CCL3L1 gene reported in previous studies was also the highest differentiated regions between Tibetan and other populations. Besides, by jointly analyzing CNVs and SNPs, we found a CNV region containing gene CTDSPL were in almost perfect linkage disequilibrium between flanking SNPs in Tibetan while not in other populations except HapMap CHD. Furthermore, we found the SNP taggability of CNVs in Chinese populations was much lower than that in European populations. Our results suggest the necessity of a full characterization of CNVs in Chinese populations, and the CNV map we constructed serves as a useful resource in further evolutionary and medical studies.It has been shown that the human genome contains extensive copy number variations (CNVs). Investigating the medical and evolutionary impacts of CNVs requires the knowledge of locations, sizes and frequency distribution of them within and between populations. However, CNV study of Chinese minorities, which harbor the majority of genetic diversity of Chinese populations, has been underrepresented considering the same efforts in other populations. Here we constructed, to our knowledge, a first CNV map in seven Chinese populations representing the major linguistic groups in China with 1,440 CNV regions identified using Affymetrix SNP 6.0 Array. Considerable differences in distributions of CNV regions between populations and substantial population structures were observed. We showed that ∼35% of CNV regions identified in minority ethnic groups are not shared by Han Chinese population, indicating that the contribution of the minorities to genetic architecture of Chinese population could not be ignored. We further identified highly differentiated CNV regions between populations. For example, a common deletion in Dong and Zhuang (44.4% and 50%), which overlaps two keratin-associated protein genes contributing to the structure of hair fibers, was not observed in Han Chinese. Interestingly, the most differentiated CNV deletion between HapMap CEU and YRI containing  Copy number variation (CNV) is a type of global genetic variations in human genome, defined as a segment of DNA larger than one kilobase presenting copy-number differences by comparison of two or more genomes ST based on the inferred allele frequency. Moreover, we showed that population structure can be detected by biallelic CNPs,but only about twenty-five percent were well tagged by flanking SNPs. Finally, we conducted population-specific CNV analysis, and identified a number of CNV candidates showing significant differentiated signals among populations which might be under selections.As a source of human genetic diversity, with various genetic patterns of nucleotide and structural variability in human populations In this study, a total of 155 samples performed by the Affymetrix Genome-Wide Human SNP Array 6.0 have passed a series of quality control in CNV detection, comprising 80 Han Chinese, 41 Tibetans, 9 Dongs, 8 Yaos, 6 Zhuangs, 8 Lis, and 3 Uyghurs. By using Birdsuite algorithm CNVs observed in more than one individual with any amount of overlap were classified as non-singleton CNVs while those detected in only one individual were classified as singleton CNVs. Non-singleton CNVs dominated (96.7%) in the detection of all CNVs, and the total 732 singletons consisted of 390 deletions (2.1% of copy loss events) and 342 duplications (9.6% of copy gain events). The proportion of singleton duplications was much higher than that of singleton deletions . Additionally, since there were five replicate samples in this study, we can compare the CNP and SNP calling concordance between each pair of replicates. The average consistency of calling for CNP and SNP were 99.71% and 99.91%, respectively, which indicated the good reproducibility of the experiment.The average number of individual CNV events varied among these groups . Han andBy extending the boundaries of overlapping CNVs in different individuals, that is, the union region of any amount of overlapping CNVs which we called Copy Number Variable Region (CNVR) in the study , we haveWe assigned each CNVR a genotype with a copy state occupying the largest length in that region (Methods). http://projects.tcag.ca/variation). A total of 235 CNVRs were not overlapped with any amount of CNV loci in DGV, including 224 non-singleton CNVs and 11 singleton CNVRs. However, these numbers might be underestimated, as many previous studies used low-resolution experiment platform providing rougher boundaries of CNVs We also compared CNVRs in this study with previously reported CNVs in the Database of Genomic Variants   and three within-Asian groups , respectively. It showed that African has more specific CNVs than Asian or European, and the number of CNVRs shared by Asian and European was smaller than that shared either by Asian and African or by European and African . On the Under the assumption of Hardy-Weinberg Equilibrium and a three-allele system , we can use Expectation-Maximization algorithm to calculate CNV allele frequency. According to the genotypes assigned to each CNVR we identified here, 175 regions had loss allele with frequency >10% and 32 regions had gain allele with frequency >10% across 7 ethnic groups . The allSTST between YRI and any other groups was larger than that between any other two populations, which was consistent with the knowledge of recent African origin of modern humans. We also expectedly observed the flat FST curve between our Chinese samples and HapMap Asian samples  which have been found under selective pressure elsewhere CCL3L1 was previous identified as the most differentiated gene between CEU and YRI KRTAP9-3 and KRTAP9-9) contributing to the structure of hair fibers (RefSeq). Considering the small sample size of the ethnic groups, we cannot neglect the occurrence of CNVs on other regions, even though they are minor.Previous studies have revealed some CNV outliers of population differentiation, which suggested the existence of ethnic-specific selective pressures 2r> = 0.8, 2r> = 0.8) was marginally significant than that (11.7) of low taggability CNPs (2r<0.8). , which indicates that further efforts should be made on array design if we want to fully understand CNV characteristics in Chinese populations.Previous studies focused on the linkage disequilibrium of CNPs and SNPs in CEU and found a moderate or higher degree between them 2r> = 0.8, 2r = 1) than Han Chinese. Interestingly, by manually checking pairwise difference of 2r of each SNP between Tibetan and Han Chinese, we found a common deletion  where the flanking SNPs were in strong LD with this CNV in Tibetan but not in Han Chinese. Whereas this deletion was common in other HapMap populations, none of them had such strong SNP LDs except CHD  singleton CNVs and 5 (0.7%) non-singleton CNV regions were not found in previous studies submitted to DGV or other HapMap populations. However, these numbers might be underestimated due to large boundaries reported by previous studies using low-resolution platforms CNVR is a widely used term to represent the union of overlapping CNVs at a particular region and measures the cumulative extent of copy number variant ST to measure the differentiation between populations. Generally, the results of FST distribution accord to our knowledge and the diverse sample sites in our Han Chinese might account for the slight deviation in the FST distribution. However, the complexity of Han Chinese genetic background in essential should not be ignored when conducting disease studies.Whereas some allelic states of CNV cannot be determined, their allele frequencies are attainable. We inferred allele frequency by using the EM algorithm based on two major assumptions - ‘three allele system’ and Hardy Weinberg Equilibrium (HWE). The first assumption holds true as long as the genotype varies from 0-copy to 4-copy, which stands for most CNP loci  according to Birdsuite, version 1.5.5 The genetic distance devised in this study has been proven to be capable of recovering the genetic relationship among Chinese ethnic groups, which, to some extent, reflects a linguistic pattern. Previous study using other genetic data has suggested that Y-chromosome haplogroups correlates with linguistic classification ST >0.01 comprised ∼40% (42/106) of the total CNPs with FST>0.01. Given such a big proportion of information loss, the poor power of inferring population structure by biallelic CNVs at minute-difference scale is predictable. Considering the success of applying high-density SNPs as markers to detect population structure, it is unwise to use CNV data for structure inference. However, what is interesting is to compare the patterns inferred by CNV and SNP when they have comparable information.We explored the population structure of our Chinese samples at different scales by using PCA of biallelic CNP. The population genetic structure of CNVs does exist at various levels and can be inferred at certain scale by biallelic CNVs; however, the power of discriminating variants inherent to each ethnic group decreased as the analysis scale gets narrowed. The differentiation of each ethnic group in our Chinese samples could not be easily distinguished when all of our ethnic groups were involved. This is due to the paucity of informative loci in biallelic CNPs. Most of the multi-allelic CNPs excluded are more informative; for example, multi-allelic CNPs with FCCL3L1, which was identified as the most frequency differentiated CNV overlapping gene between CEU and YRI A2BP1) - the one overlapped with Tibetan specific loss region may contribute to the restricted pathology of spinocerebellar ataxia type 2 (SCA2). Ataxin-2 is the gene product of the SCA2 gene which causes familial neurodegenerative diseases. The other interesting gene overlapped with Han Chinese specific loss region is human gene NADH-ubiquinone oxidoreductase flavoprotein 3 (NDUFV3). The protein produced by this gene a subunit of the NADH-ubiquinone oxidoreductase complex which is part of the mitochondrial respiratory chain and catalyzes the rotenone-sensitive oxidation of NADH and the reduction of ubiquinone.There are several signals highlighted as we compare CNV frequency difference among