On the basis of the results of Δ(SNP-index) analysis, a 4.0 Mb candidate region including 22 putative SNPs was mapped on chromosome C04. Combining the RNA-seq, gene function annotation, and target sequence analysis among two parents and other breeding lines, an orthologous gene of the Arabidopsis gene SOC1, Bo4g024850 was presumed as the candidate gene, and an upstream SNP likely resulted in riceyness phenotype via influencing the expression levels of Bo4g024850. These results are helpful to understand the genetic mechanism regulating riceyness, and to facilitate the molecular improvement on cauliflower curds.“Riceyness” refers to the precocious development of flower bud initials over the curd surface of cauliflower, and it is regarded as undesirable for the market. The present study aimed to identify the candidate loci and genes responsible for the morphological difference in riceyness between a pair of cauliflower sister lines. Genetic analysis revealed that riceyness is controlled by a single dominant locus. An F Brassica oleracea var. botrytis) is constituted by a mass of spirally arranged floral branches with whorls of inflorescence and floral meristems, the whole held tightly together and forms a hypertrophied corymb [Curd of cauliflower  and CAULIFLOWER (CAL) were intensively studied in cauliflower, as the ap1 cal double mutants of Arabidopsis form floral meristems similar to cauliflower curds [Arabidopsis, AP1 and CAL belong to the MADS-box regulatory gene family and have the function of promoting the flowering process [AP1 and CAL, curd-like organs can only be formed when both of these two genes are mutated in Arabidopsis [CAL results in the functional deficiency of its encoding product, whereas AP1 has normal function [CAL allele is present in different cauliflower varieties from different regions, suggesting that CAL is responsible for the cauliflower phenotype in Brassica oleracea species [CAL gene, including one mutant CAL-T and one wild CAL-G, coexisting in the riceyness cauliflower and broccoli, indicating that riceyness could be related to the sequence or functional variation of the CAL gene in cauliflower [CAL gene is introduced into cauliflower, the transgenic lines give rise to inflorescence composed of green flower buds and lose the ability to form curds [Plant flowering regulation is controlled by an intricate network of genetic regulators and their interaction with environmental factors . Similarer curds . In Arab process . Becausebidopsis . In caulfunction . Subsequ species . Interesliflower . Howeverrm curds , which arm curds  and 16.In this paper, we subject bulked segregant analysis using the sequencing (BSA-seq) method and RNA-seq analysis in order to perform the gene mapping and candidate gene identification of riceyness using a pair of sister lines showing morphological differences in riceyness tolerance.Two parents (QNF11-3 and QNF11-16) showed high similarity in botanical morphology except curd morphology. There were numerous canary yellow ricey tissues covering on the curd surface of QNF11-3, whereas the curd of QNF11-16 showed a smooth surface . Scannin1 and one BC1 generated from QNF11-3×F1 displayed non-segregation with uniform riceyness phenotype. The riceyness and non-riceyness segregation of F2 population and another BC1 population (QNF11-16×F1) accorded with the expectable 3:1  and 1:1 , respectively, indicating a monogenic inheritance. Riceyness is dominant to non-riceyness.Phenotypic variations were also visible among segregation populations. A total of 2,24,791,210 and 1,89,137,118 clean reads was generated from QNF11-3 (53.05X mean depth coverage) and QNF11-16 (44.28X mean depth coverage), respectively, while 206,281,340 and 205,882,398 clean reads were generated from bulk-R (48.77X mean depth coverage) and bulk-N (48.94X mean depth coverage), respectively. After the reads were compared with the reference genome sequence, 3,897,753 and 3,887,134 SNPs were identified in QNF11-3 and QNF11-16, respectively, while 3,894,335 and 3,893,700 SNPs were identified in bulk-R and bulk-N, respectively. According to the SNP-index and Δ(SNP-index) of these SNPs, only one region that was on chromosome C04 from 3 to 7 Mb exhibited a significant difference between bulk-R and bulk-N . The peaTo acquire insights into the transcriptomic differences between the ricey and non-ricey parental lines, RNA-seq analysis was performed between the curd tissues of QNF11-3 and QNF11-16. About 298 million clean reads were obtained from six libraries and were used for quantitative gene expression analysis; 90.73% to 91.04% of the clean reads could be mapped to predict gene regions, including 88.48% to 88.88% unique mapped reads, and 2.12% to 2.43% multiple mapped reads. The detailed data of sequencing is shown in Differential expression analysis revealed a total of 213 DEGs. In ricey parent QNF11-3, 128 genes were upregulated expressed and 85 genes were downregulated expressed as compared with the non-ricey parent QNF11-16 .Bo4g024850 encoding an MADS-box protein SOC1 (suppressor of overexpression of CO1), showed significantly different expression levels between two parents. The corresponding SNP (coded as SNP-5) located at the upstream of Bo4g024850, and its physical position  was very close to Δ(SNP-index) peaked for the riceyness trait  not only suppressed the late flowering of plants that had functional FRI and FLC alleles but also promoted flowering and inflorescence meristem identity in A. thaliana [Bo4g024850 was presumed to be the most likely candidate gene responsible for the riceyness phenotype, and the morphological difference of curd between two parents possibly resulted from the upstream SNP-5, which could have functions of regulating the expression levels of Bo4g024850.Out of the 29 genes identified by genetic mapping, only ss trait . It has thaliana ,18. ThusBo4g023880) was also detected among these materials, but it did not show a clear correlation between genotype and phenotype  of QNF11-3 and QNF11-16. The expression levels of Bo4g024850 in QNF11-3 and QNF11-16 were increasing continuously along with the curd development  did not show a correlation with riceyness phenotype among the other breeding lines , but weaker during late curd development (30 DPC and 40 DPC). This trend is consistent with previous research results, because there has already been a mass of floral meristems and flower buds on the curd surface in QNF11-3 but only inflorescence meristems in QNF11-16  and QNF11-16 . A single F1 individual from the cross of QNF11-16  and QNF11-3  was, then, used to produce F2 and BC1 populations. All the entries were seeded in growing matrix on 10 October, 2018. Then, 25-day seedlings were planted into a greenhouse in Haining County  with 50 cm row spacing and 65 cm line spacing. When the curd diameter reached 10 cm, the phenotype of each individual was identified by visual observation and classified as “riceyness” and “non-riceyness”.”Qingnong65” is a cauliflower commercial cultivar widely cultivated in China. When the temperature during the curd development period is below 12 °C, its curd shows different degrees of riceyness. QNF11-3 and QNF11-16 are a pair of sister lines generated from an individual of an Fgnong65” . This pagnong65” . F1 plan2 population were selected to construct the extreme phenotype bulks for BSA-seq. Genomic DNA of these F2 individuals and parents was isolated from fresh leaves using a DNA Secure Plant Kit . DNA concentration and quality were measured by an ND-1000 spectrophotometer  and electrophoresis on 1.0% agarose gel with a standard lambda DNA. Riceyness bulk (bulk-R) and non-riceyness bulk (bulk-N) were then constructed by pooling equal quantity of DNA from 38 riceyness individuals and 50 non-riceyness individuals.On the basis of the phenotype determination, 38 individuals showing remarkable riceyness and 50 individuals with unambiguous non-riceyness curd from the FBrassica oleracea reference genome sequence (https://www.ncbi.nlm.nih.gov/ assembly/GCF_000695525) using BWA software. SAM tools were used to perform single-nucleotide polymorphism (SNP) calling. The SNP-index and Δ(SNP-index) were calculated to identify candidate regions associated with the riceyness trait [The BSA-seq was performed on the Illumina HiSeq X10 platform  following the detailed procedure as described by Wang et al. (2018). After filtering the low-quality raw data using NGSQC toolkit software, the clean reads of bulk-R and bulk-N were aligned to the ss trait .2 and BC1 population, a monogenic inheritance model was used to identify the putative SNPs associated with riceyness. The criterion is that SNP ratio should be higher than 0.9 for QNF11-3, higher than 0.5 for bulk-R, lower than 0.1 for QNF11-16, and lower than 0.2 for bulk-N [The SNP ratio  of each SNP detected within the candidate region were calculated for bulk-R, bulk-N, and two parents, respectively. On the basis of the genetic analysis of riceyness in Fr bulk-N .The curd tissue of parental line QNF11-3 and QNF11-16 was respectively collected at the early curding stage (1 day post curding), to isolate total RNA using a Plant RNA Mini Kit . Three biological replicates were performed for each sample . A total amount of 1 μg purified RNA per sample was subjected to construct the cDNA libraries using a NEBNext UltraTM RNA Library Prep Kit for Illumina . The library quality was assessed on the Agilent Bioanalyzer 2100 system .Brassica oleracea reference genome sequence using HISAT2; only reads with a perfect match or one mismatch were further analyzed and annotated based on the reference genome. Gene expression levels were estimated by fragments per kilobase of transcript per million fragments mapped (FPKM). Differential expression analysis between two parental lines was performed using the DEseq. The resulting p values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted p-value < 0.05 found by DEseq were assigned as differentially expressed [The six library preparations were subsequently sequenced on an Illumina HiSeq2500 platform and paired-end reads were generated. Data analysis was performed following the procedures described by Jian et al. (2019) . Generalxpressed . SignifiSOC1. qRT-PCR was conducted in an ABI StepOne-Plus machine using SYBR® Premix Ex Taq™ . The detailed reaction system and PCR procedure was performed as that described by our previous paper [−ΔCt value was used to measure the relative expression levels for putative SNPs validation.For qRT-PCR, specific primers (5′-acaaactgagcagcccaagca-3′ and 5′-ctcgtcgtcgcctcttccac-3′) were designed based on the cDNA sequence of us paper . The 2−ΔIn order to further validate if the putative SNPs were associated with riceyness phenomenon, putative SNPs were detected in two parents and other 10 cauliflower inbred lines  using the Sanger sequencing method. The 250 bp sequences flanking each candidate SNP on either side were used to design the PCR primers. The purified PCR products were sequenced following the chain termination protocol at Sangon Biotech (Shanghai) Co., Ltd.


