Principal Component Analysis (PCA) in Animal Breeding

Principal Component Analysis (PCA) in Animal Breeding REVIEW OF LITERATURE Global Animal data bank for genetic resources documents more than 14,017 breeds of domesticated livestock species in over 181 countries. However, this is underestimations of indigenous farm animals of developing countries are yet to be documented (FAO, 2011). Hall, (2004) studied the two stage process to develop the enormous livestock biodiversity. 1) Domestication and 2) breed differentiation. Both theses stages involved genetic legitimate changes and are thus evolutionary and cultural. In these stages, farm animals/ populations have acquired unique adaptation in response to diverse environment. Genetic diversity of farm animal and their adaptability provides building blocks for very successful breed improvement programs. Moreover, the conservation and improvement of farm animal diversity is an opportunity to respond in future needs. The farm animal’s resources lost due to the heavy cross breeding schemes in recent past. There is urgent need for conservation of these resource s and documents Biometric traits generally used to characterize the different breeds of livestock as they are body confirmation. These traits are also used for comparison of growth in different individuals. Mostly body dimensions are used to indicate breed, origin and relationship or shape and size of individuals (Buchenauer, 1993). Body measures and indices estimated from different combinations of different body traits produced superior guide to weight and also used as indicator of type and function in farm animals (Schwabe and Hall, 1989; Salako, 2006). The objective of to measure the body dimensions could be improved the body shapes by enabling the breeders to recognize early maturity and late maturity of different animals size (Brown et al., 1973; 1974). The body dimensions could be achieved by grouping them more meaningfully. Significant difference in different body dimensions is due to the sex age and some reports in different breeds and species (Gilbert et al., 1993; Shahin et al., 1995; Pundir et al., 2007a,b,c; Singh et al., 2008; Yakuba et al., 2009 and Khuram, 2013) in Cattle; (Biedermann and SSchmucker, 1989, Jakubec et al., 1999; Miserani et al., 2002 and Sadak et al., 2006) in Horses and Sarako et al., 2006 in Sheep. Analyses of multivariate are used to obtained relationships among different body measurements. The PCA analysis can explain relationships in a better way when the recorded traits are used to obtain relationships among different body measures Hammock et al. (1986). This type of analysis transform original group of variables. The purpose of PCA analysis is to reduce a data set and describe to use easily (Yakuba et al., 2009). For breed improvement, multifactor analysis is mostly considering a group of attributes, which may be used for selection (Fumio et al., 1982). Multivariate analysis used to study the different biometric traits in Japanese black cattle, Swiss Dairy Cattle and White Fulani cattle (Fumio et al., 1982; Hammock et al., 1986; Karacaroen et al., 2008). Salako, 2006 and Sadek et al., 2009 used PCA analysis to study the different biometric traits in Uda sheep and in Arabian Horses, respectively. Tolenkhomba et al. (2012) measure 18 different biometric traits in Manipuri local cattle in India. They measure height at whiter, neck girth, body length, puch girth, ear length, tail length, switch length horn diameter circumference of horn, length of horn in 107 bulls. The average of these traits in this local animal indicates that this is a smaller type cattle breed. Promax rotation revealed 6 factors which explained about 69.77 % of the total variation. Factor 1 described the general body confirma tion and explained 21.93% of total variation. The results of this study revealed a significant high loading of height at wither, body length, heart girth, Puch girth and ear length. The ranged of communality from 0.493 to 0.782 and unique factor ranged from 0.507 to 0.218 for all traits. Pundir et al. (2011) studied the Kankaraj cattle and also used 18 different traits for body confirmation. Average body length, height at wither, height at shoulder, height at knee, heart girth were 123.44Â ±0.37, 124.49Â ±0.28, 94.68Â ±0.30, 38.2Â ±0.14, 162.56Â ±0.56, 178.95Â ±0.70, 44.09Â ±0.10, 15.91Â ±0.05, 42.47Â ±0.53, 26.07Â ±0.19,13.34Â ±0.08, 31.24Â ±0.12, 16.10Â ±0.05, 50.63Â ±0.18, 73.21Â ±0.32, 111.62Â ±0.53, 89.34Â ±0.34 and 17.28Â ±0.10 cm, respectively. They measured correlation of coeffiecnt between traits’ ranged from -0.806 to -0.815. Most of the correlations were positive and significant. They also used promax rotation with power 3 and find three factors, which explained about 66.02% of the total variation. Factor 1 described the body confirmation and explained 19.68% of total variation. The second factor described the front view and third back of the animal 7.44. Yakuba et al. (2009) also studied the factor analysis for body confirmation in white Fulani cattle. They find that age group significantly influenced all the measured traits. The correlation coeffiecnt of body measures ranged from 0.5-0.9 and 0.22-0.9 for 1.5-2 and 2.5-3.6 years. In factor solution of PCA analysis two factor with ratio of variation 85.37 in first group. In second age group four factors which explained 86.47% of the generalized variation were extracted. Salako et al. (2006) measured ten different biometric traits. The body measured includes Wither height (WH), Rump Width (RW), Tail length, rump length, rum height. Appling trational use of body size estimation, the animals measurements were BL=59.37Â ±4.50,RL=22.1Â ±1.12, RH=65.18Â ±6.06, RW=12.90Â ±1.24, FL=41.62Â ±2.29, SW=14.40Â ±1.45, FaL=21.47Â ±1.82, TL=40.72Â ±2.71 and HG=71.98Â ±4.30cm. Variation was observed within the body measurements. The first and second factor of their study was 67.6 and 11.03%. the first component contained measurements that are closely associated with bone growth while the second one to produce dimensions that are relatively less associated. In another study, Yakuba et al. (2011) determine the interdependence among the confirmation traits of Uda ram. The various constituent parts of the body developed at varying rates. This accounted for 86.3 % of the total variance. The first component alone explained 80 % of the variance and tended to describe general size, while second component for meat traits. Yakubu et al. (2013) studied the biometric traits of Yankasa sheep and measure different body traits. The body measurements taken were: withers height, rump height, body length, heart girth, tail length, face length, shoulder width, head width, rump width, ear length, fore leg length, hind leg length and rump length. General linear model was used to study the age group effect. Luanna et al. (2012) measured the Moroccan goat population and find the importance of body measure among individual’s and populations. The traits were wither height (WH), brisket height (BH) and ear length (EL). Thorax depth (WH-BH) and the three indices, TD/WH, EL/TD and EL/WH, were also calculated. The first component explained 99 % of the total variance. Importance of PCA analysis for Breed conservation Livestock species is results of a combination of various processes. These includes domestatication, migration, genetic isolation, environmental adaptation, selective breeding (Lacy, 1997). Small populations lose genetic variability because of genetic drift and inbreeding within populations. Lower variability suppress individuals fitness (Rege, 1999), resistance to disease and parasities and flexibility in coping with environmental challenges (Rege and lipner, 1992). Moreover, lower variation decreases fitness of population, resilience and ling term adaptability. Sheep breeds are far from uniform (Kruger, 2001) and this difference is the overall results of the fact that in thousands of years since animals were first domesticated (kohler-Rollefson, 2001; Hall, 2004). A large verity of breeds has been developed through adaptation to various ecological niches. These breeds grow quikly, produce tones of meat, wool and milk but rely on high quilty feed and need intensive veterinary care (Ramsey et al., 2000; FAO, 2007; Kunene et al., 2009) . Therefore the purpose of this study was to document the important native/ indigenous cattle breed and this review explain some important measurement for factor analysis. Correlations are established to set relationships between different body measurements. The factor and Principal Component Analysis (PCA) can best explain the relationships in more appropriate way when the recorded traits are correlated. Principal components are the linear combination of original variables and in these kinds of analysis original group of variables are transformed into another group. The data set will be shortened by factor and principal component analysis so that it could be described in more accurate way. In recent years biometric measurements/traits of many others animal species has been published. Most of these worker studies the Body length (BL), Heart girth (HG), Face length (FL), Shoulder width (SW), Head width (HW), Foreleg length (FL), Hind leg length (HL), Rump height (RH), Rump width (RW), R ump length (RL), Cannon circumference (CC), Withers height (WH) and Tail length (TL) of some cattle breeds. A review on these parameters is as follows. Body length (BL): Production traits of beef cattle are directly related to body length (BL). A study on Fulani cattle showed a relationship with production (Yakubu et al., 2010). In a study on Kankrej cows it was formed that biometric trait has a strong relationship with high production of milk (Pundir et al., 2011). Muhammad et al. (2012) by using regression tree (RT) estimated body lengths of some sheep breeds in Baluchistan province. Face length (FL): Face length, lips area and nasal circumference show the behavior of animal feeding. In case of selection of elite animal face length parameter has an important role. Comparison between two breed, native and Holstein cattle showed that the production performance is greatly affected by differences in face length parameter (Meyer, 2005). A study was conducted on Uda sheep and it was observed that this breed has static variation for face length (Salako et al., 2006). Kankrej cattle have a wide variation with face length and it shows relationship with production traits (Pundir et al., 2011). Head width (HW): Head width is also an important parameter in the beef and dairy production. Significant effect in milk production is observed in Swiss dairy cattle and Frisian cattle due to a wide variation in head width (Karacaroen and Kadarmindeen, 2008). In a comparative