Copy number variation mapping and genomic variation of autochthonous and commercial turkey populations

This study aims at investigating genomic diversity of several turkey populations using Copy Number Variants (CNVs). A total of 115 individuals from six Italian breeds (Colle Euganei, Bronzato Comune Italiano, Parma e Piacenza, Brianzolo, Nero d\u2019Italia, and Ermellinato di Rovigo), seven Narragansett, 38 commercial hybrids, and 30 Mexican turkeys, were genotyped with the Affymetrix 600K single nucleotide polymorphism (SNP) turkey array. The CNV calling was performed with the Hidden Markov Model of PennCNV software and with the Copy Number Analysis Module of SVS 8.4 by Golden Helix\uae. CNV were summarized into CNV regions (CNVRs) at population level using BEDTools. Variability among populations has been addressed by hierarchical clustering (pvclust R package) and by principal component analysis (PCA). A total of 2,987 CNVs were identified covering 4.65% of the autosomes of the Turkey_5.0/melGal5 assembly. The CNVRs identified in at least two individuals were 362\u2014189 gains, 116 losses, and 57 complexes. Among these regions the 51% contain annotated genes. This study is the first CNV mapping of turkey population using 600K chip. CNVs clustered the individuals according to population and their geographical origin. CNVs are known to be indicators also of adaptation, as some researches in different species are suggesting

Genetic analysis of carcass traits of steers adjusted to age, weight, or fat thickness slaughter endpoints

Carcass measurements from 1,664 steers from the Germ Plasm Utilization project at U.S. Meat Animal Research Center were used to estimate heritabilities (h2) of, and genetic correlations (rg) among, 14 carcass traits adjusted to different endpoints (age, carcass weight, and fat thickness): HCW (kg), dressing percent (DP), adjusted fat thickness (AFT, cm), LM area (LMA, cm2), KPH (%), marbling score (MS), yield grade (YG), predicted percentage of retail product (PRP), retail product weight (RPW, kg), fat weight (FW, kg), bone weight (BNW, kg), actual percentage retail product (RPP), fat percent (FP), and bone percent. Fixed effects in the model included breed group, feed energy level, dam age, birth year, significant (P \u3c 0.05) interactions, covariate for days on feed, and the appropriate covariate for endpoint nested (except age) within breed group. Random effects in the model were additive genetic effect of animal and total maternal effect of dam. Parameters were estimated by REML. For some traits, estimates of h2 and phenotypic variance changed with different endpoints. Estimates of h2 for HCW,DP, RPW, and BNW at constant age, weight, or fat thickness were 0.27, —, and 0.41; 0.19, 0.26, and 0.18; 0.42, 0.32, and 0.50; and 0.43, 0.32, and 0.48, respectively. Magnitude and/or sign of rg also changed across endpoints for 54 of the 91 trait pairs. Estimates for HCW-LMA, AFTRPW, LMA-YG, LMA-PRP, LMA-FW, LMA-RPP, and LMA-FP at constant age, weight, or fat thickness were 0.32, —, and 0.51; −0.26, −0.77, and —; −0.71, −0.89, and −0.66; 0.68, 0.85, and 0.63; −0.16, −0.51, and 0.22; 0.47, 0.57, and 0.27; and −0.44, −0.43, and −0.18, respectively. Fat thickness was highly correlated with YG (0.86 and 0.85 for common age and weight) and PRP (−0.85 and −0.82 for common age and weight), indicating that selection for decreased fat thickness would improve YG and PRP. Carcass quality, however, would be affected negatively because of moderate rg (0.34 and 0.35 for common age and weight) between MS and AFT. Estimates of h2 and phenotypic variance indicate that enough genetic variation exists to change measures of carcass merit by direct selection. For some carcass traits, however, magnitude of change would depend on effect of endpoint on h2 and phenotypic variance. Correlated responses to selection would differ depending on endpoint

Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef

BACKGROUND: Africa is home to numerous cattle breeds whose diversity has been shaped by subtle combinations of human and natural selection. African Sanga cattle are an intermediate type of cattle resulting from interbreeding between Bos taurus and Bos indicus subspecies. Recently, research has asserted the potential of Sanga breeds for commercial beef production with better meat quality as compared to Bos indicus breeds. Here, we identified meat quality related gene regions that are positively selected in Ankole (Sanga) cattle breeds as compared to indicus (Boran, Ogaden, and Kenana) breeds using cross-population (XP-EHH and XP-CLR) statistical methods. RESULTS: We identified 238 (XP-EHH) and 213 (XP-CLR) positively selected genes, of which 97 were detected from both statistics. Among the genes obtained, we primarily reported those involved in different biological process and pathways associated with meat quality traits. Genes (CAPZB, COL9A2, PDGFRA, MAP3K5, ZNF410, and PKM2) involved in muscle structure and metabolism affect meat tenderness. Genes (PLA2G2A, PARK2, ZNF410, MAP2K3, PLCD3, PLCD1, and ROCK1) related to intramuscular fat (IMF) are involved in adipose metabolism and adipogenesis. MB and SLC48A1 affect meat color. In addition, we identified genes (TIMP2, PKM2, PRKG1, MAP3K5, and ATP8A1) related to feeding efficiency. Among the enriched Gene Ontology Biological Process (GO BP) terms, actin cytoskeleton organization, actin filament-based process, and protein ubiquitination are associated with meat tenderness whereas cellular component organization, negative regulation of actin filament depolymerization and negative regulation of protein complex disassembly are involved in adipocyte regulation. The MAPK pathway is responsible for cell proliferation and plays an important role in hyperplastic growth, which has a positive effect on meat tenderness. CONCLUSION: Results revealed several candidate genes positively selected in Ankole cattle in relation to meat quality characteristics. The genes identified are involved in muscle structure and metabolism, and adipose metabolism and adipogenesis. These genes help in the understanding of the biological mechanisms controlling beef quality characteristics in African Ankole cattle. These results provide a basis for further research on the genomic characteristics of Ankole and other Sanga cattle breeds for quality beef. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-016-0467-1) contains supplementary material, which is available to authorized users

Genetic variation in wholesale carcass cuts predicted from digital images in cattle

peer-reviewedThe objective of this study was to quantify the genetic variation in carcass cuts predicted using digital image analysis in commercial cross-bred cattle. The data set comprised 38 404 steers and 14 318 heifers from commercial Irish herds. The traits investigated included the weights of lower value cuts (LVC), medium value cuts (MVC), high value cuts (HVC), very high value cuts (VHVC) and total meat weight. In addition, the weights of total fat and total bones were available on the steers. Heritability of carcass cut weights, within gender, was estimated using an animal linear model, whereas genetic and phenotypic correlations among cuts were estimated using a sire linear model. Carcass weight was included as a covariate in all models. In the steers, heritability ranged from 0.13 (s.e.50.02) for VHVC to 0.49 (s.e.50.03) for total bone weight, and in the heifers heritability ranged from 0.15 (s.e.50.04) for MVC to 0.72 (s.e.50.06) for total meat weight. The coefficient of genetic variation for the different cuts varied from 1.4% to 3.6%. Genetic correlations between the different cut weights were all positive and ranged from 0.45 (s.e.50.08) to 0.89 (s.e.50.03) in the steers, and from 0.47 (s.e.50.14) to 0.82 (s.e.50.06) in the heifers. Genetic correlations between the wholesale cut weights and carcass conformation ranged from 0.32 (s.e.50.06) to 0.45 (s.e.50.07) in the steers, and from 0.10 (s.e.50.12) to 0.38 (s.e.50.09) in the heifers. Genetic correlations between the same wholesale cut traits in steers and heifers ranged from 0.54 (s.e.50.14) for MVC to 0.79 (s.e.50.06) for total meat weight; genetic correlations between carcass weight and carcass classification for conformation and fat score in both genders varied from 0.80 to 0.87. The existence of genetic variation in carcass cut traits, coupled with the routine availability of predicted cut weights from digital image analysis, clearly shows the potential to genetically improve carcass value