Optimal Combination of Different Selection and Mating Strategies on Exploiting Genetic Diversity and Genetic Gain in Small Pig Conservation Populations

ABSTRACT

Both selection and mating systems are essential tools for breeders to conserve the genetic variance and improve the performance of livestock animals. How to effectively balance the genetic gain and inbreeding has always been an important issue in quantitative genetics research. In this study, a total of 11 selection methods, including random and truncation selection, six conventional selection methods, three different optimal contribution selection (OCS) methods and three mating strategies including random mating, minimum‐coancestry mating based on pedigree (MCPed) and genomic information (MCmarker), were performed using stochastic simulations. The long‐term effects of different combinations of selection and mating strategies on the genetic gain, the rate of inbreeding and genetic diversity in the small‐scale pig conservation populations were investigated. The results showed that different strategies of selection and mating methods had different effects on genetic gain and inbreeding rate. For maintaining additive genetic variance, the optimal strategy was random selection with random mating, followed by SIREhalf‐DAMfullRandom selection (which means selecting dams randomly from each full‐sib family) and random mating. For mainting the number of common ancestors, the optimal strategy was SIREhalf‐DAMfull selection (which means selecting dams with the highest estimated breeding value within each full‐sib family) and random mating, followed by SIREhalf‐DAMfullRandom selection and random mating, OCS and MCPed mating. For genetic diversity metrics, taking He and Ho as an example, the optimal strategy was GOCS (optimal contribution selection based on genomic information) with MCmarker mating. For genetic gain, the optimal strategy was truncation selection and MCmarker mating, followed by POCS (optimal contribution selection based on pedigree information) and MCmarker mating, truncation selection and MCPed mating. For the rate of inbreeding, the optimal strategy was SIREhalf‐DAMfull selection and MCPed mating. Our findings can help breeding managers and farmers choose a more suitable and sustainable strategy for maintaining the genetic diversity and improving the genetic gain of local pig breeds.

Abbreviations

A _e

effective allele number

delta F

the rate of inbreeding

delta G

the rate of genetic gain

EBV

estimated breeding value

GOCS

optimal contribution selection based on genomic information

GOCS‐0cM

optimal contribution selection based on genomic relationship matrix constructed using all markers

GOCS‐1cM

optimal contribution selection based on genomic relationship matrix constructed using the markers excluding those with a distance from a random QTL less than 1 cM

GS

genetic selection

H _e

expected heterozygosity

H _o

observed heterozygosity

MC

minimum coancestry mating

MCAC

minimising the covariance between ancestral contributions

MCmarker

minimum‐coancestry mating based on genomic information

MCPed

random mating, minimum‐coancestry mating based on pedigree

OCS

optimal contribution selection

POCS

optimal contribution selection based on pedigree information

ROH

runs of homozygosity

Sirehalf‐Damfull

scenario selected dams with the highest EBV within each full‐sib family

Sirehalf‐DamfullRandom

scenario selected dams randomly from each full‐sib family

Sirehalf‐Damhalf

scenario selected dams with the highest EBV within each half‐sib family

Sirehalf‐DamhalfRandom

scenario selected dams randomly from each half‐sib family

Sirehalf‐DamRandom

scenario selected dams randomly without considering the families

Sirehalf‐Damtrunc

scenario selected dams with the highest EBV without considering the families

1. Introduction

Genetic selection (GS) usually pays more attention to the genetic gain of the target traits in the current generation, but less to the genetic relationship between breeding animals, and thus leads to an increase in inbreeding and a decrease in genetic diversity of the population. The loss of diversity, in turn, limits long‐term genetic gain of the traits under selection (Jannink 2010). Optimal contribution selection (OCS) provides an approach that achieves a balance between rate of inbreeding and genetic gain (Meuwissen 1997; Grundy, Villanueva, and Woolliams 1998; Woolliams et al. 2015). In addition to selection, mate allocations have an influence on the overall genetic merit and inbreeding of future offspring (Akdemir and Sanchez 2016). Selection and mating systems are both essential tools for breeders to conserve the genetic variance and improve the performance of livestock animals. By improving mating system, the rate of inbreeding and probability of gene loss can be reduced without increasing the number of boars and sows. The easiest way to control the rate of inbreeding through mating is to artificially avoid the mating among individuals with close genetic relationship such as full‐sibs families or half‐sibs. Another approach is to allocate mating by minimising genetic relationship between mates. Appropriate mating strategy plays a vital role in the conservation and breeding plan of livestock.

