Ne), and can provide insights into the genetic diversity history of populations. Estimates of the consistency of linkage phase across breeds  can be used to determine if there is sufficient relationship to use pooled reference populations in multi-breed GS programs. The objective of this study was to estimate LD levels, persistence of phase and effective population size in Hereford and Braford cattle populations sampled in Brazil.The existence of moderate to high levels of linkage disequilibrium (LD) between genetic markers and quantitative trait loci (QTL) affecting traits of interest is fundamental for the success of genome-wide association (GWAS) and genomic selection (GS) studies. Knowledge about the extent and the pattern of LD in livestock populations is essential to determine the density of single nucleotide polymorphisms (SNP) required for accurate GWAS and GS. Moreover, observed LD is related to historical effective population sizes (r2), obtained between adjacent SNP across all chromosomes were 0.21 ± 0.27 for Herefords  and 0.16 ± 0.22 for Brafords . Estimated r2 was > 0.2 and 0.3, respectively, for 34 and 25 % of adjacent markers in Herefords, and 26 and 17 % in Brafords. Estimated Ne for Brafords and Herefords at the current generation was 220 and 153 individuals, respectively. The two breeds demonstrated moderate to strong persistence of phase at all distances . The largest phase correlations were found in the 0 to 50 Kb bins . Estimated LD decreased rapidly with increasing distance between SNP, however, useful linkage for GWAS and GS (r2 > 0.2) was found spanning to ~50 Kb.Mean LD estimates, measured using the squared correlation of alleles at two loci (Panels containing about 50,000 and 150,000 SNP markers are necessary to detect minimal levels of LD between adjacent markers that would be useful for GWAS and GS studies to Hereford and Braford breeds, respectively. Markers are expected to be linked to the same QTL alleles in distances < 50 Kb in both populations due to observed high persistence of phase levels. The evolution of molecular biology tools and techniques occurred over the last decades helped unveiling underlying genetic factors and improving rates of genetic gains for economically important traits in livestock. The availability of high-density single nucleotide polymorphisms (SNP) genotyping assays for cattle coupled with advances in computational and statistical methods allowed the generation and use of large amounts of genomic data in genome-wide association (GWAS) and genomic selection (GS) studies for production-relevant traits . These aExisting genome-wide linkage disequilibrium (LD) levels are usually affected by genetic  and non-genetic (marker ascertainment bias) factors –6 and thRhipicephalus (Boophilus) microplus) however, infestation peaks can be observed sporadically, raising risks of losses associated with tick-borne diseases [Hereford and Braford cattle are important breeds for beef production in southern Brazil, where subtropical climates are observed with average low temperatures in winter months. Local climate conditions naturally control the incidence of tropical ectoparasites such as the bovine tick .Samples from a total of 391 Hereford and 2079 Braford (with a breed composition ranging between ½ Hereford + ½ Zebu and ¾ Hereford + ¼ Zebu) cattle born between 2008 and 2010 in commercial farms associated with the Delta G Connection Genetic Improvement Consortium  were useP < 10-6) from Hardy-Weinberg Equilibrium (HWE) were also excluded. Moreover, whenever multiple SNP were observed in the same physical map position, only one SNP with highest MAF was retained. The high-density (HD) panel was filtered to select only the SNP also present in the 50K panel.Additional QC was performed with R snpStats package , 23. SamHaplotype reconstruction and imputation of sporadically missing genotypes (0.39 %) were carried out using FImpute 2.0 . Expectar2 [pA1, pA2, pB1 and pB2, and the frequency of haplotypes with allele 1 at marker locus A and allele 1 at locus B, for example, denoted pA1B1. Following Hill and Roberson [Linkage disequilibrium was calculated as pairwise r2 , which rRoberson ,1\documer2 were calculated for each bin.For each population, LD values between all pairs of SNP of all chromosomes were binned according to pairwise physical distances into intervals of 100 Kb starting from 0 up to 10 Mb. Average values of r2 between adjacent markers was calculated for different marker densities, sequentially removing SNP from the total dataset using every second, fourth, fifth, sixth, seventh and 14th marker . Linkage disequilibrium estimates were calculated according to Badke et al. [https://www.msu.edu/~steibelj/JP_files/LD_estimate.html.To evaluate the feasibility of successfully using sparser marker panels in GWAS and GS studies, average e et al. , using Re et al.  availablTo investigate intra and inter chromosomal and breed variation in LD, two analysis of covariance with general linear models were fitted. The following linear model Equation  was usedi of breed j, μ is the overall mean of i, j, dk is the observed physical distance for marker pair k, 10 transformed in an attempt to linearize the relationship between LD and the log-transformed distance [were distance .r2 in a broader range of inter marker distances than that observed in Equation Additionally, a more complex linear model was fit to all 12,911,174 syntenic SNP pairs to investigate the adjusted mean 10 transformation of physical distances to linearize the relationship between LD and the log-transformed distances, we decided to fit these higher order coefficients to consider possible deviations of the expected linearity and interaction with breed and chromosome effects .In addition to the variables already described above for Equation r2 using Equation k) of 20 Kb. Accordingly, chromosome by breed predicted r2 values were obtained from estimated parameters of Equation The physical distances of interest to predict The degree of phase concordance between the two breeds for pairs of SNP was calculated according to Badke et al.  with theRB,H is the correlation of phase between rij(B) in the Braford (B) population and rij(H) in the Hereford (H) population, SB)( and SH)( are the standard deviations of rij(B) and rij(H), respectively, rij across all SNP i and j within interval p for populations B and H, respectively.where r values are expected when two markers are in LD and show equal linkage phase in both studied populations [RB,H were calculated for each bin, using markers common to both breeds.Positive ulations . Marker Ne) of the two studied cattle breeds. The relationship between r2 and Ne can be expressed by the formula:Estimates of LD decay in relation to different SNP distances were used to infer past effective population sizes (c is the genetic distance between two markers expressed in Morgans [Net) for a given generation t = 1/2c [c values. When applied in t = 1/2c, the resulting t value was rounded to the target generation. For example, r2 of all SNP pairs with distance between 33.3 cM (t = 1.5) and 1 M (t = 0.5) of the same autosomal chromosome were selected to calculate Ne at t = 1. Then, within each bin, the average values of r2 and c were obtained and applied in the formula:where  Morgans . Effecti Morgans , 33. Bect = 1/2c  in the pr2 < 1.0. Longer c ranges were used to define generation bins as we moved further in the past, because they correspond to shorter distances with fewer markers and we wanted to ensure sufficient numbers of SNP pairs for reliably estimating Net within each bin [t between 1 and 10, e.g. a range from 0.5 to 1.5 for the current generation; five generations for t between 15 and 100, and of 50 generations for t between 150 and 1000 (see Table for 0.0 < each bin . The actP < 10-6). Estimated means for call rate, MAF and HWE were 0.998, 0.271, and 0.541, respectively. Additionally, 34 monomorphic markers in the subset of Hereford samples were excluded from subsequent analyses of this breed. The final resulting dataset contained a total of 41,241 autosomal SNP (75.52 %) in Brafords and 41,207 autosomal SNP (75.46 %) in Herefords.Data QC excluded 43 samples with call rates < 0.90, 24 samples with heterozygosity deviations > 3.0 standard deviations, eight samples with incorrect sex assignment and eight duplicated genotypes with different sample identification. The final resulting dataset contained 2435 samples (391 Hereford and 2044 Braford). Some samples were excluded for not meeting more than one criteria of QC. Marker QC also removed 4232 SNP with call rates < 0.98, 5712 SNP with MAF < 0.03, and 1342 SNP with highly significant HWE deviations  and 25 %  of adjacent markers in Herefords, and 26 %  and 17 % (7011 SNP) in Brafords.Average r2 values > 0.2 were observed in the 0 to 60 Kb bins in Herefords (range = 0.20 to 0.49), and in the 0 to 20 Kb bins in Brafords (range = 0.21 to 0.43). Average r2 values > 0.3 were observed in the 0 to 1 Kb bins in Herefords (0.49) and Brafords (0.43). Linkage disequilibrium estimates obtained from sparse maps of markers were low , average r2 decreased to 0.09 in Herefords and to 0.08 in Brafords. Similarly, as observed in LD values, the percentage of pairs of markers with r2 values > 0.2 and with average r2 > 0.3 decreased when sparse maps were analyzed  to 0.26 (BTA6) in Herefords, and from 0.13  to 0.20 (BTA6) in Brafords. Squared correlation estimates between adjacent SNP (Equation r2 (P < 0.001), whereas the interaction between breed and chromosome was not significant. Therefore, predicted marginal (least square) means ± SE of r2 