RETN) increased susceptibility to type 2 diabetes by enhancing its promoter activity. Plasma resistin was highest in Japanese subjects with G/G genotype, followed by C/G, and C/C. In this study, we cross-sectionally analyzed plasma resistin and SNPs in the RETN region in 2,019 community-dwelling Japanese subjects. Plasma resistin was associated with SNP-638 (rs34861192), SNP-537 (rs34124816), SNP-420, SNP-358 (rs3219175), SNP+299 (rs3745367), and SNP+1263 (rs3745369) . SNP-638, SNP -420, SNP-358, and SNP+157 were in the same linkage disequilibrium (LD) block. SNP-358 and SNP-638 were nearly in complete LD (2r = 0.98), and were tightly correlated with SNP-420 . The correlation between either SNP-358 (or SNP-638) or SNP-420 and plasma resistin appeared to be strong . In haplotypes determined by SNP-420 and SNP-358, the estimated frequencies for C-G, G-A, and G-G were 0.6700, 0.2005, and 0.1284, respectively, and C-A was rare (0.0011), suggesting that subjects with A at −358, generally had G at −420. This G-A haplotype conferred the highest plasma resistin . In THP-1 cells, the RETN promoter with the G-A haplotype showed the highest activity. Nuclear proteins specifically recognized one base difference at SNP-358, but not at SNP-638. Therefore, A at -358 is required for G at −420 to confer the highest plasma resistin in the general Japanese population. In Caucasians, the association between SNP-420 and plasma resistin is not strong, and A at −358 may not exist, suggesting that SNP-358 could explain this ethnic difference.Insulin resistance is a feature of type 2 diabetes. Resistin, secreted from adipocytes, causes insulin resistance in mice. We previously reported that the G/G genotype of single nucleotide polymorphism (SNP) at −420 (rs1862513) in the human resistin gene ( In the general Japanese population, subjects with the G/G genotype had the highest plasma resistin levels, followed by C/G and C/C RETN, remains to be elucidated.We previously carried out systematic analyses of SNPs in the human resistin gene  analysis RETN mRNA is localized on chromosome 19p, where the strongest positional candidate is RETN itself RETN mRNA was positively correlated with its simultaneous serum level RETN would be of special interest.Melzer et al. reported that a SNP rs2431866, located at a distance of ∼100 Kb from RETN, we cross-sectionally analyzed 2,019 Japanese subjects. Plasma resistin was found to be most strongly associated with SNP-358 and SNP-420, where the relevant allele, A at-358, was tightly linked to G at −420.In view of this, to determine the SNPs responsible for circulating resistin around P = 0.049 for SNP+157, and P = 0.037 for SNP+1263 did not remain significant after Bonferroni's correction. Based on a confidence interval analysis, SNP-638, SNP -420, SNP-358, and SNP+157 were determined to be in the same LD block (2r = 0.98), and were tightly correlated with SNP-420 .We first typed the 8 SNPs listed in LD block . Four mahttp://www.genomatix.de/)) revealed the presence of transcription factor binding sites for SNP-358 but not for SNP-638. Therefore, we tentatively concluded that SNP-358 was a more functional SNP than SNP-638, and used SNP-358 when necessary in this study.To determine which SNP, i.e., SNP-358 or SNP-638, is potentially functional, we examined whether nuclear proteins specifically bound to DNA sequences using electrophoretic mobility shift assay (EMSA) . NuclearP = 2.75×10−329), SNP-537 (P = 4.63×10−14), SNP-420 (P = 1.71×10−133), SNP-358 (P = 4.94×10−324), SNP+299 (P = 5.24×10−85), and SNP+1263 (P = 2.33×10−16). These P values remained significant after Bonferroni's correction. A low P value and high 2rwere found for SNP-358 (or SNP-638), and SNP-420 (2r = 0.5224 (0.5275), and 0.2616, respectively). The difference in plasma resistin levels between each genotype appears to be highest for SNP-358 (or SNP-638), followed by SNP-420 . These findings remained unchanged when assessed using multiple regression analyses adjusted for age, gender, and BMI . Since oP = 0.2700), it was not possible to verify an effect of A at −358 itself, independent of G at −420, due to its low frequency (0.0011). The G-G haplotype, with G at −420, but not A at −358, showed an effect similar to that for C-G . Therefore, G at −420 was not sufficient to confer the highest plasma resistin, and A at −358 was required in the general Japanese population. It is difficult to determine whether A at −358 alone or in combination with G at −420 is more responsible for this effect, based on only statistical analyses of genetic data.To determine which combination of these potentially functional SNPs is most responsible for plasma resistin, we examined the relation between plasma resistin and haplotypes defined by SNP-420 and SNP-358 in this order . The estRETN promoter activity with G at −420 and A at−358 showed the higher activity than the reference control promoter with C at −420 and G at−358   . The proRETN in the general Japanese population. The lowest P value was observed for SNP-358 (or SNP-638), followed by SNP-420. SNP-420, and SNP-358 were in the same LD block, and a risk allele, A of SNP-358, was tightly linked with a risk allele, G of SNP-420. The G-A haplotype defined by SNP-420, and SNP-358, which included these risk alleles, conferred the highest plasma resistin. Functionally, the RETN promoter with this haplotype showed the highest activity in THP-1 cells. Nuclear proteins specifically recognized a difference in one base at SNP-358, but not at SNP-638.These findings herein indicate that plasma resistin levels are associated with SNP-638, SNP-537, SNP-420, SNP-358, SNP+299, and SNP+1263 around RETN promoter including G at −420 was higher than that of the wild type including C at −420 in vitroRETN promoter activity with G at −420 and G at−358 in the absence of SNP-638 in SL2 cells RETN promoter activity with G at −420 and A at−358 without SNP-638 was ∼60% higher than that with the other combinations. Therefore, both the A of SNP-358 and the G of SNP-420 appear to be required for the highest RETN promoter activity.We found that SNP-358 was associated with plasma resistin in the order of A/A >G/A>G/G in a large number of Japanese subjects. Subjects having the relevant allele for high plasma resistin, i.e. an A at−358, generally had a G at −420. In four independent studies, the activity of the mutant During the preparation of this manuscript, Asano et al. reported on-line that rs34861192 (SNP-638G>A) and rs3745268 (3′ UTR) are significant determinants of plasma resistin in an aged Japanese population http://www.genomatix.de/). The induction of RETN expression by lipopolysaccharides is inhibited by an NF-κB inhibitor in human macrophages P values clearly merit further investigation.Since the A at −358 was tightly linked with the G at−420, the issue of whether the A of SNP-358 itself is largely responsible for high plasma resistin levels, or whether both the A of SNP-358 and the G of SNP-420 are required remains to be elucidated. Also, the possible relevance of SNP-358 is suggested, since the sequences around SNP-358 are similar to the NF-κB consensus sequences based on the MatInspector program (http://www.hapmap.org/index.html.en). Therefore, SNP-358 is likely to be monophormic in Caucasians, which could account for the ethnic differences in the association between plasma resistin and SNP-420.We previously reported that a functional promoter SNP-420 was tightly associated with plasma resistin, its final gene product, in Japanese subjects retn is disrupted, a high fat diet was found to induce insulin resistance with inflammation of adipose tissue Most recently, in “humanized resistin mice”, where human resistin is specifically expressed in macrophages and endogenous RETN promoter with this G-A haplotype showed the highest activity. It is not clear why A of SNP-358 is required for G of SNP-420 to confer the highest plasma resistin, and whether the A of SNP-358 indeed accounts for ethnic differences in the association between plasma resistin and SNP-420. Further studies will be required to clarify these points.In summary, of the potentially functional SNPs, SNP-358 appears to be most strongly associated with plasma resistin in the general Japanese population, followed by SNP-420. Subjects having the responsible allele for the highest plasma resistin, A at −358, generally had G at −420. The G-A haplotype defined by SNP-420 and SNP-358 conferred the highest plasma resistin. Functionally, the All subjects were informed of the purpose of the study and their written consent was obtained. The study was approved by the ethics committee of the Ehime University Graduate School of Medicine.This cross-sectional study included community-dwelling Japanese subjects who were attending an annual medical check-up in a rural town located in Ehime prefecture in Japan. We previously analyzed resistin SNP-420 and plasma resistin levels in 2,078 subjects in this population RETNhttp://www.hapmap.org/index.html.en). These SNPs included all of the 4SNPs reported in HapMap around RETN. SNPs were typed by TaqMan analysis using assay on demand probes and primers sets (Applied Biosystems) except rs3760678, rs34861192, rs34124816, and rs3219177 which were generated as assay by design. The rs1862513 was previously typed as described The seven SNPs were selected, based on our systemic analysis of SNPs in Fasting plasma resistin was determined using a human resistin ELISA kit , according to the manufacturer's protocol 2 incubator. Nuclear extracts of THP-1 cells were prepared with NE-PER® Nuclear and Cytoplasmic Extraction Reagents (Thermo SCIENTIFIC). The following oligonucleotides were used for EMSA as probes or competitors, sense hRE-358A, 5′-CACTGTCTGCTCAGGAGCTTCCTCTTGGCCA-3′; antisense hRE-358A, 5′-GATCTGGCCAAGAGGAAGCTCCTGAGCAGACAGTGGTAC-3′; sense hRE-358G, 5′-CACTGTCTGCTCAGGGGCTTCCTCTTGGCCA-3′; antisense hRE-358G, 5′-GATCTGGCCAAGAGGAAGCCCCTGAGCAGACAGTGGTAC-3′, sense hRE-638A, 5′-CGTCACTGTAGCTTCAAACTCCCGGGCTCAA-3′; antisense hRE-638A, 5′-GATCTTGAGCCCGGGAGTTTGAAGCTACAGTGACGGTAC-3′; sense hRE-638G, 5′-CGTCACTGTAGCTTCGAACTCCCGGGCTCAA-3′; antisense hRE-638G, 5′-GATCTTGAGCCCGGGAGTTCGAAGCTACAGTGACGGTAC-3′. Complementary oligonucleotides were annealed and labeled with 32P. A 10 µg of nuclear extract was incubated for 30 min with a 32P-labeled oligonucleotide (0.01 pmol) and 1 µg of poly(dAdT) in reaction buffer containing 25 mM HEPES (pH 7.9), 0.5 mM EDTA, 0.5 mM DTT, 40 mM KCl and 4% Ficoll (v/v). Nonidet P-40 (0.5%), and a 100-fold molar excess of the unlabeled single-stranded sense and antisense oligonucleotides, corresponding to each labeled probe were added to reduce nonspecific binding. Competition experiments were performed by the addition of a 200-fold molar excess of unlabelled double-stranded oligonucleotides to the reaction mixture. After the binding reaction, the mixture was subjected to 6% PAGE in 45 mM Tris, 45 mM boric acid, 1 mM EDTA at 100V for 2 h at 4°C. The gel was dried and exposed to a FUJIFILM imaging plate. Signals were detected with the FUJIFILM FLA-5100 image analyzing system.THP-1 cells were grown in RPM1 medium supplemented with 10% fetal bovine serum at 37°C in a 5% CORETN was amplified by PCR using DNA from either a subject homozygous for a wild-type allele (−420C) or a subject homozygous for a mutant allele (−420G). These products were inserted into pGL3 basic vector  to generate pGL3(−420G) and pGL3(−420C), both of which harbor G at the −358 locus, as described previously5′-CTTACTGTCTGCTCAGGAGCTTCCTCTTGGCCCCG, and 5′- CGGGGCCAAGAGGAAGCTCCTGAGCAGACAGTAAG. The DNA fragments between −450 and −206 of RETN with or without this point mutation were subsequently cloned into a luciferase reporter vector pGL4.11 (Promega) at the site between Kpn I and Bgl II. The sequence of the generated four plasmids, pGL4(−420C/−358G), pGL4(−420C/−358A), pGL4(−420G/−358G) and pGL4(−420G/−358A), were confirmed by DNA sequencing. All plasmids used for the transfection were prepared by use of a Qiagen plasmid kit, followed by CsCl2 density-gradient ultracentrifugation as described previously The 5′ flanking region between −450 and −206 of 5 cells/well. Trans IT-Jurkat Reagent  was used for transient transfection following the manufacturer's protocol. Each of the pGL4(−420C/−358G), pGL4(−420C/−358A), pGL4(−420G/−358G) and pGL4(−420G/−358A) vectors was transfected into these cells along with pGL4.11 basic vector , pGL4.11 promoter vector (positive control), and pRL-SV40 internal control vector. The THP-1 cells were then incubated with RPMI1640 containing 10% FCS for 48 hours. Luciferase assay were performed using the Dual-LuciferaseTM Reporter Assay System according to the manufacturer's protocol (Promega).THP-1 human monocytes purchased from Human Science Research Resources Bank  were grown in RPMI1640 containing 10% FCS. One day before transfection, THP-1 cells were plated on a 6 well plate at 4.0×10P value ×8). To assess isolated effects of each SNP on plasma resistin, multiple regression analyses involving each SNP, age (year), gender , and BMI as independent variables, and plasma resistin (ng/ml) as a dependent variable were performed. In these analyses, we assume that the major and minor alleles based on the allele frequency are normal (N), and susceptibility (S) alleles, respectively. Each SNP genotype, N/N, N/S, and S/S, were denoted by two dummy variables   = , , and , respectively. This first dummy variable estimates the difference between N/S and N/N, and the second between S/S and N/N. These analyses were performed using a commercially available statistical software package . Linkage disequilibrium (LD) was assessed using the Haploview software P<0.05.In the human population study, values are expressed as the mean±standard deviation. Differences in plasma resistin levels among genotypes were assessed by analysis of variance (ANOVA). Bonferroni's correction was applied to


DLK1-MEG3 intergenic differentially methylated region (IG-DMR) and the postfertilization-derived secondary MEG3-DMR, together with multiple imprinted genes. Although previous studies in cases with microdeletions and epimutations affecting both DMRs and paternal/maternal uniparental disomy 14-like phenotypes argue for a critical regulatory function of the two DMRs for the 14q32.2 imprinted region, the precise role of the individual DMR remains to be clarified. We studied an infant with upd(14)pat body and placental phenotypes and a heterozygous microdeletion involving the IG-DMR alone (patient 1) and a neonate with upd(14)pat body, but no placental phenotype and a heterozygous microdeletion involving the MEG3-DMR alone (patient 2). The results generated from the analysis of these two patients imply that the IG-DMR and the MEG3-DMR function as imprinting control centers in the placenta and the body, respectively, with a hierarchical interaction for the methylation pattern in the body governed by the IG-DMR. To our knowledge, this is the first study demonstrating an essential long-range imprinting regulatory function for the secondary DMR.Human chromosome 14q32.2 harbors the germline-derived primary  DLK1-MEG3 intergenic differentially methylated region (IG-DMR) and the postfertilization-derived secondary MEG3-DMR, together with multiple imprinted genes. Perturbed dosage of these imprinted genes, for example in patients with paternal and maternal uniparental disomy 14, causes distinct phenotypes. Here, through analysis of patients with microdeletions recapitulating some or all of the uniparental disomy 14 phenotypes, we show that the IG-DMR acts as an upstream regulator for the methylation pattern of the MEG3-DMR in the body but not in the placenta. Importantly, in the body, the MEG3-DMR functions as an imprinting control center. To our knowledge, this is the first study demonstrating an essential function for the secondary DMR in the regulation of multiple imprinted genes. Thus, the results provide a significant advance in the clarification of underlying epigenetic features that can act to regulate imprinting.Genomic imprinting is a process causing genes to be expressed in a parent-of-origin specific manner—some imprinted genes are expressed from maternally inherited chromosomes and others from paternally inherited chromosomes. Imprinted genes are often located in clusters regulated by regions that are differentially methylated according to their parental origin. The human chromosome 14q32.2 imprinted region harbors the germline-derived primary  PEGs) such as DLK1 and RTL1 and non-coding maternally expressed genes (MEGs) such as MEG3 , RTL1as (RTL1 antisense), MEG8, snoRNAs, and microRNAsHuman chromosome 14q32.2 carries a cluster of protein-coding paternally expressed genes (DLK1-MEG3 intergenic DMR (IG-DMR) and the postfertilization-derived secondary MEG3-DMR MEG3-DMR is rather hypomethylated The 14q32.2 imprinted region also harbors two differentially methylated regions (DMRs), i.e., the germline-derived primary MEG3-DMR show a hierarchical interaction for the methylation pattern in the body, and function as imprinting control centers in the placenta and the body, respectively. To our knowledge, this is the first study demonstrating not only different roles between the primary and secondary DMRs at a single imprinted region, but also an essential regulatory function for the secondary DMR.However, the precise role of individual DMR remains to be clarified. Here, we report that the IG-DMR and the We studied an infant with upd(14)pat body and placental phenotypes (patient 1) and a neonate with upd(14)pat body, but no placental, phenotype (patient 2) . DetailemRNAs, snoRNAs, and microRNAs) from fresh leukocytes of patients 1 and the parents of patients 1 and 2, from fresh skin fibroblasts of patient 2, and from formalin-fixed and paraffin-embedded placental samples of patient 1 and similarly treated pituitary and adrenal samples of patient 2 . We also made metaphase spreads from leukocytes and skin fibroblasts. For comparison, we obtained control samples from fresh normal adult leukocytes, neonatal skin fibroblasts, and placenta at 38 weeks of gestation, and from fresh leukocytes of upd(14)pat/mat patients and formalin-fixed and paraffin-embedded placenta of a upd(14)pat patient We isolated genomic DNA (gDNA) and transcripts , the deletion size was 8,558 bp  for the microdeletion in patient 1 and her mother, and 4,303 bp  for the microdeletion in patient 2. The microdeletion in patient 2 also involved the 5′ part of MEG3 and five of the seven putative CTCF binding sites A–G MEG3 intron 5 . Direct sequencing of the exonic or transcribed regions detected no mutation in DLK1, MEG3, and RTL1, although several cDNA polymorphisms (cSNPs) were identified (We first examined the structure of the 14q32.2 imprinted region . Upd(14)atient 2 . The micatient 2  and quanatient 2 , and seratient 2 . Accordientified . Oligoarentified .MEG3-DMR (CG7)  and MR (CG7)  [2], usiatient 1 .We also examined methylation patterns of the seven putative CTCF binding sites by bisulfite sequencing . The sitMEG3 and SNORD114-29 in a control subject and the mother of patient 1 but not in patient 1. For skin fibroblasts, although all MEGs but no PEGs were expressed in control subjects, neither MEGs nor PEGs were expressed in patient 2. For placentas, although all imprinted genes were expressed in control subjects, PEGs only were expressed in patient 1. For the pituitary and adrenal of patient 2, DLK1 expression alone was identified.Finally, we performed expression analyses, using standard reverse transcriptase (RT)-PCR and/or q-PCR analysis for multiple imprinted genes in this region . For leuMEG3 expression in the leukocytes of the mother of patient 1 (RTL1 expression in the placenta of patient 1 (no informative cSNP was detected for DLK1) and biparental DLK1 expression in the pituitary and adrenal of patient 2   , as welllacentas . AlthougMEG3-DMR in the body and caused paternalization of the imprinted region and typical upd(14)pat body and placental phenotypes, whereas loss of the hypomethylated MEG3-DMR of maternal origin in patient 2 permitted normal methylation pattern of the IG-DMR in the body and resulted in maternal to paternal epigenotypic alteration and typical upd(14)pat body, but no placental, phenotype. In this regard, while a 66 bp segment was inserted in patient 2, this segment contains no known regulatory sequence http://genome.lbl.gov/vista/index.shtml). Similarly, while no control samples were available for pituitary and adrenal, the previous study in human subjects has shown paternal DLK1 expression in adrenal as well as monoallelic DLK1 and MEG3 expressions in various tissues MEG3-DMR exhibits a non-DMR in the placenta MEG3-DMR on the maternally inherited chromosome in the body, but not in the placenta; (2) the hypomethylated MEG3-DMR functions as an essential imprinting regulator for both PEGs and MEGs in the body; and (3) in the placenta, the hypomethylated IG-DMR directly controls the imprinting pattern of both PEGs and MEGs. These notions also explain the epigenotypic alteration in