We analysed 8929 regions harboring genes for potential long non-coding RNA that were retrieved from data from the ENCODE project. Of these regions, 60% were annotated as intergenic, which implies that they do not overlap with protein-coding genes. Focusing on the intergenic regions, and using stringent analysis of the allele-specific expression data, we detected robust cis-regulatory SNPs in 258 out of 489 informative intergenic regions included in the analysis. The cis-regulatory SNPs that were significantly associated with allele-specific expression of long non-coding RNAs were enriched to enhancer regions marked for active or bivalent, poised chromatin by histone modifications. Out of the lncRNA regions regulated by cis-acting regulatory SNPs, 20% (n = 52) were co-regulated with the closest protein coding gene. We compared the identified cis-regulatory SNPs with those in the catalog of SNPs identified by genome-wide association studies of human diseases and traits. This comparison identified 32 SNPs in loci from genome-wide association studies that displayed a strong association signal with allele-specific expression of non-coding RNAs in monocytes, with p-values ranging from 6.7×10−7 to 9.5×10−89. The identified cis-regulatory SNPs are associated with diseases of the immune system, like multiple sclerosis and rheumatoid arthritis.We applied genome-wide allele-specific expression analysis of monocytes from 188 samples. Monocytes were purified from white blood cells of healthy blood donors to detect  ASE analysis is more powerful for detecting cis-regulated gene expression than the total expression levels of genes determined by regular eQTL analysis cis-regulatory SNPs (cis-rSNPs) stems from the fact that the differential expression of the two alleles of a SNP are measured in the same environment and have been exposed to the same environmental conditions in the cells from which the RNA was extracted trans-acting and environmental factors that may affect gene expression are controlled for in ASE analysis. To adjust for possible methodological differences in the efficiency of the genotyping assay to measure the two alleles of a SNP, such as the sequence context of a SNP or copy number variations, the relative amounts of the alleles measured in DNA, is used as a reference for quantification of the allelic expression. Genome-wide ASE analysis by SNP genotyping has been applied to map cis-regulation of protein coding genes associated with human diseases and traits in lymphoblastoid cell lines Determination of allele-specific gene expression (ASE) levels by quantitative genotyping of heterozygous SNPs on the RNA level cis, and especially with genes that overlap with the antisense strand of a lncRNA. By investigating the effect of regulatory SNPs on the expression of lncRNAs using traditional microarray-based expressed quantiative trait locus (eQTL) analysis of peripheral blood cells a recent study identified 112 cis-regulated lncRNAs Long non-coding RNAs (lncRNAs) are involved in gene regulation and other cellular processes. LncRNAs such as XIST and TSIX, which are involved in X chromosome inactivation cis-acting SNPs that regulate the expression of lncRNAs and to highlight previously unknown roles for lncRNAs in human complex diseases. We reasoned that ASE-analysis that is highly sensitive for detecting cis-rSNPs would be a powerful tool for studying cis-regulation of lncRNAs that are expressed at lower levels than protein-coding genes. To our knowledge ASE-analysis has not been previously used for studies of lncRNAs.The aim of our study was to apply ASE-analysis to identify cis-acting regulatory SNPs out of which 3910 are located in intergenic regions and 571 of these are located in enhancer regions marked as active or poised by histone modifications in monocytes. We also compared the indentified SNPs that regulate expression of lncRNAs with risk SNPs for complex diseases and traits previously identified in genome-wide association studies (GWAS). In this way we were able to obtain new functional clues for these disease associated GWAS loci. Additionally, we analysed co-expression between lncRNA genes and nearby protein coding genes, and found that 20% of the intergenic lncRNAs with a cis-rSNP were co-regulated with a protein-coding gene.We analysed RNA extracted from human monocytes purified from white blood cells of 188 healthy blood donors using a genome-wide panel of SNPs. Monocytes were selected for analysis because they are a relevant cell type for multiple diseases. Our analysis included 8929 genomic regions that harbor genes for potential long non-coding RNA that were retrieved from the GENCODE database Circulating monocytes were collected from healthy adult blood donors of European origin (n = 188) recruited from the United Kingdom National Blood Service Centre in Cambridge, UK as part of the Cardiogenics Transcriptomic Study DNA was extracted from peripheral blood leukocytes using the guanidine hydrochloride - chloroform method. CD14+ magnetic microbeads  were used to isolate monocytes from whole blood. RNA was extracted from cell pellets of freshly isolated monocytes by homogenization with Trizol-reagent , chloroform-ethanol extraction and purification using Qiagen RNAeasy columns and reagents, followed by on-column DNase treatment. cDNA was synthesized using reagents from the Illumina TotalPrep RNA Amplification Kit, except that the poly-dT primers were substituted by random decamers .All participants in the study provided their written consent after a personal meeting with a study nurse. Written consent from each participant was recorded by name, date and signature on a standardized consent form. Both the consent form and the study has been reviewed and approved by the Cambridgeshire 1 Research Ethics Committee. The Research Ethics Committee is independent from the research institute and part of the UK Health Research Authority.A total of 8929 genomic regions containing lncRNAs were retrieved from the GENCODE version 7 database cis-rSNPs and the ASE-levels of lncRNAS in a genomic region. Genotypes were called in gDNA using Genome Studio version 2009.2 (Illumina) with a call rate of 99% as the threshold for genotype calls for SNPs and 98% for samples. SNPs were further filtered on deviations from Hardy-Weinberg equilibrium with a p-value cutoff of 10−6 (Chi-squared test). One sample with higher than 40% call rate in cDNA were removed due to possible DNA contamination. The raw two-colour fluorescence signals from the Infinium assay were normalized to remove dependency of the allele fractions on the signal intensities of the fluorophores using a quadratic function with medians of bins for the two fluorescence colors. From the regression, the intensities were predicted based on the log10 values of the raw signal, which were used to adjust the allele fraction, see cDNA/(Allele1cDNA+Allele2cDNA)] – [Allele1gDNA/(Allele1gDNA+Allele2gDNA)]. Here, gDNA, where the two SNP alleles are present in 1∶1 ratio serves as a quantification standard for the relative expression levels of the two alleles in RNA.RNA (cDNA) and genomic DNA (gDNA) from the monocyte samples were genotyped using the Infinium assay and Human 1.2 M Duo custom BeadChips  as described previously cis-rSNPs  was 42797. To retain only protein coding genes, all genes annotated as RNA genes in the GeneCards database Protein coding regions extracted from the Refseq database were analysed to detect SNPs with association signals that overlap with those from lncRNAs. The total number of transcripts in Refseq  data was retrieved from the Blueprint project cis-rSNP were correlated with gene expression of the closest protein coding gene. For this purpose we divided the samples into three groups based on three genotype combinations of the most significantly associated cis-rSNP . For each expressed gene region we calculated the average signal intensity for both alleles and used this value as a measure of gene expression levels. Next, we performed linear regression analysis with the three genotype groups as x-values and the average total expression levels as y-values in order to obtain a p-value for the co-expression.We investigated to what extent expression of lncRNA genes with a cis-regulation of 8929 genomic regions harboring lncRNA from the ENCODE project using allele-specific expression (ASE) analysis of 188 RNA samples from human primary monocytes. To determine ASE, we genotyped RNA (cDNA) and genomic DNA from the monocytes using a genome-wide panel of 1.2 million SNP markers We explored The definitions of exon and intron boundaries were taken from Refseq. As protein coding genes are on average expressed at higher levels than lncRNAs, the ASE signal for lncRNA that overlap with protein coding genes is hard to distinguish from that of a protein-coding gene. Because of this, we focus on the intergenic lncRNA regions where the ASE data is most reliable.