populations. For example, As members of a big Chinese family, the genetic contribution of other minority ethnic groups  cannot be ignored. Considering the small sample size of each minority group, we combined them as one and compared it with Han Chinese by searching for non-singleton specific regions. Thirty-one regions were identified, of which 40% overlap with genes. Although these CNVs did not occur so frequently, given the limited sample size we included in this study, some of them were probably common in each specific group. All these differentiated CNVs that might play a role in phenotypic differences among human populations are interesting candidates for future studies. Accordingly, the diversity of Chinese remains to be discovered and the large sample size of ethnic groups will be needed in future genomic variant analysis.CTDSPL (RBSP3) which is a member of small C-terminal domain phosphatases gene family possibly controlling the RNA polymerase II transcription machinery In our study, SNP taggability of CNPs in Han Chinese and Tibetan population is not as high as that in previous studies reported in CEU Our results provide a comprehensive CNV map of Chinese from seven ethnic groups. At population level, we analyzed CNV genomic distribution and diversity which indicate the genetic relationship among human populations and suggest a linguistic pattern within Chinese ethnic groups. Different populations share a large percentage of CNV regions: the closer the relationship, the higher the sharing. We also demonstrated that the impacts of CNVs among populations could be reflected by detecting population structure as well. Our joint analyzing results highlighted those most differentiated CNVs among Chinese ethnic groups which are candidates to be further studied about their functional consequences as they are more likely under selective pressure than the others. In summary, our results present here will serve as a Chinese CNV resource to assist characterizing the pattern of human genetic variation, performing population level analyses and investigating medical disorders.There are 184 samples in total, collected from 7 Chinese populations representing major linguistic groups in China, including 101 Han Chinese, 46 Tibetans, 9 Dongs, 8 Yaos, 8 Zhuangs, 8 Lis, 4 Uyghurs. All samples were assayed on the Affymetrix Genome-Wide Human SNP Array 6.0 which contains both SNP probes and copy number probes for the simultaneous detection of SNPs and CNVs Samples which were also assayed by Affymetrix Genome-Wide Human SNP Array 6.0 from the International HapMap Project CNV detection was performed by Birdsuite, version 1.5.2 The genotypes of 1,315 copy number polymorphisms (CNP) which are defined by a previous study of HapMap samples Only the results of autosomes were included in the analyses because CNV callings of the sex chromosomes are probably not as reliable as those of autosomes due to the segmenting problem with both X and Y chromosomes. Moreover, a great portion of large CNV events (>1 Mb) was analyzed separately from those CNVs less than 1 Mb long, because they could be artifacts, for example, due to the scarcity of probes near the centromeres of the chromosome. The workflow of detecting and filtering CNV is shown in All probe coordinates were mapped to the human genome assembly build 36 (hg18). Because of the platform limitation of SNP array in determining CNV boundaries, they could only be approximated by the first and the last probe positions. In order to construct the comprehensive autosomal CNV map in Chinese population, we apply the widely-used term Copy Number Variable Region (CNVR) to delineate the characteristics. A CNVR refers to a union region of overlapping CNVs on the chromosome In order to calculate the allele frequency and carry out population analysis, we assigned each individual a ‘genotype’ for each CNVR by the following criteria: if no CNV was found in that CNVR of the individual, we assign the calling as normal state (which is assumed as ‘two’); if one was found, then the genotype is the CNV state; if more than one CNV were found in that CNVR of the same individual, the genotype of that CNVR is assigned by the copy state of CNV with the largest length in that CNVR . AlthougWe proposed a measure to characterize the genetic difference of CNV genotypes between two individuals for all the CNV regions. Distance between two populations is defined as the average of pairwise genotype differences between two individuals from the two populations respectively. Based on this, we constructed a phylogenetic tree by Neighbor-joining and UPGMA with 1,000 bootstrap replications. The consensus phylogenetic tree was constructed using PHYLIP CNV sharing analysis was conducted by counting the number of sharing regions among populations. However, the larger the sample size of the population, the more CNV regions will be generated (including singletons). The results of sharing analysis would be biased if we do not consider the affect of sample size. Therefore, to make the analysis comparable, we specified the smallest sample size of compared populations as ours. For those whose sample size were larger than this, we randomly sampled the same number of individuals and averaged 100 random sampling results.STST and analyzed together with HapMap samples.The calling results of CNVs by Canary and Birdseye contained five copy states: 0-copy state (homozygous deletion), 1-copy state (heterozygous deletion), 2-copy state , 3-copy state (single copy duplication) and 4-copy state (double copy duplication). Such five copy state results can be explained by a three-allele  system. Based on the three-allele system, we applied Expectation-Maximization (EM) algorithm to calculate the allele frequency under the assumption of Hardy-Weinberg Equilibrium (HWE), which allows us to obtain the widely-used statistic FST calculation. We coded biallelic CNPs with genotype ‘0, 1, 2, 3, 4’ as ‘0/0, 0/1, 1/1, 1/2, 2/2’ for the pair of two chromosomes. PCA was performed by software Eigenanalysis 2.0 Population structure was inferred by principal component analysis (PCA). Due to technology limitations, allelic copy number states were only available for biallelic CNVs. Therefore, we used CNVs observed as biallelic in the analysis of populations. In order to combine the results with HapMap samples, we focused on CNPs as we did in FWe identified population specific CNVs using two complementary strategies. Firstly, we made pairwise frequency comparison one by one of our test group with the reference groups that consist of all other HapMap populations. The p-value was generated by testing on a contingency table with two populations and two types of allele counts . CNV loci were ranked according to the number of significant p-values in all the comparisons. Secondly, some of the low-frequency CNVs with occurrence only in one population might not be captured by the frequency comparison. Thus, we conducted a search for those non-singleton CNVs observed only in one ethnic group.2r mainly for 155 Chinese, 80 Han Chinese and 41 Tibetan, respectively.Linkage disequilibrium analysis was performed in biallelic CNVRs. Biallelic CNVRs with frequency larger than 10% and SNPs within 20 kb of both ends of CNVRs were phased into haplotype by cnvHap Figure S1CNV detection flow chart. See the detailed description of the filtering procedure in Methods.(PDF)Click here for additional data file.Figure S2CNV length distribution. While the number of deletion  is more than 5 times of duplication , duplication has much larger length  than deletion .(PDF)Click here for additional data file.Figure S3Genomic distribution of CNVs in Chinese population. Red and blue triangles indicate the chromosomal location of deletions and duplications respectively.(PDF)Click here for additional data file.Figure S4Phylogenetic tree of Chinese ethnic groups constructed by Neighbor-joining. Phylogenetic tree of Chinese ethnic groups based on average pairwise genetic population distance between ethnic groups with 1,000 bootstrap replications by Neighbor-joining.(PDF)Click here for additional data file.Figure S5Allele frequency distribution in Chinese population. (A). Cumulative allele frequency distribution of Chinese population in 1440 CNVRs. (B). Allele frequency distribution of Chinese population in 254 multi-allelic CNVRs.(PDF)Click here for additional data file.Figure S6The state of Individual CNVs on non-singleton CNVRs (CNVR length < 100 kb). Black bars denote CNVR, and below which each bar denote a CNV call in one individual. Red and blue represent deletion and duplication, respectively.(PDF)Click here for additional data file.Figure S7The state of Individual CNVs on non-singleton CNVRs (CNVR length> = 100 kb). Black bars denote CNVR, and below which each bar denote a CNV call in one individual. Red and blue represent deletion and duplication, respectively.(PDF)Click here for additional data file.Figure S8CNV sharing. Venn diagram of (A).Deletion, (C).Duplication and (E).Multi-allelic CNVs sharing results among African, Asian and European groups (each group with sample-size 425). Venn diagram of (B).Deletion, (D).Duplication and (F).Multi-allelic CNVs sharing results among Han Chinese, Chinese minority and Japanese groups (each group with sample-size 75).(PDF)Click here for additional data file.Table S1A map of Chinese Copy Number Variable Regions (CNVRs) and Copy Number Variable Segments (CNVsegs) with Allele frequency and overlapping genes including overlapping OMIM genes.(XLS)Click here for additional data file.Table S2Gene ontology analysis of CNVR overlapping genes. According to the type of variants, CNVRs have been divided into deletion, duplication and multi-allelic groups.(XLS)Click here for additional data file.Table S3Han Chinese specific CNV regions and highly differentiated CNV regions between Han Chinese and other populations.(XLS)Click here for additional data file.Table S4Tibetan specific CNV regions and highly differentiated CNV regions between Tibetan and other populations.(XLS)Click here for additional data file.Table S5Chinese Minority  Specific non-singleton CNV regions compared with Han Chinese.(XLS)Click here for additional data file.


