Introduction

The Pacific white shrimp, Litopenaeus vannamei (L. vannamei), is famous worldwide as a high-quality food in the human diet (FAO 2012). With the high economic value and good stress resistance of this species (Li et al. 2007, Wang et al. 2013a, b), L. vannamei has become one of the most important aquatic species for culturing, and L. vannamei farming areas have been widely introduced in nonnative environments worldwide (Huang et al. 2018). Total shrimp production reached 4 million tons, and the total output value achieved 24 billion US dollars in 2016 (Yu et al. 2019). Advances in shrimp breeding projects might further promote the high economic value of the shrimp culture industry.

Soil salinization significantly impacts crop yields and poses a threat to regional human life; therefore, the strategy of reusing and transferring saline-alkali lands as cultivated resources is becoming imperative (Li et al. 2014; Wang et al. 2017; Huang et al. 2018). The development of aquaculture industries in saline-alkali water areas, including inland lakes, rivers, and marine coasts in China, has been thought to be an effective method for taking full advantage of these territorial resources (Liang et al. 2013; Liu et al. 2016) and has economic (production of saline-alkali water reached USD$1.14 million) (Liu et al. 2016) and environmental (the top pH value of the saline-alkali lands dropped from 9.01 to 8.56) (Xue 2018) value in the Hebei and Shaanxi Provinces of China. L. vannamei is one of the most important aquatic species for culturing in saline-alkali water areas in China (Luan et al. 2003; Liu et al. 2008; Zhang 2016). However, compared with the farming of L. vannamei under normal seawater conditions, the immunity and production of the shrimp might be weakened when the species is cultured in high-pH environments (Li and Chen 2008; Wang et al. 2009; Huang et al. 2018). The high-pH tolerance trait of the shrimp has become an important economic trait for the breeding and culturing industries.

The development of marker-assisted selection (MAS) has substantially accelerated genetic breeding work (Yue 2014; Abdelrahman ElHady et al. 2017; Li et al. 2017; Yu et al. 2019; Li et al. 2019). Most targeted traits of species are governed by multiple genes or loci, and quantitative trait loci (QTLs) mapping with dense genetic linkage maps is employed to reveal the locations of the trait-related genes (Yue 2014; Das et al. 2015; Wan et al. 2017; Li et al. 2017; Kong et al. 2019; Li et al. 2019). Single nucleotide polymorphism (SNP) markers, which represent the most abundant source of variation in the genome, are increasingly utilized for the construction of high-density genetic linkage maps (Lien et al. 2011; Shao et al. 2015; Wan et al. 2017). High-resolution genetic maps with 12,712 high-confidence SNPs and 24 consensus linkage groups (LGs) were constructed for the Japanese flounder (Paralichthys olivaceus, P. olivaceus), and 9 positive QTLs and 4 major genes for Vibrio anguillarum disease resistance were detected (Shao et al. 2015). Yu et al. (2015) used a total of 6146 high-quality SNP markers for QTL mapping, and an average marker distance of 0.7 cM was obtained for the linkage map (Yu et al. 2015).

With the development of next generation sequencing (NGS) technology, high-throughput marker development and genetic map construction has become possible to finely map trait-related QTLs (Shao et al. 2015, Wan et al. 2017). In recent years, many advanced methods for mapping trait-related QTLs have been developed, such as genome-wide association studies (GWAS) (Abdelrahman ElHady et al. 2017), expression quantitative trait loci (eQTLs) (Imprialou et al. 2017), specific-locus amplified fragment sequencing (SLAF) (Miller et al. 2007), genotyping-by-sequencing (GBS) (Baird et al. 2008), and restriction site-associated DNA sequencing (RAD-seq) (Sun et al. 2013). In particular, as a reliable, affordable method to reduce genomic complexity, RAD-Seq has been very useful for SNP discovery and genotyping (Berthier-Schaad et al. 2007; Rowe et al. 2011; Wang et al. 2012; Shao et al. 2015; Fu et al. 2016; Wan et al. 2017). RAD-Seq technology has now been applied for the genetic study of various aquatic species, such as P. olivaceus (Shao et al. 2015), Hypophthalmichthys nobilis (Fu et al. 2016), Megalobrama amblycephala (Wan et al. 2017), Trachinotus blochii (Zhang et al. 2018), Larimichthys crocea (Kong et al. 2019), and Oreochromis spp. (Li et al. 2017; Li et al. 2019).

