breeding test 2

heritability

heritability

population estimate, relationship of difference (variance) in animal performance due to inheritance (genes and cost), vary from population to population and environment to environment

if a trait is highly heritable

the trait will always be heritable and only the numbers will change

broad sense heritability

measure of strength of relationship between performance (P) and genotypic values (G) for a trait in a population, not commonly used in livestock

what is broad sense heritability used in

only used in identical individuals

broad sense heritability equation

H² = r²_P,G

narrow sense heritability

measure of strength of relationship between performance and BV for a trait in a population, used in animal production since doesn’t have to be identical

narrow sense heritability equation

h²=r²_P,BV

compare and contrast broad vs narrow

broad: includes BV and GCV, complete genotypic values, more G in estimate

narrow: only BV, GCV not inherited but genes are inherited, less G in estimate

narrow sense

positive, ranges from 0 to 1, more environment changes the less heritable the trait

what are the ranges for high and low in narrow sense

<0.2 is low

0.2-0.4 is moderate

>0.4 is high

low h²

parental performance is not a good indicator of offspring performance

as narrow sense heritability increases and or decreases what happens with the environment

increase: less environment

decrease: more environment

fitness

fertility and survivability, low h², if get bred will be because of BV, have to make equal to environment or minimize

production

milk production, growth rate, moderate h²

terminal

measured later in life, carcass and skeletal and mature weights, high h²

why is heritability important

want to choose for survivability and to achieve the end goal have to have fertility and survivability to get the rest

alternate narrow sense definition

ratio of additive genetic variance (Va) to phenotypic variance (Vp), the more Vp the smaller the h²

alternate narrow sense equation

h² = Va/Vp= Va/(Va+Vd+Vi+Vep+Vet)

Va

genotypic variance

high environmental effect

low h²

high h²

parental performance is a good indicator of the offspring performance

low environmental effect

high h²

in phenotypic selection

low h²: performance reveals little about the BV, genetic change is slow

high h²: performance is a good indicator of BV, genetic change is fast

repeatability

measure of strength of the relationship between repeated records for a trait in a population, population estimate, vary from populating to population and environment to environment, r = r_p1,r_p2

charachteristics of repeatability

mostly positive, -1 to 1, high: 1st record is good indicator, low: 1st record is bad indicator, <.2 low and .2-.4 moderate and >.4 high

alternate definition of repeatiability

r= (Va+Vd+Vi+Vep)/(Va+Vd+Vi+Vep+Vet)

ways to improve h² and r

environment uniformity, accurate measurement, contemporary groups

environment uniformity

environment is the same for different animals, not making environment better just smaller

accurate measurement

the more accurate the better the estimate, adjust for known environmental effects: age of dam and animal, sex of animal, parity, milkining per day

contemporary groups

group of animals that have experienced a similiar environment with respect to the expression of a trait, same location age and management, compare as deviation from group mean, use when all animals cant be managed similary and create stronger relationship between PA and P, large groups are better, ensure better grouping, P-Pcg

contemporary group equations

P-Pcg=BV + GCV + Ep +Et

P-Pcg=PA + GCV + Ep + Et

trait ratios

expression of relative performance, ratio of an individual performance to the average performance of all individuals in the contemporary group, below 100 is below average, above 100 is above average

trait ratio equation

TR= (Pi/Pcg)*100

what are the four factors that affect genetic change

accuracy of selection, selection intensity, genetic variation, generation interval

rate of genetic change

rate of change in the mean BV of a population caused by selection

accuaracy of selection

accuracy of breeding value predictions, the more accurate we predict the better chance of selected the best animals, the more information the better: performance, pedigree, progeny, r_BV,BV(hat)

selection intensity

measures how “choosy” breeders are in selection individuals, high intensity: selecting very best animals, no intensity: selecting animals at random, selection criteria: phenotypic values and predictors of breeding values, in standard deviation units, more pressure is further away from the mean

selection criterion (sc)

