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Full Research Article

Uncovering the Underlying Patterns in Different Economic Traits of Layer Chickens using Principal Component Analysis

G
Gitanjali1
https://orcid.org/0009-0001-0302-9808
P
Prem Prakash Dubey1,*
https://orcid.org/0000-0001-8381-7419
S
Saroj Kumar Sahoo1
https://orcid.org/0000-0002-3966-2496
D
Daljeet Kaur2
https://orcid.org/0009-0007-4394-6735
1Department of Animal Genetics and Breeding, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana-141 012, Punjab, India.
2Department of Livestock Production and Management, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana-141 012, Punjab, India.

ABSTRACT

Background: Principal component analysis (PCA) is a multivariate statistical tool to reduce the dimensionality of large datasets while preserving most of the original variability. By transforming a set of correlated variables into a new set of uncorrelated variables known as principal components, it simplifies data interpretation and highlights the underlying structure of complex traits.

Methods: This study aimed to identify principal components (PCs) explaining the economic traits in layer chickens. A total of 1,500 birds were evaluated, comprising 750 birds from each generation and 250 birds from each genetic group- Desi Cross 1, Desi Cross 2 and RIR (Rhode Island Red), considering two generations. Birds of both generations were reared up to 52 weeks of age, during which data were collected in the years 2023 to 2025 on growth, production, reproduction and egg quality traits. A total of twenty three economic traits were considered viz. tri-weekly body weight from hatch to 21 weeks in grams (BW0-BW21; g), body weight at 40 weeks (BW40; g) and 52 weeks (BW52; g), age at sexual maturity (ASM; days), body weight at first egg production (BWFEP; g), weight of first egg (WFE; g), average egg weight at 40 (EW40; g) and 52 (EW52; g) weeks of age and cumulative egg production up to 40 (EP40) and 52 (EP52) weeks. Egg quality traits such as egg weight (EW; g), yolk height (YH; mm), yolk diameter (YD; mm) and yolk index (YI) were also included. The correlation among all the variables was established and further PCA using the factor program of IBM SPSS 25.0 statistical package was used, followed by varimax rotation to achieve simplified and interpretable components.

Result: A total of three PCs were obtained, which explained a total variance of 68.59%. The first three PCs explained 54.366%, 7.699% and 6.525% of the total variance, respectively. PC1 exhibited strong loadings for BW15 and BW18, PC2 for YD and PC3 for YH across distinct genetic groups. The Kaiser-Meyer-Olkin (KMO) value was 0.931, which indicated the results of PCA were accurate.  Among all the variables, higher communalities were observed for BW15, BW18, EP40, EW52, YH, YD and YI, indicates components models (PC1, PC2 and PC3) well explain these variables. From this study, it was concluded that the identified components can be used for selecting the parent birds to execute the next generation. In addition to that, it will effectively reduce the cost and time required for genetic evaluation of multiple traits.

INTRODUCTION

Economic traits in layer chickens such as body weight at various ages, age at sexual maturity, egg production rate and egg weight, are critical to productivity and profitability in poultry production. These traits are often correlated; for example, early body weight may influence later egg production or the timing of sexual maturity. Multicollinearity among such traits can complicate statistical analyses and make it difficult to identify which traits are most informative for selection. PCA is a multivariate statistical technique developed to address such challenges. It is used to reduce the dimensionality of large datasets while preserving most of the original variability (Phookan et al., 2026). By transforming a set of correlated variables into a new set of uncorrelated variables known as principal components, PCA simplifies data interpretation and highlights the underlying structure of complex traits. These components capture most of the variation present in the original traits, thus facilitating data reduction, interpretation and trait selection (Sarma et al., 2025). It is used to identify the minimum number of combined variables that explain the maximum proportion of variation present among the studied variables (Mishra et al., 2022). Applying PCA to economic traits in layer chickens enables the identification of key factors influencing productivity and profitability, assists in trait selection and supports breeding decisions. By reducing complexity, PCA facilitates a better understanding of trait relationships, aids in the development of selection indices and contributes to the optimization of breeding programs.
       
PCA was used in body measurements for determining the morphological structures of Indigenous Chicken. A total of 360 non-descript hens were used and a total variance of 63.9% was extracted from two Principal Components (Vilakazi et al., 2020). PCA was used to predict body weight from morphometric measurements (body length, chest circumference, shank length, shank circumference) under smallholder management. The PCA-derived models explained a large proportion of the variance in body weight and were more accurate than traditional multiple regression in some cases (Negash, 2021). PCA was used to analyze morphometric and shape traits of the Haringhata Black chicken breed; they found that only a subset of traits had high loadings on the first principal component, which explained a major share of variability in size and shape. However, it is also important to consider sampling adequacy (e.g., via Kaiser-Meyer-Olkin or Bartlett’s test), communality of the variables and correct interpretation of component loadings, especially when traits have low communalities or when different components load differently in different populations (Saikhom et al., 2018). PCA was used to study egg quality traits of Indigenous free-range chickens in Kabwe, Zambia and a total variance of 79% was collectively accounted for by three principal Components (Liswaniso et al., 2020).
       
The relationship among morphological traits and body weight (BW) of the Ross 308 chicken breed was explored. Correlation findings in females ranged from -0.16 to 0.51, while they ranged from -0.07 to 0.56 in males. The specified principal components extracted contributed excellently to describe overall structuring and regression results revealed that use of principal components was more appropriate than the use of correlated morphological traits in predicting BW (Bila and Tyasi, 2022). PCA was applied to 18 biometric traits of Tripura Desi cattle to evaluate body conformation. Six principal components explained 75.67% of the total variation, demonstrating that PCA can reduce the number of measurements required while supporting breeding programmes, breed conservation and characterization  of this non-descript indigenous cattle breed (Majumder et al., 2025).
       
Keeping this in view, the present study aimed to reduce the dimensionality of traits and identifying the key traits to explain maximum variability of data through principal components (PCs) considering different economically important traits in layer chickens.

MATERIALS AND METHODS

Source of data
 
The data comprised two generations from the years 2023 to 2025 involving 1500 layer birds of three genetic groups (Desi cross 1, Desi cross 2 and RIR). The data used in this study belonged to Poultry Research Farm, Directorate of Livestock Farms, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, India.
 
Data collection
 
The following performance traits were included viz. tri-weekly body weight from hatch to 21 weeks in grams (BW0-BW21; g), body weight at 40 weeks (BW40; g) and 52 weeks (BW52; g), age at sexual maturity (ASM; days), body weight at first egg production (BWFEP; g), weight of first egg (WFE; g), average egg weight at 40 (EW40; g) and 52 (EW52; g) weeks of age and cumulative egg production up to 40 (EP40) and 52 (EP52) weeks. Egg quality traits such as: egg weight (EW; g), yolk height (YH; mm), yolk diameter (YD; mm) and yolk index (YI) were also taken, which were measured and analysed using the Nabel Digital Egg Tester (DET-6500, Nabel Co., Ltd., Kyoto, Japan). The DET-6500 is a precision digital egg quality analyser widely used for rapid and accurate evaluation of both external and internal egg quality characteristics. The instrument is equipped with sensitive electronic sensors and a digital display system that enables reliable measurement of parameters with minimal operating error.
 
Statistical analysis
 
Pearson correlation coefficients were computed by employing the IBM SPSS 25.0 statistical software package (2017). The correlation matrix derived from these analyses served as the foundational dataset for conducting PCA. The appropriateness of the data for factor analysis, which included 1500 birds and twenty-three traits, was evaluated through Bartlett’s test of sphericity (1950), in accordance with the recommendation by Maxwell (1959), while sampling adequacy was assessed using the Kaiser-Meyer-Olkin (KMO) measure. A KMO value of 0.60 or higher was considered satisfactory (Eyduran et al., 2010). To facilitate clearer interpretation of the extracted principal components, a variance-maximizing orthogonal (varimax) rotation was applied to the factor loading matrix.
       
According to Everitt et al. (2001), PCA is a statistical technique used to transform a set of correlated variables (X1, X2, …, Xn) into a new set of uncorrelated variables (Y1, Y2, …, Yn), known as principal components. These new components account for progressively smaller proportions of the total variance present in the original variables, specified as:
 
Y1 = a11X1 + a12X2 +…+ a1nXn
Y2 = a21X1 + a22X2 +…+ a2nXn
Yn = an1X1 + an2X2+…+ anXn
 
The principal components Y1, Y2, …, Yn summarize the total variance of the original variables while reducing dimensionality. The PCA was conducted using the factor analysis module in IBM SPSS 25.0 (2017).

RESULTS AND DISCUSSION

Phenotypic correlations
 
The correlation coefficients among the studied traits ranged from -0.86 to 0.99, indicating a wide spectrum of relationships between the parameters. Most of the correlations were positive, suggesting that an increase in one trait was generally associated with a proportional increase in others. However, negative correlations were observed between EP40 and body weights at different ages, ASM, BWFEP, WFE and EW40. The negative correlation with ASM implies that birds reaching sexual maturity earlier tend to demonstrate higher EP40, highlighting the advantage of early onset of lay in maximizing cumulative egg production. However, this early maturity may be associated with lower body weight. The inverse association with BWFEP and WFE suggests that birds with higher production levels may produce their first eggs at a lower body weight and with smaller initial egg size.
       
Phenotypic correlations revealed that ASM was negatively associated with egg production at 40 and 52 weeks (EP40: r = -0.50; EP52: r = -0.51), yolk height (YH: r = -0.07), yolk diameter (YD: r = -0.07) and yolk index (YI: r = 0.02). The moderate negative correlations between ASM and egg production traits (EP40 and EP52) indicate that early-maturing individuals enter the laying phase sooner, thereby extending their productive period within a fixed timeframe. Conversely, delayed sexual maturity reduces the effective laying duration, resulting in lower overall egg output. In contrast, the correlations between ASM and yolk-related traits (YH, YD and YI) are negative but relatively weak in magnitude, suggesting only a minor influence of sexual maturity timing on internal egg quality characteristics. From a physiological perspective, these findings suggest that the mechanisms governing the onset of sexual maturity are more closely linked to reproductive efficiency (i.e., egg number) than to egg quality parameters such as yolk morphology. Among all the examined relationships, WFE showed highly significant (P<0.01) correlations with body weights at 8, 9, 12, 18, 21, 40 and 52 weeks of age, suggesting that early body growth is strongly related to the physiological onset of egg production. Moreover, the correlation between BW20 and BWFEP was also highly significant, underscoring the importance of pre-laying body weight in determining reproductive performance. In the present study, the correlation patterns among growth and reproductive traits were consistent with several previous reports.
       
In addition, YD and YH exhibited significant associations with both EW40 and EP40, further highlighting the interdependence between egg quality and production traits. Overall, the observed correlation pattern emphasizes the intricate relationship between growth performance, onset of sexual maturity and egg production parameters, providing valuable insights for genetic selection and management strategies aimed at improving both growth and reproductive efficiency. Similar findings of correlation coefficients were reported, in which body weight at 8 weeks was positively correlated with 12 week whereas its correlation with first egg weight and age at sexual maturity was relatively low. A negative phenotypic correlation of body weight at 10 to 20 weeks was found with age at sexual maturity. Moreover, body weight at 10 and 14 weeks was highly significant (p<0.001) with egg production at 52 weeks and egg weight at 40 weeks, respectively (Yousefi Zonuz et al., 2013). In contrast, the opposite trend was observed in which ASM exhibited a positive correlation with egg production at both 40 and 52 weeks. Egg production at 40 weeks (EP40) showed a positive phenotypic association with egg production at 52 weeks (EP52) and egg weight at 52 weeks (EW52), but a negative correlation with egg weight at 40 weeks (EW40) and first egg weight (FEW). The relationship between EP40 and EW40 was statistically significant (p<0.05), suggesting that birds that start laying earlier may initially produce smaller eggs but maintain better persistency of lay throughout the production cycle (Liu et al., 2019). Consistent with these observations, body weight at 8 weeks was positively correlated with body weight at 12 and 16 weeks, although the correlation with egg production traits was relatively weak (Dana et al., 2011). Collectively, these findings indicate that early growth traits are reliable indicators of later performance and can be used as indirect selection criteria for improving productivity. However, the moderate to low correlations observed between growth and reproductive traits also suggest that selection programs should balance both aspects to avoid antagonistic effects on age at sexual maturity and egg quality.
 
Principal component analysis
 
PCA was performed on twenty-three economic traits across three distinct genetic groups, namely Desi Cross 1, Desi Cross 2 and RIR. The results of the PCA provided valuable insights into the inter-relationships among growth, reproductive and egg quality traits in layer chickens. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is commonly used to assess whether the dataset is suitable for principal component analysis (PCA) by determining if each variable contains sufficient information for reliable factor extraction. A KMO value greater than 0.6 is generally considered acceptable, whereas values above 0.8 are regarded as highly desirable (Tabachnick and Fidell, 2013). The sampling adequacy, as assessed by the Kaiser-Meyer-Olkin (KMO) statistic, was 0.931 (Table 1), indicating that the dataset was highly suitable for PCA. The overall significance of the correlation matrix was verified using Bartlett’s test of sphericity, which yielded a highly significant chi-square value (χ2 = 59,285.377; P<0.01) as illustrated in Table 1. Eigen values (Fig 1) and the percentage of variance explained by each component are presented in Table 2. Based on Kaiser’s criterion (Eigen value > 1), three principal components were retained, collectively accounting for 68.59% of the total variance. The component plot of the major components in rotated space is illustrated in Fig 2. The interpretation of the extracted components was carried out based on the factor loadings of individual traits on each principal component. Traits exhibiting high positive loadings within a component were considered to have strong associations, thereby representing a common underlying factor influencing performance. The first principal component (PC1) accounted for 54.366% of the total variance. This component was predominantly associated with body weight traits measured at various ages BW4, BW15, BW18, BW20, BW21, BW40, BW52, suggesting that this component captured growth-related attributes and highlighting its importance for economic efficiency. Moreover, since body weight is closely linked to feed efficiency, market value and overall production output, the dominance of this principal component underscores its relevance for economic efficiency. Selecting individuals with higher scores on this component could therefore lead to improved performance, reduced production costs and increased profitability in livestock systems. Among these, BW15 and BW18 exhibited particularly high component loadings, indicating that PC1 primarily represented overall body growth performance. The dominance of PC1, represented by body weight traits across different ages, underscores the significance of growth performance as a major determinant of overall productivity. The second principal component (PC2) explained 7.699% of the total variation and showed strong loadings on YD and YI, thus representing egg quality characteristics.

Fig 1: Scree Plot showing component numbers with Eigenvalues.



Fig 2: Component plot in rotated space showing three different components.



Table 1: KMO and bartlett’s test.



Table 2: Total variance explained by different components in three genetic groups of chicken.


       
The coefficients of the rotated component matrix derived from PCA are presented in Table 3. The third principal component (PC3) contributed 6.525% of the total variance, with high loadings on YH. PC2 and PC3, which were defined by egg quality traits such as yolk diameter, yolk index and yolk height, reflect the influence of egg characteristics on market preference and breeding value. Communalities in PCA represent the proportion of each original variable’s variance explained by the retained PCs and range from 0 to 1. A high communality closer to 1, indicates the component model well explains that variable, while low values suggest the variable is not well explained.

Table 3: Varimax rotated component matrix showing different component loadings and communalities for performance traits in two genetic groups of chicken.


       
Traits exhibiting higher communalities, indicate their strong contribution to the explanation of total variance and their greater effectiveness in describing the economic traits of layer chickens. The communalities of the traits ranged from 0.096 (WFE) to 0.976 (BW18) (Table 3). Collectively, these components provide an integrated understanding of the underlying structure of performance variation among the genetic groups studied. In the present study, varimax rotation was applied to maximize the sum of squared loadings and facilitate the interpretability of extracted components (Fernandez, 2010). Comparable studies have demonstrated similar trends in which the total variation of body weight was determined, with the first three PCs explaining 67.78% and 65.93% of the total variance of the morphostructure of male and female birds, respectively. The KMO value of 0.80, indicates the robustness of the PCA results, which concluded, that the body measurements of PC1 were influenced by the BW of birds (Tamzil and Putra, 2024). A similar trend was depicted in which PC1 had the largest share of body weight.
       
The second component explained 27.072% of total variation, consistent with the pattern of major components contributing significantly to morphometric variation (Vilakazi et al., 2020). A study was conducted where the first component illustrated body size and explained about 36% of total variation (Mishra et al., 2017). Likewise, PC1, explaining 38.3% of total variation, was documented, further confirming the prominence of body weight traits in defining structural variation (Egena et al., 2014). A similar trend was identified with four principal components explaining a total of 69.52% of variation, with the first component alone accounting for 28.68%, emphasizing the consistent contribution of PC1 across diverse chicken populations (Dahiya et al., 2020). Similar studies conducted on internal egg quality and performance traits of native hens revealed that the application of PCA could effectively reduce the computing cost and time required for genetic evaluation of multiple traits (Babajani et al., 2018).
       
A comparable study was also reported in which PC1 loaded mostly on the size and weight of albumen and yolk for 22 egg quality traits of chickens, whereas the five principal components extracted accounted for 79.70% of the total variance (Goto et al., 2015). The comparatively higher proportion of variance explained may be attributed to differences in the genetic background of the chicken population, degree of trait intercorrelation, environmental uniformity and the nature of the traits included in the analysis. Moreover, egg quality traits are often more closely associated physiologically than combined growth and performance traits, resulting in greater clustering of variability within fewer components.

CONCLUSION

In conclusion, the use of these principal components in breeding programs can enhance selection accuracy, minimize redundancy in trait recording and contribute to more efficient use of time and resources, ultimately supporting improved genetic progress in Desi 1, Desi 2 and RIR. The extraction of three principal components from the original twenty-three traits successfully captured a substantial proportion (68.59%) of the total variability, with each component representing key performance indicators such as BW15, BW18, YD, YI and YH.PC1 accounted for 54.366% of total variance and exhibited strong loadings for BW15 and BW18. PC2 explained 7.699% of the total variation and showed strong loadings on YD and YI, thus representing egg quality characteristics. PC3 contributed 6.525% of the total variance, with high loadings on YH. The present findings validate the efficacy of PCA as a robust multivariate statistical approach, enabling the systematic identification and prioritization of key traits (BW15, BW18, EP40, EW52, YH, YD and YI) that substantially influence the productivity and profitability of layer chicken.

ACKNOWLEDGEMENT

The authors thank the funding agency, National Livestock Mission (NLM), Department of Animal Husbandry and Dairying (DAHD), Government of India (GoI), for financial support and Guru Angad Dev Veterinary and Animal Sciences University for providing basic infrastructure to conduct the research work.

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest.

REFERENCES


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    Full Research Article

    Uncovering the Underlying Patterns in Different Economic Traits of Layer Chickens using Principal Component Analysis

    G
    Gitanjali1
    https://orcid.org/0009-0001-0302-9808
    P
    Prem Prakash Dubey1,*
    https://orcid.org/0000-0001-8381-7419
    S
    Saroj Kumar Sahoo1
    https://orcid.org/0000-0002-3966-2496
    D
    Daljeet Kaur2
    https://orcid.org/0009-0007-4394-6735
    1Department of Animal Genetics and Breeding, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana-141 012, Punjab, India.
    2Department of Livestock Production and Management, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana-141 012, Punjab, India.

    ABSTRACT

    Background: Principal component analysis (PCA) is a multivariate statistical tool to reduce the dimensionality of large datasets while preserving most of the original variability. By transforming a set of correlated variables into a new set of uncorrelated variables known as principal components, it simplifies data interpretation and highlights the underlying structure of complex traits.

    Methods: This study aimed to identify principal components (PCs) explaining the economic traits in layer chickens. A total of 1,500 birds were evaluated, comprising 750 birds from each generation and 250 birds from each genetic group- Desi Cross 1, Desi Cross 2 and RIR (Rhode Island Red), considering two generations. Birds of both generations were reared up to 52 weeks of age, during which data were collected in the years 2023 to 2025 on growth, production, reproduction and egg quality traits. A total of twenty three economic traits were considered viz. tri-weekly body weight from hatch to 21 weeks in grams (BW0-BW21; g), body weight at 40 weeks (BW40; g) and 52 weeks (BW52; g), age at sexual maturity (ASM; days), body weight at first egg production (BWFEP; g), weight of first egg (WFE; g), average egg weight at 40 (EW40; g) and 52 (EW52; g) weeks of age and cumulative egg production up to 40 (EP40) and 52 (EP52) weeks. Egg quality traits such as egg weight (EW; g), yolk height (YH; mm), yolk diameter (YD; mm) and yolk index (YI) were also included. The correlation among all the variables was established and further PCA using the factor program of IBM SPSS 25.0 statistical package was used, followed by varimax rotation to achieve simplified and interpretable components.

    Result: A total of three PCs were obtained, which explained a total variance of 68.59%. The first three PCs explained 54.366%, 7.699% and 6.525% of the total variance, respectively. PC1 exhibited strong loadings for BW15 and BW18, PC2 for YD and PC3 for YH across distinct genetic groups. The Kaiser-Meyer-Olkin (KMO) value was 0.931, which indicated the results of PCA were accurate.  Among all the variables, higher communalities were observed for BW15, BW18, EP40, EW52, YH, YD and YI, indicates components models (PC1, PC2 and PC3) well explain these variables. From this study, it was concluded that the identified components can be used for selecting the parent birds to execute the next generation. In addition to that, it will effectively reduce the cost and time required for genetic evaluation of multiple traits.

    INTRODUCTION

    Economic traits in layer chickens such as body weight at various ages, age at sexual maturity, egg production rate and egg weight, are critical to productivity and profitability in poultry production. These traits are often correlated; for example, early body weight may influence later egg production or the timing of sexual maturity. Multicollinearity among such traits can complicate statistical analyses and make it difficult to identify which traits are most informative for selection. PCA is a multivariate statistical technique developed to address such challenges. It is used to reduce the dimensionality of large datasets while preserving most of the original variability (Phookan et al., 2026). By transforming a set of correlated variables into a new set of uncorrelated variables known as principal components, PCA simplifies data interpretation and highlights the underlying structure of complex traits. These components capture most of the variation present in the original traits, thus facilitating data reduction, interpretation and trait selection (Sarma et al., 2025). It is used to identify the minimum number of combined variables that explain the maximum proportion of variation present among the studied variables (Mishra et al., 2022). Applying PCA to economic traits in layer chickens enables the identification of key factors influencing productivity and profitability, assists in trait selection and supports breeding decisions. By reducing complexity, PCA facilitates a better understanding of trait relationships, aids in the development of selection indices and contributes to the optimization of breeding programs.
           
    PCA was used in body measurements for determining the morphological structures of Indigenous Chicken. A total of 360 non-descript hens were used and a total variance of 63.9% was extracted from two Principal Components (Vilakazi et al., 2020). PCA was used to predict body weight from morphometric measurements (body length, chest circumference, shank length, shank circumference) under smallholder management. The PCA-derived models explained a large proportion of the variance in body weight and were more accurate than traditional multiple regression in some cases (Negash, 2021). PCA was used to analyze morphometric and shape traits of the Haringhata Black chicken breed; they found that only a subset of traits had high loadings on the first principal component, which explained a major share of variability in size and shape. However, it is also important to consider sampling adequacy (e.g., via Kaiser-Meyer-Olkin or Bartlett’s test), communality of the variables and correct interpretation of component loadings, especially when traits have low communalities or when different components load differently in different populations (Saikhom et al., 2018). PCA was used to study egg quality traits of Indigenous free-range chickens in Kabwe, Zambia and a total variance of 79% was collectively accounted for by three principal Components (Liswaniso et al., 2020).
           
    The relationship among morphological traits and body weight (BW) of the Ross 308 chicken breed was explored. Correlation findings in females ranged from -0.16 to 0.51, while they ranged from -0.07 to 0.56 in males. The specified principal components extracted contributed excellently to describe overall structuring and regression results revealed that use of principal components was more appropriate than the use of correlated morphological traits in predicting BW (Bila and Tyasi, 2022). PCA was applied to 18 biometric traits of Tripura Desi cattle to evaluate body conformation. Six principal components explained 75.67% of the total variation, demonstrating that PCA can reduce the number of measurements required while supporting breeding programmes, breed conservation and characterization  of this non-descript indigenous cattle breed (Majumder et al., 2025).
           
    Keeping this in view, the present study aimed to reduce the dimensionality of traits and identifying the key traits to explain maximum variability of data through principal components (PCs) considering different economically important traits in layer chickens.

    MATERIALS AND METHODS

    Source of data
     
    The data comprised two generations from the years 2023 to 2025 involving 1500 layer birds of three genetic groups (Desi cross 1, Desi cross 2 and RIR). The data used in this study belonged to Poultry Research Farm, Directorate of Livestock Farms, Guru Angad Dev Veterinary and Animal Sciences University, Ludhiana, India.
     
    Data collection
     
    The following performance traits were included viz. tri-weekly body weight from hatch to 21 weeks in grams (BW0-BW21; g), body weight at 40 weeks (BW40; g) and 52 weeks (BW52; g), age at sexual maturity (ASM; days), body weight at first egg production (BWFEP; g), weight of first egg (WFE; g), average egg weight at 40 (EW40; g) and 52 (EW52; g) weeks of age and cumulative egg production up to 40 (EP40) and 52 (EP52) weeks. Egg quality traits such as: egg weight (EW; g), yolk height (YH; mm), yolk diameter (YD; mm) and yolk index (YI) were also taken, which were measured and analysed using the Nabel Digital Egg Tester (DET-6500, Nabel Co., Ltd., Kyoto, Japan). The DET-6500 is a precision digital egg quality analyser widely used for rapid and accurate evaluation of both external and internal egg quality characteristics. The instrument is equipped with sensitive electronic sensors and a digital display system that enables reliable measurement of parameters with minimal operating error.
     
    Statistical analysis
     
    Pearson correlation coefficients were computed by employing the IBM SPSS 25.0 statistical software package (2017). The correlation matrix derived from these analyses served as the foundational dataset for conducting PCA. The appropriateness of the data for factor analysis, which included 1500 birds and twenty-three traits, was evaluated through Bartlett’s test of sphericity (1950), in accordance with the recommendation by Maxwell (1959), while sampling adequacy was assessed using the Kaiser-Meyer-Olkin (KMO) measure. A KMO value of 0.60 or higher was considered satisfactory (Eyduran et al., 2010). To facilitate clearer interpretation of the extracted principal components, a variance-maximizing orthogonal (varimax) rotation was applied to the factor loading matrix.
           
    According to Everitt et al. (2001), PCA is a statistical technique used to transform a set of correlated variables (X1, X2, …, Xn) into a new set of uncorrelated variables (Y1, Y2, …, Yn), known as principal components. These new components account for progressively smaller proportions of the total variance present in the original variables, specified as:
     
    Y1 = a11X1 + a12X2 +…+ a1nXn
    Y2 = a21X1 + a22X2 +…+ a2nXn
    Yn = an1X1 + an2X2+…+ anXn
     
    The principal components Y1, Y2, …, Yn summarize the total variance of the original variables while reducing dimensionality. The PCA was conducted using the factor analysis module in IBM SPSS 25.0 (2017).

    RESULTS AND DISCUSSION

    Phenotypic correlations
     
    The correlation coefficients among the studied traits ranged from -0.86 to 0.99, indicating a wide spectrum of relationships between the parameters. Most of the correlations were positive, suggesting that an increase in one trait was generally associated with a proportional increase in others. However, negative correlations were observed between EP40 and body weights at different ages, ASM, BWFEP, WFE and EW40. The negative correlation with ASM implies that birds reaching sexual maturity earlier tend to demonstrate higher EP40, highlighting the advantage of early onset of lay in maximizing cumulative egg production. However, this early maturity may be associated with lower body weight. The inverse association with BWFEP and WFE suggests that birds with higher production levels may produce their first eggs at a lower body weight and with smaller initial egg size.
           
    Phenotypic correlations revealed that ASM was negatively associated with egg production at 40 and 52 weeks (EP40: r = -0.50; EP52: r = -0.51), yolk height (YH: r = -0.07), yolk diameter (YD: r = -0.07) and yolk index (YI: r = 0.02). The moderate negative correlations between ASM and egg production traits (EP40 and EP52) indicate that early-maturing individuals enter the laying phase sooner, thereby extending their productive period within a fixed timeframe. Conversely, delayed sexual maturity reduces the effective laying duration, resulting in lower overall egg output. In contrast, the correlations between ASM and yolk-related traits (YH, YD and YI) are negative but relatively weak in magnitude, suggesting only a minor influence of sexual maturity timing on internal egg quality characteristics. From a physiological perspective, these findings suggest that the mechanisms governing the onset of sexual maturity are more closely linked to reproductive efficiency (i.e., egg number) than to egg quality parameters such as yolk morphology. Among all the examined relationships, WFE showed highly significant (P<0.01) correlations with body weights at 8, 9, 12, 18, 21, 40 and 52 weeks of age, suggesting that early body growth is strongly related to the physiological onset of egg production. Moreover, the correlation between BW20 and BWFEP was also highly significant, underscoring the importance of pre-laying body weight in determining reproductive performance. In the present study, the correlation patterns among growth and reproductive traits were consistent with several previous reports.
           
    In addition, YD and YH exhibited significant associations with both EW40 and EP40, further highlighting the interdependence between egg quality and production traits. Overall, the observed correlation pattern emphasizes the intricate relationship between growth performance, onset of sexual maturity and egg production parameters, providing valuable insights for genetic selection and management strategies aimed at improving both growth and reproductive efficiency. Similar findings of correlation coefficients were reported, in which body weight at 8 weeks was positively correlated with 12 week whereas its correlation with first egg weight and age at sexual maturity was relatively low. A negative phenotypic correlation of body weight at 10 to 20 weeks was found with age at sexual maturity. Moreover, body weight at 10 and 14 weeks was highly significant (p<0.001) with egg production at 52 weeks and egg weight at 40 weeks, respectively (Yousefi Zonuz et al., 2013). In contrast, the opposite trend was observed in which ASM exhibited a positive correlation with egg production at both 40 and 52 weeks. Egg production at 40 weeks (EP40) showed a positive phenotypic association with egg production at 52 weeks (EP52) and egg weight at 52 weeks (EW52), but a negative correlation with egg weight at 40 weeks (EW40) and first egg weight (FEW). The relationship between EP40 and EW40 was statistically significant (p<0.05), suggesting that birds that start laying earlier may initially produce smaller eggs but maintain better persistency of lay throughout the production cycle (Liu et al., 2019). Consistent with these observations, body weight at 8 weeks was positively correlated with body weight at 12 and 16 weeks, although the correlation with egg production traits was relatively weak (Dana et al., 2011). Collectively, these findings indicate that early growth traits are reliable indicators of later performance and can be used as indirect selection criteria for improving productivity. However, the moderate to low correlations observed between growth and reproductive traits also suggest that selection programs should balance both aspects to avoid antagonistic effects on age at sexual maturity and egg quality.
     
    Principal component analysis
     
    PCA was performed on twenty-three economic traits across three distinct genetic groups, namely Desi Cross 1, Desi Cross 2 and RIR. The results of the PCA provided valuable insights into the inter-relationships among growth, reproductive and egg quality traits in layer chickens. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is commonly used to assess whether the dataset is suitable for principal component analysis (PCA) by determining if each variable contains sufficient information for reliable factor extraction. A KMO value greater than 0.6 is generally considered acceptable, whereas values above 0.8 are regarded as highly desirable (Tabachnick and Fidell, 2013). The sampling adequacy, as assessed by the Kaiser-Meyer-Olkin (KMO) statistic, was 0.931 (Table 1), indicating that the dataset was highly suitable for PCA. The overall significance of the correlation matrix was verified using Bartlett’s test of sphericity, which yielded a highly significant chi-square value (χ2 = 59,285.377; P<0.01) as illustrated in Table 1. Eigen values (Fig 1) and the percentage of variance explained by each component are presented in Table 2. Based on Kaiser’s criterion (Eigen value > 1), three principal components were retained, collectively accounting for 68.59% of the total variance. The component plot of the major components in rotated space is illustrated in Fig 2. The interpretation of the extracted components was carried out based on the factor loadings of individual traits on each principal component. Traits exhibiting high positive loadings within a component were considered to have strong associations, thereby representing a common underlying factor influencing performance. The first principal component (PC1) accounted for 54.366% of the total variance. This component was predominantly associated with body weight traits measured at various ages BW4, BW15, BW18, BW20, BW21, BW40, BW52, suggesting that this component captured growth-related attributes and highlighting its importance for economic efficiency. Moreover, since body weight is closely linked to feed efficiency, market value and overall production output, the dominance of this principal component underscores its relevance for economic efficiency. Selecting individuals with higher scores on this component could therefore lead to improved performance, reduced production costs and increased profitability in livestock systems. Among these, BW15 and BW18 exhibited particularly high component loadings, indicating that PC1 primarily represented overall body growth performance. The dominance of PC1, represented by body weight traits across different ages, underscores the significance of growth performance as a major determinant of overall productivity. The second principal component (PC2) explained 7.699% of the total variation and showed strong loadings on YD and YI, thus representing egg quality characteristics.