There have been a number of studies on investigating the effects of mating strategy on rate of inbreeding and genetic gain in livestock. Sonesson and Meuwissen (2000) compared the impact of several mating methods on genetic gain using stochastic simulations, which included random mating, compensatory mating, minimum coancestry mating (MC) and minimum variance of relationship of offspring mating. Henryon, Sorensen, and Berg (2009) concluded that the allocation of mates by minimising the covariance between ancestral contributions (MCAC) would result in a lower level of inbreeding without compromising genetic gain in breeding schemes when using truncation selection. Liu, Henryon, and Sorensen (2017) further found that MCAC based on genomic information would lead to lower inbreeding than that based on pedigree. Berodier et al. (2021) studied how to use genomic information to improve the mating plan of dairy cattle at the herd level. Pryce, Hayes, and Goddard (2012) compared the results of mating using different inbreeding coefficient based on pedigree, genome or runs of homozygosity (ROH) information in Holstein cattle. Sun et al. (2013) investigated the impact of mating plans including dominant effects on three dairy cattle populations. Akdemir and Sanchez (2016) concluded that the approach combined the measures of inbreeding and risk into the mating plan for breeding complex traits was more favourable than phenotypic and genomic selection.

There are a number of small‐scale local pig breeds in China, and it therefore is very important to protect and utilise these precious local pig breeds. In order to protect these local pig breeds, the breeders and farmers usually keep the same number of offspring of each family, then proceed to mating plan in the actual operation of these conservation farms. He et al. (2020) investigated different selection methods including truncation selection and genomic OCS in Ningxiang pig breeds, and indicated that genomic OCS appeared to be a sustainable strategy for the genetic improvement of local breeds. Zhao et al. (2021) also indicated that genomic OCS was a better choice if considering both genetic gain and genetic diversity in a conservation population. However, there are limited studies on investigating the effects of a combination of different selection and mating scenarios on genetic diversity and genetic gain in small pig conservation populations in a long time horizon. Therefore, the objective of this study was to investigate the long‐term effects of different mating strategies with different selection scenarios on inbreeding rate, genetic diversity and genetic gain in small conservation pig populations using stochastic simulations.

2. Methods

2.1. Selection Methods

A total of 11 selection scenarios was simulated, which includes six conventional methods (Sirehalf‐Damfull, Sirehalf‐DamfullRandom, Sirehalf‐Damhalf, Sirehalf‐DamhalfRandom, Sirehalf‐Damtrunc and Sirehalf‐DamRandom), three optimal contribution selection methods (GOCS‐0cM, GOCS‐1cM and POCS). In addition, random selection and truncation selection methods are regarded as two reference methods. In each conventional methods, the animals with the highest estimated breeding value (EBV) within each sire family were selected as the sires of next generation. The dams were selected using six different methods. Sirehalf‐Damfull scenario selected dams with the highest EBV within each full‐sib family. Sirehalf‐Damhalf scenario selected dams with the highest EBV within each half‐sib family. Sirehalf‐Damtrunc scenario selected dams with the highest EBV without considering the families. Sirehalf‐DamfullRandom scenario selected dams randomly from each full‐sib family. Sirehalf‐DamhalfRandom scenario select dams randomly from each half‐sib family. Sirehalf‐DamRandom scenario select dams randomly without considering the families. As for optimal contribution selection (OCS) methods, three relationship matrices were used to constrain the rate of inbreeding. POCS used pedigree‐based relationship matrix, GOCS‐0cM used a genomic relationship matrix constructed using all markers, and GOCS‐1cM used a genomic relationship matrix constructed using the markers excluding those with a distance from a random QTL less than 1 cM. OCS methods allocated selection candidates in all generations according to EBV and relationships between all the involved animals. More details about OCS method can be found in Zhao et al. (2021). A range of penalty $\omega$ (1, 5, 10 …100) was applied to examine the pattern of genetic gain with different inbreeding rates when using OCS. EVA programme (Berg, Nielsen, and Sørensen 2006) was used to perform POCS and GOCS.

2.2. Mating Methods

We used two mating methods including random mating and minimum‐coancestry mating (MC) in this study. In the random mating scenario, male and female animals were mated with no mating restrictions, which means all the individuals are potential partners. Minimum‐coancestry mating method was previously found to minimise the relationship between breeding male and female animals and the rate of inbreeding of their offspring using pedigree information (Sonesson and Meuwissen 2000). In the current study, the additive genetic correlation matrix between individuals was constructed using pedigree ($A$ matrix) and genomic information ($G$ matrix). The construction method refers to Meuwissen and Luo (1992) and Yang et al. (2011). The mating methods were called MCPed and MCmarker. $A$ and $G$ are a matrix of $N_s\times N_d$, where $N_s$ and $N_d$ are the number of sires and dams selected. The expected coancestry of individuals $i$ and $j$ is $\frac{1}{2}{A}_{\mathit{ij}}$ (or $\frac{1}{2}{G}_{\mathit{ij}}$), and this coancestry is equal to the inbreeding coefficient of the offspring (Liu, Henryon, and Sorensen 2017).

MC mating was performed using the approach described in Henryon, Sorensen, and Berg (2009). The algorithm used to find the minimum‐coancestry matings was carried out by simulated annealing algorithm (Sonesson and Meuwissen 2000), but modified it to obtain the MC matings including the dams and sires from four randomly sampled matings in each circle of sampling (Henryon, Sorensen, and Berg 2009).

2.3. Simulation Procedure

Stochastic simulation was used to estimate the long‐term genetic gain and genetic diversity in different selection and mating strategies. Data of 100 replicates for each selection and mating methods were generated by QMSim software (version1.10) (Sargolzaei and Schenkel 2009). The simulated population structure and the genome parameters are shown in Tables 1 and 2.

TABLE 1.

Simulation parameters of the population.

Stages	Population structure	Value
Stage 1: Historical population	Number of historical generations	2000
	Population size	200
	Sex ratio of the offspring	0.5
	The number of males in the last historical generation	100
	The number of females in the last historical generation	100
Stage 2: Base population	Number of selected sires	12
	Number of selected dams	120
	Litter size	5
Stage 3: Recent population	Number of generations	20
	Number of replicates	100
	Phenotypic variance	1
	Heritability	0.2

TABLE 2.

Simulation parameters of the genome.

Genome parameters	Value
Number of chromosomes	18
Chromosome length	100 cM
Starting allele frequencies in the historical population	0
Number of marker loci on one chromosome	2000
Marker positions	Evenly
Number of QTL loci on one chromosome	20
QTL positions	Randomly
QTL allele effect	A gamma distribution with a shape parameter of 1.48
Mutation rate	0.00025
Minimum of QTL allele frequency	0.01
Minimum of marker allele frequency	0.05
Number of loci on each chromosome for tracking IBD	100

Briefly, each scenario was run for 20 discrete generations and 100 replicates. The animals were selected based on a single trait controlled by 360 quantitative trait loci (QTL). For OCS methods, the sires were mated with 0, 1, 2, … or 120 dams by EVA software (Berg, Nielsen, and Sørensen 2006). The number of selected dams is 120 and these dams had one single mating and produced five offspring in each generation. For other methods, 12 males were selected and each male was mated to 10 females in each generation. More details about simulation procedure can be found in Zhao et al. (2021).

2.4. Statistical Analysis

The rate of inbreeding and genetic gain for each combination of selection and mating strategies were presented as mean of the 100 replicates. The rates of inbreeding in each generation were calculated as $\left(F_t-F_{t-1}\right)/\left(1-F_{t-1}\right)$, where $F_t$ is the level of inbreeding in the $t$ th generation. The rate of genetic gain in each replicate was calculated as a linear regression of $G_t$ on $t$, where $G_t$ is the average true breeding value of animals born in generation, $t=1\dots 20$ in each replicate.

We also calculated some common genetic diversity metrics, such as expected heterozygosity ($H_e$), observed heterozygosity ($H_o$), effective allele number ($A_e$) and the number of the polymorphic gene loci with minor allele frequency no less than 0.01 (M01) or 0.05 (M05). More details for the formulas of above metrics can be seen in Zhao et al. (2021).

