scholarly journals Crossbreeding Combination Test for the Production of New Synthetic Korean Native Commercial Chickens

2021 ◽  
Vol 48 (3) ◽  
pp. 101-110
Author(s):  
Sea Hwan Sohn ◽  
Eun Sik Choi ◽  
Eun Jung Cho ◽  
Bo Gyeong Kim ◽  
Ka Bin Shin ◽  
...  
Keyword(s):  
Combination Test ◽  
Commercial Chickens

scholarly journals Comparison of Selection Signatures between Korean Native and Commercial Chickens Using 600K SNP Array Data

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 824
Author(s):  
Sunghyun Cho ◽  
Prabuddha Manjula ◽  
Minjun Kim ◽  
Eunjin Cho ◽  
Dooho Lee ◽  
...  
Keyword(s):  
Snp Array ◽  
Initial Step ◽  
Reproductive Organs ◽  
High Linkage Disequilibrium ◽  
Primary Data ◽  
Multiple Traits ◽  
Indigenous Chicken ◽  
Primary Data Analysis ◽  
The Government ◽  
Commercial Chickens

Korean native chickens (KNCs) comprise an indigenous chicken breed of South Korea that was restored through a government project in the 1990s. The KNC population has not been developed well and has mostly been used to maintain purebred populations in the government research institution. We investigated the genetic features of the KNC population in a selection signal study for the efficient improvement of this breed. We used 600K single nucleotide polymorphism data sampled from 191 KNCs (NG, 38; NL, 29; NR, 52; NW, 39; and NY, 33) and 54 commercial chickens (Hy-line Brown, 10; Lohmann Brown, 10; Arbor Acres, 10; Cobb, 12; and Ross, 12). Haplotype phasing was performed using EAGLE software as the initial step for the primary data analysis. Pre-processed data were analyzed to detect selection signals using the ‘rehh’ package in R software. A few common signatures of selection were identified in KNCs. Most quantitative trait locus regions identified as candidate regions were associated with traits related to reproductive organs, eggshell characteristics, immunity, and organ development. Block patterns with high linkage disequilibrium values were observed for LPP, IGF11, LMNB2, ERBB4, GABRB2, NTM, APOO, PLOA1, CNTN1, NTSR1, DEF3, CELF1, and MEF2D genes, among regions with confirmed selection signals. NL and NW lines contained a considerable number of selective sweep regions related to broilers and layers, respectively. We recommend focusing on improving the egg and meat traits of KNC NL and NW lines, respectively, while improving multiple traits for the other lines.

scholarly journals ALV-J-contaminated commercial live vaccines induced pathogenicity in Three-Yellow chickens: one of the transmission routes of ALV-J to commercial chickens

2021 ◽  
Vol 100 (7) ◽  
pp. 101153
Author(s):  
Peikun Wang ◽  
Min Li ◽  
Haijuan Li ◽  
Yuyu Bi ◽  
Lulu Lin ◽  
...  
Keyword(s):  
Live Vaccines ◽  
Commercial Chickens

Sequence and phylogenetic analysis of the haemagglutinin genes of H9N2 avian influenza viruses isolated from commercial chickens in Iran

2010 ◽  
Vol 42 (6) ◽  
pp. 1291-1297 ◽  
Author(s):  
Ali Reza Homayounimehr ◽  
Habibollah Dadras ◽  
Abdolhamid Shoushtari ◽  
Seyyed Ali Pourbakhsh
Keyword(s):  
Phylogenetic Analysis ◽  
Avian Influenza ◽  
Influenza Viruses ◽  
Avian Influenza Viruses ◽  
Commercial Chickens

Detection of Wild- and Vaccine-Type Avian Infectious Laryngotracheitis Virus in Clinical Samples and Feather Shafts of Commercial Chickens

2009 ◽  
Vol 53 (4) ◽  
pp. 618-623 ◽  
Author(s):  
Irit Davidson ◽  
Sagit Nagar ◽  
Israel Ribshtein ◽  
Irena Shkoda ◽  
Shimon Perk ◽  
...  
Keyword(s):  
Clinical Samples ◽  
Infectious Laryngotracheitis Virus ◽  
Vaccine Type ◽  
Infectious Laryngotracheitis ◽  
Laryngotracheitis Virus ◽  
Commercial Chickens

scholarly journals Longitudinal linear combination test for gene set analysis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Elham Khodayari Moez ◽  
Morteza Hajihosseini ◽  
Jeffrey L. Andrews ◽  
Irina Dinu
Keyword(s):  
Linear Combination ◽  
Pathway Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Sets ◽  
Wide Range ◽  
Combination Test ◽  
Microarray Studies ◽  
Study Designs ◽  
Linear Combination Test

Abstract Background Although microarray studies have greatly contributed to recent genetic advances, lack of replication has been a continuing concern in this area. Complex study designs have the potential to address this concern, though they remain undervalued by investigators due to the lack of proper analysis methods. The primary challenge in the analysis of complex microarray study data is handling the correlation structure within data while also dealing with the combination of large number of genetic measurements and small number of subjects that are ubiquitous even in standard microarray studies. Motivated by the lack of available methods for analysis of repeatedly measured phenotypic or transcriptomic data, herein we develop a longitudinal linear combination test (LLCT). Results LLCT is a two-step method to analyze multiple longitudinal phenotypes when there is high dimensionality in response and/or explanatory variables. Alternating between calculating within-subjects and between-subjects variations in two steps, LLCT examines if the maximum possible correlation between a linear combination of the time trends and a linear combination of the predictors given by the gene expressions is statistically significant. A generalization of this method can handle family-based study designs when the subjects are not independent. This method is also applicable to time-course microarray, with the ability to identify gene sets that exhibit significantly different expression patterns over time. Based on the results from a simulation study, LLCT outperformed its alternative: pathway analysis via regression. LLCT was shown to be very powerful in the analysis of large gene sets even when the sample size is small. Conclusions This self-contained pathway analysis method is applicable to a wide range of longitudinal genomics, proteomics, metabolomics (OMICS) data, allows adjusting for potentially time-dependent covariates and works well with unbalanced and incomplete data. An important potential application of this method could be time-course linkage of OMICS, an attractive possibility for future genetic researchers. Availability: R package of LLCT is available at: https://github.com/its-likeli-jeff/LLCT

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