1. C. Maroteau, I. Palhière, H. Larroque, V. Clément,
63 rd Annual Meeting EAAP 2012
27-31 august – Bratislava, Slovakia
Genetic parameter estimation for major fatty acids in french dairy goats
C. Maroteau, I. Palhière, H. Larroque, V. Clément,
G. Tosser-Klopp, R. Rupp
INRA, Genetic Department
Toulouse

2. This presentation focuses on the possibility of using genetic selection to improve goat milk fatty acid composition
PhénoFinlait program and data collection
y = Xß + Za + e
Statistical models of estimation
Mapping slide for the presentation (note that a background slide or a slide justifying the importance might precede this slide). A common mistake with mapping slides is to give the audience simply a boring and unmemorable vertical list of topics (including the names “Introduction” and “Conclusion” and “Questions”). Such a list is quickly forgotten after the slide is removed.
On a mapping slide, take the opportunity to show a key image or perhaps a representative image for each major section of the presentation. In the second case, each image would be repeated on the first visual of the corresponding section and would remind the audience that they have arrived to a major section of the presentation’s middle.
In regards to the names “Introduction” and “Conclusion,” every talk has those sections, and the names are ignored by audiences. So why state them? Also, for the divisions that you do have, find a logical and parallel grouping. Note that groups of two’s, three’s, and four’s are much easier to remember and are not so nearly intimidating as groups of five’s, six’s, and seven’s. See the example mapping slide in the textbook. (CSP, pages 55-56, 74-75, 86, 143, 147, and 148)
Genetic parameter results

3. The PhénoFinlait program created the 1st collection of goat fatty acid (FA) milk composition from farms
Prediction equations
FA composition
(18 FA, 2 units)
Mid Infra Red spectra
Body slide from the first section of the presentation’s middle. Consider having the first-topic image from the mapping slide repeated here. Use the headline (no more than two lines) to make an assertion about this topic. In the body of the slide, support that headline with images and with words, if necessary. See CSP, pages and
This slide shows one orientation for the image and text in the body of the slide. Other orientations appear in this template. Choose the one that best supports your headline assertion.
Some institutions will insist that you place an institutional logo on each slide. Other institutions recommend a logo on the first slide and the last. If you do place a logo on each slide, make sure that logo is at the bottom of the slide rather than the top. Placing the logo at the top (the place on a slide that receives the most emphasis) shifts the emphasis away from the work and to the people or place doing the work.
Alpine
Saanen
Estimations
24 267
20 992
Animals
7 359
6 337
Pedigrees
32 580
27 926
1st lactation

4. A multitrait animal mixed model of 1st lactation repeated data to compute heritabilities and correlations
FA estimations
20 DIM
364 DIM
From 2 to 7 estimations/animal
y2 = Xß + Za + Wp + e
(REML method of estimation WOMBAT)
y2: 3.5 estimations / animal over the lactation
Fixed effects: Herd within test-day
Lactation stage
Month at kidding
Season of control
Moment of control
Analysers
Sample slide from the middle of a presentation. Notice how animation works to allow the speaker to present fairly complex connections in a way that the audience can digest. Notice how the arrows show connections—something that bullets could not achieve.
Reference
This slide is a composite slide from a number of mechanical engineering seniors in ME 4006, a laboratory course at Virginia Tech. These presentations occurred during the Fall 2004 semester.
Random effects: a, genetic
p, permanent environment
e, residual

5. 20% to 40% of the variability of FA traits is controlled by genetics
g/100g of milk
g/100g of milk fat h²
Alpine
Saanen
C6:0
0.24
0.21
C8:0
0.23
C10:0
0.30
0.26
C12:0
0.38
0.37
C14:0
0.32
C16:0
0.20
C18:1
0.19
0.18 h²
Alpine
Saanen
C6:0
0.37
0.36
C8:0
0.23
C10:0
0.28
0.26
C12:0
0.32
0.33
C14:0
0.25
C16:0
0.38
0.30
C18:1
0.24
*(SE between 0.02 and 0.03)
A change in the FA composition could be expected

6. The relationship between FA mainly reflects the metabolic pathways
Genetic correlations between FA (g/100g of milk fat)
Saanen
C6:0
C8:0
C10:0
C12:0
C14:0
C16:0
0.78
0.40
0.59
0.03
0.34
0.80
-0.36
-0.30
0.33
0.51
-0.26
-0.51
-0.38
-0.44
C18:1
-0.16
-0.69
-0.62
-0.07
*(SE between 0.02 and 0.13)

7. Increasing contents would probably increase short chain saturated FA
Genetic correlation with fat and protein contents
rg from 0.5 to 0.7 with protein content in g/100g of milk
rg from 0.8 to 0.9 with fat content in g/100g of milk
Saanen (g/100g of fat)
Protein Content
Fat Content
C6:0
0.10
0.17
C8:0
0.36
0.34
C10:0
0.28
C12:0
0.49
0.18
C14:0
-0.18
-0.35
C16:0
-0.37
-0.01
C18:1
-0.28
-0.17
*(SE between 0.02 and 0.13)

8. In summary, genetic selection to improve FA composition in goat milk could be possible
1st caprine results
Heritability estimates are moderate (from 20% to 40%)
Improving the nutritional value of milk by selection is possible
Genetic parameters reflect the main FA pathways
Increasing fat and protein contents would probably lead to a moderate increase of short chain saturated FA at the expense of medium and long chain saturated FA in fat
On going researches: Protein prediction equations
-QTL detection

9. Thank you for your attention