1	Genetic Evaluation for Small Ruminants
2	Why small ruminants? Important contributors to the world supply of meat, milk, and fiber Can utilize pasture not suitable for cattle More suitable for small scale operations People enjoy associating with them
3	Why genetic selection? Genetic selection can improve fitness, utility, and profitability Females must be bred to provide replacements and initiate milk production Mate selection is an opportunity to make genetic change
4	Selection is a continuous process Decisions Which females to breed Which males to use Which specific matings to make Which progeny to raise Which females to keep and breed Goals Improve production and efficiency Avoiding inbreeding Correct faults
5	Why genetic evaluations? A valuable tool for genetic selection Allows for comparison of animals in different environments Can include all of the information available for each animal Greatest impact on progress is from selection for males
6	What is an evaluation? Phenotype is measurable Pounds of milk produced Stature An evaluation is an estimate of Genotype
7	Steps in genetic evaluation Define a breeding goal Measure traits related to the goal Record pedigree to allow detection of relationships across generations Identify non-genetic factors that affect records and could bias evaluations Make adjustments Include in the model Define an evaluation model
8	Examples of breeding goals Increased milk, fat, or protein yield Increased average daily gain Increased weaning weight Optimal birth weight Optimal litter size Improved conformation score (overall and linear)
9	Trait and pedigree data collection
10	Examples of non-genetic factors Age Lactation Season Litter size Milking frequency Herd
11	Evaluation model An equation that indicates what factors contribute to an observation Separates the genetic component from other factors Solutions predict the genetic potential of progeny
12	Yield Model: y = hys + hs + pe + a + e y = yield of milk, fat, or protein during a lactation hys = herd-year-season Environmental effects common to lactations in the same season, within a herd hs = herd-sire Effects common to daughters of the same sire, within a herd pe = permanent environment Non-genetic effect common to all of a doe’s lactations a = animal genetic effect (breeding value) e = unexplained residual
13	Type Model: y = h + pe + a + e y = adjusted type record h = herd appraisal date pe = permanent environment Non-genetic effect common to all of a doe’s lactations a = animal genetic effect (breeding value) e = unexplained residual
14	Correlations between type traits
15	Evaluations indexes An index combines evaluations for a group of traits based on their contribution to a selection goal Example: Milk-Fat-Protein Dollars MFP$ = 0.01(PTA_Milk) + 1.15(PTA_Fat) + 2.55(PTA_Protein)
16	Why evaluations go wrong Important factors ignored Litter size Milking Frequency Preferential treatment Unlucky Current data not representative of future data Traits with low heritability require large numbers to be accurate Recording errors Wrong daughters assigned to a sire
17	Factors affecting value of data Completeness of ID and parentage reporting Years herd has collected data Size of herd Frequency of testing and component determination
18	Factors affecting evaluation accuracy Number of daughters Number of lactation records Completeness of pedigree data Numbers of females kidding in same herd-year-seasons Numbers of males with daughter records in same herd-year-seasons
19	How accurate are evaluations? Reliability measures the amount of information contributing to an evaluation Increases at a decreasing rate as daughters are added Also affected by: Number of contemporaries Reliability of parents’ evaluations Heritability of the trait
20	What do the numbers mean? Evaluations are predictions The true value is unknown The predictions rank animals relative to one another using a defined base The base is the zero- or center-point for evaluations For example: the performance of animals born in a given year
21	Expressing evaluations Estimated Breeding value (EBV) Animal’s own genetic value Predicted Transmitting ability (PTA) ½ EBV Expected contribution to progeny
22	Factors in genetic improvement Heritability is the portion of total variation due to genetics Milk: 25% Type: 19% (r. udder arch) — 52% (stature) Rate of genetic improvement is determined by: Generation interval Selection intensity Heritability
23	Increasing genetic improvement Use artificial insemination (AI) to use better males in more herds Identify promising young males for progeny testing (PT) Use in a representative group of breedings and observe the actual success of progeny Focus on larger herds to improve accuracy
24	Dairy cattle improvement program Pre-select only promising bulls for PT Select only the best of the PT bulls for widespread use Only about 1 in 10 PT bulls enter active service Remove bulls from active service as better new bulls become available Bulls remain active only a few years
25	Alternative to waiting for PT Use young males for most breedings Replace males quickly Bank semen of young males Use frozen semen from superior proven males as sires of next generation of young males
26	Central vs. on-farm testing Availability of: Central Test Stations Effective genetic evaluation system Traits analyzed support selection goals Active participation of many breeders in the centralized data repository
27	Centralized performance test Determine genetic differences of individuals from different herds Does NOT compare herds or breeders Optimal environment Allows for ADG and feed conversion testing Ultrasound testing of final meat products Marketing venue Typically only males evaluated Phenotype compared
28	On-farm testing Comparisons Within herd Across herd through evaluations Data collection for many traits Low cost Whole herd test Records and genetic evaluation of all animals Genotype compared
29	Available evaluations AIPL Dairy goat Milk, fat, and protein yields 14 conformation traits http://aipl.arsusda.gov Boer Goat Improvement Network http://www.abga.org National Sheep Improvement Program http://www.nsip.org Ram testing stations
30	Pennsylvania meat goat and ram performance tests Livestock Evaluation Center (LEC) in Centre County Purebred males born Sept — Feb Starts in April 84 days for rams 70 days for goats ADG and US testing Results combined in an index
31	AIPL dairy goat evaluations Yield evaluations in July Type evaluations in December Evaluations provided to ADGA, DRPC, and publicly via the internet Web services at: http://aipl.arsusda.gov/query/public/ tdb.shtml#GoatsTBL
32	AIPL web services Queries provide display of: Pedigree information Yield records Herd test characteristics Genetic evaluations Does and bucks Yield and type Access information using: ID number Animal name Herd code
33	Evaluations in other countries Australia: LambPlan http://www.mla.com.au/lambplan Canada: Goats http://www.aps.uoguelph.ca/~gking/Ag_2350/ goat.htm http://www.goats.ca Israel: Dairy Sheep and Goats http://www.sheep-goats.org.il/about.htm
34	Sequencing the genome Single Nucleotide Polymorphisms (SNP) enable identification of the source for segments of chromosomes Parentage verification DNA sequences must match those of a parent Known sequences can suggest unknown parent ID EBV calculated for chromosome segments Sum the value of segments to approximate evaluation Accuracy approaches progeny test
35	Wrap up Genetic principles apply across species Selection is the method for genetic improvement Genetic evaluations improve selection accuracy Accurate evaluations also require adequate data and an appropriate model Evaluations are based on comparisons Differences for non-genetic reasons must be removed DNA technology is of great interest Still requires reliable evaluations