In previous studies on L. vannamei, the genetic mechanism of growth traits has been elucidated (Andriantahina et al. 2013; Yu et al. 2015, Yu et al. 2019), but few genes have been identified in association with growth traits (Yu et al. 2019). Further genetic studies on growth-related traits in this species are still needed. The high-pH tolerance trait of the shrimp is becoming an important economic trait for industrial breeding and culturing in saline-alkali water areas; however, the stress response QTLs are still unknown. In the present work, double-digest restriction site-associated DNA sequencing (ddRAD-Seq) technology was applied to genotype an F1 full-sib family of L. vannamei, and a high-resolution genetic map was constructed with high-quality SNP markers. High-pH tolerance and growth (body weight) trait-related QTLs were detected, high-pH response and growth-related candidate genes were discovered, and real-time PCR was carried out to validate the candidate high-pH response genes. Our results will further benefit MAS work and might be useful for promoting genomic research on the shrimp L. vannamei.

Materials and Methods

Shrimp Full-Sib Family Production

An F1 full-sib shrimp family was produced by inner species crossing at JingYang Tropical Biology Co., LTD in Maoming, Guangdong, China (August, 2017). The female shrimp were selected from the inbred line called “L. vannamei ZhongKe No.1” (a shrimp variety in China, variety registration no. GS-01-007-2010), and the candidate male shrimps were obtained from a commercial shrimp termed “Zheng Da.” According to the propagation characteristic (open thelycum) of L. vannamei, mating a single mature male shrimp with a mature female was difficult, and a full-sib shrimp family was thus produced with a new “semidirectional cross” method according to the following steps. A fertile female shrimp (containing yellow shrimp eggs) (Supplementary Fig. 1a) was placed in a seawater mating pond (5 m2, 1 m height) that contained 7–15 fertile male shrimps, whose spermatophores were packed with white spermatia (Supplementary Fig. 1b). The male shrimps in the mating pond took turns to chasing the female until mating occurred, then, the female shrimp whose thelycum contained the white spermatia (Supplementary Fig. 1c) was removed. The mated female shrimp was reared in an isolation tank (round, 70 cm in height, volume ≤ 1000 L) for spawning and served as the female parent of the F1 full-sib family. The male parent of the F1 full-sib family was visually determined by determining based on which male showed an empty spermatophore (without packed spermatium) (Supplementary Fig. 1b, d), as naturally, only one male shrimp could mate with the female shrimp. The F1 full-sib progenies were first nursed in the tank for 15 days, and the full-sib shrimps were then moved to an indoor culturing pond (5.0 m in length, 6.0 m in width, and 1.2 m in height) for further rearing.

High-pH Challenges and Preparation of the Shrimp Samples

After being reared in the culturing pond for 40 days, 35 of the full-sib progenies were randomly selected and treated in a high-pH gradient environment (gradient pH values of 8.1, 8.3, 8.5, 8.7, 8.9, 9.1, 9.3, 9.5, and 9.7) for 48 h. The pH values were maintained with Na2CO3 and NaHCO3 solutions according to the description by Huang et al. (2018). The survival statuses of the shrimp were evaluated by observing their ability to move spontaneously or after gentle prodding (Huang et al. 2017). The dead individuals were collected, and the endurance times were recorded for each pH level. The high-pH stress was determined by evaluating the number of survivors in each high-pH treatment group.

A total of 148 full-sib progenies were randomly selected from nearly 5000 whole full-sib family numbers in the rearing pond and treated in the stress high-pH environments. Afterward, the dead shrimps were collected and weighed. The data regarding the endurance times under the stress of the high-pH environment (high-pH tolerance trait, HP) and body weight (body weight, growth trait, BW) were collected. The muscle tissues of 2 parents and 148 offspring were separated for the extraction of genomic DNA, and the experiments were conducted with the TIANGEN Marine Animal DNA extraction kit (TIANGEN, Beijing, China) according to the manufacture’s protocol. The concentration of total DNA was determined with a NanoDrop 1000 spectrophotometer (NanoDrop, Wilmington, DE, USA), and the quality of each DNA sample was evaluated by gel electrophoresis (Yu et al. 2015).

