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Novel microsatellite markers for the oriental fruit moth Grapholita molesta (Lepidoptera: Tortricidae) and effects of null alleles on population genetics analyses

Published online by Cambridge University Press:  07 November 2016

W. Song
Affiliation:
Institute of Plant and Environmental Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China College of Life Sciences, Hebei Normal University, Shijiazhuang 071000, China
L.-J. Cao
Affiliation:
Institute of Plant and Environmental Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Y.-Z. Wang
Affiliation:
Institute of Plant and Environmental Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China Key Laboratory of Forest Disaster Warning and Control of Yunnan Province, College of Forestry, Southwest Forestry University, Kunming 650224, China
B.-Y. Li
Affiliation:
Institute of Plant and Environmental Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China Key Laboratory of Forest Disaster Warning and Control of Yunnan Province, College of Forestry, Southwest Forestry University, Kunming 650224, China
S.-J. Wei*
Affiliation:
Institute of Plant and Environmental Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
*
*Author for correspondence Phone: +86 010 -51503439 Fax: +86 010-51503899 E-mail: shujun268@163.com

Abstract

The oriental fruit moth (OFM) Grapholita molesta (Lepidoptera: Tortricidae) is an important economic pest of stone and pome fruits worldwide. We sequenced the OFM genome using next-generation sequencing and characterized the microsatellite distribution. In total, 56,674 microsatellites were identified, with 11,584 loci suitable for primer design. Twenty-seven polymorphic microsatellites, including 24 loci with trinucleotide repeat and three with pentanucleotide repeat, were validated in 95 individuals from four natural populations. The allele numbers ranged from 4 to 40, with an average value of 13.7 per locus. A high frequency of null alleles was observed in most loci developed for the OFM. Three marker panels, all of the loci, nine loci with the lowest null allele frequencies, and nine loci with the highest null allele frequencies, were established for population genetics analyses. The null allele influenced estimations of genetic diversity parameters but not the OFM's genetic structure. Both a STRUCTURE analysis and a discriminant analysis of principal components, using the three marker panels, divided the four natural populations into three groups. However, more individuals were incorrectly assigned by the STRUCTURE analysis when the marker panel with the highest null allele frequency was used compared with the other two panels. Our study provides empirical research on the effects of null alleles on population genetics analyses. The microsatellites developed will be valuable markers for genetic studies of the OFM.

