Chapter 12: Bio- and estimation technology

• Estimation technology for breeding value estimation

• The significance of artificial insemination (AI) for breeding value estimation

• Transgenesis and transgenic animals

• Application of DNA-markers for disease genes

• Finding disease genes or QTL‘s

The most important calculation methods for breeding value

estimation

• Selection index (SI)

• Best Linear Prediction (BLP)

• Best Linear Unbiased Prediction (BLUP)

• Animal Model (AM)

The significance of artificial insemination for breeding value

estimation

Preconditions for breeding value estimations

• Standardised environment

• Test station or correction for environment

• AI, the precondition for equality, as a bull or a boar are sire of offspring in many different herds

The significance of artificial insemination for breeding value

estimation

• Accurate estimation (large families)• Independent of ownership• Comparable• Increased selection intensity and accuracy rIA

by AI and super ovulation

Transgenese and transgenic animals

• Gene constructs

• Methods for gene transfer

• Production motives for transgenesis.

• Other motives for transgenesis

Gene constructs

• Promoter (regulator-sequence)

• Structure gene (DNA-code for a protein)

• Micro injection of DNA in male pronucleus

• Embryonic stemcells and homologue recombination

• Micro injection or other forms of gene transfer into foetal cells

Methods of gene transfer

Motives for transgenese?

• Additive effect 4A

• New metabolic traits

• Pharmaceutics

• Organ donation

Example of application of DNA-markers for disease genes

DNA-marker (A,B) within families* = disease gene, + = normal gene

• The genome is ca. 3000 centi Morgan (cM)• A marker covers 20 cM• 150 DNA-markers are needed to analyse for a given

segregation • Ca. half of the markers are informative, so ca. 300

all together have to be applied

Identification of DNA-marker linked to disease genes or QTL‘s

• Family size - ca. 50 for an analyse • At least 10 to 15 affected• The best ratio between affected and normal is 1:1• The remaining normal are excluded randomly

Family material suited for identification of a disease gene

Genotype affected not affected Total

aa 20 3 23

not aa 4 23 27

Total 24 26 50

• Family size - more than 1000

• The existence of QTL’s is unknown

• Sire or grand-sire design can be applied

• F2 animals in relation to exotic crosses

Family material suited for identification of QTL’s

QTL estimation• Grand sire design

Estimated effect on offspring’s breeding values• Sire design

Estimated effect on offspring phenotypes• Marker typing of parents and offspring, in both

cases

Sire design or Grand sire design

Graphic Sire and Grand-sire design

Sire design or Grand-sire design, test size

Number A1 Number A2 S.D. LSD for disease frequency

5000 5000 0.0060 1.8 % units 500 500 0.0190 5.7 % units 100 100 0.0420 12.6 % units

Number of affected bin(N;0.10.9/N)

Least significant difference (LSD) in disease frequency for offspring with A1 and offspring with A2

Successfull selection experiments in layers for resistance to neo plasma fatalities, Cole and Hutt (1973)

Successfull selection for high and low leukocyte count in mice, Chai (1975)

Results of selection experiments for disease resistance

Selection experiments for high or low anti sheep erythrocytes titter

in mice.

Challenge experiments in mice

Breeding measures to healthy animals

Use crosses but avoid F2 and four-way

High uniformity in production

Conclusion of experiments for general disease resistance