were obtained for each chromosome averaged across breeds, chromosome by breed interactions and adjacent SNP distances . Predicted r2 values at specific distances for Herefords and Brafords are shown in Fig. Squared correlation estimates considering all SNP pairs Equation  revealedNe = 220) is larger than for Herefords (Ne = 153), different recent Ne trends were observed for the two studied populations. Braford´s decreasing historical Ne trend was reversed and sharply increased in the last two generations, while Herefords had an accelerated Ne decline starting four generations ago. Consequently, the relative positions of the breed were changed, restoring the larger past effective size of Braford compared to Hereford, which was observed up to about thirty generations ago, when respective values were 315 and 309 for the two populations.Although current estimated effective population size Fig.  for BrafRB,H = 0.53 to 0.97) were observed for both breeds , and the proportion of SNP with reversed r signs was lower, ranging from 5 % at 1 Kb to 34 % at 10 Mb distance.Moderate to strong persistence of phase at all distances RB,H = 0.3 to 0.97r2 [D and D´.Numerous studies have been conducted to estimate the pattern and extent of LD in different domestic animal species , 28, 36.r2  to estimr2  and sampr2 , 40, wher2 of 0.17.Robust ascertainment bias towards informativeness in taurine breeds has been observed in the 50K panel  and has B. indicus breeds by Villa-Angulo et al. [B. indicus breeds may be representative of differences which occurred during the domestication and selection processes of B. indicus and B. taurus cattle, and offers a plausible explanation for the lower LD levels observed in indicine populations [r2 observed in this population can be a result of those differences reported for B. indicus and B. taurus breeds in terms of past effective population sizes and selection processes during breed formation.Lower mean LD estimates were also reported for o et al.  when anaulations , 44. Conr2 estimates in cattle [B. indicus breeds had lower r2 values at short distances and higher r2 values at longer distances between markers when compared to B. taurus breeds. As the extent of LD at short inter-marker distances reflect the historic effective population size [r2 of Braford compared to the decrease of r2 in Hereford can be associated to differences in effective population size of the breeds.Both studied populations presented an inverse relationship between LD and inter-marker distances, and this decline of LD as a function of distance agrees with other studies based on n cattle , 42, 45.n cattle  reportedion size . Rapid dNe trend observed in the last two generations .Decreases in effective population sizes have been widely observed within the last ~50 years in several cattle breeds, mostly due to the advent of artificial insemination which allowed the intense use of fewer males and high selection pressures for specific traits . As demoons Fig.  could haS) [KIT gene. The SH allele is responsible for Hereford pattern of coat colour  [Moreover, selection can lead to increased interchromosomal LD heterogeneity . Thus, hS)  is also ozygous) , 54. Sinozygous)  and to fr2 = 0.2 is established as a threshold and the 80K panel will be used for genotyping, only BTA6 and BTA10 in Hereford and BTA6 in Braford would be expected to meet the target LD threshold . Alternatively, animals genotyped with panels below the desirable density could be imputed to higher densities, provided that reference haplotype panels are available for the respective populations [Knowledge about breed and chromosome-specific variation in LD levels could be used to determine optimal marker density for performing GWAS and GS studies , 48. Thiold Fig. . To reaculations .r2 values close to 1 are observed across populations, if a large proportion of SNP are in reversed phase the result of selection for these markers will lead to antagonistic responses [The across-population, or across-breed, accuracy of GS estimates based on prediction equations derived from a specific reference population depends basically on the persistence of LD phase across populations, which reflects their genetic relationships . Even ifesponses . Our estesponses  concludeOur results indicate that at least 50K and 150K of evenly spaced SNP are necessary to effectively perform GWAS and GS studies, in Hereford and Braford respectively. For distances < 50 Kb, SNP are expected to be linked to the same QTL alleles in both breeds, due to high persistence of phase; therefore, Hereford and Braford data can be pooled into a single reference population for multibreed GS and GWAS studies performed with the above mentioned marker densities.http://www.mct.gov.br/upd_blob/0234/234054.pdf.Biological samples used for genotyping were collected according the Brazilian Guidelines for Use of Animals for Scientific and Educational Purposes, available at There was a written informed consent from cattle owners (Embrapa SAIC 10200.10/01623) to cover the use of their animals and sample collection.


Massively parallel sequencing (MPS) technologies, such as 454-pyrosequencing, allow for the identification of variants in sequence populations at lower levels than consensus sequencing and most single-template Sanger sequencing experiments. We sought to determine if the greater depth of population sampling attainable using MPS technology would allow detection of minor variants in HIV founder virus populations very early in infection in instances where Sanger sequencing detects only a single variant. We compared single nucleotide polymorphisms (SNPs) during acute HIV-1 infection from 32 subjects using both single template Sanger and 454-pyrosequencing. Pyrosequences from a median of 2400 viral templates per subject and encompassing 40% of the HIV-1 genome, were compared to a median of five individually amplified near full-length viral genomes sequenced using Sanger technology. There was no difference in the consensus nucleotide sequences over the 3.6kb compared in 84% of the subjects infected with single founders and 33% of subjects infected with multiple founder variants: among the subjects with disagreements, mismatches were found in less than 1% of the sites evaluated . The majority of the SNPs observed only in pyrosequences were present at less than 2% of the subject’s viral sequence population. These results demonstrate the utility of the Sanger approach for study of early HIV infection and provide guidance regarding the design, utility and limitations of population sequencing from variable template sources, and emphasize parameters for improving the interpretation of massively parallel sequencing data to address important questions regarding target sequence evolution. Sanger sequencing has been widely used to study the evolution of variable pathogens such as HIV, the emergence of drug resistance, and the rise of escape variants as a result of host immune pressures. Nonetheless, there are drawbacks associated with this technology. Individual template or cloned-derived sequencing is time-consuming, labor intensive, and is usually limited to tens of sequences per subject in consideration of cost. Consensus Sanger sequencing of virus populations can detect minority variants only above 10%- 25% of a heterogeneous sequence population –7 and imFour factors determine the level of minor variant resolution in MPS technologies such as 454-pyrosequencing, the related Ion Torrent system, and the Illumina platforms: a) error rate associated with the initial PCR amplification rounds  prior to sequencing; b) accurate quantitation of the number of amplifiable input template molecules; c) number of pyrosequences (reads) that map to the genomic position with the observed polymorphism; and d) resolution of errors that are inherent to the sequencing process.Incorrect base incorporation by DNA polymerases during cDNA sysnthesis and PCR amplification has the potential to introduce errors within DNA products prior to pyrosequencing thus making these mismatch errors indistinguishable from real variation after sequencing. These PCR introduced errors have been shown to introduce biases when characterizing microbial community structure –16 and hWhile the term “coverage” is most often used to refer to the number of reads mapped to a genomic position, this quantity alone cannot be used to gauge the number of actual viral templates sequenced using MPS technologies. Indeed, careful estimation of the number of amplifiable templates is rarely performed, but is essential to accurately measure population diversity –24. The Errors within pyrosequences also follow distinct patterns compared to traditional Sanger sequencing. Consecutive runs of the same nucleotide (homopolymers) are particularly error-prone, resulting in inclusion of more or less bases in the read than is actually present in the DNA template. In pyrosequences generated by the GS-FLX Titanium technology, the mean homopolymer-associated error has been estimated at 1.1%, with errors showing a non-random distribution and certain positions showing error rates as high as 50% . Sanger Over the past few years, several error correction algorithms for identifying and correcting pyrosequencing artifacts have been described , 33–42. There have been studies comparing the prevalence of low frequency <20%) HIV-1 drug resistance mutations within clinical samples sequenced through Sanger sequencing, especially when determining consensus bases at each position, and pyrosequencing –47. Thes0% HIV-1 gag, gp120 and nef, in 32 subjects who became infected with HIV-1 during the MrkAd5 