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.

Whole genome sequencing of Guzerá cattle reveals genetic variants in candidate genes for production, disease resistance, and heat tolerance.

In bovines, artificial selection has produced a large number of breeds which differ in production, environmental adaptation, and health characteristics. To investigate the genetic basis of these phenotypical differences, several bovine breeds have been sequenced. Millions of new SNVs were described at every new breed sequenced, suggesting that every breed should be sequenced. Guzerat or Guzerá is an indicine breed resistant to drought and parasites that has been the base for some important breeds such as Brahman. Here, we describe the sequence of the Guzerá genome and the in silico functional analyses of intragenic breed-specific variations. Mate-paired libraries were generated using the ABI SOLiD system. Sequences were mapped to the Bos taurus reference genome (UMD 3.1) and 87% of the reference genome was covered at a 26X. Among the variants identified, 2,676,067 SNVs and 463,158 INDELs were homozygous, not found in any database searched, and may represent true differences between Guzerá and B. taurus. Functional analyses investigated with the NGS-SNP package focused on 1069 new, non-synonymous SNVs, splice-site variants (including acceptor and donor sites, and the conserved regions at both intron borders, referred to here as splice regions) and coding INDELs (NS/SS/I). These NS/SS/I map to 935 genes belonging to cell communication, environmental adaptation, signal transduction, sensory, and immune systems pathways. These pathways have been involved in phenotypes related to health, adaptation to the environment and behavior, and particularly, disease resistance and heat tolerance. Indeed, 105 of these genes are known QTLs for milk, meat and carcass, production, reproduction, and health traits. Therefore, in addition to describing new genetic variants, our approach provided groundwork for unraveling key candidate genes and mutations.

Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle.

A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre's polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre's polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from -0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from -0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits.

Estrutura populacional da raça Girolando / Population structure of Girolando breed

O objetivo neste estudo foi avaliar a estrutura genética da população de bovinos da raça Girolando no Brasil. Analisou-se o arquivo de pedigree de 26.969 animais, composto de 3.031 machos e 23.938 fêmeas. O nível de conteúdo de informação do pedigree na geração atual foi 61%, mostrando ser de qualidade moderada. O coeficiente de endogamia médio e o coeficiente de relação médio da população Girolando foram 0,11 e 0,13%, respectivamente. O tamanho efetivo da população, considerando a geração completa traçada, foi 188, acima do nível crítico. Do total de 9.457 ancestrais que contribuíram para a população de referência, 457 explicaram 50% da variabilidade genética da população. O número efetivo de fundadores foi 551 e o de ancestrais 393. O intervalo médio de geração foi de 5,26 anos, sendo ligeiramente maior nas trilhas gaméticas mãe-filho e pai-filha. A partir dos coeficientes estimados, pode-se concluir que a endogamia nos rebanhos da raça Girolando foi de pequena magnitude e que as práticas de acasalamento foram adequadas durante o período avaliado. No entanto, é importante continuar com o monitoramento desses coeficientes a fim de prevenir perda de variabilidade genética.

The aim of this study was to evaluate the population structure of Girolando cattle in Brazil. The pedigree file contained 26,969 individuals, from which 3,031 were males and 23,938 were females. The average level of completeness of the pedigree in the current generation was of reasonable quality (61%). Inbreeding and average relatedness coefficients were low: 0.11 and 0.13%, respectively. Estimates of effective population size considering the full generations traced was 188, which is above the critical level range. The number of ancestors that contributed to the reference population was 9,457 animals, from which 457 explained 50% of the genetic variability of the population. The effective number of founders and the effective number of ancestors in this population were, respectively, 551 and 393. The average generation interval was 5.26 years, slightly higher in genetic pathways dam-son and sire-daughter. The inbreeding in the Girolando breed was of small magnitude, indicating that the current practices of mating were adequate during the study period. However, it is important to continue monitoring these coefficients in order to prevent loss of genetic variability.

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.