Since the times of domestication, cattle have been continually shaped by the influence of humans. Relatively recent history, including breed formation and the still enduring enormous improvement of economically important traits, is expected to have left distinctive footprints of selection within the genome. The purpose of this study was to map genome-wide selection signatures in ten cattle breeds and thus improve the understanding of the genome response to strong artificial selection and support the identification of the underlying genetic variants of favoured phenotypes. We analysed 47,651 single nucleotide polymorphisms (SNP) using Cross Population Extended Haplotype Homozygosity (XP-EHH).TG, ABCG2, DGAT1, GH1, GHR and the Casein Cluster) within selection signatures of at least one breed. A literature search for genes lying in outstanding signatures revealed further promising candidate genes. However, in concordance with previous genome-wide studies, we also detected a substantial number of signatures without any yet known gene content.We set the significance thresholds using the maximum XP-EHH values of two essentially artificially unselected breeds and found up to 229 selection signatures per breed. Through a confirmation process we verified selection for three distinct phenotypes typical for one breed . Moreover, we detected six genes strongly associated with known QTL for beef or dairy traits (MAP2K6 is the only annotated gene of two signatures detected in Galloway and Gelbvieh cattle and is already known to be associated with carcass weight, back fat thickness and marbling score in Korean beef cattle. Based on the confirmation process and literature search we deduce that XP-EHH is able to uncover numerous artificial selection targets in subpopulations of domesticated animals.These results show the power of XP-EHH analyses in cattle to discover promising candidate genes and raise the hope of identifying phenotypically important variants in the near future. The finding of plausible functional candidates in some short signatures supports this hope. For instance,  Since the Neolithic Transition humans have systematically made use of many different production capabilities  associated with various domesticated animal species. For cattle, the domestication of the aurochs took place about 10,000 years ago in two isolated events, one in the region of the Fertile Crescent, the other in the Indus Valley . During H Test [et al.[In general, recent technological and analytical advances of genomics hold great promise for the future when it comes to moving from hypothesis-driven candidate gene studies to hypothesis-generating genome-wide scans in various species . EssentiH Test  , (ii) pt [et al.-15, and t [et al., iHS [17t [et al.  based on the ratio of the integrated EHH-curves of the two  core-alleles [During the “classical selective sweep” a typical signature of selection arises around the selected or so-called “core”-alleles ,6. Thesety . EHH is-alleles . Cross-P-alleles ) is esseFST-values as well as the effective population size of these breeds, discuss the consequences of applying different concepts to selection signature studies and provide some perspectives on future strategies for identifying the selected variants in an artificially sub-divided population.This study explores the adaptive genetic variation fixed or concentrated within artificially sub-divided and divergently selected breeds. The between-population scan for signatures of strong recent artificial selection was accomplished by applying Sabeti’s XP-EHH  to genomNo extra sampling was applied for this study, thus no formal ethical approval was required. Instead, we used hair roots, blood and semen samples that are collected by breeding associations and insemination stations on a regular basis. They were as follows: for Anatolian Black, Illyrian Mountain Buša, Red Holstein, Blanc-Bleu Belge and Galloway and some individuals from German Fleckvieh, Murnau-Werdenfelser and Franken Gelbvieh we used existing DNA from a previous study, which had already been prepared . For othSamples from more than 1,800 animals representing ten distinct cattle breeds were collected from 1992 to 2008. The sampled breeds can be divided into four breeding-purpose groups. The Anatolian Black (ABB) and the Illyrian Mountain Buša (IMB) are local varieties of an initially large Buša cattle population kept in almost the entire Balkan region and parts of Turkey . Since tDNA was genotyped with Illumina BovineSNP50 BeadChip  using stBEAGLE 3.0.4[Haplotypes were reconstructed by a Hidden Markov Model implemented in the program GLE 3.0.4. Since aNa, Table Differences in the degree of familial relationships within analysed breeds may have an impact on the distribution of haplotypes and allele frequencies and could therefore lead to biased results. To sample pure bred and most independent animals we used pedigree records collected by breeding associations where possible. For the two “artificially unselected” cattle breeds (ABB and IMB) there were no breeding associations and no pedigree records available. Instead, we used written and oral evidences from animal owners to ensure the largest possible independence of the sampled animals. To ensure a similar population structure within each breed, regardless of pedigree availability, we estimated genome-wide unified additive relationships  betweenFST) based on Weir and Cockerham [pcaMethods[hierfstat[FST-calculation.To ensure we didn’t have population stratification within the sets of animals and to be able to assess the genetic diversity of the ten breeds, we conducted a principal component analysis (PCA) and estimated the pair-wise fixation index  separately so as to have zero mean and unit variance. For each pair of breeds, the breed, which was searched for selection signatures, was called “case breed” and the one used as reference population “control breed”. Due to the absence of genomic position we instead used the physical position (1 Mb ≈ 1 cM).Ne), and an inevitable selection for overall fitness . In conWe were primarily interested in breed specific selection signatures confirmed by multiple evidences. Therefore, we merged all significant SNPs of a case breed regardless of the respective control breeds. For each breed, we integrated all significant SNPs to a common signature, if separated by one non-significant SNP at the most. Moreover, the significant signatures included half of the physical distance to the neighbouring non-significant marker on both sides  was estimated based on its known relationship with linkage disequilibrium (LD) and the inter-marker genetic distance c[et al.[r2-values using Haploview [r2-values over all 29 autosomes in distance bins with an increment of 50 kb starting the first bin at 975 kb and ending the last at 10,025 kb for each breed separately. The bin-midpoints therefore varied from 1,000 kb (~0.01 Morgan) to 10,000 kb (~0.10 Morgan). For the estimation of Ne the physical distance was used as an approximation of the genetic distance c (1 Mb = 0.01 Morgan). Since Ne is estimated (2c)-1 generations ago [Ne from 50 to 5 generations ago. For each bin we calculated the mean of the r2-values E(r2) and finally estimated Ne based on the equationThe effective population size  and dairy cattle production traits . As shown in Table After conformation process of our XP-EHH-method that confirmed a selection signature for all three phenotypes tested, we checked if it was able to detect significant selection signatures close to previously published candidate genes of six known QTL associated with beef  and 217 (MWF-BBV) comparison-specific signatures including singletons, finally totalling and complementing to between 100 (OBV) and 229 (MWF) breed-specific confirmed signatures without singletons. The numbers of comparison-specific and breed-specific signatures as well as the average number of SNPs per signature are listed in Table Ne of all ten breeds for 5 (Ne5) to 50 (Ne50) generations ago , shows lower negative correlation to the number of detected confirmed signatures (-0.76) than the more recent Ne represented by Ne5 (-0.82). The number of SNPs per signature shows stronger correlation with Ne50 (-0.69) than with Ne5 (-0.47). Interestingly, we found the strongest correlation (0.89) between the number of SNPs per signature and the recent decline in Ne, represented by the Ne5/Ne50 ratio. In contrast, Ne5/Ne50 does not significantly affect the number of detected signatures in each breed  between signature lengths and the Ne5/Ne50 ratio points indirectly to the fact that highly selected  and effectively smaller  seems to be involved in the regulation of calcium, particularly in the non-lactating mammary gland [The most extended signature in IMB within 450 kb on BTA17 includes eight genes. Most outstanding are GF2alpha  whereas ry gland .single-minded (Drosophila) homologue 1 (SIM1) is a reasonable target for selection in a beef breed. SIM1 is thought to regulate body weight and is associated with obesity in humans and mice  and  and singce , shown to be expressed in a wide range of tissues, including the mammary gland. Moreover, it is suggested that the presence of BTC in milk might play a fundamental role in growth and development of neonates’ gastrointestinal tracts [ANKRD17, has already been reported to be necessary for vascular maturation during embryonic development in mice [IL8, whose mutations were significantly associated with a substantial number of tested dairy traits in Chinese Holstein [CSN1S1, CSN2, CSN1S2 and CSN3, which are associated with parameters like improved milk-clotting as well as protein and milk yield [The positive selection signature on BTA6 in the dairy breed BBV (~12 Mb) encompasses 68 genes. Among these is l tracts . Another in mice . Some adHolstein  and the lk yield ,61.SALL1[NKD1[NOD2 also named CARD15) is involved in dairy traits [MC1R also MSHR) responsible for red coat colour [The selection signature on BTA18 in RH extends almost 12.2 Mb. Among the positional candidate genes there are four striking examples. Two can be roughly summarised as fertility related, SALL1 and NKD1ALL1[NKD1, one  lying in this area are assumed to be of special interest. Many of these candidate genes have already been associated with interesting traits. IBSP and MEPE were identified as possible candidates for protein and fat percentage in Israeli Holstein [LAP3 have been shown to impact fat as well as protein percentage and milk somatic cell score [NCAPG was associated with bovine carcass weight [et al.