research advances in the genomics and applications for
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Aquaculture
journal homepage: www.elsevier.com/locate/aquaculture
Review
Research advances in the genomics and applications for molecular breedingof aquaculture animals
Xinxin Youa,1,⁎, Xinxin Shana,b,1, Qiong Shia,b
a Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGIMarine, Shenzhen 518083, Chinab BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
A R T I C L E I N F O
Keywords:Aquaculture animalsGenomeGenome-wide association studyGenomic selectionQuantitative trait lociSingle nucleotide polymorphism
A B S T R A C T
High-throughput sequencing technologies have been extensively applied to genetics and breeding in the aqua-culture industry. To date, many aquaculture animal genomes have been sequenced, and single nucleotidepolymorphisms (SNPs) have become the most popular genetic markers. SNP applications include high-densitygenetic linkage map construction, quantitative trait loci (QTL) mapping, genome-wide association study(GWAS), and genomic selection (GS). In this review, we assess the availability of complete genomes of aqua-culture animals and then briefly discuss the sequencing technologies and SNP array for SNPs genotyping. Finally,we summarize the current status of genetic linkage map construction, QTL mapping, GWAS, and GS in aquaticanimals. As the overall research programs and aims are similar across aquaculture animals, successful practicesin some species might act as reference frameworks for in-depth investigations in other species.
1. Introduction
Aquaculture is the fastest growing industry for food production (Yueand Wang, 2017); it is predicted that aquaculture will play an im-portant role in providing vital food and nutrition to over 9 billionpeople by the middle of the twenty-first century (FAO, 2018). However,rapid and sustainable aquaculture development has been hindered byseveral factors, including a lack of genetically improved stocks, slowanimal growth, low feed conversion rates, various environmentalstressors, and emerging pathogens or diseases. Aquaculture breedingprograms aim to improve production efficiency in order to satisfyconsumer demands and improve commercial profits (Gjedrem, 2012).To improve the production efficiency of any given aquaculture species,it is helpful to characterize genomic structure, genomic variations, andthe genetic basis of economically important traits. As the next genera-tion of sequencing technologies have flourished over recent decades,whole genome sequencing (WGS) and various molecular genetic toolshave been widely applied in aquaculture.
In addition, the genomes of many aquaculture animals have been, orare being, sequenced. Indeed, as the cost of high-throughput sequencinghas decreased substantially, genotyping by sequencing (GBS) technol-ogies has led to several advances in aquaculture genetics and breeding(Robledo et al., 2018b). Restriction-site-associated DNA sequencing
(RAD-Seq) and related techniques, as well as whole genome resequen-cing (WGR), have generated large volumes of population-level single-nucleotide polymorphism (SNP) data. With a large number of SNP re-sources, it is feasible to design and use SNP arrays for SNPs genotyping.High-volume SNP genotype data generated by GBS and SNP arrays havefacilitated the construction of high-density genetic linkage maps forquantitative trait loci (QTL) mapping and genome-wide associationstudy (GWAS) (Robledo et al., 2018b). Such studies have identifiedmany SNPs associated with economically-important performance traits,including growth rate, disease resistance, sexual determination, andtolerance of various environmental stressors. Based on these SNPs,marker-assisted selection (MAS) has been applied in some aquaculturebreeding programs. Genomic selection (GS) breeding programs are alsoin progress for some aquaculture animals.
In this review, we first summarize the available genome assembliesof aquatic animals and discuss the sequencing technologies and SNParray that were used to discover SNPs. We then review the currentstatus of genetic linkage map construction, QTL mapping, GWAS, andGS in representative aquatic animals. As overall research programs andgoals are similar across aquaculture species, current progress in severalimportant species might provide practical guidance for other species.
https://doi.org/10.1016/j.aquaculture.2020.735357Received 2 March 2020; Received in revised form 8 April 2020; Accepted 9 April 2020
⁎ Corresponding author at: Shenzhen BGI Academy of Marine Sciences, BGI Marine, Shenzhen 518083, China.E-mail address: [email protected] (X. You).
1 These authors contributed equally to this work.
Aquaculture 526 (2020) 735357
Available online 10 April 20200044-8486/ © 2020 Elsevier B.V. All rights reserved.
T
Table 1Whole genome sequencing for genome assemblies of aquaculture animals.
Species Sequencing strategy (Sequencingcoverage)
Estimated/Assembled genomesize (Mb)
Contig/Scaffold N 50(kb)
Chromosome or scaffoldlevel
BUSCO (genomemode)
Reference
Atlantic code (Gadus morhua) 454 (40) + Illumina (480) + BAC (0.1)1 830/6111 3/6881 Scaffold1 –1 Star et al. (2011)1
454 (40) + BAC (0.1) + Illumina (480)+ PacBio (19)2
613/6432 116/11502 Scaffold2 93.2%2 Malmstrøm et al. (2017)2
Pacific oyster (Crassostrea gigas) Illumina (155) + Fosmid (10) 637/559 19/401 Scaffold – Zhang et al. (2012)Japanese eel (Anguilla japonica) Illumina (35–55)1 1630/11501 –/531 Scaffold1 –1 Henkel et al. (2012)1
Illumina (43)2 1030/11302 11/10332 Scaffold2 92.2%2 Chen et al. (2019c)2
Pearl oyster (Pinctada fucata martensii) 454 (16) + Illumina (27)1 1150/10241 2/151 Scaffold1 –1 Takeuchi et al. (2012)1
Illumina (134) + BAC (5)2 –/9912 21/3242 Chromosome (genetic map)2 90.2%2 Du et al. (2017)2
Pacific bluefin tuna (Thunnus orientalis) 454 (12) + Illumina (43) 800/740 8/137 Scaffold – Nakamura et al. (2013)Soft-shell turtle (Pelodiscus sinensis) Illumina (106) 2210/2210 22/3332 Scaffold – Wang et al. (2013)Half-smooth tongue sole (Cynoglossus semilaevis) Illumina (117) 545/477 27/867 Chromosome (genetic map) – Chen et al. (2014)Common carp (Cyprinus carpio) 454 (7) + Illumina (80) + SOLiD (44) +
BAC (0.03)1830/1690 68/1000 Chromosome (genetic map) – Xu et al. (2014a, 2014b)
Blue-spotted mudskipper (Boleophthalmuspectinirostris)
Illumina (237) 983/966 20/2310 Scaffold – You et al. (2014)
Large yellow croaker (Larimichthys crocea) Illumina (1287)1 728/6441 26/4901 Scaffold –1 Wu et al. (2014)1
Illumina (103) + BAC (4.3)2 691/6792 63/10302 Scaffold 98.6%2 Ao et al. (2015)2
Illumina (103) + BAC (4.3) + PacBio(25)3
670/6723 283/65503 Chromosome (genetic map) 99.1%3 Mu et al. (2018)3
Illumina (149) + PacBio (114) +HIC(168)4
708/7244 2830/–4 Chromosome (HIC) 97.1%4 Chen et al. (2019b)4
Nile tilapia (Oreochromis niloticus) Illumina (269)1 1010/9281 29/28001 Chromosome (genetic map)1 –1 Brawand et al. (2014)1
PacBio (44)2 1082/10102 3325/–2 Chromosome (genetic map)2 95.4%2 Conte et al. (2017)2
Rainbow trout (Oncorhynchus mykiss) 454 (19) + Illumina (54) 2400/1877 8/384 Chromosome (genetic map)1 – Berthelot et al. (2014)Grass carp (Ctenopharyngodon idellus) Illumina (132) 891/901 41/6400 Chromosome (genetic map) – Wang et al. (2015c)Atlantic salmon (Salmo salar) Sanger (4) + Illumina (202) + PacBio
(19)2970/2970 58/2970 Chromosome (genetic map) – Lien et al. (2016)
Amur ide (Leuciscus waleckii) Illumina (237) 900/752 37/448 Scaffold – Xu et al. (2016)Chinese mitten crab (Eriocheir sinensis) Illumina (156) 1660/1120 6/224 Scaffold – Song et al. (2016)Channel catfish (Ictalurus punctatus) Illumina (143) + Fosmid (0.15) + PacBio
(5.4)11021/7831 77/77271 Chromosome (genetic map)1 –1 Liu et al. (2016c)1
Illumina (240)2 839/8452 49/72482 Scaffold2 –2 Chen et al. (2016)2
Asian seabass (Lates calcarifer) Illumina (80) + PacBio (90) + BAC(0.12)
700/587 1066/25849 Chromosome (genetic map) – Vij et al. (2016)
Golden-line barbel (Sinocyclocheilus grahami) Illumina (157) 1792/1754 29/1156 Scaffold – Yang et al. (2016)Tiger tail seahorse (Hippocampus comes) Illumina (190) 695/502 35/1800 Scaffold – Lin et al. (2016)Yesso scallop (Patinopecten yessoensis) Illumina (297) 1430/988 38/804 Chromosome (genetic map) 95.2% Wang et al. (2017b)Zhikong scallop (Chlamys farreri) Illumina (382) 1000/780 22/602 Chromosome (genetic map) 93.5% Li et al. (2017b)Blunt snout bream (Megalobrama amblycephala) Illumina (179) 1117/1116 49/839 Chromosome (genetic map) 90.5% Liu et al. (2017a)
Illumina (162) + PacBio (10) 1120/1088 143/1403 Scaffold 96.2% Ren et al. (2019)Northern snakehead (Channa argus) Illumina (125) 670/615 81/4500 Scaffold 82.9% Xu et al. (2017)Chinese clearhead icefish (Protosalanx hyalocranius) Illumina (315) 525/536 17/1163 Scaffold 97.1% Liu et al. (2017c)Ridgetail white prawn (Exopalaemon carinicauda) Illumina (30) 5730/5568 0.7/1 Scaffold 88.4% Yuan et al. (2017)Lined seahorse (Hippocampus erectus) Illumina (243) 489/458 15/1970 Scaffold 85% Lin et al. (2017)Japanese flounder (Paralichthys olivaceus) Illumina (120) 640/546 31/3881 Chromosome (genetic map) – Shao et al. (2017)Eurasian perch (Perca flfluviatilis) Illumina (9–39)1 /6311 4/61 Scaffold1 74%1 Malmstrøm et al. (2017)1
Illumina (68)2 1051/9582 18/62612 Scaffold2 95%2 Ozerov et al. (2018)2
Sea cucumber (Apostichopus japonicus) Illumina (356)1 820/6601 6/101 Scaffold1 60.7%1 Jo et al. (2017)1
Illumina (295) + PacBio (73)2 880/8052 190/4862 Chromosome (genetic map)2 –2 Zhang et al. (2017)2
Illumina (346) + PacBio (24)3 1000/9523 45/1963 Chromosome (genetic map)3 –3 Li et al. (2018d)3
Greater amberjack (Seriola dumerili) Illumina (200)+ PacBio (10) –/671 –/5813 Chromosome (genetic map) – Araki et al. (2018)Peruvian scallop (Argopecten purpuratus) Illumina (75) + PacBio (21) + 10X
Genomics (227)855/725 80/1020 Scaffold 89% Li et al. (2018a)
(continued on next page)
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Table 1 (continued)
Species Sequencing strategy (Sequencingcoverage)
Estimated/Assembled genomesize (Mb)
Contig/Scaffold N 50(kb)
Chromosome or scaffoldlevel
BUSCO (genomemode)
Reference
Yellow catfish (Pelteobagrus fulvidraco) Illumina (72) + PacBio (54) + HIC(205)1
714/7331 1100/258001 Chromosome (HIC)1 91.2%1 Gong et al. (2018)1
Illumina (437) + PacBio (35)2 720/7142 970/36502 Scaffold2 96.5%2 Zhang et al. (2018b)2
Chinese black porgy (Acanthopagrus schlegelii) Illumina (258) 740/688 17/7600 Scaffold 91.6% Zhang et al. (2018c)Kanglang white minnow (Anabarilius grahami) Illumina (189) 1020/1006 26/4410 Scaffold 97.2% Jiang et al. (2018)Yellow drum (Nibea albiflora) Illumina (260) 596/565 50/2200 Scaffold 98.6% Han et al. (2019)Pacific white shrimp (Litopenaeus vannamei) Illumina (338) + PacBio (54) + BAC
(0.46)2600/1664 58/606 Chromosome (genetic map) – Zhang et al. (2019e)
Giant grouper (Epinephelus lanceolatus) Illumina (71) + PacBio (27)1 1165/11281 1469/15061 Chromosome (genetic map)1 93.1%1 Wang et al. (2019a)1
Illumina (133.72) + HIC (0.12)2 1064/10862 120/462002 Chromosome (HIC)2 98.2%2 Zhou et al. (2019a)2
Red-spotted grouper (Epinephelus akaara) Illumina (49) + Nanopore (100) + HIC(102)
1111/1135 5250/46030 Chromosome (HIC) 98.3% Ge et al. (2019)
Spiny head croaker (Collichthys lucidus) Illumina (63) + PacBio (109) + HIC(233)
830/877 1100/35900 Chromosome (HIC) 97.4% Cai et al. (2019)
Golden pompano (Trachinotus ovatus) Illumina (114) + PacBio (26) + HIC(175)
655/648 1800/5050 Chromosome (HIC) 95.7% Zhang et al. (2019a)
Giant devil catfish (Bagarius yarrelli) Illumina (90) + PacBio (34) 599/571 1600/3100 Scaffold 95.4% Jiang et al. (2019)Sterlet (Acipenser ruthenus) Illumina (136) 1870/1832 19/191 Scaffold 88.2% Cheng et al. (2019)Chinese seabass (Lateolabrax maculatus) Illumina (189) + HIC (267) 641/597 182/2790 Chromosome (HIC) 97.03% Chen et al. (2019a)Blue tialapia (Oreochromis aureus) Illumina (235) 1020/920 53/1100 Chromosome (genetic map) 97.8% Bian et al. (2019)Goldfish (Carassius auratus) Illumina (70) + PacBio (71) 2034/1821 1373/22763 Chromosome (genetic map) 97.3% Chen et al. (2019d)Black rockfish (Sebastes schlegelii) Illumina (115) + PacBio (66) + HIC
(189)868/811 3846/3848 Chromosome (HIC) 97.5% He et al. (2019)
Barred knifejaw (Oplegnathus fasciatus) Illumina (117) + PacBio (81) + HIC(118)
778/769 2131/33549 Chromosome (HIC) 98.1% Xiao et al. (2019)
Yellowbelly pufferfish (Takifugu flavidus) Illumina (110) + PacBio (73) + HIC(132)
377/366 4400/15700 Chromosome (HIC) 98.4% Zhou et al. (2019c)
Two-spot puffer (Takifugu bimaculatus) Illumina (136) + PacBio (74) + HIC(118)
402/404 1313/16785 Chromosome (HIC) 96.3% Zhou et al. (2019e)
Topmouth culter (Culter alburnus) Illumina (194) + PacBio (6) 947/1018 72/3669 Scaffold 98.4% Ren et al. (2019)Chinese tapertail anchovy (Coilia nasus) Illumina (266) + PacBio (80) 857/870 1600/2100 Chromosome (genetic map) 90.1% Xu et al. (2020)
Note: For the BUSCO assessment, the superscript numbers mean that the results were collected from the corresponding references, and the “–” means that related data were unavailable. The value of BUSCO (genomemode) in this table represents the proportion of complete and fragmented orthologues.
