Lec 10

• Quantitative Trait

  Also known as metric/continous trait, Measurable, quantifiable, Not forming into classes or categories, Controlled by many genes at many loci – each gene has small effect, Of economic importance to the livestock industry
• Quantitative traits of economic importance

  • Dairy cattle: milk yield, % milk fat, % milk protein, calving interval, no. services per conception
  • Beef cattle: weaning weight, yearling weight, average daily gain (ADG), dressing %, % lean
  • Meat goats: weaning weight, yearling weight, ADG, age at puberty, prolificacy rate, dressing %, % lean
  • Poultry - layers: no. of eggs, egg weight, feed intake
  • Poultry - broilers: feed intake, feed conversion ratio (FCR), weight for age
  • Swine/pigs: weight for age, ADG, FCR, carcass weight, % lean, backfat thickness
• Phenotype (P)

  Genotype (G) + Environment (E)
• G and E effects are independent, no covariance between G and E
• Quantitative or Metric Characters

  Always looking at variation, Breaking down phenotypic value (P) into its components, Asking what proportion of the variation due to genetic and environment
• Variance

  The average squared deviation of the observations from the mean, A measure of the dispersion of a set of data points around their mean value, Measures the variability from an average/mean
• Variance = mean of squared deviation values from the mean
• Mean

  The sum of all measurements divided by the number of measurements
• Variance

  The average squared deviation of the observations from the mean
• In breeding we are always looking for best animals to become parents, animals with best transmitting ability/genotypic value/breeding value
• Breeding Value

  Value of all genes of a parent which are passed on to the next generation (transmittable part)
• Phenotype (P)

  Genotype (G) + Environment (E), where G = A + D + I + E, A = Additive genetic effects or breeding value (BV), D = Dominance/intra locus interaction, I = Epistasis/inter allelic interaction
• Breeding Value (BV)

  Sum of all genes contributed by parents to the offspring of the next generation (transmittable part)
• Mendelian principles of segregation and independent assortment apply to Breeding Value
• Additive/Co-dominance

  AA = +4, Aa = +2, aa = +0
• Permanent environmental effect (Ep)

  Differences common to members of the same group
• Temporary environmental effect (Et)

  Differences common to all individuals at any one time
• Permanent environmental effects (Ep)

  Severe illness, Failure to have a calf one year, Injury to udder, Physical injury
• Temporary environmental effect (Et)

  Heat wave, Slight illness, Off feed
• Phenotypic variance (VP)

  VP = VG + VE, where VG = Genetic variance, VE = Environmental variance
• Genetic variance (VG)

  VG = VA + VD + VI, where VA = Additive variance, VD = Dominance variance, VI = Epistatic variance
• Environmental variance (VE)

  VE = VEp + VEt, where VEp = Permanent environmental variance, VEt = Temporary environmental variance
• Quantitative traits have a normal distribution of phenotypes with underlying normal distributions of Additive effects, Dominance effects, Epistatic effects, Permanent environmental effects, Temporary environmental effects
• If we have identified a best animal, the animal may be best because it has: Best additive genotype, Best dominance genotype, Best epistatic genotype, Best permanent environment, Best temporary environment, and/or best combination of these effects
• Population

  All the members of a group
• Parameter

  Value that describes a population
• Statistic

  Estimate of a parameter derived from a sample
• Sample

  A subset of a population
• Normal Distribution

  Bell curve or bell shaped distribution, One of many distributions, Many observations close to the middle, Fewer observations far from the middle, Most traits in livestock are normally distributed
• Empirical Rule for Normal Distribution

  68% of the data will fall within 1 standard deviation of the mean, 95% of the data will fall within 1.96 standard deviations of the mean, Almost all (99.7%) of the data will fall within 3 standard deviations of the mean
• Within the interval μ ± 1.96 δ there are theoretically 95% of the observations
• Mature Weight (kg) of Cows

  • Skewed set of data points which is not normally distributed
  • Normally distributed set of data points
• Calculating Mean and Standard Deviation

  1. Mean = Xi / n-1

  2. Variance = (Xi2 - (Xi)2/n)/n-1

  3. Standard deviation = sqrt(variance)
• Mean

  Describes the 'middle value' of a normal distribution or average value for the population
• Standard deviation

  Describes the width of a normal distribution or the mean deviation of values from the mean
• Measures of Variability

  • Range = difference between maximum and minimum
  • Variance = average squared deviation about the mean
  • Standard deviation = square root of variance
  • Coefficient of variation (CV) = δ / μ (CV in percentage, %)
• Coefficient of Variation (CV) = (Standard deviation / Mean) x 100%
• Regression

  Change in Y per one unit change in X, Y = b1X1 + b2X2 + …+ bnXn
• Correlation


  Degree to which two variables vary together, Range of values -1 to +1