[NCPAG-LCORL locus with feed intake and body weight traits.Interestingly FGV and MWF overlap in their most extended signatures on a region of almost 5.7 Mb. The other dual purpose breeds of alpine origin, DFV and OBV, as well as the closely related BBV also show selection signatures on the same part of BTA6, overlapping with the common section of FGV and MWF in almost 1.6 Mb. Therefore, the seven genes  will be challenging. More promising for the deciphering of new adaptive pathways will be the investigation of less extended signatures that incorporate only a few or even just one positional candidate gene like those presented in the subsequent section.We investigated less extended signatures harbouring only one to three positional candidate genes (named “short signatures” in the following) for possible associations cited in literature. In doing so, we were able to identify about 80 potential candidate genes that have been associated or suspected to be related to important traits in human, goat, pig, cattle and other species  and the other at the beginning. At this location there is also a selection signature in GLW but, unlike to FGV, it is a discrete signature that is separated from the one around MAP2K6. This shows possible evidence for the signature in FGV to be due to two different targets of selection.One of those short signatures includes f cattle ) on BTA1All selection signatures and positional candidate genes are listed in the breed specific additional files  is around twice the average for IMB  were also declared as positional candidate genes in at least one of the investigated breeds in our study. The Creole cattle represents an admixture of European, African and zebu cattle kept in tropical environments. However, pure European cattle breeds investigated in this case are mainly kept in -and are therefore adapted to- a temperate climate. Considering these discrepancies, the overlap in question is quite remarkable. Moreover, corresponding signatures can be assumed to represent more adaptions to selective pressures concerning performance than environment.Using XP-EHH we were able to identify between 32 (IMB) and 229 (MWF) selection signatures per breed and some promising candidate genes. The overlap with other studies was only moderate. This moderate agreement was no surprise at all and might be due to a number of parameters. One is the discrepancy in the power of methods used to identify selection signatures in different designs. Since this is the first genome-wide study using XP-EHH in cattle it is not possible to compare the results to another study utilizing the same method. Some previous studies based on long haplotypes using methods like EHH  or iHS  were looMSTN and MC1R (two obvious targets of selection) were more than 520 kb (MSTN) respectively 490 kb (MC1R) apart. Such huge gaps can bias haplotyping and the XP-EHH estimation on both sides of the gap thus precluding an adequate signature mapping within the gap. Although both, MSTN and MC1R, are surrounded by significant SNPs it is obvious  and number of positively selected sites  are potentially able to affect Ne-results. However, sample sizes are not chosen freely but dependent on the degree of familial relationship within the sampled subpopulations as measured by UAR. Filtering subpopulations using UAR will reduce the sample of populations that have a smaller Ne more than those with higher Ne, since individuals in effectively small populations are in general closer related. The observed relationship between estimated Ne and sample size might thus be a result of UAR filtering that determined sample size. Taken together, we conclude that the estimated Ne is related to the true Ne. This conclusion is strongly supported by the fact that the estimated Ne–values match with our expectations especially regarding the relative sizes and recent demographical processes of the investigated breeds. Therefore, we assumed that different sample size or Ne do not bias our design although great differences between compared breeds might not be advantageous. However, the final number of selection signatures after excluding all singletons was more strongly correlated with estimated Ne and the estimated negative correlation was even stronger for Ne5 than Ne50. Since Ne50 coincides with the breed founding period, here detected selection signatures reflect a combination of breed specific phenotypes and quantitative traits under partly divergent artificial selection in artificially isolated subpopulations.The intensity of the artificial selection is inevitably associated with the number of parents of the next generation . This ises Table . Therefop-values debatable [Regardless of whether breed characteristics or quantitative traits are predominantly mapped, the main question is where to set the significance threshold in order to get as few as possible false positives due to genetic drift, while retaining as much as possible of the truly selected sites. Unlike for human evolution, a precise demographic model as a basis for the coalescence simulation is not available for the diversification of cattle breeds. But even for human data there is considerable uncertainty in the models simulated, which makes the usefulness of assigning formal ebatable ,36. Therebatable ,36,83,84ebatable ,85. ThisMAP2K6 . Howeve6 Figure .We also detected a number of signatures  that did not incorporate any gene at all. This is not new in genome-wide searches for signatures of selection and has been discussed in human studies ,83,84,86In general, combining different methods of detecting selection signatures  or combiMany differentially selected phenotypes within cattle breeds were the subject of selection for novelty. This practice was common due to the human tendency to select phenotypes visibly distinguishing particular breeds. We adapted a design so that it should be able to detect a large fraction of differentially selected breed characteristics as well as differentially selected (quantitative) traits. Nevertheless, a final validation for both the way of defining the significance thresholds based on artificially unselected breeds as well as for the found signatures of selection will only be possible when further studies are performed and some candidate genes are irrevocably identified. This proof of causality will be easier to achieve for less extended signatures that include only a few or just one candidate gene. The experiences gathered by resolving these “simpler” signatures might in turn lay the foundation for dissecting the more extended signatures.Our results demonstrate that the XP-EHH approach can be very useful for mapping selection signatures in artificially selected breeds of livestock animals. Specific genetic architecture could make the detection of selection signatures in domesticated animals  more rewarding and less costly than in humans even where classic sweeps were not a dominant mode of adaptation after domestication 10,000 years ago. The determination of the significance threshold in studies of selection signatures within and between breeds of domesticated species is still a major challenge. We made use of the artificially unselected breeds ABB and IMB to account for the maximum XP-EHH value that seems to be achievable if genetic drift and natural or weak artificial selection are acting only. In doing so we were able to detect up to 229 selection signatures per breed, thereby recovering a number of genes among the selected sites that are already known to be involved in important breeding traits. Nevertheless, distinguishing the effects of positive selection from those of population demographics or genetic drift is still a major challenge that needs to be resolved. We found hundreds of signatures without any genes. About one fifth of those were mapped in more than one breed and even more (~40%) reached significance in the respective case breed with more than one control breed. The proof of causality for most significant signatures with obvious candidate genes and the setting up of a correct adaptation hypothesis for signatures without genes present further challenges in the effort to uncover new adaptive pathways. Our results clearly highlight numerous genes associated with important traits and characteristics selected within breeds or breed groups. These might eventually contribute to the identification of the selected variants that caused the signatures found. In most cases advanced studies will be needed to distinguish between signatures caused by breed specific characteristics or traits of practical interest for agriculture. Both are of interest for a better understanding of mechanisms and targets of artificial selection in species of domesticated animals.EHH: Extended haplotype homozygosity; iHS: Integrated haplotype score; XP-EHH: Cross population extended haplotype homozygosity; sXPEHH: Standardised XP-EHH values; Rsb: Relative integrated EHH of a site between populations; FST: Population fixation index; ABB: Anatolian Black; IMB: Illyrian Mountain Buša; BBB: Blanc-Bleu Belge; GLW: Galloway; RH: Red Holstein; BBV: Braunvieh; OBV: Original Braunvieh; MWF: Murnau-Werdenfelser; DFV: German Fleckvieh; FGV: Franken Gelbvieh; Na: Number of animals of a breed used for selection signature analyses; Ng: Number of sampled individuals of a breed; UAR: Unified additive relationship; maxABB: Chromosomal maximum sXPEHH value of the case breed ABB compared to a specific control breed; maxIMB: Chromosomal maximum sXPEHH value of the case breed IMB compared to a specific control breed, gene symbols are given according to the UCSC Genome Browser.The authors declare that they have no competing interests.FST values, XP-EHH- and LD-analyses as well as Ne–estimations, contributed to results interpretation and drafted the manuscript. IM designed experiments, supervised the study, organized sampling, performed plausibility control of the data, supplied database applications for all the subsequent data analyses, partly performed data analyses, contributed to the interpretation of data, revised and edited the manuscript. DS generated and performed the initial analysis of the genotyping data. MF supplied material and critically revised the manuscript. All authors read and approved the final manuscript.SR assisted in designing experiments, carried out principal component analysis, estimation of pairwise Bos taurus autosome (BTA) 6. A) The distribution of the sXPEHH values of BTA6 in the IMB-RH comparison. According to the convention used in the sXPEHH-program, the positive sXPEHH values suggest selective pressure in the first breed of comparison IMB-RH (i.e. IMB) while the negative values suggest selective pressure in the second breed (i.e. RH). The maximal positive sXPEHH values of the comparison IMB-RH (maxIMB: 2.658523) points to a SNP under possible selection pressure in artificially unselected population IMB. B) Similar to the above, the maximal positive sXPEHH values of comparison ABB-RH (maxABB: 3.056731) points to a SNP under possible selective pressure in the artificially unselected population ABB. C) The distribution of the sXPEHH values of BTA6 in the DFV-RH comparison. The chromosome-wide significance threshold for XP-EHH comparisons of the control breed RH with any other artificially selected breed, e.g. DFV, is defined as the mean value of maxIMB and maxABB (2.857627). All sXPEHH values above this threshold are declared significant for selection in DFV if contrasted to RH on BTA6 and shaded in red.Determination of significance thresholds. The PDF illustrates the determination of the significance threshold for DFV in comparison to RH on Click here for fileBos taurus autosomes (BTA) and all eight artificially selected breeds (breed abbreviations). Each breed represents the respective control breed for XP-EHH calculation. If a marker in a XP-EHH comparison reaches a sXPEHH value above the threshold of the control breed on the specific chromosome it is called significant. These thresholds were calculated as the mean of the chromosome-wide maximum values reached by ABB and IMB  when calculating XP-EHH with the respective control breed , B) and C) represent all assumed breed-comparisons of DFV for which at least one of the 22 SNPs was significant. Now each SNP of the comparison-independent row D) that represents DFV in total is significant if the respective SNP is significant in at least one breed-comparison A), B) or C). All remaining SNPs stay non-significant. All significant SNPs belong to the same selection signature if they are not separated by more than one non-significant SNP in D). Finally, the signature is extended at each side by half the distance to the neighbouring non-significant SNPs (marked with a green dashed line) to get the final signature of selection spanning the distance marked by a double arrow.Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Galloway. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Blanc-Bleu Belge. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Red Holstein. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Braunvieh. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Original Braunvieh. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Murnau-Werdenfelser. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Franken Gelbvieh. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed German Fleckvieh. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Anatolian Black. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileBos taurus autosom (BTA). The breed-abbreviations represent the respective control breed . In case of a significant comparison to the respective control breed the sXPEHH-value is shown in these columns, otherwise the field is left empty. nSC shows the overall number of significant breed comparisons for each marker. Furthermore, all genes within the signature, which starts at position (bp) “Start of Sig” and ends at “End of Sig” according to the UCSC Genome Browser, are listed.Significant markers of the breed Illyrian Mountain Buša. Listed are all significant markers with their SNP-Number (SNP-No), representing the range of the respective SNPs within all 47,651 SNPs ordered per chromosome and position, the SNP position and corresponding Click here for fileCandidate genes of short signatures. This excel file shows candidate genes of short signatures (that means at most three annotated genes) found on a given chromosome (BTA) in the respective case breed. All listed genes have already been associated or at least suspected to be involved in interesting traits and metabolic pathways in different species. The respective trait/pathway as well as the species it was investigated in and the corresponding reference are given.Click here for file


Zea mays L.) grain. Given that individual tocochromanols differ in their degree of vitamin E activity, variation for tocochromanol composition and content in grain from among diverse maize inbred lines has important nutritional and health implications for enhancing the vitamin E and antioxidant contents of maize-derived foods through plant breeding. Toward this end, we conducted a genome-wide association study of six tocochromanol compounds and 14 of their sums, ratios, and proportions with a 281 maize inbred association panel that was genotyped for 591,822 SNP markers. In addition to providing further insight into the association between ZmVTE4 (γ-tocopherol methyltransferase) haplotypes and α-tocopherol content, we also detected a novel association between ZmVTE1 (tocopherol cyclase) and tocotrienol composition. In a pathway-level analysis, we assessed the genetic contribution of 60 a priori candidate genes encoding the core tocochromanol pathway (VTE genes) and reactions for pathways supplying the isoprenoid tail and aromatic head group of tocochromanols. This analysis identified two additional genes, ZmHGGT1 (homogentisate geranylgeranyltransferase) and one prephenate dehydratase parolog (of four in the genome) that also modestly contribute to tocotrienol variation in the panel. Collectively, our results provide the most favorable ZmVTE4 haplotype and suggest three new gene targets for increasing vitamin E and antioxidant levels through marker-assisted selection.Tocopherols and tocotrienols, collectively known as tocochromanols, are the major lipid-soluble antioxidants in maize ( Tocochromanols are a class of lipid-soluble antioxidants synthesized by photosynthetic organisms and include the biosynthetically related compounds tocopherols and tocotrienols. Tocopherols have a saturated tail derived from phytol diphosphate, whereas the tail of tocotrienols is derived from geranylgeranyl diphosphate and contains three unconjugated double bonds. α-, β-, δ-, and γ-Tocopherols and tocotrienols refer to four species of compounds distinguished by the degree and position of methyl groups  on the aromatic ring of each compound class . A givenLike all essential nutrients, vitamin E is required at minimal levels in the human diet to maintain optimal health. Clinical vitamin E deficiency is rare and results in various neurological conditions, including ataxia  and myopathy (muscle weakness). Suboptimal dietary intake has been associated with increased risk to cardiovascular disease, some cancers, and decreased immune function .Zea mays L.) is not only a global staple crop but also a primary food source for hundreds of millions of people in developing countries. Unfortunately, the maize varieties providing grain for human consumption typically do not provide adequate daily levels of vitamin E  by the cytosolic enzyme p-hydroxyphenylpyruvic acid dioxygenase  or geranylgeranyl diphosphate by homogentisate geranylgeranyltransferase (HGGT) to yield the committed precursors to tocopherols and tocotrienols, respectively. Three subsequent activities, two methyltransferases and tocopherol cyclase , generate the full chemical diversity of tocochromanols . An additional source of phytol for tocopherol synthesis is provided by phytol kinase (VTE5), which in green seeds recycles phytol from chlorophyll degradation into the tocopherol pathway , including some that contain within their support intervals tocochromanol biosynthetic pathway genes, primarily VTE3, VTE4, and HPPD  markers contributes to the process of resolving QTL down to single genes or causative variants. We conducted the present (1) genome-wide association study (GWAS) and (2) pathway-level analysis to identify genes responsible for quantitative variation of grain tocochromanol levels in a maize inbred association panel.We evaluated an association panel of 281 diverse maize lines that captures a significant fraction of the common alleles present in temperate and tropical public maize breeding programs around the world . Mobile phases were A: methanol:methyl tert-butyl ether and B: methanol:H2O  at a flow rate of 0.8 mL/min using the following gradient: 0 to 12 min: 100% B to 60% B; 12 to 17.5 min: 60% B to 22.5% B; 17.5 to 19.5 min: 22.5% B to 100% B; 19.5 to 21 min held at 100% B for re-equilibration. Tocochromanols were detected by fluorescence using 290 nm excitation and 325 nm emission and quantified relative to curves with γ-, α-, and δ-tocopherol  and γ-, α-, and δ-tocotrienol standards .Dry maize grain samples were ground using a commercial Stein Mill  and aliquoted into cryogenic tubes for long-term storage at −80°. For high-pressure liquid chromatography (HPLC) analysis, 15−20 mg of ground tissue was weighed into a 1.4 mL of U-bottom bar coded extraction tube  containing two 5-mm glass beads. Four hundred microliters of extraction buffer [60:40 v/v acetone: ethyl acetate containing 1 mg/mL butylated hydroxytoluene and 1 mg/mL DL-α-tocopherol acetate  as an internal recovery control] and 150 μL of deionized water was added and extraction accomplished using a commercial paint shaker  for 10 min. Samples were centrifuged for 10 min at 2500 × g in a Sorvall Legend RT centrifuge . Two-hundred microliters of the upper organic phase was then transferred into a 750-μL tube, dried in a speedvac and resuspended in 100 μL of 3:1 (v:v) methanol:methyl Similar to other tocochromanol studies in maize , γT3/(γT3+αT3), δT/(γT + αT), δT/αT, δT/γT, δT3/(γT3 + αT3), δT3/αT3, δT3/γT3, and total T/total T3. Levels of δT3 and δT were below minimum HPLC detection limits in five and three samples, respectively, and values were approximated for these samples by uniform random variables ranging from 0 to min, where min is the minimum HPLC detection value for a given compound. This approach is similar to one recommended by We evaluated 20 tocochromanol traits based on HPLC data collected on grain samples from 252 lines of the maize association panel that yielded sufficient quantities of physiologically mature grain. Specifically, the phenotypes analyzed were α-tocopherol (αT), δ-tocopherol (δT), γ-tocopherol (γT), α-tocotrienol (αT3), δ-tocotrienol (δT3), γ-tocotrienol (γT3), total tocopherols , total tocotrienols , total tocochromanols  in μg·gSupporting Information, Table S1) was predicted from a mixed linear model fitted across environments in ASReml version 3.0 (r) were used to assess the relationship between untransformed tocochromanol trait BLUPs. Before the GWAS, the Box-Cox procedure  for each line (rocedure  was implrocedure  to find http://www.panzea.org/genotype_search.html). In this study, the diversity panel was also genotyped with a genotyping-by-sequencing (GBS) protocol (http://www.panzea.org/dynamic/derivative_data/genotypes/Maize282_GBS_genos_imputed_20120110.zip). Removal of monomorphic and low-quality SNPs generated a data set with 591,822 SNPs, of which 294,092 SNPs had minor allele frequencies (MAFs) greater than or equal to 0.05 in the panel. Of the 294,092 SNPs used for association analysis, 7964 of them were redundant as a result of loci overlapping among the three SNP data sets. These redundant SNPs were included in the association analysis because of the variable missing data patterns and error rates attributable to the different genotyping technologies. Before association analysis, missing SNP genotypes were conservatively imputed with the major allele.The diversity panel was previously genotyped with the Illumina MaizeSNP50 BeadChip    was usedi.e., chromosome-wide) with a major effect locus were considered for inclusion into the final model. The optimal model was selected using the extended Bayesian information criterion  (riterion . We thenVTE1 through VTE5) have been well characterized in Arabidopsis  aromatic head group synthesis (HGA production); (2) prenyl group synthesis; and (3) core tocochromanol pathway genes . We then created a subset of GWAS results for each trait that included raw (unadjusted) P-values for the 6122 SNPs located ±250 kb of these 60 a priori genes. The P-values within each subset were corrected for multiple testing by using the We conducted a pathway-level analysis that used prior knowledge pertaining to the biosynthesis of tocochromanols to select a prioritized subset of candidate genes. The genes encoding enzymes leading to synthesis of the tocochromanol precursors HGA, GGDP, and phytol-PP and for the core tocochromanol pathway (bidopsis  and HGGTr2) in TASSEL