Characterization of bovine (Bos taurus) imprinted genes from genomic to amino acid attributes by data mining approaches.

Genomic imprinting results in monoallelic expression of genes in mammals and flowering plants. Understanding the function of imprinted genes improves our knowledge of the regulatory processes in the genome. In this study, we have employed classification and clustering algorithms with attribute weighting to specify the unique attributes of both imprinted (monoallelic) and biallelic expressed genes.

Using quantile regression for fitting lactation curve in dairy cows.

The main objective of this study was to compare the performance of different 'nonlinear quantile regression' models evaluated at the τth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression.

Attribute selection and model evaluation for the maternal and paternal imprinted genes in bovine (Bos Taurus) using supervised machine learning algorithms.

Imprinted genes display biased expression of paternal and maternal alleles in mammals. They are marked through epigenetic process during gametogenesis. Characterization of imprinted genes has expanded our understanding of the regulation and function of genes.

Incorporating Prior Knowledge of Principal Components in Genomic Prediction.

Genomic prediction using a large number of markers is challenging, due to the curse of dimensionality as well as multicollinearity arising from linkage disequilibrium between markers. Several methods have been proposed to solve these problems such as Principal Component Analysis (PCA) that is commonly used to reduce the dimension of predictor variables by generating orthogonal variables. Usually, the knowledge from PCA is incorporated in genomic prediction, assuming equal variance for the PCs or a variance proportional to the eigenvalues, both treat variances as fixed.

Comparative In silico Study of Sex-Determining Region Y (SRY) Protein Sequences Involved in Sex-Determining.

Background: The SRY gene (SRY) provides instructions for making a transcription factor called the sex-determining region Y protein. The sex-determining region Y protein causes a fetus to develop as a male. In this study, SRY of 15 spices included of human, chimpanzee, dog, pig, rat, cattle, buffalo, goat, sheep, horse, zebra, frog, urial, dolphin and killer whale were used for determine of bioinformatic differences.

Comparison of linear mixed model analysis and genealogy-based haplotype clustering with a Bayesian approach for association mapping in a pedigreed population.

Background: Despite many success stories of genome wide association studies (GWAS), challenges exist in QTL detection especially in datasets with many levels of relatedness. In this study we compared four methods of GWA on a dataset simulated for the 15th QTL-MAS workshop. The four methods were 1) Mixed model analysis (MMA), 2) Random haplotype model (RHM), 3) Genealogy-based mixed model (GENMIX), and 4) Bayesian variable selection (BVS).

A two step Bayesian approach for genomic prediction of breeding values.

Background: In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size p on the posterior distribution of the marker variances will be p df.