Estimating genetic parameters with molecular relatedness and pedigree reconstruction for growth traits in early mixed breeding of juvenile turbot

Song Sun Weiji Wang Yulong Hu Sheng Luan Ding Lyu Jie Kong

Song Sun, Weiji Wang, Yulong Hu, Sheng Luan, Ding Lyu, Jie Kong. Estimating genetic parameters with molecular relatedness and pedigree reconstruction for growth traits in early mixed breeding of juvenile turbot[J]. Acta Oceanologica Sinica, 2021, 40(9): 66-73. doi: 10.1007/s13131-021-1799-z
Citation: Song Sun, Weiji Wang, Yulong Hu, Sheng Luan, Ding Lyu, Jie Kong. Estimating genetic parameters with molecular relatedness and pedigree reconstruction for growth traits in early mixed breeding of juvenile turbot[J]. Acta Oceanologica Sinica, 2021, 40(9): 66-73. doi: 10.1007/s13131-021-1799-z

doi: 10.1007/s13131-021-1799-z

Estimating genetic parameters with molecular relatedness and pedigree reconstruction for growth traits in early mixed breeding of juvenile turbot

Funds: The Agriculture Variety Improvement Project of Shandong Province under contract No. 2019LZGC013.
More Information
    • 关键词:
    •  / 
    •  / 
    •  / 
    •  
  • Figure  1.  Number of offspring in each family.

    Figure  2.  Relationship between estimated breeding values (EBVs) from molecular relatedness (MR) and pedigree reconstruction (PR).

    Figure  3.  Relationship between MR-based observed and predicted values.

    Figure  4.  Relationship between the observed and predicted values based on PR.

    Figure  5.  Distribution of Person correlation coefficients for 500 times cross validations. PR, pedigree reconstruction; MR, molecular relatedness; BW, body weight; BL, body length.

    Table  1.   Characteristics of SSR primers (Ruan et al., 2010)

    LociPrimer sequence (5′-3′)RepeatsSize/bpFluorescent dyeTemperature/°C
    YSKr61F: TCAGTGGGCAGTGAGGTG
    R: AAGTCAGAGAAACATCCAGA
    GTCT164–173HEX62.0
    YSKr71F: TGGGATACATACACATTC
    R: AGTGAGTTGACAGACAGAG
    ACGC172–178TAMRA53.4
    YSKr72F: CCAGACAGATAACTACACA
    R: GTAAGGCTCGTTAGTCAC
    ACGC132–168FAM58.0
    YSKr85F: TACTTACACTGTGTATGTGC
    R: GAGAACCGAAGAAATGAGA
    GTGC252–290ROX56.0
    YSKr92F: CCACGCTGTGTATTTCCTCAT
    R: GGTCAACATTCAAACCCAACT
    GTGC188–208HEX60.0
    YSKr101F: CGGATAGTTAGTACCTCAT
    R: GAAAACTGAAGCTGAATG
    ACGC112–133TAMRA56.0
    YSKr111F: AACTGGGACTGGAGTGGAC
    R: CTCATTAGAGCCGCTGTAT
    TGCG340–366FAM62.0
    YSKr119F: GCTCTTCCAAGTGCCA
    R: TGTAGTGTACCAAATGC
    ACGC242–271ROX54.6
    YSKr121F: CAGAGGACAGCGACGAAGAC
    R: AGCATTGCATTGGGTTGAGT
    ACGC183–188HEX62.0
    YSKr124F: CAGCCGTTCTGACCTCGTAG
    R: ACCCTCCACTGCTTGTCCTTG
    GTGC178–187TAMRA62.0
    YSKr125F: ACTTATTTGCCTATGGAGAG
    R: TTCATTCACATCACTGGTC
    CGTG138–151FAM56.0
    YSKr6F: CTAACAAACAACGCAGTCG
    R: AGAAACAGGGTAGCATCAC
    CTT299–313ROX62.0
    YSKr141F: TTCTGCTCCCTTCTTCGTGT
    R: TCGGTGCTTGTGGAAATCG
    GCG171–189FAM61.0
    YSKr169F: TAATCTCCTGTTGCCTAATG
    R: AACGGACGAGTTCGGTGC
    AAC179–185ROX62.0
    YSKr170F: GCTACAGTGATGTCGCA
    R: ATTTATCCAGTGTTTCG
    AAC276–304HEX54.6
    YSKr173F: CTGGATTTGCCACGTCAGTAC
    R: TCTCGCTAACGCTTCACCTC
    AAG323–474TAMRA59.0
    下载: 导出CSV

