A Preliminary Genetic Linkage Map of Sinonovacula constricta(Lamarck, 1818) Based on Microsatellites Derived fromRAD Sequencing

WU Xueping, FENG Yanwei, JIANG Hailin, LIU Xiangquan, *,and PAN Ying



A Preliminary Genetic Linkage Map of(Lamarck, 1818) Based on Microsatellites Derived fromRAD Sequencing

WU Xueping1), 2), 3), #, FENG Yanwei1), #, JIANG Hailin1), LIU Xiangquan1), *,and PAN Ying2), *

1),,264006,2),,530004,3),,530008,

isone of the important economic aquaculture species in China. In this study, we constructed genetic linkage maps ofbased on 300 microsatellite markers derived from RAD-seq using an F1 full-sib family. The female map contained 204 microsatellites assigned to 22 linkage groups, which covered 1529.5cM with an average interval of 10.3cM. The male consisted of 187 microsatellites in 19 linkage groups corresponding to the haploid chromosome number (n=19), which spanned 1429.3cM with an average interval of 8.7cM. The genome coverage was approximately 83.5% and 81.4%, respectively. An integrated map was constructed according to the common markers in parental linkage groups, which had a total length of 1683.8cM with an average interval of 7.3cM. The genome coverage of the integrated map was approximately 86.3%. The genetic linkage map would form the foundation for further studies on the quantitative trait loci (QTL), as well as accelerating the breeding process of this species.

linkage maps;; microsatellite marker; RAD sequencing

1 Introduction

Razor clam,, belonging to Mollusca (Bivalvia: Veneroida: Solecurtidae), is widely distributed in the intertidal coastal and estuarine waters of China, Japan, Korea and Vietnam (Xu and Zhang, 2008). It is one of the four major commercial aquatic clams in China.As well as,and,has been farmed for over 500 years in Zhejiang and Fujian Provinces due to its short culture cycle, high nutritive value and delicious taste (Xie, 2003). In recent years, culturinghas expanded greatly, and the output has reached 793708 tons (FAO, 2016). However, there are many challenges in the artificial culture process, such as low growth rate, disease and low survival (Feng., 2010). To counter these challenges, conventional selections have been performed (Du., 2016; Li., 2016). However, directional selections that depend onphenotype are time-consuming, labor-intensive and ex- pensive (Guo., 2012).

Generally, the economic traits of a species, such as rapid growth and disease resistance, are determined by multiple loci and are quantitatively inherited. Molecular markers provide a powerful tool for identification of quantitative trait loci (QTL) in a selective program. In contrast to traditional selection, marker-assisted selection (MAS) and phenotypic selection in breeding strategies can greatly accelerate breeding efforts. A saturated gene- tic linkage map is regarded as an essential step towards establishing MAS programs and allowing QTL detection. Genetic maps have been constructed in many important aquaculture species,..,(Zhan.,2012),(Liu., 2013),(Luzón., 2015), and(Qiu., 2016).

Construction of a fine genetic map requires many mo- lecular markers. Microsatellites (or simple sequence re- peats, SSR) are easily transferrable among laboratories and are ideal co-dominant marker systems (Song., 2012; Chu., 2014; Tsigenopoulos., 2014). The development of microsatellite markers using traditional methods requires significant efforts. In contrast, restric- tion site-associated DNA sequencing (RAD-seq), a next- generation sequencing (NGS) technology, provides a more cost-effective and higher throughput method to generate useful markers and has been widely used to study non- model organisms (Baird., 2008; Gonen., 2014).

In this study, we constructed genetic linkage maps ofbased on microsatellite markers derived from RAD-seq, aiming at providing the basis for future QTL- mapping and MAS efforts.

2 Materials and Methods

2.1 Mapping Family and DNA Extraction

According to the pseudo-testcross mapping strategy, an F1 full-sib family was established by single-pair mating a wild female and a wild male in October 2012, which were both captured from Rizhao, Shandong Province, China. After 10-day rearing, 150 early umbo-veliger larvae were randomly selected and kept in 100% ethanol (changed three times). Adductor muscle tissues of the parents were also sampled. All samples were frozen at −20℃ until DNA extraction.

Parental DNA was extracted with the standard phenol- chloroform method (Sambrook and Russell, 2000). Larval DNA was prepared using the Chelex-100 extraction tech- nique as previously described (Li and Kijima, 2006), fol- lowed by amplifying genomic DNA with REPLI-g®Mini Kit(QIAGEN, Duesseldorf, Germany).