the previous cases with epimutations or microdeletions affecting both DMRs  is dependent on that of the germline-derive IG-DMR MEG3-DMR.It remains to be clarified how the IG-DMR and the MEG3-DMR regulate the expression of both PEGs and MEGs in the placenta and the body, respectively. For the MEG3-DMR, however, the CTCF binding sites C and D may play a pivotal role in the imprinting regulation. The methylation analysis indicates that the two sites reside within the MEG3-DMR, and it is known that the CTCF protein with versatile functions preferentially binds to unmethylated target sequences including the sites C and D MEGs in this imprinted region can be transcribed together in the same orientation and show a strikingly similar tissue expressions pattern PEGs are transcribed in different directions and are co-expressed with MEGs only in limited cell-types MEGs as a large transcription unit and represses all the PEGs perhaps by influencing chromatin structure and histone modification independently of the effects of expressed MEGs. In support of this, CTCF protein acts as a transcriptional activator for Gtl2 (the mouse homolog for MEG3) in the mouse It also remains to be elucidated how the IG-DMR and the H19-DMR and from the non-coding RNA-mediated model implicated for several imprinted regions including the KvDMR1 http://www.essex.ac.uk/bs/molonc/spa.html). Thus, while each imprinted region would be regulated by a different mechanism, a CTCF protein may be involved in the imprinting control of multiple regions, in various manners.Such an imprinting control model has not been proposed previously. It is different from the CTCF protein-mediated insulator model indicated for the Gtl2-DMR (ΔGtl2-DMR) MEG3/Gtl2-DMR is differentially methylated in the body and exhibits non-DMR in the placenta. Second, the IG-DMR and the MEG3/Gtl2-DMR show a hierarchical interaction on the maternally derived chromosome in both the human and the mouse bodies. Indeed, the MEG3/Gtl2-DMR is epimutated in patient 1 and mice with maternally inherited ΔIG-DMR, and the IG-DMR is normally methylated in patient 2 and mice with maternally inherited ΔGtl2-DMR. Third, the function of the IG-DMR is comparable between human and mouse bodies and different between human and mouse placentas. Indeed, patient 1 has upd(14)pat body and placental phenotypes, whereas mice with the ΔIG-DMR of maternal origin have PatDi(12)-compatible body phenotype and apparently normal placental phenotype. It is likely that imprinting regulation in the mouse placenta is contributed by some mechanism(s) other than the methylation pattern of the IG-DMR, such as chromatin conformation This imprinted region has also been studied in the mouse. Clinical and molecular findings in wildtype mice Gtl2-DMR mice appears to be drastically complicated by the retained neomycin cassette in the upstream region of Gtl2. Indeed, it has been shown that the insertion of a lacZ gene or a neomycin gene in the similar upstream region of Gtl2 causes severely dysregulated expression patterns and abnormal phenotypes after both paternal and maternal transmissions Gtl2-DMR is reminiscent of that in sheep homozygous for the callipyge mutation Gtl2-DMR mice MEG3/Gtl2-DMR has a similar or different function between the human and the mouse.Unfortunately, however, the data of ΔMEG3-DMR in the placenta and grossly deleted MEG3-DMR in the body. Second, the clinical features in the mother of patient 1 such as short stature and obesity are often observed in cases with upd(14)mat mat phenotype is primarily ascribed to loss of functional DLK1 mat . Howevernal DLK1  [2], [34MEG3-DMR in imprinting control. Thus, this study provides significant advance in the clarification of mechanisms involved in the imprinting regulation at the 14q32.2 imprinted region and the development of upd(14) phenotype.In summary, the results show a hierarchical interaction and distinct functional properties of the IG-DMR and the This study was approved by the Institutional Review Board Committees at National Center for Child health and Development, University College Dublin, and Dokkyo University School of Medicine, and performed after obtaining written informed consent.All the primers utilized in this study are summarized in DLK1, MEG3, RTL1, MEG8 and snoRNAs, and with mirVana miRNA Isolation Kit (Ambion) for microRNAs. For paraffin-embedded tissues including the placenta, brain, lung, heart, liver, spleen, kidney, bladder, and small intestine, gDNA and RNA samples were extracted with RecoverAll Total Nucleic Acids Isolation Kit (Ambion) using slices of 40 µm thick. For fresh control placental samples, gDNA and RNA were extracted using ISOGEN (Nippon Gene). After treating total RNA samples with DNase, cDNA samples for DLK1, MEG3, MEG8, and snoRNAs were prepared with oligo(dT) primers from 1 µg of RNA using Superscript III Reverse Transcriptase (Invitrogen), and those for microRNAs were synthesized from 300 ng of RNA using TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems). For RTL1, cDNA samples were synthesized with RTL1-specific primers that do not amplify RTL1as. Control gDNA and cDNA samples were extracted from adult leukocytes and neonatal skin fibroblasts purchased from Takara Bio Inc. Japan, and from a fresh placenta of 38 weeks of gestation. Metaphase spreads were prepared from leukocytes and skin fibroblasts using colcemide (Invitrogen).For leukocytes and skin fibroblasts, genomic DNA (gDNA) samples were extracted with FlexiGene DNA Kit (Qiagen), and RNA samples were prepared with RNeasy Plus Mini (Qiagen) for Microsatellite analysis and SNP genotyping were performed as described previously Methylation analysis was performed for gDNA treated with bisulfite using the EZ DNA Methylation Kit (Zymo Research). After PCR amplification using primer sets that hybridize both methylated and unmethylated clones because of lack of CpG dinucleotides within the primer sequences, the PCR products were digested with appropriate restriction enzymes for combined bisulfite restriction analysis. For bisulfite sequencing, the PCR products were subcloned with TOPO TA Cloning Kit (Invitrogen) and subjected to direct sequencing on the CEQ 8000 autosequencer.DLK1, RTL1, MEG3, MEG8, and snoRNAs using primers hybridizing to exonic or transcribed sequences, and one µl of PCR reaction solutions was loaded onto Gel-Dye Mix (Agilent). Taqman real-time PCR was carried out using the probe-primer mixtures  on the ABI PRISM 7000. Data were normalized against GAPDH  for MEG3 and MEG8 and against RNU48 (assay ID: 0010006) for the remaining miRs. The expression studies were performed three times for each sample.Standard RT-PCR was performed for MEG3 in the leukocytes of the mother of patient 1, direct sequence data for informative cSNPs were compared between gDNA and cDNA. To analyze the imprinting status of RTL1 in the placental sample of patient 1 and that of DLK1 in the pituitary and adrenal samples of patient 2, RT-PCR products containing exonic cSNPs informative for the parental origin were subcloned with TOPO TA Cloning Kit, and multiple clones were subjected to direct sequencing on the CEQ 8000 autosequencer. Furthermore, MEG3 expression pattern was examined using leukocyte gDNA and cDNA samples from multiple normal subjects and leukocyte gDNA samples from their mothers, and RTL1 expression pattern was analyzed using gDNA and cDNA samples from multiple fresh normal placentas and leukocyte gDNA from the mothers.To examine the imprinting status of Figure S1D14S985 is present in two copies  Quantitative real-time PCR analysis (q-PCR) for four regions (q-PCR-1-4) in patient 2. The q-PCR-1 and q-PCR-2 regions are present in two copies whereas q-PCR-3 and q-PCR-4 regions are present in a single copy in patient 2. The four regions are present in two copies in the parents and a control subject, in a single copy in the two previously reported patients with microdeletions involving the examined regions Click here for additional data file.Figure S2MEG3 expression in the leukocytes of normal subjects. Genotyping has been performed for three cSNPs using genomic DNA (gDNA) and cDNA of leukocytes from control subjects and gDNA samples of their mothers, indicating that both maternally and non-maternally  derived alleles are delineated in the gDNA, whereas maternally inherited alleles alone are identified in cDNA. These three cSNPs have also been studied in the mother of patient 1  derived alleles are delineated in the gDNA, whereas a non-maternally  inherited allele alone is detected in cDNA. This cSNP has also been examined in the placenta of patient 1  Maternal atient 1 . (B) Patatient 1 . Further(0.39 MB TIF)Click here for additional data file.Figure S3MEG3-DMR of maternal origin in the body, and that of the IG-DMR of maternal origin in the placenta (the MEG3-DMR is rather hypomethylated in the placenta)  is predicted to have impaired the IG-DMR as the primary target, followed by the epimutation (hypermethylation) of the MEG3-DMR after fertilization; (2) Loss of the hypomethylated MEG3-DMR of maternal origin leads to paternalization of the imprinted region in the body; and (3) Loss of the hypomethylated IG-DMR of maternal origin results in paternalization of the imprinted region in the placenta. Furthermore, epigenotype-phenotype correlations imply that the severity of upd(14)pat phenotype is primarily determined by the RTL1 expression dosage rather than the DLK1 expression dosage MEG3-DMR of paternal origin  is predicted to have affected the IG-DMR as the primary target, followed by the epimutation (hypomethylation) of the MEG3-DMR after fertilization; and (2) Loss of the hypermethylated DMRs of paternal origin has no effect on the imprinting status DLK1 and RTL1 from the paternally derived chromosome mat, it remains to be determined whether the difference in the MEGs expression dosage has major clinical effects or not. (C) Normal and upd(14)pat/mat subjects.Schematic representation of the observed and predicted methylation and expression patterns in previously reported cases with upd(14)pat/mat-like phenotypes and in normal and upd(14)pat/mat subjects. For the explanations of the illustrations, see the legend for (2.72 MB TIF)Click here for additional data file.Table S1The results of microsatellite and SNP analyses.(0.19 MB DOC)Click here for additional data file.Table S2Clinical features in the mother of patient 1.(0.09 MB DOC)Click here for additional data file.Table S3Primers utilized in the present study.(0.14 MB DOC)Click here for additional data file.


