Research on the genetic rules of secondary traits of different staple types in Inner Mongolia Cashmere goats

ABSTRACT

Inner Mongolia Cashmere goats are excellent genetic resources in China. Cashmere yield/body weight (g/kg), staple diameter (It is the cross-sectional diameter of coarse wool)/staple length (μm/cm), and fiber diameter (It is the cross-sectional diameter of fluff)/fiber length(μm/cm) were studied as secondary traits. Using the CORR and REG programs of SAS9.2, it was found that there is a significant phenotypic correlation and regression between the three secondary traits. The variance components analysis and genetic parameters estimation using the AIREML of the WOMBAT software. The results showed that all secondary traits have moderate heritability (0.17–0.30), and the heritability of each secondary trait of the long staple type is higher than other types. The genetic correlation of each secondary trait is between −0.39 and 0.27, and the genetic correlation of each secondary trait increases with the increase of staple length. Therefore, selection staple length can be to indirect selection of secondary traits.

KEYWORDS:

• Inner Mongolia Cashmere goat
• secondary traits
• genetic parameters
• indirect selection

Inner Mongolia Cashmere goats are an important breed for cashmere and meat and are a crucial genetic resource of the Chinese goat population. The cashmere produced by Inner Mongolia Cashmere goat is famous worldwide for its brightness, color, elasticity, thin diameter and softness, notwithstanding the harsh environmental conditions where Inner Mongolia Cashmere goats usually live. Research has found that the staple length significantly affects the cashmere yield, body weight, fiber length, and fiber diameter [1]. The genetic correlation between staple length and fiber length was positive and relatively strong, which means the longer the staple length, the longer the fiber length. A positive genetic correlation was observed between fiber length and Cashmere yield, indicating that improvement of Cashmere yield could increase fiber length. At the same time, Li has found that the heritability of the cashmere yield, body weight, fiber diameter, and fiber length of different staple types were different in Inner Mongolia Cashmere goats [2,3]. Therefore, it is necessary to study the different staple types of Inner Mongolia Cashmere goats. And the main economic traits for Inner Mongolia Cashmere goats include cashmere yield, body weight, staple length, staple diameter, cashmere length, and fiber diameter. In the process of individual genetic evaluation and breeding guidance, due to the cumbersome handling of six traits, it is more efficient to merge these six traits into three for genetic assessment to reduce the difficulty of data processing and make calculation quicker. In previous studies, secondary traits refer to traits with higher economic significance, relatively low heritability, or larger measurement difficulties [4]. Secondary traits have been deeply studied in dairy cow breeding [5,6]. Many studies have estimated the genetic parameters of important economic traits in cashmere goats. However, the numbers of animals in these studies were commonly small, from hundreds to thousands. With the accumulation of sample size, the accuracy of genetic parameter estimation can be improved.

Although early studies also reported genetic evaluation results of some traits. However, there are relatively few reports on the genetic correlation of these traits. This study uses traditional quantitative genetic methods to analyze the phenotypic rules of secondary traits, which can effectively guide the indirect selection of the goat population. Using Cashmere yield, body weight, staple diameter, staple length, fiber diameter, and fiber length as basic traits. The cashmere yield/body weight (g/kg), hair fineness/hair length (μm/cm), and cashmere fineness/cashmere length (μm/cm) are studied as secondary traits. The interrelationship between secondary traits of different staple food types was revealed, and the genetic law of secondary traits was discovered. It provides a scientific theoretical basis for genetic evaluation and establishment of reasonable breeding programs, and lays a theoretical foundation for improving cashmere quality and production efficiency.

1 Materials and methods

1.1. Data source

The data used in this study comes from the data records of cashmere yield, body weight, staple diameter, staple length, fiber diameter, and fiber length of 7385 cashmere goats during 2008 to 2001 from the Arbas Stock Farm (latitude 39°06′N and longitude 107°59′E) in southwestern Inner Mongolia, China. Before cashmere combing in May, patch samples of 10 cm² on the side of the shoulder were obtained by shaving the area. The diameter of fiber samples was measured with an Optical Fiber Diameter Analyser (OFDA). The farm has completed and detailed records of production performance and pedigree data.