Tomato spotted wilt virus (TSWV) that exists among populations of Thrips tabaci has been associated with the presence of cryptic species that exhibit different modes of reproduction and host ranges. However, recent findings suggest that vector competency of T. tabaci at any given location depends on the thrips and virus populations that are present. This study characterizes the population genetic structure of T. tabaci collected from four locations in North Carolina and examines the relationship between population genetic structure and variation in TSWV transmission by T. tabaci. Mitochondrial COI sequence analysis revealed the presence of two genetically distinct groups with one characterized by thelytokous, parthenogenetic reproduction and the other by arrhenotokous, sexual reproduction. Using a set of 11 microsatellite markers that we developed to investigate T. tabaci population genetic structure, we identified 17 clonal groups and found significant genetic structuring among the four NC populations that corresponded to the geographic locations where the populations were collected. Application of microsatellite markers also led to the discovery of polyploidy in this species. All four populations contained tetraploid individuals, and three contained both diploid and tetraploid individuals. Analysis of variation in transmission ofTSWV among isofemale lines initiated with individuals used in this study revealed that ‘clone assignment,’ ‘virus isolate’ and their interaction significantly influenced vector competency. These results highlight the importance of interactions between specific T. tabaci clonal types and specific TSWV isolates underlying transmission of TSWV by T. tabaci.Knowledge of population-level genetic differences can help explain variation among populations of insect vectors in their role in the epidemiology of specific viruses. Variation in competency to transmit  Thrips tabaci Lindeman, is a polyphagous insect pest species that is also a vector of two important plant infecting Tospoviruses (Genus: Tospovirus Family: Bunyaviridae), Tomato spotted wilt virus (TSWV) and Iris yellow spot virus (IYSV) T. tabaci is the sole vector of IYSV, which is responsible for an estimated U.S. $60–90 million in onion crop losses in the western U.S. T. tabaci was the first species reported to transmit TSWV and was believed to be the primary vector until transmission studies revealed extensive variation in its competence as a vector of TSWV. Brazilian and Canadian populations of T. tabaci did not transmit TSWV in transmission assays T. tabaci is the primary vector of TSWV in tobacco and vegetable production systems in Europe and Tasmania, respectively T. tabaci is not reported as an important vector of TSWV, however, both poor and efficiently transmitting populations have been observed Knowledge of population-level genetic differences and clonal diversity in sexual and asexual species has improved our understanding of the role insect vectors have in the epidemiology of specific viruses T. tabaci populations has long been associated with the presence of cryptic species that exhibit different modes of reproduction and host ranges T. tabaci, one exhibiting arrhenotokous parthenogenesis that is found on tobacco and capable of transmitting TSWV, the other exhibiting thelytokous parthenogenesis that is not found on tobacco and not capable of transmitting TSWV. Two recent studies examining mitochondrial cytochrome oxidase subunit one (mtCOI) sequence variation have provided evidence for at least three genetically distinct groups based on host plant (leek versus tobacco) T. tabaci individuals in relation to species or population genetic structure. It has been shown that a large amount of variation in transmission of different TSWV isolates exists within purely inbred isofemale lines initiated from individual, parthenogenetic females collected from the same location T. tabaci is specific to certain isolate and thrips population combinations, and is more commonly observed when the tested TSWV isolate and thrips combinations were collected from the same location T. tabaci as a TSWV vector on the basis of genetic groupings associated with host plant or reproductive mode alone is not sufficient for understanding the potential of this vector in disease epidemics.Variation in transmission efficiency by T. tabaci collected from four different locations in North Carolina using mtCOI sequences and microsatellite markers, and to examine whether there is a relationship between genetic structure and variation in TSWV transmission by T. tabaci. Isofemale lines established from parthenogenetically reproducing, field-collected females that were previously characterized for both reproductive mode type and their ability to transmit several TSWV isolates were used in this study T. tabaci are geographically structured across the sampled region, and that the effect of clonal assignment in statistical models was a significant factor used to estimate the probability of TSWV transmission by T. tabaci.The objectives of this study were to characterize the population genetic structure of No specific permits were required for the described field studies. No permission was required to collect insects or plant material from Jackson Springs or Mills River collection sites. These reside on university research farms (with whom we are affiliated) or unregulated public lands. Verbal permission was obtained from private landowners for the Apex and Faison collections sites, respectively. The field studies did not involve regulated, endangered, or protected species.Thrips tabaci individuals were collected from cultivated and weed plant hosts at different locations in North Carolina during May and June of 2010  according to manufacturer's instructions. DNA was eluted in 30 µl of AE buffer.5′-ATTCAACCAATCATAAAGATATTGG-3′) and LepR1 (5′-TAAACTTCTGGATGTCCAAAAAATCA-3′) 2, 100 µM dNTPs, 3 pM of each primer, 0.5 U Taq DNA polymerase , 2 µl of DNA, and ddH2O to 25 µl. PCR cycling conditions were comprised of an initial denaturation stage of 1 min at 94°C, followed by 35 cycles each consisting of 1 min at 94°C, 1.5 min at 45°C, and 1.5 min at 72°C, with a final elongation step at 72°C for 5 minutes, carried out using a PTC-200 thermal cycler (MJ Research Inc.). PCR products were visualized on a 2.5% agarose gel to confirm samples contained a single band, and 5 µl of PCR product was subsequently purified using the ExoSAP-IT PCR purification kit  and bidirectionally sequenced following the methodology outlined in Copren et al. From the sampled populations, a subset of individuals were amplified for a 629-bp fragment of the mitochondrial cytochrome oxidase I (COI) gene using primers LepF1 . Phylogenetic relationships were examined using Molecular Evolutionary Genetics Analysis (MEGA), Version 4 T. tabaci specimens, selected from geographically distant sampling locations, DNA was extracted using the DNEasy Blood & Tissue kit . DNA quality and concentration from each specimen were determined using the BioSpec-nano spectrophotometer . Pooled DNA from the five specimens was then subjected to shotgun sequencing using the Roche 454 Genome Sequencer FLX  with the Titanium Sequencing kit XLR 70, performed at the Genomic Sequencing Laboratory located at the North Carolina State University. Sequencing was performed on a 1/16 GS-FLX PTP.From five CACGACGTTGTAAAACGAC). Amplification products were chosen to be within a 100 to 400 bp range (including M13 tag), with an optimal annealing temperature of 59°C (range 57°C–63°C), an optimal GC content of ∼50%, low levels of self- or pair-complementarity, and a maximum stability of 8.0 A total of 97,611 reads were obtained with an average read length of 354 bp and a total of 34,514,909 bp. Using MSATCOMMANDER version 0.8.2 T. tabaci. PCRs were carried out in 12 µl total volumes, each containing 1× PCR buffer, 1.75 mM MgCl2, 100 mM dNTPs, ∼20 ng DNA template, 0.5 U Apex Taq DNA polymerase , and ddH2O to 12 µl. Primer concentration varied between 0.10 and 0.15 µM -statistics The R program Polysat T. tabaci, and suggest that local adaptation may be occurring between TSWV isolates and thrips. To further investigate the virus-vector relationship between TSWV isolates and T. tabaci, the analysis of TSWV transmission was re-run using 12 isofemale lines that came from one of the four populations used in the genetic analysis for which both clonal assignments and TSWV transmission data in individuals were available, along with two additional isofemale lines, one from Kinston, NC and one from Cove City, NC, for which transmission and microsatellite clone assignment information was available. Not enough transmission data were available for the Mills River isofemale lines to include them in this analysis. Models were run using Proc Glimmix . The mtCOI haplotypes were not included in this analysis because the small number of arrhenotokous individuals available did not permit a robust comparison.A previous TSWV transmission study conducted with individuals from the same isofemale line showed that isolate, isofemale line and their interaction were statistically significant effects describing the probability of successful transmission of TSWV NJ, UPGMA, and Minimum Evolution phylogenetic methods employed in MEGA produced identical tree topologies . The indMinimum spanning networks generated with TCS produced two main subgroups comprised of thelytokous and arrhenotokous individuals, respectively . The theT. tabaciAll of the individuals collected from Apex, Jackson Springs, and Faison exhibited thelytokous parthenogenesis, and all of the individuals from Mills River exhibited arrhenotokous parthenogenesis. Both diploid and tetraploid females were found in Faison, Jackson Springs and Mills River collection sites, whereas only tetraploids were collected from Apex. Males from Mills River included in the study appeared to be haploid based on the maximum number of alleles observed at each microsatellite locus, however, ploidy has not been formally examined for male FST   .P = 0.0552) was not significant, but ‘virus isolate’ (P<0.0001) and ‘isofemale line×virus isolate’ interaction (P = 0.0070) were statistically significant with this subgroup. When the variable ‘sympatry’, replaced the interaction term in this model to investigate whether this interaction was significant among virus isolates and thrips isofemale lines that occur in the same location, ‘virus isolate’ (P<0.0001) and ‘isofemale line’ (P<0.0001) were significant, but ‘sympatry’ (P = 0.4436) was not significant.The probability of TSWV transmission based on the variables ‘virus isolate‘, ‘isofemale line’ and ‘sympatry’ P<0.0001) and ‘virus isolate’ (P<0.0001) were significant main effects, but ‘sympatry’ was not (P = 0.9577). When ‘clone assignment’ (P<0.0001), ‘virus isolate’ (P<0.0001) and their interaction term (p = 0.0399) were included in the model, however, all three terms were statistically significant. These results highlight the importance of interactions between specific T. tabaci clonal types and specific TSWV isolates in determining transmission of TSWV by T. tabaci.Next, these models were run using genetic clonal groups, identified from microsatellite marker analysis, as the variable to describe the thrips isofemale lines. A comparison of transmission efficiency based on mtCOI haplotype could not be conducted due to the small sample sizes of transmission phenotypes available for arrhenotokous haplotypes. When ‘isofemale line’ was replaced with ‘clone assignment’ as the variable to model thrips transmission, ‘clone assignment’ (T. tabaci collected from four different locations in NC using mtCOI sequences and microsatellite markers. The results of the mtCOI sequence analysis support previous evidence for the existence of genetically distinct groups within this species The first objective of this study was to characterize the population genetic structure of T. tabaci populations using microsatellite markers. Microsatellite markers developed here revealed genetic structure among the four NC populations that corresponded to both the geographic locations where the populations were collected, and the grouping of individuals observed in the phylogenetic analysis conducted with mtCOI sequences. Pairwise FST values ranged from 0.06–0.20 indicating moderate genetic differentiation between all populations (This is the first study to examine the population genetic structuring of ulations . The PCAulations , howeverT. tabaci populations during microsatellite marker development was unexpected, and is the first report of polyploidy in Thysanoptera T. tabaci appeared to be diploid and tetraploid based on genome size estimates for multiple individuals. Because we did not expect populations to be polyploid our sample collection, storage, and processing methodology only allowed for the determination of ploidy and allele dosage in these populations using allele frequency based estimation methods. Based on these methods, all populations sampled contained tetraploid individuals, and three of the populations were comprised of both diploid and tetraploid individuals. Polyploidy represents a significant barrier to the establishment of, and gene flow among populations due to the elevated likelihood of genetic incompatibility amongst individuals of different ploidy level T. tabaci are more common worldwide; males and arrhenotokous populations are rarely reported in the literature, and nothing is known about the abundance and distribution of polyploids. Although this analysis does not suggest that genetic structuring occurs as a result of polyploidy in the NC populations sampled, tetraploidy was almost twice as common in these populations, and may be contributing to the successful establishment of clones in some areas. Specific effects of polyploidy have been reviewed recently in a series of publications (T. tabaci, especially with regard to the origin and presence of polyploidy, the relative distributions of diploid and polyploid individuals, and how polyploidy influences population structure in arrhenotokous populations. Another important area of research will be whether or not elevated gene copies are maintained and expressed, especially in relation to traits that may influence acquisition, infection and transmission of viruses, or genes related to insecticide metabolism or target sites whose elevated expression has been related to insecticide resistance in hemipteran and dipteran species The discovery of polyploidy in T. tabaci population structure to transmission efficiency of TSWV was born from observations that thrips and TSWV isolate, when grouped by location, were both significant explanatory variables in accounting for variation in the probability of TSWV transmission, and that, on average, transmission rates were higher among sympatric TSWV isolate-thrips pairings T. tabaci in relation to a specific ‘clonal assignment’ variable that uses the genetics of individual thrips, rather than using general population definitions such as ‘isofemale line’ that groups thrips transmission phenotypes based on where they were collected, revealed different, yet related, thrips tospovirus interactions. These interactions offer insights into the localized importance of this species as a vector of TSWV and highlight the role of genetic variation in isolate-specific vector competency among individual thrips within and among populations of T. tabaci in determining this species role in the epidemiology of TSWV over a broad range of spatial dimensions. While local adaptation between TSWV and T. tabaci, suggested by the significance of sympatry in earlier models, contributes to observed variation in vector competency among T. tabaci populations, ultimately, the interaction of specific thrips genotypes with specific virus isolates underlies transmission of TSWV by T. tabaci. Therefore, when assessing the importance of T. tabaci as a vector of TSWV, efficient transmission more likely depends on specific genotypic interactions between thrips clonal groups and virus isolates than on cryptic species group alone. However, cryptic species group, reproductive mode, and other life history traits affecting the fitness of these individuals will influence the abundance and distribution of efficient vectors through time and space. A better understanding of thrips population biology and defining the abundance and distribution of cryptic species and clonal groups over time would augment our understanding of thrips population dynamics and help to explain patterns of intra-and inter-population variation of economically important traits including vector competency.The premise of this study to relate


TreeMix, is available at http://treemix.googlecode.com.Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In our model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data, we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry  to wolves subsequent to domestication and that East Asian toy breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy breeds and “ancient” Asian breeds. Software implementing the model described here, called  With modern genotyping technology, it is now possible to obtain large amounts of genetic data from many populations in a species. An important question that can be addressed with these data is: what is the history of these populations? There is a long history in population genetics of inferring the relationships among populations as a bifurcating tree, analogous to phylogenetic trees for representing the evolution of species. However, it has long been recognized that, since populations from the same species exchange genes, simple bifurcating trees may be an incorrect representation of population histories. We have developed a method to address this issue, using a model which allows for both population splits and gene flow. In application to humans, we show that we are able to identify a number of both previously known and unknown episodes of gene flow in history, including gene flow into Cambodia of a population only distantly related to modern East Asia. In application to dogs, we show that the boxer and basenji breeds have a considerable component of ancestry from grey wolves subsequent to domestication. The extant populations in a species result from an often-complex demographic history, involving population splits, gene flow, and changes in population size. It has long been recognized that genetic data can be used to learn about this history There are many statistical approaches to demographic inference from genetic data. One approach is to develop an explicit population genetic model for the history of a set of populations, framed in terms of the effective population sizes of the populations, the times of population splits, the times of demographic events (such as population bottlenecks), and other relevant parameters. The values of these parameters can then be learned from the data using a variety of techniques, often involving simulation post hoc as representing historical populations, and individuals or populations that are mixtures of different components as evidence of admixture between these populations . We then calculate the residual covariance matrix, After finding the single migration edge that most increases the likelihood, we attempt a series of local changes to the graph structure (Methods). We then iterate over this procedure to add additional migration edges. In principle, migration edges could be added until they are no longer statistically significant (see the following paragraph). In our experience, however, we prefer to stop adding migration events well before this point so that the resulting graph remains interpretable.After building the maximum likelihood graph, we would like to quantify our uncertainty in the resulting graph structure. In particular, we would like to quantify our confidence in individual migration events. However, because the likelihood in exceptConsider a given migration edge, with corresponding weight TreeMix method in simulations. We generated coalescent simulations from several histories; the basic structure was a set of 20 populations produced by a serial bottleneck model like that used by DeGiorgio et al. We tested the performance of the First, we tested the performance of the algorithm on truly tree-like data. We generated 100 independent simulations of 20 chromosomes from each population using the above demographic model without migration, and inferred population trees. The inferred trees perfectly matched the simulated model in all cases , and the lengths .We used these simulations without migration to test the calibration of our p-values for migration events. For each simulation, after building the maximum likelihood tree, we introduced a migration event between two random populations and tested it for significance. As expected if the p-values are properly calibrated, their distribution is approximately uniform .Finally, we performed tree simulations in a situation where fixed differences and new mutations (rather than shared polymorphisms inherited from a common ancestor) were common between the populations; in this context the population genetic interpretation of the model breaks down. We performed simulations where all the true branch lengths were 50 times longer than in the original model, corresponding to a history where the 20 populations share a common ancestor approximately 100,000 generations in the past. Again, we see no errors in the topology of the inferred trees . In this situation, the covariances between closely-related populations tend to be slightly underestimated; in more extreme situations this could lead to spurious inferences of migration . However, overall, these simulations suggest that the model will still be useful even in situations where the population genetic interpretation is not strictly correct.TreeMix and allowed it to infer a migration event. We then judged the error rate of the algorithm as the fraction of times the inferred topology of the graph was not exactly correct . In general, TreeMix was able to correctly infer the graph structure in these simulations  from one of the other populations. We tried ten different pairwise combinations of populations, and generated 100 simulations for each pair. For each simulation, we ran ulations . Howeverulations . The majulations .We next asked whether the mixture “weights” inferred in the model can be interpreted as admixture proportions. To do this, we simulated admixture events of varying proportion between the first and tenth population in the serial bottleneck model described above, set the graph to the true topology, and estimated the mixture weight. The weights are indeed correlated with the true ancestry fraction, but underestimate relatively high admixture proportions in these simulations . The preTreeMix model with real data, we applied it to humans, whose genetic history has been studied extensively To test the performance of the TreeMix with random orders of input populations). This graph model explains 99.8% of the variance in relatedness between populations. As expected from examination of We then sequentially added migration events to the tree. In Several additional migration events in the human data have not been previously examined in detail, but are consistent with previous clustering analysis of these populations Two inferred edges were unexpected. First, perhaps the most surprising inference is that Cambodians trace about 16% of their ancestry to a population equally related to both Europeans and other East Asians (while the remaining 84% of their ancestry is related to other southeast Asians). This is partially consistent with clustering analyses, which indicate shared ancestry between Cambodians and central Asian populations TreeMix on these data  to southern European populations . We infer a significant genetic contribution from wolves to the boxer  result from admixture between a population related to ancient East Asian dog breeds and a modern population related to the Brussels griffon and the pug  do not cluster with the other sight hounds in the tree, namely greyhound, whippet and Irish wolfhound ; howeverOverall, we conclude that there has been considerable gene flow between dog breeds over the course of domestication; there are many additional migration events that merit further examination , Text S1In this paper, we have developed a unified model for inferring patterns of population splits and mixtures from genome-wide allele frequency data. We have shown that this model is accurate in simulations, largely recapitulates the known relationships between well-studied human populations, and is able to identify new relationships between populations in both humans and dogs.TreeMix model can be thought of as a complement to methods for the identification of population structure how it arose, and are only indirectly informative about the