Step vaccine trial [In the present study we comprehensively compared the minor and major SNP variations observed in Sanger sequences and pyrosequences across three HIV-1 genomic regions, ne trial . We perfne trial , 26. Fingag, pol and nef inserts [All 32 subjects were in early HIV-1 infection (within 1.5 months of the first HIV-positive visit) and enrolled in the MrkAd5 Step HIV-1 vaccine trial , a double-blind phase IIb test-of-concept study of the Merck Adenovirus-5 (MRK Ad5) HIV-1 clade B vaccine with  inserts –53. Insthttp://indra.mullins.microbiol.washington.edu/DIVEIN/insites.html) [The Sanger sequences used in this study were derived from single amplifiable near-full-length viral genome (NFLG) and half genome templates, and have been deposited in GenBank under accession numbers JF320002-JF320643 . Sequences.html)  was usedThe number of variants establishing productive infection (henceforth referred as founders) for each subject was identified from Sanger sequences based on phylogenetic and genetic distance analyses . Probablgag, gp120, and gp41-nef, using the first-round reverse PCR primer. The list of primers used is provided in gag, env and nef. The MrkAd5 vaccine insert included gag and nef and hence these gene regions were selected for studying impact of vaccine-induced immune responses. The env region was not part of the vaccine, and this was selected as a control.Using the plasma samples from the same visit date as the previously derived Sanger sequences, RNA was extracted using the Qiagen Viral RNA Mini Kit . cDNA was synthesized using Superscript III Reverse Transcriptase , over three 1.5kb regions corresponding to gag, gp120, and gp41-nef. The second round of PCR was done separately for each gene using nested primers . Once amplifiable template numbers were determined, additional PCR reactions were performed to amplify a target of up to 5000 templates for specimen. PCR reactions were subsequently cleaned using Agencourt AMPure XP beads  and DNA concentrations were determined spectrophotometrically. Products from all three gene regions from individual study participants were pooled for 454-pyrosequencing.PCR amplification prior to pyrosequencing was done using Advantage LA or Advantage 2 DNA Polymerase . The viral template input was estimated using clinical viral load measures. First round PCR was a multiplex reaction, using primers to simultaneously amplify all three non-overlapping genomic regions,  primers . Endpoin7 molecules/μl in TE buffer. Libraries generated from multiple samples (each with distinct sequence tags) were mixed at equimolar ratios. Emulsion PCR (Roche) was performed on the combined libraries using a ratio of 2–3 DNA molecules per bead. PCR-positive beads (~10–20% of emulsion PCR products) were then selectively enriched. Four million enriched beads were loaded onto a 454 picotiter plate and pyrosequences were generated using the 454 GS FLX system. Median number of pyrosequences generated per gene region for the subjects in this study is given in Pooled and purified PCR amplified products were quantified using the Quan-it PicoGreen dsDNA assay (Invitrogen). GS-FLX Titanium kits were used for Rapid Library Preparation and Rapid Library MID Adaptor addition . 500ng of each sample was nebulized, end repaired, and ligated with 454 library adaptors and MIDs. Fragments between 600–900bp were selected for and purified using AMPure beads. Library quality was assessed using the Agilent High Sensitivity DNA Bioanalyzer kit and chip , and the quantity of DNA was measured using the Quan-It PicoGreen dsDNA assay. Library concentrations were calculated using the online Roche Rapid Library Quantitation calculator. Each DNA library was diluted to a working stock of 1x10gp41 region region being excluded in subsequent variant analyses. Following the construction of multiple-sequence alignments, a collection of Perl programs [Pyrosequences and their associated signal intensities were processed using the error correction program CorQ . Brieflyprograms  were runThe sensitivity of minor variant detection in pyrosequencing experiments is determined in part by the PCR conditions used to generate the templates for pyrosequencing, and the number of amplifiable templates in the reaction. Subsequent to the PCR amplifications done for this study, a number of DNA polymerases and varying PCR conditions were assessed to identify differences in sensitivity and mismatch error-rates . Based oMinor SNP variants were those observed at a frequency between 1–50% in the sequences of a given subject. A major variant difference was defined as a polymorphism at a position in which the consensus base varied between the two sequencing methods. In most cases, the differences in major variants were due to a “frequency reversal” of the two relatively abundant variants. SNPs observed only in pyrosequences or Sanger sequences were classified as pyrosequencing-specific (PS-SNPs) or Sanger-specific (SS-SNPs) SNPs, respectively. Shared minor SNPs are nucleotide differences from the consensus that occur at the same genomic location in both the Sanger and pyrosequences in a given study subject.http://indra.mullins.microbiol.washington.edu/quality/). Sequencing depth is used as a measure of template coverage. The read coverage is the number of reads that map to a genomic position and is determined after a reference based multiple sequence alignment of pyrosequencing reads. Mean number of amplifiable templates and mean sequencing depth for each subject is given in The metric sequencing depth is defined as the number of reads mapped to a genomic position (read coverage) divided by the number of estimated amplifiable genomic templates in the sequencing reaction. PCR amplification following end point dilution of templates is performed to estimate the number of amplifiable viral copies per gene using the quality template-estimating program  (http://Spearman’s rank correlation coefficient (ρ) was used to estimate correlation between SNP frequencies. Kruskal-Wallis tests were performed to compare the correlation among multiple groups with Dunn’s error correction for multiple comparisons. The non-parametric Mann-Whitney test was used to compare two distributions.gp120). As expected, private (found only in one sequence) SS-SNPs and PS-SNPs were more prevalent across all gene regions in the individuals with replicating multiple founders , 23 in gp120 (88%), and 22 in nef (84%), with the number of positions with consensus mismatches ranging from 1–3 (median = 1), A consensus nucleotide sequence was generated from both pyrosequences and Sanger sequences for each subject over the 1500, 1530, and 610 nucleotide regions in Not surprisingly, for subjects with multiple founders, the consensus sequence concordance was lower, with all consensus base mismatches resulting from frequency reversals between the two most common variants  Figs. Ingag, gp120 and nef, respectively) represented <2% of the sequence population in subjects with single founders . Among those infected with multiple founders, only one subject had informative SNPs that were absent in the subject’s pyrosequences . Howeverequences , while 3gag and nef. In gp120 there was a significant (p < 0.05) increase in mean PS-SNPs frequencies when comparing positions with depth of < 1 to positions with depth > 5 . There was also high concordance between the frequencies of both shared major and minor variants in individuals with single . In subject gp120 sequences, we observed reduced frequency difference with increasing sequencing depth  . In gag sequences we observed reduced frequency differences with increasing sequencing depth  . In subjgag . However, a single subject with 11 Sanger sequences largely drove this result as this subject had a large proportion of positions with frequency reversals  of the PS-SNPs observed were rare in the viral sequence population (<2%), whereas only 9% of the PS-SNPs observed in pyrosequences were found in more than 25% of the sequences. That the majority of SNPs fell below 2% of sequence population is not surprising given the large number of reads generated through pyrosequencing. Based on above results from consensus and minor variant comparisons we found that founder sequences identified from 5 single template-derived sequences did not differ from those established through pyrosequencing. Furthermore, the greater depth afforded by MPS technology did not results in the detection of additional minor founder variants in the subjects where Sanger sequencing identified only a single founder establishing infection.Only one informative Sanger SNP  was absent in pyrosequences. However, the pyrosequencing depth at this position was less than 0.1, which likely explains its absence. Among subjects with multiple founders, only one subject had phylogenetically informative SS-SNPs, despite the majority of positions having a pyrosequencing depth of at least one. This subject also showed the highest number of consensus base mismatches of all subjects. These differences could be the result of using different plasma vials and cDNA preparations, or an indication of the stochastic nature of quantitation of multiple viral variants. All other phylogenetically informative Sanger SNPs were observed in the pyrosequencing dataset above the 1% threshold. This result lends confidence that all the consensus viral templates observed within the Sanger sequence population were also adequately sequenced by pyrosequencing. Private-site Sanger SNPs were found at a frequency of 0.22% per nucleotide sequenced. Interestingly, 70% of these private Sanger SNPs were observed below