version 3.0 (r2) and common haplotype patterns were also assessed in Haploview version 4.2 . Even though the average amount of α-tocopherol was greater than its tocotrienol complement, the correlation between these two species was stronger (r = 0.40). Total tocopherols and tocotrienols were also very weakly correlated (r = 0.10) although, as expected, both were strongly correlated with total tocochromanols . This suggests that synthesis of the tocopherol and tocotrienol compound classes are largely independently regulated. The average heritability on a line-mean basis for the six tocochromanol compounds and their fourteen derivative traits was 0.90, with a range of 0.78 to 0.96 . Given that the sample size of the association panel (n = 252) only has adequate statistical power to repeatedly detect large effect QTL (Table S4). In general, this less conservative FDR level increased the density of SNPs at strong association signals that were initially detected at a genome-wide FDR of 5%. Although all of the 34 SNPs with significant associations at 5% FDR had MAFs ≥0.09 for temperate lines (n = 207) of the association panel, 23.5% of these SNPs had MAFs ≤0.05 for tropical lines  located within ZmVTE4 (GRMZM2G035213)—the gene that encodes γ-tocopherol methyltransferase (γTMT) in maize (ZmVTE4 (2r) for marker pairs with the peak SNP locus were ≤0.36 over distances of > 3.5 kb. This SNP locus was also strongly associated  with three αT-related trait ratios: αT/γT, γT/(γT + αT), and δT/αT . An additional 24 of the 34 SNPs significant at 5% FDR were associated with at least one of these four αT-related traits and present on chromosome 5. However, only seven of these 24 SNPs were located within 3 kb of the ZmVTE4 start and stop codons (a conservative definition of the ZmVTE4 gene). Of the 17 remaining significant SNPs, 15 were within 3.7 Mb of ZmVTE4 but also within 3 kb of genes that encode other proteins such as a WYRKY transcription factor (GRMZM5G823157), a pentatricopeptide repeat-containing (PPR) protein (GRMZM2G325019), and an amino acid permease .The strongest association signal was detected for αT on chromosome 5, with the peak SNP locus . The three MLMM-identified SNPs were essentially in linkage equilibrium (2r ≤ 0.023) with each other and tagged two of the four short ZmVTE4 haplotype blocks detected by Haploview  on chromosomes 5, 6, and 9 were likely spurious associations.Given that nearly all of the statistically significant SNPs at 5% FDR were located within a 5.9 Mb segment on chromosome 5, the multilocus mixed model (MLMM) , shown in Table S6). The most favorable haplotype  had an average αT concentration of 15.95 ± 3.90 μg·g−1(n = 28), whereas the other four haplotypes (n = 224) averaged 7.15 ± 4.63 μg·g−1. When contrasting the most and least favorable haplotype classes, we observed a 5.76-fold change in average αT values. Although the most favorable haplotype occurred at a low frequency (11%) in the maize association panel, it was the only haplotype that was essentially equally distributed between temperate (n = 15) and tropical (n =13) germplasm.We used a haplotype approach to assess the joint effect of the three MLMM-identified ZmVTE4 was identified for the ratio of δT3 to the sum of γT3 and αT3 [δT3/(γT3 + αT3)] on chromosome 5, with the peak SNP locus  located 70 bp from the transcriptional start site of the gene that encodes tocopherol cyclase, ZmVTE1  for marker pairs with the peak SNP locus revealed long-range patterns of LD  limiting mapping resolution, especially downstream of ZmVTE1. We also identified two SNPs  within a putative transcription factor  and ~168 kb away from ZmVTE1 that were significantly associated with δT3 and δT3/(γT3 + αT3) at the 5% and 10% FDR levels, respectively. When the FDR was relaxed to 10%, an additional 27 SNPs spanning a 3.3-Mb interval that included ZmVTE1 were associated with at least one of these two tocotrienol-related traits (Table S4).A moderately strong association signal relative to r = 0.81). The optimal model obtained for δT3 and δT3/(γT3 + αT3) contained only a single SNP  and explained 9–10% of the total phenotypic variation (Table S5). Both of these SNPs were significant for their respective traits at 5% FDR in the initial GWAS with the unified mixed model (Table S4). Although separated by a distance of 168.3 kb, these two SNPs were in moderate linkage disequilibrium (2r = 0.42). However, only S5_133333561 was previously significantly associated with both tocotrienol-related traits at 10% FDR. When we reconducted GWAS using the unified mixed model with one of these two SNPs as covariates for their respective traits, all of the SNPs previously significant for these two traits at 5% or 10% FDR were no longer significant at either FDR level .As was conducted for the four αT-related traits, MLMM was used on a chromosome-wide level to better clarify association signals for the two highly correlated tocotrienol-related traits . Given this limitation and the potential that additional a priori candidate genes from well-characterized biochemical pathways may contribute to variability of tocochromanol levels in maize grain . Of the retested 6122 SNPs, 91 were statistically significant at 5% FDR for at least one of 15 tocochromanol traits. Not surprisingly, 64 of these significant SNPs were within ± 250 kb of ZmVTE4 and ZmVTE1, whereas new findings consisted of 27 significant SNPs within ± 250 kb of an additional 11 candidate genes. Of these 11 candidates, one was from the tocochromanol biosynthetic pathway, six were from the prenyl group synthesis pathway, and four were from the aromatic head group pathway (Table S7).In a large-scale GWAS, it is challenging to identify variants with moderate to weak effect sizes because of the need to control for the problem of simultaneously testing hundreds of thousands of SNP markers , prenyl group synthesis (2), and aromatic head group (2) pathways (Table S7). All but one of these 39 SNPs were a subset of the 91 significant SNPs detected in the pathway-level analysis without the inclusion of ZmVTE4 or ZmVTE1 SNPs as covariates. When the two MLMM identified ZmVTE1 SNPs were similarly included as covariates, we identified 57 significant SNPs at 5% FDR that were within ±250 kb of 10 genes involved in the tocochromanol biosynthetic (2), prenyl group synthesis (4), and aromatic head group (4) pathways. With the exception of a SNP on chromosome 9, these SNPs were also previously detected in the pathway-level analysis that did not control for ZmVTE4 or ZmVTE1 effects. Taken together, these two conditional analyses detected 12 of the 13 genes previously detected in the pathway-level analysis without the inclusion of MLMM identified ZmVTE4 or ZmVTE1 SNPs as covariates. Although the gene encoding a hydroxymethylbutenyl 4-diphosphate synthase (GRMZM2G137409) was not reidentified in either conditional analysis, a gene that encodes for a shikimate kinase (GRMZM2G070218) was newly detected when the three MLMM identified ZmVTE4 SNPs were included as covariates.Because of the moderate-to-strong association signals at ZmVTE4 and two ZmVTE1 SNPs as covariates resulted in a more stringent conditional analysis with only 10 SNPs, all for tocotrienol-related traits, being significant at 5% FDR within ±250 kb of two candidate genes (Table S7). The start codon for a gene encoding a prephenate dehydratase (GRMZM2G437912) was 23.4 kb away from two SNPs (S2_59013838 and S2_59013840) that were significantly associated with both γT3 and total tocotrienols. We also identified three SNPs  significantly associated with total tocotrienols and/or γT3 that were located 137.4 to 170.1 kb upstream of the start codon for a gene that encodes a homogentisate geranylgeranyl transferase . In addition, two SNPs (S9_92548696 and S9_92554465) that were located 61.4 to 67.2 kb downstream of the ZmHGGT1 stop codon were associated with γT3 and/or the ratio of total tocopherols to total tocotrienols , whereas three SNPs  located 231,403 to 231,441 bp downstream of the stop codon were significantly associated with both γT3 and the ratio of γT3 to the sum of γT3 and αT3 [γT3/(γT3+αT3)]. Although there were five additional SNPs within ±3 kb of the start and stop codons for both the prephenate dehydratase and ZmHGGT1 genes, none were significantly associated at 5% FDR with any of the tocochromanol-related traits in the pathway-level analysis.Controlling for the effect of both QTL with the three A. thaliana and Synechocystis sp. PCC6803  in grain from a maize association panel that was assembled to represent the genetic diversity found in public sector maize breeding programs worldwide (ZmVTE4 (γTMT) and ZmVTE1 (tocopherol cyclase). The strong associations of ZmVTE4 SNPs with αT (the product of γTMT) are in agreement with results from a GWAS conducted by ZmVTE4, as well as two separately scored indels within the ZmVTE4 5′ UTR and promoter regions that were significantly associated with αT at a Bonferroni-corrected threshold of 5%. Similar to ZmVTE1 SNPs with δT3/(γT3 + αT3) was observed.The maize association panel was scored for 536,988 SNPs with a GBS protocol , but the optimal models obtained with MLMM always only included three ZmVTE4 SNPs. Thus, our findings do not support the presence of an enhancer element ~85 kb upstream of ZmVTE4. However, our results do provide a second line of evidence for multiple ZmVTE4 alleles , but with the caveat that multiple rare variants could also have created highly significant independent signals of synthetic association over an extensive genomic region , whereas a SNP within a putative transcription factor (GRMZM2G105494), ~168 kb away from ZmVTE1, was identified by MLMM for δT3. This was an unexpected result given that these two tocotrienol traits are strongly correlated (r = 0.81), thus they are more likely to be under highly similar genetic control. From a biosynthetic perspective, it is straightforward to rationalize ZmVTE1 as a causative gene affecting tocotrienol levels as tocopherol cyclase from a variety of organisms have been shown to utilize both tocopherol and tocotrienol substrates  in vitro and in vivo  and ZmVTE1 (9–10%) accounted for only a portion of the estimated heritability for their associated traits. Why is the heritability still unexplained or “missing” for these traits? Even though GBS increased the number of SNPs scored in the panel by ~10-fold and enabled a GWAS of the 2r >0.8) for a GWAS in diverse maize with such a rapid breakdown of LD  than the genotyped SNPs used for the GWAS . In addition to the strong genome-wide association with δT3/(γT3 + αT3), ZmVTE1 showed more modest pathway associations with δT3, δT3/αT3, δT3/γT3, and one tocopherol trait, δT/γT. Similarly, in addition to the four strongly associated, genome-wide αT-related traits, ZmVTE4 also had pathway-level associations with three tocotrienol traits: αT3, αT3/γT3, and γT3/(γT3 + αT3). Thus, genome-wide associations suggested ZmVTE4 and ZmVTE1 only impacted specific tocopherols and tocotrienols, respectively, a finding at odds with the known biosynthetic activities of the encoded enzymes toward both tocopherols and tocotrienols  and GRMZM2G437912, which encodes one of at least four prephenate dehydratase paralogs in the maize