ARG1 gene and bronchodilator response.Genome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases and traits. There has been a rapid increase in the number of GWAS over the past decade. As additional GWAS are being conducted, it is unclear whether a novel signal associated with the trait of interest is independent of single nucleotide polymorphisms (SNPs) in the same region that has been previously associated with the trait of interest. The general approach to determining whether the novel association is independent of previous signals is to examine the association of the novel SNP with the trait of interest conditional on the previously identified SNP and/or calculate linkage disequilibrium (LD) between the two SNPs. However, the role of epistasis and SNP by SNP interactions are rarely considered. Through simulation studies, we examined the role of SNP by SNP interactions when determining the independence of two genetic association signals. We have created an R package on Github called gxgRC to generate these simulation studies based on user input. In genetic association studies of asthma, we considered the role of SNP by SNP interactions when determining independence of signals for SNPs in the  Genome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases  as well Epistasis is defined as the interaction between different genes or SNPs and refers to the departure from independence of the effect of different genetic loci on the disease or trait of interest ,6. ThereGiven epistasis, it is important to consider SNP by SNP interactions when determining whether genetic signals in GWAS are considered independent. For example, if an SNP is associated with the trait of interest in a GWAS, an investigator may want to determine if this SNP is an independent signal or purely the result of a correlated SNP that was previously associated with the trait of interest. In order to determine if the two signals are independent, the most popular approaches are to fit a regression of the trait of interest with the novel SNP conditional on the previously identified SNP and/or calculate LD between the two SNPs.ARG1 gene and bronchodilator response in genetic association studies of asthma.Through simulation studies, we examined the impact of SNP by SNP interactions when determining whether two signals are independent in a GWAS. We have created the R package, called gxgRC, which implements these simulation studies for user specified parameters. In addition, we considered the role of SNP by SNP interactions when determining independence of signals for SNPs in the X1 conditional on the SNP X2. We generated 1000 subjects for 5000 simulations using a significance level of 5*10−8. SNP X1 is generated from a binomial distribution with a binary genetic coding  and P(X1 = 1) = 0.5. SNP X2 is generated from a logistic regression based on X1 such that:X1 and X2 assume a binary genetic coding for simplicity, the results are generalizable to an additive genetic coding . The continuous outcome Y is generated from a normal distribution with a variance of 1 and a mean such thatIβ varies from 0.3 to 1 by 0.05. We considered additional simulation scenarios for different values for 0, In the following simulation scenarios, we examined the impact of SNP by SNP interactions when determining the independence of two SNPs by regressing the trait of interest Y with the SNP After the data were simulated using Equations (1) and (2), we then fit 3 algorithms and tested the following null hypotheses for each algorithm:Algorithm 0: Fitting Algorithm 1: Fitting Algorithm 2: Fitting Then, we determined when the following scenarios were true.Scenario 1: Rejected Algorithm 0 Scenario 2: Rejected Algorithm 0 Scenario 3: Rejected Algorithm 0 Scenario 4: Rejected Algorithm 0 H0 for Algorithms 1 and 2.Scenario 5: Failed to reject Algorithm 0 Iβ closer to 1 in Equation (2)), the majority of simulations concluded scenario 1: rejecting Algorithm 0 In ARG1], which has previously been associated with bronchodilator response (BDR) in asthma [N = 560) [N = 206) [N = 126) [ARG1 gene on BDR among subjects of European ancestry  adjusting for cohort, age, sex, body mass index (BMI) as a categorical variable , and genetic ancestry. We picked these covariates based on other studies of BDR [To illustrate the effect of SNP by SNP interactions when determining conditional independence of genetic signals, we considered SNPs in chromosome 6 [n asthma . In the N = 560) , CARE NN = 206) ,14, and N = 126)  cohorts,s of BDR ,17,18, as of BDR ,20, sex 6 ,14, ans of BDR  and BMI s of BDR . Three os of BDR  and one AGR1. In our cohorts, rs2781659 was associated with BDR adjusting for cohort, age, sex, BMI as a categorical variable , and genetic ancestry  = 0.32, Beta = −0.15, sd = 0.05, p-value = 0.0037). In order to determine if the association of rs2781659 with BDR is independent of the other three SNPs, we considered the following algorithms:Based on previous studies , rs27816Algorithm 1:Algorithm 2:C is a vector of the covariates: cohort, age, sex, BMI as a categorical variable , and genetic ancestry.where p = 0.003) and rs185631674 (p = 0.004) but not when conditioning on rs2781665 (p = 0.78). The SNP by SNP interaction on BDR was marginally significant for rs2781663 (p = 0.06) and rs2781665 (p = 0.06), and significant for rs185631674 (p = 0.03). However, rs2781659 is in LD with rs2781663 (2r = 0.995) and rs2781665 (2r = 0.891) and rs185631674 is a rare variant in a study with a small sample size (N = 892).As seen in r2. This also shows that special consideration needs to be given to rare variants.This shows that if a researcher were to only consider Algorithm 1, where the association of rs2781659 with BDR is still significant conditioning on rs2781663 and rs185631674, the researcher would falsely conclude that there is more than one independent genetic signal with BDR. This false conclusion would not be reached with the SNP by SNP interaction considered in Algorithm 2 as well as the LD as measured by ARG1 with BDR, we demonstrate that it is not sufficient to consider independence of two genetic signals by considering Algorithm 1: r2 or D’. Also, prior knowledge, for example protein–protein interactions or biological pathways, should be considered when examining SNP by SNP interactions [Through simulation studies and a data analysis of SNPs in ractions .n = 892). Since the power to detect an interaction is substantially smaller than detecting a main effect, it should be noted that a larger sample size may be needed or the study may be underpowered to detect SNP by SNP interactions. While we have only presented two simulation studies here, we have created an R package on Github called gxgRC (https://github.com/SharonLutz/gxgRC (accessed on 12 December 2020)) [There are some potential limitations for our simulation studies and data analysis. For the data analysis, all subjects are of European origin. A more diverse population could provide varying results, which should be considered for future analyses. Additionally, our data analysis considered genetic association studies of asthma but there is opportunity to explore the role of SNP by SNP interactions in conditional analyses by examining other diseases or traits. The sample size for the data analysis was relatively moderate (r 2020))  to generUnderstanding the role of epistasis and SNP by SNP interactions is important for the development of pharmacogenetic tests and personalized medicine. To date, studies in asthma pharmacogenetics have not resulted in clinical practice changes; however, exploring the role of SNP by SNP interactions has the potential to increase the likelihood of translatable findings.


BMPRIB, BMP15, and GDF9 genes are present and associated with the litter size of Mongolia sheep; (2) to identify novel variants in GDF9 and perform association analysis; and (3) to validate the effects of these GDF9 promoter variants on the activity of the gene. The results of the 19 known variants showed that the BFecB affected the litter size of Mongolia sheep (p < 0.001). The association analysis results of novel variants showed that the g.46544883A>G  in the 3’ untranslated region (3’ UTR), the c.1040T>C (Phe347Ser) in the exon 2, and the g.46547859C>T SNP in the promotor of GDF9 were significantly associated with litter size of Mongolia ewes . In addition, the GDF9 promoter activity analysis showed that the C allele at the −332 position (g.46547859C>T) could decrease luciferase activity compared with the T allele (p < 0.01). Our findings may facilitate effective marker-assisted selection to increase litter size in Mongolia sheep populations, as well as bring new insights into GDF9 expression.Litter size is an economically important trait in sheep breeding. The objectives of this study were as follows: (1) to ascertain if any of the 19 known variants in the  The protocol was approved on 15/05/2015 by the Institutional Animal Care and Use Ethics Committee of Inner Mongolia University, with the permit number for conducting animal experiments of (IMU-2015-03).The total of 260 purebred Mongolia (MG) sheep were sampled from Inner Mongolian Sainuo Grassland Sheep Industry Co., Ltd of China. The owners randomly selected more than 1000 ewes that produced twins when they were one and two years old from many farms in the Sonid Right and Left Banners, and the Dorbod Banner of Inner Mongolia of China in 2011. Since then, the ewes that produce twins were left, and the ewes that produce a single lamb were removed from group. All matings were natural in autumn from 2011 to 2013. There was no specific ram used for mating from 2011 to 2013. Until 2014, the 77 MG-T (twins) that stably produced twin lambs were alive and sampled for genotyping. The ewes that stably produced a single lamb were randomly sampled from many farms in the Sonid Left Banner and the Dorbod Banner. There was no genetic relationship among ewes and crossbreeding with any other sheep breed. All of the Mongolia sheep were raised under similar conditions of free access to food and water.In addition, 30 Hulunbuir sheep (big tail type) (HBB) ewes and 30 Hulunbuir sheep (short tail type) (HBS) ewes were collected from Old Barag Banner and Evenk Banner of Inner Mongolia, 36 Ujimqin (UM) sheep ewes were collected from East Ujimqin Banner of Inner Mongolia, and 30 small-tailed Han (STH) sheep ewes and 30 Hu sheep ewes were collected from Zhengzhou of Henan Province, China. There was no strong bias for a specific father or a specific maternal grandfather of the ewes within each sheep breed, and the animal panel for each breed likely represents a random sample of the population of each sheep breed. The information of the six sheep breeds is shown in R spectrophotometer  and by agarose gel electrophoresis.Ten milliliters of blood was collected from each ewe and used for variants sequencing and genotyping. Genomic DNA samples of total 416 sheep were extracted from blood samples with a TIANamp Blood DNA kit . The quality and quantity of the extracted DNA were evaluated using a NanodropGDF9 gene of MG. Ovine specific PCR primers were designed using Premier Primer 5.0 software  and the National Center for Biotechnology Information (NCBI) primer BLAST tool was applied to amplify the full length of the GDF9 gene, according to the ovine GDF9 DNA sequence (NCBI reference sequence: NC_040256) (Twenty ewes (13 ewes that stably produced twin lambs and seven ewes stably produced a single lamb) were used to detect variants in the _040256) . PCR ampRFecX, GrFecX, OFecX, and BarFecX in BMP15, and the TFecG, VFecG, AFecG, and 1FecG in GDF9 genes, were genotyped with the MassARRAY® SNP genotyping system  in the 260 MG population. PCR and extension primers were designed from sequences containing each target mutation and ~100 upstream and downstream bases with Assay Design Suite (http://agenabio.com/assay-design-suite-20-software) using the default settings. The genotype of each allele