Integrating epigenetic data with genome-wide association study (GWAS) results can reveal disease mechanisms. The genome sequence itself also shapes the epigenome, with CpG density and transcription factor binding sites (TFBSs) strongly encoding the DNA methylome. Therefore, genetic polymorphism impacts on the observed epigenome. Furthermore, large genetic variants alter epigenetic signal dosage. Here, we identify DNA methylation variability between GWAS-SNP risk and non-risk haplotypes. In three subsets comprising 3128 MeDIP-seq peripheral-blood DNA methylomes, we find 7173 consistent and functionally enriched Differentially Methylated Regions. 36.8% can be attributed to common non-SNP genetic variants. CpG-SNPs, as well as facilitative TFBS-motifs, are also enriched. Highlighting their functional potential, CpG-SNPs strongly associate with allele-specific DNase-I hypersensitivity sites. Our results demonstrate strong DNA methylation allelic differences driven by obligatory or facilitative genetic effects, with potential direct or regional disease-related repercussions. These allelic variations require disentangling from pure tissue-specific modifications, may influence array studies, and imply underestimated population variability in current reference epigenomes. Genomic polymorphisms affect the epigenome, which in turn influences how epigenome- and genome-wide analysis are interpreted. Here, the authors characterise allelic differences in DNA methylation driven by obligatory or facilitative genetic effects, which may affect disease-related loci. However, linkage disequilibrium (LD) between variants, incomplete coverage and gaps in their functional annotations make it difficult to establish a firm causal and functional mechanism between the statistically identified variants and the phenotypes they associate with. Integration with disease-relevant and tissue-specific functional indicators or epigenetic marks within these regions, such as DNase I hypersensitivity sites (DHSs)2, histone modifications4 and DNA methylation variation6, can highlight candidate active variants. This dissection of GWAS signals enables progress from associated SNP to mechanistic understanding8.The hunt for disease-implicated genetic sequences is a major focus of medical research because it may reveal precise molecular insights into disease pathophysiology. Genome-wide association studies (GWAS) have successfully identified thousands of common genetic loci associated with human diseases and phenotypes9. Thus, defining how epigenomes vary with respect to genetic influence and the mechanistic role this plays over phenotypic expression can greatly increase understanding of genetic regulation in heath and in disease. Genetic influences on the epigenome, such as enhancer variation, are strong enough to be observed in only 19 diverse ancestry individuals10.Epigenetic variation in relation to genome sequence falls into three main categories: ‘pure’ if under no genetic influence, ‘facilitated’ when genetic polymorphism enable variability and ‘obligatory’, if sequence variants directly predict the epigenetic state1) and DNA methylation within the LD block harbouring it. These GWAS regions are not only robustly associated with human traits but are also known to be enriched for functionality11. We used genome-wide methylation-dependent immunoprecipitation second-generation sequencing (MeDIP-seq) data derived from peripheral blood and high-quality SNP array genotype data from 3128 samples of European ancestry. Therefore, we broadly capture DNA methylation differences between risk and non-risk GWAS haplotypes, or haplotype-specific DNA methylation (HSM) variation12. The underlying strong extent of genetic influences on the epigenome is increasingly acknowledged13. CpG density is fundamental in defining the background DNA methylome, along with cell-specific usage of available binding sequences for transcription factors14. Common population variation in CpGs is significant via SNPs within this dinucleotide (CpG-SNPs) as well as larger variants. Recent base-resolution data has further supported that regional epigenetic effects exist within the DNA methylome15. We took advantage of the fact that this immunoprecipitation-derived signal is strongly influenced by the number of methylated cytosines in the DNA fragment16 to capture these regional allelic variation effects, and can be considered more akin to ChIP-seq than base-resolution analyses. This leads to a distinct signal of population variation in both facilitated and obligatory genetic effects on the DNA methylome. Allelic variations in genetic dosage effects on this DNA methylation score are allelic signal differences not epigenetic variability. However, these may as well contribute to regional or neighbouring allelic epigenetic variation, with effects on traits, or even bias other analytical measures, including bisulphite arrays and sequencing.Here we report an analysis of the relationship between allelic dosage of genetic risk SNPs that have previously been significantly and reliably associated with human phenotypic variation  listed in the NHGRI-EBI GWAS catalogue1. Due to some SNPs being associated with more than one trait, this amounted to 5474 unique SNP by GWAS LD block analyses. In addition, because of GWAS results co-locating in the same LD block, this reduced to a total of 2709 blocks, which cover 22.1% of the entire length of the genome.We investigated 8093 GWAS phenotype-associated results (N = 1343) included 1343 siblings and additional time-point  samples from the individuals of 1DISC. The replication data set (3REPL) comprised 890 additional individuals, unrelated to any of the previous two sets. To identify differentially methylated regions (DMRs) between risk and non-risk GWAS haplotypes, the haplotype-specific DNA methylation (HSM) peak analysis assessed the linear relationship between the allelic count of the GWAS SNP and DNA methylation scores in 500 bp overlapping windows across the LD block. This included adjustments for critical covariates (Methods). Only HSM peaks retaining significance beyond multiple testing correction  in all three data sets and with a consistent direction of effect are reported here.The total peripheral blood DNA methylome samples were split into three separate data sets  analyses that identify via array data significant SNP associations with DNA methylationWe identified a consensus set of 16,060 500 bp windows, which were then merged due to overlapping and adjacent locations into the final set of 7173 distinct HSM peaks (Supplementary Data p < 2.2 × 10−16). The total count for overlap with the major variant classes with their p-value distributions is shown in Supplementary Fig. We subsequently investigated the overlap of these significant windows with known common genetic variants: copy number variants (CNVs), insertions and deletions (Indels) and short tandem repeats (STRs). 36.8% of the HSM peaks overlap these non-SNP variants or combinations of them. As would be expected there is strong proportional enrichment for non-epigenetic dosage effects driven by these CNVs (green), indels (blue), STRs (orange) and regions overlapping multiple variant types (purple) compared to their fractions both across the genome and within the GWAS LD block regions . However, they are predominately not within CpG-dense ‘CpG island’ regions (see later). There is also an increased number of SNPs within HSM peaks (6.60 SNPs/kb) compared to the GWAS SNP LD block regions . Due to hypermutability of methylated cytosines, CpG-SNPs are a significant proportion of all SNPs and we calculated that 32.7% of common SNPs (MAF ≥ 1%) within the GWAS LD Block Regions are CpG-SNPs. This gives a background density of 0.26% CpG-SNP sequence in these regions, however, within HSM peaks they are found at more than twice this density  than both the background genome (1.84%) and the GWAS LD block regions (2.15%) . 