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2. WGS and genome assembly in aquaculture animals
In many aquaculture species, WGS has been used for genome as-sembly, gene annotation, and reference genome construction. Thegenomes of aquaculture animals are important and useful for severalresearch areas, including genetics, development, immunology, and re-production; genome availability also supports innovations in breedingtechnologies. As high-throughput sequencing technologies have beenimproved, many WGS and genome assemblies for important aqua-culture animals have been completed (Table 1). Several indicators areused to reflect the quality of these genome assemblies, such as se-quencing coverage, estimated genome size, total size of the genomeassemblies, contig and scaffold N50 values, and genome integrity in-dexes (Abdelrahman et al., 2017). Genome integrity is commonly as-sessed using the Benchmarking Universal Single-Copy Orthologs(BUSCO) index, which presents quantitative measures for the assess-ment of genome assembly, gene set, and transcriptome completeness,based on the evolutionarily informed expectations of gene content fromnear-universal single-copy orthologs (Simão et al., 2015; Waterhouseet al., 2017).
Many factors intrinsic to the genome itself affect the quality of agenome assembly, particularly heterozygosity, repeat content, wholegenome duplication, and ploidy. For example, the oyster and Chinesemitten crab genomes are highly polymorphic and rich in repetitivesequences (Zhang et al., 2012; Song et al., 2016); for this reason, theoyster genome assembly is of poor quality compared to the genomeassemblies of other aquaculture animals. Using long reads helps toameliorate the difficulties engendered by heterozygosity and repetitivesequences, as exemplified by the genome assemblies of the scallop(Wang et al., 2017b) and the Pacific white shrimp (Zhang et al., 2019e;Table 1). In general, molluscan and crustacean genomes are moreheterozygous than those of fishes. Therefore, high-quality genomes forthese organisms are more difficult to assemble. However, the assem-blies of some teleost fish genomes also present serious challenges, dueto whole genome duplication events (Meyer and Van de Peer, 2005;Steinke et al., 2006). The genomes of other teleost fishes, such ascommon carp, Atlantic salmon and other salmonid fishes, are evenmore complex, since these genomes are allotetraploid due to additionalrounds of whole genome duplication (Moghadam et al., 2009;Moghadam et al., 2011). Importantly, by generating three chromo-some-level reference genomes for the common carp and comparingthem to related diploid Cyprinid genomes, it was possible to divide theallotetraploid genome into two subgenomes (Xu et al., 2019a, 2019b).This approach may provide a framework for analyses of other allote-traploid genomes. More challenges present in polyploid fishes. Amongthem, the surgeons (e.g., order Acipenserformes, family Acipenseridae,genus Acipenser) have been recognized as octaploid and may possess upto about 500 chromosomes (Ludwig et al., 2001). The assembly of suchcomplex genomes will require huge volumes of sequencing data andclever assembly algorithms.
The quality of reported genome assemblies is also affected by ex-trinsic factors, such as sample quality, sequencing strategy (e.g., readlength, sequencing depth, and sequencing coverage), and assemblingmethod. For example, the genome of the large yellow croaker was re-cently assembled by two different groups: one assembly was 672 Mb,with a contig N50 of 283 kb (Mu et al., 2018), and one assembly was7224 Mb, with a contig N50 of 2.83 Mb (Chen et al., 2019b). Thediscrepancies between these two assemblies were primarily due todifferences in sequencing coverage and assembly pipelines. Indeed, thegenome assemblies of several aquaculture animals have recently beenimproved using updated technologies. For instance, the contig N50 ofthe Nile tilapia (Oreochromis niloticus) was increased from 29.3 kb to 3.3Mb by using long reads from a homozygous clonal XX female for as-sembling (Brawand et al., 2014; Conte et al., 2017). As the costs of long-read-generating sequencing platforms (e.g., PacBio or Nanopore) havedecreased, the WGS data of more aquaculture animals have been
produced as long-read sequences, which facilitates the assembly of re-petitive sequences. However, short reads are also essential for genomesize estimation, long-read polishing, and final genome assembly. Toensure compatibility among different genome assemblies, it is urgent todevelop gold standards for genome assembly pipelines that couldcombine short and long reads to a much higher degree.
Chromosome-level genome assemblies may be more useful thanscaffold-level genome assemblies, because they not only provide re-ference genomes and genetic resources for economic traits, but alsoserve as a source of chromosome information for subsequent studies(Gong et al., 2018). High-density genetic linkage maps can be used asguides for construction of chromosome-level genome maps. High-re-solution chromosome-level genome maps have been generated in thisway for several economic species, including the Nile tilapia (Brawandet al., 2014), the common carp (Xu et al., 2014a, 2014b), and thechannel catfish (Liu et al., 2016c; Table 1). However, full-sib popula-tions are required for the construction of genetic linkage maps, and it isrelatively straight forward to produce full sibling populations for manyaquaculture animals, as long as suitable experimental facilities exist. Incases where physical separation of siblings is challenging, DNA markerscan be used for family assignment. High-resolution chromosome con-formation capture (HIC) techniques (Lieberman-Aiden et al., 2009;Duan et al., 2010) have helped to assemble chromosome-level genomesfrom scaffold-level genomes. HIC sequencing requires only simplesample procedures with blood and even fresh muscle tissues for ana-lyzing. Therefore, this technique is very convenient for the studies onaquaculture animals. In addition, HIC sequencing libraries are builtwith paired-end short reads and have relatively low sequencing costs.HIC methods and software tools are widely available and frequentlyused (Ay and Noble, 2015; Yardımcı and Noble, 2017). To date, manychromosome-level genomes of aquaculture animals have been as-sembled using HIC techniques (Table 1). Indeed, the popular combi-nation of short reads, long reads, and HIC will be widely used inaquaculture animals over the next few years, either to decode presentlyunassembled genomes or to improve already sequenced genomes.However, it is also worthwhile to be mindful of new sequencing tech-niques, as sequencing advances may reduce the monetary and humancosts associated with the production of high-quality aquaculture animalgenomes.
3. RAD-Seq genotyping and related techniques
RAD-Seq, which was first described in 2007, combines a reductionin genome complexity using restriction enzymes with high-throughputsequencing (Miller et al., 2007; Baird et al., 2008). Because RAD-Seqallows SNP discovery and genotyping without a reference genome, thismethod is especially suitable for non-model organisms (Davey andBlaxter, 2010; Andrews et al., 2016). The advantages of RAD-Seq haveled to further innovation, and several related methods have been de-veloped (Table 2). These techniques primarily differ in the details ofenzyme digestion and size selection (Andrews et al., 2016; Table 2).RAD-Seq and related techniques are cost-effective and have thus be-come the most popular genomic approaches for high-throughput SNPdiscovery and genotyping (Robledo et al., 2018b). However, there areseveral specific potential sources of bias in RAD-Seq and related tech-niques that should be considered (Andrews et al., 2016). Herein, wefocus on allele dropout.
Allele dropout occurs when polymorphisms are present in the re-striction enzyme recognition sites, and DNA fragmentation thus fails.That is to say, alleles missing the appropriate cut sites are not se-quenced and thereby become “null alleles”. The failure to sequence thenull alleles results in genotyping errors, and individuals heterozygous atthe null allele sites may be mistakenly identified as homozygotes(Andrews et al., 2016). In techniques based on size selection, the ab-sence of the restriction enzyme recognition sites might also lead to al-lele dropout for loci at neighboring cut sites, because the enzyme-
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digested fragment lengths may be larger than the target sizes (Gautieret al., 2013; Andrews et al., 2016). As the probability of mutationsincreases with sequence length, restriction enzymes that target longrecognition sites will increase the frequency of allele dropout. This biasshould be carefully considered when analyzing aquaculture animalswith heterozygous genomes, particularly in mollusks and crustaceans.Choosing a restriction enzyme that targets a short recognition sequencewill decrease this bias. In addition, the recognition sequence should beblasted against the examined genome with various matching para-meters to evaluate whether polymorphisms occur in the recognitionsequence at the cut sites. The results of this analysis may help toidentify reliable restriction enzymes. Other sources of genome bias in-clude PCR duplicates, shearing bias, higher major allele frequencies,and variances in the depth of coverage among loci (see more details inAndrews et al., 2016; Flanagan and Jones, 2018).
The most common techniques used to construct high-density geneticmaps, QTLs, and GWASs of economic traits are RAD-Seq, 2b-RAD,ddRAD, and SLAF-Seq (Table 3). Although the SNPs derived by theseRAD-Seq-related techniques generally satisfy the research objects, thepercentages of genome coverage will be no more than ~10% when tworestrictions are used, and no more than ~5% when one restriction en-zyme is applied (Fuentes-Pardo and Ruzzante, 2017; Flanagan andJones, 2018). This is because RAD-Seq-related approaches identifySNPs based only on sequences associated with the restriction enzymesites. Simulation studies also indicate that RAD-Seq fails to scan manyregions of the examined genomes, especially for species with shortlinkage disequilibrium (Lowry et al., 2017) and for which genetic di-versity is underestimated (Cariou et al., 2016). Additionally, RAD-Seq-related methods identify single-base polymorphisms only (i.e., SNPs)and rarely detect structural variations, such as fragment deletions andinsertions. Therefore, if a higher target marker density is required tomore accurately phase the genetic loci of phenotypic traits or to over-come the problems associated with small linkage disequilibrium blocksizes, other genotyping techniques should also be introduced.
4. Genotyping using WGR
Despite the large volumes of data and sequences produced via denovo genome assembly, it remains challenging to identify genetic dif-ferences among individuals or populations and to understand the re-lationships of various genetic to phenotypic differences. WGR, identi-fying comparatively small changes between a genome and a referencesequence, is a pivotal approach to allow investigation of basic evolu-tionary biological questions that have not been fully resolved usingtraditional methods (Fuentes-Pardo and Ruzzante, 2017). In contrast toRAD-Seq, WGR supplies ultra-dense markers and detects a wide di-versity of genetic variations, including structural variants. Thus, WGR
can be used to investigate the genetic bases of phenotypic traits, as wellas the signatures of evolution (Fuentes-Pardo and Ruzzante, 2017).