was analyzed using the Sequenom MassARRAY iPLEX platform [The platform . The resBFecB in BMPRIB; the BFecX, GFecX, HFecX, and IFecX in BMP15; as well as the HFecG, EFecG, and FFecG in GDF9 were genotyped using the PCR-restriction fragment length polymorphism (RFLP) method, as described previously [The eviously ,5,19 , were identified during the re-sequencing and genotyped across the 416 ewes by direct sequencing. PCR amplification was performed as described above. The primers and the annealing temperature are shown in g.46547859C>T, g.46548061A>G, and g.46548326C>T SNPs, as well as the g.46547934T>G, g.46548232A>G, and g.46548307C>T SNPs, had strong linage disequilibrium (LD). These were validated in this experimental MG population and named LD-M1 and LD-M2, respectively.The quencing   were purchased from the Chinese Academy of Sciences  and cultured in Dulbecco’s modified Eagle’s medium (DMEM)  with 10% fetal bovine serum (FBS)  and 0.5% penicillin/streptomycin  at 37 °C under an atmosphere containing 5% COGDF9 promoter region and gradually delete the g.46548326C>T SNP (between −1292 and −726 bp), the g.46548061A>G SNP (between −726 and −460 bp), and the g.46547859C>T SNP (between −460 and −177 bp) of LD-M1. The primers used to construct the vectors containing the NheI and MluI restriction sites  are shown in NheI and MluI sites of the pGL3 Basic vector . Nine promoters were obtained from the DNA templates of a random sheep with the C allele at the g.46547859C>T, the C allele at the g.46547876C>T, the T allele at the g.46547934T>G, the A allele at the g.46548061A>G, the A allele at the g.46548232A>G, the C allele at the g.46548307C>T, the C allele at the g.46548326C>T, and the T allele at the g.46548349T>C SNP, except for the −460 bp fragment with the T allele at the g.46547859C>T SNP, which was derived from the other sheep. The −726 bp fragment with the G allele at the g.46548061A>G SNP and the −1292 bp fragment with the T allele at the g.46548326C>T SNP were amplified from the same sheep by primers with mutated allele (GDP9 gene (NCBI reference sequence No. NC_040256).Fragment primers for −1533, −1,292, −726, −460, −177, −110, and +49 bp  were desd allele . ColonieRenilla reporter were co-transfected into HEK293T cells with Lipofectamine™ LTX and PLUS™ reagents . Transfections were incubated for 48 h at 37 °C in growth medium (DMEM + 10% FBS). Firefly and Renilla luciferase activity levels were measured after 48 h transfection with a Dual Luciferase Report Array System  in a Varisskan Flash Microplate Reader  according to the manufacturers’ protocols. The firefly luciferase activity levels in each well were normalized by those for Renilla luciferase. There were three replicates per experiment and each assay was performed thrice.Differences in promoter activity caused by variations in promoter length, LD-M1 sites, and alleles at each SNP of LD-M1 were measured by luciferase assay. HEK293T cells were transfected with DNA for each reporter plasmid and incubated in a 96-well plate. For the promoter assays, each of the seven reporter plasmids and the TK-http://www.cbs.dtu. dk/services/Promoter/). The transcription factor (TF) binding sites in the GDF9 promoter were identified with JASPAR (http://jaspar.genereg.net/) [http://www.uniprot.org) online tools. PROVEAN 1.1 online software (http://provean.jcvi.org/seq_submit.php) predicts whether a protein sequence variation affects protein function based on alignment of clusters of closely related sequences generating a PROVEAN score. If the PROVEAN score is equal to or less than a predefined threshold (−2.5 in this study), the protein variant is predicted to have a deleterious effect [http://genetics.bwh.harvard.edu/pph2/) is a sequence and structure-based method that determines the structural and functional consequences of non-synonymous SNPs. PolyPhen-2 calculates the posterior probability that a non-synonymous SNP is damaging by a Bayesian classifier [The core promoter was predicted with the Promoter v. 2.0 Prediction Server (eg.net/)  using sts effect . PolyPheassifier . The conassifier  in Maestassifier  of Schroassifier ).e), and polymorphism information content (PIC) were calculated by Nei’s methods [2 test. The LD, including D’ and r2, was assessed with HAPLOVIEW v. 4.2 [t-tests.Genotypic and allelic frequencies and Hardy–Weinberg equilibria were calculated for MG, HBB, HBS, UM, STH, and Hu breeds. Population genetic indices, gene heterozygosity (He), effective allele numbers   and significantly higher than those in the 260 MG (p < 0.01) (1FecG and AFecG of GDF9 and litter size of MG was exhibited (p > 0.05). The genetic indices of BFecB, 1FecG, and AFecG in MG are shown in None of the in lambs a. Statis< 0.001) b. The di the STH  Table 3 < 0.01) c. No evi < 0.01 cGDF9 gene of MG. Among them were eight SNPs  in the promoter region, one novel single nucleotide mutation (c.1040T>C) in exon 2, and one SNP (g.46544883A>G) in the 3’ untranslated region (3’ UTR) of the GDF9 gene  in GDF9 protein. The c.1040T>C mutation was predicted to have a deleterious effect on protein function with a PROVEAN score of −2.934 (cut off = −2.5) and was predicted to be “possibly damaging” with a score of 0.810 in Polyphen-2. The Phenylalanine at residue 347 in sheep GDF9 is related and highly conserved among vertebrates ranging from fish to mammals, except humans  than those with the CC genotype  more lambs than those with the genotype TT  more lambs than those with the AA genotype. No significant difference between the g.46547934T>G SNP of LD-M2, g.46547876C>T as well as g.46548349T>C SNPs and litter size was detected in this experimental MG sheep population (Owing to the effect of genotype . For theotype TT . For thepulation .p < 0.05) more lambs than those with the H1H1, and 0.47 (p < 0.001) more lambs than those with the H1H2  had the highest frequency (0.734), followed by haplotype 2  and haplotype 3  . By haplthe H1H2 .e, and PIC). No significant departures at the 5% level were detected by any test for each variant , which were associated with litter size of MG, showed a related low distribution in each sheep breed, as well as the values of PIC of the three variants presented with related low polymorphism in each sheep breed [T allele in the g.46547859C>T SNP of LD-M1 was significantly higher in MG-T than that in MG (p < 0.01)  .GDF9 promoter activity, the recombinant plasmids pGL3-1533, pGL3-1292-C, pGL3-726-A, pGL3-460-C, pGL3-177, and pGL3-110 were synthesized and transfected into HEK293T cells. The luciferase reporter assay revealed that, when the GDF9 promoter was deleted to −177 bp, its activity was significantly increased than −460, −726, −1292, and −1533 bp (p < 0.01) in HEK293T (p < 0.01) in HEK293T . The luciferase activity of pGL3-460-T was significantly higher than that of pGL3-460-C (p < 0.01), and higher than the control (p < 0.05) in the HEK293T , E2F transcription factor 1 (E2F1), and Spi-C transcription factor (SPIC)  TF (The  HEK293T a. There  HEK293T a. In silr (SPIC) b. In conPAX2) TF b, and maBFecB mutation in MG population is still unclear. A previous study also found the BFecB mutation in the MG breed [BFecB mutation in the purebred MG population of this study should be the result of large collections at the beginning, ongoing selection, and retention from 2011 to 2013 by the owner. However, this speculation still needs additional MG population to be proven. Although the G allelic frequency was higher in 77 MG-T than that in MG, it was still significantly lower than those in STH and Hu sheep , c.1040T>C (rs589708049), g.46547859C>T (rs428242918), g.46547876C>T (rs591690695), g.46547934T>G (rs406740996), g.46548061A>G (rs400597589), g.46548232A>G (rs411447528), g.46548307C>T (rs401365297), and g.46548326C>T (rs412658916) variants had been submitted to NCBI dbSNP (https://www.ncbi.nlm.nih.gov/SNP/snp_viewBatch.cgi?sbid=1059601) through an International Sheep Genomics Consortium project (http://www.sheephapmap.org/participants.php), which discovered SNP from the whole genome sequence of 68 domestic sheep from 44 breeds and 5 wild sheep. Our present study is the first to discover the g.46548349T>C SNP at −882 bp upstream from the GDF9 TIS. Five breeds or populations other than MG exhibited comparatively low genetic diversity because of their small population sizes.Our current study firstly describes the identification of previously unreported variants in g.46544883A>G in 3’ UTR and c.1040T>C in exon 2, as well as the g.46547859C>T, g.46548061A>G, and g.46548326C>T variants of LD-M1 in promoter of GDF9, are the first variants confirmed to be associated with litter size of the MG. In particular, the r2 indicated that the g.46544883A>G and g.46547859C>T SNPs were in related low LD .The LD 0.46;  in the eat in MG . This reg.46547859C>T SNP of LD-M1. Because only the H3 haplotype has the T allele for the g.46547859C>T SNP of LD-M1, the significant association with litter size of MG was obtained in H1H3. Furthermore, because the frequencies of haplotypes including the C allele of the c.1040T>C mutation were 0.007 (TAGCCCA), 0.010 (TAGCCCG), 0.010 (TATCCCA), 0.004 (TATCCCG), 0.002 (CATTCCG), and 0.002 (TGGCTCG), the C allele could not be included in the haplotype-based association analysis. Thus, the results of single SNP and haplotype-based associations should be confirmed in a bigger sheep population in the future study.The results of haplotype-based association analyses further proved the effect of the LFecX (affecting BMP15) and LFecL (affecting B4GALNT2) in Lacaune sheep. In the same manner, BFecB (affecting BMPRIB) and GFecX (affecting BMP15) combined to influence litter size in STH sheep [g.25921552C>T  and g.25935026C>T  in the B4GALNT2 gene also have effects on litter size in STH sheep [BFecB was associated with litter size in MG, although there was an extra-low allelic frequency of the FecB mutation in MG ewes with twin lambs. Simultaneously, we found that the g.46544883A>G SNP in 3’ UTR, the c.1040T>C in exon 2, as well as the g.46547859C>T SNP in the promotor of GDF9 were significantly associated with litter size in MG. Together with the results of this study, we speculated that the MG breed could be genetically regulated by a set of different genes, each having a small effect as in the Romanov sheep breed [Meanwhile, reproduction is a complex process, and traits such as ovulation rate and litter size are genetically affected by some major genes as well as many minor genes ,52. DrouTH sheep . AdditioTH sheep . These fHFecG (Ser395Phe) in GDF9 associated with ovulation rate and sterile in the Belclare and Cambridge breeds, which introduces a non-conservative amino acid change, is thought to disrupt interaction with a type 1 TGF-β family receptor [VFecG (Arg315Cys), which affected ovulation rate and litter size in Ile de France ewes, provokes a residue change in the cleavage site for the proprotein convertase subtilisin kexin proteases, highlighting the necessity of the proteolytic processing for the biological activity of GDF9 [EFecG (Phe345Cys) has been linked to an increased ovulation rate when ewes are homozygous for the nonconservative change in an amino acid predicted to be involved in dimer formation [c.1040T>C (Phe347Ser) found in our current study is close to EFecG (Phe345Cys) mutation, and also exhibited that mutation to serine may interrupt the dimer formation. Furthermore, the c.1040T>C may have a deleterious effect or be “possibly damaging” on protein function, which was predicted by PROVEAN and Polyphen-2, respectively. We hypothesize that this c.1040T>C mutation may reduce or abolish biological activity by predisposing GDF9 to dimer instability. Because the 77 MG-T that stably produced twin lambs were retained from more than 1000 ewes that produced twins from many farms, together with the in silico analysis, suggesting that there was a high selective pressure on the 77 MG-T, thus the association is likely to be true. In