Type
Research Papers
Copyright
Copyright © Cambridge University Press 2016 

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References

A'Hara, S. & Cottrell, J. (2013) Development and characterisation of ten polymorphic microsatellite markers for the pine-tree lappet moth Dendrolimus pini (Lepidoptera: Lasiocampidae). Conservation Genetics Resources 5, 11351137.Google Scholar
An, B., Deng, X., Shi, H., Ding, M., Lan, J., Yang, J. & Li, Y. (2014) Development and characterization of microsatellite markers for rice leaffolder, Cnaphalocrocis medinalis (Guenee) and cross-species amplification in other Pyralididae. Molecular Biology Reports 41, 11511156.Google Scholar
Antao, T., Lopes, A., Lopes, R.J., Beja-Pereira, A. & Luikart, G. (2008) LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method. BMC Bioinformatics 9, 323.CrossRefGoogle ScholarPubMed
Anthony, N., Gelembiuk, G., Raterman, D., Nice, C. & R. Ffrench-Constant (2001) Isolation and characterization of microsatellite markers from the endangered Karner blue butterfly Lycaeides melissa samuelis (Lepidoptera). Hereditas 134, 271273.Google Scholar
Blacket, M.J., Robin, C., Good, R.T., Lee, S.F. & Miller, A.D. (2012) Universal primers for fluorescent labelling of PCR fragments – an efficient and cost-effective approach to genotyping by fluorescence. Molecular Ecology Resources 12, 456463.Google Scholar
Bouanani, M.A., Magné, F., Lecompte, É. & Crouau-Roy, B. (2014) Development of 18 novel polymorphic microsatellites from Coccinella septempunctata and cross-species amplification in Coccinellidae species. Conservation Genetics Resources 7, 445449.Google Scholar
Carlsson, J. (2008) Effects of microsatellite null alleles on assignment testing. Journal of Heredity 99, 616623.Google Scholar
Chapuis, M.P. & Estoup, A. (2007) Microsatellite null alleles and estimation of population differentiation. Molecular Biology and Evolution 24, 621631.Google Scholar
Cong, Q., Borek, D., Otwinowski, Z. & Grishin, N.V. (2015) Tiger swallowtail genome reveals mechanisms for speciation and caterpillar chemical defense. Cell Reports 10, 910919.CrossRefGoogle ScholarPubMed
Cox, M.P., Peterson, D.A. & Biggs, P.J. (2010) SolexaQA: at-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics 11, 485.Google Scholar
Dakin, E.E. & Avise, J.C. (2004) Microsatellite null alleles in parentage analysis. Heredity 93, 504509.Google Scholar
Dharmarajan, G., Beatty, W.S. & Rhodes, O.E. (2013) Heterozygote deficiencies caused by a Wahlund effect: dispelling unfounded expectations. Journal of Wildlife Management 77, 226234.Google Scholar
Du, L., Li, Y., Zhang, X. & Yue, B. (2013) MSDB: a user-friendly program for reporting distribution and building databases of microsatellites from genome sequences. Journal of Heredity 104, 154157.Google Scholar
Earl, D.A. & vonHoldt, B.M. (2011) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4, 359361.Google Scholar
Estoup, A. & Angers, B. (1998) Microsatellites and minisatellites for molecular ecology: theoretical and empirical considerations. Advances in Molecular Ecology Nato Sciences 38, 6975.Google Scholar
Falush, D., Stephens, M. & Pritchard, J.K. (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 15671587.Google Scholar
Flanagan, N.S., Blum, M.J., Davison, A., Alamo, M., Albarrán, R., Faulhaber, K., Peterson, E. & Mcmillan, W.O. (2002) Characterization of microsatellite loci in neotropical Heliconius butterflies. Molecular Ecology Notes 2, 398401.Google Scholar
Gaetano, J. (2013) Holm-Bonferroni Sequential Correction: An EXCEL Calculator - Version 1.2. Available online at https://www.researchgate.net/publication/242331583_Holm-Bonferroni_Sequential_Correction_An_EXCEL_Calculator_-_Ver._1.2.Google Scholar
Gagneux, P., Boesch, C. & Woodruff, D.S. (1997) Microsatellite scoring errors associated with noninvasive genotyping based on nuclear DNA amplified from shed hair. Molecular Ecology 6, 861868.Google Scholar
Goudet, J. (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. Journal of Heredity 86, 485486.Google Scholar
Guichoux, E., Lagache, L., Wagner, S., Chaumeil, P., Leger, P., Lepais, O., Lepoittevin, C., Malausa, T., Revardel, E., Salin, F. & Petit, R.J. (2011) Current trends in microsatellite genotyping. Molecular Ecology Resources 11, 591611.Google Scholar
Habel, J.C., Finger, A., Meyer, M., Schmitt, T. & Assmann, T. (2008) Polymorphic microsatellite loci in the endangered butterfly Lycaena helle (Lepidoptera: Lycaenidae). European Journal of Entomology 105, 361362.CrossRefGoogle Scholar
Jakobsson, M. & Rosenberg, N.A. (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 18011806.Google Scholar
Jiang, W., Zhu, J., Zhan, L., Chen, M., Song, C. & Yu, W. (2014) Isolation and characterization of microsatellite loci in Polytremis nascens (Lepidoptera: Hesperiidae) and their cross-amplification in related species. Applied Entomology & Zoology 49, 177181.Google Scholar
Jiggins, C.D., Mavarez, J., Beltran, M., McMillan, W.O., Johnston, J.S. & Bermingham, E. (2005) A genetic linkage map of the mimetic butterfly Heliconius melpomene . Genetics 171, 557570.Google Scholar
Jombart, T., Devillard, S., Dufour, A.B. & Pontier, D. (2008) Revealing cryptic spatial patterns in genetic variability by a new multivariate method. Heredity 101, 92103.CrossRefGoogle ScholarPubMed
Jurka, J. & Pethiyagoda, C. (1995) Simple repetitive DNA sequences from primates compilation and analysis. Journal of Molecular Evolution 40, 120126.CrossRefGoogle ScholarPubMed
Kirk, H., Dorn, S. & Mazzi, D. (2013) Worldwide population genetic structure of the oriental fruit moth (Grapholita molesta), a globally invasive pest. BMC Ecology 13, 12.Google Scholar
Lebigre, C., Turlure, C. & Schtickzelle, N. (2015) Characterisation of sixteen additional polymorphic microsatellite loci for the spreading but locally rare European butterfly, Brenthis ino (Lepidoptera: Nymphalidae). European Journal of Entomology 112, 389392.Google Scholar
Li, X., Fan, D., Zhang, W., Liu, G., Zhang, L., Zhao, L., Fang, X., Chen, L., Dong, Y. & Chen, Y. (2015) Outbred genome sequencing and CRISPR/Cas9 gene editing in butterflies. Nature Communications 6, 8212.Google Scholar
Meglécz, E., Costedoat, C., Dubut, V., Gilles, A., Malausa, T., Pech, N. & Martin, J.F. (2010) QDD: a user-friendly program to select microsatellite markers and design primers from large sequencing projects. Bioinformatics 26, 403404.Google Scholar
Park, S.D.E. (2001) Trypanotolerance in west african cattle and the population genetic effects of selection . PhD Thesis, University of Dublin, Dublin, Ireland.Google Scholar
Pavinato, V.A.C., Silva-Brandao, K.L., Monteiro, M., Zucchi, M.I., Pinheiro, J.B., Dias, F.L.F. & Omoto, C. (2013) Development and characterization of microsatellite loci for genetic studies of the sugarcane borer, Diatraea saccharalis (Lepidoptera: Crambidae). Genetics and Molecular Research 12, 16311635.Google Scholar
Peng, Y., Leung, H.C.M., Yiu, S.M. & Chin, F.Y.L. (2010) IDBA – a practical iterative de Bruijn Graph De Novo Assembler. Lecture Notes in Computer Science 6044, 426440.Google Scholar
Primmer, C.R., Møller, A.P. & Ellegren, H. (1995) Resolving genetic relationships with microsatellite markers: a parentage testing system for the swallow Hirundo rustica . Molecular Ecology 4, 493498.Google Scholar
Pritchard, J.K., Stephens, M. & Donnelly, P. (2000) Inference of population structure using multilocus genotype data. Genetics 7, 574578.Google Scholar
Quaintance, A.L. & Wood, W.B. (1916) Laspeyresia molesta, an important new insect enemy of the peach. Journal of Agricultural Research 7, 373378.Google Scholar
Raymond, M. & Rousset, F. (1995) GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. Journal of Heredity 86, 248249.Google Scholar
Rosenberg, N.A. (2004) distruct : a program for the graphical display of population structure. Molecular Ecology Notes 4, 137138.Google Scholar
