Meiotic crossovers are the major mechanism by which haplotypes are shuffled to generate genetic diversity. Previously available methods for the genome-wide, high-resolution identification of meiotic crossover sites are limited by the laborious nature of the assay (as in sperm typing).Several methods have been introduced to identify crossovers using high density single nucleotide polymorphism (SNP) array technologies, although programs are not widely available to implement such analyses.Here we present a two-generation "reverse pedigree analysis" method  and a web-accessible tool to determine and visualize inheritance differences among siblings and crossover locations on each parental gamete. This approach is complementary to existing methods and uses informative markers which provide high resolution for locating meiotic crossover sites. We introduce a segmentation algorithm to identify crossover sites, and used a synthetic data set to determine that the segmentation algorithm specificity was 92% and sensitivity was 89%. The use of reverse pedigrees allows the inference of crossover locations on the X chromosome in a maternal gamete through analysis of two sons and their father. We further analyzed genotypes from eight multiplex autism families, observing a 1.462 maternal to paternal recombination ratio and no significant differences between affected and unaffected children. Meiotic recombination results from pediSNP can also be used to identify haplotypes that are shared by probands within a pedigree, as we demonstrated with a multiplex autism family..Using "reverse pedigrees" and defining unique sets of genotype markers within pedigree data, we introduce a method that identifies inherited allelic differences and meiotic crossovers. We implemented the method in the pediSNP software program, and we applied it to several data sets. This approach uses data from two generations to identify crossover sites, facilitating studies of recombination in disease. pediSNP is available online at  Meiotic recombination or crossing over assures that each child inherits distinct genetic material from parental chromosomes. The process of meiotic recombination occurs between homologous chromosomes that pair and form chiasmata that promote normal chromosomal segregation during meiosis. In humans, mice, and other eukaryotes the sites of recombination characteristically occur in "hot spots" which are narrow regions of several thousand base pairs or less . The PhaRecombination rates have been determined using several main approaches: cytogenetics, direct molecular assays, and genetic linkage analysis . CytogenWhile early studies employed up to hundreds of highly polymorphic markers, the introduction of SNP array technology allows from thousands to over a million markers to be assessed. Using an array with 11,454 SNP loci, Wirtenberger et al. (2005) implemented a schema (described below) to detect crossover regions in 16 members of a three-generation family. They separately compared the individual maternal and paternal haplotypes of a pair of siblings at all positions where the mother or the father were heterozygous. This allowed them to delineate regions of identical and nonidentical haplotype blocks that represented crossovers in the two siblings. More recently, Coop et al. [The determination of locations and patterns of meiotic recombination in pedigree data remains a challenge. We previously reported the SNPtrio program for the analysis of inheritance patterns in high density SNP genotype data . SNPtrioSNPtrio uses a schema to define inheritance patterns in genotype data from trios ; AdditioSNPtrio was programmed to analyze genotype data from trios in the format father/mother/child. In the present work we applied an adaptation of the SNPtrio algorithm with "reverse pedigrees" in the format child1/child2/father and child1/child2/mother with the capacity to analyze pedigrees with at least two and as many as ten offspring. It should be noted that data are entered as standard pedigrees and the program automatically generates the appropriate reverse comparisons. We applied the SNPtrio schema to the reverse pedigrees, and assigned novel interpretations in order to identify regions in which the children have inherited the same (or different) parental haplotypes Table . For exaThe output of pediSNP for chromosome 5 of a pedigree consisting of both parents  and two children (brothers B1 and B2) contains four panels as well as a chromosomal ideogram Figure . The fir1) The two reverse pedigree plots display overlapping boxes with track 2 (ID) patterns indicated with red dots. This is a region where the parent contributed DNA sequences from the same physical chromosome to both siblings. Therefore, these two siblings have inherited DNA sequences that are identical by descent on that parental haplotype. The identical genotypes of each sibling are described by the pediSNP schema  Both reverse pedigree plots contain a SNP pattern on track 4 (indicated with black OPP dots) denoting inheritance of non-identical alleles in the siblings for that parent. In this case, both parents contributed non-identical haplotypes to these two siblings. Thus, these two siblings did not share any single allele. This is referred as semi-identical inheritance region with only one parental haplotype shared identically by descent. The corresponding IBS plot included IBS-1 patterns as expected for one shared allele  One parent's reverse pedigree plot is part of a red track 2 box while the other parent's plot has a blank space in the corresponding region. This pattern indicates that for the parent with a red track 2 box the two siblings inherited the same allele, and for the parent with blank space the two siblings inherited two separate alleles. This is a e Figure .We note that in Figure (4) All four panels  can be blank, indicating no informative BPI or MI-S SNPs for SNPtrio Figure , arrow. The schema employed by pediSNP is closely related to but distinct from those used by Wirtenberger et al. and Coop et al.  and six reversed pedigree panels (three for two children relative to the father and three for two children relative to the mother). An example is shown in Figure Analysis of the three reverse pedigree panels associated with each parent allowed the determination of meiotic crossover sites in each child. The edges of identical inheritance regions (track 2 ID regions) aligned into pairs (with the exception of the telomeric and centromeric regions that lacked SNPs). We infer that each meiotic crossover caused a change in the block type pattern of that child compared to the other children for the parent in which the crossover occurred. For example, the first vertical pink line with arrows in Figure With four children, pediSNP generates four standard SNPtrio panels and twelve reverse trio panels (six for each parent) as shown in Figure The graphical output of pediSNP can be used to infer meiotic crossover sites. This can be done manually by visual inspection of the output to determine breakpoints in the tabular data , including genotype data from three generations, and in which all crossover sites were known. A crossover site was defined as "found" if the region of minimum overlap included the known crossover location. The algorithm exhibited approximately 89% sensitivity and 92% specificity Figure . Thus, 8We investigated the causes underlying the false positive and false negative calls. Out of about 238 calls 214 were true synthetic recombination sites; 18 were false positive calls, and 24 true sites were not called .550 K Illumina SNP data for eight families were obtained from the Autism Genetic Resource Exchange (AGRE) and analyzed via pediSNP , the genome-wide maternal to paternal recombination ratio was 1.526. These ratios of recombination events here , are comparable with previous studies: 1.555/1.625 for genetic length (cM) ratios in ; 1.378/1The average number of autosomal recombination events among the seventeen affected children was 26.00 on the paternal gamete and 37.71 on the maternal gamete, similar to the averages from the eleven non-autistic children: 26.73 on the paternal gamete and 39.64 on the maternal gamete. The differences between affected and unaffected children were not significant on a combined genome-wide or chromosome-by-chromosome basis (p = 0.337), nor on the paternal gamete (p = 0.700) or maternal gamete (p = 0.479).Recombination on the X chromosome occurs in females but not on the hemizygous male X. Recombination can occur between the pseudoautosomal regions PAR1 and PAR2 of the male X chromosome and the Y chromosome, although both PAR and Y chromosome SNPs are represented only sparsely on most commercial SNP platforms. The properties of female recombination are similar to those of other autosomes  and 4 (D4) were diagnosed with autism and daughters 2 (D2) and 3 (D3) were diagnosed unaffected. pediSNP was used to generate a series of 16 panels of all chromosomes. Results are shown for chromosome 9 Figure . Each plIt is important to understand the nature of human recombination for many reasons including its role in generating genetic diversity and the effect of aberrant recombination in causing aneuploidy, a leading cause of both miscarriages and disorders such as Down syndrome. Recombination history generates patterns of linkage disequilibrium, which is the correlation or covariance between neighboring SNPs.The reverse trio approach that we introduce in this paper uses a schema to define informative SNPs that originated with the SNPtrio tool . Using "A novel feature of pediSNP is that it permits the analysis of crossovers on the maternal X chromosome, even when analyzing SNP patterns from one or more sons and the father (who has an unrelated X). Our approach allowed us to determine the maternal crossovers based on separate analyses of paternal and maternal genotypes. Notably for SNP-based methods, hemizygous SNPs (A or B), such as SNPs on a male X chromosome, are interpreted as homozygous (AA or BB) with most current genotype calling algorithms. This reduces the number of heterozygous calls and thus limits the ability to detect crossovers on the X chromosomes in males with alternative algorithms. For example, Coop et al.  provide Meiotic recombination is an essential feature of chromosomal biology and is largely responsible for generating haplotype diversity in offspring. Until recently, methods to detect meiotic crossovers were either laborious (e.g. sperm typing) or required three generation pedigrees. We introduce a novel reverse