92.6% of ‘Other’ HSM peaks contain a CpG-SNP with an average of 2.26 CpG-SNPs within these CpG-SNP containing peaks, thus indicating, as expected, that clusters of CpG-SNPs are strong drivers of HSM signal outside other known variants . These are therefore expected to contain numerous SNPs, including methylation signal influencing CpG-SNPs4 , weak enhancer (Fisher’s exact p = 6.01 × 10−4) and CTCF loci (Fisher’s exact p = 2.58 × 10−8) as well as the large genomic regions of transcribed and repressed regions (Fisher’s exact both p < 2.2 × 10−16). Generalised MeDIP-seq coverage differences were compared via the proportion of zero coverage across the differing functional units and indicated no significant influence on our enrichment calculations  overlap enhancer signal from this combined segmentation in at least one of these six tissues, which rises to 1089 HSM peaks (~15.2%) if weak enhancers are also included.p < 0.05) compared to the entire genome. By contrast CpG Islands (CGI) and vertebrate conserved regions show a significant depletion. Whilst we expect depletion within CGI of MeDIP-seq signal across the genome, as these regions are predominately unmethylated, the 32 CGI in which we do find GWAS-related DNA methylation variation are of obvious interest for those traits. This indicates potential genetic variability within these CGI, in LD with the GWAS SNP, which is strongly influencing their DNA methylation state and may have canonical CGI functional consequences. Overall, the HSM regions are, therefore, representative of the functionally enriched GWAS LD block regions they are derived from, including enrichment for Fantom5-derived enhancers, DHSs and CTCF elements , and dynamic regions30 all remain significantly enriched . Thus, there is strong enrichment across both of the base-resolution data sets used to identify regions of high DNA methylation variability, the VMRs, (54 DNA methylomes across 21 cell/tissue types29) and ‘Dynamic’ regions . This implies that genetic variation between samples may in fact be a significant contributor to the identified regions of increased DNA methylation variability within both these studies.Then compared to the average GWAS LD block regions themselves, the HSM regions show significant depletion in 6 of the 16 categories, Fig. , such as32. As above, we found increased DHSs within HSM peaks. To further investigate the enrichment of CpG-SNPs we also identified in the HSM peaks, we explored SNPs that influence allele-specific DHSs. SNPs altering transcription factor (TF) binding were identified in a study by Moyerbrailean et al. by interrogating DHS footprinting data within the binding sites of 1372 TFs across 153 tissues33. About 66% of the ~5.8 million SNPs that reside within TF motifs were predicted to significantly modify binding. However, only 3217 SNPs demonstrated allele-specific differences in DHSs. Interestingly, we calculate that these allele-specific DHS SNPs are very strongly enriched for CpG-SNPs: 54.14% (1742) compared to the genome average of ~31.1% . This further points to the potential functional importance of CpG-SNPs that we and others have suggested previously35. 15 HSM peaks (~0.21%) overlap with these 3217 SNPs identified in this study by Moyerbrailean et al., and despite this small number, are enriched compared to the genome . 11 of these 15 SNPs (73.3%) are in fact CpG-SNPs, clearly fitting with their potential to directly influence allelic methylation and impact functionally.DNase I hypersensitivity sites identify accessible regions of the genome and therefore act as broad functional indicatorsχ2p < 0.05), across a range of different tissues  of DHSs Fig. . In fact32. We see no evidence for HSM peaks to reside within GWAS LD blocks of blood-specific or related diseases above any of the other categories, either by number of HSM peaks per GWAS LD block or size-corrected HSM peaks/kb . Nineteen of these matched significantly with known sequences via the TOMTOM algorithm37 . These younger repeats still possess mobilisation ability39 and are more likely to be significant contributors to the population variation and haplotypic differences through direct and regional positional effects. Additionally, albeit smaller numbers, the even younger hominid-specific and Alu-containing SVA (SINE-VNTR-Alu) transposable element shows a significant enrichment  that influenced binding in ChIP-seq CTCF data. They found 1837 AS-CTCF binding events across the genome and 26 of these overlap HSM peaks, an extreme enrichment compared by chance in comparison to the genome and even within GWAS LD block regions themselves . This result suggests that these HSM peaks may be contributing to haplotypic variation in CTCF binding, potentially mediating population variation in 3D chromatin topography42. The haplotype allele-specific methylation (hap-ASM) results of Do et al. also identified a role for CTCF variability6. Furthermore, CTCF is known to commonly bind constitutively across many tissue types including a highly similar binding spectrum across all three germ layers in development43, which is consistent with the constitutive nature of the HSM peaks. Additional gene set enrichment analyses and multiple disease block identification are detailed in Supplementary Notes We further explored allele-specific functional differences using an allele-specific CTCF (AS-CTCF) data set from Ding et al.44. This is generally performed with arbitrary thresholds for common SNPs located at the interrogated CpG and within 10 bp or up to 50 bp of the probe sequence, although more nuanced approaches are being explored45.Excluding known common SNP effects on Illumina DNA methylation array probes is part of the standard technical quality control for epigenome-wide association study (EWAS) analysisp < 0.05) and 4192 were significant to a genome-wide Bonferroni level  for GWAS risk haplotype-capturing SNP allelic count. When common SNPs to 10 bp or 50 bp are excluded, these reduce to 21,091 and 16,878 at nominal, and 3890 and 2904 CpGs, at Bonferroni significance, respectively . These findings indicate the importance of mQTL and additional detailed genetic interrogation of array results.No allelic information is available from Illumina DNA methylation array results as methylation values are an average between both alleles. To investigate the potential genetic effects that may or may not be captured, we performed a similar analysis for possible allelic effects in GWAS LD blocks with a linear mixed model analysis in 811 Illumina 450k array peripheral blood-derived samples (88.9% overlap with MeDIP samples). The 450k array contain 169,151 probes (34.8%) within the GWAS LD block regions. After exclusion of multi-mapping probes, this reduced to 157,473 CpGs. Of these, 22,296 were nominally significant  peaks by investigating DNA methylation data in conjunction with robust GWAS SNPs and LD information. This analysis was performed in currently the largest available sequencing-based genome-wide DNA methylome data set. The identified loci are enriched for functional regions, such as enhancers, DHSs and allele-specific CTCF, illustrating their potential to play a pathogenic role.11 and robustly disease-associated GWAS LD block regions. Secondly, the MeDIP-seq data enables DNA methylation interrogation across the entire GWAS LD regions, not just predetermined CpGs. Array data does not provide a representative survey of population-level DNA methylome variation, its underlying genetic architecture46, and lacks the density of coverage required for powerful DMR calling47. When DMRs can be identified they are found to be strongly enriched for functional elements30. Thirdly, is the considerable reduction in the number of tests, by reducing these components to DNA methylation 500 bp windows and a single haplotype-tagging GWAS SNP within each LD block. Individual CpG by SNP analyses require a punitive multiple testing correction and our approach reduces this burden.This HSM analysis powerfully benefits from the combination of three main factors. Firstly, it focuses the analysis on functionally enriched48. Furthermore, whilst identifying ‘pure’ epigenetic changes by removing genetic effects has been the focus of some studies6, facilitative mechanisms are influential35. McClay et al. recently reported that the majority (75%) of cis-mQTL were due to collocating CpG-SNPs35, but only within GWAS regions were these functionally enriched. The individual genetic gain or loss of a CpG can impact upon dependent TF binding activity49. It can also led to DNA conformation changes affecting protein–DNA interaction strength, including an order of magnitude increase in adjacent DHS50. We identified a strong enrichment of CpG-SNPs within HSM peak regions as well as within allele-specific DHS sites. Previous work from Jaffe et al.51 has also investigated GWAS loci with respect to DNA methylation from array data and identified a strong enrichment for mQTL within these loci. Furthermore, many of these mQTLs were not cell-type specific and involved critical disrupting CpG-SNPs, in similar fashion to our HSM data set. That analysis also proposed that many GWAS variants and highly correlated proxy SNPs influence nearby DNA methylation and in this way impact upon disease risk. An analysis in lung tissue by Shi et al. equally found that cis-mQTL reside at CpG-SNPs distal to both genes or CpG-dense promoter regions52, and that mQTLs themselves are also enriched for CTCF loci, DHS and chromatin modifications. In our analysis a strong enrichment particularly for allele-specific CTCF was also seen, potentially indicating the ability of these loci to contribute to population variation in 3D structure42.Our LD aware approach differs from many recent excellent studies exploring genetic influences on the DNA methylome due to its direct focus on known GWAS SNPs53. However, it needs to be clearly noted that methylation is the default state for the majority of CpGs throughout the genome. Those signals driven by variation in CpG number between risk and non-risk haplotypes can not be directly interpreted as functional, as is the case for tissue-specific epigenomic data, such as DHSs32. Yet the HSM peaks identified are enriched for functional indicators such as enhancer signal as well as motifs for DNA methylation sensitive TFs. Similarly to the hap-ASM results of Do et al.6 they are also located outside CpG island promoter regions. Some of these peaks may represent population variation in distal regulatory regions, such as low methylation regions (LMRs)14. These LMR loci require a genetic infrastructure of a slightly above