RAD Library Construction and Sequencing

Genomic DNA from 2 parents and 148 offspring was used to construct the 150 L. vannamei ddRAD libraries (October 2017) using protocols described by Peterson et al. (2012) and Sun et al. (2017). Briefly, the DNA from each shrimp sample was double-digested using the restriction enzymes EcoRI and NlaIII (New England Biolabs, Ipswich, MA, USA) (Yu et al. 2015). Then, the digested samples were purified using a Qiagen MinElute Reaction Cleanup Kit (Qiagen, Valencia, CA, USA). The fragments were then ligated to adapters that included unique 4- to 8-bp multiplex identifiers (MIDs) that were used to distinguish each individual. The samples were pooled, size-selected (400 to 600 bp) on an agarose gel and subsequently purified with a Qiagen MinElute Gel Purification Kit. The paired-end (150 bp) sequencing of the ddRAD products was performed on an Illumina HiSeq 2500 platform (Illumina, Inc., San Diego, CA, USA).

SNP Discovery and Genotyping

The Illumina short reads that lacked the sample-specific MIDs and the expected restriction enzyme motifs were discarded. The raw data were then filtered using Trimmomatic software (v0.32) (Bolger et al. 2014) in three steps: removal of adapters; removal of reads with bases from the start or end of a read with a quality threshold below 3; and scanning reads with a 4-bp sliding window, removing those with an average Phred quality per base below 20 (Sun et al. 2017). The STACKS (v1.41) (Catchen et al. 2013) pipeline was employed for de novo assembly of the loci and for SNP discovery. The Ustacks program was used to cluster the enzyme cutting sequences of each individual, and the Cstacks program was applied to identify the congruent loci and make the alleles in the parental data uniform. The Sstacks program was utilized to compare the progeny and parent loci, confirming the genotype of each progeny locus; and the Genotype program was utilized to proofread the genotype data. The following parameters were used:

  • Ustacks: -t gzfastq -i -m 3 -M 9 -p 15 -d -r –f –o

  • Cstacks: -b 1 –o –s –n 2 –p 15

  • Sstacks: -b 1 –c –p 15

  • Genotype: -b 1 –P -r 1 -c -s -t CP

The miss rates (number of samples with no genotype information/number of total samples) were less than 10%, and biallelic SNPs were used to avoid sequencing errors and to advance the segregation analysis (Sun et al. 2017; Lu et al. 2016). The sequencing depth of the SNP loci was no less than 5.

Linkage Map Construction

Only biallelic SNPs were introduced to construct the genetic linkage map, and the SNP genotypes were defined as follows: (1) SNPs that were heterozygous in the maternal parent (lm) and homozygous in the paternal parent (ll); (2) SNPs that were heterozygous in the paternal parent (np) and homozygous in the maternal parent (nn); and (3) SNPs that were heterozygous in the maternal and paternal parent (hk) (Lu et al. 2016). Biallelic SNPs that presented significant segregation distortion in the χ2 goodness-of-fit tests (chi-square test, P < 0.05) were also eliminated in the linkage analysis (Liu et al. 2017). LG assignments were conducted with JoinMap 4.1 software (Stam 1993) using a logarithm of odds (LOD) score ≥ 6.0. A pseudo-testcross strategy (a mapping population was developed by hybridizing two unrelated highly heterozygous parents to produce a set of F1 progeny) was utilized to construct the linkage map (Shao et al. 2015; Sun et al. 2017). The regression mapping algorithm and Kosambi’s mapping function were used for map construction with the following settings: Rec = 0.4, LOD = 1.0, and Jump = 5. The genetic distance of 30 cM was set as the longest gap between two markers in one LG. The resulting linkage maps were drawn using R software (version of R x64 3.5.2, https://www.r-project.org/) with the package LinkageMapView (Ouellette et al. 2018).

QTL Mapping

QTL mapping of the HP and BW traits was performed with MapQTL 5.0 software (Van Ooijen 2011). The Multiple QTL Mapping (MQM) program was used to detect the QTL region and calculate the percentage of explained phenotypic variance (Jansen and Stam 1994); an LOD ≥ 2.50 was set for the trait-related QTL regions. A mapping step size of 1 cM and five neighboring markers were used in the QTL analysis (Louro et al. 2016; Liu et al. 2017). The genome-wide LOD threshold (significance level) or group-wide LOD threshold (suggestive level) was determined in MapQTL5.0 by a Permutation Test on the basis of 1000 permutations with a confidence interval of 95% (Piepho 2001; Lu et al. 2016).