3. Results

3.1. Rate of Genetic Gain and True Inbreeding

On the whole, the results of the rate of genetic gain and true inbreeding of different selection methods based on MCmarker and MCPed are shown in Figure 1. As shown in Figure 2 and Table 3, the rate of genetic gain of these two minimum‐coancestry mating methods was slightly higher than that of random mating regardless of the selection method. Among all the selection methods, the highest relative increase was observed in random selection scenario where the rate of genetic gain was 0.0013 when using random mating, 0.0016 when using MCmarker, corresponding to an increment s about 25.9%. In Sirehalf‐Damtrunc scenario, there was a minimal relative increase (from 0.3007 to 0.3075), which was about 2.3%. However, the Sirehalf‐Damfull scenario had the highest absolute increase (from 0.2418 to 0.2794), which corresponded to a relative increase of 15.5%. In the truncation selection, the rate of genetic gain was increased from 0.3232 to 0.3422 when changing mating method from random mating to MC mating.

FIGURE 1.

The rate of genetic gain and true inbreeding of different selection and mating strategies. (a) Different selection methods with MCPed mating; (b) different selection methods with MCmarker mating. [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 2.

The rate of genetic gain of different selection and mating strategies in the 20th generation. delta G, The rate of genetic gain. [Colour figure can be viewed at wileyonlinelibrary.com]

TABLE 3.

The rate of genetic gain of different selection and mating strategies in the 20th generation.

Selecting method	Mating method
	Random	MCmarker	MCPed
Random	0.001	0.002	−0.002
Truncation	0.323	0.342	0.333
SIREhalf‐DAMfull	0.242	0.279	0.282
SIREhalf‐DAMfullRandom	0.193	0.209	0.209
SIREhalf‐DAMhalf	0.274	0.295	0.291
SIREhalf‐DAMhalfRandom	0.193	0.213	0.209
SIREhalf‐DAMRandom	0.198	0.212	0.211
SIREhalf‐DAMtrunc	0.301	0.308	0.319
GOCS‐0cM (p10)	0.278	0.287	0.281
GOCS‐1cM (p10)	0.282	0.292	0.287
POCS (p10)	0.323	0.331	0.327

Compared with traditional selection methods, switching random mating to MC mating led to less increment of genetic gain in three OCS methods. The relative increase was 3.0% in GOCS‐0cM, 3.4% in GOCS‐1cM and 2.4% in POCS. Among them, the rate of genetic gain increased by 1% in GOCS‐1cM than that in POCS. And the increment rate of genetic gain in GOCS‐1cM was 0.4% higher than that in GOCS‐0cM (Figure 2 and Table 3). These results showed that, as for the rate of genetic gain, there was more increment in two GOCS methods than that in POCS if we used MC mating rather than random mating. However, no matter what mating method was used, the rate of genetic gain in POCS was higher than that in GOCS.

On the whole, the selection method with the highest genetic gain was truncation selection, followed by POCS, SIREhalf‐DAMtrunc and SIREhalf‐DAMfullRandom regardless of mating strategies. Furthermore, the genetic gain obtained by MCmarker and MCPed was almost the same when using traditional selection methods. However, the rate of genetic gain from MCmarker was slightly higher than that from MCPed when using GOCS and POCS. Compared to random mating, the MC mating could effectively improve the rate of genetic gain in the truncation selection scenario, and the effect of MCmarker was better than that of MCPed.

Figure 3 and Table 4 showed that truncation selection caused the highest rate of inbreeding compared to other selection methods, especially in the case of random mating. The minimum‐coancestry mating based on genome and pedigree information effectively reduced inbreeding rate in the case of truncation selection. The rate of inbreeding was reduced by 56% in MCmarker and 55% in MCPed, respectively. In traditional selection methods (such as SIREhalf‐DAMfull, SIREhalf‐DAMfullRandom and SIREhalf‐DAMhalf), the rate of inbreeding with the random mating was slightly lower than that with two MC mating scenarios. But in the OCS method, the MC mating strategies, especially MCmarker mating had a better effect in controlling the rate of inbreeding than that random mating. However, in general, the rates of inbreeding of these three mating strategies in each selection method were only little different except for truncation selection.

FIGURE 3.

The rate of inbreeding of different selection and mating strategies in the 20th generation. delta F, the rate of inbreeding. [Colour figure can be viewed at wileyonlinelibrary.com]

TABLE 4.