2.2 RAD Library Preparation and Sequencing

DNA sample was obtained from the adductor muscle of a living(named SC15, sampled from Yantai, Shandong Province, China), using the same method as the parental DNA. Genomic DNA digested byI and an adapter (P1) was ligated to the fragment’s compatible ends. This adapter contained forward amplification and Illumina sequencing primer sites, as well as a nucleotide barcode for sample identification. The adapter-ligated fragments were subsequently pooled, randomly sheared and ligated to a second adapter (P2) that has divergent ends. Finally, the fragments of 200bp to 400bp were col- lected for libraries construction. After quality assessment, the libraries were sequenced on an Illumina HiSeq2500platform and 125bp paired-end reads were generated.

2.3 RAD-seq Data Assembly and MicrosatelliteDevelopment

Prior to the assembly, stringent filtering was conducted, and only high-quality reads were sent to the VelvetOpti- miser assembler with parameter settings: -s 23 -e 31 -x 4 (Daniel., 2008). The contigs assembled were as ref- erence genome for obtaining double-end SSR (100bp) fragments. SSR primers were designed using PRIMER 3.0 program (http://www.premierbiosoft.com/).

2.4 Microsatellite Analysis

Markers were genotyped in the parents and six progeny to look for polymorphism. PCR was amplified in 10µL reactions containing 0.25UDNA polymerase (Ta- kara, Dalian, China), 1× PCR buffer (Mg2+), 0.2mmolL−1dNTP mix, 0.5µmolL−1of each primer set. PCR program (Li., 2016) performed as the following thermal cy- cles: 3min at 94℃; seven cycles of 1min at 94℃, 30s at the optimal annealing temperature, and 30s at 72℃; 33 cycles of 30s at 94℃, 30s at the specific annealing tem- perature, 30s at 72℃, and a final extension of 5min at 72℃. The PCR products were evaluated by electrophore- sis on 8% non-denaturing polyacrylamide gel and visual- ized by silver staining.

Polymorphic microsatellites were used in subsequent genotyping of parents and 150 offspring to construct the linkage maps. Forward primer was labeled with either FAM or HEX fluorescence dye in Sangon BioTechno- logies, Shaihai, China. PCR reactions were carried out in 25µL volumes: 1μmolL−1of each primer set, 12.5µL 2× Easy Taq mix (Transgen, Beijing, China), and about 50ng template DNA. The PCR conditions were as follows: an initial denaturation for 3min at 94℃, followed by 35 cycles 45s at 94℃, 45s at primer-specific annealing temperature, 3min at 72℃, with a final extension of 5min at 72℃. Product of one locus was added with 0.1µL GeneScanTM-500 LIZTMsize standard (ABI, USA) and 9.9µL Hi DiTMformammide, and subjected to genotyping on an ABI 3700XL. Data collection was performed on GeneMapper ver. 3.7 software (Applied Biosystems, Fors- ter City, CA, USA).

2.5 Segregation and Map Construction

A linkage map was constructed using JoinMap 4.0 soft- ware (van Ooijen, 2006) under cross-pollinating (CP) coding scheme, which handles F1 outbred population data containing genotype configurations with unknown lin- kage phase. Basing on JoinMap software, segregation distortion of markers was detected by a chi-square (χ2) with a level of<0.01. Corrections of the significance level for multiple tests were performed following the se- quential Bonferroni correction. In order to avoid false linkages, markers deviating from Mendelian expectations after Bonferroni correction were excluded from the link- age analysis. Linkage groups were determined at an in- dependence LOD (logarithm of odds) threshold of 4.0 and a recombination threshold of 0.40. Recombination fre- quencies were converted into genetic distances using the Kosambi mapping function (Kosambi, 1944). Once the female and male maps were established, a consensus map was obtained using the ‘Map Integration’ function. The linkage map was drawn by MapChart 2.3 software (Voor- rips, 2002).

2.6 Genome Size and Coverage

The observed genome length (Go) was estimated by adding the length of each linkage group. The expected genome length (Ge) was calculated using two methods: (i)e1=of+2(Fishmen., 2001), whereofis the length of each linkage group excluding triplets and dou- blets, whilerepresents the average spacing between markers, denoted by dividing the total observed length of all linkage groups by the number of intervals (number of markers minus the number of linkage groups); (ii)e2was calculated by multiplying the length of each linkage group by a factor of (+1)/(−1), whereis the num- ber of markers of each linkage group (Chakravarti., 1991). The average ofe1ande2was used ase. The map coverage was calculated byo/e.

3 Results

3.1 RAD Sequencing Analysis

After stringent quality assessment and data filtering, a total of 48810439 clean reads were obtained, which were assembled into 498746 contigs. Totally 5700 pairs of mi- crosatellite primers were designed for subsequent analysis (Table 1).