Identification of the Candidate High-pH Response and Growth-Related Genes

To further reveal the high-pH response and growth-related genes, the tag sequences of the HP and BW QTL markers were searched in the Pacific white shrimp genome database (GenBank no. QCYY00000000.1), and the aligned shrimp genome scaffolds were identified. The genes contained in the genome scaffolds (called the scaffold genes) were obtained, and the stress- and growth-related scaffold genes were annotated and summarized according to previous studies.

All the stress-related scaffold genes were compared with the high-pH transcriptome data from our previous work (S7 Table in Huang et al. 2018), and the coexisting candidate genes for high-pH response were discovered. To verify the mRNA expression patterns of the candidate high-pH response genes, healthy cultured shrimp (supplied by Yuehai Feed Group Co., LTD, Zhanjiang, China; average weight, 11.09 ± 2.37 g) were selected, and the three shrimp groups were treated in pH 8.0 seawater (control pH), pH 9.0 seawater, and pH 9.3 seawater. The high-pH values of the seawater were maintained as described previously (Wang et al. 2009; Huang et al. 2018). Total RNA was extracted from the posttreated gill tissues of the shrimp at 0, 1, 6, 12, 24, and 48 h. Real-time PCR analysis was carried out using the primers listed in Supplementary Table 1. The relative expression levels of the candidate genes were obtained according to the 2−ΔΔCT method by normalizing to the expression of the L. vannamei β-actin gene (Livak and Schmittgen 2001).

Results

Summary of the Data

The high-pH challenge data were analyzed. In total, 21 shrimps (survival rate of 60.0%) survived after treatment in the high-pH environment of 9.1 for 48 h, 3 (survival rate of 8.6%) survived after treatment in the high-pH environment of 9.3 for 48 h, and all died after treatment in a high-pH environment of 9.5 for 12 h (Fig. 1a).

Fig. 1
figure 1

Information on the experimental and RAD-Seq libraries. a High-pH challenges in gradient high-pH environments for 48 h; the high-pH stress condition of the full-sib family is indicated. b Summary of the high-pH tolerance data of the full-sib offspring. c Summary of the body weight data of the full-sib offspring. d Quality control of the RAD-Seq libraries. The X axes represent the base position along the reads. The Y axes represent the base content percentages. e Summary of the SNP markers; high-quality markers are enclosed in a box

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The high-pH value of 9.3 was set as the stress environment, and 148 progenies were treated in pH 9.3 seawater; with the ability or inability of the shrimp to resist a high-pH environment, 9 different stages were determined (Fig. 1b). Nine individuals (6.08%) died in the first stage (0–2 h), and 11 (7.43%) progenies survived beyond 48 h of treatment (Fig. 1b). The body weights were immediately obtained when the shrimp were dying under the pH 9.3 stress condition; the minimum shrimp body weight was 0.048 g, the maximum shrimp body weight was 1.100 g, the mean body weight was 0.319 ± 0.211 g, and the body weight values of the shrimp individuals were continuous (Fig. 1c).

Sequencing and Genotyping

A total of 150 ddRAD libraries were constructed from the two parents and their 148 offspring. The mean value of the GC% in the offspring was 43.67 ± 0.73% (Supplementary Table 2). The adenine bases (A) nearly overlapped the thymine bases (T), and the guanine bases (G) nearly overlapped the cytosine bases (C) (Fig. 1d, Supplementary Table 2). After filtering, the female parent had 2.17 Gb of clean data (GC rate of 42.16% and a Q30 rate of 90.08%), and the male parent had 2.58 Gb of clean data (GC rate of 43.21% and Q30 rate of 90.18%). The clean reads of the progenies ranged from 5.42 to 27.97 million, and the clean reads of the offspring ranged from 0.80 to 4.07 Gb (average GC rate of 43.61 ± 0.73 and mean Q30 value of 89.54 ± 1.12) (Supplementary Table 3). All clean data were deposited into the US National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/Traces/sra) under GenBank accession no. PRJNA532795. A total of 10,465 markers were detected from more than 90% of the progenies (Fig. 1e), and after a Mendelian fit test (P ≥ 0.05), 3894 high-quality markers (three types of markers including 2446 “lm × ll” markers, 1308 “nn × np” markers, and 140 hk × hk markers) were used to construct the consensus genetic map (Fig. 1e).