historical relationships between different populations. The model developed in this paper is designed to more directly address these historical questions.The TreeMix model. First, we have motivated the model in terms of inferring the historical splits and mixtures of populations. However, a given covariance structure of allele frequencies between populations can be a consequence of either a non-equilibrium demography (population splits and mixtures) or an equilibrium demography (populations at long-term stasis with a fixed migration structure) There are a number of assumptions, both implicit and explicit, in the interpretation of the We have also modeled migration between populations as occurring at single, instantaneous time points. This is, of course, a dramatic simplification of the migration process. This model will work best when gene flow between populations is restricted to a relatively short time period. Situations of continuous migration violate this assumption and lead to unclear results . The relWe also rely on the implicit assumption that the history of the species being analyzed is largely tree-like. We have made this assumption to simplify the search for the maximum likelihood graph; additionally, we speculate that in graphs with complex structure, there will be many graphs that lead to identical covariance matrices, and thus several different histories will be compatible with the data. That said, improvements to the search algorithm could allow the assumption of approximate treeness to be somewhat relaxed. Currently, if the number of admixed populations is large relative to the number of unadmixed populations, this assumption breaks down. For example, in the human data, note that we see no evidence of the documented gene flow from Neandertals to all non-African populations A number of extensions to the sort of model described here are of potential interest. First, the historical relationships between populations could be useful as null demographic models for the detection of natural selection TreeMix that uses the composite likelihood in As described in the Results, we developed an algorithm called To search the space of possible graphs, we take a hill-climbing approach. We start by finding a local optimum tree, taking an algorithmic approach similar to Felsenstein After adding all populations, we calculate the residual covariance matrix, TreeMix source code is available at http://treemix.googlecode.com.The TreeMix, see the We implemented three- and four-population tests as described in Reich et al. For the four-population test for treeness, we calculate the http://www.cephb.fr/en/hgdp/ on August 16th, 2011 (the data set labeled Harvard HGDP-CEPH genotypes). They consist of several panels of SNPs ascertained from low-coverage genome sequencing of single individual from different populations and then genotyped in the Human Genome Diversity Panel TreeMix, we used a window size .Since we have only a single allele from the Neandertal and Denisova populations, we cannot calculate heterozygosity in these populations for unbiased estimation of the covariance matrix (see ). To account for this, we simply chose a relatively low level of heterozygosity and assigned it to both populations. In the Yoruba ascertained SNPs, we used a heterozygosity of 0.13, and for the French ascertained SNPs, we used a heterozygosity of 0.2. In practice, this only affected the lengths of the terminal branches to Neandertal and Denisova; running http://genome-mirror.bscb.cornell.edu/ on July 30, 2011. These data consist of counts of reference and alternate alleles at 61,468 sites in 85 dog breeds and wild canids. We removed the Jackal and Scottish Deerhound for having relatively high amounts of missing data, and the village dogs because it is unclear if they represent a coherent population. We also removed all SNPs on the X chromosome. This left us with 60,615 SNPs in 82 populations. We ran TreeMix with a window size Click here for additional data file.Figure S2Replicates of inferred trees from simulated data. We generated tree-like data using the topology in (PDF)Click here for additional data file.Figure S3Inferred trees on ascertained data. We generated tree-like data using the topology in (PDF)Click here for additional data file.Figure S4Histogram of p-values for migration in simulated data. We generated 100 tree-like datasets using the topology in (PDF)Click here for additional data file.Figure S5Consensus tree in simulations with long branches. We generated 100 tree-like datasets using the topology in (PDF)Click here for additional data file.Figure S6Example trees from simulations with long branches. We generated 100 tree-like datasets using the topology in (PDF)Click here for additional data file.Figure S7TreeMix did not reach the correct answer. A. The correct topology for the simulations presented in the other panels. B. A representative example of an incorrect topology inferred from the simulations of a migration event with weight 10% from population 1 to population 5 . C. A representative example of an incorrect topology inferred from the simulations of a migration event with weight 30% from population 1 to population 5 .Representative errors in simulations. We examined the simulations in which (PDF)Click here for additional data file.Figure S8Replicate graphs inferred in the human data. These graphs were generated in an identical manner as (PDF)Click here for additional data file.Figure S9Residual fit from graph of human data presented in the main text. Plotted are the residuals from the fit of the graph presented in (PDF)Click here for additional data file.Figure S10TreeMix on the Yoruba-ascertained data Click here for additional data file.Figure S11Trees inferred using the human data including the Oceanians. We show the maximum likelihood trees and residuals for the human data including the Oceanian populations, plotted in the same manner as in (PDF)Click here for additional data file.Figure S12Graphs inferred using the human data including the Oceanians. We show the maximum likelihood graphs for the human data including the Oceanian populations, plotted in the same manner as in (PDF)Click here for additional data file.Figure S13Residual fit from graph of dog data presented in the main text. Plotted are the residuals from the fit of the graph presented in (PDF)Click here for additional data file.Figure S14TreeMix run on populations with continuous migration. We simulated a set of populations on a lattice, where each population has constant gene flow at a rate of ms command is given in TreeMix inferred no consistent tree structure. Three representative trees are presented in B.-D.(PDF)Click here for additional data file.Figure S15TreeMix run on human data using only a single non-African population. We inferred the maximum likelihood tree (A.) using only the African populations and one non-African population (French), using SNPs identified in a single Yoruban individual. In examining the residuals (B.), a relationship between the French and the Neandertal is clear. We then inferred three migration events (C.), where we do see that the French contain some Neandertal ancestry ((PDF)Click here for additional data file.Text S1Supplementary Information.(PDF)Click here for additional data file.


Franciscanas are the most endangered dolphins in the Southwestern Atlantic. Due to their coastal and estuarine habits, franciscanas suffer from extensive fisheries bycatch, as well as from habitat loss and degradation. Four Franciscana Management Areas (FMA), proposed based on biology, demography, morphology and genetic data, were incorporated into management planning and in the delineation of research efforts. We re-evaluated that proposal through the analysis of control region sequences from franciscanas throughout their distribution range (N = 162), including novel sequences from the northern limit of the species and two other previously unsampled localities in Brazil. A deep evolutionary break was observed between franciscanas from the northern and southern portions of the species distribution, indicating that they must be managed as two Evolutionarily Significant Units (ESU). Furthermore, additional FMAs should be recognised to accommodate the genetic differentiation found in each ESU. These results have immediate consequences for the conservation and management of this endangered species. Pontoporia blainvillei , is a small dolphin endemic to the Southwestern Atlantic, from the state of Espírito Santo, Brazil (∼18°S), to the province of Chubut, Argentina (∼42°S) Inia geoffrensisThe franciscana Franciscanas are the most endangered dolphins in the Southwestern Atlantic Pontoporia populations, it is fundamental that their limits be clearly identified. Delimitation is vital to access demographic parameters and, thus, the impact of non-natural mortality. Being demographically independent, populations need to be managed separately. Genetic data have the unrivalled ability to disclose demographically independent units. In conservation, those units are called Evolutionarily Significant Units sensu Moritz To help the conservation of Secchi et al. Pontoporia blainvillei (GenBank NC005277) to design a new set of primers, flanking 577 base pairs (bp) of the mitochondrial control region, . PCR amplification was performed in 25 µL reactions containing 1 unit of GoTaq polymerase (Promega); 0.20 mM dNTPs; 2.5 mM MgCl2; 25 µg BSA and 0.5 µM of each primer. PCR cycling was as follows: 3 min. at 93°C; 30 cycles of 1 min. at 92°C, 1 min. at 50°C and 1 min. at 72°C; plus 5 min. of final extension at 72°C. PCR products were purified and sequenced in both directions in an ABI 3130 automated sequencer. Sequences were edited with program SeqMan 7 (Lasergene Inc.), visually aligned in MEGA 4We collected samples from 68 franciscana carcasses that had washed ashore along the Brazilian coast . No animPreviously published sequences from different localities N = 94  were incDNASp 5Arlequin 3.5Arlequin 3.5. A median joining haplotype network was built with Network 4.611 (www.fluxus-engineering.com).Haplotype and nucleotide diversities were estimated with BEAST 1.6jModelTestWe investigated the demographic past of the species with a Bayesian skyline plot reconstruction conducted in Due to the shorter length of published sequences, analyses were conducted using an alignment of 455 bp. Thirty-six substitutions were observed, defining 30 haplotypes, of which six had not been reported previously. Haplotype and nucleotide diversities were 0.868 (±0.018) and 0.009 (±0.00035), respectively. A gradient of haplotype diversity was evident, decreasing from south to north, and all samples from the northernmost sampling area (Espírito Santo) shared the same, exclusive haplotype .The most frequent haplotype (H3) was found in all localities south of SPC, and the second most common haplotype (H2) occurred in all localities between SC and RJS . HaplotyCT = 0.44; P<10−5) to a two-population scenario . Thus, sub-structuring was further investigated among all localities south of RJS : ARG+URU+RS/SC+PR+SPS+SPC/SPN+RJS   from two to seven, were tested using the AMOVA framework . The poph of RJS . The mos/SPN+RJS . Similar P<10−5) . Combini P<10−5) .The population groups detected by AMOVA analyses were evaluated in relation to possible population expansions, and all of them  had mismatch distributions compatible with the sudden population and geographic expansion models . ExpansilocusThe Bayesian skyline plots revealed contrasting demographic histories among the four populations analysed . Very reThis is the geographically most comprehensive study on the genetic structure and molecular demography of franciscanas to date. The analyses reveal that the species is subdivided into two Evolutionarily Significant Units, each with a higher number of populations (Franciscana Management Areas) than previously recognised. The corollary is that the four current FMAs are inadequate to ensure the best protection for all populations, thus prompting the need for reassessing FMAs.Pontoporia. Interestingly, our results unequivocally show that samples from that area (ES) and those from northern Rio de Janeiro (RJN) comprise populations that are different from each other and much differentiated from those southwards along the South-American coast. Franciscanas from those two areas were provisionally pooled in Franciscana Management Area I .Here, we provide clear evidence that franciscanas