baseline CpG level and co-locate with transcription factor binding and enhancer evidence. This may then facilitate time- or tissue-specific epigenetic variability.On average ~2.65 HSM peaks were identified per GWAS LD blocks regions (59.0%≥1) and these will enable further hypotheses to be proposed in these disease susceptibility regions. The strong genetic influences on the DNA methylome leads to many more significant results than are seen for other potentially ‘pure’ epigenetic changes. For example, only 71 Bonferroni significant ageing-specific DMRs were identified in this same data set (including an LD correction for genetic effects)54. The functional potential of these large-scale variants is significant and these regions, as would be expected due to modulation of available CpGs, influence signal and strongly overlap with HSM peaks. Additionally, STRs contribute to HSM peaks and these variants have shown enhancer activity55 as well as the ability to influence expression56. Furthermore, DNA methylation within transposable elements can influence nearby gene expression57. Our data indicated enrichment for functionally implicated LTR repeat elements, HERV-H and LTR12C40. As well we see enrichment for younger primate-specific Alus and hominid-specific SVA repeats, which have known germline insertion mobility and population variability58. Thus, some of the HSM peaks represent their direct or adjacent positional effects on the regional epigenome58.Global diversity in large deletions, duplication and CNVs have had a significant influence on shaping the genome of human populations through selection, mutation and demography59. Therefore, there may be multiple genetic variants impacting to varying degrees, with some acting via this observed DNA methylation variability. Whilst functional overlap with HSM peaks was identified, this may itself be an underestimate, as the data sets for comparison themselves, such as chromatin segmentation, have only been performed in small numbers46. Population epigenomic variation will exist beyond that in current reference epigenomes. Our evidence of genetic effects in both the VMR and ‘Dynamic’ methylome data further imply that genetic polymorphism drives some of these findings assumed to be purely epigenetic. Additionally, strong HSM effects were previously identified in the FTO GWAS locus12. This located a peak within an enhancer region, which influences IRX3 expression60, that highlighted a SNP (rs7202116) subsequently found in a large meta-analysis to influence trait variability61. Thus, the potential of all 7173 HSM peaks is worthy of exploration and make excellent candidates for further functional analyses.GWAS results are capturing haplotypes and a single variant may not be the only causal element. Recent analysis has proposed that the fraction of causal variation tagged by common SNPs is higher than previously calculated47. It is notable the pausity of significant directional disease-associated findings in robustly controlled experiments that have reduced genetic and cell-type heterogeneity issues by the use of isolated cell types in monozygotic twin discordant models62. Therefore, researchers need to be as aware as possible of the potential genetic confounding and/or interaction, both directly or due to regional or neighbouring effects. Our data set adds to the available genetic effects to be integrated in these analyses  by their lead SNP captured haplotype background, even after excluding probes with common SNP co-location. Identified effects require exploration for evidence of obligatory, facilitative, or dosage factors, as previously observed in array analysis64. Do et al. also identified haplotypic effects on a DNA methylation array analysis of Alzheimer’s disease being driven by the nearby genetic susceptibility factor at HLA-DR*6. Furthermore, of the 278,873 probes on the new Illumina EPIC (850k) within these GWAS regions, 1815 directly overlap an identified HSM peak, and this further rises to 6671 probes including those within a 1 kb flanking region. Technological advances, including direct assessment of DNA modifications in long reads to enable more robust genetic and epigenetic haplotypic assessment will obviously improve our knowledge of these interactions and their interplay in disease risk.These extremely strong HSM signals clearly point to the caution and extra exploration required in the interpretation of population or non-inbred MeDIP as well as other DNA methylome analyses. Adjusting for known genetic influences impacts greatly on EWAS array analysis for common disease and phenotypesWe identified functionally enriched DNA methylome variation between risk and non-risk GWAS haplotypes. This robust set of HSM peaks propose potential new mechanisms to combine with tissue-specific data to further understand these diseases. Thus, this integrative analysis is a step in the process of defining population variation in the facilitative and obligatory DNA methylome. Complete allelic integration of both genetic and epigenetic variability will be required to fully understand these human disease-associated regions. Future advances with direct epigenetic detection and longer reads, will help define more precisely this variability. These HSM peaks begin this process by giving strongly demarcated loci across these GWAS regions, for further exploration and integration in human disease susceptibility understanding.For this study, peripheral blood-derived DNA was provided by the adult volunteers from the UK Twin Register (TwinsUK Resource) who are rigorously phenotyped at visits at St Thomas’ Hospital, London. Blood is collected for haematological analysis and DNA extraction at these appointments. Tobacco smoking is queried at this time or via questionnaire within the nearest 5 years. Samples are stored at −80 °C in EDTA tubes before extraction via the Nucleon Genomic DNA Extraction Kit. DNA is subsequently stored in TE Buffer at −20 °C. The majority of samples have full blood count data. Twinning questionnaire determines twin zygosity and is confirmed by genotyping. Ethics were approved by Guy’s & St Thomas’ NHS Foundation Trust Ethics Committee (EC04/015—15-Mar-04) and written informed consent was obtained from all subjects in accordance with this.f2 = 0.15)65 as we estimate these can be identified with 95% power in ~500 samples. Furthermore, we possess the added benefit in this analysis of the two additional data sets for conformation and replication.The initial Discovery analysis set (1DISC) comprised of 895 DNA methylomes of 895 unique individuals. These included only a single sample from each family i.e. non-related individuals and only female samples to exclude sex-specific variation in DNA methylation. The second Follow-Up data set (2FOLL) comprised of 1343 methylomes and included siblings and some longitudinal data set from Set 1. The third Replication data set (3REPL) comprised 890 DNA methylomes of non-related individuals to the first two sets. Our discovery set is well powered to detect moderate sized effects (Cohen’s 66 was used to produce reads per million (RPM). These data were produced as BED files of genomic windows . Additional quality checks were also employed with Principle Components Analysis and correlation matrix, hierarchical clustering, dendogram, heatmap, and density plots. All analysis and co-ordinates cited are for genome build Homo sapiens hg19/GRCh37.The methods and analysis pipeline made use of Methylation Dependent Immunoprecipitation Illumina second-generation sequencing (MeDIP-seq). BGI-Shenzhen  performed DNA sample preparation, followed by Sonication with a Covaris system  that fragmented whole peripheral blood DNA. Then the MeDIP reaction was performed and 5 ug of fragmented genomic DNA was used for sequencing library preparation. Illumina Single-End DNA Sample Prep kit was employed and end repair, < A > base addition and adaptor ligation steps were performed. Adaptor-ligated DNA was incubated with an antibody for 5-methylcytosine (5mC) Cat. No.: CO2010021 mc-magme-048 from Diagenode . The protocol for the MagMeDIP kit (mc-magme-048) was followed: combining 0.5 μl antibody + 0.5 μl water; then add 0.6 μl MagBuffer A, 1.4 μl water, 2 μl MagBuffer C, therefore resulting in a final volume of 5 μl for the IP reaction. Immunocapture was performed with magnetic bead conjugation to capture the enriched DNA fraction. Quantitative PCR validated this resultant MeDIP, which was then purified with Zymo DNA Clean & Concentrator-5 (Zymo Research), and amplified with adaptor-mediated PCR. Gel excision for size selection of fragments (200–500 bp) was performed and these were quality assessed by Agilent BioAnalyzer . The libraries were subjected to highly parallel 50-bp single-end sequencing on the Illumina HiSeq2000 platform. FASTQC (v0.10.0) assessed initial base composition QC successfully. MeDIP-seq data was aligned with BWA , and duplicates were removed. The average high-quality BWA aligned reads was ~16.9 million per sample. Quality control with SAMTools and FastQC and for MeDIP-specific analysis MEDIPS(v1.0)11 were ascertained from the GRCh37 genetic map, downloaded from Center of Statistic Genetics, University of Michigan, Locuszoom 1.367. Recombination rate 10 cM/Mb block boundaries were