pedigree algorithm to analyze crossover locations within pedigrees, and implement this method with the pediSNP program. This provides a user-friendly, accessible way to identify meiotic crossovers. We anticipate that this tool will be useful for studies related to disease (e.g. in identify haplotypes harboring mutations that are shared by siblings) or chromosomal biology (e.g. characterizing events such as crossover interference or variation in crossover frequency and location).The pediSNP program is publicly available  and was SNP data based on Illumina's 550 K SNP chip were obtained from AGRE as Illumina BeadStudio data files. SNP data of genotype calls were exported from BeadStudio as text files, and used as the input to pediSNP. Forward trio analysis was performed by steps described in SNPtrio and plotted as trio tracks. Meiotic recombination sites were identified among reverse trio tracks of identical or opposite inheritance, based on patterns and descriptions shown in Figure Figure A unique dimension introduced by pediSNP lies in blocks of dots shown in both the OPP and the ID tracks  Thus, for example, in Figure Each contiguous red ID box region on any one of those two reversed trio plots identifies a segment of that particular chromosome where one of that parent's allele was not transmitted to either of the two siblings. For that chromosomal region, the two siblings inherited the same allele from this parent. The beginning and the end points of each such chromosomal region mark two different meiotic crossover events on that parent's chromosome among these two siblings. Each crossover was transmitted to only one sibling and not to the other sibling. Both crossover events could have been transmitted to the same child.Note that OPP dots signify that parent1 is AB while one child is AA and the other child is BB. Consider a region of semi-identical (IBS-1) inheritance  such as hemizygous deletions, as well as Mendelian inconsistencies. SNPtrio panels are plotted as part of the pediSNP output. Similar patterns are occasionally evident on reverse trio plots.  and 4 (opposite inheritance) and is thus unaffected by genotyping errors which would generate signals in tracks 1, 3, or 5. The presence of a single signal in tracks 2 or 4 that was due to genotyping error would not disrupt the segmentation algorithm because it relies on locating the beginning and end of blocks to find the crossovers. A single MI-S or BPD in the middle of another block type would not generate enough points to create a block (and would be too far away from other blocks) to be considered. Furthermore, if there were by chance two or more dots interrupting a block, it could correspond to a true recombination event. This can be assessed by finding consensus among n-1 siblings. Even if it were found, an erroneous block would be ignored unless it matched in multiple individuals. For these reasons the segmentation algorithm is insensitive to single outliers (genotyping errors) and would only to start to be affected when there were multiple points clustered together. And then the clusters would only cause a problem if they were replicated between multiple siblings .Synthetic SNP data were generated to test the segmentation algorithm's sensitivity and specificity using data in which recombination sites and gamete choices were known. Data were based on a HapMap genotype scaffold  and distributed throughout the genome at their annotated coordinates. A three generation pedigree was designed to include maternal and paternal grandparents, two parents, and four children/grandchildren.The first step in creating the synthetic pedigree was to create the grandparents' genotypes from which all individuals would be derived. The synthetic parents for each grandparent were defined as two unrelated HapMap Yoruba individuals for which SNP data were available. The father/mother for the Paternal Grandfather, Paternal Grandmother, Maternal Grandfather, and Maternal Grandmother were individuals NA18507/NA18858, NA18871/NA18517, NA19138/NA19172, and NA19160/NA19116. The HapMap founders' genotypes were initially randomly segregated into haplotypes. Location and number of crossovers were determined for each individual. Each chromosome arm could have one or two crossover events, and the probability of each scenario was dependent on chromosome arm size and sex Table , chosen Once the number of recombinations was determined, crossover locations were determined. For each crossover a 1 Mb bin was selected based on the weighted sex-specific recombination probability  availablWith crossover locations decided, the haplotypes were swapped in the appropriate positions to create a choice of two non-recombinant and two recombinant gametes for each chromosome. The choice of which gamete to transmit to progeny was independent for all chromosomes in females. Males did not recombine their X, and therefore could only transmit the X to female offspring or the Y chromosome male offspring. The probability of selecting a non-recombinant chromosome was 35%, while the probability of selecting a recombinant chromosome was 65%.Once the synthetic gametes were generated for each founder pair, the gamete haplotypes were stored for the individual they created, and that individual's genotypes were derived from the inherited haplotypes. This ensured that the individuals in the first generation (the grandparents) had allele frequencies representative of the sampled population and had genotypes that were based on real data.With the grandparents established, synthetic children were derived in a very similar manner, the only difference being that haplotypes were already established. Each crossover location and gamete choice was recorded in the output, giving a complete list of all of the crossovers that occurred to create each individual. The reported data were given as tabular genotypes annotated by chromosome and position for each member of the pedigree.Project name: pediSNP• Project home page: • Operating system(s): Platform independent web interface, Linux only webserver• Programming language: Perl, R, C, html• Other requirements: none• License: GNU GPL• Any restrictions to use by non-academics: none• BPI: biparental inheritance; MI-D: double Mendelian inconsistency; MI-S: single Mendelian inconsistency; SNP: single nucleotide polymorphism; UPI: uniparental inheritance, OPP: opposite inheritance; ID: identical inheritance; IBS: identity-by-state; IBD: identity-by-descent.The authors declare that they have no competing interests.JCT developed the project, implemented the original software, and co-wrote the manuscript. EDOR optimized software, generated synthetic data, contributed to the segmentation algorithm development, and contributed to the writing of the manuscript. DGC programmed the segmenting algorithm, calculated sensitivity and specificity, and contributed to the writing of the manuscript. JP conceived the project and contributed to data analysis and writing the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:Schema for SNPtrio. The SNPtrio schema as reported in Ting et al. (2007). This schema includes AA, AB, or BB genotype calls for a trio consisting of two parents and one child. Informative SNP combinations are plotted on tracks  and identical inheritance patterns in forward and reverse pedigrees. (A) In forward trios, MI-S patterns occur when a child has a single allele not present in either parent (i.e. father/mother/child AA/AA/AB or BB/BB/AB). In this figure, as in Additional File Click here for fileBiparental inheritance (BPI) and opposite inheritance patterns in forward and reverse pedigrees. (A) In forward trios, BPI occurs when the parents are opposite homozygotes, making the child an obligate heterozygote . BPI typically occur ~5% of the time in analysis of trios, and they are expected to occur across the genome unless there are interruptions by anomalies such as deletions or duplications. BPI signals are indicated as black dots. (B) In reverse pedigrees, genotypes of two children are analyzed relative to one parent . The child1/child2/mother trios are shown both in the conventional pedigree format (with children at the bottom) or as reverse pedigrees with children at the top; these two representations are equivalent, offering two perspectives on the pediSNP schema. The output on track 2 ("opposite") in pediSNP includes black dots that only occur when the two children have homozygous SNPs with no shared alleles (i.e. AA/BB or BB/AA in child1/child2).Click here for fileWirtenberger and Coop schemas. Schemas for defining informative SNP patterns by Wirtenberger et al. and Coop et al. In the Wirtenberger schema, father/mother/child combinations are defined from which transmission from a maternal haplotype (column Wirt. hapM) or paternal haplotype (Wirt. hapF) are considered in two separate analyses at each SNP position. For example, for pattern AA/AA/AA it is inferred that the child inherited an A from each parent. The Wirtenberger schema includes additional analysis for missing data (not shown). For the Coop et al. schema, informative positions occur where one parent has a homozygous call and the other parent is heterozygous (e.g. AA/AB/AA)(see arrowheads).Click here for fileMeiotic crossover sites in eight autism families. Meiotic crossover sites were located with pediSNP in eight autism families as a function of chromosome. Children identified with a single asterisk were diagnosed as having autism, while a double asterisk indicates a diagnosis of Autism Spectrum Disorder. Values represent the number of meiotic crossover events. In some instances it is possible to determine that a crossover has occurred, but it is not possible to unambiguously assign the crossover to a child or his/her sibling. In such cases we recorded a value of 0.5 crossovers for each sibling.Click here for fileIdentity by state on the X chromosome. IBS analysis of the X chromosome in two brothers (S1 and S2). While the male chromosome X is hemizygous, current SNP platforms interpret the genotype (A or B) as the biallelic calls AA or BB, and there are essentially no heterozygous (AB) calls. In the telomeric regions, these two males have an IBS 2 signal that corresponds to identically shared segments (i.e. AA matches AA or BB matches BB). In the central portion of the chromosome , an IBS 0 signal is present consistent with these two X chromosomes being unrelated across the region. The lower panels show the genotype calls. The results of this SNPduo analysis are consistent with those of pediSNP.Click here for fileRecombination in pedigrees with autistic probands. Identification of shared alleles on chromosome 16p13.3 in probands with autism that are not inherited by unaffected siblings. The same analysis described in Figure Click here for file