WGR has been successfully used to detect signatures of selection,investigate the genetic bases of phenotypic traits, and identify localadaptations in several fish taxa, including three-spined stickleback(Jones et al., 2012), African cichlids (Brawand et al., 2014), commoncarp (Xu et al., 2014a, 2014b), Asian seabass (Vij et al., 2016), Atlanticherring (Barrio et al., 2016), and Amur Ide (Xu et al., 2016). Two WGRstrategies are available, depending on biological questions to be ad-dressed, budget for shotgun sequencing, and availability of computingresources and storage. One of the strategies pools individual DNA se-quences by population (population-based approach), while the otherdirectly sequences individuals within a population (individual-basedapproach). In the latter, one sequencing library is prepared per in-dividual, with a high read depth (≥ 15 X). This method is designed toaccurately detect SNPs and insertion-deletion variations across in-dividuals. In the population-based approach, a single pooled sequen-cing library is prepared per population, thereby substantially reducingsequencing costs, and it can be effectively used for SNP discovery forother platforms. However, the population-based approach has two mainshortcomings. First, individual genotypes cannot be determined, asDNA sequences from multiple individuals are mixed; this also makes itimpossible to distinguish true variation from sequencing errors. Second,rare alleles may be underestimated when pooled datasets are analyzed,possibly leading to imbalances in allele frequency estimations.
Previous WGR studies on aquaculture animals have primarily fo-cused on important commercial fish species, including common carp(Xu et al., 2014a, 2014b), Atlantic salmon (Ayllon et al., 2015; Kjærner-Semb et al., 2016), Asian seabass (Vij et al., 2016), Nile tilapia (Joshiet al., 2018), rainbow trout (Gao et al., 2018), turbot (Zhang et al.,2019c), Atlantic cod (Clucas et al., 2019), and groupers (Xu et al.,2019a, 2019b; Table 4). Although WGR is affordable and yields manymarkers, WGR datasets are extremely large, and the analyses of thesedatasets thus cost significant time and labor. In addition, the computingresources and storage required for such analyses may pose a greatchallenge for aquaculture researchers. Reference genomes for manyaquaculture animals have been released in recent years, and the ap-plications of WGR in these aquaculture animals need a course to gothrough. We believe that, in the coming future, due to cost reductionand advances in bioinformatics, WGR will become a routine task inaquaculture animal analysis, and the availability of these WGR datasetswill greatly facilitate the discovery and mapping of the genetic bases ofphenotypic traits.
5. Development of SNP arrays in aquaculture animals
Except for the sequencing technologies, SNP array has also been a
Table 2Key features of RAD-Seq and related techniques.
Technique Key features Reference
RAD-Seq (restriction-site associated DNAsequencing)
Digestion with one restriction enzymes, and use mechanical shearing to reduce fragments Miller et al. (2007), Baird et al.(2008)
RRL (reduced representation libraries) Digestion with a blunt-end common-cutter restriction enzyme, followed by a size selection stepand use a proprietary Illumina library preparation kit
Van Tassell et al. (2008)
MSG (multiplexed shotgun genotyping) Digestion with one common-cutter restriction enzyme and have a size selection step Andolfatto et al. (2011)GBS selection (genotyping by sequencing) Digestion with a common-cutter restriction enzyme and PCR preferentially amplifies short
fragmentsElshire et al. (2011)
2b-RAD (type IIB restriction enzymes RAD) Digestion with type IIB restriction enzyme, which cleave DNA upstream and downstream of therecognition site, resulting in short fragments of uniform length (33–36bp)
Wang et al. (2012)
SBG (Sequence-based Genotyping) Digestion with a rare-cutter and one or two common-cutters restriction enzymes and PCRpreferentially amplifies short fragments
Truong et al. (2012)
ddRAD (double-digest RAD) Digestion with uses two restriction enzymes, with adaptors specific to each enzyme, and sizeselection by automated gel cut
Peterson et al. (2012)
SLAF-Seq (specific-locus amplified fragmentsequencing)
Digestion with a blunt-end common-cutter restriction enzyme, followed by a size selection stepand double barcode system for large populations
Sun et al. (2013)
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Table 3Summary of aquaculture animal studies using RAD-Seq and related techniques.
Species Technique Samples SNPs Research object Reference
FishesAtlantic salmon (Salmo salar) RAD 32 6712 Infectious Pancreatic Necrosis (IPN) disease resistance
QTL-linked SNPHouston et al. (2012)
RAD 96 8257 Linkage map Gonen et al. (2014)Orange-spotted grouper (Epinephelus coioides) MSG 144 4608 Linkage map You et al. (2013)
ddRAD 70 3029 Growth QTL Yu et al. (2016)RAD 198 261,366 Growth GWAS Yu et al. (2018)
Atlantic halibut (Hippoglossus hippoglossus) RAD 93 7572/5954 Sex determination QTL Palaiokostas et al. (2013a)Nile tilapia (Oreochromis niloticus) RAD 88 3904/4477 Sex determination QTL Palaiokostas et al. (2013b)
ddRAD 372 1279 Sex determination QTL Palaiokostas et al. (2015a)Chinook salmon (Oncorhynchus tshawytscha) RAD 422 3534 Thermotolerance and growth QTL Everett and Seeb (2014)Half-smooth tongue sole (Cynoglossus semilaevis) RAD 216 12,142 Linkage map Chen et al. (2014)Common carp (Cyprinus carpio) RAD 107 3470 Linkage map Xu et al. (2014a, 2014b)
RAD 1425 12,311 Growth GWAS and genomic selection Palaiokostas et al. (2018b)RAD 1425 15,615 Genomic selection (KHV) Palaiokostas et al. (2019)
Rainbow trout (Oncorhynchus mykiss) RAD 456 4661 Disease resistance QTL (BCWD and IHNV) Campbell et al. (2014)RAD 19 145,168 SNP resource Palti et al. (2014)RAD 252 5612/4946 Disease resistance QTL (BCWD) Palti et al. (2015a, 2015b)RAD 234 4874 Cortisol response to crowding GWAS and QTL Liu et al. (2015a)RAD 301 7849 Disease resistance QTL (BCWD) and spleen size QTL Liu et al. (2015b)RAD 711 24,465 Genomic selection Vallejo et al. (2016)RAD 310 9654 Disease resistance QTL (Flavobacterium psychrophilum) Fraslin et al. (2018)
Large yellow croaker (Larimichthys crocea) RAD 127 2889 Linkage map Ao et al. (2015)SLAF 60 1782 Identify SNP dataset Chen et al. (2018b)
European sea bass (Dicentrarchus labrax) RAD 187 6706 Sex determination QTL Palaiokostas et al. (2015b)ddRAD 81 804 Gene-centromere map Oral et al. (2017)RAD 1538 9195 Disease resistance GWAS and genomic selection (VNN) Palaiokostas et al. (2018a)
Turbot (Scophthalmus maximus) RAD 151 6647 Sex determination and growth QTL Wang et al. (2015b)RAD 672 755 Genetic structure do Prado et al. (2018)2b-RAD 1800 18,125 GWAS disease resistance and endurance Saura et al. (2019)
Common pandora (Pagellus erythrinus) ddRAD 99 920 Linkage map Manousaki et al. (2016)Japanese flounder (Paralichthys olivaceus) RAD 218 13,362 Disease resistance QTL (Vibrio anguillarum) Shao et al. (2015)Asian seabass (Lates calcarifer) ddRAD 144 3321 Growth QTL Wang et al. (2015a)Sockeye salmon (Oncorhynchus nerka) RAD 491 11,457 Thermotolerance and growth QTL Larson et al. (2015)Hāpuku (Polyprion oxygeneios) ddRAD 59 1609 Sex determination and growth QTL Brown et al. (2016)Bighead carp (Hypophthalmichthys nobilis) 2b-RAD 119 3323 Growth QTL Fu et al. (2016)Gilthead sea bream (Sparus aurata) 2b-RAD 777 12,085 Genomic selection (Disease resistance to
Pasteurellosis)Palaiokostas et al. (2016)
2b-RAD 1296 22,544 Disease resistance GWAS Aslam et al. (2018)ddRAD 112 2258 Growth GWAS Kyriakis et al. (2019)
Blunt snout bream (Megalobrama amblycephala) RAD 189 14,648 Growth and gonad QTL Wan et al. (2017)Mandarin fish (Siniperca chuatsi) ddRAD 157 3283 Sex determination QTL Sun et al. (2017)Brown trout (Salmo trutta) ddRAD 304 3977 Linkage map Leitwein et al. (2017)Crucian carp (Carassius auratus) 2b-RAD 102 8487 Body weight QTL Liu et al. (2017b)Red drum (Sciaenops ocellatus) RAD 74/83 1383/2620 Linkage map Hollenbeck et al. (2017)Pacific halibut (Hippoglossus stenolepis) RAD 95 24,911 Sex QTL Drinan et al. (2017)Golden pompano (Trachinotus blochii) RAD 104 12,358 Growth QTL Zhang et al. (2018a)Albock (Coregonus sp.) RAD 158 5395 Linkage map De-Kayne and Feulner
(2018)Yangtze River common carp (Cyprinus carpio
haematopterus)2b-RAD 106 8115 Growth and sex QTL Feng et al. (2018)
Coho salmon (Oncorhynchus kisutch) ddRAD 764 9389 Piscirickettsia salmonis disease resistance GWAS and GS Barría et al. (2018)Channel catfish (Ictalurus punctatus) RAD 158 4768 Growth and sex QTL Zhang et al. (2019d)Largemouth bass (Micropterus salmoides) RAD 152 6917 Growth and sex QTL Dong et al. (2019)
MollusksZhikong scallop (Chlamys farreri) 2b-RAD 98 7458 Sex determination and growth QTL Jiao et al. (2013)Perl oyster (Pinctada fucata) RAD 100 1373 Growth QTL Li and He (2014)
2b-RAD 98 3117 Growth QTL Shi et al. (2014)RAD 150 4463 Linkage map Du et al. (2017)
Small abalone (Haliotis diversicolor) RAD 142 3717 Growth QTL Ren et al. (2016)Chilean blue mussel (Mytilus chilensis) RAD 190 1240 Genetic structure Araneda et al. (2016)Yesso scallop (Patinopecten yessoensis) 2b-RAD 349 2364 Genomic selection (growth) Dou et al. (2016)
2b-RAD 66 109,258 Shell color GWAS Zhao et al. (2017)Razor clam (Sinonovacula constricta) SLAF 153 7516 Growth QTL Niu et al. (2017)Pacific oyster (Crassostrea gigas) RAD 232 21,499 Genetic structure SNP array Vendrami et al. (2019)Blue mussel (Mytilus edulis) RAD 40 14,212 SNP marker panel Wilson et al. (2018)
CrustaceansChinese mitten crab (Eriocheir sinensis) RAD 122 10,358 Sex determination QTL Cui et al. (2015)
SLAF 151 17,680 Growth QTL Qiu et al. (2017)Pacific white shrimp (Litopenaeus vannamei) SLAF 207 6359 Linkage map Yu et al. (2015)Kuruma prawn (Marsupenaeus japonicus) RAD 152 9289 Temperature tolerance and growth QTL Lu et al. (2016)Swimming crab (Portunus trituberculatus) SLAF 122 10,963 Growth QTL Lv et al. (2017)Blue swmming crab (Portunus pelagicus) RAD 33 45,464 Genome assembly and molecular marker Wu et al. (2018)
(continued on next page)
X. You, et al. Aquaculture 526 (2020) 735357
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feasible choice for largescale SNPs genotyping. Compared with DNAsequencing which is dependent on complicated library preparation andintensive subsequent bioinformatics processing steps, the experimentalworkflows and bioinformatic analyses are much simpler for using SNParrays (Qi et al., 2017). Additionally, sequencing data probably sufferfrom certain error rates derived from multiple factors, including base-calling and alignment errors (Wall et al., 2014). The repeatability andreproducibility are higher for SNP arrays than sequencing (Robledoet al., 2018b). However, SNP array also have drawbacks, such as, inspecies with strong population structure, having a fixed set of lociwould result of ascertainment bias. The utility of a SNP array willhighly depending on the genetic structure of the discovery population(Robledo et al., 2018b). SNP arrays have been applied in several im-portant commercial aquaculture animals (Table 5), and the SNP densityvary from 6.5 K to 930 K. A variety of SNP array platforms have beendeveloped, of which the Affymetrix Axiom (Thermo Fisher Scientific,Waltham, USA) and the Illumina iSelect (Illumina, San Diego, USA) aremost popular. These arrays differ in their principles for SNP detection,as well as in their requirements for marker numbers, cost, and samplesize (Qi et al., 2017).
