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      High-Density Genetic Linkage Maps Provide Novel Insights Into ZW/ZZ Sex Determination System and Growth Performance in Mud Crab ( Scylla paramamosain)

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          Mud crab, Scylla paramamosain is one of the most important crustacean species in global aquaculture. To determine the genetic basis of sex and growth-related traits in S. paramamosain, a high-density genetic linkage map with 16,701 single nucleotide polymorphisms (SNPs) was constructed using SLAF-seq and a full-sib family. The consensus map has 49 linkage groups, spanning 5,996.66 cM with an average marker-interval of 0.81 cM. A total of 516 SNP markers, including 8 female-specific SNPs segregated in two quantitative trait loci (QTLs) for phenotypic sex were located on LG32. The presence of female-specific SNP markers only on female linkage map, their segregation patterns and lower female: male recombination rate strongly suggest the conformation of a ZW/ZZ sex determination system in S. paramamosain. The QTLs of most (90%) growth-related traits were found within a small interval (25.18–33.74 cM) on LG46, highlighting the potential involvement of LG46 in growth. Four markers on LG46 were significantly associated with 10–16 growth-related traits. BW was only associated with marker 3846. Based on the annotation of transcriptome data, 11 and 2 candidate genes were identified within the QTL regions of sex and growth-related traits, respectively. The newly constructed high-density genetic linkage map with sex-specific SNPs, and the identified QTLs of sex- and growth-related traits serve as a valuable genetic resource and solid foundation for marker-assisted selection and genetic improvement of crustaceans.

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          SLAF-seq: An Efficient Method of Large-Scale De Novo SNP Discovery and Genotyping Using High-Throughput Sequencing

          Large-scale genotyping plays an important role in genetic association studies. It has provided new opportunities for gene discovery, especially when combined with high-throughput sequencing technologies. Here, we report an efficient solution for large-scale genotyping. We call it specific-locus amplified fragment sequencing (SLAF-seq). SLAF-seq technology has several distinguishing characteristics: i) deep sequencing to ensure genotyping accuracy; ii) reduced representation strategy to reduce sequencing costs; iii) pre-designed reduced representation scheme to optimize marker efficiency; and iv) double barcode system for large populations. In this study, we tested the efficiency of SLAF-seq on rice and soybean data. Both sets of results showed strong consistency between predicted and practical SLAFs and considerable genotyping accuracy. We also report the highest density genetic map yet created for any organism without a reference genome sequence, common carp in this case, using SLAF-seq data. We detected 50,530 high-quality SLAFs with 13,291 SNPs genotyped in 211 individual carp. The genetic map contained 5,885 markers with 0.68 cM intervals on average. A comparative genomics study between common carp genetic map and zebrafish genome sequence map showed high-quality SLAF-seq genotyping results. SLAF-seq provides a high-resolution strategy for large-scale genotyping and can be generally applicable to various species and populations.
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            A maximum likelihood method for estimating genome length using genetic linkage data.

            The genetic length of a genome, in units of Morgans or centimorgans, is a fundamental characteristic of an organism. We propose a maximum likelihood method for estimating this quantity from counts of recombinants and nonrecombinants between marker locus pairs studied from a backcross linkage experiment, assuming no interference and equal chromosome lengths. This method allows the calculation of the standard deviation of the estimate and a confidence interval containing the estimate. Computer simulations have been performed to evaluate and compare the accuracy of the maximum likelihood method and a previously suggested method-of-moments estimator. Specifically, we have investigated the effects of the number of meioses, the number of marker loci, and variation in the genetic lengths of individual chromosomes on the estimate. The effect of missing data, obtained when the results of two separate linkage studies with a fraction of marker loci in common are pooled, is also investigated. The maximum likelihood estimator, in contrast to the method-of-moments estimator, is relatively insensitive to violation of the assumptions made during analysis and is the method of choice. The various methods are compared by application to partial linkage data from Xiphophorus.
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              SMOOTH: a statistical method for successful removal of genotyping errors from high-density genetic linkage data.

              High-density genetic linkage maps can be used for purposes such as fine-scale targeted gene cloning and anchoring of physical maps. However, their construction is significantly complicated by even relatively small amounts of scoring errors. Currently available software is not able to solve the ordering ambiguities in marker clusters, which inhibits the application of high-density maps. A statistical method named SMOOTH was developed to remove genotyping errors from genetic linkage data during the mapping process. The program SMOOTH calculates the difference between the observed and predicted values of data points based on data points of neighbouring loci in a given marker order. Highly improbable data points are removed by the program in an iterative process with a mapping algorithm that recalculates the map after cleaning. SMOOTH has been tested with simulated data and experimental mapping data from potato. The simulations prove that this method is able to detect a high amount of scoring errors and demonstrates that the program enables mapping software to successfully construct a very accurate high-density map. In potato the application of the program resulted in a reliable placement of nearly 1,000 markers in one linkage group.

                Author and article information

                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                05 April 2019
                : 10
                1Guangdong Provincial Key Laboratory of Marine Biotechnology, Shantou University , Shantou, China
                2STU-UMT Joint Shellfish Research Laboratory, Shantou University , Shantou, China
                3Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao) , Qingdao, China
                4Institute of Tropical Aquaculture, Universiti Malaysia Terengganu , Kuala Terengganu, Malaysia
                Author notes

                Edited by: Gen Hua Yue, Temasek Life Sciences Laboratory, Singapore

                Reviewed by: Shikai Liu, Ocean University of China, China; Le Wang, Temasek Life Sciences Laboratory, Singapore

                *Correspondence: Hongyu Ma, mahy@

                These authors have contributed equally to this work

                This article was submitted to Livestock Genomics, a section of the journal Frontiers in Genetics

                Copyright © 2019 Waiho, Shi, Fazhan, Li, Zhang, Zheng, Liu, Fang, Ikhwanuddin and Ma.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 3, Tables: 6, Equations: 0, References: 88, Pages: 16, Words: 0
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