−6, based on the number of tests performed and adjusted for SNPs in high LD (>0.9), was used to correct the association signals for multiple testing. In addition a threshold of 0.05 for the slope of the regression line was applied to exclude SNPs with low effects on ASE. This value corresponds to approximately a 20% expression difference between the two alleles and was set as a reasonable threshold for biological relevance. To avoid false positives with inflated p-values, we only retained associations that were based on at least four data points for each allele combination for the calculation of the p-value cis-rSNP (−6) in the 188 analysed monocyte samples, and for the intergenic lncRNA regions 3910 out of 79628 tested SNPs (4.9%) were significantly associated cis-rSNPs.For the ASE analysis, the genotypes of 194530 SNPs located within 250 kb upstream and downstream of the lncRNA regions were tested for their association with the ASE levels of the corresponding expressed lncRNAs, using linear regression, see Manhattan plot in cis-rSNP . The asscis-rSNP out of 108881 tested SNPs (7.9%) in 471 out of 10345 regions (4.6%) that were associated with ASE of a Refseq gene.To allow comparison between ASE of lncRNA genes and protein coding Refseq genes, we applied the same procedure for ASE analysis to the Refseq gene regions as for the lncRNAs. This analysis identified 8604 cis-rSNPs located in enhancer regions marked as active or poised by histone modifications in monocytes. To determine if the cis-rSNPs for lncRNAs are enriched in enhancer regions, we calculated the fold enrichment of significantly associated cis-rSNPs defined by ASE analysis of intergenic lncRNA regions to histone marks for active enhancers (H3K27ac) and bivalent, poised enhancers (H3K4me1) in monocytes. The fold enrichment was determined by comparing the fraction of significantly associated SNPs in enhancer regions with the fraction of non-significant  SNPs in enhancer regions. Of the significantly associated SNPs, 2.1% were located in H3K27ac peaks compared to 1.2% for non-significantly associated SNPs, which corresponds to a 1.72-fold enrichment . For H3K4me1 we observed a 1.54-fold enrichment  of significantly associated SNPs, with 12.5% of the significant SNPs located in the enhancer peaks, while this number was 8.1% for non-significant SNPs. For protein coding genes the percentages and fold enrichment are similar to those for lncRNA for H3K27ac with 2.2% and 1.5% for significant and non-significant SNPs, respectively, and 1.43 fold enrichment  and less pronounced for H3K4me1 with 9.8% and 8.5% for significant and non-significant SNPs, respectively, and 1.15 fold enrichment . This result shows that enhancers have a regulatory cis-effect on expression of not only protein coding genes, but also lncRNAs.We identified 571 cis-rSNP. In our data 52 (20%) of the identified intergenic lncRNA regions to which a cis-acting regulatory SNP was associated were also correlated with the expression level of a protein-coding gene at a false discovery rate (FDR) of 10%, suggesting co-regulation of the genes, see cis-rSNPs are significantly associated both with ASE of a lncRNA and ASE of the closest protein coding gene, which implies that the expression of the lncRNA and the protein coding genes are regulated by the same cis-rSNP. For 19 cis-rSNPs the ASE signal is unique to a lncRNA, which could be an indication of involvement of the lncRNA in the regulation of the expression of an adjacent protein coding gene as has been suggested in previous studies cis-rSNPs had a distance >250 kb to the nearest protein coding gene and the ASE signal could therefore not be evaluated.Next we investigated whether the lncRNA genes and nearby protein coding genes are co-expressed and co-regulated by a cis-rSNP associated with ASE of intergenic lncRNAs in monocytes with SNPs identified in GWAS of human diseases and traits, listed in the National Human Genome Research Institute (NHGRI) GWAS Catalog th of January 2013) includes 9617 entries, 641 traits and 7797 unique SNPs. We identified SNPs associated with 32 loci high-lighted by GWAS that are also cis-rSNP for lncRNAs. The cis-rSNP are the same as the GWAS associated SNP for 25 of the loci, while seven cis-SNPs are linkage disequilibrium (LD) proxies to the lead GWAS SNPs, with five SNPs having LD = 1.0 and two having LD> = 0.88. cis-rSNP associated with ASE of lncRNA regions as well as the phenotypes to which these SNPs were associated in GWAS. cis-rSNP that were significantly associated with ASE of a lncRNA are located in an intron of a protein coding gene, illustrating a direct or indirect regulatory function of an intronic SNP. One locus is reported by GWAS to be associated with a non-coding gene (genomic region: 2:19067188–19369296), which we confirm here by a cis-rSNP for the same lncRNA gene. Furthermore, when searching for overlaps between cis-rSNP associated with lncRNA and GWAS SNPs that are located in active enhancer regions in monocytes, we detected three GWAS SNPs that are located in a ChIP-seq peak for H3K4me1, and seven additional SNPs that are in the 2.5 kb enhancer regions flanking the histone mark. For H3K27ac no overlap between the cis-rSNP and the actual ChIP-seq peak was found, but five SNPs were located in the 2.5 kb flanking region. The number of overlapping GWAS SNPs – cis-rSNPs that map to an enhancer region are enriched at the same level as cis-rSNPs in general. For these GWAS SNPs the effect on the enhancer could be the functional mechanism.Next we superimposed the cis-rSNP or a proxy cis-rSNP in high LD . An advantage of RNA-seq compared to ASE analysis using SNP genotyping is that alternatively spliced transcripts and strand specific gene expression can be detected, provided that the sequencing coverage is sufficient. However, ASE analysis using RNA-seq suffers from the drawback of being highly affected by the reference bias The combination of a large number of samples from primary monocytes and the sensitive genotyping method for detecting cis-rSNP. We observed correlated expression of 20% of the lncRNAs with their neighbouring protein-coding genes, and co-regulation of a lncRNA and a neighbouring protein coding gene by the same cis-rSNP for approximately half of these lncRNA genes. This finding is consistent with a recent study of peripheral blood cells, where 75% of 112 lncRNAs that were mapped using traditional eQTL analysis were found to be regulated independently of nearby protein-coding genes cis-regulation, ASE analysis allows the dissection of cis-effects by lncRNAs that influence the transcription of nearby genes. If the expression level of a lncRNA and a nearby protein coding gene are correlated, and they both show significant ASE association with the same cis-rSNP, it is likely that the cis-rSNP directly regulates the protein coding gene. If the expression levels between a protein-coding and lncRNA gene are correlated, but there is no shared association with a cis-SNP, the lncRNA may be a regulatory factor that affects the expression of the adjacent protein coding gene. In our ASE data set we found that these two putative mechanisms occur at similar frequencies. However, using the ASE approach, trans-acting regulatory mechanisms for co-expressed genes would remain undetected. A recent study used traditional eQTL analysis of monocytes found several trans-regulated modules of co-expressed protein coding genes. However, lncRNA expression was not addressed in this study In this study we focused our ASE analysis of monocytes on 5346 lncRNAs that were annotated by the ENCODE project as intergenic without overlap with protein coding genes. Thus we ensured that our analysis targeted truly lncRNA genes and was not confounded by overlapping protein-coding genes. Using stringent criteria for calling ASE, we identified 258 lncRNAs that were regulated by at least one A drawback of our study is that this specific dataset was not replicated by an independent method. However, in a previous study we have shown that a similar method for genome-wide ASE-analysis using NS-12 BeadChips (Illumina) was highly reproducible between replicate samples, with correlation coefficients of 0.9969 for gDNA and 0.9956 for cDNA. ASE-levels determined using NS-12 BeadChips and those determined by quantitative Sanger sequencing were also strongly correlated (0.86) in nine representative genes cis-rSNPs that regulate the expression of lncRNAs have been identified in GWAS as risk variants for human diseases or traits. The diseases or phenotypic traits associated with these cis-regulatory SNPs according to the lncRNA ASE analysis, include diseases of the immune system; multiple sclerosis, Graves’ disease, eosinophilic esophagitis, celiac disease, rheumatoid arthritis, immune response to small pox vaccine, soluble E-selectin levels, soluble ICAM-1, and ankylosing spondylitis, for which monocytes are a relevant cell type . Figure adapted from Almlöf et al(PDF)Click here for additional data file.Figure S2Signal intensity threshold. The figure shows the distribution of signal intensities for the two alleles. The x-channel represents the A-allele and the y-channel represents the B-allele. In the xraw panel the fraction of SNPs with the BB genotype having intensity levels above the cutoff level of 1000 is very low and similarly for the yraw panel.(PDF)Click here for additional data file.Figure S3Regression lines. Regression lines for the 32 risk SNPs from GWAS that are significantly associated with a lncRNA region.(PDF)Click here for additional data file.Figure S4lncRNA in the GWAS catalog. Illustration of all lncRNA regions that have a significant association to a SNP that is also significantly associated in the GWAS catalog. The tracks are from top to bottom in each panel: Horizontal red bars represent transcript windows (with genomic coordinates) used for determination of ASE levels; grey lines show p-values for the association of GWAS SNPs with ASE levels in the transcript window; a grey line overlayed with a red dotted line indicates that this is the SNP that overlaps with the reported SNP in the GWAS catalog; red vertical lines are median ASE-levels for each SNP; annotated transcripts are shown in black below the tracks.(PDF)Click here for additional data file.Table S1Significantly associated lncRNA regions.(DOCX)Click here for additional data file.Table S2SNPs associated with allele-specific expression of Refseq protein coding genes with published trait- or disease-associations from genome-wide association studies.(DOCX)Click here for additional data file.Methods S1Quadratic normalization. Detailed explanation of the quadratic normalization performed on the intensity levels obtained from the genotyping.(DOCX)Click here for additional data file.


Mycobacterium tuberculosis complex (MTBC) is an important factor in pathogenesis that may affect virulence, transmissibility, host response and emergence of drug resistance. Several systems have been proposed to classify MTBC strains into distinct lineages and families. Here, we investigate single-nucleotide polymorphisms (SNPs) as robust (stable) markers of genetic variation for phylogenetic analysis. We identify ~92k SNP across a global collection of 1,601 genomes. The SNP-based phylogeny is consistent with the gold-standard regions of difference (RD) classification system. Of the ~7k strain-specific SNPs identified, 62 markers are proposed to discriminate known circulating strains. This SNP-based barcode is the first to cover all main lineages, and classifies a greater number of sublineages than current alternatives. It may be used to classify clinical isolates to evaluate tools to control the disease, including therapeutics and vaccines whose effectiveness may vary by strain type.Strain-specific genomic diversity in the  Genetic variation in Mycobacterium tuberculosis complex (MTBC) bacteria is responsible for differences in factors such as virulence and transmissibility. Here, the authors analyse the genomes of 1,601 MTBC isolates from diverse geographic locations and identify 62 SNPs that may be used to resolve lineages and sublineages of these strains. Mycobacterium tuberculosis complex (MTBC) results in a variety of outcomes including latent infection and/or progression to pulmonary or extra-pulmonary manifestations of disease. Such diversity has been historically attributed to host and environmental factors, and the MTBC was previously considered genetically monomorphic in natureMycobacterium africanum I, 6 West Africa or M. africanum II), distinct from a M. bovis clade. Lineages 1, 5 and 6 are considered ‘ancient’, and 2 to 4 ‘modern’. A novel phylogenetic lineage of MTBC that appears to be intermediate between the ancient and modern has been described in Ethiopia and the Horn of Africa5Infection with bacteria of the M. africanum strains16171819Lineage is of importance to tuberculosis control as it has been shown that strain type may play a role in disease outcome, variation in vaccine efficacy1112n=1,601) from a diverse geographic spread. We identify a single panel of 62 SNPs that may be used to resolve all seven lineages and a total of 55 sublineages.In this study, we have sought to provide an improved system by examining whole-genome data from a large number of strains , as well as M. bovis  and sublineages were subsequently identified based on the spoligotype and regions of difference (RD) composition of the clades in the SNP-based phylogeny (see Methods). The phylogeny revealed the presence of new clades for which RDs do not discriminate. In particular, there were gaps in the Euro-American lineage for which molecular fingerprint classifications are less accurateA genomic analysis was performed on whole-genome sequences of 1,601 MTBC isolates from eleven independent sequencing studies from different areas of the world, with representation of all seven major lineages  and 6 (π=0.0093), and the least within lineage 2 (π=0.0039) and 3 (π=0.0040) strains . The distribution of functional categories of genes containing the 91,648 and 6,915 SNP sets did not differ , there was some evidence of difference in the distribution of functional categories between lineages, namely a greater proportion of lipid metabolism non-synonymous polymorphism in lineage 2  robust SNPs in essential genes with mutations that lead to synonymous amino acid changes, therefore less likely to be under selective pressure . RedundaM. bovis, when it was found to predict 100% their reported strain types  that can be used to construct high resolution and reproducible phylogenies, which can be incorporated in diagnostic assays and assess genotype–phenotype associations. Future work should focus on other types of lineage-specific polymorphisms , which are less common than SNPs, but may have major functional consequences. The proposed system has the flexibility to incorporate novel strain types should they be reported. Incorporating anti-tubercular drug-resistance loci will further enhance the usefulness of the barcode as an important tool for tuberculosis control and elimination activities worldwide.http://www.ebi.ac.uk/ena/). The dataset consists of nine independent whole-genome sequencing studies, available under ENA accessions ERP000192, ERP000276, ERP000520, ERP001731, ERP000111, SRP002589, ERP002611 and ERP000436 (ref. SRA065095 (ref. ERP001885 (ref. ERP001567 (ref. NC_000962.3) using BWASAMtools/BCFtoolsGATKn=1,601).The raw sequence data of 1,804 MTBC isolates available in the public domain were downloaded from the European Nucleotide Archive  using SpolPred softwarede novo assembled using VelvetAll samples were N=100, x=12,345) combined with maximum likelihood search was chosen to construct the phylogenetic tree. The resulting tree was rooted on M. canettii (Genbank accession number: NC_019950.1) and nodes were annotated. Subsequently, the ancestral sequence at all internal nodes was computed using DnaPars from the Phylip packageRAxML trees were constructed for the 1,601 samples from alignments using other proposed lineage-informative SNPs . Similarly, the ancestral reconstructed sequence for the clade-defining node was compared with its closest ancestral node, and the SNP differences derived. A high-confidence set of clade-specific SNPs was obtained by selecting those at the clade-defining internal node and having FST values of >0.99 in between group comparisons. To ensure that clade-specific SNPs were also suitable markers for their use in strain typing assays, we applied filtering criteria: (1) only synonymous SNPs were retained as they are generally under lower selection pressure; (2) SNPs at non-coding regions were discarded since insertions or deletions (indels) are usually more frequent. The density of small indels and large deletions is five and 17 times smaller, respectively, in coding regions of the genome compared to non-codingwww.tbdreamdb.com) and recent studieswww.iedb.org). The gene functional categories were extracted from Tuberculist (tuberculist.epfl.ch).For each lineage and sublineage, the dataset was split into two populations: one containing all samples descending the clade-defining node and the other with remaining samples. The F.C. and T.G.C. conceived the project. J.A.G.-A., J.R.G., J.P., M.V., I.P. and A.P. contributed to the construction of data. F.C. analysed the data. R.M., N.M. and T.G.C. jointly supervised the research. F.C., R.M. and T.G.C. wrote the paper.How to cite this article: Coll, F. et al. A robust SNP barcode for typing Mycobacterium tuberculosis complex strains. Nat. Commun. 5:4812 doi: 10.1038/ncomms5812 (2014).Supplementary Figures 1-6, Supplementary Tables 1-7 and Supplementary ReferencesMycobacterium tuberculosis complex lineages and sub-lineages


For this reason, the FTO gene is a physiological and functional candidate gene for carcass and meat quality traits in beef cattle. The objectives of this study were to identify SNPs in the exonic regions of FTO gene and to evaluate the association of these SNPs with carcass traits in Hanwoo (Korean cattle). In this study, we newly identified two exonic SNPs in Hanwoo population. The g.125550A > T SNP was located in exon 3 and the g.175675C > T SNP was located in exon 6. Genotyping of the two SNP markers was carried out using PCR-RFLP analysis in Hanwoo steers to evaluate their association with carcass traits. As a result, g.125550A > T SNP genotype was significantly associated with effects on marbling score. Animals with the AA and TT homozygous genotypes had a significantly higher marbling score (p < 0.001) than those with AT heterozygous genotype, and this was significant after Bonferroni correction of the significance threshold (p = 0.003). Dominance effect was also observed for the marbling score (P < 0.05) with higher marbling score of homozygous animals. However, no significant associations with meat quality traits were observed for the g.175675C > T SNP. Our results suggest that the exonic SNP g.125550A > T in the FTO gene may be used as a DNA marker for the selection of Hanwoo with higher marbling.The  Marbling is the most economically important trait in beef cattle industry of Korea. Generally, marbling means the amount and distribution of intramuscular fat in a cross section of musculus longissimus muscle . In partfat mass and obesity associated (FTO) gene have been shown to have a relatively large effect on body mass index (BMI) and obesity-related traits in various human populations, suggesting that FTO associated with the development of fat tissue and adiposity [Recently, the diposity –9. Some diposity . In livediposity –15. In aHoengseong Hanwoo population in Korea, were used to genotype and collect carcass traits. To confirm genetic inheritance of the identified SNPs, 51 Hanwoo animals, which were used for the evaluation of performance ability for proven sires in the national progeny testing programs, were genotyped. The carcass data analyzed in the current study included marbling score (MS), meat color (MC), fat color (FC), meat texture (MT), meat maturity (MA), backfat thickness (BF), Longissimus dorsi muscle area (LMA), and carcass weight (CW). The means carcass traits values were 5.98 ± 1.73 for MS, 4.91 ± 0.37 for MC, 3.00 ± 0.10 for FC, 1.09 ± 0.28 for MT, 2.03 ± 0.19 for MA, 11.99 ± 4.01 mm for BF, 90.05 ± 9.57 cm2 for LMA and 427.11 ± 46.96 kg for CW.A total of 300 steers, which were registered in a national database of cattle and guided with standardized breeding programs provided by the Meat samples were collected from 13th thoracic rib to the first lumbar vertebrae of the steers within 24 hr of slaughter and evaluated according to the Animal Product Grading System of Korea. Genomic DNA was extracted from tail root-hair by using a NaCl precipitation protocol  with a sBos taurus UMD 3.1 primary assembly) using a web-based software Primer 3.0 program (http://frodo.wi.mit.edu/primer3). To determine SNP identification, pooled DNA samples from the sixty unrelated animals were amplified by PCR using the each primer pair. The PCR amplification was performed in a DNA thermal cycler . The PCR reaction was performed in a 20 μL reaction mixture containing 0.1 μM of each primer, 1.5 mM MgCl2, 250 μM of each dNTP and 1 unit of Taq DNA polymerase, 10 X reaction buffer and 50 ng of pooled DNA as template. The PCR conditions were at 94°C for 5 min, followed by 35 cycles of 94°C for 30 s, annealing at 55°C to 57°C for each primer for 20 s and 72°C for 1 min, with a final extension at 72°C for 5 min. The PCR products were verified by 2% agarose gel electrophoresis and purified with Wizard Prep PCR purification kit . The purified PCR amplicons were directly sequenced in both directions using BigDye™ Terminator V3.1 Cycle Sequencing Kit in an ABI PRISM 3730 Genetic Analyzer  according to the manufacturer's instructions. GENESCAN 3.7 ANALYSIS software  was used to assemble the sequences and to identify polymorphisms.The bovine FTO gene is mapped to chromosome 18, which includes nine exons coding 505 amino acids  and the fragments were visualized using a UV transilluminator . To define each genotype according to band patterns, the PCR products of different RFLP type corresponding to each genotype were sequenced and analyzed for nucleotide changes.Two SNPs, g.125550A > T and g.175675C > T, newly identified in the exons 3 and 6 of the Hanwoo FTO gene respectively, were genotyped using PCR-RFLP method. PCR primers used for PCR-RFLP analysis were 5'-TTCCTCAAGCTCAACAGCTACC-3' and 5'-ACGGTTCCTCTTTCAGGTATGG-3' for the g.125550A > T SNP, and 5'-AGAGTCAGTTCTAGGTGCTGTGGT-3' and 5'-CCACAGTTCTCAGAAGCCCTTA-3' for the g.175675C > T SNP. PCR amplifications were carried out as for the SNP detection protocol. For the RFLP analysis, amplified fragments were digested with restriction enzyme ijk = μ + Gi + Sj + Pk + βAl + eijkl, Where Yijk =the value of carcass traits; μ = the overall mean for each trait; G = fixed effect of single SNP marker genotype; S = random effect of sire, P = fixed effect of parity; A = fixed effect of age as a covariate; and eijkl = random residual effect. The Bonferroni correction for multiple testing was performed to remove any false positives. The additive and dominance genetic effects were also estimated using REG procedure of SAS according to Falconer and Mackay [Allele and genotype frequencies of SNPs and Hardy-Weinberg equilibrium were estimated and tested using PROC ALLELE . Associations between SNP genotypes and phenotypes of carcass traits were analyzed with the following liner mixed model using the MIXED procedure of SAS . The data were analyzed according to the following model: Yd Mackay . This rep < 0.001) than the animals with AT heterozygous genotype and this was significant after Bonferroni correction of the significance threshold (p = 0.003). Dominance effect was also observed for the marbling score (P < 0.05) with higher marbling score of homozygous animals. However, no significant association was detected between the g.175675C > T SNP genotype and carcass traits measured in this study.In this study, we sequenced all exon regions of the bovine FTO gene and newly identified two exonic SNPs in Hanwoo steers Figure . An A toThe development of functional genomics has revealed a large number of genes related with fat accumulation and lipid metabolism. Several QTLs for carcass traits and a large number of potential candidate genes based on a known relationship with physiological or biochemical processes and carcass traits have been reported in beef cattle –21. Howelongissmus muscle area traits in five Chinese native cattle breeds. In this study, however, the effect of genotypes of the two novel SNPs was not statistically significant for backfat thickness and longissmus muscle area. This might be supported by the fact that Hanwoo breed exhibits very low or negative genetic correlation between marbling and backfat thickness [Many regulatory factor genes are involved in the formation of marbling , and inchickness , 30. Alshickness  have rephickness , 33. As hickness .In conclusion, we identified two novel exonic SNPs of the FTO gene in Hanwoo population and the g.125550A > T SNP genotype showed significant effect on marbling score. These findings suggest that the FTO gene-specific SNP identified in this study may be useful molecular marker for selection to increase the levels of marbling in Hanwoo. This study also will contribute to a better understanding of the molecular mechanisms of marbling in beef cattle. Further studies are will be needed to confirm the associated effect on other population and breeds.