It can be infered that that genotyping by sequencing and SNP arraywill towards a peaceful coexistence, and the choice of genotyping bysequencing or SNP array will depend on the species and project-specificfactors (Robledo et al., 2018b). For example, for species with completegenomic resources and the commercial breeding practice is relativesuccessful (e.g. salmonids), SNP arrays are more suitable; while for newaquaculture species and/or those at the the initial phase of breeding aremore suitable for sequencing techniques.
6. Construction of high-density genetic linkage maps
The recombination frequency of a given pair of genetic markersreflects the genetic distance between them. This rule underlies theconstruction of genetic linkage maps. Rather than specific physicaldistances along a chromosome, recombination frequencies are used torepresent the distances among genetic markers in a single linkage group(LG). The advantage of genetic distances versus physical distances onreference genome assemblies is that it can be used to anchor scaffolds tochromosomes. However, the drawback is also notable, such as, onegenetic marker can be aligned to different scaffolds owning to thegenome duplication, and these genetic markers are useless for genomeassembling. High-density genetic linkage maps with large numbers ofmarkers have many important applications in aquaculture breedingprograms.
Generation of reference families is a prerequisite for construction ofa genetic linkage map. Most aquaculture animals have high fecundity,generating abundant progeny from a single mating. Therefore, the firstgeneration (F1) of full-sib or half-sib families, generated by crossinggenetically diverse parents, are the most commonly used type of re-ference family in aquaculture animals (Yue, 2014). Parents and off-spring should be genotyped simultaneously, and only those genotypeswith high quality and heterozygote in at least one parent can be usedfor subsequent linkage analysis (Li et al., 2017a). The notable increasein the number of genetic markers available due to high-throughputsequencing has sparked the development of high-density geneticlinkage maps in aquaculture animals. To date, high-density genetic
linkage maps have been constructed for over 40 aquaculture animals,and several species are represented by more than one map (seeTable 6). Most high-density linkage maps for aquaculture animals areusually based on SNPs and/or simple sequence repeats (SSRs; Table 6).In aquaculture animals, the recombination rate of female usually largerthan this in male (You et al., 2013).
Several software tools are available for linkage map construction,including JoinMap (Stam, 1993), MapChart (Voorrips, 2002) OneMap(Margarido et al., 2007), CRI-MAP (Green et al., 1990), MapMaker(Lincoln, 1992), Lep-Map (Rastas et al., 2013), and High Map (Liu et al.,2014a). These software packages have been described in detail, withstep-by-step illustrations, elsewhere (Li et al., 2017a). JoinMap, HighMap, and Lep-Map are the most widely used in aquaculture animals(Table 5). Each tool has advantages and disadvantages. For example,the genetic distances calculated by the JoinMap regression algorithmare shorter than those from High Map and Lep-Map which tend to in-flate map distances due to genotype errors (Li et al., 2017a), andthereby are considered relatively accurate. However, the computationalrequirements for JoinMap increase exponentially when each examinedLG has more than 200 markers. When the number of markers is verylarge, Lep-Map and High Map are more suitable for construction ofhigh-density or ultra-density linkage maps. However, these tools tend toexaggerate genetic distances due to genotype errors. Therefore, severalsoftware packages should be evaluated simultaneously with genotypedata before choosing the best for final linkage map construction (Liet al., 2017a). We recommend using combinations of various softwarepackages. For example, JoinMap could be used for linkage group as-signment, and then Lep-Map could be used to order the markers anddetermine the genetic distances among them (Zhang et al., 2018a;Wang et al., 2019d).
7. Identification of QTLs using linkage mapping
It is important to know whether traits are polygenic or monogenicfor developing breeding programmes. Analysing the genetic archi-tecture related to traits is necessary to identify genomic loci that controltraits. Many economically important traits of aquaculture animals, suchas growth, disease resistance, and stress tolerance, may be described asquantitative traits. The phenotype of a quantitative trait is mostly ef-fected by many genes, and the purpose of QTL mapping is to localizecorresponding genomic loci that contribute to variations in this quan-titative trait. QTL mapping is the first step toward detection of thepolymorphisms and genes directly contributing to population variationsin quantitative traits (Gutierrez and Houston, 2017). In a broad sense,QTLs mapping can be divided to linkage mapping and associationmapping (described in the next section). Association mapping typicallyassesses linkage disequilibrium in a natural population (i.e., an un-related group of individuals), while linkage mapping is based ontracking QTLs and the co-segregation of markers in sib families(Mackay, 2009; Gutierrez and Houston, 2017). Practically, QTL map-ping is the detection of QTLs using linkage mapping, which is per-formed based on phenotypic characters and genetic linkage data amongsib families. Therefore, QTL mapping populations are similar to thoseused for genetic linkage mapping. Indeed, the F1 progeny derived viaoutbreeding are typically used as a mapping population for aquacultureanimals. For QTL mapping, it is important that phenotypic data should
Table 3 (continued)
Species Technique Samples SNPs Research object Reference
Mud crab (Scylla paramamosain) SLAF 131 16,701 Sex determination QTL Waiho et al. (2019)RAD 20 1,780,706 Sex determination system Shi et al. (2018b)
Red swamp crayfish (Procambarus clarkii) 2b-RAD 221 22,043 Population genetic structure Yi et al. (2018)Black tiger shrimp (Penaeus monodon) RAD 100 6524 Sex QTL Guo et al. (2019)Crucifix crab (Charybdis feriatus) RAD 103 1,160,218 Sex determination technique Fang et al. (2020)
X. You, et al. Aquaculture 526 (2020) 735357
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beprecise.QTL
mapping
evaluatesdiff
erencesbetw
eenthe
mean
phenotypeof
individualspossessing
onegenotype
atalocus
versusthose
possessinganother
genotypeat
thesam
elocus
(Gutierrez
andHouston,2017).A
tpresent,com
positeintervalm
appingisthe
mostprecise
method
forQTL
mapping;
itdetects
multiple
QTLs
inone
shot(Zeng,
1994).Ad-
ditionally,using
linkagemapping
andlinkage
disequilibriumsim
ulta-neously
maps
QTLs
more
accuratelythan
linkagemapping
alone(M
euwissen
etal.,
2002;Hayes
etal.,
2006).The
software
packagesavailable
forQTL
mapping
includeGridQ
TL(Seaton
etal.,
2006),MapQ
TL(V
anOoijen
andKyazm
a,2009),R/qtl(Brom
anet
al.,2003),and
R/qtlbim
(Yandell
etal.,
2007).These
toolsare
userfriendly
andhave
beenfrequently
usedfor
QTL
mapping
inaquaculture
animals
(Gutierrez
andHouston,2017).
Todate,
QTL
mapping
hasbeen
performed
in44
aquacultureani-
mals
(Table7),
including29
fishes,
8mollusks,
6crustaceans,
and1
echinoderm.For
severalim
portantaquaculture
animals
suchas
Asian
seabass,Atlantic
salmon,
rainbowtrout,
andturbot,
more
thanthree
QTL
maps
areavailable.
Themapped
traitsare
primarily
associatedwith
growth,
diseaseresistance,
andsex
determination.
Other
traitswith
lessmapping
includestress
responses,morphological
indexes,tem
peraturetolerance,
andnutrient
contents.Most
selectivebreeding
programsfor
aquacultureanim
alsaim
toim
provegrow
th,since
thegrow
thtraitis
easyto
phenotypeand
highlyheritable
(Gjedrem
,2000).Therefore,grow
thhas
becomeapopular
focusof
QTL
mapping
studiesin
aquacultureanim
als,andmany
growth-associated
QTLs
orcandidate
geneshave
beenidentifi
ed.How
ever,as
growth
isapolygenic
trait,critical
growth-associated
QTLs
orgenes
aregenerally
inconsistentam
ongvarious
taxa(Table
7).Indeed,
growth-associated
QTLs
appearto
shiftam
ongdiverse
families,
populations,and
environments.
Geno-
type-environment
interactionsto
affect
growth
regulationhave
beendetected
inseveralaquaculture
animals
(Sae-Limet
al.,2014;Luet
al.,2017;
Heet
al.,2017),
andsuch
interactionsare
worthy
ofin-depth
investigation.Disease
substantiallyaffects
thecultivation
ofmost
aquacultureanim
als,anddisease
resistanceistherefore
amajor
focusofaquaculture
breedingprogram
s.A
growing
bodyof
evidenceindicates
that,in
aquacultureanim
als,disease
resistanceis
anim
portantgenetic
trait,and
theutilization
ofbrood
stockswith
geneticallyim
proveddisease
resistanceis
aprom
isingstrategy
tocom
batdisease-associated
losses(R
obinsonet
al.,2017).
Quantifi
ableand
reliablephenotypes
arecri-
ticalfor
estimation
andexploitation
ofgenetic
variationsrelated
todisease
resistance.Studies
onindividual
diseaseresistance
typicallyassess
mortality
afterinfectious
challenge.How
ever,tim
e-until-deathmeasures
duringachallenge
canbe
scoredas
continuousquantitative
charactersand
would
betterdiscrim
inateam
ongpotentialvariations
indisease
resistance(Shi
etal.,
2018a).Additionally,
indexesfor
char-acterizing
pathogenproliferation
orthe
immunological
capacityof
anaquaculture
animal
might
alsouseful
forprediction
ofindividual
hostresistance;
theseparam
etersmay
representconvenient
alternativesto
infectiouschallenges
(Robinson
etal.,
2017).More
studiesshould
beperform
edto
identifyadditional
reliableindicators
ofindividual
dis-ease
resistancefor
practicaluse.
Over
thepast
decade,severalQTLs
andcandidate
geneshave
beenidentifi
edfrom
variousaquaculture
animals
usinglinkage
mapping
(Table7).
How
ever,the
shortcomings
oflinkage
mapping
havere-
peatedlybeen
highlighted.Forexam
ple,most
QTLs
spanhuge
regionsof
theexam
inedgenom
es,and
itis
thusdiffi
cultto
identifyim
portantQTLs
orcandidate
genes(ifthey
exist).Inaddition,the
QTLs
associatedwith
complex
traitshave
notmapped
particularlysuccessfully
acrosspopulations
(Gutierrez
andHouston,2017).M
oreim
portantly,linkagemapping
isnot
powerfulenough
todetect
QTLs
with
polygenicgenetic
architecture(A
shtonet
al.,2017).
Therefore,association
mapping
isa
necessarycom
plement
tolinkage
mapping,
asit
facilitatesthe
identi-fication
ofQTLs
forcontrolling
complicated
traits.Asthe
costsof
ob-taining
high-densitymarkers
froma
largenum
berof
samples
have
Table 4Whole genome resequencing (WGR) studies of aquaculture animals.
Species Approach Sample (Population) Average sequencing depth(X)
SNPs Research object Reference
Common carp (Cyprinus carpio) Individual-based 33 (10) 7 18,949,596 Demonstrates a single origin for C. carpio in 2subspecies
Xu et al. (2014a, 2014b)
Atlantic salmon (Salmo salar) Population-based 120 (2) 12 4,326,591 Regulate age at maturity in males Ayllon et al. (2015)population-based 570 (8) + 120 (2) 27 4,450,990 Genes and genomic regions of genetic differences Kjærner-Semb et al.
(2016)Asian seabass (Lates calcarifer) Individual-based 61 (3) 7 5,642,327 Population structure Vij et al. (2016)Nile tilapia (Oreochromis niloticus) Individual-based 32 (1) 18 2,760,000 SNP array and linkage map Joshi et al. (2018)Rainbow trout (Oncorhynchus mykiss) Individual-based 61 (2) 15 31,441,105 SNP database Gao et al. (2018)Greater Amberjack (Seriola dumerili) Individual-based 20 (1) 27 186,259 Genome structural variation Araki et al. (2018)Pacific oyster (Crassostrea gigas) Population-based 138 (3) 35 5,810,000/5,940,000/
5,640,000Salinity adaptation mechanism She et al. (2018)
Individual-based 427 (1) 20 52,142,764 Nutrient trait GWAS Meng et al. (2019)Turbot (Scophthalmus maximus) Population-based 300 (3) 29 370,502/450,897/487,465 Identified markers associated with Vibrio anguillarum
resistanceZhang et al. (2019c)
Atlantic cod (Gadus morhua) Individual-based 306 (1) 1 10,886,831 Preserve adaptive genetic diversity and evolutionarypotential
Clucas et al. (2019)
Orange-spotted grouper (Epinephelus coioides) Individual-based 300 (1) 5 2,783,352 Ammonia tolerance GWAS Xu et al. (2019a, 2019b)
X.Y
ou,etal.
Aquaculture 526 (2020) 735357
8
decreased, GWAS are now commonly used as an alternative for iden-tification of QTLs.