    Table  2.   Genetic diversity information of 16 SSR loci

    LociKnHObsHExpPICNE-1PNE-2PNE-PPNE-INE-SIHWF (Null)
    YSKr6139090.3590.4980.4370.8760.7460.6060.3130.580***0.175 0
    YSKr7197150.8150.7620.7270.6250.4470.2580.0920.392***0.042 9
    YSKr72109090.7920.8310.8130.4950.3250.1460.0470.346***0.026 3
    YSKr85149040.8850.8480.8310.4620.2990.1280.0400.336***0.021 4
    YSKr9299070.8820.8010.7740.5660.3880.2050.0670.366***–0.050 0
    YSKr101109010.6850.7080.6740.6830.5020.3020.1190.426***0.018 5
    YSKr111149010.9210.8610.8480.4280.2710.1060.0330.328***0.035 7
    YSKr119118990.8710.8430.8230.4830.3150.1440.0440.340***0.014 7
    YSKr12189060.9250.8240.8010.5230.3490.1720.0540.352***0.060 3
    YSKr124109050.7830.7530.7310.6140.4280.2230.0830.394***0.023 1
    YSKr12569090.6740.7060.6540.7150.5460.3690.1390.432***0.029 9
    YSKr669030.8580.7620.7200.6460.4680.2900.0980.394***0.061 9
    YSKr14169020.6270.6190.5710.7880.6220.4420.1930.489***0.008 8
    YSKr16949010.6150.6270.5540.7930.6490.4890.2120.490***0.013 2
    YSKr170138880.5550.8260.8050.5100.3380.1580.0510.350***0.197 1
    YSKr173258670.7800.9020.8940.3310.1980.0630.0180.304***0.072 8
    Note: Number of individuals, 931; number of loci, 16; mean number of alleles per locus, 9.875 0; mean proportion of loci typed, 0.955 0; mean expected heterozygosity, 0.760 8; mean polymorphic information content (PIC), 0.728 5; combined non-exclusion probability (first parent), 0.000 157; combined non-exclusion probability (second parent), 0.000 000 55; combined non-exclusion probability (parent pair), 2.396×10–11; combined non-exclusion probability (identity), 1.366×10–18; combined non-exclusion probability (sib identity), 0.000 000 28. K, number of alleles at the locus; n, number of individuals typed at the locus; HObs, observed heterozygosity; HExp, expected heterozygosity; PIC, polymorphic information content; NE-1P, average non-exclusion probability for one candidate parent; NE-2P, average non-exclusion probability for one candidate parent given the genotype of a known parent of the opposite sex; NE-PP, average non-exclusion probability for a candidate parent pair; NE-I, average non-exclusion probability for identity of two unrelated individuals; NE-SI, average non-exclusion probability for identity of two siblings; HW, significance of deviation from Hardy-Weinberg equilibrium; F (Null), estimated null allele frequency. ***, significant at the 0.1% level. The significance level includes a Bonferroni correction if the Bonferroni correction option was selected.
    下载: 导出CSV

    Table  3.   Variance component of body weight

    $ {\sigma }_{a}^{2} $$ {\sigma }_{d}^{2} $$ {\sigma }_{e}^{2} $$ {\sigma }_{p}^{2} $$ {h}^{2} $±SE
    PR0.326±0.1720.705±0.3030.488±0.1241.519±0.2980.214±0.121
    MR0.139±0.0360.631±0.2510.608±0.0331.379±0.2530.101±0.031
    Note: $ {\sigma }_{a}^{2} $, additive genetic variance (g2); $ {\sigma }_{d}^{2} $, maternal common environmental variance (g2); $ {\sigma }_{e}^{2} $, residual variance (g2); $ {\sigma }_{p}^{2} $, phenotypic variance (g2); h2, heritability; SE, stardard error.
    下载: 导出CSV

    Table  4.   Variance component of body length

    $ {\sigma }_{a}^{2} $$ {\sigma }_{d}^{2} $$ {\sigma }_{e}^{2} $$ {\sigma }_{p}^{2} $$ {h}^{2} $±SE
    PR0.038±0.0460.126±0.0520.162±0.0340.327±0.0530.117±0.141
    MR0.031±0.0010.106±0.0450.169±0.0010.306±0.0450.102±0.034
    Note: $ {\sigma }_{a}^{2} $, additive genetic variance (cm2); $ {\sigma }_{d}^{2} $, maternal common environmental variance (cm2); $ {\sigma }_{e}^{2} $, residual variance (cm2); $ {\sigma }_{p}^{2} $, phenotypic variance (cm2); h2, heritability; SE, stardard error.
    下载: 导出CSV

    Table  5.   Genetic correlation and phenotypic correlation of body weight (BW) and body length (BL) based on pedigree reconstruction (PR) and molecular relatedness (MR)

    Correlation coefficientGenetic correlationPhenotypic correlation
    MR0.8560.668
    PR0.7230.624
    下载: 导出CSV