Genetic influence on DNA methylation is potentially an important mechanism affecting individual differences in humans. We use next-generation sequencing to assay blood DNA methylation at approximately 4.5 million loci, each comprising 2.9 CpGs on average, in 697 normal subjects. Methylation measures at each locus are tested for association with approximately 4.5 million single nucleotide polymorphisms (SNPs) to exhaustively screen for methylation quantitative trait loci (meQTLs).Using stringent false discovery rate control, 15 % of methylation sites show genetic influence. Most meQTLs are local, where the associated SNP and methylation site are in close genomic proximity. Distant meQTLs and those spanning different chromosomes are less common. Most local meQTLs encompass common SNPs that alter CpG sites (CpG-SNPs). Local meQTLs encompassing CpG-SNPs are enriched in regions of inactive chromatin in blood cells. In contrast, local meQTLs lacking CpG-SNPs are enriched in regions of active chromatin and transcription factor binding sites. Of 393 local meQTLs that overlap disease-associated regions from genome-wide studies, a high percentage encompass common CpG-SNPs. These meQTLs overlap active enhancers, differentiating them from CpG-SNP meQTLs in inactive chromatin.Genetic influence on the human blood methylome is common, involves several heterogeneous processes and is predominantly dependent on local sequence context at the meQTL site. Most meQTLs involve CpG-SNPs, while sequence-dependent effects on chromatin binding are also important in regions of active chromatin. An abundance of local meQTLs resulting from methylation of CpG-SNPs in inactive chromatin suggests that many meQTLs lack functional consequence. Integrating meQTL and Roadmap Epigenomics data could assist fine-mapping efforts.The online version of this article (doi:10.1186/s13059-015-0842-7) contains supplementary material, which is available to authorized users. Methylation of DNA cytosine residues is an important mechanism in the control of gene expression and the determination of cell fate in development –3. DNA mSeveral early reports focusing on candidate loci found instances of DNA methylation levels correlated with sequence variants . HoweverIn this context, next-generation sequencing (NGS) methods offer a significant advance over array-based methylation detection . Whole gWe used MBD-seq to measure methylation at over 4.5 million unique loci in DNA from peripheral blood from normal subjects. We then tested each locus-specific DNA methylation measure for association with a high density SNP genotype panel, augmented with imputed genotypes from 1000 Genomes , to exhar2 > 0.5 as thresholds. Our MBD-seq data consisted of 31.6 million methylation-enriched reads per subject (standard deviation (SD) = 13.4 million) after alignment and quality control (QC). We used these reads to estimate fragment coverage at each of the ~27 million autosomal CpG sites in the human reference genome (hg19), where higher coverage indicates higher levels of methylation [Our study population comprised 697 subjects from Sweden  [p values. The π1 statistic was 95 %, 98.7 %, and 99.3 % for local, distant same-chromosome, and cross-chromosome tests, respectively, indicating very high replication rates. Fig. Our methylation data were obtained from control subjects who formed part of a larger study of the psychiatric disorder schizophrenia . While mibution)  for thesSNPs that create or abolish CpGs (CpG-SNPs) have been suggested as genetic drivers of individual differences in methylation . We hypop values generated via up to four million permutations. We initially focused on local findings, first examining enrichment among all local meQTLs and then split our data to characterize enrichment patterns for meQTLs with CpG-SNPs and those without. In the second bioinformatics analysis phase, we looked at specific annotations in more detail, reducing the number of permutations to one-tenth of those carried out in the first phase and including FDR control due to the larger number of tests. Initial phase 1 analysis found some enrichment of genomic duplications, copy number variants, and pseudogenes among meQTL findings. This was modest for local effects but substantially greater for cross-chromosome compared with local meQTLs  were enriched in several categories of genomic annotations. We performed our bioinformatics analyses in two phases. In the first phase, we looked at aggregate annotation categories , with p = 2.6 × 10−75). It should be noted that in this instance we consider a very specific overlap, i.e., the GWAS catalog SNP is within the boundary of the methylation site. This finding was also observed in our replication sample (odds ratio (OR) = 3.4, p = 4.8 × 10−79). Of the 393 methylation sites under local genetic control overlapping the GWAS catalog in our main analysis  CpG-SNPs were present at most (75 %), but not all, local meQTLs. This implies that different mechanisms are operating to influence methylation at meQTLs without CpG-SNPs. To compare patterns of genomic features for local meQTLs with CpG-SNPs and those without, we reran our phase 1 analysis stratifying by common CpG-SNP (MAF > 0.05) presence or absence. Thus, in this analysis, local meQTLs with CpG-SNPs were compared with a background set of all methylation sites with CpG-SNPs (out of the 4.5 million assayed in our study). Similarly, local meQTLs without CpG-SNPs were compared with all methylation sites lacking CpG-SNPs.p = 7.24 × 10−18) and DNase clusters . Notably, these findings became more pronounced when we excluded sites with any CpG-SNP, regardless of MAF . These findings were also observed in the replication sample, with similar ORs and significance levels , p300  and TR4 . As shown in Table S5 in Additional file We found that local meQTLs with common CpG-SNPs showed similar patterns to our observations for all local meQTLs, except that they were much less enriched for G-quadruplexes . Chromatin state classification was according to an 18 state model from the Roadmap Epigenomics Consortium [p = 1.9 × 10−4) than all local meQTLs with CpG-SNPs. These results suggest that meQTLs with CpG-SNPs in regions of active chromatin associated with disease could be high priority targets for further characterization in disease studies.Of the 393 local meQTLs that overlapped the NHGRI GWAS catalog, the vast majority (>93 %) encompassed a common CpG-SNP (MAF > 0.05), while practically all (>98 %) encompassed a CpG-SNP with MAF > 0.01. However, our chromatin state analysis showed that meQTLs with CpG-SNPs were much more likely to occur in quiescent or heterochromatin regions. Given that most local meQTLs with CpG-SNPs are in regions unlikely to affect phenotype, we speculated that those overlapping the NHGRI GWAS catalog were in some way distinct. We therefore compared the local meQTLs with CpG-SNPs overlapping the NHGRI GWAS catalog with all local meQTLs with CpG-SNPs. Due to the smaller numbers of meQTLs being tested, this analysis had lower power relative to those above and none of the findings passed our stringent threshold of FDR < 0.01. However, five tests did pass a more typical FDR < 0.05 threshold . This association was also apparent in the replication sample , suggesting that some cross-chromosome findings may have functional relevance  were significantly more likely to overlap with exons  [. [Several different processes appear to be working to cause meQTLs. For those meQTLs overlapping CpG-SNPs, absence of the CpG sequence in some individuals will clearly prevent methylation. However, in others the presence of a CpG means only that it  (>75 %) . This su75 %) [.  found thTCF7L2 locus on chromosome 10  and the associated SNP was rs34872471 (p = 1.4 × 10−126). The CpG-SNP within the boundary of this site, rs7901695 , was associated at a similar level of significance (p = 2.1 × 10−114). It is considered to be a confirmed susceptibility variant in diabetes [One expectation based on these observations could be that meQTLs with CpG-SNPs typically have no effect on phenotype. However, this is somewhat incongruent with our finding that local meQTLs were enriched in loci from the NHGRI GWAS catalog and almost all sites overlapping the catalog encompassed CpG-SNPs. For example, the most significant site among the overlap spanned 157 bp at the diabetes , 42, witdiabetes . We obsediabetes  or accesdiabetes . Recent diabetes . Based ocis-acting factors that, in turn, affect methylation levels. For example, binding of Sp1 serves to prevent methylation at some CpGs in promoter regions [Where local meQTLs lack CpG-SNPs, alternative mechanistic explanations must be considered. It is known that non-CpG variants influence the binding of  regions . We obseOur use of high-density genome-wide SNP genotyping and imputation based on 1000 Genomes data enabled us to capture much of the common SNP variants in this sample. Our use of NGS to assay DNA methylation enabled us to assess most methylated CpGs in the non-repetitive portion of the genome. We confirmed that genetic influence on methylation is a pervasive phenomenon throughout the genome and, for the most part, highly localized in its effect. Our findings suggest that several mechanisms can generate meQTLs. These include CpG-SNPs and variants that interfere with chromatin binding for several classes of proteins. The very large number of local meQTL effects