Unit 4 Quantitative Genetics Concepts: Heritability, Repeatability and Correlation

Traits:

• Quantitative traits – measurable, continuous, polygenic, environmentally affected
• Qualitative traits – fall into distinct categories, discontinuous traits, can’t be measured, only graded.

From now on, we will be talking only about quantitative traits.

• Economic traits – characters of economic importance (related to the economic value/productivity/profitability of the animal production system)
  • Production Traits – milk yield, fat percentage, egg production traits
  • Reproduction Traits – Age at first calving, inter calving interval, conception rate etc.
  • Growth Traits – Birth weight, FCR, carcass traits etc.
  • Since economic traits are majorly controlled by quantitative genes, there is a great deal of variation observed in their expression (i.e. phenotype)
• The expression of a particular phenotype (P) for a particular trait is affected by both the genotype (G) of the individual and the environment (E) in which the individual is placed. Both genotype and environment contribute their effects separately, but they also interact with each other. This interaction also affects the expression of the trait.
• Explanation for G*E interaction:
  • For example, consider the growth of an apple tree. If you have two apple trees with the same genetic constitution (i.e. the same genotype), and if you plant one of them in Kashmir and one in Karnataka, which one do you think will grow better and give tasty apples? Answer is: the one in Kashmir. This is because the genes of that apple are better suited for that environment than the hot conditions of Karnataka. That means, production will be better in one environment and lesser in the other, even though the genotype is the same.

∴We can say that,

P = G + E + (G*E)

• The ‘G’ can be further divided into additive genetic effect (A) and dominance effect (D).
  • Additive genetic effect (A) – In polygenic inheritance of quantitative traits, each of the genes has a small effect and the phenotype observed is the overall cumulative effect of all these genes, i.e., their additive effect. This is the portion that will be transmitted to the offspring without change, i.e., determines the breeding value of the parent.
  • Dominance effect (D) – This is the effect of the interaction between the two alleles for a gene, where the dominant allele influences the character.
  • Interaction effect (I) – The influence of the inter-genic interaction (viz., epistasis, complementary genes, etc.) on the expression of the phenotype.
  • Dominance and interaction effects exist only in an individual and cannot be passed on to the next generation, as the combination of alleles and genes breaks during gamete formation.

Due to all these effects (G, E, and G*E), there is a range of various phenotypes observed for a trait. This is known as its ‘variation’ and to study it, we calculate the ‘variance’

Variance

• Components of Variance:
  • Quantitative Variance of a population is of three types:
    • Phenotypic Variance (V_P)
    • Genotypic Variance (V_G)
    • Environmental Variance (V_E)
  • Genetic Variance is further divided into three components
    • Additive genetic Variance (V_A)
    • Dominance Variance (V_D)
    • Epistatic Variance (V_I)

V_P = V_G + V_E

V_P = (V_A+V_D+ V_I) + (V_Ep + V_Et)

Breeding Value

• It is, the average effect of the parent’s genes that determine the mean genotypic value of the progeny.
  • The value of an individual is judged by the mean value of its progeny, which is called the breeding value of the individual.
  • Thus, Breeding value of an individual is the mean phenotypic value of its progeny.
  • If an individual is mated to a number of individuals taken at random from the population, then its breeding value is twice the mean deviation of the progeny from the population mean.
  • Each parent contributes half of the genes to their offspring, so the mean deviation of the progeny reflects only half of the parent’s genetic potential. To determine the true breeding value of a parent, we double the mean deviation of their progeny. Since the parent mates randomly with others in the population, the average genetic contribution from the other mates is zero relative to the population mean. Therefore, the deviation has to be doubled because the parent in question provides only half of the genes of the progeny, the other half is coming at random from the population.
  • In terms of average effect of gene, the breeding value of an individual is equal to sum of the average effect of genes it carries, the summation being made over the pair of alleles at each locus and over all loci.

Heritability

Definition and Concept

• Heritability measures the degree to which offspring resemble their parents in trait performance.
• It represents the strength of the relationship between phenotypic values and breeding values for a trait in a population.
• Denoted by the symbol h².
• It is necessary for determining the breeding value of the individuals

Types of Heritability

• Broad sense heritability: h² = V_G / V_P
  • Represents the influence of the entire genotype on the phenotype.
  • Not very useful for selection: the whole genotype (including dominance & interaction effect) can’t be transmitted to the progeny, only the additive genetic effect is transmitted.
• Narrow sense heritability: h² = V_A / V_P
  • More useful for breeding purposes.
  • Represents the additive genetic portion of phenotypic variance.