8. GWAS
Association mapping is performed based on the principle of linkagedisequilibrium (LD) at a population level, and GWAS involves detectinggenome-wide genetic variants of many individuals to test genotype-phenotype associations (Tam et al., 2019). Compared with linkagemapping, association mapping more precisely detects QTL regions butrequires more markers to achieve this high level of precision. Associa-tion mapping can be performed using populations of wild or captiveunrelated individuals (considered as natural population in practice), aswell as family-based populations, which are usually generated by di-rectional mating; conversely, linkage mapping requires family-basedpopulations (Ashton et al., 2017). Selection of good mapping popula-tion is critical to GWAS studies. The ideal population should behomogenous in genetic background without population stratificationbut with abundant phenotypic variations (Geng et al., 2017b).
Population stratification, caused by allele frequency variations amongsubpopulations, decreases the reliability of association tests. To excludethe effects of population stratification, population structure should beevaluated prior to the GWAS. Compared with natural populations, fa-mily-based populations are less likely to be affected by populationstratification. Indeed, because of their homogenous genetic back-ground, the progenies produced by a few parents are suitable for GWAS.However, family-based populations lack the power to detect causalalleles that vary among subpopulations (Geng et al., 2017b). Further-more, family-based population may have relatively limited numbers ofrecombination events, resulting in low mapping resolutions.
As sample size critically affects the power of association tests, largersample sizes improve the association detection and result in identifi-cation of additional loci (Tam et al., 2019). However, the costs ofgenotyping and phenotyping increase substantially as sample sizes in-crease. The genotyping of individuals with extreme phenotypes requiresfewer samples but maintains the accurate detection of causal alleles(Yang et al., 2015; Dong et al., 2016). However, this strategy may po-tentially overestimate the magnitude of the effects of alleles on
Table 5Application of SNP arrays in aquaculture animals.
Species SNP array platform Density Aim Reference
Atlantic Salmon (Salmo salar) Illumina iSelect 15 K Genotype calling and mapping of multisite variants Gidskehaug et al. (2011)Affymetrix Axiom 220 K GS for salmon lice (Lepeophtheirus salmonis) resistance Ødegård et al. (2014)Affymetrix Axiom 132 K Development the SNP array Houston et al. (2014)
GWAS and GS for growth trait Tsai et al. (2015a)GWAS for growth trait Tsai et al. (2016b)Construction of high-density linkage map Tsai et al. (2016c)
Illumina iSelect 6.5 K Detection of quantitative trait loci (QTL) for grilsing and late sexualmaturation
Gutierrez et al. (2014)
GWAS for growth rate and age at sexual maturation Gutierrez et al. (2015)Reveal recent signatures of selection during domestication Gutierrez et al. (2016)
Affymetrix Axiom 200 K Discovery genomewide SNP Yáñez et al. (2016)Comparing genomic signatures of domestication in two populations López et al. (2019)
Affymetrix Axiom 930 K Genome assembly Lien et al. (2016)Affymetrix Axiom 25 K/78 K GS for sea lice resistance and body weight Tsai et al. (2017)Affymetrix Axiom 17 K GWAS and GS for amoebic gill disease (AGD) resistance Robledo et al. (2018a)Affymetrix Axiom 50 K Reveal genetic relationship and chromosomal fusions to contribute to
local adaptationWellband et al. (2019)
GWAS for sexual maturiation Mohamed et al. (2019)GWAS for age at maturity Sinclair-Waters et al. (2020)
Common carp (Cyprinus carpio) Affymetrix Axiom 250 K Development and characterization of the first high-density SNPgenotyping array
Xu et al. (2014a, 2014b)
Channel catfish (Ictalurus punctatus) Affymetrix Axiom 250 K Development the SNP array for GWAS Liu et al. (2014)Construction of high-density genetic map Li et al. (2014)GWAS to identify QTLs for columnaris disease resistance Geng et al. (2015)GWAS to identify QTLs for head size Geng et al. (2016)Construction of high-density genetic linkage map Liu et al. (2016)GWAS for heat tolerance Jin et al. (2017)GWAS to identify multiple novel QTL associated with tolerance tolow dissolved oxygen
Zhong et al. (2017)
GWAS for albino phenotype in the Hermansky-Pudlak syndrome 4(Hps4) gene
Li et al. (2017)
GWAS to identify QTLs for hypoxia tolerance Wang et al. (2017c)GWAS for growth trait Li et al. (2018a, 2018b, 2018c,
2018d)Affymetrix Axiom 690 K Construction of high-density linkage map and genetic mapping Zeng et al. (2017)
GWAS to identify intra-specific QTL associated with resistance toenteric septicemia disease
Shi et al. (2018a)
Rainbow trout (Oncorhynchusmykiss)
Affymetrix Axiom 57 K Development and characterization of the first high-density SNPgenotyping array
Palti et al. (2015a, 2015b)
GWAS for fillet yield, body weight and carcass Gonzalez-Pena et al. (2016)GS for bacterial cold water disease (BCWD) resistance Vallejo et al. (2016); Vallejo
et al. (2017a)Population genetic analysis of seven salmonid species in China Zhang et al. (2018)Compare the accuracy of GEBVs of P-BLUP to ssGBLUP for infectiouspancreatic necrosis virus disease resistance
Yoshida et al. (2019a)
Affymetrix Axiom 32 K/35 K LD analysis/GS for BCWD resistance Vallejo et al. (2018)Pacific oyster (Crassostrea gigas) Affymetrix Axiom 190 K The first commercially available high-density SNP chip for mollusks Qi et al. (2017)
Affymetrix Axiom 23 K GS for growth trait Gutierrez et al. (2018b)Affymetrix Axiom 41 K Reveal the genetic structure in Europe Vendrami et al. (2019)
Nile tilapia (Oreochromis niloticus L.) Affymetrix Axiom 58 K Construction of high-density linkage map Joshi et al. (2018)
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9
Table 6Available high-density genetic linkage maps for aquaculture animals.
Species Mapping population Number and type ofmarkers
Software/Tools Map size (cM) Average interval(cM)
No. of LGs reference
FishesGrass carp (Ctenopharyngodon idella) 192 fish from 2 F1 families 279 SSR and SNP MapChart 1176.10 4.22 24 Xia et al. (2010)Japanese Flounder (Paralichthys olivaceus) 45 fish from 1 F1 family 1375 SSR and SNP MapChart 833.80/1147.70 0.78/0.98 24 Castaño-Sánchez et al. (2010)
94 fish from 1 F1 family 1487 SSR JoinMap4.0 1763.30 1.19 24 Song et al. (2012b)218 fish from 24 F1 families 12,712 SNP Lep-MAP 3497.29 0.28 24 Shao et al. (2015)
Atlantic salmon (Salmo salar) 3297 fish from 143 F1 families 5650 SNP CRI-MAP 2.4 2402.30/1746.20 - 29 Lien et al. (2011)92 fish from 2 F1 families 6458 SNP OneMap 2807.00/2170.00 2.14/1.18 29 Gonen et al. (2014)622 fish from 60 F1 families 96,396 SNP Lep-MAP2 7153.00/4769.00 0.07/0.05 29 Tsai et al. (2016c)
Asian seabass (Lates calcarifer) 380 fish from 2 F1 families 790 SSR and SNP CRI-MAP 2411.50 3.00 24 Wang et al. (2011)144 fish from 1 F2 family 3321 SNP JoinMap4.1 1577.67 0.52 24 Wang et al. (2015a)179 fish from 1 F1 family 3000 SSR and SNP JoinMap4.1 2957.79 1.27 24 Liu et al. (2016b)
Common carp (Cyprinus carpio L.) 190 fish from 1 F1 family 732 SSR and SNP JoinMap4.0 3278.00 4.50 50 Zhang et al. (2013)107 fish from 1 F1 family 4243 SSR and SNP JoinMap 4.0 3946.70 0.90 50 Xu et al. (2014a, 2014b)106 fish from 1 F1 family 28,194 SNP MergeMap 10,595.94 0.38 50 Peng et al. (2016)
Half-smooth tongue sole (Cynoglossus semilaevis) 94 fish from 1 F1 family 1009 SSR and SNP JoinMap4.0 1624.00 1.67 21 Song et al. (2012a)48 fish from 1 F1 family 325 SSR MapChart 1041.00/1154.00 5.39/5.92 21 Jiang et al. (2013)216 fish from 1 F1 family 13,084 SSR and SNP JoinMap4.0 - - 21 Chen et al. (2014)
Rainbow trout (Oncorhynchus mykiss) 120 fish from 2 F1 families 2226 SSR and SNP CarthaGene 3600.00 1.62 29 Guyomard et al. (2012)298 fish from 2 F1 families 7595 SNP MultiMap 5483.00/2964.00 8.59/6.77 29 Liu et al. (2015a)2464 fish from 56 F1 family 47,939 SNP Lep-MAP 4248.00/2214.00 - 29 Gonzalez-Pena et al. (2016)
Orange-spotted grouper (Epinephelus coioides) 142 fish from 1 F1 family 4608 SNP JoinMap4.0 1581.70 0.34 24 You et al. (2013)68 fish from 1 F1 family 3029 SNP JoinMap4.1 1231.98 0.40 24 Yu et al. (2016)
Channel catfish (Ictalurus punctatus) 576 fish from 1 F1 family 29,081 SNP JoinMap4.0 3505.40 0.12 29 Li et al. (2014)288 progenies from 3 F2families
23,238 SNP JoinMap4.0/OneMap 3240.00 0.14 29 Liu et al. (2016c)
478 fish from 4 F1 families 253,087 SNP Lep-MAP2 3004.70 0.01 29 Zeng et al. (2017)156 fish from 1 F1 family 4768 SNP JoinMap4.1/Lep-MAP 2480.25 0.55 29 Zhang et al. (2019d)
Gilthead sea bream (Sparus aurata L) 50 fish from 1 F1 family 321 SSR and SNP CRI-MAP3.0 1769.70 5.51 27 Tsigenopoulos et al. (2014)European seabass (Dicentrarchus labrax) 175 fish from 1 F2 families 6706 SNP Lep-MAP 4816.00 0.72 24 Palaiokostas et al. (2015b)Yellowtail (Seriola quinqueradiata) 94 offspring from 1 F1 family 2081 SNP and SSR MapChart 1029.24/1227.95 0.99/1.18 24 Aoki et al. (2015)Turbot (Scophthalmus maximus) 149 fish from 1 F1 family 6647 SNP JoinMap4.0 2622.09 0.40 22 Wang et al. (2015b)
1268 fish from 1 F1 family 11,845 SNP JoinMap4.1 3753.90 0.32 22 Maroso et al. (2018)Large yellow croaker (Larimichthys crocea) 125 fish from 1 F1 family 10,150 SNP Lep-Map 5451.3 0.54 24 Ao et al. (2015)