genome. Both loci were associated exclusively with tocotrienol traits. Relative to homogentisate phytyltransferase−type enzymes , HGGTs show a marked preference for GGDP over phytol-PP  in maize grain, the tocochromanol compound with the highest vitamin E activity (ZmVTE4 haplotype comprised of the three MLMM-identified SNPs was estimated to produce an unprecedented maximal 5.76-fold increase of αT levels in maize grain, nearly twice that reported in a previous association study of the locus (ZmVTE4 occurred at an 11% frequency in the maize association panel and importantly was distributed equally between temperate and tropical germplasm. Therefore, some of the inbreds containing this haplotype can be readily used as donors for marker-assisted selection within their respective germplasm type, be it temperate or tropical. Inbreds with best adaptation to the targeted breeding region can therefore be identified and would likely not have the issues of linkage drag of unadapted alleles surrounding the ZmVTE4 locus. In this study we have also reported the first association of loci with natural variation for tocotrienol levels in maize grain: ZmVTE1, ZmHGGT1, and one of at least four prephenate dehydratase paralogs. Importantly, these three novel associations provide a foundation for tocotrienol biofortification in maize and can provide the foundation for producing grain that balances enhanced antioxidant capacity with vitamin E activity considerations. In general, the observed low correlations between the individual tocopherol and tocotrienol compounds suggest that these traits can be independently manipulated genetically; thus. MAS of these and other yet to be identified loci could be potentially used to specifically modify the tocochromanol profile for optimal vitamin E and antioxidant contents within a single maize grain.Our study identified a activity . Selecti


Phylloscopus trochilus to detect genes potentially underlying traits associated with migration.Animal migration requires adaptations in morphological, physiological and behavioural traits. Several of these traits have been shown to possess a strong heritable component in birds, but little is known about their genetic architecture. Here we used 454 sequencing of brain-derived transcriptomes from two differentially migrating subspecies of the willow warbler Taeniopygia gutatta. The mapped reads were situated within at least 12,101 predicted zebra finch genes, with the greatest sequencing depth in exons. Reads that were mapped to intergenic regions were generally located close to predicted genes and possibly located in uncharacterized untranslated regions (UTRs). Out of 85,000 single nucleotide polymorphisms (SNPs) with a minimum sequencing depth of eight reads from each of two subspecies-specific pools, only 55 showed high differentiation, confirming previous studies showing that most of the genetic variation is shared between the subspecies. Validation of a subset of the most highly differentiated SNPs using Sanger sequencing demonstrated that several of them also were differentiated between an independent set of individuals of each subspecies. These SNPs were clustered in two chromosome regions that are likely to be influenced by divergent selection between the subspecies and that could potentially be associated with adaptations to their different migratory strategies.The transcriptome sequencing resulted in 1.8 million reads following filtering steps. Most of the reads (84%) were successfully mapped to the genome of the zebra finch Our study represents the first large-scale sequencing analysis aiming at detecting genes underlying migratory phenotypes in birds and provides new candidates for genes potentially involved in migration. Sylvia atricapilla[Carpodacus mexicanus[Animal migration requires an integrated set of physiological, morphological and behavioural adaptations -3. Some ricapilla and in Nmexicanus. In the mexicanus. Similarmexicanus.et al.[Danaus plexippus. Using a candidate gene approach a length polymorphism in the 3′ untranslated region (UTR) of the ADCYAP1 gene that was found to explain a small but significant part of the variation in migratory activity within and between different European populations of blackcaps [Quantitative genetic studies of blackcap populations have shown a strong heritable basis of several components of migratory behaviour, such as timing, duration and direction -10. Howeet al. found 40lackcaps . Increaslackcaps . The hugPhylloscopus trochilus. In Scandinavia, P. t. trochilus breeds in the south and P. t. acredula in the north, and a hybrid zone is found in the central part of the region [In this study we attempt to identify genes potentially associated with migratory traits by using next-generation sequencing of a brain-derived transcriptome from two subspecies of a small passerine, the willow warbler e region ,15. Anale region -18. Preve region ,15,19. De region -25. Giveet al. in prep). Here we describe the use of genomic resources of a closely related bird species, the zebra finch Taeniopygia guttata, to assemble and annotate reads, and demonstrate the efficiency of this approach in detecting sequence variation in a non-model species.With our data set we specifically wanted to investigate sequence variation in coding genes and determine how this variation is distributed between the subspecies. The sequence data provides a genome-wide set of markers and allows for detection of differences in protein-coding regions that could give rise to phenotypic differences. We used normalized cDNA libraries in order to increase the sequencing depth of low abundance transcripts and to maximize the number of sequenced genes. The normalization in combination with a pooling of samples has the consequence that expression differences could not be reliably quantified from this data set. Expression differences, which are likely to be important for differences in migratory phenotypes, are instead investigated in another study using microarray expression profiling of the same samples (Boss R = 0.89) between the number of reads per chromosome and chromosome length. A small number of reads  were defined as chimeric by the mapping software and had two separate alignments included in the output, which in the vast majority of cases were situated on different chromosomes. The mapping quality (MQ) of reads ranged from 0 to 40, with a mean value of 32.9. MQ is the phred-scaled probability (−10 log10P) that a read has been incorrectly aligned in the genome, with for example a value of 40 corresponding to a probability of 0.0001 [Two runs of 454 sequencing produced 1,312,317 and 1,197,626 raw reads, respectively  of the aligned nucleotide positions overlapping with one or more gene feature in the zebra finch genome. These overlapping gene features originate from 12,101 unique genes when excluding positions where features from multiple genes were overlapping. Including positions in multiple overlapping genes, the number of unique genes was 12,975. Within genes, more aligned positions were located in introns than in exons, but the mean sequencing depth was higher in exons than in introns , and willow warblers from southern Sweden in the validation set  were matched to the zebra finch genome, but the applicability of cross-species genome mapping is dependent on the similarity between the genomes and is not expected to work equally well among different non-model species. The specific karyotype of the willow warbler has not been determined and it is not known how the arrangement of genes within and between chromosomes differs between the willow warbler and zebra finch genome. Within the order Passeriformes (passerines), to which both the willow warbler and zebra finch belong, a remarkable conservation of genome structure has been reported, with both gene order and gene content on chromosomes being largely unchanged even between distantly related species -33. It tParus major[As expected from the enrichment of mature mRNA in the sample preparation steps, the greatest sequencing depth was observed in exons Table . Unexpecrus major, most ofA large number of SNPs were detected in the sequence data, which were located on all but a handful of small chromosomes. The difference in SNP density observed across features is likely to be associated with their different evolutionary constraints. For example, exonic positions had a lower density of SNPs than what is found in both intronic and intergenic positions Table . OverallADCYAP1R1 gene. This gene encodes a membrane receptor that binds to the product of the ADCYAP1 gene [Due to the construction of the cDNA library it is impossible to trace the sequencing efficiency of each individual and this could bias the genetic differentiation estimate (DI) for each SNP. In order to get a more unbiased estimate, we filtered SNPs by their minimum sequencing depth in each of the pools. Assuming that all or most individuals in each pool have similar expression levels of transcripts, and that these pooled transcripts are randomly sequenced, increasing the sequencing depth makes it more probable that more individuals are represented among the sequence reads. However, larger expression differences between individuals would result in a higher probability for transcripts from certain individuals to be sequenced. In this case, filtering by sequencing depth may not necessarily provide a more unbiased estimate. Nevertheless, the general distribution of DIs of SNPs between the subspecies pools Figure , Table 3AP1 gene , which hAP1 gene . Even thacredula) and express the same migratory behavior as northern birds in Scandinavia. The environment, however, is more similar to what is found at the same latitude in Southern Sweden. If the highly differentiated SNPs identified in this study were associated with adaptations to the different environments of southern and northern Sweden, we would expect the Lithuanian birds to have genotypes much more similar to birds in Southern Sweden. In contrast, we observed comparable levels of differentiation for SNPs between southern Swedish and Lithuanian samples as between southern and northern Swedish samples. This corroborates the hypothesis that the identified genetic differences are associated with the subspecies in general and are potentially linked to adaptations involved in their different migratory strategies.The identified genetic differences between southern and northern Swedish willow warblers could also reflect adaptations to different environments. The only obvious large environmental and ecological contrast within the sampling area is between the Scandinavian mountains and the rest of Scandinavia. In Sweden, only the northern subspecies occurs in the mountains and some adaptations to this drastically different environment are expected. Indeed, two alleles of an earlier identified genetic marker show a distribution that is strongly associated with the different environments . To addret al.