Single nucleotide polymorphisms (SNPs) have been used extensively in genetics and epidemiology studies. Traditionally, SNPs that did not pass the Hardy-Weinberg equilibrium (HWE) test were excluded from these analyses. Many investigators have addressed possible causes for departure from HWE, including genotyping errors, population admixture and segmental duplication. Recent large-scale surveys have revealed abundant structural variations in the human genome, including copy number variations (CNVs). This suggests that a significant number of SNPs must be within these regions, which may cause deviation from HWE.We performed a Bayesian analysis on the potential effect of copy number variation, segmental duplication and genotyping errors on the behavior of SNPs. Our results suggest that copy number variation is a major factor of HWE violation for SNPs with a small minor allele frequency, when the sample size is large and the genotyping error rate is 0∼1%.Our study provides the posterior probability that a SNP falls in a CNV or a segmental duplication, given the observed allele frequency of the SNP, sample size and the significance level of HWE testing. Single nucleotide polymorphisms (SNPs) are common biallelic variations that are widely used as genetic markers in linkage analyses and association studiesA copy number variation (CNV) is a genomic segment larger than 1 kb that occurs in variable numbers in the genome. When the variant frequency is larger than 1% in a population, it is called a copy number polymorphism (CNP). In some contexts, CNV stands for copy number variantsA segmental duplication (SD) refers to a large duplicated sequence in the genome, conventionally longer than 1 kb with at least 90% sequence identity between duplicate copies  and extension reactions cannot differentiate them. Finding out the exact genotypes for CNVs is also a challenging problem and only relative quantification is available to dateOur study focused on relatively small scale SNP studies with limited information. Detection and validation of CNVs through experimental and computational methods have been an ongoing problem. However such information is often limited due to difference in population (e.g. ethnicity), lack of confirmed boundaries, and quantification relative to the population average than the absolute number of copies.Methods have been developed specifically for detecting CNVs using a large number of SNPs. SNP arrays (BeadArray™ by Illumina and GeneChip® by Affymetrix) became available recently that allow simultaneous genotyping of CNVs and SNPs. Software that detects CNVs from the SNP arrays has been developed , r = 0. When duplication is fixed in all individuals in the population , r = 1. For convenience, here r∈ (i.e. 0<r<1) is treated equivalent to a CNV, r = 0 to a regular genomic region, and r = 1 to a SD.Under a biallelic CNV assumption, we can imagine a situation as depicted in If both sites are polymorphic with different pairs of bases, the observed SNP will be triallelic , which are not considered in the current study. Here, we assume the observed SNP is biallelic, as well as the true sites and the CNV itself.1 and p2 and CNV allele frequency r, observed SNP genotype frequencies Given true SNP allele frequencies pIn theory, SNP genotyping errors can be in both ways and its rate depends on which nucleotides are involved. However, it is more common to misread a heterozygote as a homozygote. In our mixture model, we take a conservative approach and assume that all genotyping errors mistake a heterozygote as a homozygote, and not the other way around. If we consider both directions, the two effects counterbalance each other and contribute less to HWD. Thus, our assumption of one-way genotyping error means that the genotyping error fully contributes to HWD and does not cancel out within itself.1, p2 and Our first goal is to understand the relationship between HWD, r, pfUnder HWE, θ = 1. When there are excessive heterozygotes, θ>1. When there are more homozygotes than expected under HWE, θ<1. Unlike other HWD measures such as the disequilibrium parameter D1 and p2. This indicates that the ectopic site contributes to increasing the number of observed heterozygotes relative to homozygotes. Based on the assumption of no other causes of HWD such as SNP genotyping errors, θ never goes below 1 (log(θ) is always ≥0). Thus, duplication always results in excessive heterozygotes.As seen in 2. The plots in 2.Given the observed minor allele frequency, the possible values of r vary widely depending on the assumption of p1 can be very large. Thus, in this case the observed allele frequency cannot serve as a substitute for the true allele frequency. In the majority of the cases, the minor allele frequency is overestimated. g). Several hypothesis tests for HWE have been proposed, including the most commonly used chi-square goodness-of-fit testP(CNV|HWD), or the probability that a SNP is in a CNV ), given that the SNP is in HWD, was computed at different observed allele frequency. At a 5% or higher genotyping error, CNV or SD is less likely to be the cause of HWD.Out results show that the probability of a SNP being in a duplicated region given HWD depends on the observed allele frequency. In case of a high observed minor allele frequency, HWD tends to be due to duplication, whereas in case of a small Hosking et al.in silico analysis that 3.7% of validated SNPs and 13.1% of nonvalidated SNPs were found in segmental duplicons. We interpret this as 7% on average, considering 65.2% of the SNPs used were valid in their analysis. It is similar to but slightly higher than the estimated proportion of SD in the genome. We simply used the reported genomic proportions of CNV or of SD as the prior probabilities of a SNP being in a CNV or an SD. Considering the previous reportsFor the prior distribution of r, we incorporated estimates from previous studies about CNVs. Fredman et al.Our beta prior assumes about 50% of the CNVs have a minor allele frequency (MAF) more than 3.5% and about 13% and 1.5% have >10% and >20% MAFs, respectively, which are approximately consistent with Iafrate et al.'s estimateGenotyping error rates for Sequenom , Illumina  and other new methods were reported as less than 1% . Sources and types of genotyping errors may vary and such heterogeneous effects were not considered in our model.Cox and KraftHunter et al.Although at least some CNVs are generated in tandemIn addition, we assumed that an underlying CNV itself is under HWE. Sebat et al.Nguyen et al.A<|0.03| as the limit of HWD in human population, as suggested by a National Research Council report A = 0 indicates HWEOur model assumes duplication, genotyping error and random variation as the only sources of HWD. In reality, there are other sources of HWD. One of them is the noise in the actual population. Shoemaker et al.Our model does not consider population admixture effect. Population admixture is an important confounding factor in case-control studies and it is known that the admixture effect causes deviation from HWE, as we mentioned in the background section of our manuscript. Nevertheless, with sample size <1000, population admixture can be detected by HWE testing only when f>0.4 and k>0.2, where f is the allele frequency difference between the mixed populations and k the proportion of the minor populationOur study shows that the degree of HWD increases with respect to r, the frequency of two-copy alleles. Duplication acts in the direction of increasing observed heterozygotes. The results of our Bayesian analysis suggest that copy number variation can be a major contributor to HWD, when sample size is large and genotyping error is small. The relative contribution of CNV and SD to HWD varies with observed SNP allele frequency.1 and p2 and observed genotype frequencies and allele frequencies were computed. Values for log2(θ) were also obtained from the computed genotype frequencies. The simulation was done using a Perl script that we wrote, and the plots were drawn using the R language.We varied r, p2, we have plotted the range of possible values of the true allele frequency p1, given observed allele frequency Given a value of r, either estimated or derived from genotyping experiments, we asked whether the true allele frequency for the SNP could be derived. For varying values of p1 is not less informative than a posterior distribution of p1, because in this case the posterior probability depends only on p2, for which we assumed a uniform prior except in marginal cases.Here, the range of p1 and p2. When r is fixed, p1 and p2 have complementary effect on 1 can be obtained by assuming the minimum and maximum values of p2, given r and 1 for different values of r and 1, p2 values ranging from 0 to 1, for a given r.1, given the observed allele frequency measured using a pooled-sample technique. The pooled-sample SNP allele frequency, which is different from the allele frequency derived from genotype frequencies (equations (4)), can also be expressed in terms of r, p1 and p2:Additionally, we have looked at the range of pExperimentally, a pooled sample allele frequency can be obtained by pooling DNA samples and measuring the relative quantities of each allele in the pooled samplen, frequency of allele A of the observed SNP, 2 goodness-of-fit test without continuity correction at α = 0.05 or 0.01. Though it has been proposed that other tests are superior under certain conditions2 test, to provide a practical perspective. Four different genotyping error models were tried including 0%, 1%, 5% and 25%. x% genotyping error is defined as follows: x% of heterozygote are read as one of the homozygotes and another x% is read as the other homozygote. This results in excessive homozygotes. Our genotyping error model only misreads heterozygotes as homozygotes, but not vise versa. It is trivial to include the opposite trend, but we do not for the following reasons: an additional genotyping error in the opposite direction would only decrease the overall deviation from HWE by counterbalancing the increased number of observed homozygotes. Experimental techniques often miss one of the two existing alleles, but less often identify an allele that does not exist, unless there is contamination or a high noise level. The 25% genotyping error is not realistic but it provides a comparative perspective.The second goal is to compute P(CNV|HWD) and P(HWD|CNV), given sample size  1 and p2 and likelihood of 1, p2, n and α.A procedure for computing the conditional probabilities P(CNV|HWD) and P(HWD|CNV) is described below , r = 0, r = 1 were first set to 14%, 81% and 5%, and within r∈, the density of r was set to either a beta or a uniform distribution. The beta function parameters were determined so that the mean of r within r∈ is 0.05.1 and p2 was also set to a hierarchical distribution, so that the probability of being biallelic is reasonably smaller than that of being monomorphic, for each site. Within p1∈ or p2∈, p1 and p2 are uniformly distributed  or pairs or singlets were drawn from the prior distribution within 3, 2 or , respectively. In order to approximate the integrals, M independent random samples of triplets .All the codes were written in the R language (Method S1PDF file describing method detail.(0.06 MB PDF)Click here for additional data file.Figure S1Range of p1, given r and pooled sample allele frequency. The black diagonal line is the case where the true frequency p1 is identical to the observed frequency. Red and blue lines represent p2 = 0 and p2 = 1, respectively.(3.64 MB EPS)Click here for additional data file.Figure S2The posterior probabilities given HWD computed using the uniform prior, at n = 100, a = 0.05 (left), and n = 1000, a = 0.01 (right), with respect to observed allele frequency. Each row respresents error rate of 0%, 1%, 5% and 25%, from top to bottom, respectively. Estimates are the sample mean of two replicates and the standard deviations are depicted with error bars.(4.98 MB EPS)Click here for additional data file.Figure S3The likelihoods computed using the uniform prior, at n = 100, a = 0.05 (left), and n = 1000, a = 0.01 (right), with respect to observed allele frequency. Each row respresents error rate of 0%, 1%, 5% and 25%, from top to bottom, respectively. Estimates are the sample mean of two replicates and the standard deviations are depicted with error bars.(4.99 MB EPS)Click here for additional data file.Figure S4Convergence of the 15 integrals. The Y values represent joint probabilities .(1.30 MB EPS)Click here for additional data file.