136 fish from 1 F1 family 5261 SNP JoinMap4.1 1885.67 0.36 24 Kong et al. (2019)Bighead carp (Hypophthalmichthys nobilis) 117 fish from 1 F1 family 3121 SNP JoinMap4.0 2341.27 0.75 24 Fu et al. (2016)Gold fish (Carassius auratus) 79 fish from 1 F2 family 8521 SNP JoinMap 5252.00 0.62 50 Kuang et al. (2016)Tambaqiu (Colossoma macropomum) 124 fish from 1 F1 family 68,584 SNP JoinMap4.0 2811.00 0.39 27 da Silva Nunes et al. (2017)Brown trout (Salmo trutta) 596 fish from 2 F1 families 3977 SNP JoinMap4.0 1403.00 0.35 40 Leitwein et al. (2017)Mandarin fish (Siniperca chuatsi) 152 fish from 1 F1 family 3283 SNP JoinMap4.1 1972.01 0.61 24 Sun et al. (2017)Crucian carp (Carassius auratus) 160 fish from 1 F1 family 8487 SNP JoinMap4.1 3762.88 0.44 50 Liu et al. (2017b)Blunt snout bream (Megalobrama amblycephala) 187 fish from 1 F1 family 14,648 SNP JoinMap4.0 3258.38 0.22 24 Wan et al. (2017)Yellowtail kingfish (Seriola lalandi) 752 fish from 1 F1 family 3998 SNP Lep-Map3 1166.00 0.29 24 Nguyen et al. (2018b)Yellow drum (Nibea albifora) 111 fish from 1 F1 family 8094 SNP JoinMap4.0 3818.24 0.47 24 Qiu et al. (2018)Golden pompano (Trachinotus blochii) 102 fish from 1 F1 family 12,358 SNP JoinMap4.1/Lep-Map 3810.03 0.31 24 Zhang et al. (2018a)Nile tilapia (Oreochromis niloticus) 1872 fish from 1 F1 family 40,186 SNP Lep-Map2 1469.69 0.04 22 Joshi et al. (2018)Yangtze River common carp (Cyprinus carpio haematopterus) 104 fish from 2 F2 families 8115 SSR and SNP JoinMap4.1 4586.56 0.57 50 Feng et al. (2018)Northern snakehead (Channa argus) 74 fish from 1 F1 family 2974 SNP JoinMap4.1 1690.64 0.57 24 Wang et al. (2019b)Snapper (Chrysophrys auratus) 169 fish from 14 F2 families 10,716 SNP Lep-Map2 1363.00 0.13 24 Ashton et al. (2019)Red spotted grouper (Epinephelus akaara) 142 fish from 1 F1 family 3435 SNP JoinMap4.1/Lep-Map 2300.12 0.67 24 Wang et al. (2019d)
MollusksZhikong scallop (Chlamys farreri) 96 offspring from 1 F1 family 3806 SNP MergeMap 1543.40 0.41 19 Jiao et al. (2013)Silver-lipped pearl oyster (Pinctada maxima) 219 offspring from 8 F2 families 887 SNP CRI-MAP 831.70 0.94 14 Jones et al. (2013)
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Table 6 (continued)
Species Mapping population Number and type ofmarkers
Software/Tools Map size (cM) Average interval(cM)
No. of LGs reference
Pearl oyster (Pinctada fucata) 98 offspring from 1 F1 family 1373 SNP JoinMap4.0 1091.81 0.80 14 Li and He (2014)96 offspring from 16 F1 families 3117 SNP MergeMap 990.74 0.32 14 Shi et al. (2014)
Oyster (Crassostrea gigas x Crassostrea angulata) 212 offspring from 2 F1 families 3367 SNP JoinMap4.0 1084.30 0.80 10 Wang et al. (2016)Triangle sail mussel (Hyriopsis cumingii) 157 offspring from 1 F1 family 4920 SSR and SNP High Map 2713.00 0.55 19 Bai et al. (2016)Small abalone (Haliotis diversicolor) 140 offspring from 1 F1 family 3717 SNP JoinMap4.1 2190.10 0.59 16 Ren et al. (2016)Manila clam (Ruditapes philippinarum) 119 offspring from 1 F1 family 9658 SNP JoinMap4.0 1926.98 0.20 19 Nie et al. (2017)Razor clam (Sinonovacula constricta) 200 offspring from 1 F1 family 7516 SNP High Map 2383.85 0.32 19 Niu et al. (2017)Pacific oyster (Crassostrea gigas) 169 offspring from 1 F1 family 5024 SNP JoinMap4.1 1982.07 0.68 10 Li et al. (2018b)
CrustaceansPacific White Shrimp (Litopenaeus vannamei) 43 offspring from 1 F2 family 451 SNP and SSR MapChart 3627.60 8.04 49 Andriantahina et al. (2013)
205 offspring from 1 F1 family 6146 SNP High Map 4271.43 0.70 44 Yu et al. (2015)631 offspring from 49 F1families
4817 SNP CarthaGene1.3 4552.50 0.95 44 Jones et al. (2017)
Black Tiger Shrimp (Penaeus monodon) 1024 offspring from 7 F1families
3959 SNP MapChart 4060.00/2917.00 1.20/0.90 44 Baranski et al. (2014)
98 offspring from 1 F2 family 6524 SNP Lep-MAP3 3275.40 0.50 44 Guo et al. (2019)Chinese mitten crab (Eriocheir sinensis) 120 offspring from 1 F1 family 10,358 SNP JoinMap4.0 5125.53 0.49 73 Cui et al. (2015)
147 offspring from 1 F1 family 18,309 SNP and SSR High Map 14,894.90 0.81 73 Qiu et al. (2017)Kuruma prawn (Marsupenaeus japonicus) 150 offspring from 20 F1
families9289 SNP JoinMap4.0 3610.90 0.39 41 Lu et al. (2016)
Swimming carb (Portunus trituberculatus) 120 offspring from 1 F1 family 10,936 SNP High Map 5557.85 0.51 53 Lv et al. (2017)Mud carb (Scylla paramamosain) 129 offspring from 1 F1 family 16,701 SNP High Map 5996.66 0.81 49 Waiho et al. (2019)
EchinodermSea cucumber (Apostichopus japonicus) 98 offspring from 9 F1 families 7839 SNP JoinMap4.0 3706.60 0.47 22 Tian et al. (2015)
Note: Two values in the map size (cM) and average interval (cM) represent the data of both female and male, respectively. Sometimes, there is only one value in these columns for one species, it is calculated from a sex-average map.
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Table 7Identification of QTLs in aquaculture animals using linkage mapping.
Species Number and traits of QTLs Number of candidategenes
Reference
FishesAsian seabass (Lates calcarifer) 32 QTLs for growth - Wang et al. (2006)
16 QTLs for growth 4 Wang et al. (2008)14 QTLs for growth 3 Wang et al. (2011)55 QTLs for Omega-3 fatty acids - Xia et al. (2014)6 QTLs for growth 1 Wang et al. (2015a)13 QTLs for viral nervous necrosis (VNN) disease resistance - Liu et al. (2016a)8 QTLs for VNN disease resistance 1 Liu et al. (2016b)
Japanese flounder (Paralichthys olivaceus) 1 QTL for lymphocystis disease resistance Fuji et al. (2006)4 QTLs for growth - Song et al. (2012b)10 QTLs for Vibrio anguillarum disease resistance 12 Shao et al. (2015)
Atlantic salmon (Salmo salar) 3 QTLs for infectious pancreatic necrosis (IPN) diseaseresistance
- Houston et al. (2008)
15 QTLs for IPN disease resistance - Moen et al. (2009)10 QTLs for growth, 3 QTLs for flesh color - Baranski et al. (2010)2 QTLs for male grilsing, 1 QTL for late sexual maturation - Gutierrez et al. (2014)37 QTLs for growth - Tsai et al. (2015b)6 QTLs for pancreas disease resistance - Gonen et al. (2015)3 QTLs for sea lice resistance 2 Robledo et al. (2019)2 QTLs for cardiomyopathy syndrome resistance 4 Boison et al. (2019)2 QTLs for piscine myocarditis virus (PMCV) disease resistance 6 Hillestad and Moghadam
(2019)Arctic charr (Salvelinus alpinus) 26 QTLs for growth, 12 QTLs for salinity tolerance, 16 QTLs for
body weight and condition factor- Küttner et al. (2011)
European seabass (Dicentrarchus labrax) 2 QTLs for body weight, 6 QTLs for morphometric, 3 QTLs forstress response
- Massault et al. (2010)
7 QTLs for sex - Palaiokostas et al. (2015b)36 QTLs for growth 39 Louro et al. (2016)
Common carp (Cyprinus carpio L.) 5 QTLs for muscle fiber - Zhang et al. (2011)15 QTLs for growth - Laghari et al. (2014a)22 QTLs for growth, 7 QTLs for sex 13 Peng et al. (2016)18 QTLs for head size 10 Chen et al. (2018a)1 QTL for koi herpesvirus (KHV) disease resistance 1 Palaiokostas et al. (2018c)
Rainbow trout (Oncorhynchus mykiss) 1 QTL for whirling disease resistance - Baerwald et al. (2011)6 QTLs for osmoregulation capacity - Le Bras et al. (2011)10 QTLs for response to crowding stress - Rexroad et al. (2013)12 QTLs for growth 5 Kocmarek et al. (2015)11 QTLs for bacterial cold water disease (BCWD) resistance 100 Palti et al. (2015a, 2015b)4 QTLs for bacterial cold water disease (BCWD) resistance, 1QTL for spleen index
- Liu et al. (2015b)
4 QTLs for cortisol responses to crowding 22 Liu et al. (2015a)14 QTLs for BCWD - Vallejo et al. (2017a)5 QTLs for BCWD - Fraslin et al. (2019)21 QTLs for infectious hematopoietic necrosis diseaseresistance (IHN)
- Vallejo et al. (2019)
Gilthead seabream (Sparus aurata) 1 QTL for morphometric, 2 QTLs for stress response - Boulton et al. (2011)2 QTLs for fish pasteurellosis resistance - Massault et al. (2011)1 QTL for body weight and sex, 1 QTL for sex - Loukovitis et al. (2011)3 QTLs for vertebral fusion, 2 QTLs for lordosis, 2 QTLs for jawdeformity
- Negrín-Báez et al. (2015)
Turbot (Scophthalmus maximus) 5 QTLs for aeromonas resistance - Rodríguez-Ramilo et al. (2011)11 QTLs for growth - Sánchez-Molano et al. (2011)9 QTLs for philasterides dicentrarchi disease resistance - Rodríguez-Ramilo et al. (2013)3 QTLs resistance to viral haemorrhagicSepticaemia (VHS)
5 Rodríguez-Ramilo et al. (2014)
45 QTLs for growth,175 QTLs for sex - Wang et al. (2015b)1 QTL for philasterides dicentrarchi disease resilience 32 Saura et al. (2019)
Half-smooth tongue sole (Cynoglossus semilaevis) 4 QTLs for growth, 7 QTLs for sex - Song et al. (2012a)Nile tilapia (Oreochromis niloticus) 1 QTL for sex - Palaiokostas et al. (2013b)
25 QTLs for growth 1 Liu et al. (2014b)1 QTL for sex - Palaiokostas et al. (2015a)
Yellowtail (Seriola quinqueradiata) 2 QTLs for Monogenean parasite resistance - Ozaki et al. (2013)Chinook salmon (Oncorhynchus tshawytscha) 3 QTLs for temperature tolerance, 1 QTL for body weight - Everett and Seeb (2014)Sockeye salmon (Oncorhynchus nerka) 6 QTLs for thermotolerance, 20 QTLs for growth - Larson et al. (2015)
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phenotypic values.Several software packages have been developed for GWAS studies.
For example, PLINK handles large whole-genome SNP data and con-ducts association tests in a computationally efficient manner (Purcellet al., 2007; Chang et al., 2015). It has been employed in many GWASworks. Recently, SNPTEST (Marchini et al., 2007) and BOLT-LMM (Lohet al., 2015) have also been frequently used for GWASs (Tam et al.,2019). Step-by-step descriptions of various GWAS procedures havebeen previously published (Geng et al., 2017b).
GWAS has been used extensively in studies of mammals, includinghumans, to identify variants associated with complex phenotypic traitsand diseases (Hindorff et al., 2009; Tam et al., 2019). However, GWASreports in aquaculture animals have been relatively limited, withmostly focusing on fishes such as Atlantic salmon and rainbow trout
(Table 8). The traits studied in aquaculture animals include diseaseresistance (Lu et al., 2015; Fraslin et al., 2019), growth (Neto et al.,2019; Yu et al., 2019), head size (Geng et al., 2016; Chen et al., 2018a),hypoxia tolerance (Zhong et al., 2017), heat tolerance (Jin et al., 2017),muscle yield (Salem et al., 2018), shell color (Zhao et al., 2017), andsexual maturation (Ayllon et al., 2015; Mohamed et al., 2019; Cacereset al., 2019).
