Dataset Information

Genetic variations associated with six-white-point coat pigmentation in Diannan small-ear pigs.

ABSTRACT: A common phenotypic difference among domestic animals is variation in coat color. Six-white-point is a pigmentation pattern observed in varying pig breeds, which seems to have evolved through several different mechanistic pathways. Herein, we re-sequenced whole genomes of 31 Diannan small-ear pigs from China and found that the six-white-point coat color in Diannan small-ear pigs is likely regulated by polygenic loci, rather than by the MC1R locus. Strong associations were observed at three loci (EDNRB, CNTLN, and PINK1), which explain about 20 percent of the total coat color variance in the Diannan small-ear pigs. We found a mutation that is highly differentiated between six-white-point and black Diannan small-ear pigs, which is located in a conserved noncoding sequence upstream of the EDNRB gene and is a putative binding site of the CEBPB protein. This study advances our understanding of coat color evolution in Diannan small-ear pigs and expands our traditional knowledge of coat color being a monogenic trait.

SUBMITTER: Lu MD

PROVIDER: S-EPMC4897638 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic variations associated with six-white-point coat pigmentation in Diannan small-ear pigs.

Lü Meng-Die MD Han Xu-Man XM Ma Yun-Fei YF Irwin David M DM Gao Yun Y Deng Jia-Kun JK Adeola Adeniyi C AC Xie Hai-Bing HB Zhang Ya-Ping YP

Scientific reports 20160608

A common phenotypic difference among domestic animals is variation in coat color. Six-white-point is a pigmentation pattern observed in varying pig breeds, which seems to have evolved through several different mechanistic pathways. Herein, we re-sequenced whole genomes of 31 Diannan small-ear pigs from China and found that the six-white-point coat color in Diannan small-ear pigs is likely regulated by polygenic loci, rather than by the MC1R locus. Strong associations were observed at three loci ...[more]

PMID: 27270507

Similar Datasets

Project description:Abstract Aims The detection of white-coat hypertension/white-coat uncontrolled hypertension (WCH/WUCH) with out-of-office blood pressure (BP) monitoring is time- and resource-consuming. We aim to develop a machine learning (ML)-derived prediction model based on the characteristics of patients from a single outpatient visit. Methods and results Data from two cohorts in Taiwan were used. Cohort one (970 patients) was used for development and internal validation, and cohort two (464 patients) was used for external validation. WCH/WUCH was defined as an office BP of ≥140/90 mmHg and daytime ambulatory BP of <135/85 mmHg in treatment-naïve or treated individuals. Logistic regression, random forest (RF), eXtreme Gradient Boosting, and artificial neural network models were trained using 26 patient parameters. We used SHapley Additive exPlanations values to provide explanations for the risk factors. All models achieved great area under the receiver operating characteristic curve (AUROC), specificity, and negative predictive value in both validations (AUROC = 0.754–0.891; specificity = 0.682–0.910; negative predictive value = 0.831–0.968). The RF model was the best performing (AUROC = 0.884; sensitivity = 0.619; specificity = 0.887; negative predictive value = 0.872; accuracy = 0.819). The five most influential features of the RF model were office diastolic BP, office systolic BP, current smoker, estimated glomerular filtration rate, and fasting glucose level. Conclusion Our prediction models achieved good performance, underlining the feasibility of applying ML models to outpatient populations for the diagnosis of WCH and WUCH. Further validation with other prospective data sets should be considered in the future. Graphical Abstract Graphical Abstract Two cohorts were used for model development after splitting and external validation respectively. With oversampled training set, four algorithms were developed through the process of feature ranking with SHapley Additive exPlantions values, feature selection, and threshold tuning under five-fold cross-validation. Models were then evaluated by variant metrics. Present study demonstrated the feasibility of applying machine learning models to outpatient population for the identify of white-coat hypertension and white-coat uncontrolled hypertension. ANN, artificial neural network; AUROC, area under the receiver operating characteristic curve; DBP, diastolic blood pressure; LR, logistic regression; NPV, negative predictive value; PPV, positive predictive value; RF, random forest; ROC, receiver operating characteristic; SBP, systolic blood pressure; Sen, sensitivity; SHAP, SHapley Additive exPlanations; Spe, specificity; WCH/WUCH, white-coat hypertension/white-coat uncontrolled hypertension; XGboost, eXtreme Gradient Boosting.