used. The NHGRI-EBI GWAS catalogue as at December 2014 provided the 8093 GWAS SNPs with p value<1 × 10−7 deposited to that time point. These are in fact 5522 unique individual SNPs due to co-associations for the same SNP. Within the above-identified LD blocks 5474 of these SNPs were positioned and due to SNPs co-locating in the same block these represented a total of 2709 blocks, covering ~22.1% of the genome.Linkage disequilibrium blocks of the SNPs in the NHGRI-EBI GWAS catalogue12. This identifies broad DNA methylation differences between risk and non-risk GWAS haplotypes. With MeDIP there is a direct relationship between the number of methylated cytosines in the DNA fragment and the amount of DNA captured by the antibody16. Therefore, genetic gains or losses of CpGs will influence the enrichment of fragments strongly. Consequently, this analysis results in a distinct signal due to population variation in both facilitated and obligatory genetic effects on the DNA methylome. These regions may have direct or regional impacts on further surrounding CpG methylation states with the potential for significant functional effects in these GWAS regions. DNA methylation was scored within 500 bp windows with 250 bp overlap by normalised RPM for each window. In the discovery set (1DISC) a linear model was fitted including chronological age at blood extraction for DNA sample, blood cell subtypes , smoking status and batch. In the Follow-Up (2FOLL), a linear mixed effect model was used for allelic count to DNA methylation with the fixed effects of 1DISC with additional family and zygosity as random effects. The replication (3REPL) set analysis was the same as for 2FOLL, but included sex and excluded blood and smoking covariate information. The described two linear effects models were compared with null models that excluded allelic count by the ANOVA function by likelihood ratio test for calculation of p-values. The lme4 R package was used to perform the linear mixed effect analysis of the relationship between allelic count of the haplotype-tagging SNP and normalised DNA methylation assayed by MeDIP-seq.DNA methylation within the LD blocks of the GWAS catalogue association SNPs was compared by linear regression with respect to allelic count of the haplotype-tagging SNP, termed HSM analysisp-value significance level of <1.85 × 10−8. The mean p-value was calculated for each window for GWAS LD block regions containing greater than one GWAS SNP. ENCODE poor mappability blacklist regions31 were subsequently removed from any further interpretation . To identify a robust set of HSM peaks we determined those windows that passed the Bonferroni threshold in all three  subsets. The R (3.0.0) environment was used for all analysis, with graphing via the ggplot2 package with results and code available at http://www.epigenome.soton.ac.uk/hsm/hsm.php.To correct for multiple testing, a strict Bonferroni cut-off was calculated by the total number of DNA methylome windows tested in the analysis, 2,708,462. Thus, a 68. Indel data was obtained from the TwinsUK data set with 1000 Genomes imputation of MAF>0.05. Short tandem repeats or Microsatellite data was obtained from the landscape of human STR variation from Willems et al. of 689,512 STRs69. Furthermore, a subset of these STRs with recent evidence of effects on gene expression, 2060 expression STRs (eSTRs), was also investigated for overlap70. Large study numbers reduced substantially the potential influence of rare or private CNVs, indels and STRs on this analysis.Common genetic variants that overlapped locations within the GWAS LD block regions were defined. This included copy number variants (CNVs), insertions and deletions (Indels) and short tandem repeats (STRs). As above the known Blacklist regions were overlapped  and then removed before subsequent enrichment analyses below. Common CNV data was ascertained from the Stringent set of the copy number variation map of the human genome in the Database of genomic variants from Zarrei et al. which includes CNV sized 50 bp to 3 MbThe dbSNP build 142 common SNP data set was downloaded including SNP alleles and surrounding base sequence. This represents 12,449,124 common SNPs found in ≥1% of samples within autosomes. These were then interrogated for those that were CpG-SNPs, i.e. where the allelic variation created or abrogated a CpG dinucleotide. 3,873,489 (~31.1%) of these SNPs were determined to be CpG-SNPs.71 for assessment of enrichment for chromatin state (ChromHMM), histone modifications and TFBSs. We downloaded the additional Combined segmentation data for 6 tissue types  from UCSC. Additional functional enrichments were also downloaded from UCSC data on CpG islands, ENCODE DHS in 125 cell types2, Vertebrate Multiz Alignment and Conservation (100 Species) from 100Vert_El_phastConsElement100way bedfile (~10.1 m regions), and TFBSs from ENCODE v3 (690 data sets from wgEncodeRegTfbsClusteredV331). ‘Dynamic’ DNA methylation regions were taken from Ziller et al.30 and eRNA validated FANTOM5 enhancers regions from Anderson et al.72. BEDTools (v 2.17.0)73 IntersectBed with the f -0.1 parameter tested overlap between 500 bp non-overlapping windows of these elements and within the GWAS LD block regions.We used Epiexplorer for our first examination of the HSM peaks74. The binomial analysis with default setting for basal, and extension parameters  was employed. Curated regulatory domains were also included. Two background sets were used for comparison, firstly the entire genome and secondly the GWAS LD block regions (Supplementary Note Genomic regions enrichment of annotations tool (GREAT v3.0.0) was used for genomic-space aware Gene and Functional pathway enrichments36) with TOMTOM37 (v4.10.2) using FASTA sequence files for the HSM peaks. MEME-Chip analysis compared with a set of 1434 DNA motifs, between 5 and 30 in length (average length 13.6), from the database Vertebrates (in vivo and in silico).Transcription factor binding site motif enrichment was performed with the JASPAR core 2014 vertebrates database in the MEME suit , where the 50 bp sequence aligned to multiple locations in the genome , or failed detection in ≥1 sample and with a bead count <3 in >5% of the samples. This resulted in a data set of 450,077 probes. Further QC involved inspection for outliers using boxplots for mean and median DNA methylation across all CpG sites, β density plots, and heatmaps. The proportion of blood cell subtypes was deconvoluted for CD8+ T cells, CD4+ T cells, B cells, Natural Killer cells, granulocytes and monocytes75. To correct for probe type bias all data was normalised via BMIQ76. Probes that resided within the GWAS LD Block Regions  were assessed for any significant GWAS SNP associated differentially methylated positions. This was performed using a linear mixed effects model fitted on standardised β values per probe ), with genotype as allelic count, age, smoking status, beadchip, position on the beadchip, granulocytes, monocytes, and CD8+ T cells as fixed effects, as well as family and zygosity as random effects. To assess for significance, ANOVA was used to compare this model to a null model without allelic count. Permutation was performed in R by random shuffling of genotype assignment of individuals whilst retaining all other variables constant.A data set of 811 females (two batches: 388 and 423) were analysed by the Infinium Human Methylation450 BeadChip from bisulphite-converted DNA derived from peripheral blood. 88.9% of these samples also overlap the MeDIP samples. The quality control steps comprised the removal of probes that reside on the X or Y chromosomes .The data supporting the results of this article are available in the EMBL-EBI European Genome-phenome Archive (EGA) under Data set Accession number EGAD00010000983 (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


Babesia bovis, is a tick borne apicomplexan parasite responsible for important cattle losses globally. Babesia parasites have a complex life cycle including asexual replication in the mammalian host and sexual reproduction in the tick vector. Novel control strategies aimed at limiting transmission of the parasite are needed, but transmission blocking vaccine candidates remain undefined. Expression of HAP2 has been recognized as critical for the fertilization of parasites in the Babesia-related Plasmodium, and is a leading candidate for a transmission blocking vaccine against malaria. Hereby we identified the B. bovis hap2 gene and demonstrated that it is widely conserved and differentially transcribed during development within the tick midgut, but not by blood stage parasites. The hap2 gene was disrupted by transfecting B. bovis with a plasmid containing the flanking regions of the hap2 gene and the GPF-BSD gene under the control of the ef-1α-B promoter. Comparison of in vitro growth between a hap2-KO B. bovis clonal line and its parental wild type strain showed that HAP2 is not required for the development of B. bovis in erythrocytes. However, xanthurenic acid-in vitro induction experiments of sexual stages of parasites recovered after tick transmission resulted in surface expression of HAP2 exclusively in sexual stage induced parasites. In addition, hap2-KO parasites were not able to develop such sexual