Important features of this method are that regions are defined statistically, so that there is no dependence on a gene database, and both gene and inter-gene regions can be detected. In the analysis of blood-lipid phenotypes from the Framingham Heart Study (FHS), we compared statistically defined regions with those formed from the top single SNP tests. Although we missed a number of single SNPs, we also identified many additional regions not found as SNP-database regions and avoided issues related to region definition. In addition, analyses of candidate genes for high-density lipoprotein, low-density lipoprotein, and triglyceride levels suggested that associations detected with region-based statistics are also found using the scan statistic approach.Due to the high-dimensionality of single-nucleotide polymorphism (SNP) data, region-based methods are an attractive approach to the identification of genetic variation associated with a certain phenotype. A common approach to defining regions is to identify the most significant SNPs from a single-SNP association analysis, and then use a gene database to obtain a list of genes proximal to the identified SNPs. Alternatively, regions may be defined statistically, via a scan statistic. After categorizing SNPs as significant or not (based on the single-SNP association  Definition of an appropriate unit of gene function has been identified as a fundamental issue in genetic association analysis using high-dimensional single-nucleotide polymorphism (SNP) data . On one df) that combine information across a set of SNP markers within an identified region [df on rare haplotypes. At the other extreme, the results of multiple single df tests of SNPs within a candidate region require adjustment for multiple testing. A number of authors compared various test statistics, mainly in the case-control setting, finding that relative performance depends on the density and the correlation structure of the SNPs within a region, the selection criteria and the number of SNP markers, the placement and the number of liability/causal SNPs within a region, as well as on allele frequencies and the presence of allelic heterogeneity.Scan statistics based on single-SNP tests have been proposed to identify genomic regions associated with disease ,4, whered region . A multid region  because p-values to identify and test genomic regions simultaneously. Taking a more conventional approach, we also used external information from the UCSC gene database [p-values. Within the defined genomic regions, we then applied region-based test statistics using multiple linear regressions of sets of SNPs. We compare the two analytic strategies in GWAS with respect to the SNPs and the regions detected, and also compare the association test results in a set of regions defined by candidate lipid genes.In this contribution, we apply two region-based approaches to a genome-wide association study (GWAS) analysis of blood lipid measures taken in members of Offspring Cohort and Generation 3 Cohort of the Framingham Heart Study (FHS). Initially, we tested each of the 550 k SNPs from the Affymetrix array datasets, one at a time. In an alternate approach, we applied scan statistics based on the single-SNP database  to definn = 2584) and Generation 3 Cohort (n = 3811) using the SNP genotypes from GeneChip Human Mapping 500 k Array and 50 k Human Gene Focused Panel and the blood lipid phenotypes. All family members within these cohorts who had been genotyped and phenotyped were included in the analysis.We analyzed the Genetic Analysis Workshop 16 FHS Offspring Cohort  cholesterol and triglycerides (TG) were measured at up to four exams for the Offspring Cohort and at one exam for the Generation 3 Cohort. Low-density lipoprotein (LDL) cholesterol was calculated using the Friedewald formula  for each measurement. For the patients on lipid lowering medication, the actual total cholesterol and TG values were imputed following the method of Kathiresan et al. . Imputat-10 and call rate <90%. Samples were filtered at a call rate <90%. There were no outliers for exclusion, as determined using Eigenstrat.Quality control was completed using the computer programs PLINK  and EigeLinear regression of each of the residual phenotypes  was performed using PLINK for each of the 550 k SNPs that passed filtering, based on a simple regression of additive SNP coding, including all individuals and ignoring familial correlation. Departures from the expected asymptotic distributions were assessed via quantile-quantile (Q-Q) plots for each of the phenotypes.p-value for association at that position. A group of SNPs tends to be identified as a region if there is statistical evidence of clustering of positions and of small p-values. The locations of SNPs along a chromosome are assumed to follow a Poisson process. To detect regions of association, the original Poisson process is partitioned into two independent Poisson processes, according to a chosen p-value threshold level. The resulting sets of SNP locations are both Poisson processes, with rates proportional to the original process. When the assumption of independent processes is violated, some regions may be detected solely because of their marker correlation structure, so to reduce the correlation among SNPs, we pruned the data by choosing tagSNPs with a pair-wise linkage disequilibrium (LD) R2 threshold less than 0.5 [The scan statistic approach identifies regions of significant SNPs and tests for regional significance . It requthan 0.5 .p-value is the probability of observing the same number of significant markers over a distance as short as or shorter than observed. The scan statistic is simply the distance spanned by the group of markers of interest, i.e., the sum of inter-marker distances. Under Poisson process assumptions of independently identically distributed exponential inter-SNP distances, the scan statistic follows a gamma distribution, so that the probability of a high association cluster is a gamma cumulative distribution function. If this observed regional probability is smaller than a pre-specified significance criterion, then the group of markers is identified as a cluster of significant associations not likely to occur simply by chance. Genome-wide regional p-values were calculated empirically, using 10,000 permutations of the tag-SNP p-values across positions. In each permutation we kept the top n regions, where n is the number of identified regions in the original analysis [Using the statistical package R, we identified regions of association by evaluating windows along the chromosome including varying numbers of SNPs, and tested for region-level significance. The regional analysis .p < 10-4) was formed from the single-SNP tests. If a SNP was within ± 5 kb of a gene, then the assigned gene region was the gene endpoints ± 5 kb. Otherwise, the SNP position ± 5 kb was classified as an inter-gene region. In each of the gene and inter-gene regions thus defined, we performed region-based analyses using multi-variable regression of k SNPs within the defined region using the generalized estimating equations (GEE) robust variance to account for familial correlation, and the linear regression model: E = α + β1 xG1 + β2 xG2 + ... + βkχGk. For test statistics, we calculated the global k df test (Hotelling's test), the Schaid test . To addp-value; .R2 < 0.5), were used as input to the scan statistic analysis. The proportion of markers retained per chromosome ranged from 36 to 52%, with a mean of 40%. We specified a SNP p-value threshold of 0.01 and a regional threshold of 0.001. We categorized a scan statistic region as a gene region if it overlapped with a defined gene region (± 5 kb), and called the remaining regions non-gene regions. For HDL, 135 gene and 105 non-gene regions were detected genome-wide, with similar proportions for LDL and TG .Markers from the 500 k chip, pruned for LD  or at least one of the region-based tests . In most cases, the genes identified by the region-based tests were also found by the scan statistic. In some cases, a scan statistic region from the pruned data did not overlap with a gene, but the results from the unpruned data did, as indicated in the rank column. On the other hand, scan statistics detected some candidate genes not identified by any of the region-based tests.We also compared the region-based statistics  and scan statistic results for a list of 62 genes reported to be associated with HDL (17 genes), LDL (25 genes), or TG (20 genes) according to previously published reports ,13,14. Ip-values to obtain genome-wide regional p-values.We consider chromosomal regions as the unit of analysis, rather than SNPs, so that the dimensionality problem is reduced at the genome-level. However, when using the scan statistic, the issue of criteria for genome-wide significance is difficult to address because the dimension of the problem is not well defined with testing of many possible overlapping regions consisting of different window sizes. Here we used positional permutation of In using the statistically defined regions without referring to the top SNPs, it appears that although we missed a number of significant single SNPs, we also identified many additional regions not found as SNP-database regions. The scan-statistic approach could also be used as a first stage in GWAS analysis, followed by within-region fine-mapping and/or direct sequencing. Once a region is detected, both approaches require follow-up with additional analyses to assess specific SNP variation within a region.FHS: Framingham Heart Study; GEE: Generalized estimating equations; GWAS: Genome-wide association study; HDL: High-density lipoprotein; LD: Linkage disequilibrium; LDL: Low-density lipoprotein; SNP: Single-nucleotide polymorphism; TG: Triglycerides.The authors declare that they have no competing interests.JLA implemented the scan statistic analysis and drafted the manuscript. YJY designed and conducted the gene-based analyses. DW carried out the single-SNP analysis, including quality control and comparison of genome-wide results. LS contributed to the conception and design. SBB conceived the study, and participated in its design and coordination. SBB and YJY helped to draft the manuscript. All authors read and approved the final manuscript.