EPDs, phenotyic values, population average

selection intensity equation

i= (sc_s-sc)/stdev_sc

selection differential

(sc_s-sc), numerator of selection intensity

when accuracy and intensity are low

rate of genetic change is slow

when accuracy and intensity are high

rate of genetic change is fast

genetic variation

variabiity if BV with in a population for a trait, amounts of genetic change variation, more: best animals can be above average, less: best animals close to average, stdev of BV

generation interval

amount of time required to replace one generation with the next, in closed populations: defined as the average age of parents when their offspring are born

key equation for genetic change

change in BV/t= (r_BV,BV(hat)*i*stdevBV)/L

key equation for genetic change based on phenotypic selection

change in BV/t= (h²*i*stdevBV)/L

equation for male and female genetic change

phenotypic: (h²(im+if)stdevBV)/(Lm+Lf)

genotypic: ((r_bvm,bvm(hat)*im+r_bvf,bvf(hat)*if)*stdevBV)/(Lm+Lf)

accuracy vs L

decrease in L= decrease in acc, sires used for shorter time and decrease records of projeny

accuracy vs i

increase in acc=increase in i, increase in i= decrease in acc, amount of young males tested~economics, test fewer males so less to select (increase acc), test more males so more to select (increase i)

i vs L

increase i= increase L, replacement rate= rate at which newly selected individuals replace existing parents in population, choose relatively few high selection intensity and replacement rate low, low replacement rate ~ animals stay in population longer, different for males and females, females: increase in i is increase in L, males: less severe, fewer need and fast replacement

selection risk

risk that the true breeding values of replacements will be significantly poorer than expected

males and risk

increase i = increase risk, few sires, multiple projeny , multiple risk

females and risk

not an issue, less projeny per female, risk=0

how much genetic change is attributed to sire

up to 90%

selection index

method of genetic prediction, linear combo of phenotypic information and weighting factors that are used for genetic prediction when performance data come from contemporary groups, I= b1x1+b2x2 , x= single item of phenotypic information, b= weighting factor

what information is used in calculating genetic prediction for an individual

individuals own performance, performance records of ancestors and or relatives, performance records of descendent, genomic information

when should I be used

when performance data comes from contemporary groups thought to be genetically similar

b

regression of true values on the evidence, measure the expected change in true value per unit of change in evidence

regression for amount of information

genetic predictions being more conservative if less info and less conservative if there is more information, drived by heritability and repeatability and record number

genetic parameters

heritability and repeatability

common environmental effects

increase in similarity of performance of family members caused by their sharing a common environment, within families, half sib= common paternal, full sib= common maternal environment

factors affecting accuracy of prediction

h² - increase heritability=increase in accuracy

pedigree relationship- closer relationship of individual and animals providing performance records

number of records- larger number the higher the accuracy

pedigree estimates

solely on pedigree, accuracy not high, no mendelian genetics

BLUP

best linear unbiased prediction, types of statistical models-genetic and environmental effects, has to do with which animals receive genetic predictions: sire, maternal grandsires, animal models—all animals

what does BLUP account for

differences in the mean breeding values of contemporary groups, genetic trends, use information from all individuals in population, non random mating, culling due to performance

differences in mean breeding values of contemporary groups

genetically superior vs inferior, solve for group effects instead of deviations from the mean, environmental effects~rely on relatives to tell if environment is good or bad, compare records from totally different environments

genetic trends

undergone effective selection for sometime, average BV has moved, compare new to old

use information from all individuals in population

adds to predictions accuracy, multiple trait models ~ predict values for more than 1 trait at a time and lacking in 1 trait explained in another

non random mating

adjust animal predictions for their merit of their mates, not possible to make individual better by assigning superior mates

culling due to performance

indicates genetic relationship and genetic correlation between traits

direct component

effect of individuals genes by the performance, growth potential of sire projeny at ages and performance due to genes from sire

maternal component

environment provided by dam, affected by genes of dam, WW comprised of milk production and mothering ability and plus direct growth, predicts milking and mothering ability of the sires daughters

BLUE

best linear unbiased estimate, fixed effects, sex effect, management, environment,

total maternal

combination of BV for both maternal and direct components of a trait

before blup

central tests- compare between different herds or flocks for growth and feed efficiency, animal performance only, location preference

what livestock industry was the first to use the BLUP method

dairy in the 1980s

accuracy measures

measure of relationship between trait values and their prediction sometimes referred to as correlation between real and prediction, 0-1

why sire summaries and not dam summaries

sire selection- drives genetic change, more accessible than dams, many offspring, AI- easier, manage selection risk - accuracy, marketing tools

intrepreting genetic information

doesn’t predict an exact performance, predicts a difference in performance

meaning of zero for an EPD

represents the base breed average for a trait in a specific year aka base year, depends on stats model used and data characterisitcs, adjust EPDs so that base represents average EPD of all animals born in specific year

problems with genetic evaluation

faulty data, pedigrees— parental misidentification, less emphasis as projeny increase; performance records— falsify records and incomplete reporting (common); adjustments: known environmental effects; contemporary groups: members in wrong group (treatment); relationships: related in different groups (more); GxE interaction: significant means there is a problem (low h² means susceptible)