stickleback. The chr11 5.7 Mb haplotype, a previously identified inversion differing in orientation between marine and freshwater ecotypes . Rather than a traditional phylogenetic tree where distance measures sequence divergence over time, our data set represents shuffling of standing genetic variation over time via population genetic processes. This does blur some relationships, for example, estuarine fish tend to follow the pattern of marine fish, branching off independently from the basal clade rather than clustering with their watershed, presumably due to ongoing gene flow. Small pond populations with low heterozygosity are also often only weakly linked to other populations within the watershed, likely due to genetic drift. Apparent associations between low heterozygosity populations also need to be interpreted with care as these populations may tend to fix the same common ancestral alleles and may also maintain shared variation at loci subject balancing selection. At first consideration, the placement of marine/estuarine populations into a common, albeit weakly supported, cluster along with several freshwater populations could be taken as evidence for very limited number of origins of freshwater stickleback on Haida Gwaii. However, clustering of marine stickleback is expected to some degree because contemporary marine stickleback are likely to be more similar to each other than to ancestral marine stickleback, and due to directional selection which differentiates some genomic regions between these habitats. The key point is that genetic distances between many freshwater populations are similar to distances between marine and freshwater environments. So, while we may never know how many independent origins of freshwater stickleback happened, the ancestral genes appear to have been almost completely shuffled between colonisations of freshwater watersheds . A single freshwater origin and dispersal in this region is feasible. When sea levels were lower during and immediately following the last glacial maximum , currentPrincipal component analysis gives a slightly different perspective on genetic structuring of Haida Gwaii stickleback by highlighting similarities within three clusters that are separated by short basal branches in the SNP tree. Three overlapping clusters identified by PC1 and PC2 correspond to broad geographically cohesive areas, further principal components cluster only small numbers of populations. Of the three broad clusters, one includes all marine stickleback as well as freshwater populations extending over a large part of the archipelago, from Moresby Island to the west coast of Graham Island. Another cluster includes populations from central Graham Island (including watersheds draining east and north) and the final cluster consists of populations from the north-east lowland area of Graham Island. These groups are not being driven by a small number of shared SNPs, with several hundred SNPs showing this underlying structure. It seems unlikely that this connectivity between watersheds across the archipelago is maintained solely by recent gene flow via marine waters, because freshwater clusters do not group watersheds draining into a common marine basin and because marine fish cluster together regardless of collection location. We have considered several possible alternative explanations. One scenario is that colonization of freshwater occurred in successive waves, resulting in two distinctive freshwater lineages and a third group of more recently derived populations similar to marine stickleback. This is speculative as the present pattern would only have developed if historical drainages connected some watersheds currently flowing east and north on Graham Island. Furthermore, we have no a priori expectations of populations in different regions being colonized at different times. Another possibility is that, even though we excluded candidate SNPs in these analyses, selection has a role in producing this underlying genetic structure. The genetic clusters approximately parallel three major geographical regions  each of which contains freshwater habitat with distinctive biophysical attributes. Population in mountainous regions along western Graham Island and on Moresby Island live in lakes characterized by limited littoral zones, high water clarity and usually co-inhabiting rainbow trout. In contrast, the north-east lowlands are an expansive low-elevation muskeg, containing dark acidic water and generally small shallow lakes. Central Graham Island contains lakes spanning the range of biophysical characters. Selection between environments can have effects across the genome, as demonstrated in stickleback  therefore depending on the lake it is possible either that typical freshwater alleles are absent due to a population bottleneck during colonization, or freshwater alleles could be rare due to recent gene flow with marine fish. These populations of freshwater stickleback containing many marine-like alleles are all located in mountainous regions along the western and southern parts of the archipelago. These habitats may also limit genetic adaptation due to the absence of low-gradient streams and large brackish estuaries, habitat features that may facilitate transport of adaptive freshwater alleles between watersheds via marine stickleback . The stiAn alternative to the possibility that marine-like alleles ending up in some lakes through historical processes is that natural selection may promote retention of these alleles in certain lakes. The shared biophysical characteristics of lakes with the most marine-like alleles could be taken as evidence for this  indicating allelic recycling between watersheds via marine stickleback is probably common at additional locally selected loci. Decades of ecological research highlights the multiple layers of selection acting on these ecological and morphologically diverse stickleback populations. If suites of genetic changes underlie each layer of selection, untangling the resultant patterns of parallel and nonparallel changes will require high resolution genomic sequence data and detailed ecological information from many contrasting populations. The pristine habitats in which most Haida Gwaii stickleback populations exist combined with previous ecological work and the broad genetic survey presented here all indicate this will be an excellent system in which to advance our rapidly emerging view of the genetic basis of adaptation.