1.2. Statistical analysis method

The repeat records data of cashmere yield, body weight, staple diameter, staple length, fiber diameter, and fiber length from 2008 to 2011 were selected, choosing 5645 pieces of data with comprehensive records of 6 traits for statistical analysis. Then, the ratios of each secondary trait of cashmere yield/body weight (CY/BW), staple diameter/staple length (SD/SL), and fiber diameter/fiber length (FD/FL) were organized and used for statistical analysis.

1.2.1. Correlation and regression analysis of secondary traits of different staple types

The processed data was analyzed using the GLM program of SAS9.2 [7] to analyze the significant impact of staple type on each secondary trait. And was analyzed using the CORR program of SAS9.2 to analyze the overall correlation relationship of each secondary trait and the correlation relationship between each secondary trait under different staple types. The pre-processed data was used to calculate the linear regression coefficients of the staple type on each secondary trait using the REG program of SAS9.2.

1.2.2. Genetic parameter evaluation of different staple types

According to Zhou [8], Bai [9] and McGregor [10], the GLM program of SAS9.2 was used to determine the non-genetic factors affecting each cashmere trait. The non-genetic factors in this study include staple type, measurement year, group, individual age, birth type, and gender. The GLM program of SAS9.2 was used to determine the fixed effects of each secondary trait of different staple types and establish the genetic parameter evaluation model for secondary traits. The model combines individual additive effects and individual permanent environmental effects in the random response. Therefore, the following model was obtained by integrating fixed effects and random effects: $y=\mathrm{Xb}+\mathrm{Za}+\mathrm{Wp}+e$y is the observed value of the ith trait, b is the fixed effect, a is the individual additive effect, and p is the permanent environmental effect. X, Z, and W are the design matrices corresponding to fixed effects, individual additive effects, and permanent environmental effects, respectively, and e is the random residual effect.

2. Results and analysis

2.1. Phenotypic variation rules of secondary traits for different staple types

The results of the significant analysis of the different staple types of Inner Mongolia Cashmere goats on each secondary trait are shown in Table 1. The ratio ranges of SD/SL, FD/FL, and CY/BW of different staple types are 2.59 (long staple type) ∼ 6.28 (short-staple type), 1.3 (long-staple type) ∼ 1.43 (short-staple type), and 18.22 (long-staple type) ∼ 24.00 (short-staple type), respectively. And it decreases with the increase of staple length. The variation coefficients of the secondary traits of different staple types are all above 13.51%, indicating that each secondary trait has a high potential for improvement.

Table 1. Descriptive statistical results for each secondary trait at different types of staple length.

Staple types	Traits	n	Mean	SD	CV
	SD/SL	1998	6.28^A	1.84	29.30
Short Staple Length	FD/FL	1998	1.43^A	0.28	19.58
	CY/BW	1998	24.00^A	7.7	32.08
	SD/SL	3200	3.53^B	0.81	22.95
Intermediate Staple Length	FD/FL	3200	1.40^B	0.27	19.29
	CY/BW	3200	21.15^B	6.64	31.39
	SD/SL	447	2.59^C	0.35	13.51
Long Staple Length	FD/FL	447	1.30^C	0.27	20.77
	CY/BW	447	18.22^C	4.76	26.13

Note: Different shoulder badge letters represent extremely significant differences. CY: cashmere yield, BW: body weight, SD: staple diameter, SL: staple length, FD: fiber diameter, FL: fiber length.

2.2. Correlation analysis between each secondary trait

The CORR program of SAS9.2 was used to analyze the correlation and significance of each secondary trait under different staple types and the results are shown in Table 2. The correlation coefficients between each secondary trait under different staple types decrease with the increase in staple length, which indicates that the staple length trait has a significant impact on other secondary traits.

Table 2. The correlation coefficient among the secondary traits at different types of staple length.

Staple types	Traits	SD/SL	FD/FL	CY/BW
	SD/SL	1.00	0.35**	−0.23**
Short Staple Length	FD/FL		1.00	−0.33**
	CY/BW			1.00
	SD/SL	1.00	0.26	−0.25**
Intermediate Staple Length	FD/FL		1.00	−0.22
	CY/BW			1.00
	SD/SL	1.00	0.08**	−0.20**
Long Staple Length	FD/FL		1.00	−0.06**
	CY/BW			1.00

Note: The upper triangle represents the correlation coefficient; ** represents an extremely significant correlation; * represents a significant correlation. CY: cashmere yield, BW: body weight, SD: staple diameter, SL: staple length, FD: fiber diameter, FL: fiber length.