Table 1 Summary of sequencing data

3.2 Markers Analysis and Segregation Distortion

A total of 656 primer pairs were selected to test ampli- fication conditions and polymorphism. The result showed that 300 markers (45.7%) exhibited heterozygosity in at least one parent. Of these 300 markers, 127 markers were polymorphic in both parents and could be used to integrate the map, 99 markers were polymorphic only in female parent, and 74 markers were polymorphic only in male parent. Thus, 226 markers were used to construct the female map, and 201 markers were used to construct the male map.

Chi-square analysis indicated that 19 markers deviated from Mendelian expectation at<0.01 after Bonferroni correction among the 226 female markers. For the 201 male markers, 11 markers exhibited significant deviation from Mendelian expectation.

3.3 Linkage Analysis and Map Coverage

The final length of the female map contained 22 linkage groups with 204 markers, covering 1529.5cM (Fig.1 and Table 2). Lengths of linkage groups varied between 17.8cM and 138.5cM. The resolution ranged from 4.8cM to 30.7cM, with an average of 10.3cM. The marker den- sity was 2-26 per group. The expected genome length (Ge) was 1831.2cM. The genome coverage of the female map was approximately 83.5% (Table 3).

Fig.1(a)Genetic linkage maps ofFemale map (F) and male map (M) are shown on the left and right, respectively. The integrated genetic map is shown on the center. Map distance is given in cM at the left of each linkage group (LG), while marker names are given on the right. The female map was matched to the male map using common markers (F1/M1, F2/M3).

Fig.1(b)Same as Fig.1(a) but the female map using common markers (F3/M13, F4/M8).

Fig.1 (c)Same as Fig.1(a) but the female map using common markers (F5/M9, F6/M12).

Fig.1(d)Same as Fig.1(a) but the female map using common markers (F7/M14, F8/M17).

Fig.1(e)Same as Fig.1(a) but the female map using common markers (F9/M5, F10/M7).

Fig.1(f)Same as Fig.1(a) but the female map using common markers (F11/M10, F12/M4).

Fig.1(g)Same as Fig.1(a) but the female map using common markers (F15/M2, F14/M15).

Fig.1(h)Same as Fig.1(a) but the female map using common markers (F18/M6, F19/M11).

Fig.1(i)Same as Fig.1(a) but the female map using common markers (F20/M16, F21/M19).

Fig.1(j)Same as Fig.1(a) but the female map using common markers (F22/M18, F13, F16, F17).

Table 2 Information of the genetic linkage maps of

Table 3 Summary of the genetic linkage maps of S. constricta

The male map consisted of 19 linkage groups with 187 markers, spanning a total genetic distance of 1429.3cM (Fig.1 and Table 2). The length of each linkage group ranged from 27.0cM to 152.1cM and contained 4-20 markers. The interval of markers ranged from 7.4cM to 14.9cM, with a mean of 8.7cM. The expected genome length was 1756.3cM. The genome coverage was about 81.4% (Table 3).

The female map was matched to the male map using common markers (F1/M1, F2/M3, F3/M13, F4/M8, F5/ M9, F6/M12, F7/M14, F8/M17, F9/M5, F10/M7, F11/M10, F12/M4, F15/M2, F14/M15, F18/M6, F19/M11, F20/ M16, F21/M19, and F22/M18). The integrated map mea- sured 1683.8cM with a total of 270 markers distributed over 19 linkage groups. The length of each group ranged from 25.6cM to 186.0cM. The minimum and maximum intervals were 4.6cM and 11.4cM, respectively. The expected genome length was 1950.2cM. The genome coverage was approximately 86.3%, higher than those of the female and male maps. Linkage group 1 (LG1) included the most markers (Fig.1, Table 2 and Table 3).

3.4 Differences of Recombination Between Sexes

Compared with the male map (1429.3cM), the female map (1529.5cM) showed significantly higher recombi- nation ratio. For the integrated map, the length of some groups (LG2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14) were longer in the female map than in the male map while that of other groups were longer in the male map than in the female map (LG1, 11, 13, 15, 16, 17, 18, 19). The total length of the intervals between common markers for the female and male map was 197.7cM and 165.5cM, respectively. The female-to-male recombination ratio was 1.19:1.

