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Ogawa S, Zoda A, Kagawa R, Obinata R. Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study. Animals (Basel) 2023; 13:ani13040638. [PMID: 36830425 PMCID: PMC9951718 DOI: 10.3390/ani13040638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/04/2023] [Accepted: 02/10/2023] [Indexed: 02/15/2023] Open
Abstract
As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic relationship matrix (G matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the A matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the G matrix based on VanRaden's methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study.
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Affiliation(s)
- Shinichiro Ogawa
- Division of Meat Animal and Poultry Research, Institute of Livestock and Grassland Science, National Agriculture and Food Research Organization (NARO), Tsukuba 305-0901, Japan
- Correspondence: ; Tel.: +81-29-838-8627
| | - Atsushi Zoda
- Research and Development Group, Zen-Noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
| | - Rino Kagawa
- Research and Development Group, Zen-Noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
| | - Rui Obinata
- Research and Development Group, Zen-Noh Embryo Transfer Center, Kamishihoro 080-1407, Japan
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Okada D, Zheng C, Cheng JH. Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data. Comput Struct Biotechnol J 2022; 20:4850-4859. [PMID: 36147671 PMCID: PMC9474327 DOI: 10.1016/j.csbj.2022.08.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data. METHOD We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses. RESULT AND CONCLUSION We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.
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Affiliation(s)
- Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto 6068507, Kyoto, Japan
| | - Cheng Zheng
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto 6068507, Kyoto, Japan
| | - Jian Hao Cheng
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, South Research Bldg. No.1(5F), 53 Shogoinkawahara-cho, Sakyo-ku, Kyoto 6068507, Kyoto, Japan
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Ogawa S, Darhan H, Suzuki K. Genetic and genomic analysis of oxygen consumption in mice. J Anim Breed Genet 2022; 139:596-610. [PMID: 35608337 DOI: 10.1111/jbg.12721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/07/2022] [Indexed: 12/16/2022]
Abstract
We estimated genetic parameters for oxygen consumption (OC), OC per metabolic body weight (OCMBW) and body weight at three through 8 weeks of age in divergently selected mice populations, with an animal model considering maternal genetic, common litter environmental and cytoplasmic inheritance effects. Cytoplasmic inheritance was considered based on maternal lineage information. With respect to OC, estimated direct heritability was moderate (0.32) and the estimated proportion of the variance of cytoplasmic inheritance effects to the phenotypic variance was very low (0.01), implying that causal genes for OC could be located on autosomes. To assess this hypothesis, we attempted to identify possible candidate causal genes through selective signature detection with the results of pooled whole-genome resequencing using pooled DNA samples from high and low OC mice. We made a list of possible candidate causal genes for OC, including those relating to electron transport chain and ATP-binding proteins (Ndufa12, Sdhc, Atp10b, etc.), Prr16 encoding Largen protein, Cry1 encoding a key component of the circadian core oscillator and so on. The results, although careful interpretation must be required, could contribute to elucidate the genetic mechanism of OC, an indicator for maintenance energy requirement, and therefore feed efficiency.
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Affiliation(s)
- Shinichiro Ogawa
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Hongyu Darhan
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
| | - Keiichi Suzuki
- Graduate School of Agricultural Science, Tohoku University, Sendai, Miyagi, Japan
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Hara H, Ogawa S, Ohnishi C, Ishii K, Uemoto Y, Satoh M. An attempt of using public ambient temperature data in swine genetic evaluation for litter-size traits at birth in Japan†. ANIMAL PRODUCTION SCIENCE 2022. [DOI: 10.1071/an21463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Brunes LC, Baldi F, Lopes FB, Lôbo RB, Espigolan R, Costa MFO, Stafuzza NB, Magnabosco CU. Weighted single-step genome-wide association study and pathway analyses for feed efficiency traits in Nellore cattle. J Anim Breed Genet 2020; 138:23-44. [PMID: 32654373 DOI: 10.1111/jbg.12496] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/11/2020] [Accepted: 06/13/2020] [Indexed: 02/06/2023]
Abstract
The aim was to conduct a weighted single-step genome-wide association study to detect genomic regions and putative candidate genes related to residual feed intake, dry matter intake, feed efficiency (FE), feed conversion ratio, residual body weight gain, residual intake and weight gain in Nellore cattle. Several protein-coding genes were identified within the genomic regions that explain more than 0.5% of the additive genetic variance for these traits. These genes were associated with insulin, leptin, glucose, protein and lipid metabolisms; energy balance; heat and oxidative stress; bile secretion; satiety; feed behaviour; salivation; digestion; and nutrient absorption. Enrichment analysis revealed functional pathways (p-value < .05) such as neuropeptide signalling (GO:0007218), negative regulation of canonical Wingless/Int-1 (Wnt) signalling (GO:0090090), bitter taste receptor activity (GO:0033038), neuropeptide hormone activity (GO:0005184), bile secretion (bta04976), taste transduction (bta0742) and glucagon signalling pathway (bta04922). The identification of these genes, pathways and their respective functions should contribute to a better understanding of the genetic and physiological mechanisms regulating Nellore FE-related traits.
