CategoryAnimal Science - Breeding & Genetics
Date Full Report Received04/29/2013
Date Abstract Report Received04/29/2013
Funded ByNational Pork Board
The pig industry continues to face many challenges with regard to costs of production. Feed costs continue to be the largest variable cost. One way of reducing feed costs is by increasing the efficiency with which pigs convert feed into saleable product. This can be done by altering the composition of the diet or selecting pigs with superior genetic potential for efficient use of nutrients. Here we report results from a project designed to use the latest advances in genomic technology to improve the accuracy with which we identify pigs having superior genetic ability to utilize nutrients. Recent advances in molecular genetics have made it possible for geneticists to improve the accuracy of genetic prediction. Utilization of genetic markers to analyze economically important traits has provided opportunities to identify chromosomal regions harboring genes influencing those traits. In some cases this has led to the discovery of the causal mutations within genes which are responsible for the change in performance. The ProcineSNP60 BeadChip allows scientists to genotype a pig for 60,000 genetic markers in a single assay for less than $100 per animal. This provides a wealth of information regarding the genetic makeup of each pig. The objective of this study was to use genomic information to identify genomic regions associated with feed efficiency and production traits, including average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR), residual feed intake (RFI), ultrasound back fat thickness (BF), muscle depth (MD), inner muscular fat percentage (IMF), birth weight (BW), and weaning weight (WW) in purebred Duroc boars. Individual feed intake records on more than 1000 boars were collected from electronic feeders. While the PorcineSNP60 BeadChip does include 60,000 markers, not all markers will be useful in every experiment. For our project a total of 40,008 markers proved useful and were evaluated. Three different statistical strategies were utilized to identify the chromosomal regions of interest. The reason for using multiple approaches is to obtain the best information available. Each approach has different strengths and weaknesses.
Several important genomic regions were identified in this study, associated with the analyzed traits. A region on pig chromosome 1 was found which explained a large proportion of the genetic variance for both ADG and ADFI. Additional regions with smaller effects were identified on chromosomes 4, 6, 8, 14 and X. The genetic architecture of the analyzed traits was characterized by a limited number of genes or genomic regions with large effects and many regions with smaller effects. The region on chromosome 1 might be used to improve ADG and decrease ADFI in pigs.
To add confidence to our results, we obtained feed intake data on an additional 504 Duroc boars from the same population. Those boars were genotyped with GGP-Porcine BeadChip (8,826 markers) which has recently become commercially available. A procedure known as “imputation” was used to predict the 60k genotypes based on the GGP-Porcine BeadChip genotypes. Imputation has been shown to be highly accurate when done correctly. The advantage of this approach is that pigs can be genotyped using the GGP-Porcine BeadChip for less than one-half the cost of the Porcine60K BeadChip. After imputation, 35,871 genotypes were obtained for each of the 504 boars typed by the GGP-Porcine BeadChip. Inclusion of the data from the additional 504 boars supported the conclusions from the original analysis that chromosomal regions affecting nutrient utilization can be identified.
Regions within chromosomes influencing nutrient utilization were identified.
Genetic markers can be used to improve the accuracy with which we predict a pigs genetic potential for average daily feed intake and average daily gain which are critical components of nutrient utilization.
Improved accuracy of prediction would be expected to result in more accurate genetic selection and faster genetic improvement.