# Transmissibility model to evaluate transgenerational transmission of environmental effects in quails

Keyvan Karami, Stacy Rousse, Frederique Pitel, Sonia Eynard, David

Gourichon, Sophie Leroux, Anne Ricard, Ingrid David

► To cite this version:

Keyvan Karami, Stacy Rousse, Frederique Pitel, Sonia Eynard, David Gourichon, et al.. Transmissibility model to evaluate transgenerational transmission of environmental effects in quails. Animal, In press, pp.101636. 10.1016/j.animal.2025.101636. hal-05240726

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# Transmissibility model to evaluate transgenerational transmission of environmental effects in quails

<figure><img src="image_2.png" /></figure>

K. Karami<sup>a,*</sup>, S. Rousse<sup>a</sup>, F. Pitel<sup>a</sup>, S. Eynard<sup>a</sup>, D. Gourichon<sup>b</sup>, S. Leroux<sup>a</sup>, A. Ricard<sup>c,d</sup>, I. David<sup>a</sup>

## ARTICLE INFO

**Article history:**
Received 30 May 2025
Revised 20 August 2025
Accepted 21 August 2025
Available online 28 August 2025

**Keywords:**
Coturnix japonica
Epigenetic
Genistein
Non-genetic
Transgenerational inheritance

## ABSTRACT

Both genetic and non-genetic information, which contribute to the development of phenotypes, can be transmitted across generations. Non-genetic information can be modified by the environment, allowing environmental effects to be transmitted to the next generation. The objective of the present study was to use an experimental design in quail combined with the transmissibility models, to demonstrate the vertical transmission of environmental effects. The study involved creating two lines: a control line and a treatment line, where female quails were fed a genistein-supplemented diet. Genistein is an endocrine disruptor that affects methylation patterns. Then, reproduction of these lines was conducted in a mirrored mating design over three generations, with a total of 1 566 animals studied. Quails underwent behavioural tests measuring locomotor activity and were weighed at one, four, and 7 weeks of age. At slaughter, body and abdominal adipose weights were recorded. The transmissibility model was a mixed model with fixed effects of rearing condition, sex, generation, age at time of measurement and direct effect of the genistein (on the animal, embryo and germ cells) and the random effect of the transmissible potential of the animal which transmissibility matrix contains sire and dam path coefficients of transmission that have to be estimated conversely to the genetic model where they are fixed to 0.5. The transmissibility model with environment included additional covariance for offspring of genistein-fed dams which is then transmitted to next generations via sires and dams. Models were chosen according to the likelihood ratio test. The proportion of transmitted variance varied from 0.34 (at 1 week) to 0.72 (at 7 weeks) for BW, 0.20 for the behavioural trait and 0.22 for adiposity. The estimated sire and dam path coefficients ranged from 0.42 to 0.52, but were not significantly different from 0.50. With the transmissibility model with environment, the additional covariance was significantly different from zero for BW traits, indicating transgenerational transmission of genistein's effects. This value corresponded to a high correlation at 1 week (0.44) but low for other BW traits. The transmissibility model with environment demonstrated the transgenerational transmission of genistein-induced environmental effects on BW traits in quail, putatively mediated by epigenetic inheritance. The findings suggest that beneficial environmental factors can positively impact multiple generations, and combining this with genetic selection could accelerate adaptation to new environmental challenges.

© 2025 The Authors. Published by Elsevier B.V. on behalf of The animal Consortium. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

## Implications

Non-genetic information affected by environmental modifications have been reported as a key factor in explaining how wild species adapt to environmental constraints in complement to genetic information. The objective of this study was to demonstrate the vertical transmission of controlled environmental effect (genistein) using a quail experiment and transmissibility models.

The results confirmed transgenerational transmission of environmental effects induced by genistein on BW traits in quail. Thus, it is possible to act favourably on several generations via the transmission of environmental effects, offering the possibility to speed up response to selection.

## Introduction

It has been reported that both genetic and non-genetic information that contribute to the development of phenotypes can be transmitted across generations (Danchin et al., 2011). Non-

<footnote><sup>a</sup>GenPhySE, Université de Toulouse, INRAE, ENVT, 31326 Castanet-Tolosan, France</footnote>

<footnote><sup>b</sup>PEAT, UE1295 - INRAE Centre Val de Loire, 37380 Nouzilly, France</footnote>

<footnote><sup>c</sup>GABI, Université Paris Saclay, INRAE, AgroParisTech, 78350 Jouy-en-Josas, France</footnote>

<footnote><sup>d</sup>Institut Français du Cheval et de l'Equitation, Département Recherche et Innovation, 61310 Exmes, France</footnote>

<footnote>* Corresponding author.
E-mail address: keyvan.karami@inrae.fr (K. Karami).</footnote>
genetic information can be categorised into two groups based on their mode of transmission (David and Ricard, 2019): those transmitted via a physical transmission support, such as epigenetic mechanisms and the microbiota, and those transmitted through learning processes (behavioural/cultural inheritance). Epigenetic mechanisms include histone modifications, ncRNAs and DNA methylation, all of which modulate gene expression (Kim et al., 2009). Their impact on phenotypes has been studied for various traits such as heat stress and production (Thompson et al., 2020; Charlesworth et al., 2017). The microbiota refers to the diverse community of microorganisms (bacteria, archea, fungi... (Berg et al., 2020)) found in and on all multicellular organisms. Numerous studies have investigated its impact on various host phenotypes, including health, resilience, feed efficiency, and prolificacy (Alexandre et al., 2024; Mancin et al., 2024; Martinez Boggio et al., 2024). Certain transmitted behaviours also play a part in the construction of phenotypes, such as the transmission of self-medication behaviours in sheep, which can influence their health (Sanga, 2010). Unlike DNA, the non-genetic information is susceptible to environmental modifications, making it a key factor in explaining how wild species adapt to environmental constraints in complement to genetic information (O'Dea et al., 2016; Bonduriansky et al., 2012; Jablonka and Lamb, 2008). Therefore, considering non-genetic inherited effects for the selection of future reproducers -by mimicking a part of natural adaptive processes- may offer the possibility to accelerate response to selection and enhance the adaptation of farm animals to new environments associated with the agroecological transition and climate change (David et al., 2019). Indeed, from the point of view of the inclusive evolutionary synthesis, vertically transmitted non-genetic effects enable the transfer of recently acquired environmental information to offspring, thereby facilitating adaptation (Danchin et al., 2019). However, demonstrating the transgenerational transmission of controlled environmental effects presents an even greater challenge than proving the transgenerational transmission of non-genetic information (Fallet et al., 2023). Especially when epigenetics is suspected to mediate the transmission of the environmental effect, it is crucial to distinguish between multi- and transgenerational transmission (Skinner, 2008). Studies aiming to demonstrate such transmission typically compare the phenotypic performances of descendants from exposed and unexposed parents to particular environment using ANOVA method (Sun et al., 2023; Sobolewski et al., 2020; Leroux et al., 2017; Braunschweig et al., 2012; Guerrero-Bosagna et al., 2012; Anway et al., 2005). However, observed differences between groups may result from genetic drift, which cannot be corrected for by accounting for genetic effects without adverse effects on the difference due to non-genetic effects. Indeed, contrary to what might be expected, comparing phenotypes corrected for additive genetic effects using pedigree information is not a feasible alternative because, if there is a transmissible environmental effect, the additive genetic values will include this effect in their (best linear unbiased) predictions, which will then no longer be detectable by comparing corrected phenotypes (David and Ricard, 2023). To address this issue, David and Ricard (2023) proposed the transmissibility model with environment, which allows for the modelling of any type of vertical transmission of information across generations. This model also accounts for the transmission of environmental effect by incorporating and testing additional covariance between individuals experiencing the same environment. Its effectiveness in detecting transgenerational environmental effects without confounding them with genetic drift has been demonstrated through simulations. The objective of the present study is to use an experimental design in quail combined with the transmissibility models, to demonstrate the vertical transmission of environmental effects.

