Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens

Background: Poultry breeding programs have been focused on improvement of growth and carcass traits, however, this has resulted in correlated changes in internal organ weights and increased incidence of metabolic disorders. These disorders can affect feed efficiency or even cause death. We used a high density SNP array (600 K, Affymetrix) to estimate genomic heritability, perform genome-wide association analysis, and identify genomic regions and positional candidate genes (PCGs) associated with internal organ traits in an F2 chicken population. We integrated knowledge of haplotype blocks, selection signature regions and sequencing data to refine the list of PCGs. Results: Estimated genomic heritability for internal organ traits in chickens ranged from low (LUNGWT, 0.06) to high (GIZZWT, 0.45). A total of 20 unique 1 Mb windows identified on GGA1, 2, 4, 7, 12, 15, 18, 19, 21, 27 and 28 were significantly associated with intestine length, and weights or percentages of liver, gizzard or lungs. Within these windows, 14 PCGs were identified based on their biological functions: TNFSF11, GTF2F2, SPERT, KCTD4, HTR2A, RB1, PCDH7, LCORL, LDB2, NR4A2, GPD2, PTPN11, ITGB4 and SLC6A4. From those genes, two were located within haplotype blocks and three overlapped with selection signature regions. A total of 13,748 annotated sequence SNPs were in the 14 PCGs, including 156 SNPs in coding regions (124 synonymous, 26 non-synonymous, and 6 splice variants). Seven deleterious SNPs were identified in TNFSF11, NR4A2 or ITGB4 genes. Conclusions: The results from this study provide novel insights to understand the genetic architecture of internal organ traits in chickens. The QTL detection performed using a high density SNP array covered the whole genome allowing the discovery of novel QTL associated with organ traits. We identified PCGs within the QTL involved in biological processes that may regulate internal organ growth and development. Potential functional genetic variations were identified generating crucial information that, after validation, might be used in poultry breeding programs to reduce the occurrence of metabolic disorders

Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach

Background: Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F2 Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations. Results: Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F2 population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1–4, 6–7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens. Conclusions: The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs

Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken

Excessive fat deposition is a negative factor for poultry production because it reduces feed efficiency, increases the cost of meat production and is a health concern for consumers. We genotyped 497 birds from a Brazilian F2 Chicken Resource Population, using a high-density SNP array (600 K), to estimate the genomic heritability of fat deposition related traits and to identify genomic regions and positional candidate genes (PCGs) associated with these traits. Selection signature regions, haplotype blocks and SNP data from a previous whole genome sequencing study in the founders of this chicken F2 population were used to refine the list of PCGs and to identify potential causative SNPs. We obtained high genomic heritabilities (0.43–0.56) and identified 22 unique QTLs for abdominal fat and carcass fat content traits. These QTLs harbored 26 PCGs involved in biological processes such as fat cell differentiation, insulin and triglyceride levels, and lipid biosynthetic process. Three of these 26 PCGs were located within haplotype blocks there were associated with fat traits, five overlapped with selection signature regions, and 12 contained predicted deleterious variants. The identified QTLs, PCGs and potentially causative SNPs provide new insights into the genetic control of fat deposition and can lead to improved accuracy of selection to reduce excessive fat deposition in chickens

Gene Co-expression Analysis Indicates Potential Pathways and Regulators of Beef Tenderness in Nellore Cattle

Beef tenderness, a complex trait affected by many factors, is economically important to beef quality, industry, and consumer’s palatability. In this study, RNA-Seq was used in network analysis to better understand the biological processes that lead to differences in beef tenderness. Skeletal muscle transcriptional profiles from 24 Nellore steers, selected by extreme estimated breeding values (EBVs) for shear force after 14 days of aging, were analyzed and 22 differentially expressed transcripts were identified. Among these were genes encoding ribosomal proteins, glutathione transporter ATP-binding cassette, sub-family C (CFTR/MRP), member 4 (ABCC4), and synaptotagmin IV (SYT4). Complementary co-expression analyses using Partial Correlation with Information Theory (PCIT), Phenotypic Impact Factor (PIF) and the Regulatory Impact Factor (RIF) methods identified candidate regulators and related pathways. The PCIT analysis identified ubiquitin specific peptidase 2 (USP2), growth factor receptor-bound protein 10 (GBR10), anoctamin 1 (ANO1), and transmembrane BAX inhibitor motif containing 4 (TMBIM4) as the most differentially hubbed (DH) transcripts. The transcripts that had a significant correlation with USP2, GBR10, ANO1, and TMBIM4 enriched for proteasome KEGG pathway. RIF analysis identified microRNAs as candidate regulators of variation in tenderness, including bta-mir-133a-2 and bta-mir-22. Both microRNAs have target genes present in the calcium signaling pathway and apoptosis. PIF analysis identified myoglobin (MB), enolase 3 (ENO3), and carbonic anhydrase 3 (CA3) as potentially having fundamental roles in tenderness. Pathways identified in our study impacted in beef tenderness included: calcium signaling, apoptosis, and proteolysis. These findings underscore some of the complex molecular mechanisms that control beef tenderness in Nellore cattle

Effect of dietary soybean oil inclusion on liver-related transcription factors in a pig model for metabolic diseases.

Dietary fatty acids (FA) are components of the lipids, which contribute to membrane structure, energy input, and biological functions related to cellular signaling and transcriptome regulation. However, the consumers still associate dietary FA with fat deposition and increased occurrence of metabolic diseases such as obesity and atherosclerosis. Previous studies already demonstrated that some fatty acids are linked with inflammatory response, preventing metabolic diseases. To better understand the role of dietary FA on metabolic diseases, for the first time, a study to identify key transcription factors (TF) involved in lipid metabolism and inflammatory response by transcriptome analysis from liver samples of animal models was performed. The key TF were identified by functional enrichment analysis from the list of differentially expressed genes identified in liver samples between 35 pigs fed with 1.5% or 3.0% soybean oil. The functional enrichment analysis detected TF linked to lipid homeostasis and inflammatory response, such as RXRA, EGFR, and SREBP2 precursor. These findings demonstrated that key TF related to lipid metabolism could be modulated by dietary inclusion of soybean oil. It could contribute to nutrigenomics research field that aims to elucidate dietary interventions in animal and human health, as well as to drive food technology and science

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio