First High-Density Linkage Map and Single Nucleotide Polymorphisms Significantly Associated With Traits of Economic Importance in Yellowtail Kingfish Seriola lalandi

Abstract

The genetic resources available for the commercially important fish species Yellowtail kingfish (YTK) (Seriola lalandi) are relative sparse. To overcome this, we aimed (1) to develop a linkage map for this species, and (2) to identify markers/variants associated with economically important traits in kingfish (with an emphasis on body weight). Genetic and genomic analyses were conducted using 13,898 single nucleotide polymorphisms (SNPs) generated from a new high-throughput genotyping by sequencing platform, Diversity Arrays Technology (DArTseqTM) in a pedigreed population comprising 752 animals. The linkage analysis enabled to map about 4,000 markers to 24 linkage groups (LGs), with an average density of 3.4 SNPs per cM. The linkage map was integrated into a genome-wide association study (GWAS) and identified six variants/SNPs associated with body weight (P < 5e-8) when a multi-locus mixed model was used. Two out of the six significant markers were mapped to LGs 17 and 23, and collectively they explained 5.8% of the total genetic variance. It is concluded that the newly developed linkage map and the significantly associated markers with body weight provide fundamental information to characterize genetic architecture of growth-related traits in this population of YTK S. lalandi.

A first genetic linage map construction and QTL mapping for growth traits in Larimichthys polyactis

“…The total length of the integrated map was 3,794.08 cm, with an average inter-locus distance of 0.66 cm. The value of the average locus distance was larger than P. olivaceus (0.47) 32 , L. crocea (0.54) 40 , C. auratus (0.44) 36 , Seriola aureovittata (0.30) 41 , but smaller than Dicentrarchus labrax (0.72) 42 , Hypophthalmichthys nobilis (0.75) 43 , C. carpio (0.75) 35 , Scophthalmus maximus (0.72) 44 , and Silurus meridionalis (0.89) 45 . Compared with other fish genetic maps, our genetic map is relatively high in quality.…”

Sibship assignment to the founders of a Bangladeshi Catla catla breeding population

“…Our findings have been applied to modify the pedigree of a Bangladeshi catla breeding population to improve the accuracy of genetic parameter and breeding value estimates, and to minimise future inbreeding. Furthermore, the lack of genetic structure observed in our study, is likely to simplify any future implementation of genome-wide association studies (GWAS) and/or genomic selection [35].…”

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

“…Currently, the reported implementation of genomic selection in commercial aquaculture is still in its early days and has been limited to a handful of high-value species (i.e., rainbow trout, Abdelrahman et al., 2017; Atlantic salmon, Bangera et al., 2018; and the Tasmanian Atlantic salmon strain, Verbyla et al., 2018). However, a number of examples of demonstrating accuracy of genomic prediction across a range of traits and other species in aquaculture species have been recently published, for example, sea lice resistance in Atlantic salmon (Tsai et al., 2016, 2017), bacterial cold-water disease resistance in rainbow trout (Vallejo et al., 2017, 2018), pasteurellosis resistance in gilthead sea bream Sparus aurata (Palaiokostas et al., 2016), shell size in Yesso scallops Patinopecten yessoensis (Dou et al., 2016), Greenshell mussel Perna canaliculus (Ashby et al., 2018), body weight and meat quality in large yellow croaker Larimichthys crocea (Dong et al., 2016) and channel catfish (Garcia et al., 2018), growth traits of Pacific white shrimp (Wang et al., 2017), resistance to viral nervous necrosis in European sea bass (Palaiokostas et al., 2018a), juvenile growth rate in common carp (Palaiokostas et al., 2018b), resistance against Piscirickettsia salmonis in a farmed Atlantic and coho salmon Oncorhynchus kisutch population (Barría et al., 2018), resistance against P. salmonis and infectious pancreatic necrosis virus in rainbow trout (Yoshida et al., 2018), growth traits in yellowtail kingfish Seriola lalandi (Nguyen et al., 2018), and resistance to amoebic gill disease in Atlantic salmon (Robledo et al., 2018). The accuracy of genomic prediction reported in the aforementioned studies varies from 0.16 to 0.83 (median = 0.60) for various disease survival traits and from 0.3 to 0.8 (median = 0.6) for the growth and body size-related traits.…”