The rate of true inbreeding of different selection and mating strategies in the 20th generation.

Selection method	Mating method
	Random	MCmarker	MCPed
Random	0.011	0.010	0.011
Truncation	0.036	0.016	0.016
SIREhalf‐DAMfull	0.008	0.010	0.007
SIREhalf‐DAMfullRandom	0.008	0.009	0.010
SIREhalf‐DAMhalf	0.009	0.010	0.010
SIREhalf‐DAMhalfRandom	0.008	0.009	0.009
SIREhalf‐DAMRandom	0.009	0.009	0.010
SIREhalf‐DAMtrunc	0.011	0.011	0.012
GOCS‐0cM(p10)	0.009	0.008	0.008
GOCS‐1cM(p10)	0.009	0.008	0.008
POCS(p10)	0.011	0.010	0.011

3.2. Additive Genetic Variance and Number of Common Ancestors

As for the additive genetic variance (Figure 4a), on the whole, except for truncation selection, random mating maintained a larger additive genetic variance than MC mating, especially in traditional selection methods. However, when using OCS methods, the difference in additive genetic variance between MC mating and random mating was much smaller, compared with using other selection methods. With random mating, the highest level of additive genetic variance was observed in random selection, followed by SIREhalf‐DAMfullRandom, SIREhalf‐DAMhalfRandom and SIREhalf‐DAMRandom, while Truncation, POCS and SIREhalf‐DAMtrunc were the lowest.

FIGURE 4.

(a) Additive genetic variance and (b) number of common ancestors of different scenarios in the 20th generation. [Colour figure can be viewed at wileyonlinelibrary.com]

For the two most popular conservation methods (SIREhalf‐DAMfull and SIREhalf‐DAMhalf) in the current pig conservation farms, the additive genetic variance of SIREhalf‐DAMfull selection was lower than that of SIREhalf‐DAMhalf selection when using random mating. However, when the mating strategy was changed to MCmarker mating, the additive genetic variance of SIREhalf‐DAMfull selection was higher than that of SIREhalf‐DAMhalf selection. Furthermore, when using MCPed mating, the two selection methods led to the same additive genetic variance. Thus, it could be seen that different mating methods had different impact on maintaining additive genetic variance, dependent on different selection methods.

Figure 4b shows the number of common ancestors in the 20th generation. It could be seen that regardless of the mating strategy, SIREhalf‐DAMfull and SIREhalf‐DAMfullRandom maintained the largest number of common ancestors, followed by GOCS‐0cM, GOCS‐1cM and POCS. In some traditional selection methods, random mating maintained a larger number of common ancestors than MC mating (both MCPed and MCmarker mating). However, the effects of these three mating strategies were almost the same when using the GOCS and POCS methods.

3.3. Genetic Diversity in Different Scenarios

Consistent with the results of the rate of inbreeding, truncation selection had the lowest values of diversity indicators such as H _e , H _o , A _e and M01. As shown in Figure 5, the results for these indicators were almost the same. Taking H _e and H _o as examples, random mating maintained a higher level of genetic diversity than MC mating under traditional selection methods such as SIREhalf‐DAMfull, SIREhalf‐DAMfullRandom, SIREhalf‐DAMhalf, SIREhalf‐DAMhalfRandom and SIREhalf‐DAMRandom, which was also consistent with the results of additive genetic variance. However, the level of genetic diversity of these three mating strategies tended to be the same when the selection methods were OCS (GOCS‐0cM, GOCS‐1cM and POCS). Only when the truncation selection was used, MC mating maintained genetic diversity at a higher level than random mating. In general, according to the results of various diversity indicators, genetic diversity was highest by GOCS combined with MCmarker mating strategy.

FIGURE 5.

Genetic diversity metrics of different scenarios in the 20th generation. [Colour figure can be viewed at wileyonlinelibrary.com]

3.4. Trends Across 20 Generations for Each Metric Under MCmarker and MCPed Mating Methods

The trends across 20 generations for each metric under different selection methods with MCmarker and MCPed mating were shown in Figures 6 and 7. From the results, we could see that OCS methods were better than the conventional conservation method when the penalty p was increased to 10, either GOCS or POCS.

FIGURE 6.