Construction of Linkage Maps

A ddRAD-based linkage map of L. vannamei was constructed with high-quality markers. The LGs containing fewer than 15 markers were discarded, and the final integrated map consisted of 48 LGs, including 3567 high-quality segregating SNP markers (327 high-quality markers were discarded or not located) (Table 1, Fig. 2). The total map length was 4161.555 cM, the average interlocus length was 1.167 cM, the genetic length of the LGs ranged from 14.612 cM (LG48) to 165.922 cM (LG 1), and the average interval length of the LGs ranged from 0.393 cM (LG 43) to 3.210 cM (LG 30) (Table 1, Fig. 2). Detailed information, including the marker names, tag sequences, and positions of the LGs, is provided in Supplementary Tables 4–5.

Table 1 Summary information of the linkage groups for the shrimp full-sib family
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Fig. 2
figure 2

Linkage group lengths and marker distributions of a high-resolution genetic map of L. vannamei. cM: centimorgan. LG: linkage group

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QTL Analysis of High-pH Tolerance and Growth Traits

For the HP trait, 12 QTLs were discovered (LOD value > 2.5) (Table 2) and were distributed in 5 different LGs (LG 4, LG 13, LG 18, LG 26, and LG 36) (Figs. 3 and 4). The most prominent QTL was HP-3, with an LOD value of 3.47; this QTL was 4.660 cM in length and explained 65.2% of the phenotypic variation. The detailed QTL information for the HP trait is available in Supplementary Table 4.

Table 2 High-pH trait-related and growth trait-related QTLs in L. vannamei
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Fig. 3
figure 3

Genetic locations of high-pH tolerance-related and growth-related (body weight) QTLs on 48 LGs of L. vannamei. The red lines indicate the logarithm of odds (LOD) value of the high-pH trait. The green lines indicate the logarithm of odds (LOD) value of the body weight trait. The dashed lines indicate a cutting threshold of LOD at 2.5

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Fig. 4
figure 4

Distribution of the high-pH tolerance-related and growth-related (body weight) QTLs on 6 different linkage groups. The value of the genetic distance (centimorgan, cM) was displayed on the left side of the LGs, and the related markers are presented on the right side of the LGs

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For the BW trait, 2 QTLs were detected (LOD value > 2.50) (Table 2), and the BW QTLs were located in LG 1 (Figs. 3 and 4). BW-2 was the major QTL (interval length of 0.933 cM), and it had an LOD value of 2.92 and explained 10.3% of the phenotypic variation (Table 2). The major BW-related genes might be located between the SNP markers of R1_309702 and R1_418289. Another BW QTL is listed in Table 2 and Fig. 4, and the detailed QTL information for the BW trait is provided in Supplementary Table 5.

Identification of the High-pH Response Gene Candidates

In total 73 HP and BW QTL markers were aligned to 46 L. vannamei scaffolds (Table 3). In the HP-2 QTL, the marker “R1_60745,” with an LOD value of 2.68 in LG4, was aligned to the L. vannamei scaffold “LVANscaffold_2027,” and the marker “R1_247764” in this QTL was aligned to the same L. vannamei scaffold, indicating a close genetic distance between the two markers (Table 3).

Table 3 Summary of the aligned L. vannamei scaffolds
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The stress-related genes contained in these L. vannamei scaffolds were summarized according to previous studies (Table 4). The expression levels of the stress-related genes were investigated using high-pH transcriptome data from our previous work (Huang et al. 2018), and six candidate high-pH response genes were discovered: hypoxia inducible factor 1 beta (HIF1b), transcription initiation factor TFIID subunit 3 (TAF3), glutamyl aminopeptidase (ENPEP), discoidin domain-containing receptor 2-like (DDR2), phosphatidylcholine: ceramide cholinephosphotransferase 1-like (SGMS1) and histone H2A.V-like (H2AFV) (Table 4). With the same method, 5 growth-related genes were obtained (Table 5).