    Table  6.   Results of cross validation between observations and predicted values

    Pearson correlation
    coefficient of
    body weight
    Pearson correlation
    coefficient of
    body length
    Molecular relatedness0.717±0.0450.629±0.058
    Pedigree reconstruction0.692±0.0520.615±0.060
    下载: 导出CSV
  • [1] Bink M C A M, Anderson A D, van de Weg W E, et al. 2008. Comparison of marker-based pairwise relatedness estimators on a pedigreed plant population. Theoretical and Applied Genetics, 117(6): 843–855. doi: 10.1007/s00122-008-0824-1
    [2] Blanquer A, Alayse J P, Berrada-Rkhami O, et al. 1992. Allozyme variation in turbot (Psetta maxima) and brill (Scophthalmus rhombus) (Osteichthyes, Pleuronectoformes, Scophthalmidae) throughout their range in Europe. Journal of Fish Biology, 41(5): 725–736. doi: 10.1111/j.1095-8649.1992.tb02702.x
    [3] Blonk R J W, Komen H, Kamstra A, et al. 2010. Estimating breeding values with molecular relatedness and reconstructed pedigrees in natural mating populations of common sole, Solea solea. Genetics, 184(1): 213–219. doi: 10.1534/genetics.109.110536
    [4] Cardellino R, Rovira J. 1987. Mejoramiento Genético Animal. Buenos Aires, Argentina: Hemisferio Sur
    [5] Falconer D S, Mackay T F C. 1996. Introduction to Quantitative Genetics. 4th ed. Longman, Harlow: Benjamin Cummings
    [6] Gall G A E, Bakar Y, Famula T. 1993. Estimating genetic change from selection. Aquaculture, 111(1–4): 75–88. doi: 10.1016/0044-8486(93)90026-U
    [7] Gheyas A A, Woolliams J A, Taggart J B, et al. 2009. Heritability estimation of silver carp (Hypophthalmichthys molitrix) harvest traits using microsatellite based parentage assignment. Aquaculture, 294(3–4): 187–193. doi: 10.1016/j.aquaculture.2009.06.013
    [8] Gilmour A R, Gogel B J, Cullis B R, et al. 2009. ASReml User Guide Release 3.0. Hemel Hempstead, UK: VSN International Ltd
    [9] Gjerde B, Korsvoll S A. 1999. Realized Selection Differentials for Growth Rate and Early Sexual Maturity in Atlantic Salmon. Oostende, Belgium: Aquaculture Europe, 99: 73–74
    [10] Guan Jiantao, Wang Weiji, Luan Sheng, et al. 2016. Estimation of genetic parameters for early growth trait of turbot (Scophthalmus maximus L.) using molecular relatedness. Aquaculture Research, 47(7): 2205–2214. doi: 10.1111/are.12673
    [11] Hu Yulong, Guan Jiantao, Ma Yu, et al. 2016. An estimation of genetic parameters of growth traits in juvenile turbot (Scophthalmus maximus L.) using parental molecular relatedness. Acta Oceanologica Sinica, 35(2): 126–130. doi: 10.1007/s13131-015-0643-6
    [12] Jones O R, Wang Jinliang. 2010. COLONY: a program for parentage and sibship inference from multilocus genotype data. Molecular Ecology Resources, 10(3): 551–555. doi: 10.1111/j.1755-0998.2009.02787.x
    [13] Kalinowski S T, Taper M L, Marshall T C. 2007. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology, 16(5): 1099–1106. doi: 10.1111/j.1365-294X.2007.03089.x
    [14] Karaket T, Poompuang S. 2012. CERVUS vs. COLONY for successful parentage and sibship determinations in freshwater prawn Macrobrachium rosenbergii de Man. Aquaculture, 324–325: 307–311. doi: 10.1016/j.aquaculture.2011.10.045
    [15] Lei Jilin. 2002. Problem and suggestion of introducting species for marine culture. China Fisheries, (2): 63–65
    [16] Lei Jilin. 2006. Outlook of the marine fish culture industry in China. Marine Fisheries Research (in Chinese), 27(2): 1–9
    [17] Lei Jilin, Liu Xinfu. 1995. A primary study on culture of turbot, Scophthalmus maeoticus L. Modern Fisheries Information (in Chinese), 10(11): 1–3
    [18] Lei Jilin, Men Qiang, Wang Yingeng, et al. 2002. Review of “Green House+Deep Well Seawater” industrialized culture pattern of turbot (Scophthalmus maximus). Marine Fisheries Research (in Chinese), 23(4): 1–7
    [19] Li Dongyu. 2016. Genetic parameter estimation for growth and meat rate traits of Pacific white shrimp (Litopenaeus vannamei) in low temperature condition by microsatellite markers (in Chinese)[dissertation]. Nanjing: Nanjing Agricultural University