4 Discussion

4.1 RAD-seq Technology

Compared to the whole-genome shotgun (WGS) sequencing, RAD-seq technology is a powerful method to analyze genomic resources in non-model organisms lack- ing a reference genome, and has been successfully used for SSR or SNP discovery and genotyping. This technique provides a great opportunity to overcoming the limita- tions of traditional methods (da Fonseca., 2016). In this study, a total of 498746 contigs were assembled and 5700 pairs of primers were obtained, much more than those reported in previous studies (Niu., 2008; Jiang., 2010; Ma., 2015; Wu., 2015), which demonstrated that RAD-seq is a cost-effective and high- throughput method for the discovery of microsatellites in.

4.2 Segregation Distortion

The observed distortion rate was 8.5% for the female parent and 5.6% for the male parent. Compared to other aquatic animals, this percentage was 18.8% less than EST-SSRs and SSRs in the Pacific oyster (Guo., 2012), 19.3% of SSRs in the scallop(Li., 2012), 16.8% (female) and 15.3% (male) of SSRs in the Chinese mitten crab(Qiu., 2016). It was similar to 6.0% of SSRs in tilapia (Lee., 2005). The causes of this apparent variation of distortion rate have not been determined. Preferential fertilization (Lyttle, 1991), high genetic load, zygotic se- lection (Liu., 2010), scoring errors, amplification of the same size from different genomic regions (Fairs., 1998), and non-random sampling or sampling in finite populations (Rick, 1969) may contribute to the segrega- tion distortion.

4.3 Linkage Analysis

In this study, we constructed the first genetic linkage map using 300 SSR markers and genotyped 150 F1 offspring from a full-sib family. After excluding the sig- nificant segregation distortion (<0.01), the linkage map was constructed at a threshold of a log of odds (LOD) score of 4.0. The remaining 204 markers were assigned to 22 linkage groups in the female map, and 187 markers were arranged into 19 linkage groups in the male map. The integrated map contained 19 linkage groups. The number of linkage groups in male map and integrated map were consistent with the haploid chromosome num- ber (n=19) of(Wang., 1998). The re- sults indicated that the selected markers did not distribute evenly and more markers are required to adequately cover the linkage groups and obtain full genome coverage.

The observed map length was 1529.5cM and the ge- nome coverage was 83.5% for female, and 1429.3cM and 81.4%, respectively for male. The integrated map covered 1683.8cM with map coverage being 86.3%. These values were lower than those of other mollusks linkage maps like those for(Bai., 2015) and(Shi., 2014), but larger than(Zhan., 2009) and(Petersen., 2012)This may be due to variable marker densities However, the female map had three more linkage groups than the male map and loca- tions of loci between some linkage groups (for example, F1 and M1). Additionally, some female linkage groups, F13, F16 and F17 were not integrated. These discrepan- cies might be due to low density and small sample size, different markers between the female map (204) and the male map (187), and uneven distribution of markers. A similar phenomenon was reported in other species, such as Pacific oyster(Zhong., 2014), large yellow croaker(Ye., 2014), half smooth tongue sole(Jiang., 2013), and turbot(Ruan., 2010).

4.4 Recombination Rate Between Sexes

The recombination rate in female is commonly higher than in male in many species, such as(1.67:1, Sahoo., 2015), red drum (1.14:1, Hollenbeck., 2015), black tiger shrimp (1.60:1, Baranski., 2014), and grass carp (1:(2.0–2.03), Xia., 2010). However, in our study, some male linkage groups showed a higher recombination rate than female. There is dis- agreement among scholars about the explanation of the differences in the recombination rate. One hypothesis suggested that sex differences might account for more suppressed recombination rates in the heterogametic sex than homogametic sex (Haldane, 1922; Huxley, 1928; Singer., 2002), but Cui. (2015) and Liu. (2013) proposed that sexual environment in embryologi- cal stage plays a more important role in determination of recombination rates than genetic sex. Many other factors, including sexual selection (Triver, 1988), meiosis (Lin- dahal, 1991), and chromosome structure (Lynn., 2004), might explain recombination rates. Further inves- tigation is needed to understand the mechanism causing sex differences in recombination rate.

5 Conclusions

In this study, we developed a large number of microsatellites with RAD-seq technology and constructed genetic linkage maps ofusing 300 polymorphic microsatellite markers. The maps ofcan provide the framework to identify QTL and facilitate future selective breeding programs in this important resource.

Acknowledgements

The study was supported by the grants from the Natural Science Foundation of Shandong Province (No. ZR2012 CM037), the Shandong Provincial Agriculture Thorough- bred Project, and the Innovation Project of Guangxi Graduate Education (No. YCBZ2015007).

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(Edited by Qiu Yantao)

(Received May 4, 2017; revised July 11, 2017; accepted April 11, 2018)

© Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2018

# These authors contributed equally to this study.

E-mail: lxq6808@163.comE-mail: yingpan@gxu.edu.cn