scholarly journals Genome-wide association studies for feedlot and growth traits in cattle1

2011 ◽  
Vol 89 (6) ◽  
pp. 1684-1697 ◽  
Author(s):  
S. Bolormaa ◽  
B. J. Hayes ◽  
K. Savin ◽  
R. Hawken ◽  
W. Barendse ◽  
...  
Keyword(s):  
Growth Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Haplotype-based genome-wide association studies for carcass and growth traits in chicken

2020 ◽  
Vol 99 (5) ◽  
pp. 2349-2361 ◽  
Author(s):  
Hui Zhang ◽  
Lin-Yong Shen ◽  
Zi-Chun Xu ◽  
Luke M. Kramer ◽  
Jia-Qiang Yu ◽  
...  
Keyword(s):  
Growth Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Genome-wide association studies for growth traits in buffaloes using the single step genomic BLUP

2019 ◽  
Vol 61 (1) ◽  
pp. 113-115 ◽  
Author(s):  
Francisco Ribeiro de Araujo Neto ◽  
Daniel Jordan de Abreu Santos ◽  
Gerardo Alves Fernandes Júnior ◽  
Rusbel Raul Aspilcueta-Borquis ◽  
André Vieira do Nascimento ◽  
...  
Keyword(s):  
Growth Traits ◽  
Association Studies ◽  
Single Step ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Genome-wide association studies for growth traits in broilers

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Dachang Dou ◽  
Linyong Shen ◽  
Jiamei Zhou ◽  
Zhiping Cao ◽  
Peng Luan ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Candidate Genes ◽  
Rapid Growth ◽  
Genetic Parameters ◽  
Growth Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Methods ◽  
Genome Wide

Abstract Background The identification of markers and genes for growth traits may not only benefit for marker assist selection /genomic selection but also provide important information for understanding the genetic foundation of growth traits in broilers. Results In the current study, we estimated the genetic parameters of eight growth traits in broilers and carried out the genome-wide association studies for these growth traits. A total of 113 QTNs discovered by multiple methods together, and some genes, including ACTA1, IGF2BP1, TAPT1, LDB2, PRKCA, TGFBR2, GLI3, SLC16A7, INHBA, BAMBI, APCDD1, GPR39, and GATA4, were identified as important candidate genes for rapid growth in broilers. Conclusions The results of this study will provide important information for understanding the genetic foundation of growth traits in broilers.

scholarly journals Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future

2017 ◽  
Author(s):  
Yan Zhang ◽  
Guanghao Qi ◽  
Ju-Hyun Park ◽  
Nilanjan Chatterjee
Keyword(s):  
Effect Size ◽  
Complex Traits ◽  
Growth Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Size Distributions ◽  
Normal Mixture ◽  
Genome Wide ◽  
Level Statistics

AbstractSummary-level statistics from genome-wide association studies are now widely used to estimate heritability and co-heritability of traits using the popular linkage-disequilibrium-score (LD-score) regression method. We develop a likelihood-based approach for analyzing summary-level statistics and external LD information to estimate common variants effect-size distributions, characterized by proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of summary-level results across 32 GWAS reveals that while all traits are highly polygenic, there is wide diversity in the degrees of polygenicity. The effect-size distributions for susceptibility SNPs could be adequately modeled by a single normal distribution for traits related to mental health and ability and by a mixture of two normal distributions for all other traits. Among quantitative traits, we predict the sample sizes needed to identify SNPs which explain 80% of GWAS heritability to be between 300K-500K for some of the early growth traits, between 1-2 million for some anthropometric and cholesterol traits and multiple millions for body mass index and some others. The corresponding predictions for disease traits are between 200K-400K for inflammatory bowel diseases, close to one million for a variety of adult onset chronic diseases and between 1-2 million for psychiatric diseases.

scholarly journals Genome-Wide Association Studies for Growth Curves in Meat Rabbits Through the Single-Step Nonlinear Mixed Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Yonglan Liao ◽  
Zhicheng Wang ◽  
Leonardo S. Glória ◽  
Kai Zhang ◽  
Cuixia Zhang ◽  
...  
Keyword(s):  
Mixed Model ◽  
Growth Traits ◽  
Association Studies ◽  
Growth Curves ◽  
Single Step ◽  
The Body ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nonlinear Mixed Model ◽  
Genome Wide

Growth is a complex trait with moderate to high heritability in livestock and must be described by the longitudinal data measured over multiple time points. Therefore, the used phenotype in genome-wide association studies (GWAS) of growth traits could be either the measures at the preselected time point or the fitted parameters of whole growth trajectory. A promising alternative approach was recently proposed that combined the fitting of growth curves and estimation of single-nucleotide polymorphism (SNP) effects into single-step nonlinear mixed model (NMM). In this study, we collected the body weights at 35, 42, 49, 56, 63, 70, and 84 days of age for 401 animals in a crossbred population of meat rabbits and compared five fitting models of growth curves (Logistic, Gompertz, Brody, Von Bertalanffy, and Richards). The logistic model was preferably selected and subjected to GWAS using the approach of single-step NMM, which was based on 87,704 genome-wide SNPs. A total of 45 significant SNPs distributed on five chromosomes were found to simultaneously affect the two growth parameters of mature weight (A) and maturity rate (K). However, no SNP was found to be independently associated with either A or K. Seven positional genes, including KCNIP4, GBA3, PPARGC1A, LDB2, SHISA3, GNA13, and FGF10, were suggested to be candidates affecting growth performances in meat rabbits. To the best of our knowledge, this is the first report of GWAS based on single-step NMM for longitudinal traits in rabbits, which also revealed the genetic architecture of growth traits that are helpful in implementing genome selection.

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