    [20] Liu Baosuo, Zhang Tianshi, Kong Jie, et al. 2011. Estimation of genetic parameters for growth and upper thermal tolerance traits in turbot Scophthalmus maximus. Journal of Fisheries of China (in Chinese), 35(11): 1601–1606
    [21] Luan Sheng, Kong Jie, Wang Qingyin. 2008. Methods and application of aquatic animal breeding value estimation: a review. Marine Fisheries Research (in Chinese), 29(3): 101–107
    [22] Lucas T, Macbeth M, Degnan S M, et al. 2006. Heritability estimates for growth in the tropical abalone Haliotis asinina using microsatellites to assign parentage. Aquaculture, 259(1–4): 146–152. doi: 10.1016/j.aquaculture.2006.05.039
    [23] Lynch M, Walsh B. 1998. Genetics and Analysis of Quantitative Traits. Sunderland: Sinauer Associates, Inc: 360–361
    [24] Lyu Ding, Wang Weiji, Luan Sheng, et al. 2017. Estimating genetic parameters for growth traits with molecular relatedness in turbot (Scophthalmus maximus, Linnaeus). Aquaculture, 468: 149–155. doi: 10.1016/j.aquaculture.2016.09.049
    [25] Ma Aijun, Wang Xinan, Lei Jilin. 2009. Genetic parameterization for turbot Scophthalmus maximus: implication to breeding strategy. Oceanologia et Limnologia Sinica (in Chinese), 40(2): 187–194
    [26] Mas-Muñoz J, Blonk R, Schrama J W, et al. 2013. Genotype by environment interaction for growth of sole (Solea solea) reared in an intensive aquaculture system and in a semi-natural environment. Aquaculture, 410–411: 230–235. doi: 10.1016/j.aquaculture.2013.06.012
    [27] Men Qiang. 2002. Overview on turbot, Scophthalmus maximus (Linnaeus) introduced to China for ten years. Modern Fisheries Information (in Chinese), 17(9): 14–17
    [28] Nguyen T T T, Hayes B J, Ingram B A. 2014. Genetic parameters and response to selection in blue mussel (Mytilus galloprovincialis) using a SNP-based pedigree. Aquaculture, 420–421: 295–301. doi: 10.1016/j.aquaculture.2013.11.021
    [29] R Core Team. 2013. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/ [2018-01-07]
    [30] Ruan Xiaohong, Wang Weiji, Kong Jie, et al. 2010. Genetic linkage mapping of turbot (Scophthalmus maximus L.) using microsatellite markers and its application in QTL analysis. Aquaculture, 308: 89–100. doi: 10.1016/j.aquaculture.2010.08.010
    [31] Sambrook J, Fristch E F, Maniatis T. 1989. Molecular Cloning: A Laboratory Manual. 2nd ed. New York: Cold Spring Harbor Laboratory
    [32] Shen Xueyan, Gong Qingli, Lei Jinlin, et al. 2004. Population genetic structure analysis of the imported turbot seedlings Scophthalmus maximus L. using RAPD and microsatellite technique. Oceanologia et Limnologia Sinica (in Chinese), 35(4): 332–341
    [33] Shen Xueyan, Kong Jie, Gong Qingli, et al. 2005. The investigation and exploition of turbot (Scophthalmus maximus L.) genetic resources. Marine Fisheries Research (in Chinese), 26(6): 94–100
    [34] Vandeputte M, Kocour M, Mauger S, et al. 2004. Heritability estimates for growth-related traits using microsatellite parentage assignment in juvenile common carp (Cyprinus carpio L.). Aquaculture, 235(1–4): 223–236. doi: 10.1016/j.aquaculture.2003.12.019
    [35] Wang Gang. 2010. Turbot industry transformation period has arrived. Ocean And Fishery (in Chinese), (1): 14–16, 20
    [36] Wang Jinliang. 2007. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genetics Research, 89(3): 135–153. doi: 10.1017/S0016672307008798
    [37] Wang Jinliang. 2011. COANCESTRY: a program for simulating, estimating and analysing relatedness and inbreeding coefficients. Molecular Ecology Resources, 11(1): 141–145. doi: 10.1111/j.1755-0998.2010.02885.x
    [38] Wright S. 1922. Coefficients of inbreeding and relationship. The American Naturalist, 56(645): 330–338. doi: 10.1086/279872
  • 加载中
图(5) / 表(6)
计量
  • 文章访问数:  448
  • HTML全文浏览量:  176
  • PDF下载量:  13
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-09-30
  • 录用日期:  2020-12-29
  • 网络出版日期:  2021-06-25
  • 刊出日期:  2021-09-30

目录

    /

    返回文章
    返回