attributable to CpG-SNPs, coupled with the fact that they typically occur in non-functional regions of the genome, suggests that most have little phenotypic consequence. However, the observed enrichment of meQTLs in disease or trait-associated regions from the NHGRI GWAS catalog indicated that a small portion of CpG-SNPs have arisen in regions of the genome where they may exert significant influence on phenotype. Integration of meQTLs with other data, such as RoadMap Epigenomics, could aid in functional interpretation of SNPs identified in disease GWAS. We therefore provide all our meQTL findings, including positional information and association statistics, in Table S8 in Additional file All procedures were approved by the institutional review board at the Karolinska Institutet, Stockholm, Sweden (IRB/KI 04/-499/4) and further locally approved by the Virginia Commonwealth University institutional review board (IRB#. HM12499). Subjects provided written informed consent . All experimental methods comply with the Helsinki Declaration.A Large-Scale Schizophrenia Association Study in Sweden”. This overarching project [Subjects were controls collected as part of a larger project entitled “ project , 46, 47 π > 0.20); and suspected sample error or contamination indicated by high heterozygosity or indeterminate genetic sex. SNPs were excluded for marked departure from Hardy–Weinberg equilibrium (p < 1 × 10−6), low minor allele frequencies (<1 %), and non-random genotyping failure, inferred from the flanking haplotype background using the PLINK ‘mishap’ test (p < 1 × 10−10). Plate-based associations of p < 1 × 10−6 were taken as evidence of non-random genotyping failure, based on a comparison of allele frequency of each plate to all others and were removed on a plate-by-plate basis. To enhance coverage, we imputed SNPs from 1000 Genomes data (phase I version 3) using Minimac, after phasing genotypes with MACH 1.0 [r2 > 0.5, a total of 4,761,800 imputed and genotyped SNPs were available for meQTL association testing.Genotyping was carried out as described previously , 30. BriMACH 1.0 . After s10(p) with p being the probability of an error) per color call was 21.4 (SD = 1.1). Reads were aligned to the human reference genome (hg19/GRCh37) using BioScope 1.2 (Life Technologies). The percentage of mapped reads was 69.2 % (SD = 6.2). We deleted reads with multiple poor quality alignments and high copy number duplicate reads were collapsed to single reads. This led to the elimination of 32.1 % of the mapped reads. After all QC, we obtained, on average, 31.6 million reads per subject (SD = 13.4 million).Our methods and analysis pipeline make use of MBD protein-based enrichment and sequencing (MBD-seq) as described previously , 27, 31.Locus-specific methylation measures were obtained by summing the number of fragments expected to cover each CpG. Note that methylation of any CpG in a DNA fragment, not just the sequenced 50 bp, could lead to its capture by MBD protein. Hence we define locus-specific methylation measures as the expected number of DNA fragments covering each CpG . SpecifiThe average number of fragments covering a particular CpG depends not only on the methylation status of that site but also on the number of methylated CpGs in the region . To makeThirty-six percent (10.5 million) of all ~27 million autosomal CpGs in the reference genome (hg19/GRCh37) were eliminated because of predicted alignment problems, as observed in an in silico analysis . The majWe previously reported in-depth quality metrics for this methylation dataset . Brieflyhttp://www.r-project.org/). Methylation values were first corrected using the inverse quantile normal transformation of the ranked values. This robust approach greatly reduces the effect of outliers, while retaining more power than rank-based procedures [To test for association between genotype and methylation measurements (each SNP versus each methylation site) we used Matrix eQTL , a compuocedures . To elimocedures . As is tocedures  to identocedures –56. Subjp value thresholds were used for these three groups of test to ensure at most 1 % of discoveries were false in each group. Within each group FDR was calculated using the standard Benjamini–Hochberg procedure, which is known to be more conservative than other common FDR procedures .For the SNP data, we used four MDS dimensions to control for ancestry, as used in the original report of GWAS in this sample . Due to p values in each group were ordered in increasing order: P(1), P(2), …, Pm).The Benjamini–Hochberg FDR control procedure works as follows. First, the http://www.repeatmasker.org/), (10) conserved TF binding sequences between humans and rodents as provided in TransFac version 7.0, (11) clustered ENCODE TF binding sites mapped via chromatin immunoprecipitation sequencing (ChIP-seq) [http://www.geneimprint.com), and (18) NHGRI GWAS catalog hits (http://www.genome.gov/gwastudies) [Annotation tracks for the first analysis phase were obtained via UCSC genome browser download for human genome build hg19 and dbSNP version 135. We selected the following tracks for testing: (1) RefSeq genes and used gene positional information to calculate (2) exon, (3) intron, (4) promoter region 2 kb upstream and (5) promoter region 8 kb from transcription start site annotations. We also selected (6) conservation based on similarity between 29 eutherian mammals, (7) CpG islands , (8) CpG shores (2 kb regions flanking a CpG island [hIP-seq) , 59, (12hIP-seq) , (15) mihIP-seq) , (17) knstudies) .For each methylation site or SNP, we determined overlap with each annotation category and then compared these with the set of sites/SNPs with detected meQTLs. The significance of the enrichment of sites/SNPs in each category with sites/SNPs with meQTLs was initially assessed using Fisher’s exact test (R function “fisher.test”). However, this test requires independence of observations and the neighboring sites/SNPs are likely to be correlated and thus violate this assumption. For proper assessment of statistical significance we performed permutation analysis based on circular shifts as they preserve local dependence of sites/SNPs. A circular shift permutation analysis was conducted in the following way. First, the methylation sites are ordered by genomic location. Then, the annotation tracks for the sites are shifted by B positions forward, with annotations for the last B sites assigned to the first B sites. Then, overlap of meQTLs with annotation tracks for the shifted annotation is calculated for all values of the offset B, except those shifting the original annotation by less than 1 % of the total number of sites in either direction. The overlap of meQTLs with annotation tracks under no permutation is then compared with those under circular shift permutations.p values were then calculated from the corresponding z-scores.We observed that the permutation distribution of the overlap counts was very close to Gaussian in each of our tests (data not shown). For each test we fitted the normal distribution to the set of overlap counts and calculated the z-score for the overlap count observed for the original non-permuted data. The permutation In the phase 2 bioinformatics analyses, we used a database of genomic annotations assembled in the GenomeRunner project  to examihttp://www.ncbi.nlm.nih.gov/gap/) with the accession number phs000608.As a resource for other researchers, all of our meQTL findings, including positional information, best association statistics and basic annotation data, are provided in Table S8 in Additional file Additional file 1:Supplementary Materials. (ZIP 58629 kb)


Bemisia tabaci (Hemiptera: Aleyrodidae) cryptic species complex are among the most important agricultural insect pests in the world. These phloem-feeding insects can colonize over 1000 species of plants worldwide and inflict severe economic losses to crops, mainly through the transmission of pathogenic viruses. Surprisingly, there is very little genomic information about whiteflies. As a starting point to genome sequencing, we report a new estimation of the genome size of the B. tabaci B biotype or Middle East-Asia Minor 1 (MEAM1) population. Using an isogenic whitefly colony with over 6500 haploid male individuals for genomic DNA, three paired-end genomic libraries with insert sizes of ~300 bp, 500 bp and 1 Kb were constructed and sequenced on an Illumina HiSeq 2500 system. A total of ~50 billion base pairs of sequences were obtained from each library. K-mer analysis using these sequences revealed that the genome size of the whitefly was ~682.3 Mb. In addition, the flow cytometric analysis estimated the haploid genome size of the whitefly to be ~690 Mb. Considering the congruency between both estimation methods, we predict the haploid genome size of B. tabaci MEAM1 to be ~680–690 Mb. Our data provide a baseline for ongoing efforts to assemble and annotate the B. tabaci genome.Whiteflies of the  Bemisia tabaci (Gennadius) stands out by exhibiting a remarkable degree of plasticity in host range (over 1000 species) [Whiteflies are among the most important insect pests in the world, causing damage to agricultural, horticultural, and ornamental plants. However, among the over 1500 species of whiteflies that have been reported [species) , environspecies) .B. tabaci is responsible for transmitting numerous plant pathogenic viruses, most belonging to the genus