Characteristics of Heritability

• Estimable – Ranges from 0 to 1 or 0% to 100%
• Always positive in value
• Dimensionless – Pure ratio (ratio of variances – has no unit)
• Population parameter/measure

Heritability Categories

• Low heritability: h² < 0.2 (e.g., fertility, survivability traits)
• Moderate heritability: h² = 0.2-0.4 (e.g., production traits like milk production, growth rate)
• High heritability: h² > 0.4 (e.g., carcass traits, structural size, mature body weight)

Practical Implications

1. Selection
   • High heritability: Individual selection may yield better progeny.
   • Low heritability: Individual performance is not a reliable indicator of superior genotype.
2. Prediction
   • Low h²: Difficult to predict if the animal will pass on the trait.
   • High h²: Highly likely that the genetic potential of progeny will be similar to the parent.
3. Management
   • High heritability: Phenotype largely influenced by genetics, environment has less impact.
   • Low heritability: Environment plays a significant role, management can improve productivity.
4. Breeding Strategies
   • High narrow sense heritability: Characters governed by additive gene action, selection for improvement is rewarding.
   • Low narrow sense heritability: Non-additive gene action, heterosis breeding is beneficial.

Factors Affecting Heritability Estimates

• Sample size: Large samples necessary for accurate estimates.
• Sampling methods: Random sampling provides true estimates of genetic variance.
• Experimental design: Increasing plot size and replications can reduce experimental error.

Interesting Note

• Heritability for the number of legs in dogs is 0, despite being genetically determined, due to lack of variation in the trait within the population.

This is because heritability exists for the variation observed in traits, not for the traits themselves, even though all traits are genetically determined.

Repeatability

• Strength of relationship (Correlation) between repeated measurements for a trait in the population
  1. It can only be determined for traits which have more than one measurement in the lifetime of an animal, e.g. milk yield, egg production, etc.
  2. It can not be determined for traits which occur only once, viz, age at first calving, age at sexual maturity, carcass traits etc.
• It is denoted by ‘r’ and expressed as:

• It ranges from -1 to +1
  1. Very rarely can it be -1
  2. R=0 means there is no repetition of the performances for a particular trait
  3. R=1 (or close to 1), there is a high chance that the performance for a trait will be repeated throughout the life of the individual
  4. Ranges of repeatability:
     • r < 0.2 – low repeatability
     • 0.2 < r < 0.4 – moderate repeatability
     • r > 0.4 – high repeatability

Practical Implications:

• When repeatability is high, the first record is a good indicator of its subsequent records
• Repeatability is the relationship between single performance record and producing abilities
• When repeatability is high, differences in performance of individuals of a population are mainly due to differences in their producing abilities, not due to environment, and vice versa.  
• Repeatability is used to take culling decisions:
• If a trait is highly repeatable in a population, the first production records can be helpful in deciding which animals to cull.
• Repeatability and Prediction:
• By calculation of MPPA, repeatability can help in predicting the performance of individuals in a population, therefore it is important for selection decisions as well.

Importance:

• Sets upper limit for genetic determination and heritability
• Determines gain from repeated measurements
• Allows prediction of future performance
• It is used in the calculation of Most Probable Producing Ability (MPPA)

Correlation

• Strength of relationship between two variables (either traits or values for the same trait)
• Three types:
  • Genetic correlation – Strength of relationship between breeding values for two traits
  • Phenotypic correlation – Strength of relationship between performances for two traits
  • Environmental correlation – Strength of relationship between effect of environment on two traits
• It ranges from -1 to +1, with 0 indicating no genetic relationship between traits.

Genetic Correlation:

• Correlation can be positive and negative:
  • A positive genetic correlation means selection for one trait will lead to improvement in the other trait as well., e.g. milk yield and mastitis incidence
  • A negative genetic correlation means selection for one trait will lead to a decline in the other trait., e.g. milk yield and milk fat percentage
• Genetic correlations are important for developing breeding programs and selection indices. They help predict correlated responses to selection.
• High genetic correlations allow indirect selection for difficult-to-measure traits.
• Common genetic correlations in livestock:
  • Positive correlation between milk yield and mastitis incidence in dairy cattle
  • Positive correlation between body weight and egg production in poultry
  • Negative correlation between growth rate and reproductive performance in pigs
• Genetic correlations can change over time due to selection.
• Environmental factors can mask true genetic correlations.
• Accurate estimation requires large datasets and pedigree information.
• Understanding genetic correlations helps avoid unintended consequences of selection.
• Genetic correlations are population-specific and may vary between breeds.
• Multi-trait selection indices account for genetic correlations between traits.