study of some high and low producing cattle breeds it was observed that head width was significantly an important indicator of production (Khan et al., 2008). In case of evaluation process of production and domestication of animal this portion of body has its own importance (Yakubu et al., 2010). Heart girth (HG) Body weight of animal can be measured by measuring the heart girth (HG). Meyer, (2005) used multivariate restricted maximum likelihood analysis for measuring the some phenotypic measurements including heart girth (HG). They developed an easy procedure to handle large data sets of phenotypic observation in Australian Angus cattle. By studying the Fulani cattle it was observed that hearth girth has a close relation with production (Yakubu et al., 2009). Principal component analysis proved some meat quality variables along with body measurements including heart girth (Mulyono et al., 2009). In India a study was carried out on 407 Kankrej cattle and it was found that heart girth has a very significant relationship with production (Pundir et al., 2011). Shoulder width (SW): Shoulder width has relation in beef producing animal and it is a good indicator for the evaluation of beef producing animal (Shahin et al., 1995). Proportion of barrel attachment with shoulder width has significant indication in Red Sindhi cattle (Pundir et al., 2007b). A study was carried out on Fulani cattle and it was observed that shoulder width has a significant effect on production (Pundir et al., 2011). Foreleg length (FL): Price of animal can be fixed by considering by strength of legs. Foreleg length plays an important role in the indication of dairy and beef sector (Khan et al., 2008). Frisian cattle have less strength in forelegs as compared to Kankrej cattle (Pundir et al., 2011). Okpeku et al. (2011) Observed that the biometric traits have strong relationship with production. Hind leg length (HL): Like forelegs the animal strength is also directly proportional to the hind leg lengths (Khan et al., 2008). Frisian cattle have less strength in hind legs as compared to Kankrej cattle (Pundir et al., 2011). In a study on Perynean cattle proved that leg length is related somewhat with production (Casanova et al., 2011). Rump height (RH) : Rump height is important factor to judge the animal. Some morphometric observations were made on Swiss dairy cattle and some body measurements and functional traits were used including rump height (Karacaroen and Kadarmindeen 2008). A study on cattle also showed the relationship of this trait with production performance (Yakubu et al., 2009). In Kankerj cattle it was observed that rump height has significant effect on production performance (Pundir et al., 2011). Rump width (RW): In dairy and beef animal rump width (RW) has significant role. This part of body has its own and unique importance in evolutionary process (Simon et al., 1993). In a study on British breeds of cattle, rump width was also somewhat related with heavy weight and showed role in milk production (Schwabe et al., 1989). In Indian native cows observation showed that rump width has a role in weight of animal and production. In this study some anatomical observations were correlated with phenotypic observations (Singh et al., 2008). In study on white Fulani cattle rump width was also measured and was related to production (Yakubu et al., 2009). Rump length (RL): Rump length is an important indicator of dairy and beef animal as in rump width. It is one of the important parameter in body measurement to judge the animal. In some livestock breeds, it was observed that rump length has an effect on weight and production (Simon et al., 1993). Singh et al. (2008) explain some liner type traits with evaluation process in some native cattle breeds. Some biometric traits were studied on goat and reported relationship of rump length with meat characteristics (Okpeku et al., 2011). Casanova et al. (2011) studying Pyrenean cattle found that these biometric measurements best describe the specific beef or milk production characteristics. Wither height (WH): Wither height has its own importance in some cattle breeds for explaining body conformation. Wither height was measured in Sahiwal cattle and it was related to some production parameters (Khan et al., 2008). In Fulani cattle wither height was measured to explain body conformation and results showed that it is good parameter to judge a productive animal (Yakubu et al., 2009). In Perynean cattle PCA was used to explain the withers height (Casanova et al., 2011). In a study on Kankrej cows, the withers height was recorded in 403 cows. The average measurement of this trait was 124.49Â ±0.28 cm. The results of this study showed that withers height is very important to explain the body conformation in this breed (Pundir et al., 2011). Tail length (TL): In animal tail length is a defensive part of the body. This portion of the body has a very unique position in genetic diversity. Tail length is considered an important body portion in beef animal (Meyer, 2005).It was reported that tail length has direct importance on production performance in Fulani cattle (Yakubu et al., 2009) and has significant association with performance in Kankrej cattle (Pundir et al., 2011). The observations were also made on some Perynean cattle by studying biometric traits including tail length (Casanova et al., 2011).