2.3. Regression analysis of staple types on each secondary trait

The regression analysis of staple length on each secondary trait is shown in Table 3. The regression coefficients of staple length on each secondary trait under different staple types are different, and all the regression coefficients are negative. This also indicates that the longer the staple length, the smaller the values of SD/SL, FD/FL, and CY/BW. In the analysis of different staple types,

Table 3. The regression analysis of secondary traits at different staple types.

Staple Types	Traits	Interception	Regression Coefficient	Standard Deviation	P-value
	SD/SL	12.76	−0.66	0.16	P < 0.01
Short Staple Length	FD/FL	1.64	−0.02	0.03	P < 0.01
	CY/BW	26.87	−0.29	0.91	P < 0.01
	SD/SL	7.09	−0.20	0.08	P < 0.01
Intermediate Staple Length	FD/FL	1.64	−0.01	0.03	P < 0.01
	CY/BW	28.80	−0.44	0.86	P < 0.01
	SD/SL	25.83	−0.32	0.12	P < 0.01
Long Staple Length	FD/FL	1.61	−0.01	0.17	P < 0.05
	CY/BW	25.83	−0.32	0.12	P < 0.05

Note: P < 0.01 indicates an extremely significant difference; P < 0.05 indicates a significant difference; P > 0.05 indicates no significant difference. CY: cashmere yield, BW: body weight, SD: staple diameter, SL: staple length, FD: fiber diameter, FL: fiber length.

2.4. Non-genetic factor analysis of each secondary trait

The results of the non-genetic factor analysis of the secondary traits of Inner Mongolia Cashmere goats are shown in Table 4. As can be seen from Table 4, staple types, measurement years, groups, and individual ages have a very significant (P < 0.01) impact on secondary traits, while birth types and sex do not significantly (P > 0.05) affect secondary traits. Therefore, considering the staple types, measurement year, group, and individual age are incorporated into the fixed effects of the genetic parameter estimation of secondary traits of different staple types.

Table 4. Fixed effect of secondary traits.

Fixed effects	SD/SL	FD/FL	CY/BW
Staple types	P < 0.01	P < 0.01	P < 0.01
Measurement Years	P < 0.01	P < 0.01	P < 0.01
Groups	P < 0.01	P < 0.01	P < 0.01
Individual Age	P < 0.01	P < 0.01	P < 0.01
Birth Types (Single/twins)	P > 0.05	P > 0.05	P < 0.01
Sex(Dams/ Sires)	P > 0.05	P > 0.05	P > 0.05

Note: P < 0.01 indicates an extremely significant difference; P < 0.05 indicates a significant difference; P > 0.05 indicates no significant difference. CY: cashmere yield, BW: body weight, SD: staple diameter, SL: staple length, FD: fiber diameter, FL: fiber length.

2.5. Genetic parameter results of secondary traits for different staple types

According to the fixed effects influencing secondary traits, the variance components and parameter estimation are performed using a multi-trait repeatability model. The results of the variance components and genetic parameter estimation are shown in Table 5 and Table 6, respectively. The heritability of SD/SL, FD/FL, and CY/BW for short-staple type are 0.27, 0.20, and 0.17, respectively. For the intermediate staple type, they are 0.28, 0.26, and 0.16, respectively. For the long staple type, they are 0.30, 0.28, and 0.18, respectively. All secondary traits have moderate heritability. The genetic correlations of each secondary trait are between −0.39 and 0.27, and the genetic correlations of each secondary trait increase with the increase of staple length. Particularly, the genetic correlation of SD/SL with CY/BW in short-staple type, intermediate staple type, and long staple type are −0.36, −0.37, and −0.39, respectively, all of which are moderately high negative genetic correlations. The genetic correlations of FD/FL with CY/BW are 0.23 (short-staple type), 0.26 (intermediate staple type), and 0.27 (long staple type), all of which are moderate positive genetic correlations.

Table 5. Estimates of variance components for each secondary trait in different staple types.

Staple types	Traits	${\sigma }_a^2$	${\sigma }_p^2$	${\sigma }_e^2$	$h_T^2+\mathrm{SE}$
	SD/SL	9.43	35.55	26.12	0.27 ± 0.03
Short Staple Length	FD/FL	0.53	2.67	2.14	0.20 ± 0.04
	CY/BW	0.48	2.84	2.36	0.17 ± 0.04
	SD/SL	9.87	35.21	25.34	0.28 ± 0.04
Intermediate Staple Length	FD/FL	0.66	2.57	1.91	0.26 ± 0.05
	CY/BW	0.41	2.56	2.15	0.16 ± 0.03
	SD/SL	10.39	34.57	24.18	0.30 ± 0.03
Long Staple Length	FD/FL	0.38	1.35	0.97	0.28 ± 0.04
	CY/BW	0.37	2.06	1.69	0.18 ± 0.03

Note: ${\sigma }_a^2$: Direct additive genetic variance; ${\sigma }_p^2$: phenotypic variance; ${\sigma }_e^2$: residual variance; $h_T^2$: heritability.

CY: cashmere yield; BW: body weight; SD: staple diameter; SL: staple length; FD: fiber diameter; FL: fiber length.

Table 6. Evaluation of genetic parameters for secondary trait in different staple types.

Staple Types	Traits	SD/SL	FD/FL	CY/BW
	SD/SL	0.27 ± 0.03	0.08 ± 0.06	−0.36 ± 0.04
Short Staple Length	FD/FL	−0.02 ± 0.06	0.20 ± 0.04	0.23 ± 0.05
	CY/BW	−0.13 ± 0.06	0.22 ± 0.06	0.17 ± 0.04
	SD/SL	0.28 ± 0.04	0.15 ± 0.05	−0.37 ± 0.04
Intermediate Staple Length	FD/FL	0.06 ± 0.02	0.26 ± 0.05	0.26 ± 0.03
	CY/BW	−0.07 ± 0.02	0.11 ± 0.02	0.16 ± 0.03
	SD/SL	0.30 ± 0.03	0.18 ± 0.04	−0.39 ± 0.03
Long Staple Length	FD/FL	0.08 ± 0.04	0.28 ± 0.04	0.27 ± 0.04
	CY/BW	−0.11 ± 0.03	0.13 ± 0.04	0.18 ± 0.03

Note: The upper triangle is genetic correlation; The lower triangle is phenotypic relation; Heredity on the diagonal. Bold is to emphasize heritability.

CY: cashmere yield; BW: body weigh; SD: staple diameter; SL: staple length; FD: fiber diameter; FL: fiber length.

3. Discussion

3.1. Analysis of phenotypic regularity of secondary traits of different staple types

After research, it is found that the phenotypic value range of secondary traits of Inner Mongolia Cashmere goats is between 1.30 (long staple type FD/SL) and 24.00 (short-staple type CY/BW). The coefficients of variation of FDFL in short-staple type, intermediate staple type, and long staple type are 19.58%, 19.29%, and 20.77% respectively. However, Xuewu Li et al. (2018) found that the coefficient of variation of FD in long-staple type was the highest, while that of FD was the highest in the short-staple type [3]. This is different from the results of this study, so this may be due to different data structures. It is speculated that the heredity of FD and FL in the long-staple type is relatively stable. Selecting individuals with long staples can improve the uniformity of each production trait in the population.

3.2. Statistical analysis of the influence of different staple types on secondary traits

It is found that the phenotypic correlation between each secondary trait under different staple types of Inner Mongolia Cashmere goats is different. The phenotypic correlation between FD/FL and SD/SL decreases with the increase of staple length in different staple types, indicating that the phenotypic differences decrease with the increase of staple length. Especially the correlation between FD/FL and CY/BW in the long-staple type is the smallest (−0.06), which may be caused by the negative correlation between fineness and cashmere yield [11]. The relatively small regression coefficient of staple length to FD/FL may be because FD and FL, SD and SL are all quantitative traits, controlled by polygenes, and cashmere and staple are produced by primary and secondary staple follicles respectively [12]. It is found that the regression coefficient of staple length to FD/FL is still low, and the impact on short-staple type and intermediate type is extremely significant (P < 0.01), but the impact on FD/FL in long-staple type is not significant. It indicates that the impact of staple length on FD/FL of long staple type is smaller, so selecting individuals with long staple type, their fiber diameter and fiber length are relatively stable, but the increase in fiber length and fiber diameter is slower.

