Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software

© 2021 The Authors.In this study, a total of 18 novel productive traits, three related to carcass [cNiT] and fifteen related to morphometric [mNiT]), were measured in gilthead seabream (Sparus aurata) using Non-invasive Technologies (NiT) as implemented in IMAFISH_ML (MatLab script). Their potential to be used in industrial breeding programs were evaluated in 2348 offspring reared under different production systems (estuarine ponds, oceanic cage, inland tank) at harvest. All animals were photographed, and digitally measured and main genetic parameters were estimated. Heritability for growth traits was medium (0.25–0.37) whereas for NiT traits medium-high (0.24–0.61). In general, genetic correlations between mNiT, cNiT and growth and traits were high and positive. Image analysis artifacts such as fin unfold or shades, that may interfere in the precision of some digital measurements, were discarded as a major bias factor since heritability of NiT traits after correcting them were no significantly different from original ones. Indirect selection of growth traits through NiT traits produced a better predicted response than directly measuring Body Weight (13–23%), demonstrating that this methodological approach is highly cost-effective in terms of accuracy and data processing time.This study was funded from the European Maritime and Fisheries Fund (EMFF) by Ministerio de Agricultura y Pesca, Alimentación y Medio Ambiente (MAPAMA), framed in PROGENSA-II III project (Mejora de la Competitividad del Sector de la Dorada a Través de la Selección Genética, programa JACUMAR)

Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software

In this study, a total of 18 novel productive traits, three related to carcass [cNiT] and fifteen related to morphometric [mNiT]), were measured in gilthead seabream (Sparus aurata) using Non-invasive Technologies (NiT) as implemented in IMAFISH_ML (MatLab script). Their potential to be used in industrial breeding programs were evaluated in 2348 offspring reared under different production systems (estuarine ponds, oceanic cage, inland tank) at harvest. All animals were photographed, and digitally measured and main genetic parameters were estimated. Heritability for growth traits was medium (0.25–0.37) whereas for NiT traits medium-high (0.24–0.61). In general, genetic correlations between mNiT, cNiT and growth and traits were high and positive. Image analysis artifacts such as fin unfold or shades, that may interfere in the precision of some digital measurements, were discarded as a major bias factor since heritability of NiT traits after correcting them were no significantly different from original ones. Indirect selection of growth traits through NiT traits produced a better predicted response than directly measuring Body Weight (13–23%), demonstrating that this methodological approach is highly cost-effective in terms of accuracy and data processing time.Versión del edito

The polygenic basis of relapse after a first episode of schizophrenia

Little is known about genetic predisposition to relapse. Previous studies have linked cognitive and psychopathological (mainly schizophrenia and bipolar disorder) polygenic risk scores (PRS) with clinical manifestations of the disease. This study aims to explore the potential role of PRS from major mental disorders and cognition on schizophrenia relapse. 114 patients recruited in the 2EPs Project were included (56 patients who had not experienced relapse after 3 years of enrollment and 58 patients who relapsed during the 3-year follow-up). PRS for schizophrenia (PRS-SZ), bipolar disorder (PRS-BD), education attainment (PRS-EA) and cognitive performance (PRS-CP) were used to assess the genetic risk of schizophrenia relapse.Patients with higher PRS-EA, showed both a lower risk (OR=0.29, 95% CI [0.11–0.73]) and a later onset of relapse (30.96± 1.74 vs. 23.12± 1.14 months, p=0.007. Our study provides evidence that the genetic burden of neurocognitive function is a potentially predictors of relapse that could be incorporated into future risk prediction models. Moreover, appropriate treatments for cognitive symptoms appear to be important for improving the long-term clinical outcome of relapse

The Effect of the Deformity Genetic Background of the Breeders on the Spawning Quality of Gilthead Seabream (Sparus aurata L.)

© 2021 Lorenzo-Felipe, Shin, León-Bernabeu, Pérez-García, Zamorano, Pérez-Sánchez and Afonso-López.Fish egg quality is strongly related with the ability of the egg to be fertilized and develop a normal embryo with good survival and a lack of abnormalities. Large variations in the spawning quantity or quality impact directly in the competitiveness and sustainability of hatcheries, which create an overly large broodstock in order to satisfy the on-growing companies’ demand for undeformed fry. The present study reports, for the first time in relation to gilthead seabream, the effect of the genetic background of breeders for presence or absence of deformity on their spawning quality and the importance of considering this when creating broodstock. The spawning quality of crosses of breeders with genetic background for presence or absence of deformity (EBVdef), were evaluated during a whole spawning season, through study of the following traits: oocyte yield, fertilization rate, viability rate, hatching rate, larval survival rate, fertilized eggs, viable eggs, hatched eggs, and number of alive larvae. Breeders with a genetic background for deformity and a normal phenotype had shorter spawning periods, lower oocyte yield and, consequently, produced a lower number of alive larvae. In these two traits, the genetic background of breeders was of greater importance during intermediate spawning periods, when spawning is generally considered optimal for the industry, while environmental factors were more important at the beginning and end of the spawning season. In conclusion, these results demonstrate the importance of controlling the breeders’ genetics when creating broodstock.This work has been co-funded by three projects: PERFORMFISH project no. 727610, Consumer driven production: Integrating innovative approaches for competitive and sustainable performance across the Mediterranean aquaculture value chain (EU Horizon 2020 programme); PROGENSA III project, Mejora de la Competitividad del Sector de la Dorada a Través de la Selección Genética (JACUMAR program, European Maritime and Fisheries Fund [EMFF]); MORFOGEN project no. RTA2017-00054-C03-02, Estudio de los factores genéticos y moleculares que regulan la morfología en dorada (Sparus aurata) [Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) funded from MCIU/AEI/FEDER, UE]. Research co-financed by the Agencia Canaria de Investigación, Innovación y Sociedad de la Información de la Consejería de Economía, Industria, Comercio y Conocimiento, and by the European Social Fund (ESF) Integrated Operational Program of Canary Islands 2014–2020, Axis 3 Priority Theme 74 (85%)