We report results from the analysis of complete mitochondrial DNA (mtDNA) sequences from 112 Japanese semi-supercentenarians (aged above 105 years) combined with previously published data from 96 patients in each of three non-disease phenotypes: centenarians (99–105 years of age), healthy non-obese males, obese young males and four disease phenotypes, diabetics with and without angiopathy, and Alzheimer's and Parkinson's disease patients. We analyze the correlation between mitochondrial polymorphisms and the longevity phenotype using two different methods. We first use an exhaustive algorithm to identify all maximal patterns of polymorphisms shared by at least five individuals and define a significance score for enrichment of the patterns in each phenotype relative to healthy normals. Our study confirms the correlations observed in a previous study showing enrichment of a hierarchy of haplogroups in the D clade for longevity. For the extreme longevity phenotype we see a single statistically significant signal: a progressive enrichment of certain “beneficial” patterns in centenarians and semi-supercentenarians in the D4a haplogroup. We then use Principal Component Spectral Analysis of the SNP-SNP Covariance Matrix to compare the measured eigenvalues to a Null distribution of eigenvalues on Gaussian datasets to determine whether the correlations in the data (due to longevity) arises from some property of the mutations themselves or whether they are due to population structure. The conclusion is that the correlations are entirely due to population structure (phylogenetic tree). We find no signal for a functional mtDNA SNP correlated with longevity. The fact that the correlations are from the population structure suggests that hitch-hiking on autosomal events is a possible explanation for the observed correlations. Modern humans have a much higher life expectancy than our closest relatives, the chimpanzees Ideally, genetic analysis should include SNPs on both mtDNA and autosomal chromosomes. This would allow a genome wide analysis to identify mutations and pathways associated with longevity and their correlation with geographic clustering and migration histories of population groups as inferred from mtDNA and Y chromosome phylogeny. Unfortunately, although some initial steps have been taken in this direction In this paper we develop techniques to identify mutations for longevity by looking for correlations between the longevity phenotype, mtSNPs and mtDNA haplogroups. The correlations could be due to functional polymorphisms or to population structure. We analyze the correlations using two different methods. In the first, we use several statistical scores to directly test correlations between haplogroups and the longevity phenotype. In the second, we apply Principal Component Spectral Analysis to separate correlations due to functional polymorphisms from those due to population structure. The results are similar. We find that there is a strong selection for Longevity in the D super-haplogroup and strong progressive correlations in the D→D4→D4a lineage. However, using Gaussian generated SNPs distributions we find that this association is most likely due to population structure . We identify the mtSNP 14979C (Cytb: Ile78Thr) in D4a to be correlated with extreme longevity. To study whether this mtSNPs might be hitchhiking on nuclear SNPs, we looked for association signatures of enrichment in the Japanese D clade samples in HapMaP http://mtsnp.tmig.or.jp/mtsnp/index_e.shtml for more information). In addition, we include new data from 112 complete mtDNA sequences from semi-supercentenarians (labeled SSC in this paper), ranging in age from 105–115 years (mean age 107.3±1.2 years). These semi-supercentenarians included 96 females and 16 males and were recruited from all over Japan. The study protocol complied with the Declaration of Helsinki and was approved by the Committees on the Ethics of Human Research of Keio University School of Medicine, Gifu International Institute of Biotechnology, and Tokyo Metropolitan Institute of Gerontology. Written informed consent was obtained from each participant or a member of his/her immediate family. A detailed description of the methods used in the collection, extraction and sequencing of the mtDNA samples used in the present study is given in our website (http://mtsnp.tmig.or.jp/mtsnp/annex_e.html). In brief, sequences were analyzed with a 3130xl Genetic Analyzer from Applied Biosystems. Complete sequences were aligned, assembled, and compared with the program Sequencher 4.1 . For verification, visual inspection of each candidate mtSNP was carried out. At least 2 overlapping DNA templates amplified with different primer pairs were used for identification of each mtSNP. Newly obtained mtDNA sequences of semi-supercentenarians were deposited in GenBank as the following accession numbers (AP010661-AP010772 for SCsq0113-SCsq0113). The 112 new sequences are attached in FASTA format in http://mtsnp.tmig.or.jp/mtsnp/search_mtDNA_sequence_e.html.http://www.mitomap.org/mitoseq.html) using the Miller-Meyers http://emboss.sourceforge.net/). We applied a robust technique developed in The 672 sequences from http://mtsnp.tmig.or.jp/mtsnp/search_mtDNA_sequence_e.html). The new complete mtDNA sequences of 112 semi-supercentenarians are in Out of the 261 mtSNPs identified above, we restricted the analysis to the 235 which were more than 1% polymorphic across the 112 semi-supercentenarian samples. We also eliminated several SNPs in the region from locus 303 to 317 and from 16180 to 16195, which are in the so called hypervariable regions HV1 (16024–16383) and HV2 (57–372). This was because we determined, by examination of the sequences in these regions, that they were length polymorphisms and not true mutations. This left us with a dataset with 227 SNPs and 782 samples which were used for further analysis see  can be dSL follows:To measure the degree of association of an mtSNP to longevity, we define a mtSNP enrichment score ST is the fraction of samples in the centenarians that have the SNP, fSC is the fraction of samples in the control group  that have the SNP. σSNT is the standard deviations of the fractions of samples in the collection of the 6 other non-centenarian (ie non-target or NT) groups, corresponding to healthy non-obese males, obese young males, diabetics with and without angiopathy, Alzheimer's and Parkinson's disease patients. The statistics SL is motivated by an analogy with the t-test.This score is defined for each SNP separately. Consider the case where we are computing this score for centenarians. fA similar score that measures haplogroup enrichment for longevity is defined as: HTf is the fraction of samples in the target (longevity) group (either centenarians or semi-supercentenarians) that belong to the haplogroup H, SCf is the fraction of samples in the control group  that belong to the haplogroup H and HNTσ is the standard deviation of these fractions across the 6 non target groups.This measure is defined for each haplogroup H. website: http://genome.cs.iastate.edu/supertree/download/biclique. These bi-cliques define “patterns of mutation” which seem to be associated synergistically to longevity. The association of each pattern to longevity is estimated using an enrichment score PL for patterns. We used the method of PTf that exhibit the pattern in the target longevity group (centenarians or semi-supercentenarians) minus the fraction of samples PCf that exhibit the same pattern in the control group, divided by the standard deviation PNTσ of these fractions over all the 6 non-target phenotypes.This statistic is defined for each pattern P. It represents the difference between the fraction of samples S, LHL and PL were calculated by comparing the experimental values to a null distribution created by computing their values in 1 million datasets obtained by a random permutation of phenotype labels. The fraction of values out of 1 million, that the enrichment score was greater than S, LHL, PL respectively, is the corresponding p-value. This permutation method for calculating p values is preferred because it avoids assumptions about the underlying distribution. We correct for the false discovery rate (FDR) using the method of Benjamini and Hochberg The p-values for ij}M = {m of size n x n, where n is the number of mtSNPs seen in the patterns. The entries ijm are defined so that they measure the beneficial effect of SNP i and SNP j if found in the same individual. Let P be the space of all patterns in the data and M are:Haplogroups associated with longevity were identified as those with a larger proportion of enriched patterns out of the total number of patterns associated with the haplogroup. The Fisher Exact test was used to examine the significance of the association between patterns and haplogroups. This test gives the exact probability of seeing a certain association between the two categorical variables by chance. We looked for associations between polymorphisms in the enriched patterns by identifying mutations which appear together in patterns and are simultaneously strongly associated with the longevity phenotypes. This was done by creating an agreement matrix SNP i and SNP j appear together in the same enriched pattern and the total number of patterns that have both mutations. The agreement