Begomovirus (Family: Gemniviridae), as well as other genera such as Carlavirus, Crinivirus, Torradovirus, and Ipomovirus [i.e., following acquisition, the virus translocates from the gut, into the hemolymph, and eventually into the salivary glands where the virus is egested [omovirus ,7,8,9,10 egested . For som egested ,14,15,16 egested ,16. AsidB. tabaci (Hemiptera: Aleyrodidae) are closely related to aphids, mealybugs, psyllids, and scale insects. Their body sizes range from 2–3 mm in length and, over the course of a lifetime, transition through four instars before the adult stage. Under optimal conditions, B. tabaci can undergo 11–15 generations per year and a single female can lay between 100 to 300 eggs during its 3–6 week lifespan [B. tabaci employs a haplodiploid sex determination system, in which fertilized eggs yield diploid females and unfertilized eggs yield haploid males [B. tabaci [lifespan ,18,19. Lid males ,21. Consid males ,23,24,25. tabaci ,26,27,28B. tabaci is a complex of at least 15 cryptic species, or “biotypes” that exhibit a wide range of genetic and phenotypic variations, including differences in virus vectoring potential, host preference and specificity, endosymbiont composition, resistance to insecticides, and reproductive incompatibility [B. tabaci Middle East-Asia Minor 1  as the internal standard for comparison (2C = 2.5 pg) . As expeBemisia tabaci. Accurate determination of the whitefly genome size would be an essential starting point for downstream genomic and genetic analyses. Estimation of genome sizes using the k-mer analysis has been recently applied in various genome sequencing projects [B. tabaci MEAM1 and flow cytometric analysis of approximately 690 Mb. Together, these data provide robust evidence that the haploid genome size of B. tabaci is ~680–690 Mb, a value that is ~300 Mb smaller than that reported previously [Despite its international notoriety as an insect pest and vector of plant pathogenic viruses, little is known about the genome of the whitefly projects . Our k-meviously . We speceviously ,34 as weeviously , the chieviously ,34.B. tabaci MEAM1 collected in the United States in 2013. The use of samples from this isogenic colony was important because it reduced the amount of heterogeneity among whiteflies  [B. tabaci MEAM1. This new genome size is still relatively large in comparison to that of other insects, including fruit fly (Drosophila melanogaster) (180 Mb), mosquito (Anopheles gambiae) (278 Mb), pea aphid (Acyrthosiphon pisum) (464 Mb), and silkworm (Bombyx mori) (530 Mb) [Whiteflies used in the present study were obtained from a colony that was established from a single female iotypes) . Togethe(530 Mb) ,39,40,41Belgica antarctica) (99 Mb) [Locusta migratoria) (6500 Mb) [, although the expansion of locus-specific gene families contributed to a large genome size, the majority of the expansion was due to the proliferation of repeat elements and an increased length of intronic regions [B. tabaci genome is nearly four times larger than that of the fruit fly, it is anticipated that B. tabaci possesses a similar number of genes, and expansions likely occurred in highly repetitive intergenic and intronic regions [B. tabaci—as a complex of cryptic species, having a rich co-evolutionary history with Begomoviruses and other whitefly transmitted viruses, it will be interesting to uncover the underlying factors that have shaped the size and sequence of the B. tabaci genome. Together, the data presented herein, with our ongoing efforts to sequence and annotate the whitefly genome, will contribute to these very fundamental understandings.Examining insects that have undergone extreme reduction or expansion in genome size, such as the polar-inhabiting Antarctic midge ( (99 Mb)  and the 6500 Mb) , we see 6500 Mb) . Similar regions . In ligh regions . ConsideB. tabaci MEAM1 (B Biotype) collected from a parent colony in April, 2013 at the USDA-ARS, U.S. Vegetable Laboratory in Charleston, South Carolina, USA. The resulting isogenic population was maintained on caged collards  in a greenhouse (26 °C ± 5 °C). The MEAM1 population was confirmed by PCR using established primers that amplify the mitochondrial cytochrome oxidase 1 gene [B. tabaci from a sweetpotato (Ipomea batatas) field at the U.S. Vegetable Laboratory in 2011. The whiteflies in the parent colony were reared on collard, squash (Cucurbita pepo) and tomato (Solanum lycopersicum).An isogenic whitefly colony was established from a single female e 1 gene . The oriw/v SDS, 1% β-mercaptoethanol). The homogenate was treated with RNase A  for 1 h at 37 °C, followed by proteinase K  for 2 h at 50 °C. DNA was extracted using phenol:chloroform:isoamyl alcohol (25:24:1). The DNA was ethanol precipitated and rehydrated in TE (pH 8.0) overnight at 4 °C. DNA integrity was analyzed via gel electrophoresis and its quantity was determined on a NanoDrop spectrophotometer .Whiteflies were aspirated from collards and immediately frozen at −80 °C for 24 h. The whiteflies were then transferred to a Petri dish embedded in a block of ice and using a stereoscope, over 6500 male individuals were collected, pooled and immediately homogenized with a micropestle in the ice-cold lysis buffer  were removed from the reads using the ShortRead package . FinallyHigh-quality cleaned Illumina sequences from each of the three genomic libraries were subjected to k-mer counting using SOAPec (v2.01) in the SOAPdenovo package  with theGenome size = total number of k-mers/peak value of k-mer frequency distribution.4.7H2O, 50 mM KCl, 5 mM HEPES, pH 8.0, 3 mM dithiothreitol, 0.1 mg/mL propidium iodide, 1.5 mg/mL DNase-free RNase and 0.25% Triton X-100. The suspended nuclei were withdrawn using a pipette, filtered through a 30 µm nylon mesh, and incubated at 37 °C for 30 min before flow cytometric analysis. Suspensions of sample nuclei were spiked with a suspension of standard nuclei (prepared in the above solution) and analyzed with a FACScalibur flow cytometer . For each measurement, the propidium iodide fluorescence area signals (FL2-A) from 1000 nuclei were collected and analyzed by the CellQuest software (Becton-Dickinson). The mean position of the G0/G1 (Nuclei) peak of the sample and the internal standard were determined using CellQuest software. The mean nuclear DNA content of each sample, measured in picograms, was based on 1000 scanned nuclei. Nuclei from chicken red blood cells (2.5 pg/2C) [The procedure used to analyze the nuclear DNA content of the whiteflies was modified from Arumuganathan and Earle . Ten mal5 pg/2C) ,34 were Nuclear DNA content = (Mean position of whitefly nuclei peak/Mean position of CRBC nuclei peak) × 2.5 pg.B. tabaci (MEAM1), one of the most destructive and invasive whitefly species in the world. Results presented in this study were obtained from a population of B. tabaci MEAM1 from North America. Specifically, the insects used in this study were from a population that originated from a field site where this whitefly occurs year-round and feral populations have been known at this location since 1990  [B. tabaci MEAM1 is ~680–690 Mb. Flow cytometry on diploid female whiteflies predicted a genome size approximately twice that of males, further validating our estimations. This new estimation is ~300 Mb smaller than previously reported [In this study, we provide a new genome size estimate for tifolii) . We collreported . Our stu


Hypoglycemia unawareness affects approximately 25% of patients with type 1 diabetes and is strong associated with severe hypoglycemia. Cardiovascular Autonomic Neuropathy (CAN) is one important factor related to hypoglycemia unawareness, although its role is not fully understood due to conflicting data in the literature. Heart rate variability (HRV) in time and frequency domain has been described as a more accurate method to assess CAN. The objective of the present study was to investigate the relationship between hypoglycemia unawareness and autonomic dysfunction assessed by HRV in time and frequency domain.Type 1 Diabetes patients were prospectively enrolled. Hypoglycemia unawareness was evaluated by Pedersen-Bjegaard method and patients were classified into three groups: normal hypoglycemia awareness (NA), impaired hypoglycemia awareness (IA) and hypoglycemia unawareness (UNA). Indices of HRV were evaluated in both time and frequency domain. CAN was diagnosed using cardiovascular reflex tests (Ewing Battery).The study population comprises 99 patients with a mean age of 25.8±10.9 yrs., mean diabetes duration of 12.8±8 yrs. and mean HbA1c 8.4±1.3% (68±10.4 mmol/mol). There was a progressive increase in age (p=0.001), diabetes duration (p=0.027) and episodes of severe hypoglycemia in the last year (p=0.005) as the degree of perception of hypoglycemia decreases. Early CAN was more prevalent in the group UNA (p=0.017). Measures of HRV in frequency and time domain, high frequency band (p=0.027), total power (p=0.037), the square root of the mean squared differences of successive NN intervals (RMSSD) (p=0.041) were progressively decreased with deteriorating hypoglycemia awareness.Hypoglycemia awareness impairment in T1D patients is associated with age, duration of disease and the number of severe hypoglycemia episodes in the previous year. Importantly, as hypoglycemia awareness worsens heart rate variability (HRV) decreases denoting a progressive loss of parasympathetic activity on the heart.