the 1% threshold and the remainder not observed within pyrosequences at all. This suggests the possibility that some private SS-SNPs correspond to sequencing errors rather than simply reflecting low sampling depth. This result was unexpected since each Sanger-derived viral genome sequence corresponds to the consensus of reads derived from a single viral template and thus should not include PCR errors.An average of 43 PS-SNPs per subject across all the three genes  were observed at frequencies below their respective Sanger sequencing thresholds. However, as the majority of these SNPs were present at a frequency of <5%, diligence must be applied to minimize external sources of error such as using high-fidelity enzymes during amplification and incorporating parameters such as sequencing depth during data analysis to improve the accuracy of the observed polymorphisms. Pyrosequencing error patterns can skew minor variant distribution and frequencies , 26, 42.Sequencing depths of 1 or below are not adequate to resolve low frequency sequencing artifacts from genuine low frequency variants present within viral templates. An excess of reads compared to input viral templates will help fine-tune minor frequency SNP calls. Unfortunately, due to the uneven sequencing coverage observed with library sequencing , 25, morThe concern over higher error rates, especially from the 454 pyrosequencing and Ion Torrent platforms, necessitates the application of a frequency threshold and additional filters in order to reduce or eliminate sequencing artifacts. The metric “sequencing depth” used here illustrates that increased read coverage with respect to number of amplifiable templates is associated with increased accuracy in the SNP frequencies at that position. As sequencing depth relies on read coverage and amplifiable templates, regions with poor read coverage or samples with large numbers of viral templates can decrease sequencing depth and subsequent confidence in the validity of observations of minor variants. Newer techniques such as PrimerID  and DuplThe sequences generated through the two methods in this study differ with respect to primers and PCR procedures. The first study generated 9kb amplicons derived from single amplifiable viral RNA template molecules. This procedure is relatively inefficient compared to the PCR procedure used for the subsequent pyrosequencing study where multiplex amplification of three, 1.5Kb regions was performed. The study design required precious, earliest available specimens (only plasma specimens were available) from infected subjects enrolled in a large-scale vaccine trial. As a result there was no possibility of achieving the depth of population sampling through single template sequencing that could have made minor variants observed in pyrosequencing comparable to those observed through Sanger sequencing. Notwithstanding these differences, this study shows the adequacy of using Sanger sequencing for assessment of acute and very early HIV infection and serve as a guide regarding the design, utility and limitations of population sequencing variable template sources, and emphasize parameters for improving the interpretation of massively parallel sequencing data.S1 Tablegag, gp120 and nef primers begin with Step, and then the letters G, E and N, respectively. Forward and reverse primers are indicated with an F or R, and first and second round primers are denoted with 1 and 2, respectively. A suffix of 0, 1 or 2 is used to denote whether that primer was the initial or alternate primer. Positions relative to the HXB2 reference sequence at the 5’ (R primers) or 3’ (F primers) ends are listed in the primer name.(TIFF)Click here for additional data file.S2 TableNumber and percent of pyrosequencing-specific minor SNPs (PS-SNPs) observed in all 32 subjects that fell below the Sanger sequencing detection threshold for that subject.(TIFF)Click here for additional data file.S3 Tablegag is 1500 bases. The first available plasma sample was sequenced.The median number of reads, mean amplifiable templates, mean sequencing depth, single-template derived Sanger sequences and plasma sample collection time is shown for each subject. The average gene length for (TIFF)Click here for additional data file.S4 Tablegp120 is 1530 bases. The first available plasma sample was sequenced.The median number of reads, mean amplifiable templates, mean sequencing depth, single-template derived Sanger sequences and plasma sample collection time is shown for each subject. The average gene length for (TIFF)Click here for additional data file.S5 Tablenef is 610 bases. The first available plasma sample was sequenced.The median number of reads, mean amplifiable templates, mean sequencing depth, single-template derived Sanger sequences and plasma sample collection time is shown for each subject. The average gene length for (TIFF)Click here for additional data file.S6 TableThe fraction of SNPs observed within subjects in each of the categories was compared between vaccine and placebo groups. The categories compared include shared SNPs, SNPs observed specifically within pyrosequences  and SNPs observed specifically within Sanger sequences (Private and Informative SNPs). The p values listed are based on Mann-Whitney comparison between vaccine and placebo subjects.(TIFF)Click here for additional data file.S1 Figgag , gp120 , and nef  that correspond to each SNP category, with a linear scale and a split at 0.25. The X-axis corresponds to each subject (ID 502-XXXX) [The Y-axis indicates the proportion of nucleotide positions in 02-XXXX) . The key(TIFF)Click here for additional data file.S2 FigSubjects with (A) single or (B) multiple founder viruses are shown. Subject IDs are indicated on the x-axis (502-XXXX) .(TIFF)Click here for additional data file.S3 Figgag (A), gp120 (B) and nef (C) and are shown. Subject IDs (502-XXXX)[Positions with consensus mismatches in 502-XXXX) are list(TIFF)Click here for additional data file.S4 Fig(A-C) Stacked bar graphs are shown for each subject with the key indicating the type of SNP and the frequency of each SNP in pyrosequences. The lower panels show the pyrosequencing depth (left y-axis), defined as number of reads mapped to a position divided by mean number of amplifiable viral templates. The number of Sanger sequences are shown on the right y-axis. X-axes list the subject publication ID (502-XXXX) .(TIFF)Click here for additional data file.S5 FigThe proportion of each subjects’ minor SNP variants found adjacent to a homopolymer (diamonds) or a nonhomopolymer (triangles) is shown on the Y-axis. Results from all 32 subjects and all three gene regions are shown. The P value derives from a Mann-Whitney comparison between the two groups.(TIFF)Click here for additional data file.S6 Figgag (A), gp120 (B) and nef (C). Subjects replicating more than one founder variant correspond to the rightmost six columns in each plot.The number  of pyrosequencing-specific SNPs is plotted for all subjects . All positions with minor SNPs (red) and minor SNP positions that have a sequencing depth greater than one (black) are shown for (TIFF)Click here for additional data file.S7 Figgag, gp120 and nef (X-axis). The black line and right Y-axis shows the pyrosequencing depth at each SNP position.Frequency of SNPs (left Y-axis) found in Sanger sequences, but absent from pyrosequences, in (TIFF)Click here for additional data file.S8 Figgag , gp120  and nef  are plotted on the Y-axis, and binned according that SNP’s presence within pyrosequences. None of the Sanger sequence private SNPs were found in the pyrosequencing dataset above the 1% threshold.The frequency of all private (observed in only one of a subject’s Sanger sequences) SNPs found in Sanger sequences across all 32 subjects in (TIFF)Click here for additional data file.S9 Figgag (A), gp120 (B) and nef (C) is shown for the six subjects with multiple founders. Major (+) and minor (o) variants are plotted. Spearman correlation coefficients are shown.Error-corrected pyrosequences (Y-axis) and Sanger sequences (X-axis) among 103, 250, and 169 shared SNPs across (TIFF)Click here for additional data file.S10 Figgag (A), gp120 (B) and nef (C) with Spearman correlation coefficients noted. (D-F) Correlations between the number of Sanger sequences and the absolute frequency difference for shared SNPs. Major (+) and minor (o) variants are plotted. Bars above each panel indicate the significance of correlations  using a Kruskal-Wallis test with Dunn correction for multiple comparisons.(A-C) Correlations between the absolute frequency difference and the pyrosequencing depth of the corresponding position in 103, 250, and 169 shared SNPs across (TIFF)Click here for additional data file.S11 Figgag (A), gp120 (B) and nef (C). Subjects infected with more than one founder are shown in the rightmost six columns in each plot.The number (Y-axis) of shared minor SNPs found in all subjects (X-axis). All positions with minor shared SNPs (orange) and only minor shared SNP positions that have a sequencing depth greater than one (turquoise) are shown for (TIFF)Click here for additional data file.S12 Figgag (A), gp120 (B) and nef (C) are binned according to the estimated sequencing depth at that position. The correlation between frequencies of PS-SNPs and the sequencing depth of the position is estimated. Bars above each panel indicate the significance of correlations  using a Kruskal-Wallis test with Dunn correction for multiple comparisons. Means with SD (red bars) are shown.PS-SNPs observed in all single and multiple founder subjects across (TIFF)Click here for additional data file.


