Identification and in silico characterization of structural and functional impacts of genetic variants in milk protein genes in the Zebu breeds Guzerat and Gyr.

Whole genome sequencing of bovine breeds has allowed identification of genetic variants in milk protein genes. However, functional repercussion of such variants at a molecular level has seldom been investigated. Here, the results of a multistep Bioinformatic analysis for functional characterization of recently identified genetic variants in Brazilian Gyr and Guzerat breeds is described, including predicted effects on the following: (i) evolutionary conserved nucleotide positions/regions; (ii) protein function, stability, and interactions; (iii) splicing, branching, and miRNA binding sites; (iv) promoters and transcription factor binding sites; and (v) collocation with QTL. Seventy-one genetic variants were identified in the caseins (CSN1S1, CSN2, CSN1S2, and CSN3), LALBA, LGB, and LTF genes. Eleven potentially regulatory variants and two missense mutations were identified. LALBA Ile60Val was predicted to affect protein stability and flexibility, by reducing the number the disulfide bonds established. LTF Thr546Asn is predicted to generate steric clashes, which could mildly affect iron coordination. In addition, LALBA Ile60Val and LTF Thr546Asn affect exonic splicing enhancers and silencers. Consequently, both mutations have the potential of affecting immune response at individual level, not only in the mammary gland. Although laborious, this multistep procedure for classifying variants allowed the identification of potentially functional variants for milk protein genes.

Oocyte origin affects the in vitro embryo production and development of Holstein (Bos taurus taurus) - Gyr (Bos taurus indicus) reciprocal cross embryos.

A reciprocal crossbred embryo production approach was used to assess effects of maternal breed on embryo development in tropical conditions (average temperature 22.0 °C and 77.9% relative humidity). Oocytes were recovered by ovum pick-up (OPU) from Gyr and Holstein donors (n = 90 Holstein and 83 Gyr OPUs). Female F1 embryos were produced by fertilization with sperm bearing X-chromosomes from Holstein semen (n = 615 Gyr oocytes) or Gyr semen (n = 255 Holstein oocytes). Blastocysts were transferred to recipients 168 h post-insemination (h.p.i.) (n = 70-144) and there were assessments of pregnancies until birth. Oocyte number per OPU (Gyr 10.0 ±â€¯0.7 compared with Holstein 6.3 ±â€¯0.4) and percentage viable oocytes (Gyr 78.8 ±â€¯1.9% compared with Holstein 71.2 ±â€¯2.2%) were less for Holstein donor animals. There was a 2.8 fold fewer total number of F1 blastocysts when Holstein donors were used (Gyr: 260, Holstein: 91). Pregnancy assessment during the different stages of gestation indicated the percentage pregnancy was less when embryos were produced from Holstein oocytes (Gyr and Holstein respectively: early pregnancy, 47.9% compared with 38.6%; mid-pregnancy, 44.4% compared with 31.4%; late pregnancy, 41.0% compared with 22.9%). Pregnancy length was also affected by maternal breed (Gyr: 280.8 ±â€¯0.6, Holstein: 286.3 ±â€¯0.7). It is concluded that in a tropical environment the maternal breed affects crossbred embryo development with pregnancy rates during the latter stages of gestation being greater when Gyr oocytes are used for production of embryos.

SNPs selection using support vector regression and genetic algorithms in GWAS.

INTRODUCTION: This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. RESULTS: The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. CONCLUSIONS: The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.

Zero-inflated Poisson regression models for QTL mapping applied to tick-resistance in a Gyr × Holstein F2 population.

Now a days, an important and interesting alternative in the control of tick-infestation in cattle is to select resistant animals, and identify the respective quantitative trait loci (QTLs) and DNA markers, for posterior use in breeding programs. The number of ticks/animal is characterized as a discrete-counting trait, which could potentially follow Poisson distribution. However, in the case of an excess of zeros, due to the occurrence of several noninfected animals, zero-inflated Poisson and generalized zero-inflated distribution (GZIP) may provide a better description of the data. Thus, the objective here was to compare through simulation, Poisson and ZIP models (simple and generalized) with classical approaches, for QTL mapping with counting phenotypes under different scenarios, and to apply these approaches to a QTL study of tick resistance in an F2 cattle (Gyr × Holstein) population. It was concluded that, when working with zero-inflated data, it is recommendable to use the generalized and simple ZIP model for analysis. On the other hand, when working with data with zeros, but not zero-inflated, the Poisson model or a data-transformation-approach, such as square-root or Box-Cox transformation, are applicable.