Additional file 2: Table S2. of A new SNP-based vision of the genetics of sex determination in European sea bass (Dicentrarchus labrax)

Detailed positions of individual SNPs in the constructed genetic map. (CSV 281 kb

Heritabilities (h²), genetic correlations and phenotypic correlations estimated for variables of the three risk taking test sessions using a sire models.

<p>Genetic correlations ± SE are presented above the diagonal, heritabilities h² ±SE on the diagonal and phenotypic correlations under the diagonal.</p

Heritabilities (h²), genetic correlations and phenotypic correlations estimated for the variables of hypoxia avoidance test using a sire models.

<p>Genetic correlations ± SE are presented above the diagonal, heritabilities h² ± SE on the diagonal and phenotypic correlations under the diagonal. NE represents non estimable value due to bad model convergence.</p

Scheme of the experimental set up used for the hypoxia avoidance test.

<p>Fish were gathered in one shadowed side of a circular tank (2.25 m in diameter, 5 m³ in volume) divided into two equal chambers by an opaque divider equipped with a PIT tag antenna surrounding a circular opening (12 cm in diameter). This enabled the monitoring of fish individual movement in a group situation without any disturbance. After 30 min of acclimation, nitrogen was bubbled in the shadowed chamber (called hypoxic chamber in the text) to reduce oxygen level and fish were allowed to freely move to the lit up chamber (called normoxic chamber) with normoxic conditions.</p

Scheme of the experimental set up used for the risk taking test sessions.

<p>Fish were gathered in one shadowed side (called safe chamber in the text) of a circular tank (2.25 m in diameter, 5 m³ in volume) divided into two equal chambers by an opaque divider equipped with a PIT tag antenna surrounding a circular opening (12 cm in diameter). This enabled the monitoring of fish individual movement in a group situation without any disturbance. After 30 min of acclimation, fish were allowed to freely move in the lit up chamber (called risky chamber in the text) with normoxic conditions during the next 24 hours with the usual photoperiod used during rearing.</p

Heritabilities (h²), genetic correlations and phenotypic correlations estimated between the variables of hypoxia avoidance test and risk taking test using a sire models.

<p>Genetic correlations ± SE are presented above the diagonal, heritabilities h² ± SE on the diagonal and phenotypic correlations under the diagonal.</p

Numbers, proportions, mean body weight and sex ratios of fish characterized by the hypoxia avoidance test, the three sessions of the risk taking test and the mean of the three sessions for all fish correctly assigned and with correct sex data (N = 1155 for hypoxia avoidance test, N = 1154 for RT1 RT2, RT3 and RT_mean).

<p>Numbers, proportions, mean body weight and sex ratios of fish characterized by the hypoxia avoidance test, the three sessions of the risk taking test and the mean of the three sessions for all fish correctly assigned and with correct sex data (N = 1155 for hypoxia avoidance test, N = 1154 for RT1 RT2, RT3 and RT_mean).</p

Genetic (first line; ± S.E.) and phenotypic correlations (second line) of traits changes during overwintering and traits after overwintering (left hand side) related to traits changes during growing period and traits at market size (upper heading).

<p>Genetic (first line; ± S.E.) and phenotypic correlations (second line) of traits changes during overwintering and traits after overwintering (left hand side) related to traits changes during growing period and traits at market size (upper heading).</p

Number of observations (<i>n</i>), traits means (mean ± S.D.), <i>CV</i> (coefficient of variation), <i>V</i>_P (phenotypic variance), <i>V</i>_A (genetic variance), <i>h</i>² (heritability estimates ± S.E.), <i>m</i>² (maternal effect ± S.E.) for traits within each studied period (₁ before winter period_{, 2} after winter period_{, 3} at harvest) and for traits changes during _1–2 overwintering period and _2–3 growing period.

<p>Number of observations (<i>n</i>), traits means (mean ± S.D.), <i>CV</i> (coefficient of variation), <i>V</i>_P (phenotypic variance), <i>V</i>_A (genetic variance), <i>h</i>² (heritability estimates ± S.E.), <i>m</i>² (maternal effect ± S.E.) for traits within each studied period (₁ before winter period_{, 2} after winter period_{, 3} at harvest) and for traits changes during _1–2 overwintering period and _2–3 growing period.</p

Table_1_Potential for Genetic Improvement of the Main Slaughter Yields in Common Carp With in vivo Morphological Predictors.DOCX

<p>Common carp is a major aquaculture species worldwide, commonly sold alive but also as processed headless carcass or filets. However, recording of processing yields is impossible on live breeding candidates, and alternatives for genetic improvement are either sib selection based on slaughtered fish, or indirect selection on correlated traits recorded in vivo. Morphological predictors that can be measured on live fish and that correlate with real slaughter yields hence remain a possible alternative. To quantify the power of morphological predictors for genetic improvement of yields, we estimated genetic parameters of slaughter yields and various predictors in 3-year-old common carp reared communally under semi-intensive pond conditions. The experimental stock was established by a partial factorial design of 20 dams and 40 sires, and 1553 progenies were assigned to their parents using 12 microsatellites. Slaughter yields were highly heritable (h² = 0.46 for headless carcass yield, 0.50 for filet yield) and strongly genetically correlated with each other (r_g = 0.96). To create morphological predictors, external (phenotypes, 2D digitization) and internal measurements (ultrasound imagery) were recorded and combined by multiple linear regression to predict slaughter yields. The accuracy of the phenotypic prediction was high for headless carcass yield (R² = 0.63) and intermediate for filet yield (R² = 0.49). Interestingly, heritability of predicted slaughter yields (0.48–0.63) was higher than that of the real yields to predict, and had high genetic correlations with the real yields (r_g = 0.84–0.88). In addition, both predicted yields were highly phenotypically and genetically correlated with each other (0.95 for both), suggesting that using predicted headless carcass yield in a breeding program would be a good way to also improve filet yield. Besides, two individual predictors (P₁ and P₂) included in the prediction models and two simple internal measurements (E4 and E23) exhibited intermediate to high heritability estimates (h² = 0.34 – 0.72) and significant genetic correlations to the slaughter yields (r_g = |0.39 – 0.83|). The results show that there is a solid potential for genetic improvement of slaughter yields by selecting for predictor traits recorded on live breeding candidates of common carp.</p