The trends of genetic diversity metrics across 20 generations under the MCmarker mating method. [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 7.

The trends of genetic diversity metrics across 20 generations under the MCPed mating method. [Colour figure can be viewed at wileyonlinelibrary.com]

4. Discussion

Mating strategy in a breeding programme usually considers genetic gain, the relationship of the selected parents and inbreeding of the offspring (Oldenbroek 2007). In this study, we compared the effects of different combinations of selection and mating strategies mainly based on the rate of inbreeding, the rate of genetic gain and genetic diversity of offspring.

Regarding the rate of inbreeding, our results indicate that mating strategy has a large effect on rate of inbreeding in the situation of truncation selection, but small effect when using the selection methods that are popularly adopted in the current pig conservation farms where same number of offspring of each family were selected for breeding. Therefore, from this point of view, if the conservation farms strictly retain the same amount of offspring for each family, both random mating and MC mating can be used, choice of mating methods can be varied and the impact on inbreeding will be small. However, in the breeds popularly used for commercial pig production (such as Yorkshire, Landrace and Duroc), we always pay more attention to the genetic improvement of some economic important traits (Akdemir and Sanchez 2016) and choose truncation selection. In this case, the choice of mating strategies will be relatively important. The MC mating can effectively reduce the rate of inbreeding caused by truncation selection (Figure 3 and Table 4). According to the results of genetic diversity metrics, the levels of expected heterozygosity of the random mating were higher than that of MC mating when using the traditional selection methods. But the effect of MCmarker mating on observed heterozygosity was almost the same as that of random mating (Figure 5). This result indicated that the MC mating based on genomic information could effectively improve the level of heterozygosity to a certain degree.

The results of this study showed that there was a difference between MC mating based on genomic information and MC mating based on pedigree information, and difference was dependent of selection methods. Furthermore, the differences caused by mating strategies were smaller in the OCS scenarios than those in traditional selection method scenarios including random selection and truncation selection. In other words, if we use traditional selection methods, mating strategy will affect the genetic gain, the rate of inbreeding, additive genetic variance and genetic diversity of the pig populations. On the contrary, if we adopt the OCS method, the mating strategy will not have as much effect on these items.

The most classic traditional selection method currently used in the conservation farms is SIREhalf‐DAMfull, which keeps the same number of offspring for each sire and dam family as breeding animals (Zhao et al. 2021). In this case, the use of MCPed and MCmarker mating strategies can effectively increase genetic gain, and MCPed can also reduce the rate of inbreeding (Figures 2 and 3). Therefore, the MC mating based on pedigree may be a good choice if we use SIREhalf‐DAMfull in pig conservation farms. With regard to combination of selection methods and mating strategy, the GOCS method with the MCmarker mating strategy will be the best combination to maintain a high genetic diversity level and obtain large genetic gain at the same time.

5. Conclusion

In this study, we explored the long‐term effects of different combinations of selection and mating strategies on the genetic gain, the rate of inbreeding and genetic diversity in the small‐scale pig conservation populations. In conclusion, different combination of selection methods and mating strategies had different effects on these metrics. Furthermore, the effects of different mating strategies were affected by different selection methods. For maintaining additive genetic variance, the optimal strategy was random selection with random mating, followed by SIREhalf‐DAMfullRandom selection and random mating. For keeping the number of common ancestors, the optimal strategy was SIREhalf‐DAMfull selection and random mating, followed by SIREhalf‐DAMfullRandom selection and random mating, OCS (GOCS‐0cM, GOCS‐1cM and POCS) and MCPed mating. For genetic gain, the optimal strategy was the combination of truncation selection and MCmarker mating, followed by the combination of POCS and MCmarker mating, truncation selection and MCPed mating. For the rate of inbreeding, the optimal strategy was SIREhalf‐DAMfull selection and MCPed mating. For each genetic diversity metrics, taking He and Ho as an example, the optimal strategy was the combination of GOCS and MCmarker mating.

Author Contributions

G.S., Y.P., Q.Z. and H.L. conceived the study. Y.P., P.L. and R.H. supervised the study, Q.Z. and H.L. ran the simulation, analysed the data and wrote the manuscript. Q.Z., G.S. and H.L. interpreted the results. All authors revised and approved the final manuscript.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.