addition, there were two ewes with CT genotype and 15 ewes with CC genotype of the g.46547859C>T SNP of LD-M1 in the 17 ewes with TC genotype of the c.1040T>C, together with the c.1040T>C mutation, which was not linked to the g.46547859C>T SNP of LD-M1 in the experimental MG population, suggesting that the MG breed may be have a different regulatory mechanism in reproductive traits.GDF9 is member of the TGF-β family, which encodes prepropeptides containing a signal peptide, a proregion, and a C-terminal mature region that is the biologically active peptide. However, the GDF9 protein lack the cysteine residue in the mature domain responsible for dimeric covalent binding , giving cGH Ser39Phe in GD of GDF9 . Furtherormation ,58. The GDF9 were shown to be higher in both fetal and adult ovaries when compared with expression in other tissues [GDF9 using HEK293T cell line. The results implied that the fragment between –460 and –177 bp could be considered as a key region that could influence GDF9 promoter activity, and the fragment between –110bp and TIS might be a minimal core promoter regulating the expression of GDF9 in the HEK293T cells. Although, some studies perform GDF9 promoter luciferase in the HEK293 cell line [GDF9 promoter activity in this study could not truly restore the condition in the oocyte because the HEK293T cell line is of kidney origin. Up to now, all proven mutations affecting the ovulation rate in sheep negatively affect the BMP/GDF signaling system as a causal mechanism [T allele associated with a large litter size. This opposite result is likely owing to the experiment of luciferase reporter assay performed in the HEK293T. Our hypothesis is that the g.46547859C>T SNP might be a site to control the preferential expression of GDF9 in the oocyte. Nevertheless, this hypothesis still requires further experiments in ovine oocyte. This study identified eight SNPs in the GDF9 promoter, giving new possibilities for further studies in promoter activity and expression level of GDF9.The expression levels of ovine  tissues . In thisell line , the resechanism ,62. The g.46544883A>G (rs409657477), c.1040T>C (rs589708049), and g.46547859C>T (rs428242918) variants of the GDF9 gene may influence litter size in Mongolia sheep populations. The effect of the c.1040T>C mutation might interrupt the dimer formation of GDF9, which is the biologically active form of the protein. The results of this study, particularly the GDF9 SNP , could be applied in marker-assisted selection for the purpose of increasing the mean litter sizes in Mongolia sheep.The results of the present study suggest that the


EXOC4) gene is essential for the growth and development of humans and animals. However, the molecular mechanisms between EXOC4 and the reproductive performance of pigs is yet to be elucidated. The findings of our study revealed that single-nucleotide polymorphism (SNP) rs81471943 (C/T) was located in the EXOC4 gene by Sanger sequencing and PCR-restriction fragment length polymorphism (PCR-RFLP). We then analyzed the relationship between this SNP in EXOC4 and reproductive traits. We found that CC was the most frequent genotype on number of piglets born alive (NBA), litter weight at birth (LWB), number of piglets weaned (NW), and litter weight at weaning (LWW). Finally, 5′-deletion and luciferase assays showed a positive transcription regulatory element in the EXOC4 promoter. Therefore, we predicted that −1781G/A might regulate the expression of EXOC4 by affecting the potential binding of transcription factors P53, E26 transformation specific sequence -like 1 transcription factor (ELK1), or myeloid zinc finger 1 (MZF1).The exocyst complex component 4 (EXOC4) gene has often been reported to be involved in vesicle transport. The SNP rs81471943 (C/T) is located in the intron of porcine EXOC4, while six quantitative trait loci (QTL) within 5–10 Mb around EXOC4 are associated with ovary weight, teat number, total offspring born alive, and corpus luteum number. However, the molecular mechanisms between EXOC4 and the reproductive performance of pigs remains to be elucidated. In this study, rs81471943 was genotyped from a total of 994 Duroc sows, and the genotype and allele frequency of SNP rs81471943 (C/T) were statistically analyzed. Then, the associations between SNP rs81471943 and four reproductive traits, including number of piglets born alive (NBA), litter weight at birth (LWB), number of piglets weaned (NW), and litter weight at weaning (LWW), were determined. Sanger sequencing and PCR restriction fragment length polymorphism (PCR-RFLP) were utilized to identify the rs81471943 genotype. We found that the genotype frequency of CC was significantly higher than that of CT and TT, and CC was the most frequent genotype for NBA, LWB, NW, and LWW. Moreover, 5′-deletion and luciferase assays identified a positive transcription regulatory element in the EXOC4 promoter. After exploring the EXOC4 promoter, SNP −1781G/A linked with SNP rs81471943 (C/T) were identified by analysis of the transcription activity of the haplotypes, and SNP −1781 G/A may influence the potential binding of P53, E26 transformation specific sequence -like 1 transcription factor (ELK1), and myeloid zinc finger 1 (MZF1). These findings provide useful information for identifying a molecular marker of EXOC4-assisted selection in pig breeding.In mammals, the exocyst complex component 4 ( Pigs were the earliest livestock species to be domesticated. Pig breeding has a particularly long history involving artificial selection from domestication to modern breeding . The abuEXOC4) gene, and six QTLs associated with reproductive traits were identified within 5–10 Mb around EXOC4. This suggested that EXOC4 might be associated with reproductive traits. EXOC4 is one of the subunits of the exocyst, which is an evolutionarily conserved complex of eight proteins, comprising EXOC1, EXOC2, EXOC3, EXOC4, and other subunits. The exocyst complex is posited to be involved in protein transfer between cells [EXOC4 connects vesicles to the cell membrane and participates in vesicular-mediated transport. EXOC4 has been reported to be expressed in many human tissues, with slightly higher expression in the ovary, skeletal muscle, spleen, and hypothalamus [EXOC4-1q23.1 interaction was associated with BMI. In the pathway-based association analysis, the Tob1 pathway, which contributes to obesity through the mitogen-activated protein kinase (MAPK) pathway, showed the most significant association with BMI. These findings were also replicated in different human populations [EXOC4 gene via Haploview. However, the underlying relationship between SNP rs81471943 and reproductive traits, as well as the transcription mechanism of EXOC4, are still unclear in pigs.Porcine reproduction performance is mostly evaluated by quantitative traits, which in turn are mainly affected by minor genes. These minor genes can be identified by quantitative trait loci (QTL) mapping . Accordien cells ,20. EXOCthalamus . A studyulations . In addiEXOC4. This study contributes to the understanding of the regulatory mechanisms of the EXOC4 gene and molecular marker-assisted selection in pig breeding.To explore the relationship between SNP rs81471943 and reproduction traits in pigs, we acquired the genotype frequencies of SNP rs81471943 in Duroc pigs. The associations between SNP rs81471943 and reproductive traits such as NW, LWW, NBA, and litter weight at birth (LWB) were determined. Then, 5′-deletion and a luciferase assay were utilized to identify whether SNP rs81471943 was associated with the transcription of Experiments and animal care in this study were conducted according to the Regulations for the Administration of Affairs Concerning Experimental Animals  and approved by the Animal Care and Use Committee of the South China Agricultural University, Guangzhou, China .Ear samples from 994 Duroc sows prepared for SNP genotyping were obtained from a breeding herd and were collected from 2009 to 2017 in Fujian, China. The ear samples were collected into 75% alcohol and stored at −20 °C. TaKaRa MiniBEST Universal Genomic DNA Extraction Kit Version 4.0  was utilized to extract the genomic DNA from porcine ear tissues. The A260/280 ratios of all DNA samples were determined with a NanoDrop One . All DNA samples passed A260/280 ratio (1.7–2.0) detection and were genotyped on an Illumina PorcineSNP60 BeadChip . Four reproductive traits, including NBA, LWB, NW and LWW, were recorded. NBA and LWB were measured 24 h after delivery, and LWW and NW were recorded after weaning.EXOC4 gene, 8 Duroc pigs were sequenced by Sanger sequencing, and 10 Duroc pigs were genotyped by PCR-restriction fragment length polymorphism (PCR-RFLP) as confirmation. DNA extracted from the ear samples of 10 pigs was set as the template. The fragment containing SNP rs81471943  of EXOC4, a gene with a length of 640 bp , was amplified using PrimerSTAR® high fidelity enzyme . The primers are listed in 2O (ddH2O). The PCR procedure involved: pre-denaturation at 98 °C for 2 min, 35 cycles , and extension at 72 °C for 10 min.To determine the polymorphic locus rs81471943 (C/T) of the porcine BsrB I (identification sequence: CCGCTC) restriction endonuclease  and separated using agarose gel electrophoresis. PCR products with two digested fragments were CC genotype (565 bp + 75 bp), those with three digested fragments were CT genotype (640 bp + 565 bp + 75bp), and those with a single digested fragment were TT genotype (640 bp).Consequently, PCR products were separated by agarose gel electrophoresis and isolated using a Gel extraction kit . The isolated products were digested by 2 flasks and cultured at 37 °C under 5% CO2 in Dulbecco’s modified Eagle medium  containing 10% fetal bovine serum .GCs were cultured according to previous studies . PorcineEXOC4 from release 89 of the Ensembl genome browser , we inferred that there was no leader exon present in EXOC4. Then, we designed 10 pairs of primers to amplify the deletion fragments of the EXOC4 promoter  was defined as +1. Thus, the longest 5′ deletion fragment, which covered a 2657 bp sequence upstream of the transcription start site and a 134 bp sequence of the first exon, was named P0 (−2657/+134), and the other fragments were named P1 (−2204/+134), P2 (−1914/+134), P3 (−1682/+134), P4 (−1323/+134), P5 (−886/+134) and P6 (−518/+134). The shorter 5′ deletion fragments were named P3A (−1826/+134), P4A (−1551/+134), and P4B (−1225/+134). The PCR product was obtained by Taq DNA polymerase . The primers are listed in 2O. The PCR procedure involved: pre-denaturation at 94 °C for 3 min, 35 cycles , and extension at 72 °C for 10 min. PCR products were purified by gelatinization and were added “AAA” tails to ligate with PMD-18T were performed prior to Sanger sequencing . The DNA sequences of PCR products were determined by DNASTAR software. Each deletion fragment digested with Hind III and MIu I was cloned into the eukaryotic expression vector pGL3-vector, which was also digested with Hind III and MIu I restriction endonucleases.According to the promoter sequences of porcine promoter . The traAccording to the manufacturer’s instructions for the dual-luciferase reporter assay kit , we used the BioTek Synergy 2 multifunctional microplate reader for fluorescence detection . In the luciferase assay, firefly luciferase was set as the experimental reporter and renilla luciferase was set as the control. Relative luciferase activity is calculated by the ratio of the expression of firefly luciferase (560 nm) to renilla luciferase (465 nm). The ratio of expression can normalize the activity of an experimental reporter to the activity of an internal control, which minimizes or sometimes even eliminates the experimental variability.