should, indeed, be divided into two ESU, North (ES and RJN) and South (RJS to ARG) . The consensu Moritz Our results also reveal that the North ESU should be split into two Management Units  supported by AMOVA, is at odds with the FMA proposal of Secchi et al. ly lower , so the Still concerning FMAII, our results differ from those of Ott CT values. However, we believe that those Management Units should be maintained based on the precautionary approach, since previous studies with microsatellite data report small scale genetic differentiation within FMA III and IV Franciscana populations from both sides of the La Plata River have been treated as different Management Units (FMAIII and IV) based on infection levels and diet composition Recently, mtDNA and microsatellite data indicated geographic micro-scale differentiation among localities within FMA IV Micro-scale genetic differentiation is highly relevant to the management of franciscanas, and as such should be investigated across the entire range of the species. The goal of conservation biology is to preserve species in space and time, and that latter axis depends on maintaining the evolutionary potential contained in geographically restricted adaptive variation. Those local populations should be managed independently on a local basis, to avoid the loss of such adaptations. Therefore, we propose that FMA III and IV should also be updated to accommodate micro-scale genetic differentiation, as suggested by previous studies Fig. 3. Although microsatellite data will be helpful to address micro-scale genetic structure in franciscanas, major patterns of differentiation, as obtained through mitochondrial data, should not change. That conclusion is supported by previous studies that analysed mitochondrial and microsatellite data and observed concordant population structure across markers Combining the results of both demographic analyses  we concluded that the ARG+URU+RS and the RJN populations were the only ones to experience demographic expansions in the recent past . Stable population sizes seem to have been kept by SPN+RJS , and SC+PR+SPS+SPC seems to have suffered a decline from around 100,000 years ago. Older demographic expansions appear to have occurred in all populations, possibly coupled with spatial expansions, as indicated by mismatch analyses. Although Bayesian skyline plots have large variances, it is possible to infer that the RS+URU+ARG population has kept a larger size than the other populations, even before the last demographic expansion. That seems to support the hypothesis that the colonization of the Southwestern Atlantic happened from the south northwards, as already proposed Pontoporia demography and life history, as well as on human-related mortality, so that the conservation status of ES (FMAIa) can be evaluated before its maintenance is irreversibly jeopardised. RJN (FMAIb), on the other hand, is a relatively well known population, but there is no data on its abundance. Still, this population has suffered substantial removal through bycatch, of around 110 animals each year Our results are very relevant to franciscanas' management, by reformulating the FMA proposal of Secchi et al. The genetic discontinuity within FMAII warrants further investigation. Due to the fact that ecotoxicological data Our results do not give support to the existence of more than a single genetic population from RS to ARG. However, we believe that FMAIII and FMAIV should be managed independently, irrespective of their low genetic differentiation. Those populations are the most studied in all aspects, including abundance, population parameters and fishery-related mortality Figure S1Gradient of genetic diversity across the franciscana's geographic range. Square: haplotype diversity; circle: nucleotide diversity.(TIF)Click here for additional data file.Figure S2Mantel test based on control region sequences (N = 162). The x axis is geographic distance (in km) and the y axis is the genetic distance (Rousset's linear FST).(TIF)Click here for additional data file.Figure S3Mismatch distributions of franciscana populations. a) Sudden demographic expansion model, and b) spatial expansion model. Bars show the observed distribution and the line shows the expected distribution. Observed distributions were not statistically different from those expected under expansion models, as indicated by P values of the sum of squared deviations. “T” indicates time since expansion events, in years.(TIF)Click here for additional data file.Figure S4Bayesian skyline plots (m = 5). Derived from franciscana mtDNA control region sequences from four populations: RJN (N = 9), SPN+RJS (N = 10), SC+PR+SPS+SPC (N = 44) and ARG+URU+RS (N = 84). The x axis is in years, and the y axis is equal to Neτ (the product of the effective population size and the generation length in years). The thick solid line is the mean estimate, and the grey area show the 95% highest posterior density (HPD) limits. Estimated times to most recent common ancestor (TMRCA) of the populations, in years, are indicated.(TIF)Click here for additional data file.Table S1Genetic diversity in the mtDNA control region of franciscanas. N: sample size; n: number of haplotypes; h: haplotype diversity; π: nucleotide diversity.(PDF)Click here for additional data file.Table S2AMOVA results of all population structure scenarios tested, considering all sampling localities, compared to scenarios proposed previously.(PDF)Click here for additional data file.Table S3AMOVA results of all population structure scenarios tested, excluding RJN and ES.(PDF)Click here for additional data file.Table S4AMOVA results for scenarios of panmixia.(PDF)Click here for additional data file.


Pygospio elegans, is the result of cryptic speciation. Populations of worms identified as P. elegansooded, and intermediate larvae; these modes are found both within and among populations. We examined sequence variation among partial mitochondrial cytochrome c oxidase subunit I sequences obtained for 279 individual worms sampled across broad geographic and environmental scales. Despite a large number of unique haplotypes , sequence divergence among European samples was low (1.7%) with most of the sequence variation observed within populations, relative to the variation among regions. More importantly, we observed common haplotypes that were widespread among the populations we sampled, and the two most common haplotypes were shared between populations differing in developmental mode. Thus, our results support an earlier conclusion of poecilogony in P elegans. In addition, predominantly planktonic populations had a larger number of population-specific low-frequency haplotypes. This finding is largely consistent with interspecies comparisons showing high diversity for species with planktonic developmental modes in contrast to low diversity in species with brooded developmental modes.Development in marine invertebrate species can take place through a variety of modes and larval forms, but within a species, developmental mode is typically uniform. Poecilogony refers to the presence of more than one mode of development within a single species. True poecilogony is rare, however, and in some cases, apparent poecilogony is actually the result of variation in development mode among recently diverged cryptic species. We used a phylogenetic approach to examine whether poecilogony in the marine polychaete worm,  Most marine invertebrates have complex life cycles and show a diverse range of larval developmental modes. Developmental mode is often defined as discrete categories describing characteristics of larvae, or larval types . For exaStreblospio benedicti, Boccardia proboscidia, Many different developmental modes may be observed within genera or larger taxonomic groups, but typically only one developmental mode exists within a single species. In rare cases, species may express two or more development modes. The term poecilogony . Using these observations, and information from previous studies of nt i.e., , we charnt i.e., . This ch2 , 200 µM of each dNTP , 0.5 µM of each primer , 0.1 U of Taq polymerase, and 1 X of PCR Buffer (Biotools). Reaction conditions included an initial de- naturation step at 94°C for 2 min, then 35 cycles of denaturation at 94°C for 15 s, annealing at 55°C for 15 s, and extension at 72°C for 45 s, followed by a final extension at 72°C for 2 min. For sequencing, the PCR products were treated with Exonuclease I and Shrimp alkaline phosphatase (Fermentas), cycle sequenced in both directions using the BigDye v.3.1 kit, and visualized with an ABI 3130xl Genetic Analyzer and Sequencing Analysis v.5.2. software .From the European samples, genomic DNA was extracted using the DNeasy Blood and Tissue extraction kit  and a KingFisher magnetic processor . A 600-bp fragment of the cytochrome c oxidase subunit I (COI) gene was amplified using species-specific primers . Polymerase chain reactions (PCRs) were performed in 20 µL reactions containing 1 µL of the DNA extract, 3 mM MgClDNA extraction, amplification, and sequencing of the North American samples followed similar protocols, but sequencing artifacts at the 5′ end of the resulting sequences reduced the length of high-quality sequence reads for these samples. To be conservative, we analyzed a shorter fragment of the COI gene (567 bp) when North American samples were included. In analyses involving only the European samples, the 600-bp fragment was used.Sequences were aligned using the ClustalW option of MEGA 4 . For theFor phylogenetic analyses, a single representative of each haplotype was used. JModeltest  was usedhttp://tree.bio.ed.ac.uk/software/figtree/).For tree reconstruction, we explored both maximum likelihood and Bayesian analyses. Bayesian analysis was conducted with MrBayes v.3.1.2. . MCMC M. All parn= 3) and the North American samples (n= 6–7) were excluded. In these analyses, the 600-bp fragment of the COI gene was used. Haplotype diversity and nucleotide diversity for each population were calculated with Arlequin v.3.5.1.2. (ST) via a hierarchical analysis of molecular variance (AMOVA). In the AMOVA analysis, sequences were grouped according to geographical regions . Population structure was also investigated using BAPS 5.3 (K) was set from two to 13, and for each the analysis was run 10 times. In the end, the K with the highest likelihood was chosen to describe the samples.Our genetic diversity analyses focused on populations with sufficient sample sizes for making robust estimates, so the Russian sample .Exploratory analyses tested whether differences in haplotype and nucleotide diversity measures were evident among the European populations with different developmental mode. Here, planktonic populations  have gone through a recent population expansion, Fu's ; D test . Tajima' test  were identified. An expanded dataset included 20 additional individuals from three North American locations for a total of 299 sequences, 567 bp in length, with 123 unique haplotypes (GenBank accession numbers JN033571–JN033693). The most common haplotype, EUNA10, was shared by 36 European individuals and was found in all three Danish populations, Iceland, Sweden, and Plym Bay in the UK (English Channel). This haplotype was also observed in worms sampled from both populations on the East Coast of the United States (NAE). The second most common haplotype, EU11, was found in Denmark, Finland, France, the Netherlands, and the White Sea, Russia . Note that both EUNA10 and EU11 were found in populations differing in developmental mode . However, nucleotide diversity was not significantly different . Haplotype diversity in the North American samples was somewhat lower than in most of the European samples . Ninety percent of the haplotypes (109 out of 121) were found in only one population . Populations from the North Sea, English Channel, and Wadden Sea had the highest percentage of population-specific low-frequency haplotypes. The Baltic Sea populations  shared many haplotypes , and only one haplotype (EU8) was shared exclusively among the three populations in the UK and France. In most populations, haplotype diversity was high . However samples , but NorMean sequence divergence (Tamura Nei model) within the total European dataset was 1.7%. Divergence between the European and North American haplotypes was noticeably higher: 5.3% . Mean sequence divergence within the total North American dataset was 3.1%, higher than what we