GWAS can be used to analyze both quantitative and qualitativetraits. GWAS has several benefits, including identification of novelvariant-trait associations, detection of monogenic and oligogenic causalgenes, discovery of novel biological mechanisms, and illumination ofethnic variations in complex traits (Tam et al., 2019). Despite manysuccessful reports of GWAS to identify QTLs regions with significantvariations as well as to determine the genetic bases of economic traits
Table 7 (continued)
Species Number and traits of QTLs Number of candidategenes
Reference
Channel catfish (Ictalurus punctatus) 4 QTLs for columnaris resistance 5 Geng et al. (2015)4 QTLs for head size 33 Geng et al. (2016)6 QTLs for hypoxia tolerance 43 Wang et al. (2017c)3 QTLs for enteric septicemia of catfish (ESC) resistance 16 Zhou et al. (2017)4 QTLs for deheaded body length, 3 QTLs for body length, 3QTLs for body depth
28 Geng et al. (2017a)
6 QTLs for ESC resistance 37 Shi et al. (2018a)3 QTLs for ESC resistance 55 Tan et al. (2018)4 QTLs for growth 19 Li et al. (2018c)6 QTLs for growth, 10 QTLs for sex 25 Zhang et al. (2019d)3 QTLs for motile aeromonas septicemia disease resistance 24 Wang et al. (2019c)
Gold fish (Carassius auratus) 13 QTLs for growth - Kuang et al. (2016)Bighead carp (Hypophthalmichthys nobilis) 38 QTLs for growth 12 Fu et al. (2016)Orange-spotted grouper (Epinephelus coioides) 27 QTLs for growth 17 Yu et al. (2016)Crucian Carp (Carassius auratus) 8 QTLs for growth 5 Liu et al. (2017b)
35 QTLs for feed conversion efficiency 7 Pang et al. (2017)Blunt snout bream (Megalobrama amblycephala) 8 QTLs for growth - Wan et al. (2017)Mandarin fish (Siniperca chuatsi) 10 QTLs for growth, 1 QTLs for sex - Sun et al. (2017)Yellow drum (Nibea albifora) 22 QTLs for growth 13 Qiu et al. (2018)Yangtze River common carp (Cyprinus carpio
haematopterus)21 QTLs for growth, 4 QTLs for sex 5 Feng et al. (2018)
Golden pompano (Trachinotus blochii) 23 QTLs for growth - Zhang et al. (2018a)Largemouth bass (Micropterus salmoides) 32 QTLs for growth, 13 QTLs for sex - Dong et al. (2019)Large yellow croaker (Larimichthys crocea) 7 QTLs for Cryptocaryon irritans disease resistance 29 Kong et al. (2019)Snapper (Chrysophrys auratus) 4 QTLs for growth 13 Ashton et al. (2019)Red spotted grouper (Epinephelus akaara) 17 QTLs for growth 13 Wang et al. (2019d)
MollusksPacific oyster (Crassostrea gigas) 5 QTLs for summer mortality load - Sauvage et al. (2010)
12 QTLs for viability - Plough and Hedgecock (2011)3 QTLs for growth, 1 QTL for sex - Guo et al. (2012)15 QTLs for growth, 26 QTLs for nutritional trait 13 Li et al. (2018b)
Zhikong scallop (Chlamys farreri) 2 QTLs for growth, 1 QTLs for sex 2 Jiao et al. (2013)Perl oyster (Pinctada fucata) 39 QTLs for growth - Li and He (2014)
6 QTLs for growth - Shi et al. (2014)Triangle sail mussel (Hyriopsis cumingii) 26 QTLs for growth - Bai et al. (2016)Small abalone (Haliotis diversicolor) 15 QTLs for growth - Ren et al. (2016)Oyster (Crassostrea gigas x Crassostrea angulata) 27 QTLs for growth 36 Wang et al. (2016)Manila clam (Ruditapes philippinarum) 7 QTLs for growth, 3 QTLs for shell color - Nie et al. (2017)Razor clam (Sinonovacula constricta) 16 QTLs for growth - Niu et al. (2017)
CrustaceansPacific White Shrimp (Litopenaeus vannamei) 14 QTLs for growth - Andriantahina et al. (2013)
18 QTLs for growth - Yu et al. (2015)Black Tiger Shrimp (Penaeus monodon) 9 QTLs for white spot syndrome virus (WSSV) resistance - Robinson et al. (2014)
1 QTLs for sex - Guo et al. (2019)Chinese mitten crab (Eriocheir sinensis) 1 QTLs for sex 1 Cui et al. (2015)
3 QTLs for growth 4 Qiu et al. (2017)Kuruma prawn (Marsupenaeus japonicus) 129 QTLs for high-temperature tolerance, 4 QTLs for body
weight- Lu et al. (2016)
Swimming Crab (Portunus trituberculatus) 10 QTLs for growth 8 Lv et al. (2017)20 QTLs for sex 3 Lv et al. (2018)
Mud carb (Scylla paramamosain) 27 QTLs for growth, 2 QTLs for sex 13 Waiho et al. (2019)
EchinodermSea cucumber (Apostichopus japonicus) 1 QTLs for growth 1 Tian et al. (2015)
Note: The full candidate gene lists were provided in the supplementary file.
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and biological pathways, these studies remain controversial for severalreasons. First, GWAS requires large numbers of samples; otherwise, theresults could yield many false positives (Turner et al., 2010; Larsenet al., 2011). Second, most GWAS signals are located in the non-codingregions of the examined genome; thus, additional steps are typicallyrequired to identify the causal variants and their target genes (Schaidet al., 2018; Tam et al., 2019). Third, while assigning causality in aGWAS, too many genes may be implicated in the variations of a
complex trait (Boyle et al., 2017), and it has been too difficult to cor-rectly estimate the effect size of any single gene. Finally, studies inmodel organisms have confirmed that epistasis is a key component ofthe genetic architecture of complex traits (Mackay, 2014; Buchner andNadeau, 2015). Because practical GWAS tools lack a statistical function,the detection of loci with complicated gene interactions requires mas-sive numbers of tests (Cortes et al., 2015; Geng et al., 2017b; Tam et al.,2019).
Table 8Available genome-wide association studies in aquaculture animals.
Species Type of mapping population Sample size Traits Number ofcandidate genes
Reference
FishesAtlantic Salmon (Salmo salar) Family-based population 3191 Fillet fat content, fillet firmness - Sodeland et al. (2013)
Natural population+family-basedpopulation
240+97 Sexual maturation 1 Ayllon et al. (2015)
Family-based population 2601 Pisciricketssia salmonis resistance 7 Correa et al. (2015)Family-based population 622 Growth 19 Tsai et al. (2015a)Family-based population 631 Growth, sexual maturation 11 Gutierrez et al. (2015)Family-based population 1152 Growth 2 Tsai et al. (2016b)Natural population 500 Adaption of infectious hematopoietic
necrosis virus (IHNV)13 Kjærner-Semb et al.
(2016)Family-based population 1481 Amoebic gill disease resistance - Robledo et al. (2018a)Family-based population n1=1867 Sexual maturation 37 Mohamed et al.
(2019)n2=2739Rainbow trout (Oncorhynchus
mykiss)Natural population n1=132 Migration mechanism - Hecht et al. (2013)
n2=57Family-based population 230 Cortisol responses to crowding - Liu et al. (2015a)Family-based population 1447 Fillet yield, fillet weight, carcass
weight21 Gonzalez-Pena et al.
(2016)Family-based population n1=1473 Bacterial cold water disease resistance - Vallejo et al. (2017b)
n2=577Family-based population 878 Muscle yield 21 Salem et al. (2018)Family-based population 789 Filet firmness and protein content 55 Ali et al. (2019)Family-based population 4596 Body weight and metabolism 234 Neto et al. (2019)Family-based population 706 Bacterial cold water disease resistance - Fraslin et al. (2019)
Channel catfish (Ictaluruspunctatus)
Family-based population 192 Heat tolerance 5 Jin et al. (2017)Family-based population 347 Low oxygen tolerance 125 Zhong et al. (2017)
Half-smooth tongue sole(Cynoglossus semilaevis)
Family-based population 115 Sex reversal 8 Jiang and Li (2017)Family-based population 505 Genetic mechanisams of vibrio harveyi
disease resistance variations25 Zhou et al. (2019b)
Coho Salmon (Oncorhynchuskisutch)
Family-based population 764 Piscirickettsia salmonis diseaseresistance
56 Barría et al. (2018)
Common carp (Cyprinus carpio L.) Family-based population 1425 Koi herpesvirus (KHV) diseaseresistance
1 Palaiokostas et al.(2018c)
Natural population 302 Polyunsaturated fatty acids (PUFAs)content
15 Zhang et al. (2019b)
Gilthead seabream (Sparus aurata) Family-based population 1187 Photobacteriosis resistance - Aslam et al. (2018)Family-based population 112 Weight 4 Kyriakis et al. (2019)
European seabass (Dicentrarchuslabrax)
Family-based population 1538 Viral nervous necrosis diseaseresistance
- Palaiokostas et al.(2018a)
Yellowtail kingfish (Seriola lalandi) Family-based population 752 Body weight - Nguyen et al. (2018b)Orange-spotted grouper
(Epinephelus coioides)Natural population 198 Growth 26 Yu et al. (2018)Natural population 300 Ammonia tolerance 7 Xu et al. (2019a,
2019b)Greater amberjack (Seriola
dumerili)Natural population 20 Sex 1 Kawase et al. (2018)
Nile tilapia (Oreochromis niloticus) Family-based population 326 Sex determination 3 Caceres et al. (2019)Family-based population 7104 Fillet yield, harvest weight 55 Yoshida et al. (2019b)
Large yellow croaker (Larimichthyscrocea)
Natural population 220 Growth 40 Zhou et al. (2019d)
MollusksYesso Scallop (Patinopecten
yessoensis)Natural population 66 Shell colour 11 Zhao et al. (2017)
Pacific oyster (Crassostrea gigas) Family-based population 138 Salinity adaptation 23 She et al. (2018)Family-based population 897 Ostreid herpesvirus disease resistance 11 Gutierrez et al.
(2018a)Family-based population 427 The content of glycogen and free
amino acids21 Meng et al. (2019)
CrustaceansPacific white shrimp (Litopenaeus
vannamei)Family-based population 205 Growth 8 Yu et al. (2019)
Note: n1 and n2 represent two different populations. The full candidate gene lists were provided in the supplementary file.
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Most current GWAS limitations can be surmounted or overcome, atleast to some extent. The growing numbers of published GWAS reportsconfirm that this method can successfully elucidate the genetic bases ofmany complex traits. As the number of available reference genomesgrows and the costs of whole-genome sequencing decrease, GWAS isexpected to become more widely used for the examination of economictraits in aquaculture animals.
9. MAS in aquaculture animals
As aquaculture animals have been recently domesticated and aresubjected to relatively intense selection pressure, the potential for QTLdetection via linkage mapping or association mapping is greater thanthat for livestock species (Gutierrez and Houston, 2017). Indeed, manyQTLs underpinning disease resistance in aquaculture animals have beenreported with substantial effects in a number of cases (Fuji et al., 2006;Houston et al., 2008; Houston et al., 2010; Moen et al., 2009; Moenet al., 2015; Gonen et al., 2015). Once the genes or causative variantslinked to major QTLs are identified and confirmed in independent po-pulations, these can be used in MAS breeding schemes to improve se-lection accuracy (Laghari et al., 2014b; Gutierrez and Houston, 2017).
Only a few successful cases of MAS have been reported in aqua-culture animals. In the Japanese flounder, Fuji et al. (2006) identified asingle microsatellite locus controlling the resistance to lymphocystisdisease in Japanese flounder; this marker explained half of the totalphenotypic variation in the mapped population. Fuji et al. (2007)mated a female flounder with favorable homozygous alleles at thislocus with a male from a commercial stock with a higher growth rateand a preferred body shape, but without this favorable allele. All pro-genies were proved to be heterozygous in the favorable allele. Whenthese heterozygous progenies were exposed to lymphocystis disease attwo commercial fish farms, the fishes showed an incidence of lym-phocystis disease, as compared 4.5% and 6.3% of the fishes in thecontrol group (without the favorable allele).
MAS has also been used to increase resistance to infectious pan-creatic necrosis (IPN) in two populations of Atlantic salmon. First,Houston et al. (2008) performed a genome-wide QTL scan within 10full-sib families that had been exposed to IPN in natural seawater, andfound that the most significant QTL, located on LG21, was significantlygenome-wide in both the sire- and the dam-based analyses. Similarly,Moen et al. (2009) performed a genome-wide scan using 10 largegroups of full-sib families artificially challenged with IPN before, andidentified one major QTL for IPN-resistance. This QTL explained 29%and 83% of the phenotypic and genetic variances, respectively. Fourmicrosatellite markers were also identified to be tightly linked to thisQTL. In addition, this QTL mapped to the same location as the onepreviously detected by Houston et al. (2008). Subsequently, Houstonet al. (2010) designed a large scale of freshwater IPN challenge basedon these previously identified QTL regions, and found that these pre-viously identified QTLs explained almost all of the genetic variations inIPN mortality under the experimental conditions. Moen et al. (2015)then employed three microsatellite makers to select and produce IPN-resistant salmon, leading to a 75% decrease in the number of IPNoutbreaks across the examined salmon farms. Finally, the causal genefor IPN resistance was proved to encode an epithelial cadherin (Cdh1)protein, which binds to IPN virions, and a putative causal SNP withinthe cdh1 gene was determined to affect QTL allele patterns (Moen et al.,2015).
Applying genetic markers to identify genders is a special type ofMAS. Sex determination can be regarded as a complex trait in aqua-culture animals, because it is influenced by one or more genetic factorsin addition to environment (Martínez et al., 2014). When sex de-termination is significantly associated with a single locus, the sexmarkers can be developed for practical MAS programs. For severalaquaculture species, including Nile tilapia (Lee et al., 2003), rainbowtrout (Felip et al., 2005), half-smooth tongue sole (Chen et al., 2007),
common carp (Chen et al., 2009), yellowtail (Fuji et al., 2010), Atlanticsalmon (Barson et al., 2015), channel catfish (Zhang et al., 2019d), andblack tiger shrimp (Guo et al., 2019), critical genetic markers or causalgenes controlling sex determination have been identified.
Although several QTLs and candidate markers or genes have beenpredicted in aquaculture species (Tables 7 and 8), MAS has been ap-plied successfully in relatively few cases. In practice, if the QTLs arepolygenic without major effects, it will be difficult to implement MASpractices. Under such circumstances, genetic improvements can only befulfilled using more advanced genomics-based approaches, in which itis possible to accurately predict breeding values based on genome-wideloci (Zenger et al., 2019).