Rothschild, G.H.L. & Vickers, R.A. (1991) Biology, ecology and control of the oriental fruit moth. pp. 389412 in der Geest, L.P.S. & Evenhuis, H.H. (Eds) Tortricid Pests: Their Biology, Natural Enemies and Control. Amsterdam, Elsevier.Google Scholar
Rousset, F. (2008) genepop ’007: a complete re-implementation of the genepop software for Windows and Linux. Molecular Ecology Resources 8, 103106.Google Scholar
Schuelke, M. (2000) An economic method for the fluorescent labeling of PCR fragments. Nature Biotechnology 18, 233234.Google Scholar
Silva Brandão, K.L., Brandão, M.M., Omoto, C. & Sperling, F.A. (2015) Genotyping-by-sequencing approach indicates geographic distance as the main factor affecting genetic structure and gene flow in Brazilian populations of Grapholita molesta (Lepidoptera, Tortricidae). Evolutionary Applications 8, 476485.Google Scholar
Sinama, M., Dubut, V., Costedoat, C., Gilles, A., Junker, M., Malausa, T., Martin, J.-F., Neve, G., Pech, N., Schmitt, T., Zimmermann, M. & Meglecz, E. (2011) Challenges of microsatellite development in Lepidoptera: Euphydryas aurinia (Nymphalidae) as a case study. European Journal of Entomology 108, 261266.Google Scholar
Sousa, S.N.D., Finkeldey, R. & Gailing, O. (2005) Experimental verification of microsatellite null alleles in Norway Spruce (Picea abies [L.] Karst.): implications for population genetic studies. Plant Molecular Biology Reporter 23, 113119.Google Scholar
Timm, A.E., Geertsema, H. & Warnich, L. (2008) Population genetic structure of Grapholita molesta (Lepidoptera : Tortricidae) in South Africa. Annals of the Entomological Society of America 101, 197203.Google Scholar
Torriani, M.V., Mazzi, D., Hein, S. & Dorn, S. (2010) Structured populations of the oriental fruit moth in an agricultural ecosystem. Molecular Ecology 19, 26512660.CrossRefGoogle Scholar
Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M. & Shipley, P. (2004) Micro-checker: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes 4, 535538.Google Scholar
Wahlund, S. (1928) Zusammensetzung von Populationen und Korrelationserscheinungen von Standpunkt der Vererbungslehre aus betrachtet. Hereditas 11, 65106.Google Scholar
Wang, Y.-Z., Cao, L.-J., Zhu, J.-Y. & Wei, S.-J. (2016) Development and Characterization of Novel Microsatellite Markers for the Peach Fruit Moth Carposina sasakii (Lepidoptera: Carposinidae) using next-generation sequencing. International Journal of Molecular Sciences 17, 362.Google Scholar
Wang, Z. (1994) The genetic bases of allozyme analysis (Part 2). Chinese Biodiversity 2, 213219.Google Scholar
Wei, S.J., Cao, L.J., Gong, Y.J., Shi, B.C., Wang, S., Zhang, F., Guo, X.J., Wang, Y.M. & Chen, X.X. (2015) Population genetic structure and approximate Bayesian computation analyses reveal the southern origin and northward dispersal of the oriental fruit moth Grapholita molesta (Lepidoptera: Tortricidae) in its native range. Molecular Ecology 24, 40944111.Google Scholar
Yang, X.M., Sun, J.T., Xue, X.F., Zhu, W.C. & Hong, X.Y. (2012) Development and characterization of 18 novel EST-SSRs from the western flower thrips, Frankliniella occidentalis (Pergande). International Journal of Molecular Sciences 13, 28632876.Google Scholar
You, M., Yue, Z., He, W., Yang, X., Yang, G., Xie, M., Zhan, D., Baxter, S.W., Vasseur, L. & Gurr, G.M. (2013) A heterozygous moth genome provides insights into herbivory and detoxification. Nature Genetics 45, 220225.Google Scholar
Zhang, D.X. (2004) Lepidopteran microsatellite DNA: redundant but promising. Trends in Ecology & Evolution 19, 507509.Google Scholar
Zheng, Y., Peng, X., Liu, G., Pan, H., Dorn, S. & Chen, M. (2013) High genetic diversity and structured populations of the oriental fruit moth in its range of origin. PLoS ONE 8, e78476.Google Scholar
Zheng, Y., Qiao, X., Wang, K., Dorn, S. & Chen, M. (2015) Population genetics affected by pest management using fruit-bagging: a case study with Grapholita molesta in China. Entomologia Experimentalis et Applicata 156, 117127.Google Scholar
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