Prospects for sustainability of pig production in relation to climate change and novel feed resources.

Pig production systems provide multiple benefits to humans. However, the global increase in meat consumption has profound consequences for our earth. This perspective describes two alternative scenarios for improving the sustainability of future pig production systems.

Creating effective biocontainment facilities and maintenance protocols for raising specific pathogen-free, severe combined immunodeficient (SCID) pigs.

Severe combined immunodeficiency (SCID) is defined by the lack of an adaptive immune system. Mutations causing SCID are found naturally in humans, mice, horses, dogs, and recently in pigs, with the serendipitous discovery of the Iowa State University SCID pigs. As research models, SCID animals are naturally tolerant of xenotransplantation and offer valuable insight into research areas such as regenerative medicine, cancer therapy, as well as immune cell signaling mechanisms.

A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses.

Background: To obtain predictions that are not biased by selection, the conditional mean of the breeding values must be computed given the data that were used for selection. When single nucleotide polymorphism (SNP) effects have a normal distribution, it can be argued that single-step best linear unbiased prediction (SS-BLUP) yields a conditional mean of the breeding values. Obtaining SS-BLUP, however, requires computing the inverse of the dense matrix G of genomic relationships, which will become infeasible as the number of genotyped animals increases.

Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods.

Background: The goal of this study was to apply Bayesian and GBLUP methods to predict genomic breeding values (GEBV), map QTL positions and explore the genetic architecture of the trait simulated for the 15th QTL-MAS workshop.

Methods: Three methods with models considering dominance and epistasis inheritances were used to fit the data: (i) BayesB with a proportion π = 0.995 of SNPs assumed to have no effect, (ii) BayesCπ, where π is considered as unknown, and (iii) GBLUP, which directly fits animal genetic effects using a genomic relationship matrix.

Genomic breeding value prediction and QTL mapping of QTLMAS2010 data using Bayesian Methods.

Background: Bayesian methods allow prediction of genomic breeding values (GEBVs) using high-density single nucleotide polymorphisms (SNPs) covering the whole genome with effective shrinkage of SNP effects using appropriate priors. In this study we applied a modification of the well-known BayesA and BayesB methods to estimate the proportion of SNPs with zero effects (π) and a common variance for non-zero effects. The method, termed BayesCπ, was used to predict the GEBVs of the last generation of the QTLMAS2010 data.

A gene frequency model for QTL mapping using Bayesian inference.

Background: Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances.