Estimation of genetic parameter for wool traits in FGF5 gene-edited Chinese Merino sheep

Introduction: We previously edited the fibroblast growth factor 5 (FGF5) gene to improve wool quality and increase wool production. However, the current population of FGF5 gene-edited sheep faces low breeding efficiency during expansion. To address this challenge, we constructed multi-trait animal models to estimate the genetic parameters of key wool traits.

Methods: Phenotypic data were collected from 281 yearling FGF5 gene-edited fine-wool sheep at the Sheep Breeding Base of the Xinjiang Academy of Animal Science. Two relationship matrices were constructed: one based on pedigree information (LM_A) and another combining pedigree and genomic information (LM_H). Genetic parameters were estimated for staple length (SL), straightened length (TL), fiber diameter (FD), and greasy fleece weight (GFW).

Results: Heritability estimates for SL (0.401, 0.380), TL (0.265, 0.309), and FD (0.240, 0.302) using LM_A and LM_H were moderate, while GFW (0.153, 0.164) showed low heritability. Strong positive genetic correlations were observed among SL, TL, and GFW, with correlation coefficients ranging (0.518 - 0.969). Compared with LM_A, LM_H produced heritability estimates with smaller standard errors.

Discussion: Overall, the LM_H approach under the multi-trait animal model provides more accurate estimates of genetic parameters for wool traits. Our findings provide a basis for genetic estimation in FGF5 gene-edited fine-wool sheep and support the development of genetic evaluation for this population.

1 Introduction

Fibroblast Growth Factor 5 (FGF5) is a well-characterized functional gene regulating the hair growth cycle, playing a critical role in inhibiting the transition of hair follicles into the anagen (growth) phase. Natural mutations in the FGF5 gene have been reported in multiple species, including mice (Hébert et al., 1994), dogs (Dierks et al., 2013), and goats (Bao et al., 2015), which cause loss-of-function mutations that release the inhibitory effect on hair follicle growth, resulting in a long-hair phenotype (Hu et al., 2017). The economic value of wool primarily depends on traits such as staple length (SL), greasy fleece weight (GFW), and fiber diameter (FD), with SL being a key indicator of wool production. Since 2015, CRISPR/Cas9-mediated gene-editing technology has been applied to generate targeted knockout of the FGF5 gene in Chinese Merino fine-wool sheep under approved institutional and national animal-use ethics protocols. This gene-editing strategy effectively suppressed FGF5 function and its downstream signaling pathways, producing an F0 generation with significantly increased SL (Li et al., 2017a, 2017). However, expansion of the edited population was constrained by several factors, including the limited number of founder animals and relatively low reproductive efficiency. To overcome these constraints, edited animals from different lineages were crossed with wild-type fine-wool sheep, and edited progeny were identified through genotyping to establish a stable breeding population.

Genetic parameter estimation is a critical component in genetic improvement and breeding, forming the foundation for assessing the potential for genetic progress, predicting breeding outcomes, and guiding the optimization of selection strategies (Vargas Jurado et al., 2022). By analyzing key metrics such as heritability, selection response, and genetic correlations, it not only provides a theoretical basis for developing scientifically sound selection programs but also enhances breeding efficiency. Estimates of genetic parameters for specific traits may vary across populations due to differences in population characteristics, statistical models, and datasets (Zhang et al., 2020). In non-gene-edited sheep populations, such as the Chinese superfine Merino population (Di et al., 2011), the heritabilities of SL, FD, and GFW were 0.32, 0.22, and 0.17, respectively, whereas in the Alpine Merino population (Li et al., 2022), the corresponding values for FD, GFW, and SL were 0.20, 0.19, and 0.19. Although gene-edited fine wool sheep populations differ from conventional fine wool sheep populations, the fundamental principles of using genetic parameters to guide breeding remain consistent.

The multi-trait animal model is a method for genetic evaluation that leverages both genetic correlations among traits and genetic information across multiple traits (Henderson, 1975). By integrating the genetic covariance information among traits, this integration enhances the reliability of genetic evaluations (Nilforooshan et al., 2010). Joint analysis of multiple traits not only makes more effective use of available information and improves breeding efficiency, but also overcomes the inherent limitations of single-trait models. Relationship matrices can be constructed not only based on pedigree information but also on genomic data also combined these. However, genotyping all individuals in large breeding populations is costly and sometimes pedigree records can be incorrect due to practical reasons. Based on this, Misztal et al (Misztal et al., 2010). proposed the use of a combined pedigree-genomic relationship matrix for genetic evaluation.

The FGF5 gene-edited sheep population exhibits distinct characteristics, including the presence of multiple FGF5 mutant alleles within the population, the stable transmission of these edited genotypes across generations, and markedly increased wool production and fiber length. However, due to limited population size in earlier stages and the use of phenotypic score-based selection to retain individuals, this population faces breeding bottlenecks. Therefore, during the current population expansion, it is necessary to integrate superior individual selection with genetic parameter evaluation to improve breeding efficiency. Closely aligned with the breeding objectives of the FGF5 gene-edited fine wool sheep population, this study utilizes pedigree, genotype and phenotypic records of key wool traits from 2016 to 2024 to establish a multi-trait animal model for genetic parameter estimation in the FGF5 gene-edited fine-wool sheep population.

2 Materials and methods

2.1 FGF5 breeding population

The current FGF5 gene-edited fine wool sheep population is based at the Sheep Breeding Base of the Institute of Biotechnology, Xinjiang Academy of Animal Science. This population comprises F0 and subsequent generations of gene-edited fine wool sheep harboring insertion-deletion (InDel) mutations generated via non-homologous end joining, as well as precisely edited individuals produced through homology-directed repair (HDR) targeting the FGF5 gene (Li et al., 2017a, 2017). The population consists of a total of 281 individuals, including 130 rams and 151 ewes, spanning three generations, among which 187 individuals have been genotyped using SNP chips, including 5 F0 animals, 63 F1 animals, and 119 F2 animals. Detailed information on the edited genotypes of offspring, mating records, and breeding strategies has been reported in Supplementary Table S1 and previous studies (Yue et al., 2024).

2.2 Wool trait performance assessment

SL and GFW measurements for FGF5 gene-edited yearling sheep were collected on-site by certified wool classers following the agricultural Industry standard of the People’s Republic of China, “Testing Items, Symbols and Technical Terms of Merino Wool” (NY1-2004). TL was determined through laboratory analysis of wool samples collected from the standard mid-side position (10 cm posterior to the left scapular ridge) of fine-wool sheep. The actual TL was calculated using the following formula:

$$\text{TL}=\text{SL}+\frac{\text{SL}\times (\text{mTL}-\text{mSL})}{\text{mSL}}$$

Mean laboratory straightened length (mTL) was measured by grasping the root and tip of wool staples with forceps, straightening the fiber bundle, and recording the root-to-tip length with the mean value calculated from 10 replicates; mean laboratory staple length (mSL) was determined by measuring the root-to-tip distance of fiber bundles in their natural crimped state, averaged across 10 replicates; and SL denotes the field-measured staple length.

FD of wool samples was determined using an OFDA 2000 instrument following the GB/T 21030–2007 standard operational protocol. Testing procedures were performed by the Wool and Cashmere Quality Supervision and Inspection Center (Urumqi), Ministry of Agriculture and Rural Affairs.

2.3 Genotyping and bioinformatics processing

Blood or ear tissue samples were collected for DNA extraction. All samples were genotypes using Sheep 50K SNP chip Manufacturer:Higentec Co Ltd, containing 50,900 SNP markers. PLINK (v1.90) (Chang et al., 2015) was used to was used to filter SNPs using the following criteria. (1) Remove the SNPs containing missing data points of > 10%. (2) Remove the SNPs with the minor allele frequency (MAF) value of < 0.05.(3) Remove the SNPs HardyWeinberg (HWE) p-value< 1e-6. As a result, 45,927 SNPs were kept for further analyses.

2.4 Data processing and software analysis

After data collation in Excel 2021, 281 records of FGF5 gene-edited yearling sheep were retained for subsequent analysis of wool traits. Descriptive statistics (including record count, mean, standard deviation, maximum, and minimum values) were computed using custom functions in R v4.2.1. Data points beyond mean ± 3 standard deviations were excluded, with the quality-controlled phenotypic dataset found to conform to a normal distribution. Variance components for the four wool traits were analyzed via the ASReml-R package (Butler et al., 2017). Fixed effects were tested using the Wald function, while heritability, genetic correlations, and phenotypic correlations were estimated with the vpredict function.

2.5 Statistical models

In the present study, single-trait and multi-trait animal models were used to estimate the variance of components of four wool traits. The model formulation is:

$$\text{y}=\text{Xb}+\text{Za}+\text{e}$$

where y is the vector of phenotypic records; b is the vector of fixed effects, including the fixed effects of the i-th farm effect, j-th sex effect, and k-th editing method effect; a is the vector of additive genetic effects; and e is the vector of residual effects. The matrices X and Z correspond to the incidence matrices for fixed and random effects, respectively. These models were implemented using ASReml-R package (Butler et al., 2017).

As a prerequisite, the pedigree-based relationship matrix is denoted as matrix A, and the genomic relationship matrix as matrix G. The LM_A model uses only the A matrix, whereas the LM_H model replaces A with matrix H, which is constructed by combining both A and G matrices to more comprehensively capture the relationships among individuals. The formula for calculating combined relationship matrix (H) was:

$$H^{-1}=A^{-1}+\left[\begin{array}{cc}0& 0\\ 0& G^{-1}-A_{22}^{-1}\end{array}\right]$$

Where A^-¹ represents the inverse of the pedigree-based relationship matrix constructed from individuals with pedigree records; G^-¹ represents the inverse of the genomic relationship matrix constructed from genotyped individuals; and A₂₂^-¹ denotes the inverse of the pedigree-based relationship matrix corresponding specifically to the subset of individuals with genotype data.

The multitrait mixed model equations and variance-covariance matrices are as follows:

$$Var\left[\begin{array}{c}a\\ p\\ e\end{array}\right]=\left[\begin{array}{ccc}A\otimes G& 0& 0\\ 0& I\otimes P& 0\\ 0& 0& R\end{array}\right]$$

Where a, p, and e​ represent the vectors for additive genetic effects, permanent environmental effects, and residual effects, respectively; A is additive genetic relationship matrix; G is variance-covariance matrix among random regression coefficients for additive genetic effects; P is variance-covariance matrix among regression coefficients for permanent environmental effects;I: identity matrix; R is diagonal matrix of residual variances.

The formula for calculating heritability was:

$$h^2=\frac{{\sigma }_a^2}{{\sigma }_a^2+{\sigma }_e^2}$$

where $h^2$ is heritability, ${\sigma }_a^2$ is additive genetic variance, and ${\sigma }_e^2$ is residual variance. Genetic and phenotypic correlations were calculated using the following formula:

$$r_G=\frac{CO{V}_{G_{XY}}}{\sqrt{{\sigma }_{G_X}^2{\sigma }_{G_Y}^2}}{r}_P=\frac{CO{V}_{P_{XY}}}{\sqrt{{\sigma }_{P_X}^2{\sigma }_{P_Y}^2}}$$

where $r_G$ is the genetic correlation between traits X and Y; $CO{V}_{G_{XY}}$ is the genetic covariance matrix of traits X and Y; ${\sigma }_{G_X}^2$ and ${\sigma }_{G_Y}^2$ are the genetic standard deviation of traits X and Y; $r_P$ is the phenotypic correlation between traits X and Y; $CO{V}_{P_{XY}}$ is the phenotypic covariance matrix of traits X and Y; ${\sigma }_{P_X}^2$ and ${\sigma }_{P_Y}^2$ are the phenotype standard deviation of traits X and Y.

2.6 Population structure and matrix correlation analysis

To investigate the population structure and evaluate the consistency between pedigree-based and genomic-based relationship matrices, genomic data were analyzed using R software. The functions kinship_heatmap and kinship_pca were employed to visualize pairwise kinship relationships and to perform principal component analysis, respectively. Furthermore, the match_G2A function implemented in the ASRgenomics R package was used to assess the concordance between the pedigree-based relationship matrix (A matrix) and the genomic relationship matrix (G matrix).

3 Results

3.1 Phenotypes

Descriptive statistical analysis was conducted on the key wool traits of the FGF5 gene-edited fine-wool sheep, with results presented in Table 1. The average SL in the study population was 10.24 cm, with a coefficient of variation of 12.53%. Meanwhile, measurements for TL, FD, and GFW exhibited considerable variation, with coefficients of variation ranging from 9.06% to 33.41%. These results indicate substantial individual variation in the primary wool traits of the FGF5 gene-edited fine wool sheep population.

Table 1

Table 1. Descriptive statistics of wool traits in FGF5-edited sheep​.

3.2 Population structure and relationship

Kinship heatmap analysis indicated that the gene-edited population could be broadly divided into four familial clusters, which was consistent with the breeding design (Supplementary Figure S1). PCA based on genomic data revealed no clear population stratification between individuals generated by InDel and HDR editing methods (Supplementary Figure S2).

Correlation analysis between the pedigree-based relationship matrix (A) and the genomic relationship matrix (G) showed moderate concordance between the two matrices (Supplementary Figure S3), supporting the use of a combined relationship matrix (H) for genetic parameter estimation in this population.

3.3 Fixed effects

The effects of sex, farm, and editing method on wool traits are summarized in Table 2. Sex had significant effects on SL and GFW, while editing method and farm significantly affected SL, TL, and GFW.

Table 2

Table 2. Results of fixed-effect for wool traits.

3.4 Estimation of heritability of wool traits in multi-trait animal models

The heritability estimates based on the multi-trait animal models LM_A and LM_H are presented in Table 3. Using the LM_A model, moderate heritability estimates were obtained for SL(0.401), TL(0.265), and FD(0.240), whereas GFW(0.153) exhibited low heritability. Similar result were observed under the LM_H model(SL: 0.380, TL: 0.309, FD: 0.302, GFW: 0.164), with smaller standard errors for all traits.

Table 3

Table 3. Estimates of heritability for wool traits in FGF5 gene-edited sheep based on the multi-trait animal model.

3.5 Genetic correlation and phenotypic correlation

The genetic and phenotypic correlations of wool traits in FGF5 gene-edited fine-wool sheep were estimated using both the LM_A and LM_H methods, with the results presented in Table 4. Positive genetic and phenotypic correlations were observed among all traits in this population. Using the LM_A method, the estimated genetic correlation coefficients ranged from 0.425 to 0.969, while the phenotypic correlation coefficients ranged from 0.271 to 0.888; under the LM_H method, the genetic correlation coefficients ranged from 0.434 to 0.967, and the phenotypic correlation coefficients ranged from 0.268 to 0.886. Strong positive genetic correlations were consistently observed among SL, TL, and GFW under both models.

Table 4

Table 4. Genetic and phenotypic correlations based on LM_H and LM_A.

4 Discussion

The focus on SL, TL, FD, and GFW is due to their importance as major economic traits with relatively high weighting in fine-wool sheep breeding. Conducting selection and genetic studies on these traits is expected to facilitate the development of gene-edited fine-wool sheep populations with superior wool quality. Genomic relationship matrices can be applied not only to populations with pedigree information but also to those lacking such data (Teissier et al., 2018; Araujo et al., 2023). Since 97 individuals in this study lacked genomic information, but pedigree records were available for them, we employed the pedigree-based LM_A model and the combined pedigree-genotype LM_H model to estimate genetic parameters. Although the individuals without genomic data accounted for about one-third of the total population, which may affect the accuracy of genetic parameter estimation, the limited number of gene-edited individuals, the difficulty in collecting and measuring phenotypic data, and the need to include more individuals in genetic evaluation and genomic analyses led us to retain these individuals in the study.

4.1 The influence of fixed effects on wool traits

Wool traits are influenced by both genetic and environmental factors, including age (Gowane et al., 2010), breed (Ahmad et al., 2021; Ramos et al., 2023), and year (Wei et al., 2020). In this study, we analyzed the fixed effects that can influence wool traits in the FGF5 gene-edited sheep population. Wool traits are influenced by environmental conditions and may exhibit genotype-by-environment interactions (G×E) (Dominik et al., 2001). Once the breeding population is expanded, we will explicitly model G×E effects to better capture this variation. During nearly a decade of breeding, the FGF5 gene-edited sheep population underwent only one relocation event in 2022. Accordingly, the years before and after relocation were considered as separate fixed effects in the model. The results showed that, following the environmental change, wool length and greasy fleece weight decreased significantly, whereas straightened length increased significantly. Due to the limited population size, all individuals with phenotypic records (130 rams and 151 ewes) were included in the genetic parameter estimation. The results showed that sex had a significant effect on SL and GFW. As an FGF5 gene-edited population, individuals in this study were generated using two gene-editing methods: InDel and HDR, which influence wool traits such as SL, TL, and GFW. Therefore, editing method should be taken into account in the statistical model when estimating heritability and genetic correlations, in order to avoid bias in heritability and genetic correlations generated by confounding additive variance.

4.2 Estimation of heritability

A comprehensive understanding and accurate estimation of genetic parameters are crucial for developing effective genetic evaluation, selection, and breeding programs targeting economically important traits, including wool characteristics (Vargas Jurado et al., 2022). In this study, we estimated the heritability of wool traits in FGF5 gene-edited fine-wool sheep using a multi-trait animal model with both LM_A and LM_H approaches. The results showed that SL (0.401, 0.380), TL (0.265, 0.309), and FD (0.240, 0.302) exhibited moderate heritabilities, suggesting that continued selection within the FGF5 gene-edited sheep population may significantly improve these wool traits. These findings also imply that, beyond FGF5, other genes contribute to the determination of wool length. Moreover, the heritability estimates obtained from LM_H differed from those derived using LM_A, which may be attributed to the limited ability of pedigree-based relationship matrices to accurately capture the true genetic relationships among individuals (Meuwissen et al., 2016). This was further supported by the correlation analysis between the A and G matrices (Supplementary Figure 3). Notably, compared with single-trait models (Supplementary Table 2), the standard errors of heritability estimates derived from the multi-trait model were smaller, indicating greater stability of the results. In addition, the standard errors of heritability estimates obtained from the single-trait model under LM_H were smaller than those from the multi-trait model under LM_A, suggesting that incorporating genomic information into the relationship matrix can improve the accuracy of genetic evaluation in FGF5 gene-edited sheep. The estimation errors of the models used in this study were greater than those reported in other fine-wool sheep populations, which is likely attributable to the relatively small sample size of this population. Nevertheless, this result does not undermine the application of the multi-trait animal model for genetic evaluation in the gene-edited fine-wool sheep population. By integrating genetic correlations among traits, this approach effectively enhances the accuracy and stability of parameter estimation.

4.3 Genetic correlation and phenotypic correlation of wool traits

The genetic correlations among wool traits play a significant role in the selection of related traits. In modern efficient livestock breeding, viable breeding programs typically require simultaneous selection of multiple traits, with both genetic and phenotypic correlations among traits are crucial for optimizing breeding strategies (Zhang, 2018). In studies on fine-wool sheep, genetic correlations between SL and GFW, and FD in Australian Merino sheep were 0.29 and 0.13, respectively (Swan et al., 2008); while in the Chinese superfine Merino population (Di et al., 2011), the genetic correlations between SL and GFW, and FD were 0.53 and 0.29, respectively, with a strong genetic correlation of 0.60 observed between FD and GFW.

This study found positive genetic and phenotypic correlations to varying degrees among wool traits in the gene-edited sheep. Among these, SL and TL exhibited strong genetic and phenotypic correlations (ranging from 0.886 to 0.969), possibly because TL is derived from SL. Using the LM_H method, the genetic correlation between GFW and FD was 0.612, consistent with the findings of Di et al (Di et al., 2011). SL and GFW also demonstrated strong positive genetic correlations, ranging from 0.518 to 0.526. The genetic correlations observed in this population were higher than those reported in conventional fine-wool sheep. This may be due to the unique background of the population, including the gene-editing treatment, limited sample size, and an unbalanced age structure. Nevertheless, the consistently high correlations among traits suggest that indirect selection can be effectively applied even in a gene-edited population of limited scale, whereby improving multiple correlated traits is possible by selecting for a single key trait.

5 Conclusion

This study provides the first population-level evidence that wool traits resulting from FGF5 gene editing can be treated as stable, heritable quantitative traits suitable for long-term genetic improvement. Using multi-trait LM_H models that integrate pedigree and genomic information, we show that staple length, straightened length, fiber diameter, and greasy fleece weight exhibit moderate heritability and meaningful genetic correlations in the edited population. These results demonstrate that the phenotypic advantages conferred by FGF5 knockout possess sufficient additive genetic variation to respond to routine selection.

The detected environmental effects associated with relocation further indicate that edited traits remain sensitive to management conditions, suggesting potential genotype-by-environment interactions. Overall, our findings confirm the feasibility of incorporating FGF5 gene-edited individuals into breeding programs and lay the foundation for applying targeted gene-editing technologies to accelerate genetic improvement in fine-wool sheep.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found here: https://ngdc.cncb.ac.cn/gvm/getProjectDetail?Project=GVM001291, PRJCA055805.

Ethics statement

The animal studies were approved by Institute of Biotechnology, Xinjiang Uygur Autonomous Region Academy of Animal Husbandry Sciences. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the owners for the participation of their animals in this study.

Author contributions

CY: Software, Writing – original draft. JK: Methodology, Writing – review & editing. CL: Data curation, Investigation, Writing – review & editing. BH: Data curation, Investigation, Writing – review & editing. ZL: Conceptualization, Data curation, Project administration, Writing – review & editing. WL: Conceptualization, Project administration, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was funded by Special Fund Project for Basic Scientific Research Operations of Xinjiang Uygur Autonomous Region Academy of Animal Husbandry Sciences (KY202472) and Tianshan Talents Program (2022TSYCLJ0013).

Conflict of interest

Author JK was employed by company Beef + Lamb New Zealand Genetics.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fanim.2025.1712295/full#supplementary-material

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