stages as defined both by morphology and by expression of the B. bovis sexual marker genes 6-Cys A and B. Together, the data strongly suggests that tick midgut stage differential expression of hap2 is associated with the development of B. bovis sexual forms. Overall these studies are consistent with a role of HAP2 in tick stages of the parasite and suggest that HAP2 is a potential candidate for a transmission blocking vaccine against bovine babesiosis. Babesia bovis, is a tick borne apicomplexan parasite responsible for important cattle losses globally. Babesia parasites have a complex life cycle including asexual replication in the mammalian host and sexual reproduction in the tick vector. Novel control strategies aimed at limiting transmission of the parasite are needed, but transmission blocking vaccine candidates remain undefined. In this study we analyze the conservation and role of the hap2 gene in the erythrocyte stage of the life cycle of the parasite and found that expression of the gene is not required for the development of the parasite in erythrocytic stages, using a hap2 mutated parasite line. In addition, we developed an in vitro system for the induction of sexual forms of B. bovis and found expression of the hap2 gene and surface localization of the protein. However, hap2-KO parasites are unable to develop sexual stages. We concluded that HAP2 is a leading candidate for a transmission blocking vaccine against bovine babesiosis due of the high level of conservation, surface exposure, and specific expression in tick stage and in in vitro induced sexual stages parasites. Babesia bovis, B. bigemina, and B. divergens and is endemic in large parts of Australia, Africa, Asia, Europe, and Latin America . Z-stack image files were imported for processing into the ImageJ-based open source processing package Fiji   revealed an orf of 2,271 bp, coding for a 79.53 kDa protein containing 723 amino acids. B. bovis HAP2 protein contains a single HAP2 domain Similar to Plasmodium falciparum 7G8 HAP2 (XP_001347424).This domain is functionally involved in a highly conserved sperm protein that is essential for gamete fusion. The HAP2 domain is located between amino acids 348 and 394, suggesting a similar conserved function for this gene among these parasites. The HAP2 domain is predicted to be located in the extracellular region of the protein, which is likely exposed on the surface of the parasites.A single copy arasites –35. The B. bovis HAP2 deduced amino acid sequence appears relatively well conserved when compared with its homologues in other species. In silico predictions suggests that the B. bovis HAP2 protein lacks a glycosyl phosphatidylinositol (GPI) anchor. In addition, HAP2 is also predicted to contain a signal peptide between amino acids 1–33, a hydrophobic transmembrane domain located between amino acids 683–705, and a predicted coiled coil domain between amino acids 721–753, located near the C-terminus. The coiled coil domain is also present in an identical location in many viral fusion proteins, consistent with possible role in the membrane fusion reaction. Collectively, all these features are suggestive of the possible trafficking of HAP2 to the external surface of the parasite, and its possible role as a fusogenic protein.Overall, hap2 gene among distinct B. bovis strains. The hap2 gene is highly conserved among the distinct strains (99% to 99.9% aa identity). The analysis was performed using a sequence database including hap2 gene derived from B. bovis Texas strain, Mo7, Argentina L17 virulent and Argentina L17 attenuated. The calculated synonymous and non-synonymous S/N ratios  among the N ratios  with thehap2 transcript expression was investigated by RT-PCR analysis performed on RNA extracted from B. bovis infected erythrocytes and tick midguts. Previous transcriptome and RNA sequence analysis using short-term cultured merozoites from strains differing in origin and virulence phenotypes show that the hap2 gene is transcribed at very low or undetectable levels compared to constitutively expressed rap1 in the blood stages  against assembly contigs revealed a single hit at contig 1592. The area covered by contig 1592 was roughly 1,438,762 to 1,455,743 of chromosome 3, which contained a ~6, 448 bp insertion (GenBank accession number: KX234097). Full sequencing of the genome of the Tf-hap2KO-gfp-bsd-cln confirmed replacement of the hap2 gene by the 5’ hap2 (935 bp), EF promoter (761 bp), luciferase , gfp-bsd , 3’ rap1 , and 3’ hap2 (656 bp) fragments, present in the transfection plasmid. Thus, analysis of the structure of the hap2-KO gene in the clonal line Tf-hap2KO-gfp-bsd-cln indicates that these sequences were inserted by homologous recombination. It was the only foreign DNA insert detected by whole genome sequencing. The rest of the B. bovis Tf-hap2KO-gfp-bsd-cln genomic sequence was essentially identical to the wildtype B. bovis genome sequence [hap2 locus of B. bovis, and suggests that disruption of the hap2 locus did not affect the pattern of growth of the parasite in in vitro cultures.The clonal line B. bovis sexual stages were induced in vitro by decreasing the temperature to 26°C and the addition of xanthurenic acid to the culture media. Microscopic inspection of Giemsa stained cells from induced cultures showed the presence of extra-erythrocytic parasites with long projections and large round parasite stages, indicative of parasite sexual stage development (Tf-hap2KO-gfp-bsd-cln parasites (Tf-hap2KO-gfp-bsd-cln live parasites (in vitro cultures with induction medium xanthurenic acid (XA) at 26°C.elopment . No sucharasites , and fluarasites  developiB. bovis parasites from in vitro cultures and in tick midgut, defined the expression of the 6-Cys A and B genes as markers of sexual stage parasites [A and B and hap2 genes in parasites emerging upon in vitro sexual stage induction. In contrast non-induced parasites failed to produce hap2 and 6-cys A and B transcripts  confirmed expression of the 6-Cys A and HAP2 proteins on the surface of sexual stage induced arasites . OverallB. bovis induced cells  culture conditions. Indeed the detection of expression of the 6-cys A and B genes in these induced forms is fully consistent with the formation of sexual forms normally induced in the midgut of R. microplus ticks feeding in Babesia infected animals [Tf-hap2KO-gfp-bsd-cln parasites to change morphology, and to generate sexual stage specific expression products such as the members of the 6-Cys gene family. In contrast, these mutant parasites Tf-hap2KO-gfp-bsd-cln are fully able to develop and grow in vitro in erythrocytes, supporting the concept that an intact copy of the hap2 gene is required for sexual stage induction but irrelevant for blood stage development. The results can be compared with similar previous findings in malaria parasites, where sexual stage fusion was found dependent on the expression of the hap2 gene [B. bovis induced parasites express HAP2 in their surface. Therefore, it is likely that the B. bovis HAP2 indeed also functions as an ancestral gamete fusogen in this parasite, since highly diverse eukaryotic gametes carrying loss-of-function mutations in HAP2 also fail to fuse [In this study we were able to induce lciparum , 41. It bigemina , we were animals . Interesap2 gene –44. Impo to fuse .B. bovis during its development within R. microplus and the in vitro induction data suggests that surface exposed expression of this protein might be connected to the completion of the B. bovis life cycle during parasite development in the tick midgut. The absence of detectable hap2 transcripts by B. bovis blood stages suggested that hap2 is unnecessary for parasite development during infection of mammalian host. In contrast, the data supports that expression of HAP2 occurs in concurrence with the development of sexual stages upon induction with xanthurenic acid under in vitro culture conditions. Overall, these findings strongly suggest a role of hap2 during tick stages of the parasite, probably including sexual reproduction and supports HAP2 as a leading candidate for a transmission blocking vaccine against bovine babesiosis. Further in vivo studies are necessary to determine if disrupting hap2 interferes with the development of B. bovis within tick midgut and beyond.In summary, HAP2 is differentially expressed by S1 FigLane 1: Bacterial lysate derived from arabinose induced recombinant bacteria; Lane 2: Bacterial lysate derived from non-arabinose induced recombinant bacteria. Size markers (M) in kDa are indicated at the left side.(TIF)Click here for additional data file.S2 FigA. Microarray analysis of the virulent and attenuated parasites derived from B. bovis T2B strain. The Y axis indicates relative transcriptional levels. The X axis represents the name of the parasite strain. B. RNA seq analysis performed on the virulent and attenuated parasites derived from B. bovis L17 strain. The Y axis indicates relative transcriptional levels. The X axis represents the name of the parasite strain.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 FigB. bovis RAP-1 protein as a positive control. Size markers (M) in kDa are indicated at the left side.Monoclonal RAP-1 antibodies were used to detect (TIF)Click here for additional data file.


