Search for QTL affecting the shape of the egg laying curve of the Japanese quail

Background Egg production is of critical importance in birds not only for their reproduction but also for human consumption as the egg is a highly nutritive and balanced food. Consequently, laying in poultry has been improved through selection to increase the total number of eggs laid per hen. This number is the cumulative result of the oviposition, a cyclic and repeated process which leads to a pattern over time (the egg laying curve) which can be modelled and described individually. Unlike the total egg number which compounds all variations, the shape of the curve gives information on the different phases of egg laying, and its genetic analysis using molecular markers might contribute to understand better the underlying mechanisms. The purpose of this study was to perform the first QTL search for traits involved in shaping the egg laying curve, in an F2 experiment with 359 female Japanese quail. Results Eight QTL were found on five autosomes, and six of them could be directly associated with egg production traits, although none was significant at the genome-wide level. One of them (on CJA13) had an effect on the first part of the laying curve, before the production peak. Another one (on CJA06) was related to the central part of the curve when laying is maintained at a high level, and the four others (on CJA05, CJA10 and CJA14) acted on the last part of the curve where persistency is determinant. The QTL for the central part of the curve was mapped at the same position on CJA06 than a genome-wide significant QTL for total egg number detected previously in the same F2. Conclusion Despite its limited scope (number of microsatellites, size of the phenotypic data set), this work has shown that it was possible to use the individual egg laying data collected daily to find new QTL which affect the shape of the egg laying curve. Beyond the present results, this new approach could also be applied to longitudinal traits in other species, like growth and lactation in ruminants, for which good marker coverage of the genome and theoretical models with a biological significance are available.


Background
From the same natural oviposition cyclic process, wild birds generally lay only one or a few clutches of up to two dozen eggs per year [1] whereas quail [2] and chicken [3] from highly selected commercial or experimental lines may produce several hundred eggs over the same period. Yet, their egg production changes with time, increasing from the onset of egg laying to peak production, then plateauing and finally declining steadily. It may be described by drawing an egg laying curve which can be modelled by theoretical equations [e.g.: [4][5][6][7]] with biologically meaningful parameters. Another, more global, measure of egg production is the total number of laid eggs which has been extensively studied from the standpoint of quantitative genetics [8,9], and for which QTL were recently detected in chicken [10,11] and Japanese quail [12]. This trait, however, conveys little information for explaining the shape of the egg laying curve and its variation. On the contrary, estimations of the parameters of the individual egg laying curves fitted to the observed egg production might be used as phenotypes, and searched for underlying QTL, but this approach has not been tested until now.
The objective of the present work was to produce the first set of QTL detected in birds for the parameters which determine the shape of the egg laying curve. For that purpose, in an F 2 experiment with two quail lines selected for early egg production [13] and for high duration of tonic immobility [14], which had led already to the detection of QTL for total egg number, age at first egg and clutch length [12], the individual egg laying curves were fitted to egg production using three theoretical models [4,6,7]. The Gifu microsatellite marker panel [15] and the microsatellite map [16] developed from the same F 2 reference families were used to detect QTL for the parameters of the curves. Table 1 shows the equations of the three models, the estimations of their parameters, and their overall fit to the F 2 data from the 359 female quail. It also lists the estimations of the same parameters for the two F 0 lines (DD and LTI) used to produce the F 1 generation, parent of the F 2 . Observed and predicted egg laying curves of the F 2 population are in Figure 1 (Model 1 for egg laying rate) and Figure 2 (Models 2 and 3 for 3-week egg production). Table 2 lists the parameters of the curves for which the detection of QTL was performed, and gives their biological meaning. It also shows the corresponding statistics (number, mean, and range) from the set of the individual curves adjusted to the data. Five, 7 and 51 curves could not be obtained by nonlinear regression under Models 1, 2 and 3, respectively.  [6] y(t) = (y P/ t 2 ) × t -r × (y P/ t 2 ) × Ln [(exp(t/r) + exp(t 2 /r))/(1 + exp(t 2 /r))] + r × b 4 × Ln [(exp(t/r) + exp((t 2 +P)/r))/(1 + exp((t 2 +P)/r))] Parameter