observed from the European sample.P. elegans haplotypes. However, this analysis also reveals clusters detected in the haplotype network. For example, one well-supported group contains almost all the Finnish Ängsö haplotypes . Another well-supported group contains individuals from Germany and all of the three Danish populations, even though other haplotypes from these populations were also distributed elsewhere in the phylogenetic tree. In addition, most Swedish (except one) and all Icelandic haplotypes were included in a well-supported group, which also included EUNA10, one of the most common haplotypes also sampled from North America. The two most commonly encountered haplotypes (EUNA10 and EU11) did not group together  and that differentiation among the regions was significant although small . Additional significant variation among populations within each region  indicated that structure may also be present on smaller spatial scales.For the test of regional subdivision of sequence diversity in Europe (AMOVA), the populations were arranged into four groups according to geographical region . Clusters were not based on sampling location, each cluster containing individuals from four to ten sampling locations. Two clusters were strictly Baltic, one containing most of the individuals sampled from the Finnish Ängsö site and the other containing most of the German samples, although German individuals were also placed into four other clusters .Fs values were negative and significant. Unimodal mismatch distribution curves for these populations also indicate that a recent population expansion may have occurred . Truncated, left-skewed mismatch distribution curves were seen for the Finnish Ängsö and Iceland populations, indicating that most haplotypes were identical within these populations.Demographic analyses suggested there has been recent population expansion in the populations from the UK and France. In these populations, Fu's nificant , supportnificant . The oneP. elegans found little evidence for cryptic species. Sequence divergence was low particularly among European samples (1.7%), which originated from populations with different developmental modes. This modest degree of divergence is lower than within species divergence observed in similar studies of other polychaetes and of other poecilogonous species (discussed further below). In addition, the sequence divergence in European P. elegans was lower than threshold values used for delineating “potential” species in the DNA barcoding program . The higher divergence is not surprising given the geographic distance of the samples, but it may indicate a possible cryptic species in North America. Unfortunately, since our collections in North America were limited , we are unable to make a strong conclusion about this result, and additional data from unsampled populations in North America and larger sample sizes are needed. However, the degree of divergence among the North American and European haplotypes provides perspective and strengthens our conclusion of poecilogony among European populations.Arenicola is on the order of 14%  and lecithotrophic (brooded) larvae have been documented within the S. benedicti clade. COI sequence data have also been employed to investigate the potential for cryptic species in the marine gastropod genus Alderia. A. willowi and A. modesta, which were 20.6% divergent from one another. Within each clade, sequence divergence was less than 5%. Although the A. modesta clade produces only planktonic larvae, slugs in the A. willowi clade produce multiple larval types. In this case, both sequence data and morphological evidence  of low-frequency haplotypes found in one or a few populations. On the other hand, two haplotypes dominated the sample . Both of these haplotypes had a broad distribution in many of the European populations we sampled and EUNA10 was also sampled from North America. We also found four haplotypes that were shared among populations characterized by different modes of development, including the most common haplotypes, EUNA10 and EU11. Similar findings were reported for the poecilogonous A. willowi, for which shared haplotypes were observed by B. proboscidea, where there was no association between sequence clades and developmental mode , Sweden, and Iceland populations, which were also found in some of the few well-supported clades in our maximum likelihood phylogeny. Overall, these results indicated that the bulk of genetic variation in P. elegans resided within populations.However, the significant AMOVA results among regions and among populations within regions indicate that there is genetic structuring at multiple spatial scales within Europe. Our sampled populations cover both a latitudinal and longitudinal gradient and experience different environmental conditions, such as differing temperature extremes, salinities, and substrata, so potential barriers to dispersal may be present between our sampling localities. The AMOVA indicated significant ΦP. elegans populations  and some possible variation may have gone undetected, so it is impossible to say at present whether an increased frequency of asexual reproduction leads to a greater reduction in genetic diversity.We observed especially low genetic diversity among the worms in the two marginal populations: Ängsö, being located in the inner parts of the Finnish Archipelago, and Iceland, further away from the bulk of the sampled localities in the Atlantic Ocean. Mismatch distribution curves for the sequences from these populations were strongly skewed to the left showing the reduced diversity, which likely reflects limited gene flow to these populations because of their marginal distribution or a recent (re)colonization or other bottleneck-like event. The significant neutrality test values seen in the Ängsö population indicate that selection could also cause this pattern. However, if selection were the cause, we would expect the other two nearby Finnish populations (the distance from FIA to FIF is only 20 km) to show similar results. Alternatively, asexual reproduction could lead to lower diversity. Asexual reproduction has previously been observed in some ns e.g., . Wilson Littorina, Boulding and colleagues (L. scutulata and L. plena than in the direct-developing species L. sitkana and L. subrotundata. eN) is larger in planktonic-developing species than in direct-developing species, and as a result, the effects of genetic drift are diminished and a larger number of rare haplotypes are more likely to be retained. In another notable example, A. modesta compared to genetic diversity in the poecilogonous A. willowi.Several studies have compared genetic diversity among marine invertebrate species with different developmental modes and generally found higher genetic diversity in planktonic-developing species compared to brooded or direct-developing species . For exalleagues  observedP. elegans suggests that the patterns of genetic diversity and development mode may be correlated within a poecilogonous species as well. Haplotype and nucleotide diversity were lowest in Ängsö, Finland population for which brooding is the predominant mode of development, although reduced diversity in this population could also be the result of other demographic factors (see above). In contrast, populations showing a predominantly planktonic developmental mode were more diverse than the Ängsö population as well as those populations showing multiple developmental modes. Fu's Fs test gave significant negative values for the three populations with predominantly planktonic larvae , indicating an excess of haplotypes, while sequences from these same populations had unimodal mismatch distributions with large numbers of pairwise differences. A pattern of significant Fu's Fs and nonsignificant Fu and Li's test suggests that the populations may have undergone recent expansion and have high eN and that background selection is not likely (Our analysis with t likely ; Fu 1997Although we cannot be certain that our definition of predominant developmental mode for the different populations is correct, our results are interesting because they reflect the general expectations of how developmental mode can influence the genetic diversity within populations, as well as population genetic structure and gene flow among populations of a species (see reviews by P. elegans to re-colonize rapidly disturbed areas (Pygospio elegans from Gullmar Fjord, Sweden, have been previously reported to have intermediate and brooded larvae (Our analysis suffers from the lack of individual level data for developmental mode and has only tentatively defined populations as predominantly planktonic, brooding, or both. We cannot rule out the possibility that other developmental modes are predominant at other times of the year or that developmental mode might fluctuate temporally either due to phenotypic plasticity or population turnover. In this regard, it is interesting to note that ed areas . For othd larvae , but we P. elegans is poecilogonous. P. elegans populations from the English Channel. They found that the Plym Bay and Ryde Sand populations from UK were strictly brooding while the French Somme Bay population and English Swale Bay populations produced only planktonic larvae. Based on allozyme analysis, they observed significant population structure but found no evidence of cryptic species in the English Channel. In our study, we widened the sample area in an attempt to increase the chance of finding cryptic species if such existed in Europe, but our conclusions uphold the previous results even at the broader spatial scale. Although the previous experimental study by Our study is not the first to consider whether P. elegans sampled from a broad geographic range. Very little sequence divergence was observed among individuals despite variation in developmental mode observed both among and within populations. Using a DNA barcoding criterion based on sequence divergence, there is no evidence for cryptic species in this taxon. In addition, haplotype network and phylogenetic analyses did not point to potentially cryptic species as no clear clustering of haplotypes was resolved among the European samples. Divergence of North American P. elegans may warrant further study. Given these results, we conclude that developmental polymorphism in P. elegans is likely a true case of poecilogony. These results also confirm the previous population genetic study of In this study, we analyzed DNA sequence variation from


Recombination varies greatly among species, as illustrated by the poor conservation of the recombination landscape between humans and chimpanzees. Thus, shorter evolutionary time frames are needed to understand the evolution of recombination. Here, we analyze its recent evolution in humans. We calculated the recombination rates between adjacent pairs of 636,933 common single-nucleotide polymorphism loci in 28 worldwide human populations and analyzed them in relation to genetic distances between populations. We found a strong and highly significant correlation between similarity in the recombination rates corrected for effective population size and genetic differentiation between populations. This correlation is observed at the genome-wide level, but also for each chromosome and when genetic distances and recombination similarities are calculated independently from different parts of the genome. Moreover, and more relevant, this relationship is robustly maintained when considering presence/absence of recombination hotspots. Simulations show that this correlation cannot be explained by biases in the inference of recombination rates caused by haplotype sharing among similar populations. This result indicates a rapid pace of evolution of recombination, within the time span of differentiation of modern humans. The recombination rate is neither constant along chromosomes nor across species. The rate within genomes has been observed to vary at both the megabase level, with different chromosomal regions in the human genome showing differences in their recombination rates Measuring the fine-scale recombination rate is experimentally challenging, the resolution of recombination maps experimentally obtained is limited by relatively few meioses and a low density of markers, exception made by the recent paper by Kong et al. Differences in recombination rates among human populations provide a useful temporal framework to analyze the evolution of