matrix is diagonal and symmetric. The diagonal entries represent the greatest possible correlation . Setting the diagonal entries to 1 sets a threshold in the matrix for the greatest possible correlation between SNP i and SNP j. This is useful when we use the matrix to identify correlated cliques by thinking of it as a connectivity matrix for a graph where the nodes (labeled by the index i) are SNP locations and nodes i and j are considered linked by a bond if their correlation is greater than some threshold t. By successively lowering the value of the threshold from higher to lower values, one can identify the core set of SNPs that form a clique, which is a set of SNPs where each pair-wise correlation exceeds the threshold. Rows and columns of the matrix are then sorted (clustered) to place the clique SNPS in the top left corner. Color coding of the resulting matrix based on the entry values produces a heat map showing mtSNPs at different levels of enrichment.This score is similar to the pattern based feature selection defined in To look for an association between the mtDNA polymorphisms and their haplogroups with nuclear mutations and pathways, we analyzed the 44 Japanese samples in the HapMap database It is known that population structure can introduce artificial correlations between SNPs and phenotypes. The classic example is SNPs in the lactase gene that spuriously correlates with the height phenotype. These spurious correlations can be easily understood by considering a simple example: imagine one homogeneous population that splits into two. Each of these populations evolves independently and develops a set of SNPs (markers) that clearly differentiate them. All these SNPs appeared as linked, because of the common ancestry of these populations. If, for any reason, one population has a different phenotype  than the other, all these SNPs will correlate with the phenotype. However, this correlation does not mean that these SNPs are functional with respect to the phenotype. In fact, the association is merely due to the population structure of the data. It does not mean that the population does not have the distinguishing property as this is undisputable. What it means is that the association between the SNP and the phenotype is due to the appearance of the phenotype (for an unknown reason) after split from a common ancestor (ie. it is due to the phylogeny of haplogroups). Such correlations often introduce a high number of false positive spurious correlations in association studies.Several methods SNP x Nsamples . Assume that the entries Mij are independent standard Normal Gaussian distributed variables for each SNP location. The product of M and its transpose MT represents the correlation matrix of SNP-SNP correlations which would be obtained by chance. The distribution of eigenvalues of MMT is the Tracy-Widom distribution which represents the null-probability to get a high eigenvalue by chance in data without population structure. We use a simple method  to find the cutoff x in eigenvalue to separate population structure from functional polymorphisms. We identify the highest k eigenvalues in the real data that represent more than a fraction f = 0.95 of the variation in the data as those representing population structure i = 1k λi]/NSNP where the eigenvalues λi are sorted from highest to lowest value. The value of x is then obtained from: x = (λk+λk+1)/2.The decision on where to place the cutoff to separate “high” eigenmodes from “low” eigenmodes is made using the Tracy-Widom distribution >0.15 for p-value<0.02) we found that the following 45 mtSNPs were significantly correlated with longevity: 103A, 194T, 199C, This analysis suggests that there is no functional mtSNPs significantly correlated with longevity. All significant correlations seem to be associated only with population structure (phylogenetic tree), suggesting that the significant longevity enrichment found in D4a (which is not in doubt) has causes other than mtDNA SNPs alone.n = 1312) was 7.39%. The overall frequency of 14979C (a marker of D4a) in our control samples was 8.44% (477 of 5651); 9.6% in Gifu (263 of 2748) A strong finding of this paper, that is consistent across many different analysis techniques, is that in Japanese individuals are more likely to live longer if they belong to the D4a haplogroup. This result is also supported by previous studies Our results suggest a possible scenario for the evolution of a longevity phenotype, which may develop by combinations of protective mutations. The signal for longevity in our data is strongest in D4a because it has the greatest number of protective mutations. Our spectral analysis suggests that the longevity phenotype must require protective mutations on nuclear chromosomes in addition to the defining mtDNA SNPs for D4a. Our rather limited association study on Japanese sequences in HapMap We attribute the reduction of the longevity effect seen in D4b2b A limitation of our study is the small sample size and lack of direct correlation with SNPs on nuclear chromosomes. We are currently trying to mitigate this problem by identifying nuclear SNPs associated with our samples on the complexes formed by proteins encoded by mtDNA. Adaptive selection of mitochondrial complex I subunits during primate evolution has been reported to support the concept of co-evolution of mtDNA-encoded and nDNA-encoded subunits of the OXPHOS complexes Many mutational hotspots are known to exist in mtDNA  is enriched (80% to 20%) in D4a compared to its wild-type form 10410T. This polymorphism is a known identifying marker for subhaplogroup D4a1 There are two ways in which any group of polymorphisms might associate with a phenotype. The most direct is if their presence alone confers the phenotype. An indirect way is if the polymorphisms are merely associated (correlated) with the phenotype because of population structure. Our spectral analysis suggests that the latter association is what is actually realized in the data. This does not mean that the association between D4a and longevity is spurious – only that the correlation between the defining SNPs for D4a and longevity is spurious. The longevity phenotype appears to correlate with D4a because it appeared due to factors other than the defining SNPs of D4a. Whereas there are many other sources of the association , one possibility is that the D4a haplogroup is enriched in autosomal SNPs that confer the longevity phenotype. Whereas our paper raises this as a strong possibility, our efforts to identify such autosomal correlations in the HapMap dataset were unsuccessful because of the limited sample size of the D haplogroup and the limited SNP coverage in the HapMap study. The hypothesis of association between D4a and longevity due to autosomal polymorphisms can be tested by a study involving more individuals in the D haplogroup in Japan and elsewhere. It is particularly important in this context to include D4a individuals outside Japan to rule out associations due to diet and lifestyle.Data S1mtDNA sequences of 112 semi-supercentenarians in FASTA format.(1.87 MB TXT)Click here for additional data file.Table S1Table of mtSNPs detected in each individual.(2.75 MB XLS)Click here for additional data file.Table S2This table shows the number of samples in each robust haplogroup in each of the 8 phenotypes. Ambiguous sequences, which could not be classified based on both our clustering system and standard markers in the literature are labelled with *. Thus, M* labels those samples which can be classified into the M clade but not further and so on.(0.09 MB DOC)Click here for additional data file.


Hepatocellular carcinoma (HCC) is frequently preceded by hepatitis virus infection or alcohol abuse. Genetic backgrounds may increase susceptibility to HCC from these exposures.Mitochondrial DNA (mtDNA) of peripheral blood, tumor, and/or adjacent non-tumor tissue from 49 hepatitis B virus-related and 11 alcohol-related HCC patients, and from 38 controls without HCC were examined for single nucleotide polymorphisms (SNPs) and mutations in the D-Loop region.Single nucleotide polymorphisms (SNPs) in the D-loop region of mt DNA were examined in HCC patients. Individual SNPs, namely the 16266C/T, 16293A/G, 16299A/G, 16303G/A, 242C/T, 368A/G, and 462C/T minor alleles, were associated with increased risk for alcohol- HCC, and the 523A/del was associated with increased risks of both HCC types. The mitochondrial haplotypes under the M haplogroup with a defining 489C polymorphism were detected in 27 (55.1%) of HBV-HCCand 8 (72.7%) of alcohol- HCC patients, and in 15 (39.5%) of controls. Frequencies of the 489T/152T, 489T/523A, and 489T/525C haplotypes were significantly reduced in HBV-HCC patients compared with controls. In contrast, the haplotypes of 489C with 152T, 249A, 309C, 523Del, or 525Del associated significantly with increase of alcohol-HCC risk. Mutations in the D-Loop region were detected in 5 adjacent non-tumor tissues and increased in cancer stage .In sum, mitochondrial haplotypes may differentially predispose patients to HBV-HCC and alcohol-HCC. Mutations of the mitochondrial D-Loop sequence may relate to HCC development. Hepatocellular carcinoma (HCC) is the fifth most frequent cancer and the third leading cause of cancer death worldwide, with over a half million mortality every year . HCC is Hepatitis