[The majority of the highly differentiated SNPs, including most of the SNPs verified to be differentiated in the validation set, are not located in the protein coding part of genes, but in intergenic regions . A particularly interesting analysis would be to compare the genomic position and the functional annotation of differentially expressed genes between migrating individuals of each subspecies with to those of genes found within the differentiated chromosome regions.Future work will aim at validating the remainder of the highly differentiated SNPs in other individuals from the same populations of willow warblers. If these SNPs also are found highly differentiated in a validation set, their allelic distribution will be investigated over a larger geographical scale to determine how well it follows the distribution of the subspecies. Putative divergent chromosome regions will be more finely mapped to get a better approximation of their size and gene content. When the differentiated regions have been properly delimited, efforts should be focused on identifying the actual targets of selection. This process will be aided by the integration with data derived from a microarray expression profiling study performed on the same set of samples  and in Northern Sweden , 63°22′N, 12°36′E, 30 May 2008; Anjan (N = 4), 63°43′N, 12°32′E, 31 May 2008). Eight individuals of each subspecies were also caught during migration in Southern Sweden  and in Northern Sweden . Given the high natal dispersal of the species [Eight males of each subspecies were caught during time of breeding in Southern Sweden . Two μg of total RNA from each individual were pooled together to form subspecies-specific pools. Messenger RNA (mRNA) was isolated from 5 μg of pooled total RNA by exonuclease digestion followed by lithium chloride precipitation using the mRNA-only Eukaryotic mRNA Isolation Kit . Synthesis and amplification of cDNA was performed with the Mint-Universal cDNA synthesis Kit  with 1 μg of mRNA used for first-strand cDNA synthesis. 800 ng of amplified cDNA was used to create a normalized library with the Trimmer kit .Of the 32 samples in total, eight from each subspecies were selected for construction of cDNA libraries. These were evenly represented by samples collected during time of breeding and migration, and constitute a subset of samples that have been used in a microarray expression profiling study . Nebulization was used to randomly shear cDNA into appropriately sized sequencing fragments of 400–900 bp. This method is optimized for shearing high molecular weight genomic DNA, but is expected to lead to a sequencing bias towards the middle and the end of a transcript . In ordehttp://genome.ucsc.edu/ and was used as a reference genome for mapping. The assembly contains 33 chromosomes  and three linkage groups. It also includes 33 sequences (e.g. chr1_random) that are comprised of contigs that are associated with particular chromosomes but which have not been successfully ordered within them, and one sequence (chrUn) derived from contigs that have not been unambiguously mapped to any of the chromosomes or linkage groups. Trimmed reads were mapped to the zebra finch genome using GMAP version 2012-01-11 [Prior to genome mapping, sfi linkers used in concatemerization were removed from reads using a perl script provided by AGOWA . In order to remove potentially contaminated sequences, standalone blast (blastn) was used to match reads against the pDNR-lib vector  used in the cDNA library construction. Reads obtaining a hit with an E-value lower than 1E-5 to the vector were discarded from downstream analyses. The zebra finch genome assembly version 3.2.4 was downloaded from the UCSC genome website 12-01-11  with a cSAM files were converted into Binary Alignment/Map (BAM) format and sorted using the view and sort function in Samtools version 0.1.18 . A pileuacredula), but are as willow warblers in southern Sweden (trochilus), homozygous for the southern allele of AFLP-ww1 [For each SNP, a differentiation index (DI) was calculated between the southern and the northern pool as follows. The proportions of each of the four nucleotides in each of the pools were first calculated and then subtracted between the pools. DI represents the largest absolute difference in nucleotide proportions and ranges from 1, which occurs when reads from each subspecies pools are fixed for different alleles, and 0 when there is no difference in allele frequencies between the two subspecies pools. Highly differentiated SNPs were arbitrarily defined as SNPs with a DI ≥ 0.9. This is likely to be a conservative threshold to highlight SNPs that have a large allele frequency difference between the subspecies pools and that could be under divergent selection. Due to the construction of the cDNA library, reads could only be assigned to any of the subspecies pools and not to individuals within them. With a decreasing number of reads in each pool, the probability increases that not all of the individuals are represented. To account for this, SNPs were filtered based on the minimum number of reads from any of the pools. This threshold was varied in order to determine its effect on the general distribution of DI and the number of highly differentiated SNPs between pools. A subset of highly differentiated SNPs (N = 14), which included some with the highest number of reads in each of the pools, was validated in eight other individuals of each of the subspecies sampled in Sweden  file using a customized perl script. For each transcript, positions of introns were inferred from genomic intervals between consecutive exons. The final file contained 315,974 features from 18,618 genes and 19,334 transcripts. No features were available for chromosome 16. Positional information extracted from the pileup file of the willow warbler alignments was also converted into BED file format and compared to the zebra finch feature BED file using Bedtools version 2.15.0 [et al.[Positional information of zebra finch genome features was downloaded as a General Transfer Format (GTF) file from the Ensembl website n 2.15.0 . For comn 2.15.0 ,51, a fr0 [et al., the dis0 [et al..The authors declare that they have no competing interests.MLu performed genome mapping, annotation, various downstream analyses and drafted the manuscript. JB and BC assisted in several initial analyses of the transcriptomic data. SB, MG and AW conceived the study. All authors provided comments and approved the final manuscript.Comparison of sequencing depth, number of aligned positions and SNP statistics between the full set of aligned reads and the subset of aligned reads with a mapping quality (MQ) ≥30.Click here for fileHighly differentiated SNPs (DI ≥0.9) with a minimum of eight reads from each of the sequencing pools.Click here for fileAdditional information on validation procedure. Description of primer design, amplification and Sanger sequencing of sequences containing highly differentiated SNPs. Table S3 provides information on primers used for amplification and sequencing. Table S4 provides information on the independent set of samples that have been used for validation.Click here for file


Rapid and reliable identification of quarantine pests is essential for plant inspection services to prevent introduction of invasive species. For insects, this may be a serious problem when dealing with morphologically similar cryptic species complexes and early developmental stages that lack distinctive characters useful for taxonomic identification. DNA based barcoding could solve many of these problems. The standard barcode fragment, an approx. 650 base pairs long sequence of the 5′end of the mitochondrial cytochrome oxidase I (COI), enables differentiation of a very wide range of arthropods. However, problems remain in some taxa, such as Tephritidae, where recent genetic differentiation among some of the described species hinders accurate molecular discrimination.In order to explore the full species discrimination potential of COI, we sequenced the barcoding region of the COI gene of a range of economically important Tephritid species and complemented these data with all GenBank and BOLD entries for the systematic group available as of January 2012. We explored the limits of species delimitation of this barcode fragment among 193 putative Tephritid species and established operational taxonomic units (OTUs), between which discrimination is reliably possible. Furthermore, to enable future development of rapid diagnostic assays based on this sequence information, we characterized all single nucleotide polymorphisms (SNPs) and established “near-minimal” sets of SNPs that differentiate among all included OTUs with at least three and four SNPs, respectively.We found that although several species cannot be differentiated based on the genetic diversity observed in COI and hence form composite OTUs, 85% of all OTUs correspond to described species. Because our SNP panels are developed based on all currently available sequence information and rely on a minimal pairwise difference of three SNPs, they are highly reliable and hence represent an important resource for developing taxon-specific diagnostic assays. For selected cases, possible explanations that may cause composite OTUs are discussed. Invasive species have been recognised as one of the most important threats to global biodiversity . As a coAnastrepha, Bactrocera, Ceratitis and Rhagoletis) include morphologically similar sibling species, complexes of cryptic species, host races, and morphotypes 70] in a TM MasterMix . Amplification was generally conducted using a ramping protocol on a Perkin Elmer GeneAmp 9600  or on the models Techne Genius or TC-412 , where an initial denaturation step at 95°C for 15 min was followed by 40–45 cycles of 40 sec at 94°C, 15 sec at 45°C, 1 min ramping to 60°C, 2 min elongation at 72°C and final extension at 72°C for 7 min. PCR products and negative controls were checked on a 1.2% agarose gel and amplification products were purified with NucleoFast® 96 PCR Plates following the protocol of the manufacturer . The amount of purified PCR product was measured with a NanoDrop ND-1000 spectrophotometer . 