Variations in human genome  may be associated with hereditary diseases, their complications, comorbidities, and drug responses. Using Web service SNP_TATA_Comparator presented in our previous paper, here we analyzed immediate surroundings of known SNP markers of diseases and identified several candidate SNP markers that can significantly change the affinity of TATA-binding protein for human gene promoters, with circadian consequences. For example, rs572527200 may be related to asthma, where symptoms are circadian (worse at night), and rs367732974 may be associated with heart attacks that are characterized by a circadian preference (early morning). By the same method, we analyzed the 90 bp proximal promoter region of each protein-coding transcript of each human gene of the circadian clock core. This analysis yielded 53 candidate SNP markers, such as rs181985043 , rs192518038 (higher risk of a heart attack in patients with diabetes), and rs374778785 (emphysema and lung cancer in smokers). If they are properly validated according to clinical standards, these candidate SNP markers may turn out to be useful for physicians  and for the general population (to choose a lifestyle preventing possible circadian complications of diseases). CLOCK, ARNTL, ARNTL2, PER1, PER2, CRY1, CRY2, CSNK1E, CSNK1D, RORΑ, RORС, and NR1D1—forms the core of the molecular genetic mechanism of the circadian clock, whose functioning is based on feedback relations among its components  [K2 is an empirical estimate of the equilibrium constant of the primary corecognition between TBP and an appropriate TBP-biding site on DNA [−ln(K2) is the maximal score of Bucher's position-weighted matrix: PWMBucher]  in italics and labeled with the phrase “[This work].”Our heuristic interpretation of the keyword search results is shown inThese results are presented in Genes HBB and HBD encode β- and δ-chains of hemoglobin, respectively. In the binding sites for TBP in their promoters, these two genes contain the greatest number (seven) of known SNP markers  of thalassemia and resistance to malaria DEL); this is a marker of hyperalphalipoproteinemia that lowers the risk of atherosclerosis [ CETP expression  regarding the risk of atherosclerosis [earchers  identifiearchers . A keywoclerosis  which declerosis . For thiGene CYP2B6 encodes cytochrome P450 2B6 and contains a known SNP marker (rs34223104) of improved bioactivation of cyclophosphamide [sphamide  with a csphamide . Accordisphamide , this SNThe INS gene encodes insulin, and its promoter contains a known SNP marker (rs5505) of neonatal diabetes and hyperinsulinemia [ulinemia . A keywoulinemia , (ii) blulinemia , and (iiulinemia . Near thulinemia . ConsequGene ESR2 codes for estrogen receptor 2 (β) and, in its promoter, contains a known SNP marker (rs35036378) for prophylactic treatment (with tamoxifen) of an ESR2-deficient primary tumor pT1 [umor pT1  to preveumor pT1 ; this trumor pT1 . A keywoumor pT1 . Near th PER1 gene as an example, which encodes a protein called period 1—a subunit of the heterodimeric PER-CRY complex—which is the main negative component of the circadian clock core: this complex inhibits the activity of transcription factor CLOCK/ARNTL [These results are shown in CK/ARNTL –98. PER1 gene inhibits the proliferation of tumor cells [PER1−/−), researchers observed impairment of spatial (3D) learning capacity and enhanced manifestations of ethanol hepatotoxicity [ PER1 gene are candidate SNP markers, as we propose in As predicted by SNP_TATA_Comparator , only fior cells , 99, 100or cells , 101. Dotoxicity , 103. Th NR1D1. This result shows that preliminary computational (bioinformatic) analysis of unannotated SNPs from the 1000 Genomes Project can indeed accelerate and cheapen the search for biomedical SNP markers because of selection (for this expensive and labor-intensive procedure) of only those candidate markers whose molecular mechanisms of pathological manifestation are easily understandable within the framework of existing clinical observations, genetic knowledge, scientific theories, hypotheses, and empirical data from animal and cellular models of human diseases.Using SNP_TATA_Comparator , we analp < 0.0005) according to Fisher's exact test for 2 × 2 design. It is noteworthy (κB or RNA polymerase II (significantly more often) weaken rather than enhance the binding of these proteins to the mutated DNA in comparison with the reference genome. Taken together, these findings suggest that the reduced proportion of candidate SNP markers weakening the affinity TBP-promoter may be a specific characteristic of the 12 genes of the human circadian clock core. This phenomenon may reflect the pressure of natural selection for robustness of their functioning under the conditions of incessant genetic variability of the promoter region being analyzed.It is also worth noting that only 13 of the 52 candidate SNP markers identified here decrease affinity of ТВР for promoters of the genes of the circadian clock core, whereas the other 39 SNPs enhance it. In teworthy  that theWhy is the robustness of the circadian clock core so important for humans? As shown in In this work, we used SNP_TATA_Comparator  to analyAs an unexpected clever generalization of this mainstream approach, the authors of Web server GenomeRunner  proposedα < 10−6) to minimally acceptable (α < 0.05), the proposed markers should be properly validated using clinical standards before practical use. The results of this validation are dependent on climate, environmental conditions, and lifestyles and on the ethnic, social, age, and gender composition of cohorts [KD values of TBP-DNA affinity in vitro [KD values vary from 1 to 335 nM, whereas the extent of their variation among alleles of a given SNP may be 1 nM, which is less than 0.3% of the KD range. This level of allelic variations is too small for empirical measurement without an a priori known, fairly narrow range of KD values to be measured. Thus, the predicted KD values (Tables Because statistical significance of our predicted candidate SNP markers Tables  and 2 va cohorts . Accordiin vitro . As showFinally, pathological manifestation of SNP markers of Mendelian diseases, as a rule, is limited to the consequences of changes in the expression of only those genes that contain these SNPs and can be useful only to physicians of the narrow specialties relevant to the diseases in question. Nonetheless, candidate SNP markers of chronopathologies are associated with consequences of desynchronoses either among the nervous, immune, digestive, respiratory, and other systems of the human body or between the human body and its environment Tables  and 2. THere, we predicted candidate SNP markers of chronopathologies Tables  and 2; t


Current variant discovery methods often start with the mapping of short reads to a reference genome; yet, their performance deteriorates in genomic regions where the reads are highly divergent from the reference sequence. This is particularly problematic for the human leukocyte antigen (HLA) region on chromosome 6p21.3. This region is associated with over 100 diseases, but variant calling is hindered by the extreme divergence across different haplotypes.We simulated reads from chromosome 6 exonic regions over a wide range of sequence divergence and coverage depth. We systematically assessed combinations between five mappers and five callers for their performance on simulated data and exome-seq data from NA12878, a well-studied individual in which multiple public call sets have been generated. Among those combinations, the number of known SNPs differed by about 5 % in the non-HLA regions of chromosome 6 but over 20 % in the HLA region. Notably, GSNAP mapping combined with GATK UnifiedGenotyper calling identified about 20 % more known SNPs than most existing methods without a noticeable loss of specificity, with 100 % sensitivity in three highly polymorphic HLA genes examined. Much larger differences were observed among these combinations in INDEL calling from both non-HLA and HLA regions. We obtained similar results with our internal exome-seq data from a cohort of chronic lymphocytic leukemia patients.We have established a workflow enabling variant detection, with high sensitivity and specificity, over the full spectrum of divergence seen in the human genome. Comparing to public call sets from NA12878 has highlighted the overall superiority of GATK UnifiedGenotyper, followed by GATK HaplotypeCaller and SAMtools, in SNP calling, and of GATK HaplotypeCaller and Platypus in INDEL calling, particularly in regions of high sequence divergence such as the HLA region. GSNAP and Novoalign are the ideal mappers in combination with the above callers. We expect that the proposed workflow should be applicable to variant discovery in other highly divergent regions.The online version of this article (doi:10.1186/s12864-016-3045-z) contains supplementary material, which is available to authorized users. Genetic variations in protein-coding genes play significant roles in many human diseases , 2 and aMultiple bioinformatics methods have been developed to identify variants from whole genome or exome sequencing data. The most dominant ones are based on the mapping of reads to a reference genome , 8, whicDivergence level varies markedly across the human genome –17, whichttp://www.novocraft.com/) and Stampy [A few mappers have been tailored to aligning reads to more divergent regions, such as GSNAP , NextGend Stampy . They usd Stampy , 25. Stad Stampy  but was d Stampy . GSNAP id Stampy . The chod Stampy , 26. HowSeveral popular software packages are available for both single- and multi-sample variant calling, such as SAMtools , FreeBayNumerous studies have