® high fidelity enzyme  to obtain the fragment containing the EXOC4 promotor, and the primers used are listed in EXOC4, and this SNP also located on the potential transcription factor-binding fragment P3A-P4A (−1826/−1551) of EXOC4.For sequencing and SNP identification, 35 pigs were randomly selected from the 994 pigs used in this study. PCR was performed using PrimerSTAREXOC4, four haplotypes were identified and defined as HA-1 (GC), HA-2 (AC), HA-3 (GT) and HA-4 (AT). The 5′ deletion fragments EXOC4-P2 (−1914/+134) of four haplotypes were amplified and cloned into the eukaryotic expression vector pGL3-vector. Luciferase assays were used to detect the effect of different haplotypes on the transcription activity of EXOC4. We analyzed the general linkage disequilibrium (LD) within +/−100 Kb of the EXOC4 gene via Haploview, and the location diagram of the SNP, exons, deletion, binding sites, and promoter is shown in To detect the linkage between SNP −1781G/A and SNP rs81471943 of Estimated breeding values (EBVs) of all pigs were computed using animal model best linear unbiased prediction  and obtap < 0.05 indicates significant differences.The EBVs of reproductive traits  were used for the multiple comparisons of SNP genotype rs81471943 by the Tukey–Kramer multiple comparison via the 9.2 version of SAS software . t-test (two-tailed). ** indicates significant difference at p < 0.01, and * indicates significant difference at p < 0.05.The luciferase assay was repeated at least three times independently, and the data are shown as the mean ± standard deviation (SD) of repeated experiments. The significance of differences in the means between two groups was analyzed using Student’s EXOC4 gene in the 994 Duroc pigs was calculated , a polymorphic mutation base C/T was found on EXOC4 , which is in line with SNP rs81471943 in the commercial SNP array. To determine the polymorphic loci SNP rs81471943 of EXOC4, 10 pigs were used in the PCR-RFLP detection. As shown in BsrB I.Target fragments of quencing A–C. CompEXOC4 promoter, 5′-deletion and luciferase assays were used. Six fragments with a 5′-deletion of the EXOC4 promoter were amplified , P4A (−1551/+134), and P4B (−1225/+134) were amplified A and cloEXOC4 was amplified and sequenced. Interestingly, one SNP was identified as being located on −1781 of EXOC4. As shown in EXOC4, and four haplotypes were defined as HA-1 (GC), HA-2 (AC), HA-3 (GT), and HA-4 (AT).The −1826/−1551 fragment of p < 0.05) higher than HA-2, whilst HA-3 was significantly (p < 0.05) higher than HA-4. These observations suggested that the SNP rs81471943 might link with −1781G/A and −1781G to potentially affect the expression of EXOC4. Moreover, many potential binding sites of transcription factors were predicted on −1781G/A of EXOC4 (P53 and ETS transcription factor (ELK1) might bind at −1781A, while myeloid zinc finger 1 (MZF1) might bind at −1781G.As shown in of EXOC4 . P53 andEXOC1 to EXOC8. The exocyst mediates the docking of vesicles carrying membrane proteins [EXOC4 is a component of the exocyst complex, which is associated with various phenomena such as cell migration, endophoria formation, cytokinesis, glucose uptake, and neural development in mammals. Previous studies indicate that the mutation of specific SNP loci in EXOC4 leads to an increase in the malformation rate of human newborns [EXOC4 is also involved in insulin, triiodothyronine, and thyroxine secretion in Chinese Holstein cattle [EXOC4-1q23.1 and BMI in a European–American adult female cohort via genome-wide interaction analyses, and results suggest that EXOC4-related pathways may contribute to the development of obesity [EXOC4 knockout in embryos, mice can form gastrointestinal embryos normally, but are unable to progress beyond the primitive streak stage and die shortly after [EXOC4 and are associated with growth traits of 49–56 day old chickens [ECOX4 might be important for economic traits in livestock.The exocyst is a protein complex composed of proteins . EXOC4 inewborns . EXOC4 in cattle . Jiao et obesity . Similarly after . In addichickens , bodyweichickens , and pecchickens , respectEXOC4 gene. After analyzing the relationship between genotype of the EXOC4 and reproductive traits, three genotypes  were found in the Duroc pig population, while C was the most frequent allele. Moreover, multiple comparisons between SNP and phenotypes confirmed that CC was the most frequent genotype on NW and LWW in Duroc pigs  located on the roc pigs . Previou and LWB . In this and LWB . CollectFGFR1) to promote the transcription of FGFR1 and enhance the pro-proliferation and anti-apoptotic effect of FGFR1 to facilitate the growth of follicles [EXOC4, the SNP markers which link with rs81471943 at the promoter of EXOC4 were further investigated by constructing 5′-deletions for EXOC4. Interestingly, we found that positive regulatory elements might localize in the P3A–P4A (−1826/−1551) region  region . After en  and LWW (p < 0.01). The −1826/−1551 region of EXOC4 contains a positive regulatory element, and the haplotypes of SNP −1781G/A and SNP rs81471943 might significantly affect the transcription activity of EXOC4. Moreover, SNP −1781G/A might influence the binding of the potential cis-acting elements P53, ELK1 and/or MZF1. These findings reveal that SNP rs81471943 is significantly associated with the reproductive traits NW and LWW in Duroc sows.In Duroc pigs, we found that the CC genotype frequency was significantly higher than CT and TT in SNP rs81471943, and CC was the most frequent genotype for NW (


Integrated analysis of GWAS and GP detected potential candidate genes for each metabolite using 80–160 top-ranked SNPs that resulted in the maximum cumulative prediction value. Applying GPs and GWASs to tea accession traits will contribute to genomics-assisted tea breeding.Effectively using genomic information greatly accelerates conventional breeding and applying it to long-lived crops promotes the conversion to genomic breeding. Because tea plants are bred using conventional methods, we evaluated the potential of genomic predictions (GPs) and genome-wide association studies (GWASs) for the genetic breeding of tea quality-related metabolites using genome-wide single nucleotide polymorphisms (SNPs) detected from restriction site-associated DNA sequencing of 150 tea accessions. The present GP, based on genome-wide SNPs, and six models produced moderate prediction accuracy values ( Camellia sinensis L.) are mainly cultivated in Asia to produce green, oolong, and black tea, which are popular beverages throughout the world. Approximately two billion cups of tea are consumed daily worldwide1, and tea drinking reportedly has numerous health benefits2. In general, tea quality is defined by the balance of various functional components, such as theanine, catechins, and caffeine, which are unique to tea. Theanine, an amino acid, contributes to the major umami taste of green tea4, and it has various health benefits, such as promoting relaxation5, improving concentration and learning ability6, and reducing blood pressure7. Tea catechins are major polyphenols and have been studied for their anticarcinogenic effects8, antimutagenic effects9, antibacterial activities10, free radical scavenging activities11, and anticaries actions12. In particular, ( −)-epigallocatechin gallate (EGCG), the major component of tea catechins, has strong antiallergic effects13. Caffeine  is a kind of purine alkaloid that accumulates to high levels in tea leaves and coffee. Caffeine consumption may be associated with a reduced risk for type 2 diabetes14, but excessive intake of caffeine may cause inflammation of the digestive organs, insomnia, and arrhythmia15. There are also reports on the risks of pregnant women ingesting caffeine16. These potentially harmful effects of caffeine negatively affect the consumption of tea and tea-related products. Thus, the unique metabolites in tea produce the most important agronomic traits targeted by modern and future tea breeding.Tea plants  associated with target traits or phenotypes to select individuals that carry favourable alleles for the traits of interest. MAS is efficient for traits that are controlled by low numbers of QTLs that have major effects on trait expression, but it is not suitable for evaluating complex quantitative traits that are governed by large numbers of minor QTLs20. GP uses all the available marker data for a population as predictors of breeding value based on modelling, and it takes into account the effects of multiple genes that control a target trait; this overcomes the limitation of MAS19, although GP requires cost-effective high-throughput genotyping platforms.Marker-assisted selection (MAS) and genomic prediction (GP) are the two main types of genomics-assisted breeding21. These high-throughput genome-wide SNP genotyping platforms have enabled the use of genome-wide association studies (GWASs) and GPs in many important crops26. GWASs enable detection of QTLs or genes that control phenotypic variations in a population of cultivars or germplasm accessions without preparing a bi-parental segregating population27.Next-generation sequencing (NGS) technologies have drastically reduced the cost and time of sequencing and single nucleotide polymorphism (SNP) discovery, which led to the development of high-throughput genome-wide SNP genotyping. In particular, the emergence of restriction site-associated DNA sequencing (RAD-seq) and genotype-by-sequencing resulted from the implementation of SNPs suitable for GP in both model and non-model crops28. Very recently, the draft genomes of two major tea varieties, C. sinensis var. assamica29 and C. sinensis var. sinensis31, were sequenced using several NGS platforms. The genome size of tea plants was estimated to be 3.8–4.0 Gb33, and this size has been a major barrier to determining tea genomic information using NGS technologies. For C. sinensis var. sinensis, a high-quality chromosome-level reference genome was obtained using single-molecule real-time sequencing and chromatin conformation capture technologies31. This information enabled determination of the chromosomal positions of SNP markers and linking genes in the tea genome, and it was effectively used for GP and GWAS.Previously, we conducted SNP genotyping of 167 tea accessions and revealed the genetic structures of cultivars, landraces, and germplasm accessions for subsequent genomics-assisted breedingIn the present study, we evaluated the potential of performing integrated analysis by GP and GWAS for genetic improvement of tea quality-related metabolites using genome-wide SNPs from RAD-seq data. Our analysis showed that GP effectively predicted the contents of several catechins and caffeine, but not free amino acids (FAAs). In addition, integrated analysis of GWAS and GP detected the potential candidate genes for each metabolite.To evaluate variations in the tea quality-related metabolites of tea accessions, we quantified the contents of 19 metabolites, including 10 FAAs, 7 catechins, caffeine, and chlorophyll (Chl), in the new shoots from the first crop season. We investigated the contents in 2018 and 2019 to evaluate the annual effect. Most variations were positively correlated between 2018 and 2019 31. We re-detected SNPs using the chromosome-scale reference genome. After filtering , 9523 SNPs were detected for subsequent analyses and widely mapped across the whole genome  values of the pairwise LD were plotted using a nonlinear regression curve against physical distance to estimate the LD decay pattern. This regression curve pattern showed that LD decayed to relatively low levels (r2 < 0.13) within 10 kb -epicatechin , ( −)-epicatechin gallate , EGCG (r = 0.32–0.41), total catechins (r = 0.27 − 0.32), and caffeine (r = 0.44 − 0.51), but low for other metabolites, such as FAAs and Chl  with linear ridge kernel regression (RR) or GBLUP with non-linear Gaussian kernel regression (GAUSS), Ridge, Lasso, Elastic Net, and Random Forest. The tenfold cross-validations (CVs) showed that the p-values  that incorporated the top-ranked SNPs  and randomly selected SNPs as a reference. The value