QTL for Stress and Disease Resistance in European Sea Bass,  Dicentrarhus labrax  L.

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Abstract

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Over the last decades, many genetic tools have been developed in order to improve our knowledge and understanding of the links between physiology and genetics. In our research, European Sea Bass was selected in order to study stress response physiological indicators in relation to growth and disease resistance. Therefore, DNA samples were collected in order to identify any potential relation between the aforementioned traits and their genetic component. Genomic areas related to body weight and stress response were detected. No genomic areas related to disease resistance were identified. Based on the results, fish that hold the genetic information for increased body weight and improved stress response could be used for genetic improvement purposes.

Abstract

There is a growing interest in selective breeding in European sea bass ( Dicentrarchus labrax), especially regarding family selection based on growth performance. In particular, quantitative trait loci (QTL) identification in sea bass enhances the application of marker-assisted breeding for the genetic improvement of the production traits. The aims of the study were to identify potential QTL affecting stress and immunological indicators, body weight, and mortality after vibriosis injection in sea bass as well as to estimate heritability and genetic/phenotypic correlations for the aforementioned traits. To this end, stress test was performed on 960 offspring and a sub-group of them (420) was selected to explore the mortality after vibrio injection. Selective genotyping was performed in 620 offspring for 35 microsatellite markers and distributed into 6 linkage groups. The length of the genetic linkage map was 283.6 cM and the mean distance between the markers was 8.1 cM. QTL affecting body weight in three different growth periods detected on linkage groups LG1, LG4, LG6, and LG14. A QTL associated with weight in early growth stages (290–306 days post-hatching) was also identified on LG3. QTL analysis confirmed the existence of QTL affecting cortisol levels, on LG3 and LG14. Moreover, new QTL affecting only cortisol and glucose levels were detected on LG1 and LG23. No QTL affecting hormonal or biochemical marks was found on LG4 and LG6. Heritability of cortisol, lysozyme levels, and mortality were high (0.36, 0.55, and 0.38, respectively).

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Invited review: Genomic selection in dairy cattle: progress and challenges.

Julie Goddard, Edward P Bowman, Scott Chamberlain … (2009)

A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams. The increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBV only, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.

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Accelerating improvement of livestock with genomic selection.

Ben J. Hayes, Theo HE Meuwissen, Mike Goddard (2012)

Three recent breakthroughs have resulted in the current widespread use of DNA information: the genomic selection (GS) methodology, which is a form of marker-assisted selection on a genome-wide scale, and the discovery of large numbers of single-nucleotide markers and cost effective methods to genotype them. GS estimates the effect of thousands of DNA markers simultaneously. Nonlinear estimation methods yield higher accuracy, especially for traits with major genes. The marker effects are estimated in a genotyped and phenotyped training population and are used for the estimation of breeding values of selection candidates by combining their genotypes with the estimated marker effects. The benefits of GS are greatest when selection is for traits that are not themselves recorded on the selection candidates before they can be selected. In the future, genome sequence data may replace SNP genotypes as markers. This could increase GS accuracy because the causative mutations should be included in the data.

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Applications of genotyping by sequencing in aquaculture breeding and genetics

Diego Robledo, Christos Palaiokostas, Luca Bargelloni … (2017)

Abstract Selective breeding is increasingly recognized as a key component of sustainable production of aquaculture species. The uptake of genomic technology in aquaculture breeding has traditionally lagged behind terrestrial farmed animals. However, the rapid development and application of sequencing technologies has allowed aquaculture to narrow the gap, leading to substantial genomic resources for all major aquaculture species. While high‐density single‐nucleotide polymorphism (SNP) arrays for some species have been developed recently, direct genotyping by sequencing (GBS) techniques have underpinned many of the advances in aquaculture genetics and breeding to date. In particular, restriction‐site associated DNA sequencing (RAD‐Seq) and subsequent variations have been extensively applied to generate population‐level SNP genotype data. These GBS techniques are not dependent on prior genomic information such as a reference genome assembly for the species of interest. As such, they have been widely utilized by researchers and companies focussing on nonmodel aquaculture species with relatively small research communities. Applications of RAD‐Seq techniques have included generation of genetic linkage maps, performing genome‐wide association studies, improvements of reference genome assemblies and, more recently, genomic selection for traits of interest to aquaculture like growth, sex determination or disease resistance. In this review, we briefly discuss the history of GBS, the nuances of the various GBS techniques, bioinformatics approaches and application of these techniques to various aquaculture species.

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Journal

Journal ID (nlm-ta): Animals (Basel)

Journal ID (iso-abbrev): Animals (Basel)

Journal ID (publisher-id): animals

Title: Animals : an Open Access Journal from MDPI

Publisher: MDPI

ISSN (Electronic): 2076-2615

Publication date (Electronic): 16 September 2020

Publication date Collection: September 2020

Volume: 10

Issue: 9

Electronic Location Identifier: 1668

Affiliations

[1 ]Laboratory of Agrobiotechnology and Inspection of Agricultural Products, Dept of Agricultural Technology, School of Geotechnical Sciences, International Hellenic University, Alexander Campus, P.O. Box 141, 57 400 Sindos, Thessaloniki, Greece; chatz@ 123456ihu.gr (D.C.); valiaekonomou@ 123456hotmail.com (S.O.)

[2 ]Department of Genetics, Development and Molecular Biology, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece

[3 ]Research Institute of Animal Science, ELGO Demeter, 58100 Paralimni, Giannitsa, Greece; tsiokosd@ 123456rias.gr

[4 ]Department of Biology, University of Crete, GR-714 09 Heraklion, Greece; sam_thanasis@ 123456yahoo.gr (A.S.); pavlidis@ 123456biology.uoc.gr (M.P.)

[5 ]Department of Research & Development, Nireus Aquaculture SA, 341 00 Chalkida, Greece; a.dimitroglou@ 123456nireus.com (A.D.); l.kottaras@ 123456nireus.com (L.K.); k.papanna@ 123456nireus.com (K.P.); l.papaharisis@ 123456nireus.com (L.P.)

[6 ]Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), 71003 Heraklion, Crete, Greece; tsigeno@ 123456hcmr.gr

Author notes

[* ]Correspondence: dloukovi@ 123456rias.gr ; Tel.: +30-2310-013-337

Author information

Dimitrios Loukovitis https://orcid.org/0000-0002-4845-5747

Dimitrios Tsiokos https://orcid.org/0000-0003-2319-1738

Arkadios Dimitroglou https://orcid.org/0000-0002-6136-8226

Kantham Papanna https://orcid.org/0000-0001-7914-0521

Costas Tsigenopoulos https://orcid.org/0000-0003-0615-7941

Article

Publisher ID: animals-10-01668

DOI: 10.3390/ani10091668

PMC ID: 7552151

PubMed ID: 32948016

SO-VID: 766871a6-44a7-4290-a628-2f3cc2a50f39

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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

QTL for Stress and Disease Resistance in European Sea Bass, Dicentrarhus labrax L.

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SDG 14: Life Below Water

Most cited references 30

Invited review: Genomic selection in dairy cattle: progress and challenges.

Accelerating improvement of livestock with genomic selection.

Applications of genotyping by sequencing in aquaculture breeding and genetics

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