P < 2.81E-06) and 30 SNPs reaching the suggestively significant level (P < 5.62E-05) were identified. Five of the 10 SNPs that were suggestively associated with the antibody response to SRBC were located within or close to previously reported QTL regions. Fifteen SNPs reached a suggestive significance level for AIV antibody titer and seven were found on the sex chromosome Z. Seven suggestive markers involving five different SNPs were identified for the numbers of heterophils and lymphocytes, and the heterophil/lymphocyte ratio. Nine significant SNPs, all on chromosome 16, were significantly associated with serum total IgY concentration, and the five most significant were located within a narrow region spanning 6.4kb to 253.4kb (P = 1.20E-14 to 5.33E-08). After testing expression of five candidate genes  located in this region, changes in IL4I1, CD1b transcripts were consistent with the concentrations of IgY, while abundances of TRIM27 and ZNF692 showed reciprocal changes to those of IgY concentrations. This study has revealed 39 SNPs associated with six immune traits  in Beijing-You chickens. The narrow region spanning 247kb on chromosome 16 is an important QTL for serum total IgY concentration. Five candidate genes related to IgY level validated here are novel and may play critical roles in the modulation of immune responses. Potentially useful candidate SNPs for marker-assisted selection for disease resistance are identified. It is highly likely that these candidate genes play roles in various aspects of the immune response in chickens.The genetic improvement of disease resistance in poultry continues to be a challenge. To identify candidate genes and loci responsible for these traits, genome-wide association studies using the chicken 60k high density single nucleotide polymorphism (SNP) array for six immune traits, total serum immunoglobulin Y (IgY) level, numbers of, and the ratio of heterophils and lymphocytes, and antibody responses against Avian Influenza Virus (AIV) and Sheep Red Blood Cell (SRBC), were performed. RT-qPCR was used to quantify the relative expression of the identified candidate genes. Nine significantly associated SNPs ( Great effort has been expended globally to understand and genetically improve disease resistance in domestic animals –4. ImmunImmunological characteristics such as antibody titers have been shown to be heritable in poultry –11, indiMore recently, genome wide association studies (GWASs) have become one of the most commonly used strategies for identifying genes for complex traits in humans, as well as in animals . In chicThis study aimed to identify major genomic regions (loci) and candidate genes associated with the immune response using GWAS for an array of important immune traits, including total serum concentrations of immunoglobulin Y (IgY), numbers of, and the ratio of heterophils and lymphocytes (H/L), and antibody responses against Avian Influenza Virus (AIV) and Sheep Red Blood Cells (SRBC) in chickens. This approach may offer valuable information for understanding the molecular mechanisms of the regulation of immune traits and facilitate the application of marker-assisted selection in breeding programs for disease resistance in chickens.A total of 7,175 independent SNP markers were obtained with multidimensional scaling (MDS) analysis of these SNPs using the first two principal components  indicatiP-values, are given in P-values ) for all SNP markers of each trait is represented by a Manhattan plot shown in The descriptive statistics for six immune traits in the 720 Beijing-You chickens used for the present GWAS are shown in B-G gene (within the MHC region). Four of the SNPs  were located within CD1b and CD1c genes, an intron of GNB2L1 gene, an intron of IL4I1 gene and an intron of BMA1 gene, respectively. A QTL for titres of Natural antibodies (NA) was detected at the SNP rs15788216 on chromosome 16 in chickens [Five SNPs, associated with the serum total IgY level, were clustered within a 0.26 Mb region on chromosome 16 . One SNPThe chicken MHC, which is composed of the B and Y complexes  and playCD1b and CD1c encode two members of the CD1 family, which are structurally similar to the MHC class I antigen and have been shown to present lipid antigens for recognition by T lymphocytes [IL4I1 gene can be induced by interleukin 4 in B cells. The IL4I1 protein has L-phenylalanine oxidase activity, and oxidative deamination of phenylalanine by IL4I1 produces H2O2, and functions in the innate immune defenses of vertebrates. Previous studies have revealed that the IL4I1 protein inhibits T-cell proliferation with a preference for memory T cells, and H2O2 mediates this inhibition [phocytes . The exphibition –29.In the current study, significant SNPs on GGA1, 3, 5 and 12 were also identified that related to serum total IgY level. Those were not located within the previously known QTLs for immune traits, for example, five QTLs associated with humoral innate immune response in chickens  were recently validated in a cross between Green-Legged Partridge-like and White Leghorn , 21. The3, bacterial infection, inter alia [The heterophil to lymphocyte (H/L) ratio in the peripheral blood of chickens has been widely accepted as a reliable and accurate physiological indicator of the stress response in chickens –31. The ter alia –33. The ter alia , suggestGHITM) gene on chromosome 6. The GHITM protein is part of the interferon signaling system that includes Janus kinases and their downstream target STAT proteins [GABRA6). This gene is associated with increased production of cortisol and increased blood pressure in response to psychological stress [DNAJC16) in chromosome 21; the gene is a member of the DnaJ/Hsp40 (heat shock protein 40) family. These proteins are molecular chaperones induced in response to cellular stress [There were three SNPs detected as being suggestively associated with the H/L ratio , two of proteins . One SNPl stress . The rs1r stress  and haver stress . Human Hr stress .PID1), also called NYGGF4, on chromosome 9, which encodes a protein possessing a phosphotyrosine interaction domain (PID). Mouse and human NYGGF4 are involved in the regulation of proliferation of adipocyte precursors [NYGGF4 homolog possibly possesses multiple functions. The GGaluGA134321 SNP, is located 11kb downstream of the zinc finger E-box binding homeobox 1 (ZEB1) gene on chromosome 2. In human and mouse studies, ZEB1 is a transcriptional repressor and plays an important role in regulating lymphoid differentiation [CD4, GATA-3, α7-integrin and IL-2 [ZEB1-deficient mice have poorly developed thymuses and significant decreases in their T cell population [There were two SNPs, rs15950814 and GGaluGA134321, that were suggestively associated with the number of lymphocytes . The forecursors . The genecursors . Proteinecursors , which iand IL-2 –45. It hpulation .The direct response to selection on antibody titer to SRBC challenge is accompanied by correlated responses in production and disease-related traits , 16. TheiNOS) gene. The expression of iNOS is regulated by cytokines such as IFN-γ, IL-1β, IL-6, TNF-α, and iNOS-derived nitric oxide (NO) is well known to play important roles in infection and immunity [In this study, of special interest, five SNPs in a narrow region (9.16Mb—9.31Mb) on chromosome 19 were suggestively associated with antibody titer against SRBC . The rs1immunity . The rs1immunity . Such reGPR89B), but no known genes were found within 500 kb of the other two SNPs. A QTL has been detected in the vicinity of this region in a F2 cross of two lines divergently selected for SRBC antibody responses [In addition, three SNPs on chromosome 1 were suggestively associated with anti-SRBC titer , and rs1esponses . Two SNPesponses , 50–51.SLC25A6). The present study found that the region at about 100 Mb from the proximal end of chicken chromosome 1, including the ROBO1  and ROBO2 (homolog 2) genes, has a strong effect on the antibody response to the NDV in chickens [SPARC-related modular calcium-binding 1 (SMOC1) gene on GGA5 playing an important role in MD tumorigenesis [(GAL1–13) and interleukin-17F genes on GGA3 probably plays an important role in the immune response against IBV [SLC25A6 gene encodes adenine nucleotide translocase 3 (ANT3), an enzyme which exchanges ATP and ADP through the mitochondrial membrane. Mitochondrial ANT3 is regulated by IL-4 and IFN-γ via STAT-dependent pathways [Two SNPs on chromosome 1 were suggestively associated with AIV antibody titer . One SNPchickens . One SNPigenesis . GGA1 alinst IBV . The SLCpathways . Considepathways .HCLS1). HCLS1 is an intracellular protein expressed mainly in hematopoietic cells. It was reported to be an important actin regulator at the T-cell immunologic synapse and also to influence numerous functions of natural killer cells [One SNP rs15995401) is located 28 kb downstream of hematopoietic cell-specific Lyn substrate 1 (01 is locMPDZ) which is involved in a diverse range of pathways that maintain cell integrity and function [Eight SNPs on chromosome Z were suggestively novel and specific associated with AIV antibody titer in this study . One SNPfunction . No knowCD1b, IL4I1 (Interleukin 4-induced 1), TRIM27 (tripartite motif-containing 27), GNB2L1 , and ZNF692 (zinc finger protein 692).The present GWAS verified that the important immune-related locus was located on chromosome 16 , 22. In Salmonella enteritidis is more potent in inducing production of serum antibodies (IgY) in the infected chickens. For just that reason, it was used here to validate the association between total serum IgY level and the corresponding candidate genes identified in this study.Compared with SRBC, Salmonella enteritidis. In this study, the changes across days in expression of IL4I1 and CD1b followed a similar pattern to the concentrations of IgY, while changes in TRIM27  and ZNF692 expression were the reverse (mirror-image) of concentrations of IgY  in Beijing-You chickens. Five of the significant SNPs for total serum IgY level were located within a narrow 247 kb region on chromosome 16. Some SNPs were located within or close to the previously reported QTL regions. Five candidate genes related to IgY level by GWAS, and validated here by experimental challenge, are novel and may play critical roles in the modulation of immune responses. It is highly likely that these candidate genes play an important role in the immune response. Findings herein lay an important foundation for further identifying the immunity-related genes and provide useful information for the molecular mechanisms underlying disease-resistance traits in chickens.All of the animal experiments were conducted in accordance with the Guidelines for Experimental Animals established by the Ministry of Science and Technology . Animal experiments were approved by the Science Research Department  of the Institute of Animal Sciences, CAAS .ad libitum. All chickens received the conventional inoculation regimen (Beijing-You chickens (a Chinese indigenous breed) from the national conservation farm, Institute of Animal Sciences, CAAS, were used. A total of 728 male offspring from 50 sire families were produced in one hatch. The birds were reared in stair-step cages under standard conditions of humidity, temperature, and ventilation, and had access to water and feed  regimen , includiAll blood samples were collected from the brachial vein by venipuncture. At d 49, chickens were injected intramuscularly in the leg with 1 ml of a 0.25% suspension of SRBC. Sera were isolated from blood collected on d 55 (6d post-injection) for immediate determination of anti-SRBC titers by hemagglutination  using 96Genomic DNA (gDNA) was extracted from blood samples using phenol-chloroform. The DNA samples were genotyped using the Illumina 60K chicken SNP Beadchip . The intensity files generated were processed with the GenomeStudio GT module with default settings .P-value for Hardy-Weinberg equilibrium test > 1×10–6.Quality control was performed by PLINK 1.07  and eigh2 < 0.2; second, the pairwise IBS distance among the 720 individuals was