of the r value curves based on top-ranked SNPs was higher than that based on randomly selected SNPs , ECG (97 genes), EGCG (64 genes), total catechins (80 genes), and caffeine (83 genes) that were located within a 10-kb region using the LD decay Fig.  of the 818. They are effective in crops with long life cycles, such as woody plants. Tea plants have several functional metabolites that have human health benefits; therefore, establishment of new genomic breeding methods, such as GP and GWAS, is an important first step for the tea industry’s future. In the present study, we evaluated the potential of GPs and GWASs for genetic improvement of tea quality-related metabolites using genome-wide SNPs detected by RAD-seq data from 150 tea accessions. The LD pattern is a key factor for GP and GWAS because these two approaches are based on the LD between markers and polymorphisms that explain phenotypic variation27.Genomics-based approaches may be especially useful in crop breeding and reduce the required breeding time compared with conventional breeding34 or 5 kb31 using 415 or 78 accessions, respectively. This disparity may result from differences in the reference population. The present population comprised mainly composed of Japanese accessions, whereas those of previous studies34 comprised mainly Chinese accessions. In cross-pollinated species, such as tea plants, LD may decay as a result of extreme genetic drift during domestication and breeding during evolution38. The hypothesis that the progenitors of the Japanese accessions (C. sinensis var. sinensis) were introduced into Japan from China approximately 800 to 1200 years ago by Buddhist priests is supported by recent DNA marker analyses40. Therefore, these differences in LD decay values of each tea reference population may result from the Japanese tea population having a limited number of founders compared with the Chinese population. However, the mean r2 value (0.24) between adjacent SNPs in this population was greater than the r2 value (0.20) required for an accurate GP41. This indicated that the marker density of this population was sufficient for GP. Furthermore, the fastSTRUCTURE, hierarchical cluster analysis (HCA), and principal component analysis (PCA) results showed that these SNPs reflected sufficient genetic differentiation, similar to our previous study28.Of the 150 analysed tea accessions, the LD decayed within 10 kb Fig. . This war values for EC (r = 0.17–0.28), ECG (r = 0.25–0.31), EGCG (r = 0.32–0.41), total catechins (r = 0.27–0.32), and caffeine (r = 0.44–0.51) from six GP models  with high GP ite Fig. . We estiphenylalanine ammonia-lyase, chalcone isomerase, chalcone synthase, dihydroflavonol reductase, leucoanthocyanidin reductase, and anthocyanidin reductase in catechins biosynthesis50 or tea caffeine synthases in caffeine biosynthesis52. This may be because of the insufficient power of the present GWAS that was conducted with multiple subpopulations to detect the subpopulation-specific alleles54, which may only segregate in some subpopulations.The potential candidate genes associated with each metabolite were detected by searching the 10-kb window  in the field and then frozen (− 30 °C) within 1 h. After being freeze-dried, samples were ground into fine powder and stored at room temperature within a desiccator under dark conditions until the subsequent measurement of tea quality-related metabolites.For the metabolite analysis, new shoots at the same developmental stage were harvested from 150 accessions grown in the same tea field during the first crop seasons  of 2018 and 2019. The tea ridges were managed using conventional methods optimised for Japanese green tea cultivation. Nitrogen fertilizer was applied at 400 kg-N hag, 15 min, 4 °C), the supernatants were individually passed individually through 0.45-µm polytetrafluoroethylene filters . The resulting solutions were stored at − 30 °C until they were analysed using high-performance liquid chromatography (HPLC). The HPLC system consisted of two LC-10ADvp pumps, a D6U-14A degasser, a CTO-20AC column oven, an SPD-M20A prominence photodiode array detector, an SCL-10Avp system controller, and an SIL-10ADvp autosampler . The HPLC conditions used were as follows: injection volume, 5 μL; column, 75 mm × 4.6 mm × 2.6 μm SunShell C18 column ; column oven temperature, 40 °C; and photodiode array detector, 190 to 400 nm. Eluent A  and eluent B  were used as the mobile phases at a flow rate of 1.0 mL min−1. The elution was performed with the following gradient: initial concentration of 10% B, followed by 2.5-min hold at 10% B, 1.5-min linear gradient from 10 to 30% B, 1.0-min hold at 30% B, 2.5-min linear gradient from 30 to 80% B, 2.5-min hold at 80% B, 1.0-min gradient from 80 to 10% B, and a final concentration of 10% B for 4.0 min. The solution of this mobile phase was eluted for 15 min per sample. The seven catechins  and caffeine were quantified, and their total value without caffeine was also expressed as total catechins.The catechins and caffeine levels were measured. Briefly, dry ground plant tissues (25 mg) were added to 5 mL 50% acetonitrile and extracted by shaking (130 strokes per min) for 60 min at room temperature. After centrifugation , the supernatants were independently passed through 0.45-µm cellulose acetate filters . The resulting solution was stored at − 30 °C until analysis by HPLC. Homoserine, as an internal standard, was added to the resulting solution, and o-phthalaldehyde derivatives were analysed using the HPLC system, which consisted of the following: two LC-10AT pumps, a DGU-20A5R degasser, a CTO-10Avp column oven, an RF-20A prominence fluorescence detector, an SCL-10Avp system controller, and an SIL-10AF autosampler . The HPLC conditions used were as follows: injection volume, 5 μL; column, 75 mm × 4.6 mm × 5 μm Ascentis Express C18 column ; column oven temperature, 40 °C; excitation wavelength, 340 nm; and emission wavelength, 450 nm. Eluent A  and eluent B  were used as the mobile phases at a flow rate of 1.0 mL min−1. The elution was performed using the following gradient: initial concentration of 5% B, followed by 1.6-min linear gradient from 5 to 12% B, 5.0-min linear gradient from 12 to 22% B, 1.7-min linear gradient from 22 to 95% B, 2.2-min hold at 95% B, 0.5-min linear gradient from 95 to 5% B, 1.5-min gradient from 5 to 0% B, and a final concentration of 0% B for 1.0 min. The solution of this mobile phase was eluted for 15 min per sample. Nine amino acids were quantified , and their total value was also expressed as total FAAs.The FAA levels were also measured. Briefly, dry ground plant tissues (10 mg) were added to 10 mg polyvinylpolypyrrolidone and 5 mL ultra-pure water and extracted by shaking (130 strokes per min) for 60 min at room temperature. After centrifugation . After incubation for 24 h at 4 °C under dark conditions to allow complete decolourisation, the samples were centrifuged at 2000 × g for 30 min, and the absorbance of the supernatant was measured at 663.8 and 646.8 nm using a spectrophotometer . The Chl a and b contents were calculated using the equation of Porra et al. (1989)55, and their total value was expressed as the Chl content.In addition, Chl a and b were extracted from finely ground powder (5 mg) of freeze-dried leaf samples using 28. Reads were pre-processed using Trimmomatic ver. 0.33 with the following parameters: ‘ILLUMINACLIP TruSeq3-PE-2.fa’, 2:30:10; ‘LEADING’, 19; ‘TRAILING’,19; ‘SLIDINGWINDOW’,30:20; ‘AVGQUAL’, 20; and ‘MINLEN’, 51. After pre-processing, the remaining reads were mapped to the tea reference chromosome-scale genome, which was downloaded from the Tea Plant Information Archive56 using Bowtie2 ver. 2.3.5.1, and then, the SNPs were called using Stacks ver. 2.557. For subsequent analyses, SNP genotypes were converted to 1 (AA homozygotes), − 1 (BB homozygotes), or 0 (AB heterozygotes). The raw SNP data were filtered using VCFtools ver.0.1.16 with the following thresholds: SNP call rate within a locus ≥ 0.7 and MAF ≥ 0.05. The filtered SNP data were imputed using the R package missForest58 ver. 1.4 and used for subsequent genetic analyses. The RAD-seq data have been deposited in the DDBJ Sequence Read Archive (Accession number: DRA008166).In a previous study, we obtained the sequencing reads for SNP genotyping using RAD-seq for a tea population, which included the 150 accessions tested in this studyr2) values within the 50-kb window using VCFtools ver. 0.1.16. Pairwise LDs were plotted against physical distances. The LD decay pattern was estimated by fitting a trend line based on a nonlinear LOESS regression of r2 on physical distance using the R package ggplot2 ver. 3.3.2. Physical distances between adjacent markers ranged from 1.00 to 49.97 kb . The genetic structure and admixture of all 150 accessions can be found in our previous study28.The LD values between pairs of SNPs in the same chromosome were determined from the squared correlation coefficients 28. HCA was based on Ward’s method60 using Euclidean distance and was conducted using the R function “hclust”. PCA was performed using the R function prcomp. The principal component scores were plotted using R package ggplot2 ver. 3.3.2.To clarify the genetic structure, we used the Bayesian clustering algorithm, HCA, and PCA. The Bayesian clustering analysis was performed using fastSTRUCTURE ver. 1.061. Restricted maximum likelihood was used to estimate variance components. Ridge , Lasso , and Elastic Net  were performed as linear regression methods using the R package glmnet ver. 4.062. Random Forest, a non-linear decision tree-based ensemble learning method, was performed using the R package randomForest ver. 4.6-1463. Number of trees (ntree) was set to 1000, and default values were used for the other parameters.To evaluate the GP accuracy, we used six regression methods. In phenotype value, the mean values obtained from 2018 and 2019 were used as values in the subsequent GP modelling and GWAS. The GBLUP with RR and GAUSS were performed using the “kinship.BLUP” function of the R package rrBLUP ver. 4.6.1r values of the models were estimated using 10 replicates of tenfold CVs. Thus, r was defined as Pearson’s correlation coefficient between observed and predicted values. Using tenfold CVs, the entire population was divided into 10 equal groups. One group was predicted by regression models based on the other nine groups (reference populations). Correlations were calculated until all individuals in all groups received phenotypic predictions. Then, a single correlation was calculated between all observed and predicted phenotypes within all groups.The 61. In total, 9,523 SNPs were used for the GWAS after selecting SNPs without missing rates ≥ 0.3 and MAFs < 0.05. The principal components and a kinship matrix were included in the GWAS calculation based on the MLM. A PCA was conducted using the R function “prcomp” to estimate the population structure. The first six principal components from the plot that explained the total variation among SNPs were selected. The kinship matrix was computed using the “A.mat” function of the R package rrBLUP ver. 4.6. To illustrate the localisation of associated SNPs by GWAS, we created Manhattan plots from SNPs anchored to 15 chromosomes and 1,318 unanchored contigs. The Manhattan plots were illustrated using the “manhattan” in the R package qqman ver.0.1.464.The GWAS was performed using an MLM implemented using the “GWAS” function of the R package rrBLUP ver. 4.6.1p-values (top-ranked) in the GWAS, as described by Nakano et al. (2020) with slight modifications65. The cumulative effects of the linked loci were estimated using 20–300 top-ranked SNPs . Randomly selected SNPs throughout the genome were used as a reference. The potential candidate genes that were located within a 10-kb region using the LD decay  was performed using the SNPs with the lowest o et al. 020 with Supplementary Tables.Supplementary Figures.