Egg laying curves
18.98 ± 0. 13 0  1 : elr(t) = egg laying rate and y(t) = number of eggs laid over the three previous weeks, after t weeks of egg laying test. 2 : equations obtained with egg laying data from 359, 65 and 66 quail, in the F2 population and lines DD and LTI, respectively. a = scale parameter; b = weekly rate of decrease in egg laying after peak; c = indicator of 1/variance in sexual maturity; d = number of weeks in test until sexual maturity. y P = 3-week production at the egg laying plateau; t 2 = number of weeks in test until egg laying plateau; b 4 = weekly rate of change in egg laying after the plateau; P = persistency of constant production. k 1 = proportion of peak production during the increasing phase of the curve; k 2 = proportion of peak production during the decreasing phase of the curve; c 2 = duration of peak egg production. Table 3 shows the location of the chromosome-wide significant QTL, their position on the chromosome, the maximum F value obtained at this position, their genetic effects, the reduction of the residual variance obtained by fitting a QTL at this location, and the corresponding genome-wide significance.

QTL analysis
Out of the 8 QTL found to be chromosome-wide significant (p c <0.05) on 5 of the 12 autosomes scanned in this study, one was genome-wide suggestive (p g <0.20) and none was genome-wide significant (p g <0.05). One chromosome-wide significant QTL for the parameter t 2 related to the ascending phase of the egg laying curve in Model 2 was found on CJA13. Another QTL was close to chromosome-wide significance on CJA06 for the parameter y P which describes the plateaued phase of the curve in the same model. Chromosome-wide significant QTL for the parameters associated with persistency in Models 1 and 3 were found on CJA10 and CJA14 for b, and on CJA05 and CJA10 for k 2 . QTL were also found on CJA10 for the two parameters, c and d, of Model 1 which are related to sexual maturity.
In most cases, the genetic effect of the QTL was dominant, but the QTL was strictly additive in two instances, for y P and t 2 . The reduction of the residual variance due to fitting a QTL was small and did not exceed 2.2%. Egg laying rate of F2 Japanese quail Figure 1 Egg laying rate of F2 Japanese quail. The blue solid line with squares represents the curve drawn from the mean F2 egg laying rates calculated from the individual egg production data. The red dashed line with triangles represents the theoretical egg laying rate curve adjusted to all individual F2 egg laying records using Model 1 [4]. Egg production of F2 Japanese quail Figure 2 Egg production of F2 Japanese quail. The blue solid line with squares represents the curve drawn from the consecutive mean 3-week F2 egg productions calculated from the individual egg production data. The orange dashed line with triangles and the green dashed line with triangles represent the theoretical egg production curves adjusted to all individual F2 egg laying records using respectively Model 2 [6] and Model 3 [7].

Egg laying curves
For all three populations, the F 2 and the two F 0 lines, the three models provided a good overall fit (R 2 ) to the egg laying data as they accounted for around 90% of the total variation. Similar figures have been obtained on quail with Model 1 [17], but it seems that Model 3 underestimated the peak production of the F 2 ( Figure 2). Moreover, it was more difficult to adjust this model to individual curves as indicated by the high proportion (14%) of instances for which the numerical estimation of the parameters did not converge to an acceptable set of values. Of course, the coefficient of determination R 2 of individual curves fitted for each F 2 quail was variable and often lower (in the range 99-58%) than for the global curve. Correspondingly, the variation of the biological parameters of the individual curves was much larger because they were estimated from 19 "observations" or less (only one value by bird and 3-week period). Because of the individual between-bird variations in the actual shape of the laying curves, it might be difficult to achieve more uniform goodness of fit for the within-bird curve parameters than that obtained in the present study, and as observed also with chicken [6,7]. This means that the precision on the phenotypes (the estimated parameters of the curve) used for the QTL analysis varied between birds, possibly making the detection of QTL more difficult to achieve than for traits measured directly.

QTL
The majority of the chromosome-wide significant QTL corresponding to different parts of the laying curve obtained for the three models showed some dominance, which could be expected since there is consistent heterosis for egg production in poultry [18]. The significant genome-wide additive QTL for total egg number found in the previous analysis of the same set of quail [12] was mapped on CJA06 at the same position as the additive QTL found for y p (the number of eggs laid in 3 weeks at the time of maximum egg laying) in the present study. It is unlikely this convergence was coincidental, and it is an indication that the QTL which were only detected at the chromosome-wide level in the present work might truly correspond to regions with genes with some effect on the shape of the laying curve. Also, it is interesting to notice that the QTL analysis of the curves obtained under Model 1(for egg laying rate) and Model 3 (for 3-week egg number) led to the detection of a QTL for the decreasing phase of the curve (related to persistency) on CJA10. To our knowledge, there is no published report on similar QTL analyses of curves fitted to repeated observations of the same phenotype. Yet, this approach might be a good alternative to splitting the egg production into consecutive periods and searching for QTL for each period [11].