## Material and methods

### Animals and experimental design

An experiment, described in detail in Rousse et al. (2025), was conducted in Japanese quails (Coturnix japonica) at the INRAE UE1295 PEAT Poultry Experimental Unit (Nouzilly, France, https://doi.org/10.15454/1.5572326250887292E12). Briefly, the initial population consisted of 23 males and 23 females of different families mated to ensure family crossing to give birth to 9 offspring per couple on average. From each couple, two sets of one male and two female offspring were retained, with each male subsequently mated to two sisters from another couple: one sister on a control standard diet and the other sister on a genistein-supplemented diet using oral capsules of 100 mg (Lin et al, 2004) for 30 days. The duration was determined based on a preliminary test experiment (with unpublished results) that we conducted prior to this study to confirm the presence of genistein in the eggs of females over the 30-day period. This setup resulted in two groups: a control line (Geni-, descendants of females not eating genistein) and a treatment line (Geni+, descendants of females eating genistein) (Supplementary Figure S1). At about 21 weeks of age, these quails were reproductively mature and produced their first progeny. Genistein is an endocrine disruptor, found naturally in soy. Its effect on phenotypes is supposed to be partly mediated by modifications of methylation patterns (Silva et al., 2019). After this initial step of line creation, three generations were then produced for each line (~200 animals per line and generation) with exact parallel pedigrees via mirrored single-pair mating (~2*40 couples) at each generation. This mirrored design is employed to reduce genetic variability between lines by ensuring consistent ancestor contributions across generations. The total number of phenotyped animals in the population was 1 566 (Table 1). This population includes four generations, from the females that ingested genistein to their great-grand offspring, who were not exposed to genistein at all. This setup enables a transgenerational study of its effects. Animals were put through a behavioural test at about 16 weeks of age. The behavioural test consisted in evaluating the response of an animal to isolation by placing the individual in a square arena (80 L × 80 l × 80 h cm) of white wood with a floor made of beige linoleum for 300 s, and measuring the time spent in each area of the arena (the centre of the arena, a square area of 20 × 20 cm, or outside) and the locomotor activity that corresponded to the distance covered by the animal during the test. In addition, animals were weighed at one, four and 7 weeks of age. At slaughter (around 30 weeks of age), body and organ weights (heart, liver, testis for male, abdominal adipose tissue) were recorded for several generations. Only abdominal adipose tissue was recorded on both sexes in all generations. Reproductive performances of the females were also recorded (age at first egg, number of eggs produced from 13 to 22 weeks of age, number of cracked eggs).

### Methods

The objective of the study was to test for the vertical transmission of the genistein effect across generations using a step-by-step procedure consisting of comparing nested models. To ensure maximal power in the analysis and to avoid bias, only traits recorded on both sexes in all generations and for which the distribution, after data transformation if necessary, was close to the normal distribution were kept for further analysis. The traits retained were the body weights (BW1, BW4, BW7, BWsl), the abdominal adipose tissue weight at slaughter (AdipWeight), relative to body weight (AdipWeight/BWsl), the total distance covered during the behavioural test and the time spent in the centre of the area in
<table><caption>**Table 1**

Descriptive statistics for the quail experiment data.</caption><thead><tr><th>Phenotype</th><th>Number of animals with record</th><th>Mean</th><th>SD</th><th>Range</th></tr></thead><tbody><tr><td>BW1</td><td>1 565</td><td>27.13</td><td>3.50</td><td>[12–39]</td></tr><tr><td>BW4</td><td>1 559</td><td>121.33</td><td>10.54</td><td>[78–162]</td></tr><tr><td>BW7</td><td>1 551</td><td>155.78</td><td>11.78</td><td>[122–195]</td></tr><tr><td>BWsl</td><td>1 311</td><td>205.59</td><td>30.44</td><td>[128–284]</td></tr><tr><td>TotalDist</td><td>1 405</td><td>2 960.84</td><td>2 142.32</td><td>[59.97–14 968.27]</td></tr><tr><td>Centre Duration</td><td>1 375</td><td>131.70</td><td>87.70</td><td>[0.04–300.08]</td></tr><tr><td>AdipWeight</td><td>1 295</td><td>1.30</td><td>1.14</td><td>[0.00–10.60]</td></tr><tr><td>AdipWeight/BWsl</td><td>1 286</td><td>0.01</td><td>0.01</td><td>[0.00–0.05]</td></tr></tbody></table>

Abbreviations: BWi = Body weight in g at i weeks of age (sl = slaughter age); TotalDist = total distance covered during the behavioural test in cm; Centre Duration = time spent in the centre of the area in seconds during the behavioural test; AdipWeight = abdominal adipose tissue weight in g; AdipWeight/BWsl = abdominal adipose tissue weight relative to BW.

seconds (TotalDist, Centre Duration, respectively). To transform non-normal data into a normal distribution, the Box-Cox (BC) transformation was used for the AdipWeight and AdipWeight/BWsl, the square-root transformation was used for Centre Duration and Tukey transformation for the TotalDist. First, each trait was analysed under the assumption of a purely genetic inheritance (genetic models) using the animal model and testing for maternal genetic and maternal permanent environmental effects. Only traits with a significant direct heritability were then retained for further analysis with transmissibility models, with and without environment, after having been precorrected for significant maternal effects. We were obliged to carry out this precorrection because, even though transmissibility models can theoretically include maternal effects, the size and structure of the data in this experiment did not permit to ensure practical identifiability of the parameters of the transmissibility models with maternal effects. The general formula of the animal models used for the analysis was the following:

$$y_i = x_i\beta + u_i + m_j + pe_j + e_i$$

where $y_i$ is the phenotype of individual i, $\beta$ is the vector of fixed effects with known incidence matrix $x_i$, $u_i$ is the direct genetic effects of animal i, $m_j$ the maternal genetic effects of its dam j with $[\begin{matrix} u \\ m \end{matrix}] N(0, [\begin{matrix} \sigma_u^2 & \sigma_{um}^2 \\ \sigma_{um}^2 & \sigma_m^2 \end{matrix}] \otimes A)$, where A is the known genetic relationship matrix, $pe_j$ the permanent environmental effect of the dam pe $N(0, I\sigma_{pe}^2)$ and $e_i$ the residual with e $N(0, I\sigma_e^2)$ where I are identity matrices of appropriate size.

The general formula of transmissibility models (David and Ricard, 2019, 2023) used for the analysis was:

$$\ddot{y}_i = x_i\beta + t_i + e_i$$

Where notations are the same as in the animal model, $\ddot{y}_i$ is the phenotype of individual i, precorrected for maternal effects for traits in the case of significant maternal effects in the animal model, $t_i$ is the transmissible potential of animal i with $t N(0, M\sigma_d^2)$, where M is the matrix of transmission between individuals (transmission relationship matrix), $M^{-1} = L^T D^{-1} L$, where L is a lower triangular matrix with 1 s on the diagonal and the negatives of the sire and dam's coefficients of transmission ($\omega_s, \omega_d$, respectively), as off-diagonal entries that have to be estimated. In the transmissibility model, D is a diagonal matrix with variances of the transmissibility sampling relative to $\sigma_t^2$ as components ($\delta_t$), i.e., considering no inbreeding: $\delta_t = (1 - \omega_s^2 - \omega_d^2)$ if both parents of animal i are known, $\delta_t = (1 - \omega_d^2)$ for animals of an unknown sire, $\delta_t = (1 - \omega_s^2)$ for animals of an unknown dam, and 1 for animals for which both parents are unknown. If $\omega_s = \omega_d = 0.5$, then the M matrix is the A matrix. In the transmissibility model with environment, the D matrix includes an additional positive, and upper bounded by $(1 - \omega_d^2 - \omega_s^2)$,

covariance $r$ between animals sharing the same environmental effect due to genistein has to be estimated.

Fixed effects included in the models were the rearing condition (2 levels: cage per couple or per pairs of the same sex), sex (2 levels), generation (4 levels), age at time of measurement in classes (number of levels depends on the trait) and the direct effect of the genistein. Genistein may have a direct multigenerational impact by modifying the methylation pattern of the animal eating it, its embryo and its primordial germ cells (Fig. 1). For simplicity, to facilitate comparisons of models fitted using restricted maximum likelihood method and because the direct impact of genistein is not the objective of the study, the direct effect of the genistein was accounted for in the same way in the fixed part of all models as a four-levels cross classified factor: dams eating genistein (A), their embryo (E), their germ cells (GC), the other animals (O). The transgenerational effect of genistein can be accounted for in different ways in the D matrix of the transmissibility model with environment. If genistein directly affects the phenotype of dams consuming it, which is possible for traits recorded only after its ingestion, then, the additional $r$ covariance transmitted across generations is considered only among these dams (A). If genistein directly influences the phenotype of the embryo (when getting older) in dams that consume it, which can apply to all traits, then, the additional $r$ covariance transmitted across generations is considered only among their offspring (E). If a multigenerational direct effect of genistein is suspected, the additional covariance is applied to multiple groups of animals (A and E; A, E, and GC; or E and GC) (Fig. 1). As a first approach, we only considered the direct effect of genistein in the D matrix on one group of animals: E for traits recorded before genistein ingestion, A or E for traits recorded after genistein ingestion (traits recorded at slaughter).

Using the parameter estimates in the animal model, the direct and maternal heritabilities were calculated as $h_d^2 = \frac{\sigma_d^2}{\sigma_p^2}$ and $h_m^2 = \frac{\sigma_u^2}{\sigma_p^2}$, respectively with $\sigma_p^2 = \sigma_u^2 + \sigma_m^2 + \sigma_{pe}^2 + \sigma_{um}^2$. Using the parameter estimates in the transmissibility models, we computed the proportion of transmitted variance as $\frac{\sigma_t^2}{\sigma_t^2 + \sigma_e^2}$, the sire and dam transmissibilities as $\omega_s = \frac{\sigma_s^2}{\sigma_t^2 + \sigma_e^2}$, $\omega_d = \frac{\sigma_d^2}{\sigma_t^2 + \sigma_e^2}$, respectively, and the correlation $\rho$ between animals with known parents sharing the same environment as $\rho = \frac{r}{1 - \omega_d^2 - \omega_s^2}$.

The parameters of the models were estimated using the restricted maximum likelihood method with ASReml (Gilmour et al., 2015) and the freely available OWN Fortran program developed by David (2023) (https://doi.org/10.5281/zenodo.8223572).

To test the significance of the different variance components in the animal model, nested models (from the simple animal without any dam effect to the saturated model in Eq1) were compared using likelihood ratio tests. In the transmissibility model, the test of $\omega_s$ and $\omega_d$ to 0.5 was performed using the Wald test. The test
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** (A) Putative multi- and transgenerational effect of the genistein in quails (B) Different decompositions of the inverse of the transmissibility matrix depending on the multigenerational impact of the genistein. (a) The direct impact of genistein is on the embryo (E) of genistein-fed females. (b) The direct impact of genistein is on genistein-fed females (A) and their embryo (E). (c) The direct impact of genistein is on genistein-fed females (A), their embryo (E) and germ cells (GC), and (d) the direct impact of genistein is on the embryo (E) of genistein-fed females and their germ cells (GC).</figcaption></figure>

of transmitted environmental effect consisted of testing if *r* is sig-
nificantly different from 0. It was performed using likelihood ratio
test comparing the transmissibility model with environment to the
transmissibility model. When the test corresponds to a test at the
boundary of the parameter space (variances and *r*), the asymptotic
distribution of the likelihood ratio test under the null hypothesis is
a 50:50 mixture, $\chi_0^2$ and $\chi_1^2$ (Stram and Lee, 1994). The null hypoth-
esis was then rejected at the $\alpha$-risk of 5% if the likelihood ratio test
was greater than 1.92.

Results

Descriptive statistics

The quails showed a rapid growth of 3.012 ± 0.007 g/d on aver-
age from week one to week seven, followed by a slowdown in
growth until slaughter (0.315 ± 0.004 g/d on average). At slaughter,
a large variability in the fat cover of the animal was observed.
There was no abdominal adipose tissue for 17.3% of the animal.
The average abdominal adipose tissue weight was 1.30 g (1% of
the BW), and the median value was 1.00 g. The maximal weight
was 10.60 g that corresponded to 5% of the BW. The average dis-
tance covered by the animals during the behavioural test was
2 960 cm with a high variability between animals, the minimal dis-
tance being 60 cm and the maximum one 14 968 cm. The same
great variability was observed for the time spent in the centre of
the area, with animals remaining in the centre for the entire dura-
tion of the test and animals moving away from it as soon as the test
began, the average time spent in the centre of the area being 132 s.
Descriptive statistics of the different traits are provided in Table 1.

Statistical models

Results of the final genetic model (more parsimonious model
that best fitted the data based on likelihood ratio test) for each trait

are provided in Table 2. For all the BW traits (BW1, BW4, BW7, and BWsl), the final model included direct and maternal genetic effects but no permanent effect of the dam. For the other traits, no significant maternal effects were highlighted; the final model was thus an animal model including direct genetic effects only. Direct heritability estimated for BW traits ranged from 0.34 ± 0.08 for BW1 to 0.67 ± 0.06 for BW7. The maternal heritability tended to decrease with the age of the animal from 0.11 ± 0.04 at 1 week of age to 0.04 ± 0.03 at slaughter. The genetic correlation between direct and maternal effects was higher than 0.56 for all BW traits, but associated with a large SE. The BW traits were precorrected by the dam genetic effects for the analysis with the transmissibility models. Heritability of the fat cover (relative to BWsl or not) was moderate, 0.22 ± 0.05. The behavioural traits presented the lowest heritability, moderate for the TotalDist (locomotor activity) (0.17 ± 0.04) and low for the centre duration (0.10 ± 0.04). So, centre duration was not kept for further analysis.

Parameter estimates obtained with the transmissibility model
and transmissibility model with environment are provided in
Table 3. The sire and dam path coefficients of transmission esti-
mated in the transmissibility model ranged from 0.42 ± 0.11 to
0.52 ± 0.09 for ωs and 0.43 ± 12 to 0.50 ± 0.09 for ωd depending
on the trait and were never significantly different from 0.5. So,
the transmissibility estimates were in line with the direct heri-
tability estimated in the genetic models. Based on AIC (results
not shown), the transmissibility model with environment that best
fitted the data for traits recorded after genistein ingestion (i.e.
traits recorded at slaughter) considered the additional covariance
r in the D matrix between offspring of dams eating genistein (E).
Thus, the same transmissibility model with the environment was
considered for all traits whether they were measured before or
after genistein ingestion. Based on likelihood ratio test comparing
the transmissibility model to the transmissibility model with envi-
ronment, the covariance r was significantly different from 0 for all
BW traits, indicating that the effect of the genistein was transmit-
<caption>**Table 2**</caption>

Parameter estimates in the final genetic models (more parsimonious model that best fitted the data based on LRT) applied to traits recorded in the quail experiment.

<table><thead><tr><th>Trait</th><th>σ<sup>2</sup><sub>e</sub></th><th>σ<sup>2</sup><sub>a</sub></th><th>COV<sub>am</sub></th><th>σ<sup>2</sup><sub>m</sub></th><th>σ<sup>2</sup><sub>pm</sub></th><th>h<sup>2</sup><sub>a</sub></th><th>h<sup>2</sup><sub>m</sub></th><th>COR<sub>am</sub></th></tr></thead><tbody><tr><td>BW_1W</td><td>4.13 ± 0.37</td><td>2.52 ± 0.66</td><td>0.95 ± 0.36</td><td>0.81 ± 0.36</td><td>—</td><td>0.34 ± 0.08</td><td>0.11 ± 0.04</td><td>0.66 ± 0.33</td></tr><tr><td>BW_4W</td><td>37.97 ± 4.85</td><td>47.86 ± 9.38</td><td>10.77 ± 4.58</td><td>7.16 ± 4.58</td><td>—</td><td>0.51 ± 0.07</td><td>0.08 ± 0.03</td><td>0.58 ± 0.32</td></tr><tr><td>BW_7W</td><td>31.10 ± 4.99</td><td>76.39 ± 10.27</td><td>13.25 ± 4.91</td><td>6.15 ± 4.91</td><td>—</td><td>0.67 ± 0.06</td><td>0.05 ± 0.03</td><td>0.61 ± 0.30</td></tr><tr><td>BWsl</td><td>119.98 ± 15.02</td><td>137.91 ± 28.43</td><td>20.94 ± 13.60</td><td>10.14 ± 13.60</td><td>—</td><td>0.51 ± 0.08</td><td>0.04 ± 0.03</td><td>0.56 ± 0.51</td></tr><tr><td>TotalDist</td><td>12.01 ± 0.65</td><td>2.51 ± 0.69</td><td>—</td><td>—</td><td>—</td><td>0.17 ± 0.04</td><td>—</td><td>—</td></tr><tr><td>Centre Duration</td><td>15.13 ± 0.77</td><td>1.64 ± 0.67</td><td>—</td><td>—</td><td>—</td><td>0.10 ± 0.04</td><td>—</td><td>—</td></tr><tr><td>AdipWeight</td><td>5.37 ± 0.33</td><td>1.48 ± 0.38</td><td>—</td><td>—</td><td>—</td><td>0.22 ± 0.05</td><td>—</td><td>—</td></tr><tr><td>AdipWeight/BWsl</td><td>0.17 ± 0.01</td><td>0.05 ± 0.01</td><td>—</td><td>—</td><td>—</td><td>0.22 ± 0.05</td><td>—</td><td>—</td></tr></tbody></table>

Abbreviations: BWi = Body weight in g at i weeks of age (sl = slaughter age); TotalDist = total distance covered during the behavioural test in cm, Tukey transformation; Centre Duration = time spent in the centre of the area in seconds during the behavioural test, square-root transformation; AdipWeight = abdominal adipose tissue weight in 10^g after Box-Cox (BC) transformation; AdipWeight/BWsl = % abdominal adipose tissue weight relative to BW after Box-Cox (BC) transformation.

$\sigma_e^2$ = residual variance; $\sigma_a^2$ = additive genetic variance; $COV_{am}$ = Covariance between additive genetic and maternal genetic effect; $\sigma_m^2$ = maternal genetic variance;

$\sigma_{pm}^2$ = maternal permanent environment variance; $h_a^2$ = direct heritability (ratio of additive genetic to phenotypic variance); $h_m^2$ = ratio of maternal genetic to phenotypic variance; $COR_{am}$ = correlation between additive genetic and maternal genetic effect; LRT = likelihood ratio test.

<caption>**Table 3**</caption>

Parameter estimates obtained with the transmissibility model and transmissibility model with environment applied to traits recorded in the quail experiment.

<table><thead><tr><th>Model</th><th>Parameter</th><th>BW1</th><th>BW4</th><th>BW7</th><th>BWsl</th><th>TotalDist</th><th>Adipweight</th><th>Adipweight/BWsl</th></tr></thead><tbody><tr><td rowspan="10">Transmissibility model</td><td>σ<sup>2</sup><sub>e</sub></td><td>2.16 ± 0.38</td><td>46.04 ± 6.53</td><td>75.91 ± 8.04</td><td>133.45 ± 20.38</td><td>2.85 ± 0.98</td><td>1.48 ± 0.38</td><td>0.05 ± 0.01</td></tr><tr><td>σ<sup>2</sup><sub>a</sub></td><td>4.17 ± 0.27</td><td>36.55 ± 4.93</td><td>28.83 ± 4.75</td><td>118.65 ± 15.60</td><td>11.57 ± 1.03</td><td>5.37 ± 0.33</td><td>0.17 ± 0.01</td></tr><tr><td>h<sup>2</sup><sub>a</sub></td><td>0.34 ± 0.05</td><td>0.56 ± 0.06</td><td>0.72 ± 0.05</td><td>0.53 ± 0.07</td><td>0.20 ± 0.07</td><td>0.22 ± 0.05</td><td>0.22 ± 0.05</td></tr><tr><td>ωd</td><td>0.49 ± 0.05</td><td>0.50 ± 0.06</td><td>0.49 ± 0.04</td><td>0.50 ± 0.06</td><td>0.43 ± 0.12</td><td>0.50 ± 0.09</td><td>0.48 ± 0.09</td></tr><tr><td>ωs</td><td>0.51 ± 0.05</td><td>0.46 ± 0.05</td><td>0.47 ± 0.03</td><td>0.47 ± 0.05</td><td>0.42 ± 0.11</td><td>0.50 ± 0.09</td><td>0.52 ± 0.09</td></tr><tr><td>Dam transmissibility</td><td>0.17 ± 0.03</td><td>0.28 ± 0.04</td><td>0.36 ± 0.03</td><td>0.26 ± 0.04</td><td>0.08 ± 0.03</td><td>0.11 ± 0.03</td><td>0.11 ± 0.03</td></tr><tr><td>Sire transmissibility</td><td>0.17 ± 0.03</td><td>0.25 ± 0.04</td><td>0.34 ± 0.03</td><td>0.25 ± 0.04</td><td>0.08 ± 0.03</td><td>0.11 ± 0.03</td><td>0.11 ± 0.03</td></tr><tr><td>LogL</td><td>-2 152.75</td><td>-4 061.58</td><td>-4 135.71</td><td>-4 143.55</td><td>-2 562.50</td><td>-1 869.77</td><td>327.178</td></tr><tr><td rowspan="10">Transmissibility model with environment</td><td>σ<sup>2</sup><sub>t</sub></td><td>2.31 ± 0.41</td><td>45.16 ± 6.44</td><td>73.89 ± 7.67</td><td>129.8 ± 20.19</td><td>2.85 ± 0.98</td><td>1.48 ± 0.38</td><td>0.05 ± 0.01</td></tr><tr><td>σ<sup>2</sup><sub>e</sub></td><td>4.16 ± 0.27</td><td>35.14 ± 5.06</td><td>27.79 ± 4.78</td><td>117.37 ± 15.79</td><td>11.56 ± 1.04</td><td>5.37 ± 0.33</td><td>0.17 ± 0.01</td></tr><tr><td>h<sup>2</sup><sub>t</sub></td><td>0.36 ± 0.05</td><td>0.56 ± 0.07</td><td>0.73 ± 0.05</td><td>0.52 ± 0.07</td><td>0.20 ± 0.07</td><td>0.22 ± 0.05</td><td>0.22 ± 0.05</td></tr><tr><td>ωd</td><td>0.50 ± 0.05</td><td>0.46 ± 0.06</td><td>0.47 ± 0.04</td><td>0.48 ± 0.06</td><td>0.43 ± 0.12</td><td>0.50 ± 0.09</td><td>0.48 ± 0.09</td></tr><tr><td>ωs</td><td>0.50 ± 0.05</td><td>0.46 ± 0.05</td><td>0.47 ± 0.04</td><td>0.47 ± 0.05</td><td>0.42 ± 0.11</td><td>0.50 ± 0.09</td><td>0.51 ± 0.09</td></tr><tr><td>Dam transmissibility</td><td>0.18 ± 0.03</td><td>0.26 ± 0.03</td><td>0.34 ± 0.03</td><td>0.25 ± 0.04</td><td>0.08 ± 0.03</td><td>0.11 ± 0.03</td><td>0.11 ± 0.03</td></tr><tr><td>Sire transmissibility</td><td>0.18 ± 0.03</td><td>0.26 ± 0.04</td><td>0.34 ± 0.03</td><td>0.24 ± 0.04</td><td>0.08 ± 0.03</td><td>0.11 ± 0.03</td><td>0.11 ± 0.03</td></tr><tr><td>ρ</td><td>0.44 ± 0.27</td><td>0.02 ± 0.01</td><td>0.02 ± 0.01</td><td>0.02 ± 0.01</td><td>0.02 ± 0.02</td><td>0.02 ± 0.02</td><td>0.02 ± 0.03</td></tr><tr><td>LogL</td><td>-2 151.7</td><td>-4 059.27</td><td>-4 133.44</td><td>-4 142.08</td><td>-2 562.5</td><td>-1 869.77</td><td>327.178</td></tr><tr><td>LRT</td><td>2.10</td><td>4.62</td><td>4.54</td><td>2.94</td><td>0.00</td><td>0.00</td><td>0.00</td></tr></tbody></table>

Abbreviations: BWi = Body weight in g at i weeks of age (sl = slaughter age); TotalDist = total distance covered during the behavioural test in cm, Tukey transformation; AdipWeight = abdominal adipose tissue weight in 10^g after Box-Cox (BC) transformation; AdipWeight/BWsl = % abdominal adipose tissue weight relative to BW after Box-Cox (BC) transformation.

$\sigma_t^2$ = transmissibility variance; $\sigma_e^2$ = residual variance; $h_t^2$ = ratio of transmissibility variance to phenotypic variance; Dam transmissibility = $\omega d \frac{\sigma_t^2}{\sigma_t^2+\sigma_e^2}$; Sire transmissibility = $\omega s \frac{\sigma_t^2}{\sigma_t^2+\sigma_e^2}$; $\rho$ = correlation between transmissibility samplings of embryo of dams eating genistein; LRT = likelihood ratio test.

ted across generations for these traits. The corresponding correlation $\rho$ was high for BW1 (0.44) but low for the other BW traits (lower than 0.02). For fat cover traits and locomotor activity, the covariance $r$ was not significantly different from 0 based on likelihood ratio test.

Discussion

A specific protocol was designed to study the potential transgenerational transmission of environmental effects. To this end, we analysed the impact of genistein ingestion over several generations on various growth, body composition and behavioural traits in quail. We used genistein as the environmental stressor due to its impact on methylation profiles (Dolinoy et al., 2006), making it a strong candidate for transgenerational effects. Other environmen-

tal stressors investigated in the literature include parental nutrition, thermal conditioning, and stressful events (Vitorino Carvalho et al., 2023; Buyse et al., 2020, Brun et al., 2015).

The direct heritability values for BW traits (0.34 to 0.67) were in line with those reported by Kaya Başar and Narinç (2023) and Kaplan et al. (2016) but higher than those reported by Karami et al. (2017) or Barbieri et al. (2015). The maternal heritability for BW traits tended to decrease with age, which is a classical result reported in the literature in many species (Karami et al., 2019; Le Bihan-Duval et al., 1998; Rajkumar et al., 2021; Saatci et al., 2006). It is known that an appropriate data structure is needed to estimate the genetic correlation between direct and maternal effects without bias (Bijma, 2006; Clément et al., 2001; Koch, 1972; Lee and Pollak, 1997). This is generally not the case, which more often than not leads to highly negative estimates of the latter (David et al., 2015). On the contrary, in our study, this
correlation tends to be strongly positive (even if associated with large s.e.). This almost certain overestimation of this covariance may be the result of non-genetic inheritance with higher true dam path coefficient of transmission than sire path coefficient of transmission not taken into account in a pure genetic model which assumes identical transmission coefficients for the 2 parents (i.e. 0.5). The moderate heritability of abdominal adipose tissue is in line with the one reported by Lotfi et al. (2011) in quail (0.28 for abdominal fat weight) and smaller than those reported in broiler (0.50 to 0.62) by Le Bihan-Duval et al. (1998), Zerehdaran et al. (2004), Rance et al. (2002). Behavioural phenotypes measured in the present study may be related to the response to social isolation measured by Recoquillay et al. (2013) which reported a higher heritability (0.34) than we estimated. This lower estimate of heritability could be related to the fact that the animals were tested at a later age than those in Recoquillay et al. (2013), as it has been reported that the heritability of several behavioural traits decreases with age in chickens (Rodenburg et al., 2003).

When applying the transmissibility model, it is possible to conclude to non-genetic inheritance when at least one of the two path coefficients of transmission (sire or dam) differs significantly from 0.5. In the present study, none of the path coefficients of transmission for the different traits differed significantly from 0.5. However, this result does not confirm purely genetic heritability. This situation may result from the fact that: i) the true coefficients of transmission of the non-genetic effects are close to 0.5 and/or ii) as the transmissible potential combines the different sources of inheritance, the relative importance of non-genetic inheritance is small compared to the genetic inheritance. In addition, the dam path coefficient of transmission may be underestimated due to the necessary precorrection of phenotypes for maternal genetic effects to ensure practical identifiability of the parameters in the transmissibility models, given the size and structure of the data. A few studies have attempted to estimate the path coefficients of transmission and variance of non-genetic effects, in particular epigenetic effects. Paiva et al. (2018a) reported a wide range of epigenetic path coefficients of transmission for different BW in quail (ranging from 0.05 to 0.50) while Araujo et al. (2025) reported very low values for BW (0.05) or back fat (0.10) in pigs. However, David and Ricard (2019) have demonstrated that even if the parameters of a model dissociating the different sources of inheritance are theoretically identifiable, they are not in practice without additional information on non-genetic factors. This difficulty has also been reported by Varona et al. (2015). So, it is preferable, in the absence of additional information on non-genetic factors, to use the transmissibility model. It should also be noted that its implementation in ASReml (David, 2018, 2023) has the advantage of providing restricted maximum likelihood estimates of all the parameters (in simple model combining the different sources of inheritance and in model disentangling the different sources of inheritance even if not recommended due to identifiability issues) without the need to use grid search methods, as done by Paiva et al. (2018a, 2018b) or Araujo et al. (2025).

Comparison of the two transmissibility models indicated significant vertical transmission of genistein effect for BW traits but with low *r* values after 4 weeks of age. It is crucial to note, as explained in the introduction, that comparing the average phenotypes of the two epilines across generations is not a reliable method for concluding that the effects of genistein exposure are transmitted across generations. The difference between the mean phenotypes of the two groups, even when corrected for other fixed effects, results from a combination of environmental transmission and genetic drift. Focusing on this difference using ANOVA method can lead to erroneous conclusions. This is illustrated in this study, where the evolution of the raw mean BWs across generations for

the two lines, Geni- and Geni+, showed an increase in weights for Geni+ compared to Geni-, particularly in the last generation, with a significant *t*-test mean difference. This difference, if considered solely due to the transmission of the effects of genistein, is not biologically expected since genistein is supposed to have a negative impact on weight in general. Indeed, it has been shown that genistein reduces food intake, BW, and fat pad weight in mice (Kim et al., 2006; Rockwood et al., 2019) through modifications of methylation patterns (Silva et al., 2019). Using transmissibility models to test (via *r*) the vertical transmission of environmental effects helps to overcome the confusion with genetic drift. However, the downside is that it does not provide any information about the direction of the effect.

The transmissibility model with the environment that we used assumed a direct effect of genistein on the embryo of females eating genistein (E) transmitted to the two next generations via vertical transmission of non-genetic phenomena (probably epigenetic). For traits recorded at slaughter after genistein ingestion, the direct effect of genistein could have also been considered on the phenotypes of female eating genistein (A). However, based on AIC, the model that best fitted the data considers a direct effect of genistein on animals of group "E" and not group "A" for these traits. This can be explained by the fact that these traits, recorded at slaughter, are the result of all the events in the animal's life since birth (such as birth weight and average daily gain) and are therefore not specific to genistein intake. While genistein may have a direct impact, it is likely to be minimal compared to the entirety of the animal's life experiences. Furthermore, as stated in the material and method section, the direct effect of the ingested genistein may be on the embryo and the GC. We have also applied a transmissibility model with environment considering this impact on E and GC. Results of this model for BW1 are provided in Supplementary Table S1. These results were consistent with the simpler model with only E: based on likelihood ratio test, the covariance *r* was significantly different from 0 and around half of the estimated value with only E.

## Conclusions

Using the transmissibility model with environment, we highlighted the transgenerational transmission of environmental effect induced by genistein on BW at 1 week of age in quail. The inheritance of modified epigenetic phenomena is supposed to drive this transmission. One approach to test this hypothesis is studying DNA methylation changes across generations. Combined with genetic selection, acting favourably on several generations via the transmission of environmental effects offers the possibility to speed up response to selection which is crucial to adapt livestock population to new environmental constraints linked to the agroecological transition and climate change.

## Supplementary material

Supplementary Material for this article (https://doi.org/10.1016/j.animal.2025.101636) can be found at the foot of the online page, in the Appendix section.

## Ethics approval

Animals were bred at the UE1295 PEAT (Nouzilly, France, https://doi.org/10.15454/1.5572326250887292E12) with official authorisation for the animals (APAFIS#29977-2021010717073072 v5), and PEAT agreement (D371751). Animals were maintained under standard breeding conditions and were subjected to minimal disturbance.
## Data and model availability statement

The code source to apply the transmissibility model with environment is available on the Zenodo website (https://doi.org/10.5281/zenodo.8223572). All data are available on Zenodo, https://doi.org/10.5281/zenodo.15796146 (https://zenodo.org/records/15796146). Information can be made available from the authors upon request.

## Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) did not use any AI and AI-assisted technologies.

## Author ORCIDs

K. Karami: https://orcid.org/0000-0002-1875-6741.

A. Ricard: https://orcid.org/0000-0002-2113-5153.

I. David: https://orcid.org/0000-0002-2514-6693.

S. Rousse: https://orcid.org/0009-0007-2894-9356.

F. Pitel: https://orcid.org/0000-0002-1477-7633.

S. Eynard: https://orcid.org/0000-0002-8609-5869.

D. Gourichon: https://orcid.org/0000-0002-4631-0149.

S. Leroux: https://orcid.org/0000-0003-2840-5109.

## CRediT authorship contribution statement

K. Karami: Writing – original draft, Validation, Software, Investigation, Formal analysis. S. Rousse: Writing – review & editing, Resources. F. Pitel: Writing – review & editing, Funding acquisition. S. Eynard: Writing – review & editing. D. Gourichon: Writing – review & editing, Resources. S. Leroux: Writing – review & editing, Resources. A. Ricard: Writing – original draft, Validation, Supervision, Software, Methodology, Conceptualisation. I. David: Writing – original draft, Validation, Supervision, Software, Methodology, Conceptualisation.

## Declaration of interest

None.

## Acknowledgements

We are grateful to the entire staff of the UE1295 PEAT (Nouzilly, France, doi: 10.15454/1.5572326250887292E12) for their excellent animal care.

## Financial support statement

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement N°101000236 (GEroNIMO). This project is part of EuroFAANG (https://eurofaang.eu). Stacy Rousse's Ph.D. is co-funded by INRAE Animal Genetics Division (GA) and La Région Occitanie.

## References

Alexandre, P.A., Rodríguez-Ramilo, S.T., Mach, N., Reverter, A., 2024. Combining genomics and semen microbiome increases the accuracy of predicting bull prolificacy. Journal of Animal Breeding and Genetics 142, 237–250. https://doi.org/10.1111/jbg.12899.

Anway, M.D., Cupp, A.S., Uzumcu, M., Skinner, M.K., 2005. Epigenetic transgenerational actions of endocrine disruptors and male fertility. Science 308, 1466–1469. https://doi.org/10.1126/science.1108190.

Araujo, A.C., Johnson, J.S., Graham, J.R., Howard, J., Huang, Y., Oliveira, H.R., Brito, L. F., 2025. Transgenerational epigenetic heritability for growth, body composition, and reproductive traits in Landrace pigs. Frontiers in Genetics 15, 1526473. https://doi.org/10.3389/fgene.2024.1526473.

Barbieri, A., Ono, R.K., Cursino, L.L., Farah, M.M., Pires, M.P., Bertipaglia, T.S., Pires, A. V., Cavani, L., Carreño, L.O.D., Fonseca, R., 2015. Genetic parameters for body weight in meat quail. Poultry Science 94, 169–171. https://doi.org/10.3382/ps/peu062.

Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M.-C.-C., Charles, T., Chen, X., Cocolin, L., Eversole, K., Corral, G.H., Kazou, M., Kinkel, L., Lange, L., Lima, N., Loy, A., Macklin, J.A., Maguin, E., Mauchline, T., McClure, R., Mitter, B., Ryan, M., Sarand, I., Smidt, H., Schelkle, B., Roume, H., Kiran, G.S., Selvin, J., Souza, R.S.C.D., Van Overbeek, L., Singh, B.K., Wagner, M., Walsh, A., Sessitsch, A., Schloter, M., 2020. Microbiome definition re-visited: old concepts and new challenges. Microbiome 8, 103. https://doi.org/10.1186/s40168-020-00875-0.

Bijma, P., 2006. Estimating maternal genetic effects in livestock. Journal of Animal Science 84, 800–806. https://doi.org/10.2527/2006.844800x.

Bonduriansky, R., Crean, A.J., Day, T., 2012. The implications of nongenetic inheritance for evolution in changing environments. Evolutionary Applications 5, 192–201. https://doi.org/10.1111/j.1752-4571.2011.00213.x.

Braunschweig, M., Jagannathan, V., Gutzwiller, A., Bee, G., 2012. Investigations on transgenerational epigenetic response down the male line in F2 pigs. PLoS One 7, e30583. https://doi.org/10.1371/journal.pone.0030583.

Brun, J.-M., Bernadet, M.-D., Cornuez, A., Leroux, S., Bodin, L., Basso, B., Davail, S., Jaglin, M., Lessire, M., Martin, X., Sellier, N., Morisson, M., Pitel, F., 2015. Influence of grand-mother diet on offspring performances through the male line in Muscovy duck. BMC Genetics 16, 145. https://doi.org/10.1186/s12863-015-0303-z.

Buyse, J., Collin, A., Coustham, V., de Haas, E., Pitel, F., 2020. The use of epigenetics in poultry breeding. In: Aggrey, S.E., Zhou, H., Tixier-Boichard, M., Rhoads, D.D. (Eds.), Advances in Poultry Genetics and Genomics. Burleigh Dodds Science Publishing, Cambridge, UK, pp. 497–522. https://doi.org/10.19103/as.2020.0065.28.

Charlesworth, D., Barton, N.H., Charlesworth, B., 2017. The sources of adaptive variation. Proceedings of the Royal Society B. Biological Sciences 284, 20162864. https://doi.org/10.1098/rspb.2016.2864.

Clément, V., Bibé, B., Verrier, E., Elsen, J.M., Manfredi, E., Bouix, J., Hanocq, E., 2001. Simulation analysis to test the influence of model adequacy and data structure on the estimation of genetic parameters for traits with direct and maternal effects. Genetics Selection Evolution 33, 369–395. https://doi.org/10.1186/1297-9686-33-4-369.

Danchin, É., Charmantier, A., Champagne, F.A., Mesoudi, A., Pujol, B., Blanchet, S., 2011. Beyond DNA: integrating inclusive inheritance into an extended theory of evolution. Nature Reviews Genetics 12, 475–486. https://doi.org/10.1038/nrg3028.

Danchin, É., Pocheville, A., Huneman, P., 2019. Early in life effects and heredity: reconciling neo-Darwinism with neo-Lamarckism under the banner of the inclusive evolutionary synthesis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 374, 20180113. https://doi.org/10.1098/rstb.2018.0113.

David, I., Bouvier, F., Banville, M., Canario, L., Flatres-Grall, L., Balmisse, E., Garreau, H., 2015. The direct-maternal genetic correlation has little impact on genetic evaluations. Journal of Animal Science 93, 5639–5647. https://doi.org/10.2527/jas.2015-9548.

David, I., Ricard, A., 2019. A unified model for inclusive inheritance in livestock species. Genetics 212, 1075–1099. https://doi.org/10.1534/genetics.119.302375.

David, I., Ricard, A., 2023. An improved transmissibility model to detect transgenerational transmitted environmental effects. Genetics Selection Evolution 55, 66. https://doi.org/10.1186/s12711-023-00833-y.

David, I., Canario, L., Combes, S., Demars, J., 2019. Intergenerational transmission of characters through genetics, epigenetics, microbiota, and learning in livestock. Frontiers in Genetics 10, 1058. https://doi.org/10.3389/fgene.2019.01058.

David, I., 2018. Fortran program for transmissibility model. Retrieved on 14 November 2018 from: https://doi.org/10.5281/zenodo.1487869.

David, I., 2023. Fortran program for transmissibility model with environment. Retrieved on 8 August 2023 from: https://doi.org/10.5281/zenodo.8223572.

Dolinoy, D.C., Weidman, J.R., Waterland, R.A., Jirtle, R.L., 2006. Maternal genistein alters coat color and protects Avy mouse offspring from obesity by modifying the fetal epigenome. Environmental Health Perspectives 114, 567–572. https://doi.org/10.1289/ehp.8700.

Fallet, M., Blanc, M., Di Criscio, M., Antczak, P., Engwall, M., Guerrero Bosagna, C., Rüegg, J., Keiter, S.H., 2023. Present and future challenges for the investigation of transgenerational epigenetic inheritance. Environment International 172, 107776. https://doi.org/10.1016/j.envint.2023.107776.

Gilmour, A.R., Gogel, B.J., Cullis, B.R., Welham, S.J., Thompson, R., 2015. ASReml user guide release 4.1 structural specification. VSN International Ltd., Hemel Hempstead, United Kingdom. Retrieved on 18 August 2021 from: https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/2018/02/ASReml-4.1-Structural-Specification.pdf.

Guerrero-Bosagna, C., Covert, T.R., Haque, M.M., Settles, M., Nilsson, E.E., Anway, M. D., Skinner, M.K., 2012. Epigenetic transgenerational inheritance of vinclozolin induced mouse adult onset disease and associated sperm epigenome biomarkers. Reproductive Toxicology 34, 694–707. https://doi.org/10.1016/j.reprotox.2012.09.005.
Jablonka, E., Lamb, M.J., 2008. Soft inheritance: challenging the modern synthesis. Genetics and Molecular Biology 31, 389-395. https://doi.org/10.1590/S1415-47572008000300001.

Kaplan, S., Narinç, D., Gürcan, E.K., 2016. Genetic parameter estimates of weekly body weight and Richard's growth curve in Japanese quail. European Poultry Science 80, 1-10. https://doi.org/10.1399/eps.2016.165.

Karami, K., Zerehdaran, S., Tahmoorespur, M., Barzanooni, B., Lotfi, E., 2017. Genetic evaluation of weekly body weight in Japanese quail using random regression models. British Poultry Science 58, 13-18. https://doi.org/10.1080/00071668.2016.1236362.

Karami, K., Zerehdaran, S., Javadmanesh, A., Shariati, M.M., 2019. Assessment of maternal and parent of origin effects in genetic variation of economic traits in Iranian native fowl. British Poultry Science 60, 486-492. https://doi.org/10.1080/00071668.2019.1621987.

Kaya Başar, E., Narinç, D., 2023. Genetic parameter estimates of growth curve and feed efficiency traits in Japanese quail. Animals 13, 1765. https://doi.org/10.3390/ani13111765.

Kim, H.-K., Nelson-Dooley, C., Della-Fera, M.A., Yang, J.-Y., Zhang, W., Duan, J., Hartzell, D.L., Hamrick, M.W., Baile, C.A., 2006. Genistein decreases food intake, body weight, and fat pad weight and causes adipose tissue apoptosis in ovariectomized female mice. The Journal of Nutrition 136, 409-414. https://doi.org/10.1093/jn/136.2.409.

Kim, J.K., Samaranayake, M., Pradhan, S., 2009. Epigenetic mechanisms in mammals. Cellular and Molecular Life Sciences 66, 596-612. https://doi.org/10.1007/s00018-008-8432-4.

Koch, R.M., 1972. The role of maternal effects in animal breeding: VI. Maternal effects in beef cattle. Journal of Animal Science 35, 1316-1323. https://doi.org/10.2527/jas1972.3561316x.

Le Bihan-Duval, E., Mignon-Grastein, S., Millet, N., Beaumont, C., 1998. Genetic analysis of a selection experiment on increased body weight and breast muscle weight as well as on limited abdominal fat weight. British Poultry Science 39, 346-353. https://doi.org/10.1080/00071669888881.

Lee, C., Pollak, E.J., 1997. Relationship between sire x year interactions and direct-maternal genetic correlation for weaning weight of Simmental cattle. Journal of Animal Science 75, 68-75. https://doi.org/10.2527/1997.75168x.

Leroux, S., Gourichon, D., Leterrier, C., Labrune, Y., Coustham, V., Rivière, S., Zerjal, T., Coville, J.-L., Morisson, M., Minvielle, F., Pitel, F., 2017. Embryonic environment and transgenerational effects in quail. Genetics Selection Evolution 49, 14. https://doi.org/10.1186/s12711-017-0292-7.