RAI1 (retinoic acid induced-1) is a dosage-sensitive gene that causes Smith-Magenis syndrome (SMS) when mutated or deleted and Potocki-Lupski Syndrome (PTLS) when duplicated, with psychiatric features commonly observed in both syndromes. How common genetic variants regulate this gene, however, is unknown. In this study, we found that RAI1 mRNA expression in Chinese prefrontal and temporal cortex correlate with genotypes of common single nucleotide polymorphisms (SNPs) located in the RAI1 5′-upstream region. Using genotype imputation, “R2-Δ2” analysis, and data from the RegulomeDB database, we identified SNPs rs4925102 and rs9907986 as possible regulatory variants, accounting for approximately 30–40% of the variance in RAI1 mRNA expression in both brain regions. Specifically, rs4925102 and rs9907986 are predicted to disrupt the binding of retinoic acid RXR-RAR receptors and the transcription factor DEAF1 , respectively. Consistent with these predictions, we observed binding of RXRα and RARα to the predicted RAI1 target in chromatin immunoprecipitation assays. Retinoic acid is crucial for early development of the central neural system, and DEAF1 is associated with intellectual disability. The observation that a significant portion of RAI1 mRNA expression is genetically controlled raises the possibility that common RAI1 5′-region regulatory variants contribute more generally to psychiatric disorders. Retinoic acid induced 1 (RAI1) is a dose-sensitive gene located at Chr17p11.2, an unstable region subject to recurrent microdeletions, deletions, and duplications resulting from non-allelic homologous recombination (NAHR) between low-copy repeats (LCRs)RAI1 cause Smith-Magenis syndrome (SMS)RAI1 that reproduce many of the features of SMS in the absence of Chr17p11.2 deletions, strongly implies that RAI1 haploinsufficiency is the primary cause of the syndrome4Rai1 gene6Rai1 gene8The identification of rare nonsense and frameshift mutations in RAI1 over-expression is the primary cause of PTLS is supported by the observations that: i) heterogeneous duplications that give rise to this disorder all include RAI1 and ii) a 125 kb “smallest region of overlap” for these deletions contains only RAI1Rai1 exhibit low body weight, hyperactivity, learn and memory deficits and abnormal maternal behavior6Rai1 show abnormal early post-natal development, hyperactivity, anxiety-related behaviors, and abnormal maternal behaviorRai1 deletions or duplications imply that RAI1 functions in multiple pathways related to development and neurological function in a dosage-dependent manner.Evidence that Rai1  was first identified as a gene induced in the mouse embryonal carcinoma cell line P19 by retinoic acid, a treatment that causes the cells to differentiate into neurons and gliaRAI1 gene13Rai1, spans 130 kb and contains 6 exons. The encoded 1906 amino acid-containing protein localizes to the cellular nucleusRAI1 mRNA has been detected in human frontal and temporal lobes by northern blot analysisBioinformatics analysis9RAI1 is a dosage-sensitive gene, we wondered whether there are common regulatory variants that could potentially function as genetic modifying factors for SMS or PTLS or contribute more generally to neuropsychiatric disorders. In this study, we identified SNPs rs9907986 and rs4925102, which are located in the RAI1 5′-region within predicted binding sites for the transcription factors DEAF1 and RXRα-RARα, respectively, as possible regulatory variants for RAI1 mRNA expression in prefrontal cortex and temporal cortex. Because retinoic acid is crucial for early development of the central nervous system, and mutations in DEAF1 are associated with intellectual disability, genetic variants in RAI1 that influence the function of these upstream factors may contribute to phenotypic differences in SMS and PTLS, and, possibly, other neuropsychiatric disorders.Because RAI1 locus, we scanned 37 genotyped SNPs within a ~273 kb chromosomal segment containing the RAI1 gene for correlations with RAI1 mRNA levels in samples of Han Chinese prefrontal cortex and temporal cortex. This analysis revealed a set of common SNPs located within the 5′-upstream region of the gene that showed nominally significant correlations between genotype and RAI1 mRNA expression in both brain regions (RAI1 mRNA expression (in ΔΔCt units) in prefrontal and temporal cortex as a function of genotype for each SNP (RAI1 mRNA expression stratified by SNPs genotype are listed in To identify common regulatory genetic variants within the  regions . To ideneach SNP . Assignmhttp://www.ncbi.nlm.nih.gov/projects/SNP/). To identify potential functional SNPs within the RAI1 region, we imputed genotypes for common SNPs using genotype data from the 1000 Genomes Project as a reference. Again, nominally significant eQTL peaks identified using the combined genotyped and imputed data were all located within the 5′-upstream region of the RAI1 gene  with “index” SNPs that make the largest contributions to the variance of measured coefficients of determination (R2) for SNPs within this region. Here we use Δ2 to denote the standard “r2” linkage disequilibrium (LD) constant, to avoid confusion with the linear regression coefficient of determination (R2). As described in Methods, this analysis began with the examination of all possible sets of 2 or 3 SNPs among the set of 96 genotyped or imputed SNPs  P-values > 0.001 and pruned to eliminate SNPs with identical genotypes) to identify a specific combination of SNPs that best account for the observed set of R2-values associated with individual SNPs in the “core” region. The results of this analysis for 3-SNP combinations for the prefrontal cortex and temporal cortex are shown in 2 value in multivariable regression analysis of RAI1 mRNA expression vs. SNP genotypes) were selected as “index” SNPs for further study.To obtain insights concerning the number of regulatory variants within the ~90 kb “core”  regions , we consRAI1 SNPs according to association with the “index” SNPs rs12449964, rs10401011 and rs4636969 (identified as described above) accounts for most of the measured R2 values associated with SNPs in the RAI1 5′-region for mRNA expression in prefrontal cortex. 2 values) obtained from this analysis superimposed on the measured R2 values associated with RAI1 5′-region SNPs. By inspection, the regression line can be seen to account for most of the measured R2 values. 2(model) of 0.892. 2-values vs. the predicted R2-values calculated using the regression equation. As shown in RAI1 mRNA in temporal cortex identified SNPs rs12449964, rs4925102 and rs8071107 as the index SNPs, with highly significant P-values and an adjusted-R2(model) = 0.876.As shown in RAI1 mRNA expression is regulated by the same functional SNPs in the two brain regions, we investigated SNPs that are in LD (Δ2 > 0.5) with each of the three index SNPs in the prefrontal cortex or temporal cortex samples. This analysis showed that the index SNPs rs4636969 and rs8071107 are in complete LD and SNP rs10401011 and rs4925102 are associated with a Δ2 LD constant of 0.735 (To determine whether of 0.735 .http://regulomedb.org/) to screen lists of SNPs from the 1000 Genome Project data base that are in LD with our index SNPs, selecting SNPs with RegulomeDB scores To identify possible functional SNPs, we used RegulomeDB  binding site within the sequence C]TGCCCC .  assays using mouse monoclonal antibodies against RXRα or RAR3 transcription factors and PCR primers designed to amplify a 274 bp DNA fragment centered on rs4925102. Consistent with the predicted binding, we observed higher levels of immunoprecipitation of the target DNA fragment with both anti-RXRα and anti-RARα antibodies compared to a control RAI1 exon 3 DNA fragment in human neuronal SH-SY5Y cells treated with 1 μM all-trans retinoic acid (ATRA) (RAI1 intron 2 in electrophoretic mobility shift assays (EMSA) and observed a significant increase in luciferase activity driven by the putative DEAF1 binding sequence in heterologous reporter gene expression assays in HEK293T cells. .To provide experimental evidence for binding of RXR::RAR dimers to the putative RXR::RAR_DR5 binding site in d (ATRA) . Also coRAI1 expression in human brain, we plotted normalized ΔΔCt values for RAI1 mRNA expression in prefrontal cortex and temporal cortex as a function of genotype for each SNP to identify “low-” and “high-expression” alleles  in prefrontal cortex. Assignments of “low-” and “high-expression” alleles for each SNP, the minor allele and its frequency (MAF), and P-values and R2 values from single-variable linear regression analysis are listed in RAI1 mRNA expression in human prefrontal cortex and temporal cortex, respectively, with only a slight increase observed with the inclusion of rs12449964  binding sites, with alternative SNP alleles predicted to influence TF binding (RegulomeDB score = 2b). Specifically, the index SNP rs4925102 is located within a predicted binding site for dimeric 9-cis retinoic acid receptor  and all-trans retinoic acid receptor (RAR). The sequence for RXR::RAR binding is located within RAI1 intron 1 and comprises the DNA sequence TCCCCTCCCCGTGCCCC, , with nucleic acid residues matching the consensus RXR::RAR_DR5 binding sequence, TGACCTNNNNN[G/C]GACCTBased on data obtained from RegulomeDB, two of the RXR and RAR receptors form a heterodimer that binds retinoic acid and retinoic acid response elements (RARE) to regulate the transcription of target genesRetinoic acid (RA) signaling plays an important role in cellular proliferation, differentiation, and apoptosis, limb and skeletal defects, abnormal axis patterning of neural tube the central nervous system developmentRAI1 expression has been reported to be increased in the brains of schizophrenia, bipolar disorder, and major depression patientsRAI1 to be associated with common neuropsychiatric disorders, including Parkinson’s diseaseRAI1 was originally cloned from mouse P19 embryonal carcinoma cells exposed to retinoic acidRAI1 promoterRAI1 expression. This study is the first to show that RAI1 mRNA expression is controlled in human brain by a common genetic variant located within a binding site for RXRα/RARα retinoic acid receptors. We hypothesize that changes in expression of this dosage-sensitive gene mediated by RA in early brain development may contribute to common neuropsychiatric disorders.RAI1 index SNP, rs9907986, is located within a consensus binding site for the transcription factor deformed epidermal autoregulatory factor-1 (DEAF1). The predicted sequence for DEAF1 binding is located within RAI1 intron 2 and