To create useful gene combinations in crop breeding, it is necessary to clarify the dynamics of the genome composition created by breeding practices. A large quantity of single-nucleotide polymorphism (SNP) data is required to permit discrimination of chromosome segments among modern cultivars, which are genetically related. Here, we used a high-throughput sequencer to conduct whole-genome sequencing of an elite Japanese rice cultivar, Koshihikari, which is closely related to Nipponbare, whose genome sequencing has been completed. Then we designed a high-throughput typing array based on the SNP information by comparison of the two sequences. Finally, we applied this array to analyze historical representative rice cultivars to understand the dynamics of their genome composition.The total 5.89-Gb sequence for Koshihikari, equivalent to 15.7× the entire rice genome, was mapped using the Pseudomolecules 4.0 database for Nipponbare. The resultant Koshihikari genome sequence corresponded to 80.1% of the Nipponbare sequence and led to the identification of 67 051 SNPs. A high-throughput typing array consisting of 1917 SNP sites distributed throughout the genome was designed to genotype 151 representative Japanese cultivars that have been grown during the past 150 years. We could identify the ancestral origin of the pedigree haplotypes in 60.9% of the Koshihikari genome and 18 consensus haplotype blocks which are inherited from traditional landraces to current improved varieties. Moreover, it was predicted that modern breeding practices have generally decreased genetic diversityDetection of genome-wide SNPs by both high-throughput sequencer and typing array made it possible to evaluate genomic composition of genetically related rice varieties. With the aid of their pedigree information, we clarified the dynamics of chromosome recombination during the historical rice breeding process. We also found several genomic regions decreasing genetic diversity which might be caused by a recent human selection in rice breeding. The definition of pedigree haplotypes by means of genome-wide SNPs will facilitate next-generation breeding of rice and other crops. Coveragsoftware  and withhttp://rgp.dna.affrc.go.jp/whoga/index.html.en) using the Generic Genome Browser (GBrowse [http://koshigenome.dna.affrc.go.jp/).Genome-wide SNPs between Koshihikari and Nipponbare were detected using the MAQ software . The sco Golden Gate detection system was scored using the Illumina online scoring system Oryza sativa L. ssp. japonica that covered the entire modern history of rice breeding in Japan  and used them in our subsequent analysis.Based on known pedigrees and development dates, we selected 151 representative Japanese cultivars of We automatically visualized the genotypes of the 151 Japanese rice cultivars using a proprietary program written in Microsoft Visual Basic for Applications (VBA) in Microsoft Excel 2007 as follows. The VBA program translated the SNP information and the physical position of the SNP into a color and length (number of cells) in the Excel spreadsheet to provide a graphical representation of the genotype. The colors used in the graphical genotype representation for each cultivar were based on a comparison with the genotypes of Koshihikari and Nipponbare. If the genotype equaled that of Koshihikari, it was colored blue. If the genotype equaled that of Nipponbare, it was colored yellow. If the genotype was neither Koshihikari nor Nipponbare, it was colored dark green for heterozygous and grey for missing data. The borders of each cell in the graphical genotype representation represent points intermediate between the physical positions of two adjacent SNPs. The cell width (number of cells) used to represent the region size in the graphical representations was 13 pixels per 1 Mb. Regions of the graphical genotype smaller than about 77 kb (= 1 Mb/13 pixels) were represented as a single pixel, since this is the smallest display size in Excel.The genotype constitutions of the ancestors of Koshihikari  to reduce the influence of outliers and to obtain a median value for a better visual description of the decay in LD as a function of distance. We selected four distances as LD decay points for simulation of the size of the sliding window: 0.4, 1, 2, and 3 Mb, which correspond to window sizes of 2, 5, 10, and 15 SNPs, respectively, based on the assumed mean distance (200 kb) between adjacent SNPs in the rice genome. The haplotype diversity index was simulated only for 38 cultivars belonging to Group 1, because this was the oldest group of cultivars, and would therefore show the fewest effects of artificial selection on the haplotype diversity. The index was calculated by dividing the number of haplotypes into the number of cultivars in this group . Haplotype windows covering 2, 5, 10, and 15 SNPs were moved from the short arm of chromosome 1 to the long arm one SNP at a time, and we calculated the number of haplotypes in each window  betweenTY and HN performed most of the experiments. MY initiated and coordinated the study. TY, HN, and MY wrote the manuscript. HN performed the SNP discovery. JY performed haplotype and diversity analysis. MN and TS contributed to the SNP typing of the rice accessions. KE provided the core rice collection and performed the SNP analysis. All authors discussed the results and commented on the manuscript. All authors read and approved the final manuscript.Sequencing depth of the Koshihikari short reads generated using the Solexa Genome Analyzer with reference to the Nipponbare genome. The x-axis shows the physical distance along the rice reference genome (Nipponbare). Red lines indicate regions in which the Koshihikari short reads could not be mapped at all owing to low reliability of the Nipponbare reference sequence . The y-axis shows the sequencing depth, which represents the mean number of reads mapped in 100-kb windows. The mean total sequencing depth was 15.7× the genome  and the black line corresponds to the right vertical scale (number of SNPs detected).Click here for fileOryza sativa ssp. japonica cultivars used in the single-nucleotide polymorphism (SNP) analysis. .List of the Click here for filePopulation structure analysis plots with three K values  using STRUCTURE program [ program . The topClick here for fileGraphical representation of the genotypes of the 151 Japanese rice landraces and cultivars in additional file3 that have been grown in the past 150 years. To visualize the genome compositions of these cultivars, the SNP type is indicated in blue , in dark green (heterozygous), and in gray (missing data). The cultivar names are ordered from top to bottom by their year of development, as are the three groups .Click here for filePedigree diagram for Japanese rice cultivars that are ancestors or descendants of Koshihikari. Numbers in parentheses are the serial numbers designated in additional file Click here for fileD') between SNP pairs within a 5000-kb distance on rice chromosome 1Median values of the estimate of linkage disequilibrium (. The median D' was calculated using 200-kb distances. Triangles represent the points for the simulation of the size of sliding window at distances of 0.4, 1, 2, and 3 Mb.Click here for fileSimulation results from the sliding-window haplotype analysis of cultivars in Group 1 on chromosome 1. Changes in the haplotype diversity index are based on 2-, 5-, 10-, and 15-SNP sliding windows on the assumption of a mean distance of 200 kb between adjacent SNP pairs in the rice genome. On this basis, the window sizes of the 2-, 5-, 10-, and 15-SNP sliding windows were 0.4, 1, 2, and 3 Mb respectively.Click here for fileAverage number of haplotypes per 10-SNP interval.Click here for file