the recombination landscape, as they are recent enough to capture fast evolutionary changes. The basal branches of the genetic diversification of human populations happened some 150,000 years ago, a much shorter time than the split between humans and chimpanzees (around 6.5 million years). The comparison of the recombination patterns among human populations provides a means to verify whether recombination landscapes evolve over time. To address this issue, we analyzed whether differences in recombination rates among human populations are correlated with their genetic differences computed as genetic distances. Whole genome estimations of recombination rates based on SNP data are already available for HapMap samples which, however, consist only of four populations for HapMap Phase I and II rhomap program rhomap program were carried out . For each pair of adjacent SNPs we obtained 5 estimates of the population recombination rate (er4N/kb) in each population and the median of these 5 estimates was used in the analysis.We considered the H971 subset of the Human Genome Diversity Cell Line Panel (HGDP-CEPH) recommended by Rosenberg er = 4N), estimates of the population recombination rate in each population were normalized by eµθ = 4N, a scaled population mutation rate obtained from the same individuals and populations, where µ is the genome-wide average microsatellite mutation rate per locus and per generation Since population recombination rates (ρ) are dependent on the effective population size  for all pairs of adjacent typed SNPs between a population pair. In order to simplify the comparison with the genetic distance, the Spearman correlation values were turned into a dissimilarity measures by subtracting them from 1. The obtained 28×28 matrix is then a measure of the dissimilarity of recombination rates between each pair of populations.ST measure ST values were calculated using a routine implemented in the PopGen module of BioPerl The differentiation among human populations was estimated through the FST matrix), were compared using a standardized Mantel test The matrix of recombination dissimilarity and that of genetic distance  on the relationship between genetic distance and recombination landscape, we designed a simulation study. We simulated human demography under a model in which the recombination rate was the same for all the simulated populations, and we sought to determine whether the correlation between genetic distance and inferred recombination similarity between simulated populations was similar to the observed in empirical data.ST values that mimic the divergence found among the three human populations being simulated and to obtain similar values of the frequency distribution of alleles, among other parameters. We performed 1000 simulations using the best-fitting demographic model provided by COSI. For each simulation, we set the length of the simulated sequences to 1 Mb and adopted a sample size of 56 sequences for European and Asian populations and 42 for the African population with the aim of having the same amount of individuals as in a three chosen equivalent HGDP populations . In each simulation, the distribution of the recombination rate is the same for the three simulated populations; this leads to simulated genotypes of different populations that share common haplotypes but have not experienced differences in their recombination rate. Finally, as SNPs included on Illumina's HumanHap650Y Genotyping BeadChip are tagSNPs with r2 higher than 0.8, and in order to have a similar ascertainment bias in the simulations and in the observed data, we performed a selection of tagSNPs with the same criteria using Haploview software with the pairwise option The simulations were carried out with the COSI program In order to compare simulated data to a consistent empirical dataset, we randomly chose, along the whole genome, 1000 non-overlapping 1 Mb long windows, and we analyzed them across the three populations of Yoruba, French and Japanese.ST and recombination rates, following the same procedure as before, for real and simulated data. If the shared haplotypes were the main source of the high correlation found between recombination and genetic distance, we expect to observe this correlation also in the simulated data.We then computed FPopulation recombination rates were computed between 636,933 neighboring SNPs for 28 populations. As the recombination rate was estimated through several runs for each population, and to test for the agreement of estimates between runs of the same chromosome, 10 runs were performed for chromosome 22 for all populations. We carried out a repeated measure ANOVA testing population and run as the main effects and pairs of adjacent SNPs as a covariate. No statistical significance of runs was found, but population and pairs of adjacent SNPs were highly significant (data not shown). This result reflects that the noise in the estimation procedure is low in relation to differences between populations.27, 636,910 = 59,479.8; p<0.00001; R2 = 8.5%). A Friedman ANOVA test shows similar results . Post hoc analysis using a Bonferroni correction for the repeated ANOVA test show that differences between populations remain significant, except for two homogenous groups from Central South Asia: Pathan, Burusho and Brahui on the one hand; and Mozabite, Balochi and Makrani on the other. ST measure ST measures were stored as dissimilarity and distance matrices respectively and compared using a standardized Mantel test r correlation of 0.894 (p<0.0001) was observed, indicating that differences in recombination rates among populations increase with their genetic distance . We also calculated this correlation using only SNPs with a minor allele frequency (MAFs) above 0.1 and above 0.2 in the 28 analyzed populations. This analysis includes 141,921 SNPs and 34,706 SNPs respectively and show similar results to the mean analysis (r correlation is 0.855 for SNP with MAFs high than 0.1 and 0.830 for SNPs with MAFs high than 0.2 and highly significant for both (p<0.00001).Genetic differentiation estimated through Fanalysis , the Manr = 0.863, p = 0.002) and was statistically significant even with the low number of pairwise comparisons.It can be argued that these results could be driven by a bias in the selected populations, that is, by similar recombination rates in populations belonging to the same continental group, due to the presence of common or shared haplotypes, and - not to smaller changes in crossing-over rate. To test this hypothesis, we repeated our analysis considering only one population per continental group, thus avoiding redundancy in the genetic composition of the populations in our dataset. In particular, the analysis was performed with data from Yoruba (Africa), French (Europe), Bedouin (Middle East/North Africa), Burusho , Han (East Asia) and Maya (America) populations. The observed correlation remained very high .To test for the impact of using the same data set for estimating recombination and genetic distance, we performed a Mantel test between the Fr = −0.08 p>0.05; Spearman correlation test r = 0.34 p>0.05).Alternatively, comparisons of recombination rates among populations can be evaluated by attending to the presence or absence of recombination hotspots. This analysis has a special value as it is much less dependent on the accuracy of estimating the recombination rates. We defined a hotspot in each population as a recombination rate that exceeds 5 times the mean rate, with a threshold of 0.1×4Ner/Kb. A total of 22,413 hotspots have been detected at least in one population each. The number of hotspots vary among populations, from 2,582 for South China to 8,042 for Palestinian , Europe and Central South Asia (0.44) and between Middle East and North Africa and Central South Asia (0.41). These values are, as expected, much lower when considering Sub-Saharan African or East Asian populations . An inteST matrix, highly significant results were obtained , suggesting that differences in the location of recombination hotspots increases with genetic differentiation between human populations.We calculated the Jaccard distance between each pair of populations to measure the overall difference in presence/absence of hotspots . Comparing this distance matrix with the FWith the Mantel test analysis using only one population from each continent, we have shown that the effect of haplotype sharing in closely related populations does not explain the correlation between genetic differentiation and recombination. However, it is possible that the sharing of haplotypes and, hence of linkage disequilibrium patterns, had a considerable effect also on distant populations, since its origin can be traced back to the Out of Africa origin of modern humans. To disentangle this point, we performed a simulation study designed to recognize the impact of using shared haplotypes on the estimates of recombination rates.ST values, for both simulated and empirical data. This is a more stringent test than the previous overall comparison between FST and recombination patterns, since, rather than general means, data points correspond now to 1000 windows a 1 Mb each. The correlation between recombination values and genetic distance for empirical data are 0.26, 0.25 and 0.27 for Yoruba-French, Yoruba-Japanese, and French-Japanese respectively . The values here are much lower than before as they refer to correlations between FST and the correlation of the recombination values for windows of 1 Mb in two populations and not to distances . Conversely, these values are only 0.05, 0.06 and 0.09 for the simulated African-European, African-Asian and European-Asian . This shows that, within the simulated populations, FST and recombination rate were not correlated despite sharing common haplotypes, whereas they are clearly correlated within the three studied populations. The common origin of haplotype structure, as illustrated in the simulation data, is unlikely to have contributed to a large part of the correlation between genetic distances and structure of the recombination landscape.As the number of simulated populations is only three, the Mantel test cannot provide a robust comparison. To compare the relationship between recombination similarities and genetic differentiation in the three populations being simulated and in the three corresponding HGDP populations, we performed a Spearman correlation of the values of recombination between all neighboring SNPs in the 1Mbp region and their FST measures. As its impact is likely to be restricted to the relative low number of regions that could be under very recent positive selection and acting differentially among populations In the simulations we have not considered the possible impact of natural selection and its consequences both on the estimated taxes of recombination (as they rely on measures of linkage disequilibrium) and on FThe results of this study reveal the footprint of the evolutionary history of human populations on the recombination rate. The large differences found in the comparison of the recombination landscapes among humans and chimpanzees Figure S1Recombination rate estimates (4Ner/kb) corrected for effective population size for successive SNP-pairs for chromosome 22 and in each of 28 populations, grouped into geographical regions.(PDF)Click here for additional data file.Figure S2Mean of the recombination estimate (4Ner/kb) for all populations calculated for 10 categories of SNPs based on their minor allele frequency. MAFs are calculated using the global allele frequency of all populations together.(TIF)Click here for additional data file.Figure S3Heatmap showing patterns of hotspots observed for 300 SNPs of chromosome 22 for the 28 populations, grouped according to their geographical region. The first 300 SNPs of chromosome 22, for which a hotspot is present in at least one population, are reported on the x axis. In color, for each population the value of the recombination estimate (4Ner/kb) corrected for effective population size for that SNP in a gradient from blue  to green .(TIF)Click here for additional data file.Table S1Mantel's r values between the genetic distance and recombination dissimilarity matrices. First row shows chromosome for which the genetic distance was calculated; first column show the chromosome for which the recombination matrix was calculated. All value were highly significant (p<0.00001).(DOC)Click here for additional data file.