Lin, F., Wu, J., Abdelnabi, M.A., Ottinger, M.A., Giusti, M.M., 2004. Effects of dose and glycosylation on the transfer of genistein into the eggs of the Japanese quail (Coturnix japonica). Journal of Agricultural and Food Chemistry 52, 2397-2403. https://doi.org/10.1021/jf034921f.

Lotfi, E., Zerehdaran, S., Ahani Azari, M., 2011. Genetic evaluation of carcass composition and fat deposition in Japanese quail. Poultry Science 90, 2202-2208. https://doi.org/10.3382/ps.2011-01570.

Mancin, E., Maltecca, C., Huang, Y.J., Mantovani, R., Tiezzi, F., 2024. A first characterization of the microbiota-resilience link in swine. Microbiome 12, 53. https://doi.org/10.1186/s40168-024-01771-7.

Martinez Boggio, G., Monteiro, H.F., Lima, F.S., Figueiredo, C.C., Bisinotto, R.S., Santos, J.E.P., Mion, B., Schenkel, F.S., Ribeiro, E.S., Weigel, K.A., Peñagaricano, F., 2024. Host and rumen microbiome contributions to feed efficiency traits in Holstein cows. Journal of Dairy Science 107, 3090-3103. https://doi.org/10.3168/jds.2023-23869.

O'Dea, R.E., Noble, D.W.A., Johnson, S.L., Hesselson, D., Nakagawa, S., 2016. The role of non-genetic inheritance in evolutionary rescue: epigenetic buffering, heritable bet hedging and epigenetic traps. Environmental Epigenetics 2, dvv014. https://doi.org/10.1093/eep/dvv014.

Paiva, J., de Resende, M., Resende, R., de Oliveira, H., Silva, H., Caetano, G., Lopes, P., Silva, F., 2018a. Transgenerational epigenetic variance for body weight in meat quails. Journal of Animal Breeding and Genetics 135, 178-185. https://doi.org/10.1111/jbg.12329.

Paiva, J.T., De Resende, M.D.V., Resende, R.T., Oliveira, H.R., Silva, H.T., Caetano, G.C., Calderano, A.A., Lopes, P.S., Viana, J.M.S., Silva, F.F., 2018b. A note on transgenerational epigenetics affecting egg quality traits in meat-type quail. British Poultry Science 59, 624-628. https://doi.org/10.1080/00071668.2018.1514582.

Rajkumar, U., Prince, L.L.L., Rajaravindra, K.S., Haunshi, S., Niranjan, M., Chatterjee, R.N., 2021. Analysis of (co) variance components and estimation of breeding value of growth and production traits in Dahlem Red chicken using pedigree relationship in an animal model. PLoS One 16, e0247779. https://doi.org/10.1371/journal.pone.0247779.

Rance, K.A., McEntee, G.M., McDevitt, R.M., 2002. Genetic and phenotypic relationships between and within support and demand tissues in a single line of broiler chicken. British Poultry Science 43, 518-527. https://doi.org/10.1080/000716622000004426.

Recoquillay, J., Leterrier, C., Calandreau, L., Bertin, A., Pitel, F., Gourichon, D., Vignal, A., Beaumont, C., Le Bihan-Duval, E., Arnould, C., 2013. Evidence of phenotypic and genetic relationships between sociality, emotional reactivity and production traits in Japanese quail. PLoS One 8, e82157. https://doi.org/10.1371/journal.pone.0082157.

Rockwood, S., Mason, D., Lord, R., Lamar, P., Prozialeck, W., Al-Nakkash, L., 2019. Genistein diet improves body weight, serum glucose and triglyceride levels in both male and female ob/ob mice. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 12, 2011-2021. https://doi.org/10.2147/DMSO.S216312.

Rodenburg, T., Buitenhuis, A., Ask, B., Uitdehaag, K., Koene, P., Van Der Poel, J., Bovenhuis, H., 2003. Heritability of feather pecking and open-field response of laying hens at two different ages. Poultry Science 82, 861-867. https://doi.org/10.1093/ps/82.6.861.

Rousse, S., Leroux, S., Gourichon, D., Calandreau, L., David, I., Rau, A., Lagarrigue, S., Zerjal, T., Pitel, F., Eynard, S.E., 2025. Transgenerational transmission of an environmental modification in quails: changes in phenotypic variance components across three generations. bioRxiv 2025.07.04.663137. https://doi.org/10.1101/2025.07.04.663137.

Saatci, M., Omed, H., Ap Dewi, I., 2006. Genetic parameters from univariate and bivariate analyses of egg and weight traits in Japanese quail. Poultry Science 85, 185-190. https://doi.org/10.1093/ps/85.2.185.

Sanga, U., 2010. Maternal effects in transmission of self-medicative behavior from mother to offspring in sheep. MSc thesis, Utah State University, Logan, UT, USA. https://doi.org/10.26076/32c4-3e85.

Silva, L.B.A.R., Pinheiro-Castro, N., Novaes, G.M., Pascoal, G.D.F.L., Ong, T.P., 2019. Bioactive food compounds, epigenetics and chronic disease prevention: focus on early-life interventions with polyphenols. Food Research International 125, 108646. https://doi.org/10.1016/j.foodres.2019.108646.

Skinner, M.K., 2008. What is an epigenetic transgenerational phenotype? F3 or F2. Reproductive Toxicology 25, 2-6. https://doi.org/10.1016/j.reprotox.2007.09.001.

Sobolewski, M., Abston, K., Conrad, K., Marvin, E., Harvey, K., Susiarjo, M., Cory-Slechta, D.A., 2020. Lineage- and sex-dependent behavioral and biochemical transgenerational consequences of developmental exposure to lead, prenatal stress, and combined lead and prenatal stress in mice. Environmental Health Perspectives 128, 27001. https://doi.org/10.1289/EHP4977.

Stram, D.O., Lee, J.W., 1994. Variance components testing in the longitudinal mixed effects model. Biometrics 50, 1171-1177. https://doi.org/10.2307/2533455.

Sun, Y., Wang, X., Zhou, S., Zhou, Y., Hua, J., Guo, Y., Wang, Y., Zhang, W., Yang, L., Zhou, B., 2023. Evaluation and mechanistic study of transgenerational neurotoxicity in zebrafish upon life cycle exposure to Decabromodiphenyl Ethane (DBDPE). Environmental Science & Technology 57, 16811-16822. https://doi.org/10.1021/acs.est.3c04578.

Thompson, R.P., Nilsson, E., Skinner, M.K., 2020. Environmental epigenetics and epigenetic inheritance in domestic farm animals. Animal Reproduction Science 220, 106316. https://doi.org/10.1016/j.anireprosci.2020.106316.

Varona, L., Munilla, S., Mouresan, E.F., González-Rodríguez, A., Moreno, C., Altarriba, J., 2015. A Bayesian model for the analysis of transgenerational epigenetic variation. G3: genes| genomes|. Genetics 5, 477-485. https://doi.org/10.1534/g3.115.016725.

Vitorino Carvalho, A., Hennequet-Antier, C., Rouger, R., Delaveau, J., Bordeau, T., Crochet, S., Couroussé, N., Pitel, F., Collin, A., Coustham, V., 2023. Thermal conditioning of quail embryos has transgenerational and reversible long-term effects. Journal of Animal Science and Biotechnology 14, 124. https://doi.org/10.1186/s40104-023-00924-2.

Zerehdaran, S., Vereijken, A.J., Van Arendonk, J.A.M., Van der Waaijt, E.H., 2004. Estimation of genetic parameters for fat deposition and carcass traits in broilers. Poultry Science 83, 521-525. https://doi.org/10.1093/ps/83.4.521.