20–30 ng of the DNA was used for each sequencing reaction (8 μl) together with 2 μl BigDyeTM Terminator Cycle Sequencing Ready Reaction Kit V.1.1  and 4 μM of each primer. Sequencing reaction products were cleaned with DyeEx™ 96 Kit  and sequenced on an ABI Prism 3130×l Genetic Analyser .The polymerase chain reaction (PCR) was conducted in a total volume of 20 μl containing 1 μl of DNA extract (direct or 1/10–1/100 diluted), 6–12 μM of each oligonucleotide primer and 10 μl of HotStar Taqhttp://www.ncbi.nlm.nih.gov/genbank/) and on the “barcoding of life database”  were downloaded and those covering at least 85% of our target fragment  were used  we assessed the genetic relationships among the individual sequences by establishing phylogenetic trees. The evolutionary relationships for the genus trees Figures -6 are inTo establish the optimal threshold for taxon delimitation we used the function localMinima of the SPIDER software (Species Identity and Evolution in R) v. 1.1.2 . This meAnastrepha , Bactrocera, Ceratitis, Dacus, Rhagoletis, and the remaining samples were pooled as the group “Others”. Because the terminal groups were in almost all cases identical between the three methods the NJ method was used with the same parameters as described under “Cluster analysis” to build the trees for the six groups mentioned above.OTU’s were then defined with the module “Cluster” of the software TaxonDNA/SpeciesIdentifier v.1.7.7-dev3  using th28 and hence cannot be exhaustively explored. We therefore designed a Visual Basic script to screen one million different sets of random composition of candidate SNPs for those that fulfill the requirements . To enable this, we first generated a consensus sequence of each of the OTU’s established with TaxonDNA in geneious using IUB codes for ambiguous bases. The consensus sequences were then used as input files for the script. A maximum of twenty-five SNP’s of each taxon were chosen for the selection procedure, with preference given to non-silent SNP’s and to SNP’s with higher variance, i.e., the occurrence of more than one alternative base at that position of the alignment. The script is available from JEF upon request.In a further step, we elaborated OTU-diagnostic sets of single nucleotide polymorphisms (SNPs). Each position in the COI alignment that was not identical in all OTUs was polymorphic and hence the corresponding nucleotide of each taxon was considered a SNP. The problem was to find the minimum number of any combination of SNPs that differentiates each taxon pair by at least one or, preferably, several SNPs. The number of possible SNP combinations that have to be checked to find the minimal number of SNPs differentiating each taxon from every other by at least, say, four SNPs, well exceeds 10The agreement between the clusters obtained with the full-length barcode fragment and the concatenated sequence composed of the SNPs (of the panel designed with 3 SNP pairwise difference and “Excluding Singletons”) was tested by a Mantel test  implemenRhagoletis suavis group shows two distinct clusters. The first represents R. suavis sensu stricto, whereas the second is composed of three described species, R. completa, R. ramosae and R. zoqui. The occurrence of shared haplotypes in this Rhagoletis suavis subgroup that we call RcomGR, was tested by constructing a haplotype network based on a 1288 bp fragment of the subset of individuals for which this fragment was available in full. The minimum spanning network is embedding all minimum spanning trees computed based on the matrix of pairwise distances that is calculated between all pairs of haplotypes [The plotypes . The netplotypes  and visuplotypes .COI: Cytochrome oxidase I; OTU: Operational taxonomic unit; SNP: Single nucleotide polymorphism.The authors declare that they have no competing interests.JEF conceived the study, developed the script, performed data analyses and drafted the manuscript, LG and BF performed molecular genetic and data analyses, LG, JS, JR and MA participated in study design and manuscript writing. All authors read and approved the final manuscript.Samples used in this study. Indicated are the species name, the code used in our analyses, the origin of the data , membership of the species in the European Plant Protection Organisation (EPPO) A1 or A2 list for European quarantine organisms. Click here for fileEvolutionary relationships among taxa and OTUs for each taxon. Evolutionary relationships among taxa are based on 699 base pairs of the 5′-region of the mitochondrial COI gene. The relationships are inferred using the Maximum Likelihood method based on the genetic distance model of Tamura-Nei  using 1000 bootstrap replicates. The tree with the highest log likelihood of −33920.66 is shown. The numbered bars indicate OTUs representing groups established based on taxon delimitation levels suggested by the software SPIDER when excluding singletons  and with a minimum of three SNPs difference in all pairwise comparisons. The table is split into the five genera  OTUs andClick here for fileThe number of pairwise SNP differences between all OTUs. OTU Nr corresponds to that in Additional file Click here for file


Assembling haplotypes given sequence data derived from a single individual is a well studied problem, but only recently has haplotype assembly been considered for population-sampled data. We discuss a software tool called Hapler, which is designed specifically for low-diversity, low-coverage data such as ecological samples derived from natural populations. Because such data may contain error as well as ambiguous haplotype information, we developed methods that increase confidence in these assemblies. Hapler also reconstructs full consensus sequences while minimizing and identifying possible chimeric points.Experiments on simulated data indicate that Hapler is effective at assembling haplotypes from gene-sized alignments of short reads. Further, in our tests Hapler-generated consensus sequences are less chimeric than the alternative consensus approaches of majority vote and viral quasispecies estimation regardless of error rate, read length, or population haplotype bias.The analysis of genetically diverse sequence data is increasingly common, particularly in the field of ecoinformatics where transcriptome sequencing of natural populations is a cost effective alternative to genome sequencing. For such studies, it is important to consider and identify haplotype diversity. Hapler provides robust haplotype information and identifies possible phasing errors in consensus sequences, providing valuable information for population studies and downstream usage of resulting assemblies. Drosophila), this is easy to ensure: any variation should result from sequencing error, and the popular "majority vote" mechanism will create a correct consensus )In the single individual haplotype assembly problem, the conflict graph will necessarily be bipartite in the absence of sequencing errors. In this case, computing the bipartition reconstructs haplotypes. When sequencing errors are present, the graph may not be bipartite. Various optimizations exist for inducing bipartiteness such as Minimum SNP Removal (MSR) and Minimum Fragment Removal (MFR), though these are NP-Hard unless the reads are all gapless . Other mk [Recently, the MFR problem has been extended to when the number of known haplotypes is increased from two to a known constant k . Non-grak ,21). Thek .MG, but rather to minimally color it--or, equivalently, minimally clique cover the complement "compatibility" graph In our formulation of the population haplotype assembly problem, the goal is not to bipartition d Figure . In the  weights . Both of weights ,5.Reconstructing consensus sequences with a focus on identifying and minimizing chimerism has been minimally studied. For single individual haplotype assembly, the number of possible crossover points is fully determined by the number of connected components with at least two nodes in the bipartite conflict graph . The CelMelitaea cinxia, a well-studied butterfly. Of the known haplotypes for this gene, we chose eight from two distinct clades . For the is specified, each base of each read is mutated to one of the remaining three bases (uniformly chosen) with probability e. Unless otherwise specified, default parameters were used for Hapler .If a sequencing error exact subsequence of some original COI sequence, otherwise it is incorrect.We first tested Hapler's ability to assemble correct haplotypes by varying simulated read lengths without sequencing error and assuming perfect SNP calling. For each read length, we assembled haplotypes over 5 trials of random simulated sequencing. All haplotype regions reported here exclude the "universal" haplotype, which is composed of reads that are common to all complete haplotypes (see Methods). For all tests, we call a haplotype assembly correct if it is an Figure >250 bp reads prevents phasing across these regions, which Hapler correctly infers. For the low diversity dataset, all reconstructed haplotypes were correct; for the high diversity dataset, of the 2,766 assembled sequences only seven were incorrect chimeras, shown offset for readability in Figure Results for both datasets are largely similar: in both cases, longer sequences result in longer haplotypes, and longer haplotypes tend to have higher coverage. In both cases, we see improvements between 200 bp and 400 bp reads. This is unsurprising, as all extended COI variants share identical regions with other variants of size ≈ 250 bp. Lack of e in Figure Next, we tested Hapler's performance with sequencing errors. For these tests, we fixed read lengths to 400 bp (representative of 454 FLX Titanium technology). In order to show the results for haplotype assembly in the general case, for each sequencing error rate As the error rate increases, the average length of correct haplotypes decreases, while the number of incorrect haplotypes increases. Length of incorrect haplotypes is comparable to correct haplotypes, particularly for high error rates. However, when error rate is high the average coverage of incorrect haplotypes is low, indicating that Hapler successfully isolates sequencing errors as anomalous, rare haplotypes with little support.n = 1 and t = 80 as appropriate for lower-diversity datasets [Figure datasets . AlthougWe evaluated each of these consensus generation techniques by computing the minimum number of crossovers (pairs of SNP loci which must be supported by different haplotypes) necessary to reconstruct the consensus amongst 1) original input haplotypes, representing "true" crossovers, and 2) Hapler assembled haplotypes, representing "estimated" crossovers; in both cases, sequencing errors included in consensus sequences at SNP positions also incur crossovers (see Methods). For chimerism analyses, each data point is an average of 50 sequencings/assemblies/consensus reconstructions.Figure Hapler's estimate of the number of crossover points trends between one and two above the true number for its own and the majority vote consensus. At high error rates, the estimated number of crossovers for the ViSpA consensus diverges from the true number faster than for the other two. For the high diversity dataset . By default, Hapler runs a minimum of 20 such repetitions, and continues until the number of haplotypes assembled has stabilized for the previous 50% of repetitions. For Figure n parameter used by ViSpA from 1 to 12. When the number of repetitions is very small, we see Hapler for the first time consistently underestimating the number of crossovers. In this case, although the number of true crossovers is steady for Hapler's consensus sequences at approximately two, Hapler estimates zero crossovers because of its assembly of long  haplotypes. For both the low and high diversity datasets, the estimated chimerism approximates or exceeds the true chimerism after four colorings  keeping the read length at 400 bp and varying error rate. Figure Figure O (rn3), where r is the number of repetitions and n is the number of irredundant reads in the largest haplotype block (see Methods). Even when the largest haplotype block is small, reconstruction of the minimum chimerism consensus runs in O (mk2) time and space, where m is the number of called SNPs and k is the number of haplotype regions.Hapler's robustness comes at a computational cost: repeated weighted matching dominates the runtime at n = k). These tests were run on a dual core 2.2 GHz desktop with 8 GB of RAM, though all tests used only a single core and less than 200 MB of resident memory. Hapler used 100 repetitions.Table n3 factor would suggest). For comparison to the Table Anopheles gambiae genome assembly  comprisUntil recently, the analysis of genetically diverse short read data has focused on identifying single-locus polymorphisms such as SNPs . HoweveThe theoretical foundations of Hapler require that sequences be gapless. In support of this requirement, initial experiments that incorporated mate-pair information only produced few incorrect haplotypes, with many internal errors. For this reason Hapler currently does not incorporate mate-pair information. Hapler's added robustness does come at a computational cost, but experimental runtime analysis indicates that runtime will nevertheless be adequate for single alignments up to 100 kilobases in length, though low levels of phasing information are likely to prevent useful results over regions of this size. Runtimes also suggest that Hapler will be useful in practice for whole-transcriptome assemblies, where contig alignments are on the order of a few kilobases in length.Although low levels of phasing information may prevent assembly of many long haplotype regions in some applications, Hapler can use the few long and highly covered regions to produce a full consensus minimizing possible chimerism. In this regard, Hapler performs well compared to the majority vote and quasispecies estimation tools (using the most frequent quasispecies estimate as the consensus). Perhaps most telling, of the 350 consensus sequences produced by Hapler over various error rates for Figure Hapler can estimate the chimerism of any consensus against its own haplotype region assemblies. This estimate will naturally overestimate the true chimerism (due to Hapler's conservative methods); however, in most tests Hapler's estimated chimerism for the approaches tested preserved the ranking of true chimerism. The only exception we found was when error rates were high Figure , and HapFinally, Hapler not only minimizes chimerism in its consensus reconstructions, it also identifies where possible chimeric points might occur. This is useful information, for example, when designing microarray probes or PCR primers that may fail if they include chimeric sequence.http://www.nd.edu/~biocmp/hapler and is released under an LGPL license. Hapler is also easy to use: to reconstruct haplotypes for a list of contig IDs in an Amos bank, one can simply use the Amos tools in conjunction with Hapler: bank2contig -E eIDList.txt amosBank | java - jar Hapler.jar --input -.Hapler is packaged as a single Java.jar file, and has been tested with Java 1.6, requiring no other dependencies. It is available at Hapler takes as input read alignments either in TIGR  or SAM  format, 1.  SNP calling.2. Masking of redundant reads.MG, and determination of the "universal" haplotype.3. Determination of haplotype blocks from connected components of M ':4. For each haplotype block (a) Determining the transitive compatibility graph (b) Minimum coloring of (c) Randomly repeating step b), identifying commonalities in colorings, and assembling haplotype regions.5. Reconstructing an overall consensus, minimizing possible crossover points while maximizing SNP allele coverage.6. If given a consensus sequence to evaluate, computing the minimum number of crossovers to support it with haplotype regions. If also given a set of "true" haplotypes, computing the minimum number of crossovers through these to support the given and reconstructed consensus sequences.As the sections below discuss, it is primarily steps 3), 4b), and 4c) that improve haplotype assembly quality.Although step 4b) is designed to isolate reads that likely contain errors so they can be ignored (see below), many sequencing technologies induce frequent errors that are easy to identify. For example, 454 data is plagued by indel errors adjacent to homopolymer runs . Hapler • Simple: Any variant locus regardless of allele frequency is treated as a SNP locus.• Simple Strict (default): Any variant locus where a minority allele is present at least twice or is covered by less than 10 reads is treated as a SNP.• 454: A variant locus is a SNP if 1) the majority allele is not a '-' and 2) either a) the number of non-'-' alleles is at least two, or b) there is any non-'-' allele that is not part of a homopolymer run of length ≥ 3.e (default 0.005) and sequence alphabet A, at a non-polymorphic locus the expected occurrence rate of the second most frequent character  will be e/(|A| - 1). Thus, this method uses a Bonferroni corrected Binomial test to call SNPs: for each locus, we call a SNP if the actual second most frequent character count is greater than F-1), where F - 1 is the inverse cumulative Binomial distribution function, α is the p-value cutoff (default 0.05), L is the length of the alignment, and d is the read depth at the locus.• Binomial (default): Given an estimated error rate • User Provided: The user can provide a file containing a list of loci for each alignment to consider as SNPs, allowing for sophisticated external SNP-callers such as QualitySNP  and Pyror Ris called "redundant" if the SNPs covered by r Rare a subset of those covered by some other read R, and r Rand R do not conflict. Hapler scans the reads first in order of first covered SNP position and secondly by length; for each such read R, those reads r Rwhich are made redundant by R are subsumed, or "masked" by R. r Ris then removed from consideration as a masking read itself. This is necessary to ensure that the compatibility graph x, y and z, where y does not share SNPs with z. If x shares SNPs with both, does not conflict with y (which is redundant) but does conflict with z, this produces a non-transitive relationship.A read oriented : consideM Gmay consist of multiple connected components. In this case, no information exists to determine whether reads from different connected components could belong to the same haplotype. Thus, for each alignment, haplotypes are assembled for each connected component separately. It can be shown that SNP loci covered by reads of each component form contiguous stretches--hence the usage of the phrase "haplotype block" [For low-diversity data, e block" .Reads in connected components of size one are consistent with all possible haplotypes, and are combined into a "universal" haplotype consistent with all other assembled haplotype regions.MG' has been isolated, the directed, transitive, block compatibility graph x Sstarts before read y Sand does not conflict with yS, a directed edge is drawn from the node representing x Sin yS.Once a connected component M G'.Because we consider only unmasked reads, any compatibility graph M B' , and node x from the left of M B' connects to y from the right if x is directly connected to y in Although the application of Dilworth's theorem in  is correM B' and computing connected components  representing the same node in /(n + 1). Original edges are given weight of 1.Regarding the first point, solving a maximum unweighted matching on  of G′M′⃗. HoweverTheorem 1. Computing connected components of a maximum weighted matching of followed by contraction of nodes representing the same node in produces a minimum path cover of , while secondarily maximizing the number of paths consisting of a single node.Proof. As noted in [M B' produce a minimum path cover of , where n - k is the number of paths and k is the number of edges selected in the matching. It is easy to see that selection of any edge of weight 1 /(n + 1) in noted in , connectM B', which selects k edges. Now consider any maximum weighted matching of k'. Clearly, k' ≥ k; k' must also be less than k + 1  Thus, for Figures plotting haplotype region lengths  j Sand kS.One possibility for reconstructing an overall consensus sequence is to find a minimum tiling path through haplotype assemblies, from the first base in the alignment to the last. This formulation, however, is complicated by 1) the possibility of haplotype assemblies being non-contiguous (see above) and 2) the existence of the universal haplotype: switching to a universal haplotype region should not be penalized. Thus, we aim to reconstruct the full consensus only at SNP positions, filling in non-SNP loci with the majority vote of the overall alignment. Given a consensus sequence i Swe identify the set of haplotype assemblies that cover it, call this H(iS). Let m be the minimum number of crossovers needed to reconstruct a consensus for the first i SNPs (represented in order as S1 to m Sif there are m SNP loci), ending with haplotype assembly q Hproviding the allele for iS. At the base of the dynamic program, for all q H∈ H(S1), m = 0. The solution for other haplotype/SNP combinations can then be computed asA consensus with minimal possible crossovers can easily be computed via dynamic programming. First, for each SNP C via dynamic programming based on several criteria of decreasing importance:In fact, Hapler optimizes the consensus sequence C.1. Minimizing the number of possible crossover points in C.2. Maximizing the total supporting read coverage (both redundant and irredundant) of alleles used at SNP positions in 3. Minimizing the number of unique haplotype assemblies used.C that are high in coverage, minimizing sequencing error and increasing the probability that a possible crossover point is not actually a true crossover point in reality (similar to the uninformed majority vote). Criterion 2 also governs where a necessary crossover from one haplotype assembly to another will occur. If two assemblies overlap at SNP loci, the highest covered will be "crossed into" and "crossed out of" greedily.In practice, there may be many paths through haplotype assemblies that minimize possible crossover points. In the case of ties, criterion 2 chooses assemblies for Criterion 3 will seldom be called upon in practice, as ties in both criteria 1 and 2 are unlikely. Nevertheless, within the constraints of a minimal set of haplotype crossovers maximizing coverage, we prefer to utilize a minimum number of explanatory haplotype regions. A graphical representation can be found in Figure ' Cagainst CH(iS) is used rather than H(iS) where CH(iS) defines not only those haplotypes that cover SNP i Sbut those that cover and agree with the allele of C' at iS. If no haplotype covers and agrees with ' Cat a SNP iS, then i Sis associated with a new, unique "error" haplotype for that locus. This will incur two crossovers in the evaluation--one into the unique error haplotype at i Sand one out of it. Thus, errors at SNP loci not supported by any haplotype are heavily penalized. However, sequencing errors in the consensus are not considered if they do not coincide with SNP loci in Given a set of "true" haplotypes or a set of assembled haplotype regions The authors declare that they have no competing interests.STO designed the methods, wrote the software, performed the analysis and drafted the manuscript. SJE helped conceive the study, supervised experimental analysis and helped draft the manuscript. Both authors read and approved the final manuscript.An additional PDF file providing Supplementary Materials, including consensus reconstruction pseudocode and supplementary figures  is available online.Click here for file