    Fig 1: Scree Plot showing component numbers with Eigenvalues.



    Fig 2: Component plot in rotated space showing three different components.



    Table 1: KMO and bartlett’s test.



    Table 2: Total variance explained by different components in three genetic groups of chicken.


           
    The coefficients of the rotated component matrix derived from PCA are presented in Table 3. The third principal component (PC3) contributed 6.525% of the total variance, with high loadings on YH. PC2 and PC3, which were defined by egg quality traits such as yolk diameter, yolk index and yolk height, reflect the influence of egg characteristics on market preference and breeding value. Communalities in PCA represent the proportion of each original variable’s variance explained by the retained PCs and range from 0 to 1. A high communality closer to 1, indicates the component model well explains that variable, while low values suggest the variable is not well explained.

    Table 3: Varimax rotated component matrix showing different component loadings and communalities for performance traits in two genetic groups of chicken.


           
    Traits exhibiting higher communalities, indicate their strong contribution to the explanation of total variance and their greater effectiveness in describing the economic traits of layer chickens. The communalities of the traits ranged from 0.096 (WFE) to 0.976 (BW18) (Table 3). Collectively, these components provide an integrated understanding of the underlying structure of performance variation among the genetic groups studied. In the present study, varimax rotation was applied to maximize the sum of squared loadings and facilitate the interpretability of extracted components (Fernandez, 2010). Comparable studies have demonstrated similar trends in which the total variation of body weight was determined, with the first three PCs explaining 67.78% and 65.93% of the total variance of the morphostructure of male and female birds, respectively. The KMO value of 0.80, indicates the robustness of the PCA results, which concluded, that the body measurements of PC1 were influenced by the BW of birds (Tamzil and Putra, 2024). A similar trend was depicted in which PC1 had the largest share of body weight.
           
    The second component explained 27.072% of total variation, consistent with the pattern of major components contributing significantly to morphometric variation (Vilakazi et al., 2020). A study was conducted where the first component illustrated body size and explained about 36% of total variation (Mishra et al., 2017). Likewise, PC1, explaining 38.3% of total variation, was documented, further confirming the prominence of body weight traits in defining structural variation (Egena et al., 2014). A similar trend was identified with four principal components explaining a total of 69.52% of variation, with the first component alone accounting for 28.68%, emphasizing the consistent contribution of PC1 across diverse chicken populations (Dahiya et al., 2020). Similar studies conducted on internal egg quality and performance traits of native hens revealed that the application of PCA could effectively reduce the computing cost and time required for genetic evaluation of multiple traits (Babajani et al., 2018).
           
    A comparable study was also reported in which PC1 loaded mostly on the size and weight of albumen and yolk for 22 egg quality traits of chickens, whereas the five principal components extracted accounted for 79.70% of the total variance (Goto et al., 2015). The comparatively higher proportion of variance explained may be attributed to differences in the genetic background of the chicken population, degree of trait intercorrelation, environmental uniformity and the nature of the traits included in the analysis. Moreover, egg quality traits are often more closely associated physiologically than combined growth and performance traits, resulting in greater clustering of variability within fewer components.

    CONCLUSION

    In conclusion, the use of these principal components in breeding programs can enhance selection accuracy, minimize redundancy in trait recording and contribute to more efficient use of time and resources, ultimately supporting improved genetic progress in Desi 1, Desi 2 and RIR. The extraction of three principal components from the original twenty-three traits successfully captured a substantial proportion (68.59%) of the total variability, with each component representing key performance indicators such as BW15, BW18, YD, YI and YH.PC1 accounted for 54.366% of total variance and exhibited strong loadings for BW15 and BW18. PC2 explained 7.699% of the total variation and showed strong loadings on YD and YI, thus representing egg quality characteristics. PC3 contributed 6.525% of the total variance, with high loadings on YH. The present findings validate the efficacy of PCA as a robust multivariate statistical approach, enabling the systematic identification and prioritization of key traits (BW15, BW18, EP40, EW52, YH, YD and YI) that substantially influence the productivity and profitability of layer chicken.

    ACKNOWLEDGEMENT

    The authors thank the funding agency, National Livestock Mission (NLM), Department of Animal Husbandry and Dairying (DAHD), Government of India (GoI), for financial support and Guru Angad Dev Veterinary and Animal Sciences University for providing basic infrastructure to conduct the research work.

    CONFLICT OF INTEREST

    The authors declare that there is no conflict of interest.

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