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Affiliation(s)
- Ludmilla C Brunes
- Department of Animal Science, Federal University of Goiás (UFG), Goiânia, Brazil.,Embrapa Rice and Beans, Santo Antônio de Goiás, Brazil
| | - Fernando Baldi
- Department of Animal Science, São Paulo State University (UNESP), Jaboticabal, Brazil
| | | | - Raysildo B Lôbo
- National Association of Breeders and Researchers (ANCP), Ribeirão Preto, Brazil
| | - Rafael Espigolan
- Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Brazil
| | | | - Nedenia B Stafuzza
- Beef Cattle Research Center, Animal Science Institute, Sertãozinho, Brazil
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Decomposition of a set of distributions in extended exponential family form for distinguishing multiple oligo-dimensional marker expression profiles of single-cell populations and visualizing their dynamics. PLoS One 2020; 15:e0231250. [PMID: 32275673 PMCID: PMC7147751 DOI: 10.1371/journal.pone.0231250] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/16/2020] [Indexed: 12/27/2022] Open
Abstract
Single-cell expression analysis is an effective tool for studying the dynamics of cell population profiles. However, the majority of statistical methods are applied to individual profiles and the methods for comparing multiple profiles simultaneously are limited. In this study, we propose a nonparametric statistical method, called Decomposition into Extended Exponential Family (DEEF), that embeds a set of single-cell expression profiles of several markers into a low-dimensional space and identifies the principal distributions that describe their heterogeneity. We demonstrate that DEEF can appropriately decompose and embed sets of theoretical probability distributions. We then apply DEEF to a cytometry dataset to examine the effects of epidermal growth factor stimulation on an adult human mammary gland. It is shown that DEEF can describe the complex dynamics of cell population profiles using two parameters and visualize them as a trajectory. The two parameters identified the principal patterns of the cell population profile without prior biological assumptions. As a further application, we perform a dimensionality reduction and a time series reconstruction. DEEF can reconstruct the distributions based on the top coordinates, which enables the creation of an artificial dataset based on an actual single-cell expression dataset. Using the coordinate system assigned by DEEF, it is possible to analyze the relationship between the attributes of the distribution sample and the features or shape of the distribution using conventional data mining methods.
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Takeda M, Uemoto Y, Inoue K, Ogino A, Nozaki T, Kurogi K, Yasumori T, Satoh M. Genome-wide association study and genomic evaluation of feed efficiency traits in Japanese Black cattle using single-step genomic best linear unbiased prediction method. Anim Sci J 2019; 91:e13316. [PMID: 31769129 DOI: 10.1111/asj.13316] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 09/30/2019] [Accepted: 10/23/2019] [Indexed: 01/18/2023]
Abstract
The objectives of this study were to better understand the genetic architecture and the possibility of genomic evaluation for feed efficiency traits by (i) performing genome-wide association studies (GWAS), and (ii) assessing the accuracy of genomic evaluation for feed efficiency traits, using single-step genomic best linear unbiased prediction (ssGBLUP)-based methods. The analyses were performed in residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) during three different fattening periods. The phenotypes from 4,578 Japanese Black steers, which were progenies of 362 progeny-tested bulls and the genotypes from the bulls were used in this study. The results of GWAS showed that a total of 16, 8, and 12 gene ontology terms were related to RFI, RG, and RIG, respectively, and the candidate genes identified in RFI and RG were involved in olfactory transduction and the phosphatidylinositol signaling system, respectively. The realized reliabilities of genomic estimated breeding values were low to moderate in the feed efficiency traits. In conclusion, ssGBLUP-based method can lead to understand some biological functions related to feed efficiency traits, even with small population with genotypes, however, an alternative strategy will be needed to enhance the reliability of genomic evaluation.
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Affiliation(s)
- Masayuki Takeda
- National Livestock Breeding Center, Fukushima, Japan.,Graduate School of Agricultural Science, Tohoku University, Miyagi, Japan
| | - Yoshinobu Uemoto
- Graduate School of Agricultural Science, Tohoku University, Miyagi, Japan
| | - Keiichi Inoue
- National Livestock Breeding Center, Fukushima, Japan
| | - Atushi Ogino
- Maebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc, Gunma, Japan
| | - Takayoshi Nozaki
- Cattle Breeding Department, Livestock Improvement Association of Japan, Inc, Tokyo, Japan
| | - Kazuhito Kurogi
- Maebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc, Gunma, Japan
| | - Takanori Yasumori
- Cattle Breeding Department, Livestock Improvement Association of Japan, Inc, Tokyo, Japan
| | - Masahiro Satoh
- Graduate School of Agricultural Science, Tohoku University, Miyagi, Japan
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