Y-SNP haplogroup G (hgG), defined by Y-SNP marker M201, is relatively uncommon in the United States general population, with only 8 additional sub-markers characterized. Many of the previously described eight sub-markers are either very rare (2–4%) or do not distinguish between major populations within this hg. In fact, prior to the current study, only 2% of our reference Caucasian population belonged to hgG and all of these individuals were in sub-haplogroup G2a, defined by P15. Additional Y-SNPs are needed in order to differentiate between individuals within this haplogroup.In this work we have investigated whether we could differentiate between a population of 63 hgG individuals using previously uncharacterized Y-SNPs. We have designed assays to test these individuals using all known hgG SNPs (n = 9) and an additional 16 unreported/undefined Y-SNPS. Using a combination of DNA sequence and genetic genealogy databases, we have uncovered a total of 15 new hgG SNPs that had been previously reported but not phylogenetically characterized. Ten of the new Y-SNPs are phylogenetically equivalent to M201, one is equivalent to P15 and, interestingly, four create new, separate haplogroups. Three of the latter are more common than many of the previously defined Y-SNPs. Y-STR data from these individuals show that DYS385*12 is present in (70%) of G2a3b1-U13 individuals while only 4% of non-G2a3b1-U13 individuals posses the DYS385*12 allele.This study uncovered several previously undefined Y-SNPs by using data from several database sources. The new Y-SNPs revealed in this paper will be of importance to those with research interests in population biology and human evolution. Single nucleotide polymorphisms (SNPs) are the smallest and most abundant type of human DNA polymorphisms http://www.ysearch.org) in addition to 9 that we possessed in-house. We have uncovered a total of 15 new hgG SNPs, four of which create new sub-haplogroups with the hgG clade. Additionally, we have also discovered that the Y-STR DYS385*12 is present in (70%) of the new hg G2a3b1-U13 individuals and only 4% of non-G2a3b1-U13 individuals.This study recruited 54 hgG men from the pool available in Ysearch.org . Only two copies of the DYS385*12 allele were found in the other 53 non-G2a3b1-U13 samples (4%). Thus the DYS385*12 is highly predictive (P = 0.70) of a G2a3b1-U13 individual if the individual is known to belong to hgG. Such Y-STR information can sometimes facilitate a speedier haplogroup assignment and differentiation than would be available from a strict hierarchy-based SNP analysis.In summary, we have characterized 15 new hgG SNPs that had been previously reported but not phylogenetically defined. Ten of the new Y-SNPs are phylogenetically indistinguishable from G-M201, one is equivalent to G2a-P15 but four create new, separate hg G sub-haplogroups. Three of the latter are more common than many of the previously defined hgG Y-SNPs.Y-SNP haplogroup G (hgG), defined by the Y-SNP marker G-M201, is relatively uncommon in the European American population of the United States. This haplogroup is thought to have originated in the Caucasus region of Eurasia, especially in the North Ossetians Prior to this study, only 8 sub-markers had been described within haplogroup GThe subjects were recruited from a selection of over 500 haplogroup G men available in public genetic-genealogy databases in the fall of 2006. This was especially valuable in the case of a somewhat rare European haplogroup like haplogroup G  This study was conducted according to the principles expressed in the Declaration of Helsinki. The study was approved by the Institutional Review Board of the University of Central Florida. All participants provided written informed consent for the collection of samples and subsequent analysis.in silico. Most of the earliest mapping of the Y chromosome was done using the BAC library from the California Institute of Technology called CTC. This included the contigs: AC005942.2 CTC-298B15, AC002992.1 CTC-203M13, AC004617.2 CTC-264M20, AC002531.1 CTC-486O8, AC004474.1 CTC-475I1, AC006565.4 CTC-484O7, AC005820.1 CTC-494G17, and AC078938.3 CTC-480L15. The Y-SNP marker M201 in contig AC004474 was seen to be derived rather than ancestral in the reference sequence. This means that the man used in the CTC library belonged to YCC haplogroup G. The assumption was made that the contents of the NCBI Trace Archives were not likely to contain the re-sequencing of any other haplogroup G men . Candidate YSNPs were chosen on the basis of appearing in only the above reference contigs and none of the traces in the Trace Archives. Those candidate SNPs were typed and characterized in a panel of 63 haplogroup G men. Additionally, all previously defined hgG Y-SNPs were compiled from various publications and a 25 member candidate list of SNPs suspected to be polymorphic inside haplogroup G was developed for assay development and subsequent population studies.DNA sequence traces from the NCBI Trace Archives were used to identify SNPs of an individual from haplogroup G, http://www.ysearch.org. All DNA samples were obtained with the individual's informed consent in accordance with the University of Central Florida's Institutional Review Board.Buccal swabs were obtained from a total of 74 individuals including 63 human males belonging to Y-SNP hgG, one human female, one male chimp, and one human individual from each of the following hgs: A, B, C, E3a, F, H, I, J, and R1b. Fifty-four of the individuals were recruited from the list of individuals whose 37 locus Y-STR haplotypes and Y-SNP haplogroups are available in −4  and stored at 4°C until analysis. DNA was quantified by gel electrophoresis in a 1% Agarose gel. Samples were visualized using the ΩMega 12ic™ Gel documentation system . Quantification was accomplished by a comparison of the fluorescence intensity of the unknown bands to a set of known quantity standards that were run simultaneously.A small piece of the buccal swab was cut and placed in a Spin-ease tube  and incubated overnight at 56°C in 400 µL DNA extraction buffer . The cut material was then removed from the tube and placed in a Spin-ease basket and the basket was then placed back in the original tube and centrifuged (Eppendorf Centrifuge 5415D) at 13,200 rpm for 5 min. The extract was then purified and isolated using 25∶24∶1 phenol/chloroform/isoamyl achohol  followed by filtration using Microcon® 100 centrifugal filter devices  according to the manufacturer's instructions. Samples were brought to a final volume of 50 µL in TEAssays were developed to amplify regions flanking the SNP for use with pyrosequencing technology. Extracted female DNA was also tested to ensure male specificity. All SNPs were tested against individuals from hgs A, B, C, E, F, H, I, J, and R for haplogroup determination and ancestral vs. derived states. A male chimp was also tested to facilitate determination of the ancestral vs. derived states. PCR primers were designed using a combination of Primer3http://www.biotage.com). SNP genotype data were collected and the phylogenetic relationships were depicted in a phylogenetic tree showing the corresponding frequencies for each haplogroup observed. Haplogroups were assigned based on the most recent comprehensive Y chromosomal haplogroup tree published by Karafet et al. http://www.ysearch.org. The probability of discrimination (DP)i2, where pi is the observed frequency of the derived allele at each of the sub hg G haplogroups.SNP genotyping was performed by pyrosequencing on a PSQ™ 96 MA instrument according to the manufacturer’s recommendations (Biotage, Uppsala, Sweden,


Caenorhabditis elegans. As mutagenized strains contain a significant mutational load, it is often still necessary to map mutations to a chromosomal interval to elucidate which of the WGS-identified sequence variants is the phenotype-causing one. We describe here our experience in setting up and testing a simple strategy that incorporates a rapid SNP-based mapping step into the WGS procedure. In this strategy, a mutant retrieved from a genetic screen is crossed with a polymorphic C. elegans strain, individual F2 progeny from this cross is selected for the mutant phenotype, the progeny of these F2 animals are pooled and then whole-genome-sequenced. The density of polymorphic SNP markers is decreased in the region of the phenotype-causing sequence variant and therefore enables its identification in the WGS data. As a proof of principle, we use this strategy to identify the molecular lesion in a mutant strain that produces an excess of dopaminergic neurons. We find that the molecular lesion resides in the Pax-6/Eyeless ortholog vab-3. The strategy described here will further reduce the time between mutant isolation and identification of the molecular lesion.Whole-genome sequencing (WGS) is becoming a fast and cost-effective method to pinpoint molecular lesions in mutagenized genetic model systems, such as  C. elegansC. elegans employs single nucleotide polymorphism (SNP)-based mapping C. elegans isolate and the mutant F2 progeny of such a cross are analyzed for their distribution of SNP markers. Genomic regions close to the mutation of interest show a decreased incidence of Hawaiian SNPs while unlinked regions contain an even representation of Hawaiian vs. Bristol SNPs. However, this conventional SNP mapping strategy usually starts with a relatively small, arbitrarily chosen number of SNPs. Through re-iterative SNP mapping to finer and finer regions, the