1. Chinyere Ekine-Dzivenu (PhD Candidate) • Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada. Genetic Influence of Host on Fatty Acid Composition in Beef Cattle

2. Outline • Background • Objectives • Materials and methods • Results and discussion • Conclusion • Acknowledgement

3. Background CONSUMER AWARENESS

4. Background Cont’d • SFA Increased Plasma cholesterol - Cardiovascular diseases - Cancer - Obesity • MUFA & PUFA Reduced plasma cholesterol • CLA Anti-carcinogenic, anti-atherosclerotic • Anti-diabetic • Anti-Obesity Type of dietary fat (fatty acid profile) matters more than the amount of fat.

5. Background Cont’d Improving beef fatty acid composition • Nutrition approach • Added cost • Change not permanent • May affect flavor • Traditional genetic improvement approach • Permanent and accumulative change • BUT difficult/expensive to measure and measured after slaughter • Genomics • Marker assisted selection/genomic selection

6. Objectives Estimate heritability of fatty acids in beef brisket adipose tissue, subcutaneous adipose tissue and longissimusluborum muscle to assess the potential for genetic improvement 1. 2. Estimate phenotypic and genetic correlation between FAs within each tissue in order to prevent antagonism when genetic selection is made Discover SNP markers associated with FA profile in beef for marker assisted selection or marker based diet management 3.

7. Materials and methods

8. Phenotype 1536snps Genotype • Over 80 FA in the brisket adipose on 223 beef steers • Over 80 FA in the subcutaneous adipose and longissimusluborum muscle on 1366 animals • Heritability and correlations estimated using univariate and bivariate animal model implemented in ASreml after accounting for fixed effects. • 961 polymorphic markers for Bayesian candidate gene association study on adjusted data

9. Results and discussion

10. Fig1. Variation among individual animals for different fatty acids % FAME B.Adipose % FAME S.Adipose % FAME Muscle Each dot represents an individual animal

11. Table 1. Heritability of selected fatty acids in 3 beef tissues High Moderate Low Health Index = ΣMUFA +ΣPUFA 4X14:0+16:0 HI =

12. Table 2. Phenotypic (above diagonal) and genetic (below diagonal) correlation between selected fatty acid groups in beef tissues Brisket adipose Subcutaneous adipose LongissimusLuborum

13. Fig.2. Schematic overview of associations of fatty acids with SNPs in candidate genes. Allele substitution effect indicated by color key

14. Fig 3. Variation in FA among individuals as a result of variation in different cellular processes • SCD • PNPLA2 • LPL • F5 • CPT2 • ACADL • BDH1 • ATIC • SLC27A2 • ATP2B1 • AP2B1 • CRHR1 • RARA • TRHR • Enzyme • Transporter • Receptors • NR1H3 • RUNX1T1 • IRF2 • BRCA1 • ANKRD1 • EIF3H • Translation Regulator • Transcription Regulator

15. Conclusions

16. Variation exists in the amount of each fatty acid in beef tissues. • Individual animals vary in the amount of each FA deposited in tissues. • Each fatty acid in beef is a complex trait (influenced by several genes). • Identified markers throw light on processes that can cause variation in FA between animals. • Results show possibility of selecting beef with superior genetics to improve not only beneficial FA content but also eating quality of beef. • Results show possibility of simultaneously improving beneficial FA in the adipose. Attention should be paid to the moderate negative correlation between muscle MUFA and PUFA.

17. Work in progress Phenotypic and genetic correlation of fatty acids in the subcutaneous adipose tissue and longissimusluborum muscle with carcass and meat quality traits Future directions Use a higher density SNP panel (bovine 50K SNP chip) to capture more markers explaining a significant amount of variation for beneficial fatty acids among individual animals.

18. Acknowledgement • Supervisor: Dr. Changxi Li • Group members, co-investigators and committee • Liuhong Chen Michael Dugan • Michael Vinsky Jennifer Aalhus • John BasarabNoeliaAldai • Paul Stothard Tim McAllister • Fiona Buchanan Carolyn Fitzsimmons • Erasmus OkineZhiquan Wang • Funding:

19. Thank you