10. GS
Although MAS can be useful for some traits when the QTLs under-lying major effects have been identified, MAS has limited efficacy whenattempting to improve complex traits controlled by many genes withsmaller individual effects (Zenger et al., 2019). With more routineavailability of large-scale genotyping, GS has been proposed as an goodalternative (Meuwissen et al., 2001). GS is based on the simultaneousestimated effects of genome-wide loci, not merely a limited number ofmajor QTLs, with MAS. In general, the GS procedure consists of threesteps. Firstly, a reference (or training) population is constructed. Thispopulation should include many individuals with available data ofphenotypes and genome-wide SNP genotypes. Then, the effects of eachSNP are able to be estimated, and an equation is generated usingmultiple analytical approaches to predict genomic estimated breedingvalues (GEBVs). Secondly, a validation population with genotypic andphenotypic data is used to assess the accuracy of the prediction equa-tion by comparing the GEBVs with the actual phenotypic values. Fi-nally, the prediction equation is used to calculate the GEBVs of a ex-amined population that only genotypes are available, and outstandingindividuals can be selected based on these GEBVs. In practice, thesecond step is optional but provides important feedback on the accu-racy of the GS, and is hence recommended (Khatkar, 2017).
GS has been used frequently in breeding programs for both plants(Crossa et al., 2017) and animals (Wiggans et al., 2017; Georges et al.,2019). For example, in dairy cattle, GS improved the rates of geneticimprovement by 100% (Khatkar, 2017). To date, GS has been applied toonly a few high-value aquaculture species (Table 9). Studies in aqua-culture animals have shown that the accuracy of genomic predictionsvaries from 0.220 to 0.946 for traits associated with disease resistance,and from 0.050 to 0.900 for traits associated with body size and growth(see more details in Table 9).
Several statistical methods have been developed for GS, includingthose based on the best linear unbiased prediction (BLUP) algorithm,such as ridge regression BLUP (RRBLUP) (Meuwissen et al., 2001) andgenomic BLUP (GBLUP) (Vanraden, 2008), and those based on theBayesian algorithm, such as BayesA (Meuwissen et al., 2001), BayesB(Meuwissen et al., 2001), BayesLASSO (De Los Campos et al., 2009;Mutshinda and Sillanpää, 2010), and BayesCπ (Habier et al., 2011).Both RRBLUP and GBLUP, assuming a normal prior distribution and aconstant variance for all SNP loci, are computationally fast but maygenerate imprecise predictions when a trait is controlled by a smallnumber of QTL loci. In Bayesian methods, there are definitions of theprior distribution of SNP effects that allow for very large SNP effectsand small or even zero variances at other SNP loci, but these priordistributions are often also computationally demanding because theyare based on the Markov chain Monte Carlo (MCMC) algorithm. Toincrease computational speed, several fast Bayesian methods have beendeveloped (Meuwissen et al., 2009; Shepherd et al., 2010). In parti-cular, the fastest Bayesian method MixP, employing the Pareto prin-ciple, is remarkably quicker than the popular MCMC-based methods(Yu and Meuwissen, 2011; Dong et al., 2017). State-of-the-art machinelearning methods (Ogutu et al., 2011; Heslot et al., 2012; Grinberg
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Table 9Studies of genomic selection in aquaculture animals.
Species Number of samplesgenotyped
SNPs Traits Main research contents Accuracy of genomicprediction
Reference
Atlantic Salmon (Salmo salar) 622 111,908 Growth Compare the accuracy of predicted genomic estimatedbreeding values (GEBVs) of GBLUP and P-BLUP
0.700 Tsai et al. (2015a)
n1=531 33,000 Sea lice resistance Compare the accuracy of GEBVs of vary SNP densities in twopopulations
0.340-0.610 Tsai et al. (2016a)n2=588624 78,362 Sea lice resistance, body weight Assess the efficacy of imputed genotype data in genomic
prediction0.900 Tsai et al. (2017)
1430 7168 Amoebic gill disease (AGD) resistance Compare the accuracy of GEBVs of GBLUP and P-BLUP 0.620/0.700 Robledo et al. (2018a)610 78,362 Sea lice resistance Compare the accuracy of GEBVs of genotype imputation using
vary densities SNP panels0.530 Tsairidou et al. (2019)
Rainbow trout (Oncorhynchus mykiss) 636 10,052 Bacterial cold water disease (BCWD)resistance
Compare the accuracy of GEBVs of different genomicselection (GS) models
0.410-0.500 Vallejo et al. (2016)
1473 35,636 BCWD Compare the accuracy of GEBVs of different GS models topedigree-based
0.220-0.720 Vallejo et al. (2017a)
930 35,636 BCWD Compare the accuracy of GEBVs of vary densities SNP panels 0.500-0.720 Vallejo et al. (2018)768 38,292 infectious pancreatic necrosis virus
disease resistanceCompare the accuracy of GEBVs of P-BLUP to ssGBLUP 0.530/0.560 Yoshida et al. (2019a)
Yesso scallop (Patinopecten yessoensis) 349 2364 Growth Compare the accuracy of GEBVs of six GS models withsimulation and real dataset
0.150-0.400 Dou et al. (2016)
Large yellow croaker (Larimichthyscrocea)
500 29,748 Eviscerated weight, whole bodyweight
Reduce genotyping cost of GS with two strategies 0.050-0.450 Dong et al. (2016)
Gilthead sea bream (Sparus aurata) 825 12,085 Pasteurellosis resistance Compare the accuracy of GEBVs of GBLUP to P-BLUP 0.380-0.460 Palaiokostas et al. (2016)Pacific White Shrimp (Litopenaeus
vannamei)205 6359 Growth Compare the accuracy of GEBVs of GS models with different
traits0.284-0.413 Wang et al. (2017a)
Yellowtail kingfish (Seriola lalandi) 752 14,448 Growth SNPs were derived from DArTseqTM and GBLUP model andimputation of missing genotype was evaluated
0.440-0.830 Nguyen et al. (2018a)
Zhikong scallop (Chlamys farreri) 509 31,361 Growth Compare the accuracy of GEBVs of different traits withdifferent GS models
0.370-0.580 Wang et al. (2018)
Common carp (Cyprinus carpio) 1425 12,311 Growth Compare the accuracy of GEBVs of vary densities SNP panels 0.660-0.710 Palaiokostas et al.(2018b)
1425 15,615 Koi herpesvirus disease resistance Compare the accuracy of GEBVs of GBLUP with vary densitiesSNP panels to P-BLUP
0.470-0.550 Palaiokostas et al. (2019)
Pacific oyster (Crassostrea gigas) 820 23,388 Growth Compare the accuracy of GEBVs of GBLUP to P-BLUP withdifferent traits
0.540-0.670 Gutierrez et al. (2018b)
Channel catfish (Ictalurus punctatus) 2911 54,837 Harvest and carcass weight Compare the accuracy of GEBVs of GBLUP to P-BLUP withdifferent traits
0.220-0.380 Garcia et al. (2018)
Japanese flounder (Paralichthysolivaceus)
931 1,934,475 Edwardsiella tarda disease resistance Compare the accuracy of GEBVs of GBLUP to BayesCπ 0.946 Liu et al. (2018)
European seabass (Dicentrarchus labrax) 1538 9195 viral nervous necrosis diseaseresistance
Compare the accuracy of GEBVs of GS models to P-BLUP 0.660-0.710 Palaiokostas et al.(2018a)
Coho salmon (Oncorhynchus kisutch) 764 9389 Piscirickettsia salmonis diseaseresistance
Compare the accuracy of GEBVs of GS models to P-BLUP withdifferent traits
0.299-0.807 Barría et al. (2018)
Yellow drum (Nibea albiflora) 371 53,677 Body size-related traits Compare the accuracy of GEBVs of GS models with differenttraits
0.145-0.412 Liu et al. (2019)
Note: n1 and n2 represent two different populations that were analyzed separately.
X.Y
ou,etal.
Aquaculture 526 (2020) 735357
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et al., 2016; Wang et al., 2018) and deep learning methods (Ma et al.,2018) have also been used in GS. These non-parametric methods the-oretically detect complex epistatic effects without explicitly pre-mod-eling (Gianola and van Kaam, 2008; Khatkar, 2017). The predictionperformance of different methods may depend on genetic and statisticalfactors (Wang et al., 2018), and no one method is consistently optimalunder all conditions (Liu et al., 2019). When using GS for breedingprograms, it is necessary to evaluate various statistical methods andthen to select the most suitable.
GS accuracy depends on many factors in addition to the computingmethod. Specifically, size of the reference population strongly affectspredictive accuracy. The number of samples genotyped in the referencepopulation will mainly depend on the effective size of the population(Ne) and the heritability estimate of a trait (Khatkar, 2017). Large Nevalues and small heritability estimates require larger reference popu-lations. The required reference population size can be inferred in ac-cording to Daetwyler et al. (2008). For example, a reference populationof 3773 was required to predict body length in the yellow drum (Nibeaalbiflora) with 80% accuracy (Liu et al., 2019).
Predictive accuracy also depends on the number of identified SNPs.Ideally, SNP density should be high enough to ensure that all QTLs arein LD with at least one SNP; in this case, estimates of SNP effects cap-ture the most genetic variance (Khatkar, 2017). In general, larger SNPpanels result in more accurate predictions (Tsai et al., 2016a; Vallejoet al., 2018; Palaiokostas et al., 2018b; Tsairidou et al., 2019; Liu et al.,2019). If LD was extensive enough, a relatively small number of SNPswould be sufficient to cover the genome and can work well for GS(Werner et al., 2018). Several studies have shown that predictionsbased on small numbers of informative SNPs are as accurate as thosebased on whole-genome SNPs (Dong et al., 2016; Abed et al., 2018;Yoshida et al., 2019a; Robledo et al., 2018a; Liu et al., 2019); in-formative SNPs can be chosen based on GWAS results (Liu et al., 2019).Admittedly, however, the high predictive accuracies of low SNP densityperhaps related to relatively small size of the training population (Abedet al., 2018). It is possible that larger training population could requirea larger SNP density to ensure high prediction accuracies (Abed et al.,2018).
Genotype imputation can also be used to improve GS with cost-ef-ficiency. In a study of Atlantic salmon, genomic predictions of sea liceresistance and body weight had an imputation accuracy of up to 0.90when the parents were genotyped at medium or high densities and theoffspring were genotyped at lower densities (Tsai et al., 2017). In yel-lowtail kingfish, the accuracy of body weight predictions increasedfrom 0.69 to 0.83 when missing genotypes were imputed (Nguyenet al., 2018a).
Thus, accurate and low-cost industrial-scale genotyping and on-farm phenotyping techniques are critical for the practical uses of GS inthe aquaculture industry (Zenger et al., 2019). For most aquatic species,GS applications are currently in their infancy, but we expect that theapplication of these techniques will generate big returns in the nearfuture.
11. Conclusions and perspectives
In the first two decades of the 21st century, high-throughput se-quencing technologies have developed rapidly, and sequencing costshave decreased sharply. Due to these advances, a growing number ofaquaculture animal genomes have been sequenced, covering almost allof the most economically important species. Genome assemblies havealso become increasingly precise. The use of -omics technologies hasalso accelerated analyses of economically important traits, and a largenumber of candidate genes and loci have been identified. These reportsand data provide a solid theoretical basis for the practical breeding ofaquaculture animals. However, progress differs noticeably across var-ious aquaculture species. For example, genetic research and breedingadvances in salmonid species have progressed farther than in other
aquaculture animals. Therefore, the breeding techniques and strategiesapplied in salmonoid species may provide an instructive framework forgeneral genetic advances in other aquatic species. Additionally, thegenomic resources accumulated in aquaculture animals should be fur-ther used to develop functional genomics. It is necessary to build up anunderstanding of the gene network underlying important traits in-cluding growth, disease resistance, sex determination, etc. The preciseand detailed understanding of functional genomics is the good guide forimproving genomic selection and performing genome editing. We caneven imagine that under the rapid development of gene synthesis it willbecome possible to create aquaculture animals with ideal traits bytransgenic technology. Admittedly, the safety and effectiveness of suchtechnologies (genome editing and transgene) in aquculture animals areimportant issues for further research, however, the commercial appli-cation of these technologies not only depends on the maturity of thetechnology but also the bioethics consideration.
Declaration of competing interest
The authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influ-ence the work reported in this paper.
Acknowledgements
This work was supported by National Natural Science Foundation ofChina (No. 31872572), and Natural Science Foundation forFundamental Research in Shenzhen (No.JCYJ20190812105801661).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.aquaculture.2020.735357.
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