scholarly journals Identification of genomic regions associated with feed efficiency in Nelore cattle

BMC Genetics ◽  
2014 ◽  
Vol 15 (1) ◽  
Author(s):  
Priscila SN de Oliveira ◽  
Aline SM Cesar ◽  
Michele L do Nascimento ◽  
Amália S Chaves ◽  
Polyana C Tizioto ◽  
...  
2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 319-321
Author(s):  
Taiane S Martins ◽  
Juliana Silva ◽  
Lenise Mueller ◽  
Tamyres Amorim ◽  
Annelise Aila G Lobo ◽  
...  

Abstract The goal of this study was to evaluate the performance and the carcass traits of Nelore cattle progenies from bulls selected by contrasting traits for precocity, growth and muscularity, through the Expected Progeny Difference (EPD). One hundred and five Nelore bulls (initial weight of 350kg±15kg) and 20 months of age were confined and fed with same diet (73% of concentrate). Thirty-two animals were selected to create the contrasting groups for precocity, growth and muscularity (16 animals assigned as a low EPD group - LEPD and 16 animals assigned as a high EPD group - HEPD), based on the EPD of their parents. The ribeye area and backfat thickness were performed by ultrasonography of 12–13th rib fat thickness and longissimus muscle area (LMA), as well as rump fat thickness (RF) measurements. Animals were harvested after 100 days and during the deboning, meat cuts were weight for cutting yield. The animals selected for the HEPD group had greater average daily gain (P = 0.006), which can be explained by the higher feed intake (P = 0.006). However, there are no difference between groups for the final body weight (P = 0.254) and feed efficiency (P = 0.715). The LEPD group presented higher dressing percentage (P = 0.028). Although the groups evaluated did not presented difference in LMA (P = 0.329) and weight of longissimus muscle (P = 0.480), the weight of rump displayed heaviest in the HEPD (P = 0.037). There was no difference between groups for RF (P = 0.086). Nevertheless, backfat thickness was higher in HEPD group (P = 0.006). The present study indicates that Nelore cattle progenies, with parents displaying higher potential for precocity, growth, and muscularity, show greater backfat thickness and weightiest of rump than the other genetic backgrounds. Thanks to FAPESP for the scholarship (Grant # 2017/02349–1).


PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0164390 ◽  
Author(s):  
Bianca Ferreira Olivieri ◽  
Maria Eugênia Zerlotti Mercadante ◽  
Joslaine Noely dos Santos Gonçalves Cyrillo ◽  
Renata Helena Branco ◽  
Sarah Figueiredo Martins Bonilha ◽  
...  

2016 ◽  
Vol 94 (9) ◽  
pp. 3613-3623 ◽  
Author(s):  
R. M. O. Silva ◽  
B. O. Fragomeni ◽  
D. A. L. Lourenco ◽  
A. F. B. Magalhães ◽  
N. Irano ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jorge Augusto Petroli Marchesi ◽  
Rafael Keith Ono ◽  
Maurício Egídio Cantão ◽  
Adriana Mércia Guaratini Ibelli ◽  
Jane de Oliveira Peixoto ◽  
...  

AbstractChicken feed efficiency (FE) traits are the most important economic traits in broiler production. Several studies evaluating genetic factors affecting food consumption in chickens are available. However, most of these studies identified genomic regions containing putative quantitative trait loci for each trait separately. It is still a challenge to find common gene networks related to these traits. Therefore, here, a genome-wide association study (GWAS) was conducted to explore candidate genomic regions responsible for Feed Intake (FI), Body Weight Gain (BWG) and Feed Conversion Ratio (FCR) traits and their gene networks. A total of 1430 broilers from an experimental population was genotyped with the high density Affymetrix 600K SNP array. A total of 119 associated SNPs located in 20 chromosomes were identified, where some of them were common in more than one FE trait. In addition, novel genomic regions were prospected considering the SNPs dominance effects and sex interaction, identifying putative candidate genes only when these effects were fit in the model. Relevant candidate genes such as ATRNL1, PIK3C2A, PTPRN2, SORCS3 and gga-mir-1759 were highlighted in this study helping to elucidate the genomic architecture of feed efficiency traits. These results provide new insights on the mechanisms underlying the consumption and utilization of food in chickens.


2019 ◽  
Vol 18 (1) ◽  
Author(s):  
M.E. Carvalho ◽  
F.S. Baldi ◽  
P.A. Alexandre ◽  
M.H.A. Santana ◽  
R.V. Ventura ◽  
...  

2020 ◽  
Vol 11 ◽  
Author(s):  
Andressa O. de Lima ◽  
James E. Koltes ◽  
Wellison J. S. Diniz ◽  
Priscila S. N. de Oliveira ◽  
Aline S. M. Cesar ◽  
...  

2020 ◽  
Author(s):  
Emilie Delpuech ◽  
Amir Aliakbari ◽  
Yann Labrune ◽  
Katia Fève ◽  
Yvon Billon ◽  
...  

AbstractBackgroundFeed efficiency is a major driver of the sustainability of pig production systems. Understanding biological mechanisms underlying these agronomic traits is an important issue whether for environment and farms economy. This study aimed at identifying genomic regions affecting residual feed intake (RFI) and other production traits in two pig lines divergently selected for RFI during 9 generations (LRFI, low RFI; HRFI, high RFI).ResultsWe built a whole dataset of 570,447 single nucleotide polymorphisms (SNPs) in 2,426 pigs with records for 24 production traits after both imputation and prediction of genotypes using pedigree information. Genome-wide association studies (GWAS) were performed including both lines (Global-GWAS) or each line independently (LRFI-GWAS and HRFI-GWAS). A total of 54 chromosomic regions were detected with the Global-GWAS, whereas 37 and 61 regions were detected in LRFI-GWAS and HRFI-GWAS, respectively. Among those, only 15 regions were shared between at least two analyses, and only one was common between the three GWAS but affecting different traits. Among the 12 QTL detected for RFI, some were close to QTL detected for meat quality traits and 9 pinpointed novel genomic regions for some harbored candidate genes involved in cell proliferation and differentiation processes of gastrointestinal tissues or lipid metabolism-related signaling pathways. Detection of mostly different QTL regions between the three designs suggests the strong impact of the dataset on the detection power, which could be due to the changes of allelic frequencies during the line selection.ConclusionsBesides efficiently detecting known and new QTL regions for feed efficiency, the combination of GWAS carried out per line or simultaneously using all individuals highlighted the identification of chromosomic regions under selection that affect various production traits.


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