calculated using the remaining 7,175 SNPs; finally, the first two MDS dimensions were extracted via the “MDS-plot” command and visualized in R v3.0.1 (www.r-project.org).To evaluate if population substructure existed among the individuals, classical MDS, as implemented by PLINK 1.07 , was perYij is the phenotypic value of a given immune trait for individual i in the jth sire family, b0 is the intercept, bk-1, b50 is the regression coefficient, f is the dummy variable for the sire family, k is an integer ranging from 2 to 50, Xij denote the genotype at the given SNP, and εij is independent, normally distributed random residual, with mean 0 and constant variance. For the family variable with 50 categories, 49 binary dummy variables f (coded 0/1), created by the dummy-coding option within PLINK, were fitted in the model.Association analyses were conducted to test the additive effects of allele dosage by PLINK, using the linear regression model. The fitted equation was:P-value thresholds, including 5% genome-wide significance  and suggestive association . A global view of the p-value results for the six traits from the association analysis was created by the “gap” package [www.r-project.org).Given the correlation between SNPs in linkage disequilibrium, the strict Bonferroni adjustment appears to be overly conservative . The sum package  in R v3.Birds and Treatment. Twelve-day-old Beijing-You chickens were from the same conservation stock and were reared as described earlier, in separate cages. Birds were confirmed to be free of Salmonella by culturing fecal samples in buffered peptone water (BPW) overnight with shaking at 150 rpm and spreading on brilliant green agar containing 100 mg/ml nalidixic acid . Chickens were randomly divided into 2 groups of 30 each . Birds in the challenged group were injected intramuscularly into the leg with 0.5 ml of SE bacterial suspension containing 108 CFU and the control group was given 0.5 ml saline. Six chickens from each group were randomly chosen, and sacrificed at 12h, 24h, 3 or 6 days post-infection. Blood was collected, allowed to clot, and serum was stored. The spleen was rapidly removed, snap-frozen in liquid nitrogen, and stored at-80°C for mRNA extraction.Serological test. Concentrations of total IgY in all sampled birds were determined in triplicate by ELISA, as described earlier.mRNA abundance of candidate genes by RT-qPCR. Total RNA was isolated from each spleen sample with the RNAsimple Total RNA kit  and first-strand cDNA was synthesised from 2 μg total RNA using the Promega Reverse Transcription Kit , according to the manufacturer’s instructions. Power SYBR Green PCR Master Mix  was used to quantify mRNA abundance of the genes by Reverse Transcription-Quantitative PCR (RT-qPCR) with an ABI 7500 Real-time Detection System . The primers were designed with Primer Premier 5.0 from the GenBank sequences . All analyses were performed using Student’s t-test and data are presented as means ± S.D.equences . The PCRS1 Tablep-value for t-statistic. The same abbreviations are used in the following tables.The raw results from the linear regression analysis by PLINK. Its basic format is: CHR, Chromosome; SNP, SNP identifier; BP, Physical position (base-pair); A1, Tested allele ; TEST, Code for the test (the additive effects of SNPs); NMISS, Number of non-missing individuals included in analysis; BETA, Regression coefficient ; STAT, Coefficient t-statistic; P, Asymptotic (XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.


The new strategy is implemented in a program named RefEditor. When applying RefEditor to real data, we achieved encouraging improvements in read mapping, variant discovery and genotype calling. Compared to standard approaches, RefEditor can significantly increase genotype calling consistency  and reduce Mendelian inconsistency across various sequencing depths. Because many WGS and WES studies are conducted on cohorts that have been genotyped using array-based genotyping platforms previously or concurrently, we believe the proposed strategy will be of high value in practice, which can also be applied to the scenario where multiple NGS experiments are conducted on the same cohort. The RefEditor sources are available at https://github.com/superyuan/refeditor.With rapid decline of the sequencing cost, researchers today rush to embrace whole genome sequencing (WGS), or whole exome sequencing (WES) approach as the next powerful tool for relating genetic variants to human diseases and phenotypes. A fundamental step in analyzing WGS and WES data is mapping short sequencing reads back to the reference genome. This is an important issue because incorrectly mapped reads affect the downstream variant discovery, genotype calling and association analysis. Although many read mapping algorithms have been developed, the majority of them uses the universal reference genome and do not take sequence variants into consideration. Given that genetic variants are ubiquitous, it is highly desirable if they can be factored into the read mapping procedure. In this work, we developed a novel strategy that utilizes genotypes obtained  This is a PLOS Computational Biology Software Article.http://novocraft.com), SHRiMP [Mapping short reads onto the reference genome is a fundamental step in analyzing next generation sequencing (NGS) data and has been an area of intensive research in the past years. A wealth of successful software programs for mapping short reads, such as MAQ , SOAP 22, SOAP2 , SHRiMP , and STA, SHRiMP , have be, SHRiMP , whole e, SHRiMP , Chromat, SHRiMP –16 and t, SHRiMP ). The de, SHRiMP , 19. Des, SHRiMP , which w, SHRiMP –25 and a, SHRiMP , 27.Notably, genotype information is often available for samples that are undergoing NGS experiments. There are at least three scenarios in which the genotypes are available. First, many WGS or WES studies were conducted on samples that have been studied in the previous wave of genome-wide association studies (GWAS). These samples have already been genotyped by one of the array-based high-density genotyping platforms such as those from Illumina  and Affymetrix  . ComprehUsing array-based genotyping, we will be able to collect genotype information on a large proportion of common genetic variants. Aided by powerful genotype imputation techniques, such as MaCH , 32, MaCWe believe that the substantial pre-existing genotype information, whether assayed or imputed, can be and should be utilized to fine tune the reference genome to reflect the unique features of each individual genome. An accurate reference genome sequence will lead to improved read mapping and consequently improved variant discovery and genotype calling.Here we present RefEditor, a software package developed to improve read mapping by customizing the universal haploid reference genome to reflect individual genetic variation. It contains two components, RefEdit and RefEdit+, both converting the universal reference genome into a personalized diploid reference genome. RefEdit uses the assayed genotypes only whereas RefEdit+ adopts an additional step to augment the assayed genotypes by imputation. The idea of modifying the universal reference genome to accommodate genotype differences has been proposed in the literature before –25, 27. A. The sequencing read (ID: SRR005197.10106228) containing the alternative allele G at that locus can be successfully mapped to the personalized diploid reference genome with two mismatches. By contrast, this read fails to map to the universal reference genome because there are three mismatches, which exceeds the limit adopted by most mapping tools for this read length (36 base pairs (bp)). Downstream 18 bp at the locus chr1:154568683, multiple mapped reads show the same type of mismatch, suggesting that there might be a new SNP at that locus. The alternative allele G is not known a priori. This new SNP is verified by gold standard genotype calls based on Complete Genomics Inc. (CGI) deep sequencing data.In We conducted a series of studies using real data to evaluate the performance of RefEdit and RefEdit+ for read mapping, variant finding and genotype calling. In the first study, we focused on mapping success rates, genotype calling consistency and variant detection rates for two individuals from different populations. In the second study, we used Mendelian inconsistency (MI) among individuals in a trio as the metric for performance. In the third study, we used a different genotype gold standard to evaluate genotype calling consistency.We selected samples from the HapMap , 40 and We chose the Affymetrix Axiom series array as the array-based genotyping platform in this study. This array contains about 6 million SNPs. We used the genotypes produced by the 1000 Genomes Project, which were called based on the CGI deep sequencing data, as the gold standard. This sequencing platform discovered about 41 million SNPs among 433 individuals. Both platforms produce high quality genotype calls and have been frequently used in other studies –44.We use population-specific reference panels from the 1000 Genomes Project  for impuIt is of interest to know, from the existing array-based genotype data, how many genotypes containing the alternative allele are identified and how many more can be identified by genotype imputation. Genotype summaries  for NA 19238 and NA 12716 are displayed in The Rsq value is a good estimator of the correlation between the imputed and true genotypes, and thus is frequently used as a measure of imputation accuracy , 45, 46.Since we do not know the true genomic location of a sequencing read generated from real sequencing experiments, we are unable to directly compare mapping accuracy. The proportion of successfully mapped reads among all sequenced reads is a reasonable alternative, which had been used in other studies , 48. A sOur results indicate that RefEdit and RefEdit+ methods show consistent improvement in terms of the read-mapping rate. It is perhaps not too surprising to see that the usage of RefEdit and RefEdit+ results in improved read mapping rates, since more accurate reference genome information is provided to them. What is important to note here is exactly how much improvement RefEdit and RefEdit+ can achieve and at which sites the mapping rate improvement is achieved. An increase on the order of an additional 1% of reads being mapped is a tremendous gain, generating a significant increase in information content for the researcher. It is additionally useful that the largest gains using RefEdit come when allowing the fewest mismatches. In this way, a researcher can choose to allow one fewer mismatch during mapping while still generating the same mapping rate as he or she would have had with the universal genome. This will help to limit the number of false positive variant calls in the analysis while not reducing the information content. RefEdit allows a researcher the flexibility to ask for more information by keeping the mismatch level the same or ask for fewer false positives while keeping the information level the same. As no two studies are alike, this flexibility is a tremendous benefit of this approach.Using the CGI genotype calls as the gold standard, we evaluated the genotype calling consistency of RefEdit/RefEdit+ with three competing methods at 13 different sequencing depths  on individual NA19238. For each sequencing depth, performance comparison is conducted on the subset of genotypes that are not ref/ref (according to CGI genotypes) that are called by GATK  . Fig 4A Remarkably, we discovered that the read mapping using our RefEdit+ strategy can achieve the same level of accuracy as the read mapping using the universal reference genome, by using only a fraction of the reads required by the latter. The detailed breakdown table of genotype concordance for five methods can be found in the It has been shown that common and rare variants (MAF ≤ 1%) display different properties . It is tA drawback of evaluating performance using genotype concordance as above is that we need to designate a gold standard which may contain errors of its own, although error rate is rather low. Given that there are genotype data from two different platforms (array-based and sequencing-based) for parent-offspring trios from the International HapMap and 1000 Genomes Projects, an alternative metric for performance evaluation is MI which counts the number of loci that show Mendelian errors within the trio. MI has been used in Dewey et al. to evaluate the performance of the ethnicity-specific major allele reference genome approach . For thiBesides genotype calling consistency at known SNP sites, when conducting WGS studies, it is also important to correctly identify novel SNP variants, as was illustrated in the previous example . TherefoNext we again stratified all the SNPs identified by CGI into three categories according to their MAFs: MAF ≤ 1%, 1% < MAF ≤ 5% and MAF > 5%. In all the analyses conducted above, we set the minimum read length to be 36 bp to utilize all the reads that are being generated. With ever improving sequencing technologies, longer and longer reads are being generated. It is of great interest to know whether the improved read length has any impact on the results we have seen so far. After all, it is possible that increased read length may improve the read mapping and SNP calling such that the benefit of using our RefEdit/RefEdit+ tools is no longer significant. To make this assessment, we repeat all the aforementioned analysis using only reads with 100 bp read length. Here the default mismatch threshold value (5) is used. The results are summarized in As explained earlier, the CGI genotype gold standard inevitably contains errors of its own. For the sake of comprehensiveness, here we choose an alternative gold standard to conduct another round of performance evaluation. In a recent publication, Kuleshov et al. applied a novel long read haplotyping technology to obtain three phased human genomes . We chooSo far, we only use BWA as the read mapping algorithm. To understand whether the mapping tools used will have an impact on the relative performance of RefEdit, we tested another commonly used read mapping algorithm, Bowtie 2 . We use en masse in research laboratories and perhaps clinics with the primary goal of identifying genetic variants. Mapping the sequencing reads to the human genome is an important early step to analyze data from all sequencing-based experiments including WGS. Multiple studies [With the price of DNA sequencing continuing its rapid decline, whole genome sequencing will likely to be performed  studies , 48 have studies  or ethniThe performance improvement of RefEditor over existing mapping strategies is easy to understand, because more information is being incorporated. Our work showed that the improvement could be achieved computationally efficiently and in a straightforward fashion using RefEditor. Because array-based genotyping technologies have matured and cost less than WGS, they have been the choice for most large-scale association studies to date . A slew There are tools available for constructing personal genomes that integrate known variants into the reference genome such as vcf2diploid in AlleleSeq  and perEIt has been reported in the literature that multi-sample SNP calling strategy improves genotype calling in WGS studies . Since tAn important lesson we learned is that the genotype imputation strategy plays a key role in performance improvement for RefEdit+. Genotype imputation has been monumentally successful in GWAS analysis. We demonstrate that high quality imputed genotypes also improve the reference genome customization and therefore produce improved read mapping and genotype calling results.An extension of our customized reference genome strategy is to apply RefEditor iteratively for multiple rounds. Specifically, after genotypes were called with the help of RefEditor, we can combine these new genotypes with existing (assayed and imputed) genotypes that were used earlier to obtain an updated set of existing genotypes, and then apply RefEditor to perform read mapping and genotype calling again. The same strategy can also be applied to WGS samples without existing genotype information.Our performance comparison results demonstrate the importance and benefits of incorporating existing genotype information in read mapping, genotype calling and variants discovery in WGS studies. Admittedly, researchers need to spend extra time and effort to perform read mapping with RefEditor: unlike using a single universal reference genome, one has to generate a reference genome for each individual sample in the cohort. A post-process step is also needed after read mapping. However, with our RefEditor package, the whole read mapping process can be automated using simple scripts, and therefore very little human time and intervention is needed in adopting our personalized read mapping strategy. As for computation time, in our experiment on a single core 1.4G Hz CPU and 8GB memory, Diploid Constructor took 4 minutes and 32 seconds to construct the diploid reference genome from hg19.fa and 15,568,754 genotypes . The reference genome size increased by 0.2 GB (from 3.0 GB to 3.2 GB) and indexing time increased 5 minutes and 30 seconds (from 87m8s to 92m38s). Read mapping time increased 5 seconds (from 18m49 to 18m54s) to map 5,112,949 reads (read length is 36 bp). Mapping Converter took 49 seconds to convert the intermediate mapping results. Therefore, we believe the overall extra computation cost is quite manageable. Compared to the time spend on collecting sample and sequencing, we believe the extra computation time spent to improve SNP calling accuracy is well justified. Furthermore, for a large cohort, reference genome editing and the subsequent read mapping step can be done in parallel among individuals, which is difficult for multi-sample SNP calling.It is perhaps not a surprise that a personalized diploid reference genome, incorporating known and imputed genotype of an individual, can result in improved read mapping, and hence more accurate variant calling and discovery. However, until now it is unclear how much improvement this strategy may achieve. By conducting carefully designed, extensive and thorough comparisons, we report that RefEditor, especially when imputed genotypes are added in RefEdit+, will result in substantial improvement in the accuracy of genotype calling and discovery, even with longer read lengths (100 bp). Given the importance of accurately identifying genetic variants and in light of our results, we strongly advocate the adoption of new strategy of using personalized reference genome in population level sequencing-based genetic studies such as WGS and WES.https://github.com/samtools/hts-specs). This reference genome can then be used for mapping reads generated from any sequencing assay conducted on this individual to improve the read mapping accuracy. There is no need to modify the read mapping software itself. Since genotype information is increasingly available from more and more array-based genotyping and sequencing experiments, we believe incorporating such information in the read-mapping step is important and beneficial. This goal can be conveniently achieved with RefEdit and RefEdit+, with the later contains an additional imputation step to augment the existing genotypes set. The RefEdit+ pipeline consists of the following steps:The main objective of this project is to construct the personalized diploid reference genome using pre-existing genotype information of an individual, which is typically stored in a Variant Call Format (VCF) file  to create a new personalized diploid reference genome. This step is achieved by using the program Diploid Constructor contained in the RefEditor software package. This new reference genome can be fed into any existing mapping tool in the exact same way as the universal reference genome. During the construction process, no action is taken at loci where genotypes are homozygous wild type ; at loci where genotypes are homozygous mutant alleles we edit the corresponding nucleotides in the reference genome sequence file; at heterozygous loci we add a mini chromosome of length RefEditor can also accept an optional command line argument indicating the individual’s gender. When this argument is set for female individuals, chromosome Y will be excluded from the personalized reference genome. Using RefEdit, only non-ref/ref genotypes identified by the genotyping array will be incorporated, whereas using RefEdit+, all non-ref/ref genotypes identified from either the genotyping array or imputation will be incorporated.The customized diploid reference genome can be treated the same as the universal reference genome and used by almost all existing read mapping software. For this study, we use BWA v0.5.9  with defGenotypes are called from the reads successfully mapped with positive mapping quality found in sorted BAM format file. We use the Genome Analysis Toolkit (GATK)  to call Various strategies have been developed for dealing with sequence variants in read mapping. Here we briefly review other competing methods.In a recent study, Dewey et al. pointed out that the major alleles at many genomic loci are different among populations . Given tGSNAP uses universal reference genome and all SNPs from dbSNP in mapping. It also uses its own mapping algorithm based on hash tables generated from sampled k-mers from reference genome . GSNAP cS1 Text(DOCX)Click here for additional data file.S1 FigNon-ref/ref genotypes before and after imputation are incorporated into the customized reference genome construction for RefEdit and RefEdit+ methods respectively. (A) Genotype composition before/after imputation for sample NA19238. (B) Genotype composition before/after imputation for sample NA12716. (C) The overlapping of non-ref/ref genotypes between imputation and CGI for sample NA19238. Concordance is 98.94%. (D) The overlapping of non-ref/ref genotypes between imputation and CGI for sample NA12716. Concordance is 98.99%.(DOCX)Click here for additional data file.S2 Fig(1) There are 4,611,084 overlapping SNPs between Affymetrix Axiom array and CGI with 99.75% concordant rate. (2) There are 6,851,861 overlapping SNPs between imputed and CGI with concordance rate 98.58%. (3) There are 2,965,053 SNPs with imputed genotype but not called by CGI sequencing. (4) There are 20,295,528 SNPs that called by CGI sequencing but not from Affymetrix Axiom array or imputation. Only 321,790 are non-ref/ref genotypes.(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 FigThe red curve indicates the concordance between imputed genotypes and CGI after applying the Rsq threshold. The blue curve indicates the proportions of the genotypes that pass the Rsq threshold.(DOCX)Click here for additional data file.S5 FigThe read length is 100 bp. The phased VCF files produced by Kuleshov et al. are used as the gold standard.(DOCX)Click here for additional data file.S6 FigThe read length is 100 bp and the CGI genotypes are used as the gold standard.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableGenotypes obtained from CGI sequencing were used as the gold standard.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableThe CGI genotypes are used as the gold standard.(DOCX)Click here for additional data file.S6 TableThe sequencing depth is 22x. The differences (+/-) are the results of comparing to genotype calls using the universal reference genome method. The RefEdit and RefEdit+ methods increase the concordance (shaded parts) between genotype calls and the CGI gold standard genotypes.(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 TableThe CGI genotypes are used as the gold standard.(DOCX)Click here for additional data file.S9 TableThe differences (+/-) are the results of comparing with MI using the universal reference genome in the read mapping step.(DOCX)Click here for additional data file.S10 TableThe CGI genotypes are used as the gold standard.(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S12 TableThe phased VCF files produced by Kuleshov et al. are used as the gold standard.(DOCX)Click here for additional data file.


