Project description:This project aims to develop and apply genomic tools to enhance selective breeding of Nile tilapia, with a focus on disease resistance.
Project description:Transposable elements (TEs) are genomic parasites that constitute the most abundant portions of higher plant genomes. However, whether TE selection occurred during crop domestication remains unknown. HUO is active under normal growth conditions, present at low copy numbers, inserts preferentially into regions capable of transcription, but absent in almost all modern varieties, indicating its removal during rice domestication and modern rice breeding. HUO triggers genomic immunity and dramatically alters genome-wide methylation levels and small RNA biogenesis, as well as global gene expression. Its presence specifically affects agronomic traits by decreasing yield performance and disease resistance but enhancing salt tolerance, which mechanistically explains its domestication removal. Thus, our study reveals a unique retrotransposon as a negative target for maintaining genetic and epigenetic stability during crop domestication and selection.
Project description:Size distribution of Korean minipig is an important feature that made this breed ideal for biomedical research and safe post clinical trials. The extremely tiny (ET) minipig served as an excellent model for various biomedical research but due to prone immune system to surrounding it requires extra after born care over its large (L) size minipig breed. To overcome this pitfall, comparative analysis to the genomic region under selection in L type breed could provide us better understanding at molecular level and able us to develop enhance variety of ET type minipig breeds with advance molecular breeding program. We reports significant selective sweep genes using WGS and RNAseq data and identified a total of 35 common genes amongst 7 were highly expressed and selectively sweep in L type over ET type.
Project description:In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection.
Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML
For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.
Project description:Genomic analysis of saline-tolerant Nile tilapia strain shows signatures of selection and introgression from blue tilapia (Oreochromis aureus)
Project description:Alpine goat phenotypes for quality components have been routinely recorded for many years and deposited in the Council on Dairy Cattle Breeding (CDCB) repository. The data collected were used to conduct an exploratory genome-wide association study (GWAS) from 72 female Alpine goats originating from locations throughout the U.S. Genotypes were identified with the Illumina Goat 50K single nucleotide polymorphisms (SNP) Beadchip. The analysis used a polygenic model where the dropping criteria was the Call Rate ≥ 0.95. The initial dataset was composed of ~ 60,000 rows of SNPs, 21 columns of phenotypic traits and composed of 53,384 scaffolds containing other informative data points used for genomic predictive power. Phenotypic association with the 50KBeadchip revealed 26,074 reads of candidate genes. These candidate genes segregated as separate novel SNPs and were identified as statistically significant regions for genome and chromosome level trait associations. Candidate genes associated differently for each of the following phenotypic traits: test day milk yield (13,469 candidate genes), test day protein yield (25,690 candidate genes), test day fat yield (25,690 candidate genes), percentage protein (25,690 candidate genes), percentage fat (25,690 candidate genes), and percentage lactose content (25,690 candidate genes). The outcome of this study supports elucidation of novel genes that are important for livestock species in association to key phenotypic traits. Validation towards the development of marker-based selection that provide precision breeding methods will thereby increase breeding value. Specific aims: 1) Improve on contributions to the phenotype repository, the Council on Dairy Cattle Breeding (CDCB) for milk quality traits that are economically important for goat production while developing a corresponding DNA repository for each of the animals with significant genotype-phenotype associations. 2) Develop genomic prediction tools and provide data for a better database for tools to predict phenotypic traits by initially using the high density Goat50KSNP BeadChip for the selection of more specific SNPs associated with select signatures (genes) for phenotypic traits in American Alpine goats. 3) To establish whether a low number of goat subjects (< 300 goats) will provide statistically significant (p < 0.05) predictive capabilities for desired breeding traits in American Alpine dairy goats.