3.3. Genetic parameter estimation of secondary traits of different staple types

Staple types, measurement years, groups, and individual ages all have a very significant impact on each secondary trait, and it is also found by Bai and Wang [13,14]. Li et al. found that there are significant differences in the phenotypes of each trait under different staple types [15], so when estimating genetic parameters for other traits, staple types are used as a fixed effect for genetic evaluation. Saghi et al. found that the year should be analyzed as a fixed effect when estimating genetic parameters [16]. Wang et al. also found that group is an unignorable fixed effect when evaluating genetic parameters for important economic traits of Inner Mongolia Cashmere goats [17]. Individual age of Inner Mongolia Cashmere goats has a great difference in production performance, which is consistent with the results of Zhou Juanjuan [18]. Birth types has a very significant impact on CY/BW, but have no significant impact on the other two traits, and sex has no significant impact on three secondary traits, so the fixed effects to be considered when estimating genetic parameters for secondary traits of different staple types are staple types, measurement years, groups, and individual ages.

The heritability of SD/SL of short staple type, intermediate type, and long staple type are 0.27, 0.28, and 0.30 respectively, and heritability of FD/FL are 0.20, 0.26, and 0.28 respectively, all of which belong to moderate heritability. Zhang et al. estimated the genetic parameters of cashmere quality traits of Inner Mongolia Cashmere goats and found that the heritability of SD, SL, FD and FL were 0.27, 0.30, 0.32 and 0.18 respectively [19], which also belonged to moderate heritability, consistent with the results of this study. Since increasing cashmere yield while reducing fiber diameter is the breeding goal of cashmere goats [18], it is necessary to understand the genetic law of its cashmere through the study of cashmere traits of Inner Mongolia Cashmere goats of different staple types, to achieve the goal of increasing cashmere yield while reducing cashmere diameter. Rile et al. speculated that corrugated protein regulates the staple follicle growth cycle by affecting the outer sheath [20]. Their body weight is the main breeding target of Inner Mongolia Cashmere goats, the breeding value of body weight traits should be estimated as accurately as possible [21]. Kumar et al. estimated the genetic parameters of daily gain of different month-olds of the same breed and found that the heritability of body weight daily gain of each month-old was also different [22]. Therefore, the source and structure of body weight data also affect the estimation of body weight breeding value. The heritability of cashmere yield/body weight in the short-staple type, intermediate type, and long-staple type is 0.17, 0.16, and 0.18 respectively. Heydar et al. found that the heritability of body weight is 0.14 [23], and Brown et al. also found that the heritability of weaning weight of Australian meat sheep is 0.14, which is consistent with the results of this study [24]. Aguirre et al. found that the heritability of birth weight, post-grasping weight, 180-day weight, and 279-day weight were 0.22, 0.20, 0.39, and 0.49 respectively [25]. That is, the heritability of body weight at different times is different, so the source of data, data structure and time of collecting data will have a significant impact on the estimation of genetic parameters for body weight.

The highest and lowest phenotypic correlation between each secondary trait in short-staple type. So, the phenotypic correlation was influenced greatly by the environment. The negative genetic correlation between CY/BW and SD/SF is the highest negative genetic correlation among all traits at different staple types, while the positive genetic correlation between CY/BW and FD/FL is the highest positive genetic correlation among all traits. Ciappesoni et al. also found that the genetic correlation between dirty staple weight and staple diameter, staple length and body weight were 0.29, 0.37, and 0.38 respectively, while the genetic correlation between staple length and body weight was 0.14, and the phenotypic correlation between each trait was moderate. Their research results were consistent with the results of this study [26]. Mortimer et al. found that the genetic correlation between body weight and staple diameter was 0.3 [27], which was similar to the results of this study, both belonging to moderate genetic correlation.

4. Conclusions

All the secondary traits have a highly significant correlation between each trait. And the regression analysis of different staple types is an extremely significant difference. Staple types should be considered during genetic parameter estimation. The heritability of each secondary trait belongs to moderate heritability. The genetic correlation of each secondary trait is between −0.39 and 0.27, and the genetic correlation of each secondary trait increases with the increase of staple length. So, the selection of secondary traits can be achieved by selecting a staple length.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Doctoral Startup Fund of Panzhihua University [035200256].