A pinboard by
Robert Mukiibi

PhD Student, University of Alberta


Identification of molecular markers that can aid selection of more feed efficient beef cattle

Beef production is a very important agricultural sub-sector contributing greatly to economies of many countries in the world, for example, in Canada beef contributes $33.75 billion annual national revenue. Currently, there is a high demand of beef worldwide and this demand is expected to increase. Therefore, beef production must increase to meet that demand. However, beef producers are constrained by the scarcity feed that are expensive. This is mainly due to increased competition for grains between human food and animal feed, and increased costs of inputs for animal feed production resources such as labor, fertilizers and land. Recent research shows that feed costs account for about 55-75% of the total non-fixed costs of beef production. Therefore, feed utilization efficiency into salable meat by the animals is of immense importance to the farmers to increase net profits. Also, inefficient beef animals waste most of this expensive feed into manure, and greenhouse gases like methane that contribute global warming environmental problem, with the current estimation of 36 billion tons of methane from the beef industry.

Feed utilization efficiency varies or differs from one animal to another; and current research reveals that 40% part of these differences are due to differences in genes animals inherit from their parents. This genetic variation allows breeders to select and mate animals with high feed utilization ability for improvement of feed efficiency in the succeeding generations. Several studies have showed genetic linkage between feed efficiency and methane and manure production, hence, breeding for more feed efficient animals significantly reduces methane and manure production.

However, for successful selective breeding process for feed efficiency, individual animal feed intake measurements are required, and this currently an expensive and arduous process both at the farm or research institutes with the current estimate of $188 per animal. Technology advancement in molecular genetics has given scientists and breeders capability to select and breed animals for such expensive and difficult to measure traits basing only DNA differences or markers between animals through a technique called genomic selection. The main of my research is to identify genetic differences or markers that can be used to select for more feed efficient beef animals using different molecular biology tools, and these markers would enhance genomic selection for feed efficiency.


Whole genome single nucleotide polymorphism associations with feed intake and feed efficiency in beef cattle.

Abstract: Feed intake and efficiency are economically important traits because feed is the greatest variable cost in beef production. Feed efficiency can be measured as residual feed intake (RFI), which is the difference between actual DMI of an animal and the expected DMI based on its BW and growth rate. Feed conversion ratio (FCR) is the inverse of gross feed efficiency and is the ratio of DMI to ADG. A total of 2,633 SNP across the 29 bovine autosomes were analyzed in 464 steers sired by Angus, Charolais, or Alberta Hybrid bulls for associations with RFI. A total of 150 SNP were associated with RFI at P < 0.05 of which 23 were significant at P < 0.01. Nine of the SNP pairs show high linkage disequilibrium (r(2) > 0.80), so only 1 of the SNP pairs was used in further multiple-marker analyses. Two methods were used to create a panel of SNP that were maximally informative for RFI based on the data. In the first method, 141 unique SNP were combined in a single multivariate model and a backward elimination model was used to drop SNP until all SNP left in the model were significant at P < 0.05. The SNP had greater effects when combined in the multivariate model than when tested individually. In the second method, the estimates from the 141 SNP were used to create a sequential molecular breeding value (MBV) according to the compound covariate prediction (CCP) procedure. The sequential MBV was built by adding the estimated effects one at a time, but only keeping SNP effects in the sequential MBV if the test statistic and the proportion of variance explained were improved. Predictabilities of the 2 methods were compared by regressing RFI on a final MBV created from SNP that remained in each analytical model. The MBV from the compound covariate prediction model produced an r(2) of 0.497, whereas the multivariate model MBV had a decreased r(2) of 0.416. The significant SNP were also tested for associations with DMI and FCR. The SNP showed different combinations of associations with the 4 traits, including some that were only associated with RFI. About 9.5% of the SNP from the 2 models were within 5 cM of previously identified RFI QTL and pinpoint areas to further explore for positional candidate genes. In conclusion, this study has identified a panel of SNP with significant effects on RFI that need to be validated in an independent population and provides continued progress toward selecting markers for use in marker-assisted selection for feed efficiency in beef cattle.

Pub.: 15 Sep '09, Pinned: 28 Jun '17

Genome-wide association analyses for growth and feed efficiency traits in beef cattle.

Abstract: A genome-wide association study using the Illumina 50K BeadChip included 38,745 SNP on 29 BTA analyzed on 751 animals, including 33 purebreds and 718 crossbred cattle. Genotypes and 6 production traits: birth weight (BWT), weaning weight (WWT), ADG, DMI, midtest metabolic BW (MMWT), and residual feed intake (RFI), were used to estimate effects of individual SNP on the traits. At the genome-wide level false discovery rate (FDR < 10%), 41 and 5 SNP were found significantly associated with BWT and WWT, respectively. Thirty-three of them were located on BTA6. At a less stringent significance level (P < 0.001), 277 and 27 SNP were in association with single traits and multiple traits, respectively. Seventy-three SNP on BTA6 and were mostly associated with BW-related traits, and heavily located around 30 to 50Mb. Markers that significantly affected multiple traits appeared to impact them in same direction. In terms of the size of SNP effect, the significant SNP (P < 0.001) explained between 0.26 and 8.06% of the phenotypic variation in the traits. Pairs of traits with low genetic correlation, such as ADG vs. RFI or DMI vs. BWT, appeared to be controlled by 2 groups of SNP; 1 of them affected the traits in same direction, the other worked in opposite direction. This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting growth and feed efficiency traits in beef cattle.

Pub.: 16 Jul '13, Pinned: 28 Jun '17