Table 4 The candidate high-pH response genes in L. vannamei scaffolds
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Table 5 The candidate growth-related genes in L. vannamei scaffolds
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To further verify the results in this study, real-time PCR was carried out to identify the expression patterns of the high-pH response genes. The expression levels of the 6 candidate genes were significantly influenced by the high-pH treatment, and 5 (all except the ENPEP gene) of the 6 candidate genes were consistent with the high-pH transcriptome data (Fig. 5). Specifically, the HIF1b and SGMS1 genes were significantly upregulated for 6 h in the high-pH environment, and TAF3 and DDR2 were obviously downregulated in the first 6 h in response to the high-pH stress. The H2AFV gene was downregulated in the first few hours but was then substantially upregulated in the following hours in response to high-pH stress, and the results validate the candidate high-pH response genes (Fig. 5).

Fig. 5
figure 5

Transcriptional expression of the candidate high-pH response genes. a Expression pattern of the gene “hypoxia inducible factor 1 beta (HIF1b)”. b Expression pattern of the gene “transcription initiation factor TFIID subunit 3 (TAF3)”. c Expression pattern of the gene “glutamyl aminopeptidase (ENPEP)”. d Expression pattern of the gene “discoidin domain-containing receptor 2-like (DDR2)”. e Expression pattern of the gene “phosphatidylcholine: ceramide cholinephosphotransferase 1-like (SGMS1)”. f Expression pattern of the gene “histone H2A.V-like (H2AFV)”. Gill tissues of L. vannamei treated in high-pH environment for 0, 1, 6, 12, 24, and 48 h. The data are presented as the mean ± SE (n = 6), and the groups denoted by the same letter exhibit a similar expression levels (P > 0.05, two-way ANOVA followed by Fisher’s LSD test). Dotted lines indicate the expression pattern of the high-pH transcriptome data (the shrimp were treated in the pH 9.3 environment for 1 and 48 h) from our previous work (Huang et al. 2018)

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Discussion

Full-sib families provide basic materials for genetic breeding programs in aquatic species, and they are particularly important for mapping trait-related QTLs based on RAD-Seq technologies. For example, F1 full-sib families (including the two parents) have been generated and used to build ddRAD libraries in several aquatic species, such as Oreochromis niloticus (Li et al. 2017), Trachinotus blochii (Zhang et al. 2018), and Larimichthys crocea (Kong et al. 2019). Generally, the F1 full-sib families were constructed with methods of directional crossing (a pair of parents for mating) (Fu et al. 2016; Li et al. 2017; Kong et al. 2019) or undirectional crossing (several or more pairs of parents for mating) (Shao et al. 2015). According to the propagation characteristic of the shrimp L. vannamei, mating a pair of fertile shrimp is difficult to achieve in nature. Yu et al. (2015, 2019) used the “undirectional cross” method to create four combined full-sib L. vannamei families and then used 10 microsatellite loci to select the mapping family; an F1 full-sib family of 205 progenies was constructed, and their parental genomic DNA was identified (Alcivar-Warren et al. 2007; Yu et al. 2015; Yu et al. 2019). In the present study, the “semidirectional cross” method was used to generate the L. vannamei F1 full-sib family, with 148 progenies and 2 parental individuals (not just the parental genomic DNA) of the mapping family being visually identified. This method may be a quick way to generate full-sib families, which would be beneficial for genetic research on the shrimp L. vannamei.

SNP markers are ideal for the construction of genetic linkage maps (Bourgeois et al. 2013; Stölting et al. 2013; Wang et al. 2013a, 2013b; Zhang et al. 2018). SNPs are the most common type of DNA polymorphism in the genome, have a low mutation rate and high genetic stability, and are amenable to high-throughput genotyping (Berthier-Schaad et al. 2007; Shao et al. 2015; Zhang et al. 2018; Kong et al. 2019). In total, 3567 high-quality SNP markers were constructed for the linkage map in the present work. The number of SNPs identified was less than that identified in the previous study on L. vannamei by Yu et al. (2015) (6359 SNPs were selected for mapping) but more than that identified in the study on Hypophthalmichthys nobilis by Fu et al. (2016) (3121 SNPs were used for mapping). Genetic linkage maps constructed by SNP markers are important resources for various genetic studies, including comparative genomics, functional gene mapping, candidate gene positional cloning, and genome assembly (Yue 2014; Xu et al. 2014; Kujur et al. 2015; Zhang et al. 2018; Kong et al. 2019). In total, 48 LGs with a total map length of 4161.555 cM and an average interlocus length of 1.167 cM were obtained in the present study. The number of LGs was close to those obtained in previous studies; examples include 44 pseudochromosomes (Zhang et al. 2019), 44 sex-averaged LGs (Yu et al. 2015), and 45 sex-averaged LGs (Du et al. 2010). Our results might be useful for future genomic assembly research on the shrimp L. vannamei.

