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Hello. The topic of this lecture is genetic 
and environmental variability in quantitative

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traits of poultry. The lecture is part 
of Module 4, Precision Livestock Farming.

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The creation of this presentation 
was supported by the ERASMUS+ KA2

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grant as part of the ISAGREED project, Innovation 
of content and structure of study programs in the

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field of management of animal genetic 
and food resources using digitization.

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The difference between qualitative and 
quantitative traits lies in the number

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of genes that determine them and the influence of 
the environment on the development of the trait.

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Quantitative traits are influenced by a large 
number of genes with small effects (polygenes),

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often additive, and the expression of the 
trait is also influenced by the environment.

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The phenotypic value of a trait in an individual 
is therefore equal to the effect of genotypes and

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environmental factors. The result of this 
multifactorial action is the occurrence of

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continuous, gradual phenotypic variability 
in the population. All productive traits are

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of this type, including some polygenic 
diseases such as mastitis in cattle.

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The phenotypic value of an individual for 
a specific trait is therefore equal to the

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sum of the effects of genotype 
and environmental influence,

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including random environments that 
we are not able to detect, analyze,

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and consider. This applies, for example, 
to body weight, abdominal fat, egg size,

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egg weight, shell thickness, fertility, 
hatchability, feed consumption, and others.

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The relationship between genotype and phenotype, 
or the path from genotype to phenotype,

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is very complicated. In particular, the cascade 
of genes influencing various metabolic pathways

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involved in trait development, gene expression, 
and interactions at different levels are

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further influenced by developmental factors and 
effects of external and internal environments.

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This also affects the nature of genetic 
variability. Genetic variability is determined

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by the proportions of different genotypes in the 
population. In the case of qualitative traits,

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genetic variability can be directly 
identified based on phenotypic variability

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(for Mendelian traits). For quantitative traits, 
statistical methods are needed to quantify genetic

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variability and obtain estimates of population 
parameters (such as mean, variance, etc.)

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due to the continuous variability 
caused by a large number of genes

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with additive effects and the 
influence of environmental differences.

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Genetic variability is caused by differences 
in DNA sequences among individuals.

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Genetic differences can be caused by mutations, 
recombination, or other genetic processes.

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Genetic variability is a source of 
evolution and allows populations to adapt

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to a changing environment.

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Genetic variability is important in animal 
breeding programs because it provides a source

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for selection. By selecting animals with 
desired traits and breeding them together,

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breeders can increase the frequency of desirable 
traits in the population. This can lead to

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improvements in productivity, health, and other 
traits that are important for animal production.

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Environmental variability is divided into 
systematic and nonsystematic effects.

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Nonsystematic effects vary in direction and 
magnitude for each individual in an unknown

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manner, they cannot be corrected, they introduce 
"noise" (inaccuracy) into genetic estimates

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and predictions and increase residual error.

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Systematic effects act on a group of 
animals in the same direction and magnitude,

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they can be eliminated computationally or through 
standardization, and they are divided into:

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internal effects (age, litter frequency, 
litter order, lactation order, sex,

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etc.) and external effects (management, 
region, barn, year, season, etc.).

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Environmental variability. 
Environmental variability

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refers to differences in environmental conditions 
that can affect the performance of animals.

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Environmental variability can have a significant 
impact on quantitative traits in poultry,

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for example, temperature and humidity can 
affect growth rate and feed efficiency,

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and other environmental factors 
such as lighting and stocking

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density can also affect poultry performance.
Farmers can manage environmental variability

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in their flocks through appropriate 
housing and management practices.

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For example, farmers can use ventilation systems 
to regulate temperature and humidity, provide

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appropriate lighting conditions, and control 
stocking density to reduce stress in birds.

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Most production traits in poultry are of a 
quantitative nature (polygenic inheritance,

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additive gene action, environmental 
influence, continuous phenotypic variability).

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Estimating genetic and environmental variability 
can be used to calculate other genetic parameters.

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Genetic parameters are statistical 
parameters such as heritability,

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repeatability, genetic correlation, and phenotypic 
correlation. Estimating genetic parameters is a

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statistical method used to estimate genetic 
parameters of traits in animals. Estimating

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genetic parameters is an important issue in animal 
breeding. Estimating additive genetic and possible

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non-additive genetic variations contributes to 
a better understanding of genetic mechanisms.

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Genetic parameters play a significant role in 
designing breeding programs and are necessary

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for evaluating economically important traits.
Central genetic parameter - heritability.

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Heritability is the proportion of phenotypic 
variability that is caused by genetic variability.

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It is a measure of how much of the trait's 
variability is due to genetic factors and

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is essentially an estimate of the genetic 
structure (variability) in the population.

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Heritability ranges from 0 to 1, with 0 meaning 
all variability is due to environmental factors

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and 1 meaning all variability is due to genetic 
factors (both values are unrealistic extremes!).

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The data source is measured trait 
values of individuals in the population.

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Heritability is estimated by comparing 
the phenotypic variability of a trait

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with the genetic variability of that trait, 
which is done by comparing the phenotypic

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variability in the population with the 
expected phenotypic variability based on

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the genetic and kinship relationships 
among individuals in the population.

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In the table, we can see the typical range 
of heritability values estimated in different

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chicken populations. Higher values are associated 
with body weight or its parts, while lower values

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are associated with reproductive traits.
Currently, molecular data obtained from

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whole-genome sequencing also contribute 
to the estimation of genetic parameters.

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QTL mapping methods are actively used in chickens 
to identify chromosomal regions that contribute

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to the variability of traits related to growth, 
disease resistance, egg production, behavior,

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and metabolic parameters. However, for successful 
utilization of this information in breeding

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programs, mapping with higher resolution and 
better knowledge of the genetic architecture,

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which is the basis of QTL, is necessary.
There is a QTL database for animals called QTLdb.

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As of April 25, 2023, Chicken QTLdb has 
published 18,411 QTL/eQTL/associations.

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These data were obtained from 386 publications and 
represent 372 basic traits, 115 trait variants,

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and describe 39 eQTL genes.
An example of QTL region identification and

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candidate gene identification using whole-genome 
SNP for growth and feed efficiency in broilers

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is shown in this image. Estimation of genetic 
parameters and identification of QTL for feed

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efficiency in purebred broilers was conducted 
using a whole-genome association study.

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Broilers were genotyped using a 55 K chip 
SNP; Genomic heritability estimates for seven

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growth traits and feed efficiency traits ranged 
from 0.12 to 0.26; The region on chromosome 16

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(2.34-2.66 Mb) was associated with BW28 and 
BW42 traits, and the most significant SNP

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in this region accounted for 7.6% of genetic 
variance for BW28; Chromosome 1 (91.27-92.43

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Mb) was associated with feed intake, and the genes 
NSUN3 and EPHA6 were found in this QTL region;

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The most significant SNP in this region accounted 
for 4.4% of the genetic variance for feed intake.

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The most likely candidate genes for these 
QTL were identified as NSUN3, EPHA6, and AGK.

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These genes are involved in mitochondrial 
function and behavior regulation.

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The results of this case study contribute to the 
identification of candidate genes and variants for

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growth and feed efficiency in chickens.
And thank you for your attention.