gene can be fine-mapped, but this can be a relatively tedious and time-consuming process. We describe here the employment of a strategy - modeled on a similar strategy (“SHOREmap”) previously employed in plants Whole-genome sequencing (WGS) is a new and powerful means to identify molecular lesions that result in specific mutant phenotypes The overall strategy is schematically depicted in Due to meiotic recombination, in regions unlinked to the mutation the parental chromosomes will recombine in a largely non-biased manner. So as long as enough recombinants are pooled, unlinked SNP loci will appear in a roughly 50/50 ratio of Hawaiian vs. Bristol nucleotides in the sequence output pileup generated by the genome sequencer Fig. 1. The WGS dataset is analyzed using a software tool that we recently developed, called MAQGene C. elegans isolate from the Bristol isolate. Therefore, the WGS-SNP combination not only eliminates all technical aspects of SNP mapping , but also provides, in one step, a much finer grained resolution. Moreover, it reduces the costs of mutant identification, since it employs a single whole-genome sequencing run for both mutant mapping as well as mutant identification.A key advantage of this strategy is that rather than examining an arbitrarily chosen, limited number of SNPs to assess the map position of a specific mutation , as assessed by expression of the dopaminergic marker dat-1::gfp (expressed from the transgene vtIs1)ot266 mutants display an aberrantly increased number of dopaminergic neurons  defined a mapping interval of 2.1 megabases (Mb) or 2.34 map units on the X chromosome ;Table 2Table S1). One of the three variants is a premature stop codon (Q338Stop) in the vab-3 locus (Pax6/Eyeless homolog of C. elegansot292) homozygous recombinant pools provides fine-grained mapping information. In the case presented here, it provided a ∼2 Mb mapping interval and limited the candidate phenotype causing mutations to only 3 protein changing variants. Even the analysis of as few as 20 homozygous recombinants allowed for mapping of the mutation to a <5 Mb interval with 8 protein-changing variants . As recombination frequency is not uniform throughout the genome We have successfully applied a SNP- and WGS-based cloning strategy for mutant identification in C.elegans WGS  provides sufficient data to query the genome for Bristol/Hawaiian SNPs. The approach presented here can also be used to map a dominant mutation. To this end, one would need to test which of the individually isolated F2 animals are homozygous for the mutation by examining their F3 progeny. Only homozygous F2 animals shall be processed further. The method can also be used to map a phenotype-causing deletion We also find that the sequencing depth that we currently employ for standard C. elegans strain to a polymorphic mapping strain, the authors backcrossed their mutant strain with the starting strain used for mutagenesis. Backcrossing several times eliminated all unlinked mutagen-induced sequence variants, while linked, mutagen-induced variants remained, thereby providing mapping information C. elegans strains to eliminate background variants through comparing WGS datasets C. elegans strain for outcrossing, and it requires only a single cross. However, it does require the availability of a reasonably well-characterized polymorphic strain, such as the Hawaiian CB4856 strain.Recently, Zuryn et al. provided a proof-of-principle study for another approach to map and clone genes within a single WGS run C. elegans, can of course be applied for mutant identification in many other model system species in which positional cloning has traditionally been tedious.As pointed out before vtIs1, OH4254 ot266; vtIs1, OH4330 ot292;vtIs1, CB648 vab-3(e648). RNAi was performed on a sensitized background strain, OH9315 .The following strains were use: Hawaiian strain CB4856 vab-3 phenotype with RNAi we plated L3 stage, eri-1; lin-15b; vtIs1 worms on plates coated with bacteria expressing a vab-3 dsRNA clone and scored the F1 progeny for gfp expression from the vtIs1 transgene.OH4254 animals were mutagenized with EMS and were kept at 25°C at all times. 5 parental (P0) mutagenized animals were placed in each of 10 founder P0 plates. Three days later, 400 F1 progeny of the mutagenized P0 animals were singled. Their ensuing progeny (F2 and F3 generation) were screened under a stereomicroscope equipped with a fluorescent light source and individual mutant animals were picked to establish mutant lines. For recapitulating the ot266 animals with Hawaiian CB4856 males and singled F1 cross progeny. In the F2 progeny, we picked 20 (or 50) progeny with the Dopy phenotype and singled them onto individual plates. We allowed the F2 recombinants to self fertilize until they filled 6 cm plates. We pooled the progeny of 20 (or 50) F2 animals, using approximately similar numbers of animals from each plate. The pooled worms were lysed and genomic DNA was prepared according to the standard protocol used for sample preparation for WGS (http://biochemistry.hs.columbia.edu/labs/hobert/protocols.html). DNA libraries were prepared according to Illumina’s protocol. The pooled DNA from each experiment was then subjected to WGS on an Illumina GA2 sequencing platform, using single 100 nucleotide reads.We crossed To run MAQGene we used the general parameters previously described Table S2).For identifying the mapping interval, MAQGene was modified to generate an additional output file that provides a pileup for all annotated SNP loci, the number of Bristol reads, the number of Hawaiian reads and the sequencing depth. This file contains all annotated SNPs in the Bristol genome and needs to be filtered using, for example, Microsoft Excel to include only Hawaiian SNPs (named as haw). Using this file we calculated the ratio of Hawaiian (HA) reads in Excel . To minimize the chance that random sequencing errors would falsely appear as HA sequence in SNP loci, we did not consider loci with very low occurrence of HA sequences. In particular, we filtered the dataset to include only SNPs with at least 3 HA reads and a HA ratio of at least 0.2. In addition, we did not include in our analysis SNPs with ratios of HA sequences above 0.6. The reason for this upper ‘cutoff’ is the existence of loci annotated as SNPs in the Bristol genome that we actually found to be (in multiple Bristol based sequenced strains) identical to the Hawaiian annotated sequence for this locus. Thus, these loci  are ‘falsely’ annotated SNPs, possibly representing Bristol sequencing errors or loci of heterozygosity and are not indicative of the actual existence of Hawaiian sequences in a region. We excluded these loci from our datasets by filtering out everything that appeared too high on Hawaiian ratios (above 0.6). We have compiled a list with SNP loci that in two or more of our Bristol datasets show HA sequence .We did not filter out common variants that appear in our previously sequenced datasets as the resolution of this method does not require comparison with additional datasets.The aforementioned new SNP-specific output file contains one line per known SNP location with the following fields: chromosome, position, snp name, snp base, number of snp-containing reads, reference base, number of reference-containing reads, total number of reads.ftp.wormbase.org/pub/wormbase/genomes/c_elegans/genome_feature_tables/GFF3/c_elegans.WS201.gff3.gz lacks actual SNP base for each listed SNP. The new step additionally extracts information from ftp.wormbase.org/pub/wormbase/genomes/c_elegans/annotations/WS201_elegans_SNP_changes.txt.gz, which provides this information.In order to produce this extra results file, we added one step to MAQGene's database building part and one to the pipeline itself. The database building step is necessary because the previous version, which extracts all of its annotation information from During the pipeline itself, the pileup file  is scanned a second time to extract all positions of known SNPs, regardless of low sequencing coverage or other filters that are applied in the main results file. Together with the new information about actual SNP base, the number of reads matching either the SNP base or reference base are counted and reported in a separate tabular text file.As usual, users interested in this extra feature must update their installation of MAQGene and re-run the database building step (build_annotation_tables.sh).Table S1List of mutations in the mapping region. (DOCX)Click here for additional data file.Table S2Bristol SNP loci with Hawaiian sequence. (XLS)Click here for additional data file.


Genome-wide scans of hundreds of thousands of single-nucleotide polymorphisms (SNPs) have resulted in the identification of new susceptibility variants to common diseases and are providing new insights into the genetic structure and relationships of human populations. Moreover, genome-wide data can be used to search for signals of recent positive selection, thereby providing new insights into the genetic adaptations that occurred as modern humans spread out of Africa and around the world.We genotyped approximately 500,000 SNPs in 255 individuals  from the Human Genome Diversity Panel (HGDP-CEPH). When merged with non-overlapping SNPs typed previously in 250 of these same individuals, the resulting data consist of over 950,000 SNPs. We then analyzed the genetic relationships and ancestry of individuals without assigning them to populations, and we also identified candidate regions of recent positive selection at both the population and regional  level.Our analyses both confirm and extend previous studies; in particular, we highlight the impact of various dispersals, and the role of substructure in Africa, on human genetic diversity. We also identified several novel candidate regions for recent positive selection, and a gene ontology (GO) analysis identified several GO groups that were significantly enriched for such candidate genes, including immunity and defense related genes, sensory perception genes, membrane proteins, signal receptors, lipid binding/metabolism genes, and genes involved in the nervous system. Among the novel candidate genes identified are two genes involved in the thyroid hormone pathway that show signals of selection in African Pygmies that may be related to their short stature. The introduction of rapid, efficient, and relatively inexpensive platforms for simultaneous genotyping of hundreds of thousands of single-nucleotide polymorphisms (SNPs) has revolutionized disease-association studies, as genome-wide scans have identified many SNPs associated with complex diseases An important resource that has substantially advanced studies of worldwide genetic variation is the CEPH Human Genetic Diversity Panel (HGDP-CEPH), a collection of some 1064 cell lines from 52 worldwide populations Although most analyses of the Affymetrix and Illumina gave concordant results when analysed separately, thereby justifying merging the datasets, we did identify some important differences. Our analyses of the genetic structure and relationships of worldwide populations both confirm and extend the results of previous such analyses of the HGDP-CEPH We genotyped 255 unrelated individuals  for the Illumina SNPs than for the Affymetrix SNPs . In prinIndividual heterozygosities also change drastically when SNPs in high LD are pruned . HeterozWe next analyzed the genetic relationships and ancestry of individuals without assigning them to populations. First, principal component analysis (PCA) was carried out separately for the Illumina and Affymetrix SNPs, to see if the difference in observed heterozygosities between the platforms influenced the PCA results. The plots of PC1 vs. PC2 are virtually identical , althougreg) considered in a regression analysis to predict the genotypes for each SNP, and then performing PCA on the residuals of the regression analysis, as described previously reg, except that as Nreg increased, decreasing amounts of the variation explained . Thus, the PC analysis reflects genome-wide patterns, not just a few highly-differentiated SNPs.To ensure that these patterns do not simply reflect high differentiation at a few SNPs, we removed SNPs with Ffrappefrappe  are clearly differentiated in the PCA  from oneanalysis  at K = 4frappe analysis  as those within the upper 1% of the distribution of lnRsbAR values, and required candidate regions to have at least 25 tSNPs in a 100 kb region for the individual population comparisons, and at least 10 tSNPs in a 100 kb region for the continental regional comparisons  analyses at both the worldwide and regional levels largely recapitulate previous such analyses of the HGDP-CEPH with fewer genetic markers The merged dataset of non-overlapping, filtered SNPs from both the Illumina and Affymetrix platforms consists of nearly 1 million SNPs, making this the largest genome-wide scan of worldwide human populations. Although the genetic structure , Europe  and East Asians (Han Chinese and Japanese) st were used to identified signals of positive selection mainly at the continental region level. Since the lnRsb method in our study is similar to the XP-EHH method and is related to the iHS method, both used in the previous study Recently, another study As indicated above, we do re-capture many previously-inferred signals of positive selection, some of which are quite strong signals shared across many populations and multiple geographic regions. For example, this is the case for EDAR , TYRP1 a-represented by the HGDP-CEPH , or that genes previously studied in a limited context for recent positive selection should now be re-examined in a worldwide population context.There are three potential explanations for the failure of our analyses to detect previously-identified candidates. First, the sample sizes in our study may be too small to have sufficient power to detect signals of selection. However, the power analysis  indicateSome of the signals of recent positive selection identified in this study are shared among many populations and/or regions. It is expected that populations with more recent common ancestry would share the same past genetic adaptations, and hence the same candidate regions. This is consistent with the observations of many overlapping signals across the Middle East, Europe, and Central/South Asia, and between East Asians and the Americas or Oceania . HoweverThe GO analysis identified several classes of genes previously hypothesized to be subject to selection In addition to the previously-identified candidate genes mentioned above, many novel candidates were identified  and S3; It should be noted that not all of the top-ranking candidates contain genes. For example, the region Chr9:38623652-38761831 is the top-ranking candidate region in eight populations  and two regions (Middle East and Europe), but contains no genes. Several uncharacterized transcripts are within this region; these transcripts or other potentially functional components within this region might be interesting targets for further investigation.The short stature of Pygmy groups around the world has long intrigued anthropologists If this scenario is true, then there are two important implications. First, this would suggest that short stature was not selected for directly in the ancestors of Pygmy groups, but rather arose as an indirect consequence of selection in response to an iodine-deficient diet. Second, since different genes in the thyroid hormone pathway show signals of selection in Mbuti vs. Biaka Pygmies, this would suggest that short stature arose independently in the ancestors of Mbuti and Biaka Pygmies, and not in a common ancestral population. Moreover, most Pygmy-like groups around the world dwell in tropical forests In conclusion, obviously much more work is needed to verify the potential signals of selection at the thyroid hormone pathway genes in Mbuti and Biaka Pygmies, as well as the many other novel and intriguing signals of selection identified in this study. Nevertheless, the results of our study, and similar such studies http://hagsc.org/hgdp/files.html).We selected a subset of 255 individuals from the HGDP-CEPH  for genoThe Affymetrix GeneChip Human Mapping 500 K Array Set is comprised of two arrays; one uses the Nsp I restriction enzyme , while the second uses Sty I . Briefly, total genomic DNA (250 ng per array) was digested with each restriction enzyme, ligated to adaptors, and amplified using a generic primer that recognizes the adaptor sequence. The amplified DNA was then fragmented, labeled, and hybridized to oligonucleotide probes attached to the surface of each array. Array batches from different enzyme fractions were processed on different days in order to reduce the risk of cross-contamination. Each array was scanned using the GeneChip Scanner 3000 with the High-Resolution Scanning Upgrade. The cell intensity files were analyzed using GeneChip Analysis Software (GTYPE) v4.1 and Genotyping Console Software v2.1.The Dynamic Model (DM) mapping algorithm integrated into the GTYPE software assigned the genotype call for each SNP, and was primarily used to perform single-array quality control (QC). All runs of the DM algorithm were performed at default settings. On average, the DM SNP call rate was 93.8% (range 87.0 – 98.1%) for the Nsp I arrays, and 92.9% (range 87.1–98.3%) for the Sty I arrays. Call and discrimination rates of the additional QC Modified Partitioning Around Medoids algorithm (MCR and MDR) did not differ over ∼20%, indicating no contamination or sample mix-up. The reported DM gender calls for each array matched the gender described in the HGDP-CEPH