With the help of a high-resolution genetic map, fine mapping of QTLs is an efficient approach for identifying genetic loci and the candidate genes underlying these quantitative traits of interest (Shao et al. 2015; Wang et al. 2018). Growth is a priority trait for genetic improvement, and mapping of growth-related QTLs has been widely performed in studies on various aquatic species (Andriantahina et al. 2013, Yue 2014, Tong and Sun 2015, Wang et al. 2018). In the present work, 2 growth-related (body weight) QTLs were detected, both of which were located in LG1 of the genetic map. Several growth-related QTL studies have been previously performed on L. vannamei. For example, Andriantahina et al. (2013) used amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers to construct a genetic map, and 14 growth-related QTLs were identified (Andriantahina et al. 2013). Yu et al. (2015) detected several QTLs for body weight and body length based on a high-density linkage map (Yu et al. 2015); however, candidate gene associations with the growth trait were not identified due to limited genomic information (Yu et al. 2019). In the present study, 2 growth-related QTLs were detected in 55-day-old shrimp using L. vannamei genomic scaffolds (Zhang et al. 2019), and 5 growth-related candidate genes were detected and demonstrated to be associated with cell growth or energy metabolism. The gene “putative ATP-dependent RNA helicase DDX43” of BW-1 in this paper is thought to be required for tumor growth through the promotion of RAS protein expression (Linley et al. 2012). The Ras-related protein Rap-2a was identified as being significantly associated with the growth trait by applying GWAS in two independent populations of L. vannamei (Yu et al. 2019) and was discovered to be associated with head size regulation in catfish (Ictalurus punctatus) (Geng et al. 2016). The detection of the 5 candidate genes in this study might be relevant to the growth trait, the Ras-related proteins might be associated with the growth trait of L. vannamei at 55 days of age, and the location of the 2 growth-related QTLs (BW-1 and BW-2) in this study might be valid.

The balance of acidic-alkaline ions in water is important for aquatic crustaceans (Fehsenfeld et al. 2011; Roggatz et al. 2016; de Vries et al. 2016; Wu et al. 2017; Huang et al. 2018). The farming of L. vannamei in high-pH environments might affect the final production (Huang et al. 2018), and the high-pH tolerance of shrimp has become an important trait in the culturing industry in saline-alkali water areas. In the present study, the first genetic study on the high-pH tolerance trait for L. vannamei, 12 high-pH tolerance QTLs were discovered. With the development of genomic research on L. vannamei (Zhang et al. 2019), tag sequences of the SNP markers are now available that align with the genomic scaffolds of the shrimp; in the present study, 21 stress-related genes were summarized according to the annotation of the scaffold genes. Our previous work revealed the molecular basis of response to high-pH stress by analyzing L. vannamei transcriptome data (Huang et al. 2018); and we identified 6 candidate high-pH response genes based on RNA- and RAD-Seq results. The expression patterns of the 6 candidate genes were demonstrated to be significantly influenced by the high-pH environment, with 5 of the 6 candidate high-pH response genes showing results consistent with those of the RNA-Seq, indicating the important roles of those genes in response to high-pH stress and validating the locations of the HP QTLs for L. vannamei in this study.

In conclusion, a “semidirectional cross” method was used to generate the L. vannamei full-sib family in the present study. High-pH tolerance and growth trait-related QTLs were detected in one genetic map by ddRAD-Seq, and candidate genes associated with the high-pH tolerance and growth traits were discovered by alignment them with the L. vannamei genomic scaffolds and analysis of high-pH transcriptome data. We herein provide baseline data and the first report of shrimp breeding to induce high-pH tolerance traits. Our results will be beneficial for further MAS work and might be useful for promoting genomic research on the shrimp L. vannamei.