LPA gene. Many aspects of the transcriptional regulation of LPA are poorly understood and the role of epigenetics has not been addressed yet. Therefore, we conducted an epigenome-wide analysis of DNA methylation on Lp(a) levels in two population-based studies . We identified a CpG site in the LPA promoter which was significantly associated with Lp(a) concentrations. Surprisingly, the identified CpG site was found to overlap the SNP rs76735376. We genotyped this SNP de-novo in three studies . The minor allele of rs76735376  was associated with increased Lp(a) values (p = 1.01e-59) and explained 3.5% of the variation of Lp(a). Statistical mediation analysis showed that the effect on Lp(a) is rather originating from the base change itself and is not mediated by DNA methylation levels. This finding is supported by eQTL data from 208 liver tissue samples from the GTEx project, which shows a significant association of the rs76735376 minor allele with increased LPA expression. To evaluate, whether the association signal at rs76735376 may actually be derived from a stronger eQTL signal in LD with this SNP, eQTL association results of all correlated SNPs (r2≥0.1) were integrated with genetic association results. This analysis pinpointed to rs10455872 as the potential trigger of the effect of rs76735376. Furthermore, both SNPs coincide with short apo(a) isoforms. Adjusting for both, rs10455872 and the apo(a) isoforms diminished the effect size of rs76735376 to 5.38 mg/dL (p = 0.0463). This indicates that the effect of rs76735376 can be explained by both an independent effect of the SNP and a strong correlation with rs10455872 and apo(a) isoforms.Lipoprotein(a) [Lp(a)] is a major cardiovascular risk factor, which is largely genetically determined by one major gene locus, the  High Lipoprotein(a) [Lp(a)] concentrations represent a major risk factor for several cardiovascular diseases that is widely present in the population –3. DepenLPA gene[Lp(a) concentrations are mostly genetically determined by one major gene locus, the LPA gene, which pLPA gene and thusLPA locus explaining >90%[The apo(a) isoform determined by the KIV-2 repeat number is the strongest contributor to Lp(a) concentration variance and explains ≈30–70% of the total trait variance, with the complete ning >90%,21, Intening >90%, suggestning >90%.LPA[LPA expression in reporter assays[LPA is less well defined. Depending on whether a reference transcript with a leading non-coding first exon[Some high impact loss-of-function variants have been described–28 but rLPA, as wellLPA,33,34. Ter assays. Conversirst exon  at SNPs represents a special case of gene regulation via methylation. Up to 50,000, mostly cis-acting, ASM-SNPs have been reported,44 so faThe impact of methylation on regulation of lipid phenotypes has been studied to a lesser extent than germline SNPs on these phenotypes. The studies performed so far reported clear associations with lipid phenotypes such as VLDL-C, HDL-C, LDL-C, triglycerides and total cholesterol and even lipid-related diseases (reviewed in), but noLPA gene was significantly associated with Lp(a) in the discovery sample KORA F4  were occurring predominantly in low molecular weight apo(a) isoforms with 18 to 20 KIV repeats , the anaLPA) polymorphism is located exactly at the cytosine base of the CpG site cg17028067. The minor allele thus abolishes one potential methylation site by replacing the cytosine base of the CpG by a thymidine, thus effectively reducing the amount of available methylation target sites . This C>T  values  with conLPA pentanucleotide-repeat (PNR) polymorphism[Since rs76735376 is in close vicinity to the well-known ymorphism,38,39,53Since rs76735376 was identified actually by chance in a genome-wide methylation analysis, we investigated whether the effect of the SNP on Lp(a) is mediated through the methylation signal. A formal mediation analysis revealed no significant causal mediation effect of the methylation level of cg17028067 on log-Lp(a)  = +0.64 (0.18), p = 0.00038) despite only 15 heterozygous carriers in the data. Accordingly, bioinformatic transcription factor binding analysis using TRANSFAC[POU2F1 and POU5F1  within the T -allele of the rs76735376 SNP but not the C- or 5-mC allele  with rs76735376 (10 SNPs). 2 = 1), we included only rs10455872into the further analysis. While r2 between rs76735376 and rs10455872 is only 0.1 and 0.17 in KORA F3 and KORA F4 due to different allele frequencies, almost all carriers of the minor allele of rs76735376 carry also at least one minor allele of rs10455872  is a major genetically determined cardiovascular risk factor. Research on Lp(a) has experienced two revivals: the first by genetic studies providing strong support for a causal association with cardiovascular outcomes and the second, more recently, due to Lp(a)-lowering agents becoming available in the form of PCSK9 inhibitors and mRNA antisense therapy. Especially the advent of an antisense oligonucleotide therapy capable of reducing Lp(a) by >80% now putsDespite the strong impact of Lp(a) on CVD risk, many controversies and uncertainties remain about its metabolism and regulation. The assLPA promoter. Each minor allele abolishes a methylation site in a CpG dinucleotide and is associated with an increase in Lp(a) of about 37 mg/dL and 9 mg/dL when restricted to individuals with 18–20 KIV repeats who are the main carriers of this methylation site. To dissect the effect of the base from the methylation site we performed a statistical mediation analysis which indicated that the effect observed in the association study is driven by the base change and not by the methylation status of the locus. This was supported by pilot EMSA experiments using the three possible allele states C, 5m-C and T), which showed no clearly defined difference between the C and the 5m-C allele, but a difference between the C and T alleles.Regulatory variants and elements are less defined. Therefore, we performed the first genome-wide DNA methylation study for Lp(a). This identified the SNP rs76735376 located in a CpG site in the LPA. While Lp(a) is exclusively expressed in the liver and native tissue is hard to obtain in numbers sufficient to account for the low MAF of rs76735376. The recent large-scale multi-tissue and eQTL profiling initiative GTEx, however, provides a convenient way to still investigate the effects of rs76735376 on LPA expression. Indeed, a look-up for in the GTEx data revealed that the rs76735376 T-allele is associated with significantly increased LPA expression, which was in line with the observed increase of Lp(a) concentrations in these individuals.The location in the promoter suggests an effect on the transcriptional regulation of LPA pentanucleotide repeat (PNR). This led us to speculate that it might be the causal factor underlying the previously reported[LPA expression. This was, however, not the case as the association signal at rs76735376 was only marginally affected by including the PNR in the model and vice versa. Of note, all carriers of the minor allele of rs76735376 carried a PNR allele with 8 copies and virtually all minor allele carriers presented an apo(a) allele with 19 to 20 KIV repeat  isoforms. This underscores the complex genetic make-up of the Lp(a) trait, consisting of an intricate interplay between the KIV-2 repeat number, transcriptional regulation by enhancer and promoter regions, modifier SNPs, which may be in turn confined only to certain isoform sizes, and trans-regulating factors like apoE,65. GiveIn light of these observations are our findings still relevant and worth being discussed? Indeed, our study highlights several important messages for the community. First, effect size from the isoform- and rs10455872-adjusted analysis is still 5.69 mg/dL, which is about 50% of the median level of Lp(a) and therefore cannot be deemed to be irrelevant. The only person homozygous for the rare allele has an Lp(a) level of 124.75 mg/dL, which corresponds to the 99.4% percentile. Therefore it cannot be excluded that this SNP still might have some independent function or following a recessive mode of inheritance.2 of only 0.1 and 0.17. However, the high D’ values of 0.71 and 0.95 are sufficient to take up most of the signal. Indeed the D’ values are often neglected when evaluating LD structures and it is often implicitly assumed that only a high r2 can trigger an indirect signal. We exemplify here that this is not always the case. Moreover, it is also important to note how promising functional data may not necessarily translate to a true impact on the phenotype. Indeed mRNA levels do not necessarily always correlate strongly with the protein levels[Secondly, rs10455872 explains a considerable part of the effect of rs76735376, despite presenting an rin levels.https://bravo.sph.umich.edu/freeze5/hg38/). This exemplifies that, when drilling down from GWAS data to single SNPs, comparing the MAF from still widely used 1000 Genomes imputed data with that in the most recent resources like TOPMed can help to detect inconsistencies, which could otherwise bias the results if not handled properly.The third message is a rather technical one. Indeed, when we originally set out to investigate the impact of DNA methylation on Lp(a), the quality control procedure was supposed to purge any CpG sites affected by a SNP by using imputed 1000Genomes genotype data filtered at 5% MAF to focus only on differential methylation but not common ASM-SNPs. However, rs76735376 escaped filtering because it was badly imputed and thus incorrectly reported with a MAF of 0.1%. Since such a low MAF could not have triggered the association signal observed, we genotyped it de-novo, revealing a ten times higher true MAF. This MAF is now confirmed also by new reference datasets that became available only very recently , reports only 228 liver samples, thereof only 208 having also SNP genotype data available. While methylation is clearly a tissue-specific marker, studies also have shown that a consistent portion of the DNA methylation patterns is still conserved across tissues[Our study presents some limitations which need to be mentioned. Firstly, the methylation dataset was derived from peripheral blood, while s tissues,69. TherLPA SNPs between ethnicities, investigations in other populations are required to establish the meaning of our findings in other ethnicities[Secondly, our dataset was of Caucasian origin. Given the known differences in allele frequencies and effects of hnicities,71.LPA expression is blunted in cell culture for still unknown reasons and that the relationship of mRNA expression to secerned Lp(a) is not fully elucidated[Thirdly, it needs to be mentioned that also GTEx presents limitations. Most relucidated.In conclusion, this study represents a first step assessing the epigenetic regulation of Lp(a). We showed that the analyzed region of the Lp(a) promoter is subjected to DNA methylation and that the identified CpG site cg17028067, respectively rs76735376, is associated with increased Lp(a) concentrations. This newly identified variant, however, is another example of the complex LD structures in the Lp(a) gene, since, despite clear eQTL signals, major parts of the effect could be finally explained by correlation with a complex interplay of apo(a) isoforms and rs10455872 genotype. This exemplifies the intricate genetic architecture of Lp(a).The overall study design is outlined in Genome-wide DNA methylation patterns in the discovery and replication cohorts were analyzed using the Infinium HumanMethylation450 BeadChip Array (Illumina) and DNA derived from peripheral blood. KORA F3 and F4 samples were measured and processed separately . KORA F3To reduce the non-biological variability between observations, data were normalized using quantile normalization (QN) on the raw signal intensities: QN was performed on a stratification of the probe categories into 6 types, based on probe type and color channel, using the R package limma, v3.16.5.LPA gene locus were present on the microarray  and since probe binding might be affected by SNPs in the binding area, sites representing or being located in a 50 bp proximity to SNPs with a minor allele frequency (MAF) of at least 5% were excluded from the data set. Nine Cpcroarray .KORA F4 samples were genotyped using the Affymetrix Axiom chip array. Genotypes were called with the Affymetrix software and were annotated to NCBI build 37. Imputation was performed with IMPUTE v2.3.0 using the 1000G phase1 (v3) reference panel. Additio5’-TACAGGACAGAGACTAACT-3’ and 5’-GCATAGTATCAATCTTTCCG-3’. The PCR conditions were: Qiagen Taq DNA Polymerase (#201207), 0.2 mM dNTP, 0.5 μM primer, 1x Qiagen Q-Solution reagent, 40 ng input DNA amount. Cycling conditions were 94°C 3’ initial denaturation, 40 cycles of 94°C 30 sec, 60°C 30 sec, 72°C 30 sec, 72°C 10 minutes final elongation. Sequencing was done using ABI BigDye 1.1 chemistry .Due to the low MAF, samples being heterozygous and homozygous for the rare allele were confirmed by Sanger sequencing using the primers LPA pentanucleotide repeat (PNR) is a microsatellite with 5-12 repeats of a TTTTA element located at hg19:chr6:161,086,617–161,086,663 (according to the RepeatMasker tool in the UCSC Genome Browser[LPA pentanucleotide repeat was amplified in KORA F4 (data available in n = 2956) and SAPHIR (data available in n = 1429) in 384 well plates in a total volume of 5 μl with 2.5 μl Qiagen Multiplex PCR Plus Kit  and 1 μl Qiagen Q-solution using a Yakima Yellow-labelled primer. Fragment length was detected by fluorescent capillary fragment analysis on an ABI 3730s Genetic Analyzer and all data analyzed using the GeneMapper software . Data was quality controlled for null allele overrepresentation and deviation from Hardy-Weinberg Equilibrium using Micro-Checker[http://www.microchecker.hull.ac.uk/) and ARLEQUIN (http://cmpg.unibe.ch/software/arlequin3/) [The e Browser) and thue Browser. In brieo-Checker . 