panel information for each sample, with one exception. For sample HGDP_01239  the reported DM gender call  for both the Nsp I and Sty I arrays disagreed with the gender of the HGDP-CEPH panel information for that sample ; however genotypes from another laboratory agreed with our gender call To assess the reliability of the genotyping experiments for the positive controls, the percentage of agreement  and unweighted Cohen's Kappa statistic  were calculated using an R script. Genotypes of the Affymetrix Reference Genomic DNA 103 were compared with the consensus genotypes provided by Affymetrix. Genotypes of the HapMap #NA06985 CEPH/UTAH Pedigree 1341 were compared with the consensus genotypes obtained from the HapMap website. Genotypes for these comparisons were obtained by the Affymetrix Bayesian Robust Linear Model with Mahalanobis (BRLMM) distance classifier; all missing data were excluded. The concordance for the genotypes from both Nsp I and Sty I arrays combined together for the Affymetrix Reference Genomic DNA 103 was 98.5%, and for the HapMap #NA06985 CEPH/UTAH Pedigree 1341 was 99.4%, while the calculated Kappa values were 0.98 and 0.99 respectively for each positive control sample.http://www.stats.ox.ac.uk/~marchini/software/gwas/chiamo.html), which has been showed to outperform the standard Affymetrix BRLMM algorithm in several respects http://www.wtccc.org.uk/info/software.shtml). We then investigated genotype-calling accuracy for several parameter sets for the CHIAMO program  disequilibrium because there is low power to detect HW deviations with the sample sizes in this study, and moreover a comparable study showed that the impact of removing such SNPs was negligible We then performed filtering; first, in order to avoid inter-population biases in missing data, we defined a threshold of at least three genotypes called per population sample. This discarded 53,235 SNPs in total. We also discarded rs41388745 as it was mapped to both chromosome 1 and 7. Finally, by defining a threshold of 1% for global Minor Allele Frequency (MAF) of a SNP, we excluded an additional 18,875 SNPs, leaving a total of 416,644 filtered autosomal SNPs for further analyses. These data are available at http://pngu.mgh.harvard.edu/purcell/plink). The 84,967 filtered SNPs in common were merged following the consensus method, i.e. missing calls in one of the datasets was filled from the other dataset, and mismatches between datasets resulted in a missing call in the merged dataset. When the initial filter was re-applied to this set of SNPs in common, 2,169 SNPs were subsequently eliminated, yielding a final marker set of 954,063 SNPs. PLINK was also used to prune SNPs in high LD, using the following settings: -indep 50 5 2, resulting in a dataset of 220,247 SNPs.Merging the Affymetrix and Illumina datasets was performed with the PLINK software v1.05 smartpca program (http://genepath.med.harvard.edu/~reich/Software.htm) from the EIGENSOFT package reg previous SNPs, according to physical location.We performed principal component analysis (PCA) with the frappe software http://smstaging.stanford.edu/tanglab/software/frappe.html) to estimate individual affinities for a pre-determined number of clusters. The MaxIter setting was set to 10,000 so that the convergence criterion was always met (likelihood increase since last output less than one point per step), and we performed at least four independent runs per set of populations.We also used the maximum likelihood (ML) algorithm implemented in the AR value for each SNP in each population, that is comparable to the original lnRsb To identify candidate regions that have experienced recent local selection , we modified the lnRsb approach To evaluate the signals that are shared among populations, we merged the genotype data into the seven geographic regions  suggested by previous studies AR values in each population were used to define candidate regions if there were 25 or more such top SNPs (tSNPs) within 100 kb of each other. For the analysis of the six non-African continental regions, since there are many fewer groups in the analysis, the minimum number of tSNPs required for a candidate region was relaxed to 10, while the other conditions were the same as for the individual population tests. The tSNPs were ranked across the genome in each population and the mean rank of the top 20% tSNPs in each candidate region is used as the relative signal strength of that candidate region. Candidate regions were ordered according to this score and the top 25 candidate regions were determined for each population, and the top 100 candidate regions were determined for each regional grouping of populations.In the case of individual populations, the SNPs that carry the highest 1% lnRsbhttp://www.ebi.ac.uk/projects/BARGEN/download/FREGEN/, To evaluate the power of this approach to detect recent positive selection with the relatively small sample sizes  in this study, simulations were carried out. Forward simulations were carried out with the program FREEGENE (http://func.eva.mpg.de/doc/func.html) and carried out 5000 iterations to achieve stable results.For the gene ontogeny (GO) analysis we used the FUNC program Figure S1Comparison of PCA for the same 250 individuals genotyped on the Affymetrix vs. Illumina platforms. (A) Plots of PC1 vs. PC2. (B) The percent variation explained and the p-value of the TW statistic for the first 40 PCs. (C) Values of the first 40 PCs.(0.35 MB TIF)Click here for additional data file.Figure S2Effect of the number of preceding SNPs (Nreg) used to predict SNP genotypes in the regression analysis on the percent variation explained by the first 10 PCs. Also shown is the effect of pruning SNPs that are in high LD.(0.12 MB TIF)Click here for additional data file.Figure S3Analysis of additional PCs for the worldwide populations. (A) Amount of variation explained and associated statistical significance for the first 45 PCs. (B) Heat plot of the values of the first 45 PCs. The PC values have been normalized to range from 0 to 1.(0.44 MB TIF)Click here for additional data file.Figure S4frappe results for K = 5.Regional analysis of the Europe, North Africa, Middle East, and Central/South Asia groups. (A) Plot of PC1 vs. PC2. (B) (0.19 MB TIF)Click here for additional data file.Figure S5frappe results for K = 4.Regional analysis of the Europe, Middle East, Central/South Asia, and East Asia groups. (A) Plot of PC1 vs. PC2. (B) (0.24 MB TIF)Click here for additional data file.Figure S6frappe results for K = 2.Regional analysis of the East Asian groups. (A) Plot of PC1 vs. PC2. (B) (0.17 MB TIF)Click here for additional data file.Figure S7frappe results for K = 4.Regional analysis of the sub-Saharan African groups. (A) Plot of PC1 vs. PC2. (B) (0.12 MB TIF)Click here for additional data file.Figure S8frappe results for K = 5.Regional analysis of the groups from the Americas. (A) Plot of PC1 vs. PC2. (B) (0.12 MB TIF)Click here for additional data file.Figure S9Effect of sample size on the power of the lnRsb analysis to detect a positively-selected allele. For each sample size (n = number of chromosomes), the fraction of simulations (y-axis) in which a selected allele of a given selection coefficient (x-axis) was detected as a candidate region of selection, according to the criteria described in the Methods section, is plotted.(0.06 MB TIF)Click here for additional data file.Figure S10Effect of different parameter values on genotype calls obtained with the CHIAMO algorithm. Shown are the mean values (in percent) for heterozygous and homozygous mismatches between the Affymetrix and Illumina platforms, no calls for either or both platforms, and the sum of all mismatches and no calls. These values are given for the BRLMM algorithm for all chromosomes, and for two different parameter sets and various threshold values for the CHIAMO algorithm, for either just chromosome 22 or for all chromosomes. The threshold value is the maximum value for the total fraction of missing genotypes allowed; CHIAMO Par 1 is the WTCCC parameter set (-max1 -max2 -nmax 200 -n 0 -b 0 -f freqfile) and Par 2 is identical to Par 1 except without the -f option.(0.09 MB TIF)Click here for additional data file.Table S1List of the 255 HGDP-CEPH individuals genotyped in this study, including ID, population, location, region, population code, gender, heterozygosity for the Affymetrix SNPs, and heterozygosity for the Illumina SNPs.(0.15 MB XLS)Click here for additional data file.Table S2List of the candidate regions for recent positive selection identified in the population-level analyses, including the chromosome, start and end positions, HGNC gene ID, and populations  for which a positive signal was obtained.(0.10 MB XLS)Click here for additional data file.Table S3List of the candidate regions for recent positive selection identified in the regional-level analyses, including the chromosome, start and end positions, HGNC gene ID, and regions  for which a positive signal was obtained.(0.07 MB XLS)Click here for additional data file.