virus infection and alcohol abuse are associated with increased oxidative stress in liver cells, resulting in DNA changes including mitochondrial DNA (mtDNA) instability ,8. The hhttp://www.mitomap.org In this study, we sequenced a region of about 1 kb franking almost all the D-Loop in cancerous and adjacent noncancerous tissues, and blood from the same patients of both hepatitis B virus-related (HBV-HCC) and alcohol-related HCC . Many polymorphisms and somatic mutations were identified. When compared with controls without HCC, these genetic information are particular valuable to predict risk and to reveal natural history of the two types of HCC.Sequence changes have been examined extensively in the D-Loop in cancers ,19,20, bWe obtained histologically confirmed cancerous and corresponding noncancerous liver tissues from patients of 11 alcohol-HCC  and 49 HBV- HCC, and liver tissues with no detectable malignancies except hepatic hemangioma from 38 control patients at the Fourth Hospital of Hebei Medical University. The hemangioma patients under surgery were selected as control just because it was vascular malformation with developmental aberration and we can obtain normal liver tissue from the specimen. Clinical characteristics of HCC patients and controls were listed in Table The forward primer 5'-CCCCATGCTTACAAGCAAGT-3' (nucleotide 16190-16209) and reverse primer 5'-GCTTTGAGGAGGTAAGCTAC-3' (nucleotide 602-583) were used for amplification of a 982 bp product from mtDNA D-Loop region as described previously . PCR washttp://wbiomed.curtin.edu.au/genepopPaired and unpaired Student's t-test were used as appropriate to determine the differences SNP distribution within the D-Loop region and the number of SNPs per patient among groups. Fisher's exact test and chi-square were used accordingly to analyze dichotomous values, such as presence or absence of an individual SNP in each patient group. A p value of less than 0.05 was considered statistically significant. The Wilcoxon rank sum test was used to determine statistical differences among age, sex and Child-Pugh grade. Pairwise linkage disequilibrium between SNPs was done using GENEPOP AC_000021 were detected in 92 sites of the 982-bp mitochondria D-Loop region from blood samples. The minor allele frequency ranged from 1.0% (1/98) to 46.90 (46/98). Of these, 13 SNPs  were new, as they were not reported in a mitochondria database http://www.mitomap.org. SNP numbers ranged from 3 to 13 for individuals, no statistical difference for SNP numbers in each individual referring to sex was observed. The pairwise linkage disequilibrium analysis between the SNPs identified in the D-loop of HCC patients and controls was performed, the paired loci with linkage disequilibrium p value less than 0.001 were listed and for 16293G, 523del, and 525del alleles in alcohol-HCC (p < 0.05) patients (Table The M haplogroup, defined by the presence of 489C, was used to stratify the subjectgroups for subsequent analysis. When the status of the 489C was combined with the above frequent SNPs, predictive values for the risks of HBV-HCC and alcohol-HCC were immediately detected in several haplotypes (Table In addition to SNPs, mutations in the D-Loop region were identified by comparing the sequences in tumor and adjacent non-tumor areas with the genotype in blood of the same subject, except for patient #1 whose blood DNA was not available for sequence analysis Table . InsteadAn increase of mutations in the D-Loop region of mitochondria has been reported in HCC ,20,27. TAC_000021 sequence for Caucasians. The actual number of SNPs may be less if the reference sequence was of Chinese origin. These SNPs are less likely to arise from mutations in blood mitochondria DNA because the same SNPs were observed in corresponding non-tumor tissues. Also, they are homoplasmy with single peak detected at each SNP site. This suggests that the SNPs are germline sequence variants and also raises the possibility that some of homoplasmic mutations may actually have been SNPs in previous studies that do not have blood DNA for comparison. When compared with control, frequent SNPs in both HBV-HCC and alcohol-HCC patients provide the first evidence that a high SNP frequency seem to predisposes patients to HCC regardless of different etiology (Table SNPs appear to be common in this Chinese population with average of 7 to 9 for each patient in reference to GenBank From the SNP spectrum Fig. , it is vMutations in the D-Loop region are also frequent in HBV-HCC and the frequency of 21/49 (42.9%, Table Although the mechanism of mutation is still unclear, free radicals generated in mitochondria could be responsible at least partly for these mutations. The D-loop region of mtDNA is important for regulation of mitochondrial genome replication and expression. Mutation in this region may affect mtDNA replication and may alter electron transport chain. All of these might contribute to early stage of hepatocarcinogenesis. Our data demonstrated that the utility of SNPs and mutations in mitochondria D-Loop region to predict HCC risk and to differentiate HCCs with distinct etiology. The utility of mtDNA SNPs for prediction of HCC risks from different environmental exposures is a promising area for future cancer prevention. Our results links genetic variation of exonuclear genome with cancer risk, subsequent research for SNP in D-loop is more difficult to perform than SNP in individual genes which can be compared by expressional levels, protein property and interacted genes. Future experiments with a large sample size are needed to explore the usage of those minor alleles and to validate the predictive values of SNPs identified in this pilot study.The authors declare that they have no competing interests.RZ and FZ contributed to experimental design, data acquisition and analyses. CW and SW contributed to experimental design, specimen collection, and data acquisition. YHS participated in data analyses, interpretation of results, and preparation of the manuscript. ZG contributed to conception, experimental design, data acquisition, analyses, and interpretation, and manuscript preparation. All authors read and approved the final manuscript.Table S1: Statitical significance of the pairwise linkage disequilibrium analysis among SNP in mitochondrial D-loop.Click here for file


Recent segmental duplications are relatively large (≥ 1 kb) genomic regions of high sequence identity (≥ 90%). They cover approximately 4–5% of the human genome and play important roles in gene evolution and genomic disease. The DNA sequence differences between copies of a segmental duplication represent the result of various mutational events over time, since any two duplication copies originated from the same ancestral DNA sequence. Based on this fact, we have developed a computational scheme for inference of point mutational events in human segmental duplications, which we collectively term duplication-inferred mutations (DIMs). We have characterized these nucleotide substitutions by comparing them with high-quality SNPs from dbSNP, both in terms of sequence context and frequency of substitution types.Overall, DIMs show a lower ratio of transitions relative to transversions than SNPs, although this ratio approaches that of SNPs when considering DIMs within most recent duplications. Our findings indicate that DIMs and SNPs in general are caused by similar mutational mechanisms, with some deviances at the CpG dinucleotide. Furthermore, we discover a large number of reference SNPs that coincide with computationally inferred DIMs. The latter reflects how sequence variation in duplicated sequences can be misinterpreted as ordinary allelic variation.In summary, we show how DNA sequence analysis of segmental duplications can provide a genome-wide mutational spectrum that mirrors recent genome evolution. The inferred set of nucleotide substitutions represents a valuable complement to SNPs for the analysis of genetic variation and point mutagenesis. Let n(uxv) count the number of k-mers that are either uxv or its reverse complement, and define n(u[xy]v) similarly. We count SNPs and DIMs separately.Let p(x) = n(x)/n and p([xy]) = n([xy])/n for reference region and substitution respectively, with n the corresponding total number of nucleotides. Note that by this definition f.ex. p(A) = p(T) is the probability of any nucleotide being either A or T, each with a 1/2 probability of being on either strand. If the middle nucleotide, x or [xy], is independent of the surrounding nucleotides, the expected numbers in the reference regions areThe nucleotide and substitution pair probabilities are n(u*v) is the sum of all n(uxv) for different x, and the division by two is because there is a 1/2 chance that x is on the same strand as u*v. For substitutions,where xy] or u*v are their own reverse complements one should not divide by 2. The overrepresentation (or abundance) is defined as R(uxv) = n(uxv)/μ(uxv), and R(u[x*]v) = n(u[x*]v)/μ(u[x*]v) where u[x*]v indicates the sum over all matching u[xy]v for n and μ.except that if either [EH conceived the study and outlined data analysis tasks. SN performed data retrieval and statistical analysis with help from EAR and TR. EH and TR provided feedback on results obtained. SN and EH drafted the manuscript together. All authors read and approved the final manuscript.


