First High-Density Linkage Map and QTL Fine Mapping for Growth-Related Traits of Spotted Sea bass (Lateolabrax maculatus)

HSP90 and HSP70 Families in Lateolabrax maculatus: Genome-Wide Identification, Molecular Characterization, and Expression Profiles in Response to Various Environmental Stressors

“…Spotted seabass ( Lateolabrax maculatus; L. maculatus ) is an economically important aquaculture fish species in China of its high yield, high nutritional value, and pleasant taste ( Liu et al, 2020 ; Yu et al, 2020 ) with the annual production exceeding 190,000 tons in recent years (China Fishery Statistical Yearbook, 2021; Li et al, 2021 ). Nevertheless, it is facing various environmental threatens, such as heat stress caused by global warming, salinity stress derived from tidal water flow, and the oxidative stress along with the development of intensive aquaculture.…”

Unravelling the complex genetic basis of growth in New Zealand silver trevally (Pseudocaranx georgianus)

“…Eight significant QTLs related to growth were mapped to three of the trevally linkage groups. Other studies in teleost have found multiple QTLs affecting growth such as in Atlantic salmon (Tsai, Hamilton et al 2015, Besnier, Solberg et al 2020), Australasian snapper (Ashton, Ritchie et al 2019), spotted sea bass ( Lateolabrax maculatus ) (Liu, Wang et al 2020) or yellowtail kingfish (Nguyen, Rastas et al 2018), supporting the hypothesis of a polygenic regulation. No significant QTLs were identified with QTL mapping for the second time point, October 2018.…”

Application of second-generation sequencing (SGS) and third generation sequencing (TGS) in aquaculture breeding program

“…In general, the GS procedure consists of three main steps: i) a reference (training) population of individuals with available data of genome-wide SNP genotypes and phenotypes are used to estimate the effects of each marker on the phenotype of interest, and these SNPs effects are then used to generate genomic estimated breeding values (GEBVs) using multiple analytical models, ii) the predictive SNP effects are validated in a related test population (relatives of the reference population) and checked for accuracy of prediction against highly reliable estimated breeding values (EBV), and iii) the prediction equation is used to calculate the GEBVs of new selection candidates that have been genotyped but not phenotyped, where the outstanding individuals can be selected based on these GEBVs (Eggen, 2012;Meuwissen et al, 2001;Taylor, 2014). Genomic prediction across a range of commercial traits in aquaculture species were published, and were categorized into: i) disease resistance traits in sea lice resistance in Atlantic salmon (Tsai et al, 2017(Tsai et al, , 2016, bacterial cold-water disease resistance in rainbow trout (Vallejo et al, 2018(Vallejo et al, , 2017, pasteurellosis resistance in gilthead sea bream (Palaiokostas et al, 2016), viral nervous necrosis resistance in European sea bass (Palaiokostas et al, 2018a), Piscirickettsia salmonis resistance in Atlantic salmon and Coho salmon (Barría et al, 2018), infectious pancreatic necrosis virus resistance in rainbow trout (Yoshida et al, 2019), amoebic gill disease in Atlantic salmon (Robledo et al, 2018a) and V. anguillarum-resistant in turbot (Zhang et al, 2019b); ii) growth traits in Pacific white shrimp (Wang et al, 2017a), common carp (Palaiokostas et al, 2018b), Yellow River carp, C. carpio haematoperus (Wang et al, 2018), ridgetail white prawn, Exopalaemon carinicauda (Li et al, 2019), fugu (Shi et al, 2020), spotted scat, Scatophagus argus (Yang et al, 2020), spotted sea bass, Lateolabrax maculatus (Liu et al, 2020c), Asian rice-field eel, Monopterus albus (Shi et al, 2021); iii) shell color and/or size of Yesso scallops (Dou et al, 2016), Greenshell mussel, Perna canaliculus (Ashby et al, 2018) and silver-lipped pearl oyster, Pinctada maxima…”

“…This streamlined variation of RAD sequencing has a simpler and much rapid library construction protocol, and it is more costeffective as tag density could be adjusted by adding selective bases to the joints, thus genome coverage can be regulated more flexibly (Jiao et al, 2014;Shi et al, 2014;Wang et al, 2012). In particular, RAD and/ or 2b-RAD sequencing has been applied in studies on QTL identification (Pang et al, 2017;Feng et al, 2018;Wang et al, 2018;Liu et al, 2020aLiu et al, , 2020bLiu et al, , 2020cShi et al, 2020Shi et al, , 2021Yang et al, 2020;Liu et al, 2021;Oikonomou et al, 2021; and sex-related sequence detection (Cui et al, 2018;Liu et al, 2018;Zhou et al, 2019a;Xue et al, 2020;Zhou et al, 2020b;. Besides, a more enhanced GBS based method, notably WGR could uncover the variable regions and SNPs undetected by RAD-Seq to provide a more complete overview of genomic variation between studied individuals.…”