Whole Genome Resequencing of Arkansas Progressor and Regressor Line Chickens to Identify SNPs Associated with Tumor Regression

Arkansas Regressor (AR) chickens, unlike Arkansas Progressor (AP) chickens, regress tumors induced by the v-src oncogene. To better understand the genetic factors responsible for this tumor regression property, whole genome resequencing was conducted using Illumina Hi-Seq 2 × 100 bp paired-end read method (San Diego, CA, USA) with AR (confirmed tumor regression property) and AP chickens. Sequence reads were aligned to the chicken reference genome (galgal5) and produced coverage of 11× and 14× in AR and AP, respectively. A total of 7.1 and 7.3 million single nucleotide polymorphisms (SNPs) were present in AR and AP genomes, respectively. Through a series of filtration processes, a total of 12,242 SNPs were identified in AR chickens that were associated with non-synonymous, frameshift, nonsense, no-start and no-stop mutations. Further filtering of SNPs based on read depth ≥ 10, SNP% ≥ 0.75, and non-synonymous mutations identified 63 reliable marker SNPs which were chosen for gene network analysis. The network analysis revealed that the candidate genes identified in AR chickens play roles in networks centered to ubiquitin C (UBC), phosphoinositide 3-kinases (PI3K), and nuclear factor kappa B (NF-kB) complexes suggesting that the tumor regression property in AR chickens might be associated with ubiquitylation, PI3K, and NF-kB signaling pathways. This study provides an insight into genetic factors that could be responsible for the tumor regression property.

Evaluation of different approaches for the estimation of daily yield from single milk testing scheme in cattle

Three models for the estimation of milk, fat and protein daily yield (DY) based on a.m. (AM) or p.m. (PM) milkings were compared. A total of 518 766 test-day records from 5078 dairy cattle farms obtained between March 2004 and April 2008 were analysed. The DY model was a linear model with DY as a dependent variable. In the PYR model and the DYR model, partial yield ratios (AM:DY and PM:DY) and daily yield ratios (DY:AM and DY:PM), respectively, were used as a dependent variable in the first step. In the second step, DY was estimated as a partial yield divided (PYR model) or multiplied (DYR model) by the estimated yield ratio from the first step. Models included the effect of partial yield (only in the DY model), milking interval, stage (month) of lactation and parity. Analysis of variance indicated that partial yield was the most important source of variation for the DY model whereas milking interval had the biggest effect in the PYR model and the DYR model. Differences in accuracy (correlation between the true and the estimated DY) between the models were negligible. On the other hand, models differed in the amount of bias (average error). The DYR model on average overestimated DY by 0·13 kg, 0·01 kg and 0·01 kg for milk, fat and protein, respectively. For the other two models the overall bias was almost zero. However, the DY model overestimated low and underestimated high DY owing to the well known regression property. The DYR model progressively overestimated high DY. These problems were not observed with the PYR model which seemed to be the best model. In this paper a relatively old topic was analysed and discussed from a new point of view, where the estimation of DY is based on modelling biologically more stable partial yield ratios rather than yield values from a.m. or p.m. milking.