Conclusion
For the first time, a genome scan was carried out to detect QTL for the parameters of curves modelling a biological process estimated by nonlinear regression and used as phenotypes for the analysis. The potential interest of this approach is in the integration of all the information to discover genes which affect the expression of the trait through time, as illustrated in the present work by the egg laying curve. Only chromosome-wide QTL were obtained in this study, which might be related to the limited value of the available models for describing accurately the individual egg laying curve and to the "imprecise" nature of the statistics used as a phenotype, and could possibly be overcome by using improved models and a larger data set. In any case, the approach is interesting, as a QTL for the maximum level of production reached for one of the curves was located at the same position as a genome-wide significant QTL previously reported for the total egg number. Of course, the results should be further tested on chicken and quail for egg laying. Interestingly, similar work might be carried out on longitudinal data in other

Quail and husbandry
Two lines, LTI and DD, with different origins [19,20] and selected for early egg production [13] or for high duration of tonic immobility [14] were crossed reciprocally (12 single-pair matings) to produce the F 1 generation. Ten males and 30 egg-laying females were selected at random across the F 1 families to produce the F 2 generation. Each F 1 sire was mated to three full sisters from another F 1 family to obtain 434 F 2 female chicks (39 to 45 birds per sire family and 12 to 19 quail per full-sib family), in three consecutive hatches. The 57-week egg laying test analyzed in this work was completed by 359 birds.
The F 2 quail were given successively standard starter (2901 kcal ME/kg, 11.5% moisture, 7% ash, 27% total protein, 8% fat and 4% crude fiber) and commercial layer (2709 kcal ME/kg, 11.5% moisture, 12% ash, 20% total protein, 4% fat and 4% crude fiber) diets. At 5 weeks of age they were assigned at random to individual cages of a 4-tier battery maintained at 22°C, in which they remained until the end of the experiment with free access to feed and drinking water.

Traits
Egg production was recorded daily and individually from 5 weeks of age onwards. Global egg laying curves for all the F 2 (and F 0 ) quail and individual curves fitted to the 19 consecutive 3-week egg production of each F 2 quail were obtained under three different nonlinear theoretical models [4,6,7] of egg laying. The parameters of the models with a biological meaning related to egg production were chosen as traits for which QTL analyses were carried out. For each model, the phenotypes were the estimates of the parameters of the individual laying curves.

Model 1 was: elr(t) = a exp(-bt)/(1+exp(-c(t-d)))
, where t is the number of weeks in test, elr is the egg laying rate (%), a is a scale parameter, b is the rate of decrease of egg laying, 1/c is an indicator of the variance in sexual maturity, and d is the mean number of weeks in test until sexual maturity [4]. The traits for which QTL were sought under this model were b, c, and d. QTL analysis was not carried out on a because this parameter was highly correlated to b (r = 0.89) and has no biological interpretation.
Model 2 was: y(t) = (y P/ t 2 )t -r (y P/ t 2 ) Ln [(exp(t/r) + exp(t 2 / r))/(1+exp(t 2 /r))] + r b 4 Ln [(exp(t/r) + exp((t 2 +P)/r))/(1+ exp((t 2 +P)/r))], where t is the number of weeks in test, y is the number of eggs laid over the last 3 weeks, r is the duration of transition and was taken equal to 0.3 as in [6], y P is the level of constant production (that is, the 3-week pro-duction at the egg laying plateau), t 2 is the time at transition from rapid increase to constant egg production (that is, the number of weeks in test until the egg laying plateau), b 4 is the rate of decline in production (that is, the weekly rate of change in egg laying after the plateau) and P is the persistency of constant production (that is, the duration of the plateau, in weeks) [6]. The traits for which QTL were sought under this model were y P , t 2 , and b 4 . QTL analysis was not carried out on P because it was highly correlated to y P (r = 0.71).