Project description:BACKGROUND:Pigs have many features that make them attractive as biomedical models for various diseases, including cancer. P53 is an important tumor suppressor gene that exerts a central role in protecting cells from oncogenic transformation and is mutated in a large number of human cancers. P53 mutations occur in almost every type of tumor and in over 50% of all tumors. In a recent publication, pigs with a mutated P53 gene were generated that resulted in lymphoma and renal and osteogenic tumors. However, approximately 80% of human tumors have dysfunctional P53. A P53-deficient pig model is still required to elucidate. METHODS:Transcription activator-like effector nucleases (TALENs) were designed to target porcine P53 exon 4. The targeting activity was evaluated using a luciferase SSA recombination assay. P53 biallelic knockout (KO) cell lines were established from single-cell colonies of fetal fibroblasts derived from Diannan miniature pigs followed by electroporation with TALENs plasmids. One cell line was selected as the donor cell line for somatic cell nuclear transfer (SCNT) for the generation of P53 KO pigs. P53 KO stillborn fetuses and living piglets were obtained. Gene typing of the collected cloned individuals was performed by T7EI assay and sequencing. Fibroblast cells from Diannan miniature piglets with a P53 biallelic knockout or wild type were analyzed for the P53 response to doxorubicin treatment by confocal microscopy and western blotting. RESULTS:The luciferase SSA recombination assay revealed that the targeting activities of the designed TALENs were 55.35-fold higher than those of the control. Eight cell lines (8/19) were mutated for P53, and five of them were biallelic knockouts. One of the biallelic knockout cell lines was selected as nuclear donor cells for SCNT. The cloned embryos were transferred into five recipient gilts, three of them becoming pregnant. Five live fetuses were obtained from one surrogate by caesarean section after 38 days of gestation for genotyping. Finally, six live piglets and one stillborn piglet were collected from two recipients by caesarean section. Sequencing analyses of the target site confirmed the P53 biallelic knockout in all fetuses and piglets, consistent with the genotype of the donor cells. The qPCR analysis showed that the expression of the P53 mRNA had significant reduction in various tissues of the knockout piglets. Furthermore, confocal microscopy and western blotting analyses demonstrated that the fibroblast cells of Diannan miniature piglets with a P53 biallelic knockout were defective in mediating DNA damage when incubated with doxorubicin. CONCLUSION:TALENs combined with SCNT was successfully used to generate P53 KO Diannan miniature pigs. Although these genetically engineered Diannan miniature pigs had no tumorigenic signs, the P53 gene was dysfunctional. We believe that these pigs will provide powerful new resources for preclinical oncology and basic cancer research.

Project description:Seed-coat cracking and undesirable color of seed coat highly affects external appearance and commercial value of peanuts (Arachis hypogaea L.). With an objective to find genetic solution to the above problems, a peanut mutant with cracking and brown colored seed coat (testa) was identified from an EMS treated mutant population and designated as "peanut seed coat crack and brown color mutant line (pscb)." The seed coat weight of the mutant was almost twice of the wild type, and the germination time was significantly shorter than wild type. Further, the mutant had lower level of lignin, anthocyanin, proanthocyanidin content, and highly increased level of melanin content as compared to wild type. Using RNA-Seq, we examined the seed coat transcriptome in three stages of seed development in the wild type and the pscb mutant. The RNA-Seq analysis revealed presence of highly differentially expressed phenylpropanoid and flavonoid pathway genes in all the three seed development stages, especially at 40 days after flowering (DAF40). Also, the expression of polyphenol oxidases and peroxidase were found to be activated significantly especially in the late seed developmental stage. The genome-wide comparative study of the expression profiles revealed 62 differentially expressed genes common across all the three stages. By analyzing the expression patterns and the sequences of the common differentially expressed genes of the three stages, three candidate genes namely c36498_g1 (CCoAOMT1), c40902_g2 (kinesin), and c33560_g1 (MYB3) were identified responsible for seed-coat cracking and brown color phenotype. Therefore, this study not only provided candidate genes but also provided greater insights and molecular genetic control of peanut seed-coat cracking and color variation. The information generated in this study will facilitate further identification of causal gene and diagnostic markers for breeding improved peanut varieties with smooth and desirable seed coat color.

Dataset Information

Genetic variations associated with six-white-point coat pigmentation in Diannan small-ear pigs.

Publications

Genetic variations associated with six-white-point coat pigmentation in Diannan small-ear pigs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets