School of Animal Sciences
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The School of Animal Sciences merged Dairy Science and Animal and Poultry Science in 2022.
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Browsing School of Animal Sciences by Subject "0604 Genetics"
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- Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice PhenotypesYu, Haipeng; Campbell, Malachy T.; Zhang, Qi; Walia, Harkamal; Morota, Gota (Genetics Society of America, 2019-06-01)With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.
- A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association StudiesWang, Zigui; Chapman, Deborah; Morota, Gota; Cheng, Hao (Genetics Society of America, 2020-12-01)Bayesian regression methods that incorporate different mixture priors for marker effects are used in multi-trait genomic prediction. These methods can also be extended to genome-wide association studies (GWAS). In multiple-trait GWAS, incorporating the underlying causal structures among traits is essential for comprehensively understanding the relationship between genotypes and traits of interest. Therefore, we develop a GWAS methodology, SEM-Bayesian alphabet, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into multi-trait Bayesian regression methods. SEM-Bayesian alphabet provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait GWAS by performing GWAS based on indirect, direct and overall marker effects. The superior performance of SEM-Bayesian alphabet was demonstrated by comparing its GWAS results with other similar multi-trait GWAS methods on real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.
- Single-cell RNA Sequencing Reveals Heterogeneity of Cultured Bovine Satellite CellsLyu, Pengcheng; Qi, Yumin; Tu, Zhijian Jake; Jiang, Honglin (Frontiers, 2021-10-28)Skeletal muscle from meat-producing livestock such as cattle is a major source of food for humans. To improve skeletal muscle growth efficiency or quality in cattle, it is necessary to understand the genetic and physiological mechanisms that govern skeletal muscle composition, development, and growth. Satellite cells are the myogenic progenitor cells in postnatal skeletal muscle. In this study we analyzed the composition of bovine satellite cells with single-cell RNA sequencing (scRNA-seq). We isolated satellite cells from a 2-week-old male calf, cultured them in growth medium for a week, and performed scRNA-seq using the 10x Genomics platform. Deep sequencing of two scRNA-seq libraries constructed from cultured bovine satellite cells yielded 860 million reads. Cell calling analyses revealed that these reads were sequenced from 19,096 individual cells. Clustering analyses indicated that these reads represented 15 cell clusters that differed in gene expression profile. Based on the enriched expression of markers of satellite cells (PAX7 and PAX3), markers of myoblasts (MYOD1, MYF5), and markers of differentiated myoblasts or myocytes (MYOG), three clusters were determined to be satellite cells, two clusters myoblasts, and two clusters myocytes. Gene ontology and trajectory inference analyses indicated that cells in these myogenic clusters differed in proliferation rate and differentiation stage. Two of the remaining clusters were enriched with PDGFRA, a marker of fibro-adipogenic (FAP) cells, the progenitor cells for intramuscular fat, and are therefore considered to be FAP cells. Gene ontology analyses indicated active lipogenesis in one of these two clusters. The identity of the remaining six clusters could not be defined. Overall, the results of this study support the hypothesis that bovine satellite cells are composed of subpopulations that differ in transcriptional and myogenic state. The results of this study also support the hypothesis that intramuscular fat in cattle originates from fibro-adipogenic cells.