GALNT2 gene strongly associated to HDL-C levels. However, the lead GWAS SNP associated to HDL-C levels in this genomic region, rs4846914, is located outside of transcription factor (TF) binding sites defined by chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) experiments in the ENCODE project and is therefore unlikely to be functional. In this study we apply a bioinformatics approach which rely on the premise that ChIP-seq reads can identify allele specific binding of a TF at cell specific regulatory elements harboring allele specific SNPs (AS-SNPs). EMSA and luciferase assays were used to validate the allele specific binding and to test the enhancer activity of the regulatory element harboring the AS-SNP rs4846913 as well as the neighboring rs2144300 which are in high LD with rs4846914.Plasma levels of high-density lipoprotein cholesterol (HDL-C) have been associated to cardiovascular disease. The high heritability of HDL-C plasma levels has been an incentive for several genome wide association studies (GWASs) which identified, among others, variants in the first intron of the GALNT2.Using luciferase assays we found that rs4846913 and the neighboring rs2144300 displayed allele specific enhancer activity. We propose that an inhibitor binds preferentially to the rs4846913-C allele with an inhibitory boost from the synergistic binding of other TFs at the neighboring SNP rs2144300. These events influence the transcription level of GALNT2 rather than the reported lead GWAS SNP rs4846914.The results suggest that rs4846913 and rs2144300 drive the association to HDL-C plasma levels through an inhibitory regulation of  GALNT2 gene on chromosome 1q42 have been associated with HDL-C and TG plasma levels in several studies [GALNT2 encodes for N-actetylgalactosaminyltransferase 2, an enzyme involved in the O-linked glycosylation of proteins [Galnt2 in mouse models lowered HDL-C while knock-down of Galnt2 resulted in higher HDL-C levels [GALNT2 expression in liver is correlated to the lead GWAS SNP (rs4846914) associated to HDL-C plasma levels [Low plasma levels of high-density lipoprotein cholesterol (HDL-C) are strongly associated with high risk of coronary artery disease (CAD). Low HDL-C levels together with high levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) are common features of dyslipidemia and combined hyperlipidemia . Plasma  studies , 5. GALNproteins . It glycproteins  that infS SNP rs446914 assThe SNP reported with the strongest association in GWAS rarely is the functional one. Previous studies have shown that SNPs associated to disease are enriched in regulatory elements  and it i1) publicly available raw reads (.fastq) from ChIP-seq for several TFs in HepG2 were obtained from the ENCODE project database (ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/) and were aligned to the reference human genome (G1) UCSC hg19 assembly based on the Genome Reference Consortium Human genome build 37 (GRCh37) but excluding random and unplaced contigs and to an HepG2-specific alternative genome (G2), built using the FastaAlternateReferenceMaker GATK utility that generates an alternative reference sequence replacing the reference bases at variation sites with the bases supplied by a SNPs collection. SNP calls were made from pooled short-read datasets in house ChIP-seq data plus ENCODE genomic HepG2 reads and were validated using a genome-wide genotyping array. (2) Reads mapped specifically to G1 or G2 were counted at the heterozygous SNPs. SNPs with no reads mapped on G1 or G2 were discarded. (3) To determine whether the G1/G2 read counts difference was statistically significant a binomial test was applied against the null hypothesis of an equal G1:G2 coverage. After correcting for multiple testing (Benjamini & Hochberg or FDR), AS-SNPs with P < 0.05 were selected. (4) AS-SNPs were then intersected with the 1,000 Genomes SNPs collection  in order to retrieve AFs. (5) Extensive filtering of the selected AS-SNPs was performed to remove AS-SNPs that were in centromeric or telomeric regions (UCSC Gap table ± 1 Mb), blacklisted ENCODE regions (±100 bp) or CNVs. (6) Pruned AS-SNPs selections were finally intersected with collections of GWAS associated to liver-related and metabolic relevant traits and SNPs in LD (r2 > 0.8) with GWAS  at 37 °C with 5 % COp-values comparing RLU difference between AS-SNPs alleles were calculated using two-tailed t-test.All the luciferase expression constructs were built based on pGL4.23 from Promega. The ccdB expression cassette was inserted into KpnI and EcoRV sites of pGL4.23 to construct pGL4.23-ccdB, which was used as a basal vector to diminish false positive signal during the cloning process. Genomic sequences surrounding AS-SNPs were amplified by Phusion Hot Start Flex DNA polymerase (NEB) using HepG2 genomic DNA as template. The amplified fragments were purified by the QIAquick Gel Extraction Kit (QIAGEN) and inserted upstream of the minimal promoter sequence of pGL4.23 by SLiCE cloning methods . To testNuclear extracts were prepared from HepG2 cells using the NucBuster™ Protein Extraction kit (Novagen) and the amount was verified via the Qubit® Protein Assay Kit (Life Technologies). Oligonucleotide probes were designed with both alleles of rs4846913 flanked by 14 bp in both a cold and 5'-biotinylated form (IDT). Biotinylated and un-biotinylated oligonucleotides were annealed with the reverse complementary oligonucleotide (95 °C to 25 °C temperature stepdown). For the binding reaction, 3–6 μg of the nuclear extract were incubated with 200 fmol of each biotinylated dsDNA probe for 40 min on ice in 10 mM TrisHCl, 30 mM KCl, 1 mM DTT, 1 μg of Poly (dI-dC), 7.5 % glycerol, 0.063 % NP-40, 2 mM MgCl2, and 0.1 mM EDTA. Samples were electrophoresed in Criterion™ 5 % TBE Precast Gels (Bio-rad), and electro-transferred into a Genescreen plus™ hybridization transfer membrane (Perkin Elmer). DNA-protein complexes were cross-linked using UV-light and detected by chemiluminescence using LightShift® Chemiluminescent EMSA Kit (Thermo Scientific).We identified a collection of AS-SNPs showing differential allele binding of TFs based on ChIP-seq experiments performed in HepG2 cells. The preferential TF binding to a specific allele at heterozygous position in the genome was used to pinpoint putative functional regulatory elements. To avoid a bias toward the reference human genome the ChIP-seq reads were aligned to the maternal and paternal genomes in HepG2. The difference in the number of reads mapping to the two alleles was calculated and tested for statistical significance in order to identify suitable candidate regulatory elements for experimental validation see .Here we selected an AS-SNP in high LD with different GWAS SNPs associated to four traits related to phospholipids/cholesterol levels Table . These aGALNT2 and is in high LD (r2 > 0.8) with these four GWAS SNPs (Fig. We investigated the regulatory effect of rs4846913, an AS-SNP identified from ChIP-seq experiments in HepG2 cells see . This vaNPs Fig. . The ENCWe tested both alleles in EMSA and confirmed the differential binding with a stronger protein binding for the C allele Fig. . We thenThe genomic distribution of the GWAS SNPs Table  supportsGALNT2 rather than the reported GWAS SNP rs4846914. The luciferase assays data show a decrease in the functional activity of the construct harboring the C allele of the AS-SNP rs4846913, activity that was further lowered by the presence of the C allele of rs2144300 suggesting a synergistic inhibitory role and leading to the hypothesis that the low activity alleles rs4846913-C and rs2144300-C are preferentially bound by transcriptional repressors resulting in lower GALNT2 levels and higher HDL-C.We propose that the AS-SNP rs4846913 and rs2144300 drive the association to HDL-C plasma levels through an inhibitory regulation of