comprises the DNA sequence C[T/C]CGGCGGCTTCCGG, ), with nucleic acid residues matching the consensus DEAF1 binding sequence, TTCGGGNNNTTTCCGG32The second DrosophilaDEAF1 mRNA and protein are highly expressed in fetal and adult brain, where it is primarily located in the cellular nucleusDEAF1 has also been implicated in major depression, anxiety, suicidal tendencies, and panic disorder though one of its target genes, 5-HT1A5-HTR1A promoter that interferes with DEAF1 binding disrupts the expression of 5-HTR1ADEAF1, mutations in RAI1 have also been reported to associate with intellectual disability (ID) in a whole-exome sequencing studyRAI1 gene and activates RAI1 mRNA expression in vitro suggests that RAI1 may be a downstream effector for DEAF1 and genetic differences in the regulation of RAI1 by DEAF1 may contribute to phenotypic differences observed in SMS and PTLS patients and other common mental disorders.DEAF1 activates or represses transcription of a variety of genes, including inhibiting its own transcription by binding the motif TTCG, and has previously been shown to be essential for embryonic development in RAI1 mRNA expression, which were roughly normally distributed among our Han Chinese brain samples: standard deviation (SD) for prefrontal cortex = 0.9552 ΔCt unit and the SD for temporal cortex = 0.8873 ΔCt unit. Because ΔCt units are measured on the log2-scale, these SDs correspond to RAI1 mRNA levels in the range of mean × [0.52–1.94] and mean × [0.54–1.85] on the linear scale, for RAI1 mRNA expression in prefrontal cortex and temporal cortex, respectively, for approximately 68% if the population. Likewise, assuming a normal distribution, approximately 32% of the population would be expected to have brain RAI1 mRNA levels outside of this region.As shown in RAI1 mRNA levels was surprising, however, since deletion or duplication of the RAI1 gene, which decreases or increases mRNA expression about 50%7Large inter-sample variation of mRNA expression has been described for many genes in published PCR and array-based studies of mRNA expression in human brain4142RAI1 mRNA expression and sample properties, including: i) gender, ii) age, iii) postmortem interval (PMI) and iv) RNA integrity number (RIN), but found no statistically significant correlations  and anterior temporal cortex from Han Chinese individuals were obtained from the Chinese Brain Bank Center . Written consent for tissue donation was obtained from relatives for all samples (on file at CBBC). Use of human autopsy tissue is considered “non-human-subject research” and is Internal Review Board (IRB) exempt under NIH guidelines. The characteristics of our set of temporal cortex samples are presented in Isolation of genomic (g) DNA and total RNA and preparation of cDNA was carried out as previously describedwww.genenergy.cn) and yielded approximately 1.14 × 106 genotypes/sampleWhole genome genotyping was performed using Illumina HumanOmni1-Quad arrays  at Genergy Biotech  to detect PCR products. The normalized RAI1 mRNA level in each sample was calculated from three independent measurements of PCR cycle thresholds (Ct’s) for RAI1 mRNA and mRNAs for one  or three  “house-keeping” genes: β-actin (ACTB), cytochrome c-1 (CYC1) and hydroymethylbilame synthase (HMBS). The sequences of forward and reverse oligonucleotide primers for RAI1 were: 5′-ATAACCAGCCCGAGTCATGCA-3′ and 5′-TGATGTTTCCTGCGAGGTCTG-3′, respectively. The 91 bp PCR product amplified by these primers spans the end of the 5′untranslated region (UTR) and the first 25 amino acids of the RAI1 coding region within exon 3. Although several RAI1 mRNA splice variants have been described, northern blot analysis has demonstrated that a single 7668 nucleotide mRNA isoform (NM_030665) is the predominant RAI1 mRNA detected in all tissues examined, including brain13ACTB: 5′-TGTGGCCGAGGACTTTGATTG-3′ and 5′-GTGGCTTTTAGGATGGCALevels of CYC1: 5′-GAGCACGACCATCGAAAACG-3′ and 5′-CGATATGCC AGCTTCCGACT-3′; iii) HMBS: 5′-CCACACACAGCCTACTTTCCA-3′ and 5′-GTACCCACGCGAATCACTCT-3′. Primers were designed using Oligo 6.0  and synthesized by Sangon Biotech .AGG-3′; ii) RAI1 mRNA expression for each brain sample (in ΔCt units) was calculated by subtracting the average Ct obtained for RAI1 from the average Ct value for ACTB  or the geometric mean of average Ct values obtained for each of the three house-keeping genes . As shown in RAI1 mRNA expression levels comprise a nearly Gaussian  distribution for both prefrontal cortex and temporal cortex samples.The PCR amplification program comprised: i) 1 × [95 °C for 1 min]; ii) 40 × ; iii) 1 × . Following amplification, melting curves of the PCR products were examined to confirm the presence of a single PCR product. Normalized RAI1 mRNA expression and genotypes of SNPs within the extended or core-RAI1 locus were quantified by single variable linear regression analysis using the - -assoc command in PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/)2) for each SNP. For selected SNPs, differences in mRNA expression among genotypes were evaluated for statistical significance by ANOVA using SPSS . To minimize spurious correlations, SNPs with fewer than three heterozygous genotypes among the prefrontal cortex or temporal cortex samples were omitted from the analysis. Plots of pairwise linkage disequilibrium (LD) constants (Δ2 = r2) for SNPs in the neighborhood of the human RAI1 gene based upon genotype data for our Han Chinese brain samples were generated using Haploviewhttp://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview/). Proxy SNP analysis was carried out using SNAP version 2.2https://www.broadinstitute.org/mpg/snap/ldsearch.php). Identification of potentially functional SNPs was carried out using data available from RegulomeDBhttp://regulomedb.org/).Correlations between cis-acting regulatory variants within the same locus. Because the identities of the actual regulatory variants are usually unknown, SNPs that make the largest contributions to the observed variance in linear regression coefficient of determination (R2) values for SNPs within the gene locus (termed “index SNPs) are selected and the contributions of all additional SNPs within the locus are assumed to be proportional to the degree of linkage to one or more these SNPs, with additive contributions possible from multiple index SNPs.This method is based on the hypothesis that SNPs located within the neighborhood of a gene correlate with mRNA expression in proportion to their degree of linkage disequilibrium (LD) with 2–value obtained from linear regression analysis of the correlation between normalized log2-transformed tissue levels of mRNA and SNP genotype is taken as the measure of the contribution of an individual SNP to mRNA expression. The measure of LD between an “index SNP” and other SNPs in the gene locus is the standard pairwise r2 LD constant, which we designate as “Δ2” to avoid confusion with “R2”.The R2 values for all genotyped and imputed SNPs within a gene locus (the dependent variable) vs. sets of pairwise Δ2 values calculated between an “index SNP” and all other SNPs in the chromosome region of interest (ROI), with one set for every index SNP (the independent variables). This analysis yields a regression equation:In mathematical terms, we carry out multiple linear regression analysis of the set of measured RXij = pairwise LD constant (Δ2ij) for the ith SNP and jth index SNP and αj = regression coefficient reflecting the relative contribution of the jth index SNP to measured R2 values, with the values of the coefficients (αj) determined by the regression., where 2 values vs. predicted R2 values, which yields an adjusted-R2model and a P-value. To avoid bias in the selection of “index” SNPs, an adjusted-R2model is calculated for all possible combinations of SNPs in the chromosome ROI, sequentially, for 1, 2, 3 or 4 index SNPs and the smallest combination of SNPs yielding the highest adjusted-R2model selected as the best model. To reduce the computational burden of this analysis the list of SNPs within the gene locus is pruned to eliminate SNPs with identical genotypes within the sample set. Also, to avoid mathematical problems arising from colinearity among sets of Δ2ij values associated with potential index SNPs, only combinations of SNPs with a maximum variance inflation factor (VIF) < 5 for the associated sets of Δ2ij values are accepted as candidates. Finally, because negative contributions to R2 are difficult to interpret, only sets of SNPs that yield positive coefficients (αj) for all the terms in the above regression equation are accepted as potential index SNPs.The goodness of fit for each set of index SNPs is determined by regressing measured R2 incubator.Human neuroblastoma-derived SH-SY5Y cells were cultured in 1:1 mixtures of Dulbecco’s Modified Eagle’s Minimum Essential Medium (DMEM) and F12 Medium with 10% (v/v) fetal bovine serum (FBS) and 2 mM L-glutamine and were maintained at 37 °C in a 5% COtrans-retinoic acid (ATRA) dissolved in 0.01% dimethylsulphoxide (DMSO) for 135 ± 5 min. The chromatin was digested by micrococcal nuclease (Pierce), as indicated by the manufacturer. Each immunoprecipitation reaction mix contained 50 μg chromatin and 5% of this amount (2.5 μg) chromatin was collected separately to quantify the amount of input target DNA present in each immunoprecipitation reaction mix. One μg mouse anti-RARα antibody  or anti-RXRα antibody  were used for RARα or RXRα immunoprecipitations, respectively. Immunoprecipitation with 1 μg mouse IgG  was used as a negative control. DNA was purified using MinElute reaction cleanup kits (Qiagen) and subjected to real-time PCR using primers designed to amplify appropriate fragments of either the putative RAR::RXR_DR5 binding site containing rs4925102 or the control DNA RAI1 exon 3 fragment as a non-specific DNA control. The sequences of forward and reverse oligonucleotide primers for rs4925102-RARE were: 5′-AAACCAGGGCATCATTCCTC-3′ and 5′-CCTGACCTTCGACAATGGCTT-3′, respectively. The primers for the control DNA segments were identical to those used to quantify RAI1 mRNA expression (sequences listed above). All PCR amplifications were carried out in triplicate and the amounts of precipitated target DNA, expressed as percent (%) of input target DNA, were calculated using the formulaChIP assays were performed following the Cold Spring Harbor protocol with minor modificationsHow to cite this article: Chen, L. et al. Evidence for genetic regulation of mRNA expression of the dosage-sensitive gene retinoic acid induced-1 (RAI1) in human brain. Sci. Rep.6, 19010; doi: 10.1038/srep19010 (2016).