CAPN3) gene in cattle, a gene located within the confidence interval of the QTL, and which is a positional candidate gene based on the biochemical activity of the protein.Quantitative Trait Loci (QTL) affecting meat tenderness have been reported on Bovine chromosome 10. Here we examine variation at the Calpain 3  in the genomic sequence of the These associations confirm the location of a QTL for meat tenderness in this region of bovine chromosome 10. SNP in or near this gene may be responsible for part of the overall difference between taurine and zebu breeds in meat tenderness, and the greater variability in meat tenderness found in zebu and composite breeds. The evidence provided so far suggests that none of these tested SNP are causative mutations. CAPN1), one appearing to be more useful in taurine breeds and one more useful in zebu breeds. On the other hand, although several possible causative mutations have been identified in Calpastatin (CAST), variation at this gene appears to affect all breed types.The status of DNA tests for meat tenderness has been recently discussed  and so fCAPN3) gene could be implicated because it occurred within the confidence interval of the location of the QTL and that calpain 3 protein activity had been implicated in meat tenderness in sheep [CAPN3 have not been tested to determine whether they are associated with meat tenderness in any species.Quantitative trait loci (QTL) for meat tenderness were located to bovine chromosome 10 in a Charolais × Brahman experimental population [in sheep ,7, althoCAPN3 and the testing of these SNP for associations to meat tenderness in a large sample of cattle of several breeds. We found that polymorphisms in the gene were not common in taurine cattle, so associations were only tested in zebu and composite animals, the term crossbred being reserved for animals with parents from different breeds. Zebu breeds and their tropically adapted composites are reported to have a 2 to 3 times greater heritability for meat tenderness than taurine breeds as well as a slightly higher mean value for shear force, a measure of meat tenderness [Here we report the identification of single nucleotide polymorphisms (SNP) in nderness . These SThe Beef CRC cattle, the methods of measurement of phenotypes, and the methods of DNA extraction have been reported previously ,9,10. AfMusculus longissimus lumborum (LLPF) as described previously [N and where sire was fitted as a random effect as previously described [Meat tenderness was measured in kilograms using peak force measurements for the eviously . The leaeviously . The LLPeviously  where LLescribed ,13, and CAPN3 deposited by Ilian and coworkers (Genbank AF115744.1). The location of the exons was placed onto the sequence using a comparative analysis of sequences from other species. Then primers to amplify three segments of the gene were synthesized, one set flanking exon 6, one incorporating introns 10 and 11 and one that incorporated from intron 24 towards the 3' UTR (untranslated region) of the gene. DNA sequences were obtained using the Big-Dye 3.1 terminator kit from Applied Biosystems Inc. , using the manufacturer's instructions. Each fragment was sequenced in both directions in 10 animals including the Angus, Shorthorn and Brahman breeds. The DNA sequences were compared to sequences in Genbank using BLAST [Genomic DNA for SNP discovery was obtained by direct sequencing of PCR products. Primers were obtained from the cDNA sequence for ng BLAST  to confing BLAST . PolyPhrng BLAST  was usedng BLAST .Taqman™ MGB allele discrimination method  as before [SNP were genotyped using the s before  by two is before . Scoringr2 and differences in frequency between breeds were tested as previously described [r2 value was determined as χ21 = Nr2 where N is the sample size [a + d(q-p) where α is the allele substitution effect, a is the additive effect, d is the dominance effect, and p + q = 1 and are the allele frequencies [CAPN3 and CAST SNP was tested using the software EPEE [Genotypes were tested for Hardy-Weinberg Equilibrium (HWE), inferred haplotypes were used to calculate linkage disequilibrium (LD) as measured by escribed ,20,21. Tquencies ,22-25. ECAPN3 haplotypes on meat tenderness, the number of copies of the common haplotype was regressed against the residual meat tenderness values in the same way as the allele substitution effect. Two-locus haplotypes were used in the analysis for the following reasons. Firstly, two-locus SNP haplotypes can be obtained unambiguously from genotypes, except for the double heterozygote and it was ignored in this analysis. Secondly, the analysis was to regress the trait against the number of copies of the common haplotype. Using two-locus haplotypes reduces the genotypic heterogeneity of the one-copy and the no-copy groups, compared to analyses that use haplotypes based on more SNP. The expected number of genotypes equals 3n for n loci. Thirdly, the larger the number of loci making up the haplotype the smaller the number of individuals in the two-copies group, which will affect the accuracy of the estimates. The additive effect and the haplotype substitution effect of the common haplotype were calculated in the same way as the additive and allele substitution effects (cf. above). The use of two-locus haplotypes has the advantage that the analysis is essentially of the interval between the two SNP. With a large number of SNP one might restrict the analysis to adjacent intervals, but as there are only three SNP and therefore only three possible comparisons, all three intervals were analysed.To estimate the effect of CAPN3:c.53T>G (Met18Arg), dbSNP id ss102661473, 2) the intronic CAPN3:c.1538+225G>T, ss102661474, and 3) the intronic CAPN3:c.2443-103G>C, ss102661475.We examined the DNA sequence traces to find polymorphisms that were present in all breeds. All of the SNP that were discovered were either polymorphic in the Brahman animals in the panel or were differences between Brahman on the one hand and the Angus and Shorthorn on the other hand. We chose three SNP that were polymorphic in the Brahman breed for further study, one for each of the fragments that we had sequenced Table . These wc.2443-103G>C, there was 1 heterozygote in 124 taurine animals from the Angus, Hereford and Shorthorn breeds, so we did not use taurine samples for further genotyping because the chance of finding sufficient homozygotes of each genotype in those breeds would be low.To determine whether it was worth genotyping animals of all breeds, because these SNP were discovered in Brahman animals, we tested a group of taurine animals of three breeds. Testing the first SNP, We examined the genotypes for deviations from HWE and for differences between breeds because significant differences in these measures could affect the subsequent analysis of association between DNA markers and the trait. The genotypes for all three SNP were in HWE and there were highly significant differences in genotype frequencies between breeds. The allele frequencies of the minor allele in the composite breeds were approximately half that found in the Brahman breed Table , and givr2 was calculated from haplotypes inferred using the EM algorithm. The alleles were in significant LD in all breeds  and were significant only because the sample size was large. CAPN3:c.1538+225G>T showed moderate LD to c.53T>G and c.2443-103G>C in Brahman but very low LD in Belmont Red and Santa Gertrudis. CAPN3:c.53T>C and c.2443-103G>C showed very high LD in all breeds. All the CAPN3 SNP showed very low LD to CAST:c.2832G>A in all breeds with an average r2 = 0.003, SE = 0.001, N = 9, as expected for unlinked loci. None of the CAPN3-CAST r2 values were significant.To determine whether there may be high levels of LD between alleles, which may affect the interpretation of the allelic association to the trait, ds Table . Some ofCAPN3 SNP and LLPF in the Brahman and Belmont Red breeds but not in the Santa Gertrudis , were significant and the dominance effect was not significant. Although the Belmont Red breed also showed a large significant additive effect, this is based on a few animals and so should be treated with caution. In particular, the allele substitution effect was not significant and was essentially zero. The Belmont Red breed showed significant allele substitution effects for both c.53T>G and c.2443-103G>C. The Brahman breed showed large but non-significant effects for these latter two SNP, but the favourable genotype was reversed and so the allele substitution effect was of opposite polarity.Significant associations were found between a is Table . The strCAPN3 and LLPF (Tables c.1538+225G>T and c.2443-103G>C, showed significant additive or allele substitution effects in all breeds, where the haplotype substitution effect in Brahman equalled 0.22 σp. The effect of Int1, between c.53T>G and c.1538+225G>T, was not significant in all breeds, and the effect of Int3, between c.53T>G and c.2443-103G>C, was not significant in any breed. Notably, the effects of Int1 and Int2 were significant in the Santa Gertrudis even though the individual locus effects were not significant in that breed. The haplotype substitution effects had more similar sizes than the individual SNP allele substitution effects in the different breeds. The haplotype substitution effect had the opposite polarity in the Santa Gertrudis to the other breeds.Significant associations were found for haplotypes of F Tables  and 6. ICAST:c2832G>A were included because breed specific values have not previously been reported for this data set, only values for combined data (Table CAST SNP had similar sized effects in all three breeds. That differs from the CAPN3 SNP in this study, which showed different sizes in different breeds. In the Brahman, the CAPN3:c.1538+225G>T SNP showed a similar size to the CAST:c.2832G>A SNP.The significant associations for ta Table . The allCAPN3 SNP and CAST. One comparison out of 12 had a P value < 0.05, for the additive × additive component in the Santa Gertrudis breed for the comparison between CAPN3:c.1538+225G>T and CAST:c.2832A>G, with aa = 0.19 kg, SE = 0.100, P value = 0.038, which was close to expected for the number of tests performed.We found no strong evidence of epistatic interactions between CAPN3 gene confirm that there is a QTL affecting meat tenderness on bovine chromosome 10. The associations are consistent with CAPN3 being a positional candidate gene for the effect, with the strongest evidence found in Brahman animals. Further genotyping should be performed on neighbouring genes and the surrounding genetic region to exclude other genes. The intronic CAPN3:c.1538+225G>T SNP shows the largest effect, while the other SNP have weaker and inconsistent effects in different breeds. As c.1538+225G>T is located between the other two SNP, this might suggest that the causal mutations lie near to it. However, given the pattern of LD between the three SNP, that the two flanking SNP are in high LD in all breeds but in low LD to the central SNP in the composite breeds, it is possible that the causative mutations do not lie centrally in the gene; only the Brahman breed had some LD between c.1538+225G>T and the other SNP, and only in the Brahman breed was there similarity in the association to meat tenderness for all SNP. This suggests that haplotypes in the Brahman are more closely aligned to the causative mutation than in the composite breeds. Nevertheless, the differences in association between these SNP in different breeds could point to there being more than one causative mutation. These results suggest that it may be useful to discover other SNP in and around the CAPN3 gene and to examine these for functional, mechanistic explanations for the differences in meat tenderness as well as other traits.SNP at the c.53T>G and c.2443-103G>C in all breeds but the failure to detect a haplotype substitution effect for these two SNP may suggest that the QTL is not in strong LD with these two SNP.The haplotype substitution effect is significant in all breeds for a haplotype that spans the 3' half of the gene. The common allele at each of the three SNP is not associated consistently with increased meat tenderness in any breed, so testing a haplotype of the common alleles is not biased towards a particular result. The analysis of the haplotypes shows stronger effects than the individual SNP associations. The significant haplotype effects suggest that a QTL exists for this trait in all breeds, despite the lack of association in the Santa Gertrudis in the single SNP analysis. The failure of the single SNP analysis in the Santa Gertrudis, as well as the haplotype substitution effect being of opposite polarity to the other breeds, suggests that the Santa Gertrudis breed has a more complex LD structure of SNP around the causative mutation. This may be a consequence of its composite origins. The failure to find evidence in any particular single SNP association test is likely to be due to differences in the amount and polarity of allelic association between the SNP and the causative mutation in that particular breed, as well as the power of the experiment to detect the association at a particular significance threshold. This echos the critique of using TAG SNP  as a tooc.1538+225G>T, would be the start of a better understanding of how this gene or the surrounding genetic region affects meat tenderness.These SNP appear to be detecting a nearby QTL but they are not merely markers of difference between taurine and zebu cattle. Due to the many differences between zebu and taurine cattle, the analysis of fixed zebu-taurine differences in composite cattle is a way of tracking the influence of particular DNA segments, but such differences only explain increased genetic variability of recently crossbred cattle. However, in this case, because these DNA variants are SNP discovered in the Brahman, and are studied within that breed, the association indicated that the QTL contributes to the greater genetic variance for meat tenderness in the Brahman and other tropically adapted cattle. Identifying more SNP within the Brahman in this genetic region, particularly those in high LD to CAPN3 in a population association study confirm the location of a QTL for meat tenderness to bovine chromosome 10 originally identified in a QTL linkage experiment. The SNP variants were largely found in the zebu and composite breeds and this study implicates the QTL they detect in the greater heritability and mean trait values for meat tenderness found in zebu cattle compared to taurine cattle.SNP in the positional candidate gene CAPN3. MT sequenced the CAPN3 gene. All authors read the manuscript.WB planned the experiment, designed primers, analysed data and drafted the manuscript. BH and RB generated and scored genotypes for