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Genetic Influence of Host on Fatty Acid Composition in Beef Cattle

Chinyere Ekine-Dzivenu (PhD Candidate)Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Canada.

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Outline

• Background • Objectives• Materials and methods• Results and discussion• Conclusion• Acknowledgement

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Background

CONSUMERAWARENESS

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Background Cont’d

• SFA Increased Plasma cholesterol

- Cardiovascular diseases - Cancer - Obesity

• MUFA & PUFA Reduced plasma cholesterol

• CLA Anti-carcinogenic, anti-atherosclerotic

Anti-diabetic

Anti-ObesityType of dietary fat (fatty acid profile) matters more than the amount of fat.

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Background Cont’dImproving 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

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ObjectivesEstimate heritability of fatty acids in beef brisket adipose tissue, subcutaneous adipose tissue and longissimus luborum muscle to assess the potential for genetic improvement

Discover SNP markers associated with FA profile in beef for marker assisted selection or marker based diet management

Estimate phenotypic and genetic correlation between FAs within each tissue in order to preventantagonism when genetic selection is made

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Materials and methods

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Phenotype

Genotype

1536 SNPS

• Over 80 FA in the brisket adipose on 223 beef steers• Over 80 FA in the subcutaneous adipose and longissimus luborum 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

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Results and discussion

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eFig1. Variation among individual animals for different fatty acids

Each dot represents an individual animal

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Longissimus Luborum Muscle (n=1366)

Subcutaneous Adipose (n=1366)

Brisket Adipose (n=223)

Fatty Acids Mean h2±SE Mean h2±SE Mean h2±SE14:0 2.8033 0.6 ± 0.13 3.2036 0.5 ± 0.16 3.5452 0.17 ± 0.1215:0 0.5024 0.22 ± 0.1 0.6423 0.25 ± 0.12 0.6209 0.31 ± 0.1216:0 24.6089 0.54 ± 0.1 25.0924 0.28 ± 0.09 25.5585 0.05 ± 0.1217:0 1.5484 0.31 ± 0.11 1.7088 0.43 ± 0.14 1.4033 0.17 ± 0.1118:0 12.4073 0.33 ± 0.09 10.5448 0.43 ± 0.1 8.9234 0.12 ± 0.11

9c-14:1 0.6401 0.54 ± 0.09 1.0459 0.41 ± 0.1 1.4845 0.51 ± 0.119c-16:1 3.4078 0.69 ± 0.1 4.2466 0.51 ± 0.12 5.595 0.13 ± 0.119c-17:1 1.1913 0.17 ± 0.07 1.3774 0.18 ± 0.09 1.4865 0.04 ± 0.19c-18:1 36.6758 0.48 ± 0.09 37.9173 0.17 ± 0.07 40.1263 0.13 ± 0.1210t-18:1 2.0279 0.27 ± 0.09 2.9079 0.3 ± 0.1 0.5399 0.11 ± 0.1111c-18:1 1.8358 0.24 ± 0.09 1.9604 0.03 ± 0.04 2.4717 0.04 ± 0.1111t-18:1 0.4406 0.24 ± 0.08 0.5455 0.16 ± 0.07 0.5399 0.11 ± 0.1113c-18:1 0.3958 0.51 ± 0.09 0.4869 0.37 ± 0.09 0.7455 0.43 ± 0.118:2n-6 4.3871 0.28 ± 0.09 1.8761 0.43 ± 0.1 1.2634 0.17 ± 0.13

Sumtrans18:1 3.2517 0.28 ± 0.09 4.4859 0.32 ± 0.09 2.2963 0.11 ± 0.11SumCLA 0.395 0.21 ± 0.07 0.7043 0.30 ± 0.08 0.59 0.06 ± 0.1

SFA 42.4241 0.48 ± 0.1 41.5983 0.39 ± 0.1 40.2913 0.07 ± 0.11MUFA 48.5617 0.48 ± 0.09 52.9408 0.35 ± 0.09 55.4148 0.06 ± 0.1PUFA 6.696 0.26 ± 0.09 2.2902 0.42 ± 0.09 2.8065 0.12 ± 0.12BFA 1.3562 0.17 ± 0.08 1.7139 0.24 ± 0.08 1.4874 0.03 ± 0.1

SFA+BFA 43.7803 0.48 ± 0.09 43.3122 0.38 ± 0.1 41.7787 0.06 ± 0.11n-6 5.9261 0.27 ± 0.09 2.0457 0.42 ± 0.09 1.4631 0.16 ± 0.13

n-6/n-3 8.6281 0.44 ± 0.11 9.2625 0 ± 0 7.9869 0.03 ± 0.1Health Index 1.5654 0.54 ± 0.1 1.4875 0.38±0.11 1.4888 0.16 ± 0.12

Health Index = ΣMUFA +ΣPUFA 4X14:0+16:0

High

Moderate

Low

Table 1. Heritability of selected fatty acids in 3 beef tissues

HI =

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Trait SFA MUFA PUFA SumCLA Health_IndexSFA -0.99±0 -0.31±0.06 -0.70±0.52 -0.91±0.01MUFA -0.99±0.03 0.17±0.06 0.70±0.55 0.9±0.01PUFA -0.41±0.64 0.20±0.82 0.37±0.61 0.23±0.06sumCLA -0.29±0.06 0.20±0.06 0.61±0.04 0.19±0.06Health Index -0.99±0.01 0.96±0.05 0.48±0.45 0.68±0.44

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

Trait SFA MUFA PUFA SumCLA Health IndexSFA -0.59±0.09 -0.38±0.11 -0.32±0.09 -0.86±0.02MUFA -0.77±0.06 -0.5±0.06 -0.01±0.08 0.45±0.08PUFA -0.18±0.18 -0.47±0.13 0.1±0.24 0.43±0.1sumCLA -0.02±0.19 -0.08±0.17 0.31±0.14 0.23±0.11Health Index -0.89±0.03 0.75±0.06 0.09±0.19 -0.02±0.19

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Trait SFA MUFA PUFA SumCLA Health IndexSFA -0.98±0 0.05±0.09 -0.08±0.09 -0.85±0.03MUFA -0.98±0.01 -0.23±0.09 -0.03±0.1 0.85±0.03PUFA -0.15±0.17 -0.06±0.17 0.39±0.14 -0.09±0.11sumCLA -0.56±0.13 0.47±0.15 0.25±0.08 0.06±0.1Health Index -0.84±0.06 0.87±0.05 -0.05±0.18 0.36±0.17

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Fig.2. Schematic overview of associations of fatty acids with SNPs in candidate genes.

Allele substitution effect indicated by color key

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Fig 3. Variation in FA among individuals as a result of variation in different cellular processes

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Conclusions

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• 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.

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Future directionsUse 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.

Work in progressPhenotypic and genetic correlation of fatty acids in the subcutaneous adipose tissue

and longissimus luborum muscle with carcass and meat quality traits

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Acknowledgement• Supervisor: Dr. Changxi Li • Group members, co-investigators and committee

Liuhong Chen Michael DuganMichael Vinsky Jennifer Aalhus

John Basarab Noelia AldaiPaul Stothard Tim McAllisterFiona Buchanan Carolyn FitzsimmonsErasmus Okine Zhiquan Wang

• Funding:

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Thank you