compared the performance of variant callers on WES, without focusing on the highly divergent regions –34. ThesWe seek to develop a workflow for more accurate variant discovery from WES data, especially in highly divergent regions. By simulating reads from chromosome 6 exonic regions, we systematically evaluated five popular callers together with five mappers over a wide range of divergence level and coverage depth. Taking advantage of the existing call sets generated by whole genome and exome sequencing in NA12878, we verified the findings on two WES data in this well-studied CEU (Utah residents with Northern and Western European ancestry) sample. Our analysis revealed key factors impacting variant discovery accuracy and sensitivity. We identified the best mapper-caller combinations for variant detection in both HLA and non-HLA regions, and further demonstrated their excellence on WES data from a cohort of chronic lymphocytic leukemia (CLL) patients. Our strategies are particularly effective for WES and should be applicable to whole genome sequencing data as well.The variation level in the human genome is typically ~0.1 %  but can Five mappers were selected to align simulated reads against the hg19 reference sequence  were generated by the Genome in a Bottle Consortium from 11 whole genome and three exome sequencing datasets . The ana library . They fohttps://basespace.illumina.com/home/index). We downloaded two replicates, each with ~100x coverage on average. Considering that the simulated reads have a length of 100 bases, pairs with one or both reads shorter than 100 bases were filtered out, which excluded 5.1 and 5.6 % of the reads, respectively, from the two replicates. From the remainder the first 100 bases were extracted for mapping  paired-end exome data in NA12878  and three of the callers and applied them to WES data from 22 CLL patients. The selected three callers had relatively high sensitivity in SNP calling (GATK UnifiedGenotyper), INDEL calling (Platypus), or both  in the HLA region of NA12878 (see the http://figshare.com/articles/Low_complexity_regions_in_hs37d5/969685) [http://humanparalogy.gs.washington.edu/build37/build37.htm). In addition, if three or more SNPs clustered within a window of 20 bp, they were flagged as ‘SnpCluster’.Mapping, post-alignment processing, variant calling and the classification of variants into known versus novel followed the procedure used for NA12878 WES data. To assess the performance of individual mapper-caller combinations in complex genomic regions, we annotated variants regarding their sequence contexts. Specifically, variants were labeled with ‘LCR’ if they were located in low complexity regions (We simulated 100-bp paired reads at eight divergence levels (0–15 %) and seven coverage depths (5–100x) from exonic regions of chromosome 6. Five mappers were selected to align the simulated reads to the reference sequence hg19  of the cases, the full (100x) coverage datasets showed the highest sensitivity in both SNP and INDEL calling.Profiling sensitivity as a function of coverage should reveal the optimal coverage depth for individual callers; beyond which there would be much less gains in sensitivity. At 1 % or lower divergence, increasing coverage from 5x to 10x led to the biggest increase in sensitivity in both SNP  and high (5–10 %) divergence. In SNP calling, GATK UnifiedGenotyper had the highest sensitivity across all the divergence levels, followed by SAMtools and GATK HaplotypeCaller  for each of the ideal callers identified above, i.e., GATK UnifiedGenotyper, GATK HaplotypeCaller and SAMtools in SNP calling and GATK HaplotypeCaller and Platypus in INDEL calling.At 0.05–1 % divergence, BWA worked the best in most of the SNP and INDEL calling. The other four mappers also performed well in some cases, particularly at high coverage  and novel variants were compared to the public call set, where methods that showed higher levels of overlap with known variants in the public call set can be reasonably assumed to have higher sensitivities . The assIn the non-HLA regions, 91.3–96.1 % of the SNPs called from Stampy alignments and 96.0–99.4 % from other alignments matched known SNPs. The vast majority of the known SNPs overlapped the public call set  in NA12878, the SNP calling sensitivity was strikingly variable, ranging from 38.2 % to 100.0 % (GSNAP + GATK UnifiedGenotyper) Table . NotablyWe next examined the known SNPs that were identified by GSNAP + GATK UnifiedGenotyper but missed in the public call set Table , and thoThe SNPs unique to GSNAP + GATK UnifiedGenotyper were enriched in SNP cluster. About three-fourths 94/128) were in SNP cluster, compared to only 2 % of the total SNPs in the non-HLA regions and 38 % in the HLA region. Misalignments around INDELs (within 10-bp) can lead to high false positives 4/128 wer. HoweverOn the other hand, in GATK UnifiedGenotyper calling, GSNAP missed a total of 89 known SNPs present in the public call set Table , includide novo assembly-based calling methods. Collectively, these results strongly support the findings from the simulated data  or insufficient supporting reads (0–2 out of 9–29 mapped reads). Another 11 SNPs were at sites with high coverage (87–226x) in both replicates, but only 2–10 % of the reads supported the alternative calls. Of the remaining 16 SNPs (57–2–28–11 = 16), five had 31–75x and 11 had 142–178x coverage; nevertheless, only 0–4 reads supported the SNPs, raising the possibility that they are platform-specific calls or simply false positives in the public call set. Twelve of the 57 missed SNPs were in HLA-DQB1; 11 of them had two or fewer supporting reads. We argue that a significant proportion of the known SNPs unique to the public call set is likely identifiable only through whole genomic sequencing, longer reads and/or by  reads orWith GATK UnifiedGenotyper, GSNAP identified 39 novel SNPs that were not in the public call set Table . Fifteende novo assembly, a feature that should contribute to INDEL detection. Indeed, the two callers had higher sensitivity in both HLA and non-HLA regions  in combination with Platypus and GATK HaplotypeCaller. Overall, the number of public call set- 26–31) and method-specific INDELs (22–44) was not markedly different in the non-HLA regions. In the HLA region, however, there were two to ten times more novel INDELs unique to the public call set than unique to each of the methods . To understand how this parameter impacts variant calling efficiency, we tested three different divergence levels, 1, 5 and 10 %  for these three mappers. In the non-HLA regions, SNP calling from NextGenMap alignments at the divergence settings of 5 % and 10 % was 11–14 % higher in sensitivity compared to that from NextGenMap using 1 % divergence Fig. ; in the Finally, in order to ascertain the broad application of the selected variant calling methods (see below), we generated 100-bp WES data from a cohort of 22 CLL patient samples. Reads mapping (exclude NextGenMap due to its low sensitivity) and variant calling followed the procedure used in NA12878. The average per-base coverage varied between 67x and 113x, with 82.4–94.9 % of the bases having at least 20x coverage. As demonstrated in both simulated  among the 12 methods  is that it is over four times slower than BWA (data not shown). A possible solution would be to first map reads using BWA, extract pairs with unmapped read(s), and then re-map those using GSNAP. We tested its feasibility on the NA12878 WES data. Remarkably, in GATK UnifiedGenotyper and HaplotypeCaller calling, this two-step mapping approach recovered >99.5 % of the known SNPs previously identified from chromosome 6 using GSNAP alone. On the other hand, Stampy is oversensitive, which results in more false positive calls.We next focused on one of the five CLL samples ID 612703) with an average per-base coverage exceeding 100x , using four mappers (exclude NextGenMap) and three callers (exclude SAMtools and FreeBayes). For known SNPs, the 12 methods showed 95–99 % overlap between each other in the non-HLA regions . The five mappers are known to vary in mapping highly divergent reads –14, whicStrikingly, GSNAP + GATK UnifiedGenotyper achieved 100 % sensitivity in HLA-DRB1, HLA-A and HLA-C, three highly polymorphic genes in NA12878. In HLA-DRB1, for example, this method identified all the 70 known SNPs annotated in the public call set , 38, pluBesides the HLA region, there are many other regions in the human genome that are also highly divergent . For exaTraditionally, genotyping in the HLA region often relies on microarray hybridization  or sequeWe aimed to develop a strategy enabling more accurate variant discovery in highly divergent regions. Focusing on the HLA region that shows extreme divergence across different haplotypes, we revealed marked differences among the methods in SNP and INDEL calling from NA12878 WES data. We captured a similar trend on WES data from a cohort of CLL patients. Specifically, GSNAP and Novoalign achieve high sensitivity in mapping divergent reads without losing the specificity. Their limitation in speed could be overcome through a two-step mapping approach, in which reads are first mapped by BWA and unmapped ones are then re-mapped by GSNAP or Novoalign. Together with these two mappers, GATK UnifiedGenotyper demonstrates its excellence in SNP calling, followed by GATK HaplotypeCaller and SAMtools; in INDEL calling, GATK HaplotypeCaller and Platypus outperform the others and their joint calling clearly enhances the outcome. Given that highly polymorphic regions are distributed over many chromosomes and are often associated with human disease, our study brings additional options into the current variant calling practice.