2, 1 μl converted DNA. Cycling conditions were 94°C 4 minutes initial denaturation, 45 cycles of 94°C 20 sec, Annealing 30 sec, 72°C 60 sec, final elongation at 72°C 3 minutes. Primers for bisulfite-converted DNA were designed using the Agena EpiTyper Software Suite  and eluted in two times 10 μl. 1 μl eluted converted DNA was used as template for PCR. The region was amplified using the DNA Polymerase of the Agena PCR Accessory and Enzyme Set . PCR conditions were: 0.4 u Agena DNA Polymerase, 0.5 mM dNTP, 0.2 μM primer, 2 mM MgCl3. The plates were incubated with 100 μl antibody dilution , washed three times (1x PBS + 0.05% v/v Tween-20) and blocked with 200 μl 0.1% w/v casein in 1x PBS pH 7.3 . To ensure measuring each sample within the linear range of optical density, all samples were diluted into the ELISA plate twice  out of 1:30 and 1:1000 predilutions in 1x PBS, pH 7.3). A 7-point standard curve ranging from 0.32 mg/dL to 5 μg/dL was created . Duplicate determinations of four reference samples were used as longitudinal inter-assay controls. The coated plates were incubated with the analyte for 1 hour at 37°C. After several washing steps, the bound Lp(a) was detected using a horseradish-peroxidase-conjugated monoclonal antibody  directed against the KIV-2 domain and not cross-reacting with plasminogen[Lp(a) concentration were determined in plasma using the same standardized sandwich ELISA assay in all samples. The assay has been described in detail previously,85. In basminogen and the % agarose gel with 0.08% SDS and separated for 18 h at 0.04 A (constant current). A size standard consisting of a mixture of five plasma samples collected from individuals expressing each only one apo(a) isoform  was applied in every seventh well of the gel. The gel was subsequently blotted to a PVDF membrane  by semi-dry blotting . After blocking , the membrane was incubated with horseradish-peroxidase-conjugated 1A2 antibody (1A2 concentration was 168 ng/ml in Buffer C) for 2 h at room temperature on a shaker. After washing with TTBS (3x 15 min), ECL substrate  was added and signals recorded on an Amersham Hyperfilm™ ECL™ .Lp(a) isoform was determined by Western blotting in all samples as described. In brieIn heterozygous individuals, the smaller (and thus the commonly Lp(a) concentration-determining) of the two alleles was used for isoform-adjustment of statistical models.Linear regression models were applied to evaluate the association between DNA methylation beta values and Lp(a). Lp(a) levels were log-transformed to account for the skewed distribution. Age, sex and the first 20 principal components of the Illumina control probes  were included as potential confounders. In addition, since whole blood DNA samples were used, cell heterogeneity had to be considered as a confounder. As no measured cell count information was available, sample-specific estimates of the proportion of the major white blood cell types were obtained using a statistical method described by Houseman et al. To corrAssociation of the identified SNP with log-transformed Lp(a) was evaluated in all three studies individually  using a linear regression model and in all three studies combined using a linear mixed effects model with “study” as a random effect and adjusting for age and sex. The genotypes of the SNP were additively coded, that is, effect sizes refer to one copy of the minor allele. This analysis was repeated additionally adjusting for apo(a) isoforms or in subgroups of apo(a) isoforms. Furthermore, as a consequence of the results of the analysis described in the next paragraph (“Integrating GWAS results with eQTL results”), the association analysis was further adjusted for rs10455872 and in subgroups of individuals with at least one minor allele of rs10455872.In addition to the models with log-transformed Lp(a) values as the dependent variable, which were used to ensure the normal-distribution assumption to derive the test-statistic and p-values, Lp(a) on its original scale was used. From these models, beta estimates and standard errors (se) were derived to ease interpretability of the effect sizes.Due to the close vicinity of the cg-site/SNP with the well-known PNR polymorphism, the association of the PNR with Lp(a) concentrations was evaluated and whether this effect can be explained by the SNP or vice versa.Since both, the identified SNP and the methylation site are significantly associated with Lp(a) and since the methylation level is a consequence of the allelic variation of the SNP, it might be conceivable that there is no direct effect of the SNP on Lp(a), but that this effect is mediated by the methylation levels. To assess whether the effect of rs76735376 on Lp(a) is mediated through methylation, a formal mediation analysis was applied using the R library “mediation”.2≥0.1 with rs76735376 was derived. This SNP-list was integrated with all SNPs, for which eQTL data from GTEx (see next paragraph) are available and with results derived from a genome-wide association study (GWAS) on Lp(a). The GWAS results are based on the KORA F4 study (n = 2926) and were part of a published meta-analysis[To evaluate, whether the association signal at rs76735376 may actually be derived from a stronger eQTL signal in LD with this SNP, a list of SNPs with r-analysis. Since r-analysis.LPA gene expression in native liver tissue was assessed using the GTEx[www.gtexportal.com), which contains expression quantitative trait locus (eQTL) data with whole genome SNP data and whole transcriptome RNA-Seq data for 208 liver samples. The correlation of rs76735376 genotype with LPA expression in liver was extracted from the GTEx V8 dataset by downloading all pairwise SNP-expression correlations in liver tissue and using the SNP-ID “chr6_160665684” (chromosome and position of rs76735376 in hg38) and the Ensembl gene identifier for LPA “ENSG00000198670” to extract the data for rs76735376 and LPA. The dataset is available at https://console.cloud.google.com/storage/browser/_details/gtex-resources/GTEx_Analysis_v8_QTLs/GTEx_Analysis_v8_eQTL_all_associations/Liver.allpairs.txt.gz, accessible via the link “eQTL Tissue-Specific All SNP Gene Associations” at https://gtexportal.org/home/datasets, respectively at https://console.cloud.google.com/storage/browser/gtex-resources The effect of rs76735376 on  the GTEx data (wwhttp://genexplain.com/transfac/) [TRANSFAC (ansfac/) , a databCEBPB) , anti-Oct-1 (POU2F1)  plus anti-Oct-3/4 (POU5F1)  or anti-Nfatc1 (NFATC1) as negative control . Binding reactions with 3 × 105 cpm of labeled probe was performed for 20 min at 4°-8°C using Binding Buffer C-1 (Active Motif 37484) together with Stabilizing Solution D (Active Motif 37488). Samples were separated on a 4% native polyacrylamide gel in 0.5 × TBE for 3 h at 250 V. For competition assays, 10-fold unlabeled cold oligonucleotides identical to the radioactively labeled non-methylated Oligo2 LPA_EMAS2 or the radioactively labeled methylated Oligo6 LPA_EMAS2 probes were added to the binding reaction in addition.To perform a pilot validation of the mediation analysis results, single-stranded oligonucleotides spanning the SNP site were synthesized by Eurofins MWG Operon and then annealed to generate double-stranded probes. The single-stranded oligonucleotide sequences for the C, T and 5-mC allele of rs76735376 respectively are shown in S1 TableThe location is given relative to the UCSC Genome browser hg38 annotation with a leading non-coding exon 1. See the (PDF)Click here for additional data file.S2 TableFor Bisulfite Sanger sequencing primer the lower case letters denote tails that have been added by the design software to increase the annealing temperature of the primer. The PCR protocols are given in the Methods section of the manuscript. See (PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 FigUpper case: sequence regions, which could be covered with amplicons for bisulfite sequencing. Lower case: regions which could not be covered by amplicons for bisulfite sequencing. Blue background (light and dark): primer binding sites for bisulfite sequencing. Note that the sequence given here is the unconverted sequence. The primer given in (PDF)Click here for additional data file.S3 FigThe bold case base gives the location of rs76735376. Green background: POU2F1/POU5F1 binding site, Green font: mutated POU2F1/POU5F1 binding site, Blue CEBPB binding site. The reverse primers (-r) are given in reverse orientation, as they are annealed to the forward oligos (-f).(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 FigPanel A: Representative results of bisulfite sequencing in two homozygotes for the major allele and two heterozygotes in the SAPHIR study. The blue peak represents the unconverted C-allele, indicating that the major part is methylated in CC carriers, whereas only a minor part is methylated in CT carriers. Panel B: Boxplots of the methylation level  of cg17028067, stratified for genotypes in the KORAF4 study (panel B).(PDF)Click here for additional data file.S6 FigDistribution of the smaller of both isoforms, stratified for carriers (CT or TT) and non-carriers (CC) of the SNP rs76735376 rs76735376 (panel A) and carriers and non-carriers of rs10455872 (panel B)(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Figrs76735376 modifies binding of the transcription factor CEBPB. Left panel: Diagram of the double-stranded oligos used for EMSA analysis containing either cytosine , thymine (Oligo4 LPA_EMSA2-T) or methyl-cytosine (6 LPA_EMSA2-5mC) oligo. The transcription factor binding sites for CEBPB (blue) and POU2F1/POU5F1 (green) as predicted by Transfac analysis, 52 of the rs76735376 probes are shown. Position weight matrix of the TF is shown. The arrow indicates the orientation of the sequence matrix in the oligo. Right panel: EMSA analysis of human liver nuclear extract hybridized to Oligo2 LPA_EMSA2-C, Oligo4 LPA_EMSA2-T or Oligo6 LPA_EMSA2-5mC. Binding specificity to specific transcription factors was performed by using super-shift antibodies (s-shift AB) for the POU factors or CEBPB. An NFATC1 antibody and excess of cold unlabeled oligonucleotide (cold comp.) were used as negative control. One representative experiment out of three is shown. Note that the free probe has already left the gel because a long run time in order was required to distinguish the super-shift bands.(PDF)Click here for additional data file.S9 Fig2 of ≥ 0.1 with the index SNP (LD information derived from 1000 genomes CEU). Color coding shows magnitude of LD.The x-axis shows p-values from a GWAS on Lp(a) (using data from the KORA F4 study), the y-axis p-values from eQTL analysis (GTEx consortium V8). The index SNP (rs76735376) is marked as purple triangle. Only those SNPs are shown, which show a r(PDF)Click here for additional data file.