Brassica Infinium SNP array. We identified seven loci for PH, four for BIH, and five for BN. Subsequently, by determining linkage disequilibrium (LD) decay associated with 38 significant SNPs, we gained 31, 15, and 17 candidate genes for these traits, respectively. We also showed that PH is significantly correlated with BIH, while no other correlation was revealed. Notably, a GA signaling gene (BnRGA) and a flowering gene (BnFT) located on chromosome A02 were identified as the most likely candidate genes associated with PH regulation. Furthermore, a meristem initiation gene (BnLOF2) and a NAC domain transcriptional factor (BnCUC3) that may be associated with BN were identified on the chromosome A07. This study reveals novel insight into the genetic control of plant architecture and may facilitate marker-based breeding for rapeseed.Plant architecture is crucial for rapeseed yield and is determined by plant height (PH), branch initiation height (BIH), branch number (BN) and leaf and inflorescence morphology. In this study, we measured three major factors  in a panel of 333 rapeseed accessions across 4 years. A genome-wide association study (GWAS) was performed via Q + K model and the panel was genotyped using the 60 k  Brassica napus L., AACC, 2n = 38) is a globally significant oilseed crop used in the production of vegetable oil and oil meals, and ~95% of its total cultivation is in Asia, Europe, and North America. According to a new report from the United States Department of Agriculture (USDA), rapeseed production in 2015/16 is estimated at 66.9 million tons, down 4.7% from the previous year. Harvested area is estimated at 32.9 million hectares, down 5.5% from the previous year (http://www.pecad.fas.usda.gov/). To meet growing demands for both edible products and biofuel, it is critical to increase rapeseed productivity through efficient breeding.Rapeseed , number of seeds per silique (NSS), and seed weight per plant  , Brassinosteroids (BRs), and Strigolactones (SLs) participate in this process. Genes related to phytohormone biosynthesis and signal transduction contain mutations that affect internode elongation, which in turn regulates PH  and the model plant Arabidopsis are members of the Brassicaceae. Unlike Arabidopsis thaliana, B. napus is polyphyletic, and is related to B. rapa (AA) and B. oleracea (CC) , B. oleracea (CC), and B. napus (AACC)  for yield traits in rapeseed. However, most of these QTLs are localized to a large interval (10–20 cM) on chromosomes  in rapeseed using the 60 K Brassica Infinium SNP array. Accessions were grown in plots of 2 rows, with 10–15 plants in each row. Between eight and ten plants were selected from the center of each row to measure the three traits at maturity: PH, BIH, and BN.The association population used in this study consisted of 333 diverse rapeseed accessions  collected as part of a recently published study . Correlation analyses of PH, BIH, and BN for the association panel were performed in R3.3.0  of <5% or that showed a maximal missing frequency of >2% were excluded  was calculated using squared allele frequency correlations (r2) between all pairs of SNPs using TASSEL 5.2.28  setting from 1 to 10 with five independent runs  matrix using the GCTA tool, and a subset of 3,571 SNPs (MAF ≥ 0.2) evenly distributed across the whole genome (every 150 k) were selected to perform population structure (Q) and relative kinship analysis (K). STRUCTURE v2.3.4 was used to calculate a Q matrix, the putative number of genetic groups  = 3.39].Association analysis was performed using TASSEL 5.2.28 . The genes with GO terms for auxin, GA, IAA, SL, CK, and flowering time were highlighted, and the closest one of these genes was considered as the most likely candidate.To identify candidate genes, local LD decay was calculated using TASSEL5.2.28 to capture flanking regions of up to 450 kb on either side of significant SNPs, with a cut-off value of Extensive phenotypic variations for PH, BIH, and BN were observed in the 333 accessions that were grown over the course of 4 years 2012–2015). PH varied from 86.2 to 206.0 cm, with 1.6 to 2.4-fold variations across the 4 years Figure , Table 12–2015. PIn addition, we assessed these three traits for any significant correlation. PH and BIH were significantly correlated across the 4 years with a Pearson's correlation coefficient of 0.3 to 0.8 Table . This suBrassica 60 K SNP array. We blasted the SNP probe sequences to the B. napus genome database (http://www.Genoscope.cns.fr/brassicanapus/) and a total of 34,292 SNP markers (65.7%) were selected to genotype the panel of 333 accessions  showed strong LD, with distances ranging from 1,300 to 1,710 kb; while others exhibited modest LD, with distances ranging from 100 to 825 kb when r2 = 0.2  for all 333 accessions, in order to obtain the highest Δ k-value  across 4 years. For the groups-populations, we created a Q matrix  from SNP Bn-A02-p9505646 (Table R2 ~11.54%) from SNP Bn-A07-p21413042  to PH regulation, measuring 21.5 cm more than those of the AA allele  and measured 11.4 cm more than those of the AA allele  and was significantly greater than GG alleles on chromosome A3 . Notably, 31, 15, and 17 candidate genes for PH, BIH, and BN were located in each region, respectively (Table We searched for candidate genes from the genomic regions with significant SNPs according to GO annotations from the Arabidopsis database (ly Table .10 (p) = 5.47], two candidate genes associated with GA signaling and flowering time repression were located at 77.2 and 29.4 kb, respectively (Table 10 (p) = 4.90]  = 4.70] on chromosome A02 (Table 10 (p) = 4.88] (Table 10 (p) = 5.04, and with the largest contribution to BIH variation, with R2 = 9.87%. Notably, a MATE efflux family protein (BnaA09g14730D) involved in determining the rate of organ initiation was identified close to the peak SNP  were also identified in the candidate region around the peak SNP Bn-A02-p9505646 [−log8] Table . In addiARR11, TCP12, ARF8, LOF2, and CUC3, were identified up- and downstream of the peak SNP Bn-A07-p16996070 [−log10 (p) = 6.08] (Table 10 (p) = 3.93] contained two candidate genes (YUCC8 and IAA11). On chromosome C09, a WOX5 gene (BnaC09g49730D) was located 0.45 M from the peak SNP Bn-scaff_28053_1-p60542 [−log10 (p) = 6.18]  = 3.39, Q + K] or in the top 50 SNPs in PH control as the candidate SNPs  using two populations in rapeseed, and it identified 10 QTLs distributed on chromosomes A02, A07, A08, C04, C06, and C07  between the two SNPs was 0.27, indicating that they are separate QTLs. In addition, QTLs related to PH on chromosome A03 and A07 were found close to the QTLs related to BN on the same chromosomes, at a distance of 500 kb and 1 M, respectively. The QTL at 17.7 M on chromosome A07 related to BIH regulation was close to the QTL related to BN, at ~1.3 M. These distances are also beyond the average LD decay and the LD (r2) between the two peak SNPs for each group were relatively low, suggesting that they are separate QTLs.In our study, the QTLs at 6.3 M on chromosome A02 and at 18.0 M on chromosome A07 influenced PH and BIH, and a significant correlation was found between these two traits during our correlation assay. In the A02 QTL regions, PH and BIH traits shared three SNPs and the two traits were also co-localized, which suggests pleiotropic regulation of a single gene or the existence of closely linked genes. In contrast, in the A07 QTLs, the SNP associated with the PH QTL was separated from the related SNP associated with the BIH QTL by about 700 kb, while the LD  are also located in the same region of chromosome A02  and the rice FT homolog HEADINGDATE3a (Hd3a) gene affected flowering and PH  and BnFT (BnaA02g12130D) are main candidate genes on chromosome A02 that are involved in PH regulation.In Arabidopsis, rice, and other plant species, BR, IAA, GA, and SL biosynthesis and signaling pathways are known to regulate PH, while mutations in most of these genes cause dwarf phenotypes  encodes a MYB-domain transcription factor that functions in both lateral organ separation and axillary meristem formation, in part through interaction with CUC2, CUC3, and STM  and BnCUC3 (BnaA07g28210D) were identified as the most likely candidate genes and may play roles in both organ separation and axillary meristem formation.Several transcription factors (TF) and hormones  also affect shoot outgrowth in plant architecture (Yang and Jiao, Although several candidate genes were identified following the GWAS, the function of these genes remains ambiguous. In the future, it is necessary to isolate target genes by cloning these putative genes or by developing mapping populations for the QTLs, and to illustrate their functional role in relative trait regulation by transformation experiments.MZ, WH, and HW conceived and designed the research; HL performed GWAS; XW, MT, HY, XL, JL, and XS characterized the agronomic traits; MZ, CP, HL, and JX analyzed the data; and MZ wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.