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Methodologies for segregation analysis and QTL mapping in plants

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Abstract

Most characters of biological interest and economic importance are quantitative traits. To uncover the genetic architecture of quantitative traits, two approaches have become popular in China. One is the establishment of an analytical model for mixed major-gene plus polygenes inheritance and the other the discovery of quantitative trait locus (QTL). Here we review our progress employing these two approaches. First, we proposed joint segregation analysis of multiple generations for mixed major-gene plus polygenes inheritance. Second, we extended the multilocus method of Lander and Green (1987), Jiang and Zeng (1997) to a more generalized approach. Our methodology handles distorted, dominant and missing markers, including the effect of linked segregation distortion loci on the estimation of map distance. Finally, we developed several QTL mapping methods. In the Bayesian shrinkage estimation (BSE) method, we suggested a method to test the significance of QTL effects and studied the effect of the prior distribution of the variance of QTL effect on QTL mapping. To reduce running time, a penalized maximum likelihood method was adopted. To mine novel genes in crop inbred lines generated in the course of normal crop breeding work, three methods were introduced. If a well-documented genealogical history of the lines is available, two-stage variance component analysis and multi-QTL Haseman-Elston regression were suggested; if unavailable, multiple loci in silico mapping was proposed.

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References

  • Braak CJF, Boer MP, Bink MCAM (2005) Extending Xu’s Bayesian model for estimating polygenic effects using markers of the entire genome. Genetics 170:1435–1438

    Article  PubMed  CAS  Google Scholar 

  • Buckler ES, Thornsberry JM (2002) Plant molecular diversity and application to genomics. Curr Opin Plant Biol 5:107–111

    Article  PubMed  CAS  Google Scholar 

  • Chesler EJ, Rodriguez SL, Mogil JS (2001) In silico mapping of mouse quantitative trait loci. Science 294:2423

    Article  PubMed  CAS  Google Scholar 

  • Cui ZL, Gai JY, Carter TE et al (1999) The released Chinese soybean cultivars and their pedigree analysis (1923–1995). China Agriculture Publishing House, Beijing, China

    Google Scholar 

  • Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via EM algorithm. J Royal Stat Soc B 39:1–38

    Google Scholar 

  • Elston RC, Stewart J (1971) A general model for the genetic analysis of pedigree data. Hum Hered 21:523–542

    Article  PubMed  CAS  Google Scholar 

  • Elston RC, Steward J (1973) The analysis of quantitative traits for simple genetic models from parents, F1 and backcross data. Genetics 73:695–711

    PubMed  CAS  Google Scholar 

  • Fisher RA (1935) The detection of linkage with ‘dominant’ abnormalities. Ann Eugen 6:187–201

    Google Scholar 

  • Flint-Garcia SA, Thornsberry JM, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374

    Article  PubMed  CAS  Google Scholar 

  • Gai JY (2006) Segregation analysis on genetic system of quantitative traits in plants. Front Biol China 1:85–92

    Article  Google Scholar 

  • Gai JY, Wang JK (1998) Identification and estimation of a QTL model and its effects. Theor Appl Genet 97:1162–1168

    Article  Google Scholar 

  • Gai JY, Zhang Y-M, Wang JK (2003) Genetic system of quantitative traits in plants. Science Press House, Beijing (in Chinese)

    Google Scholar 

  • Gai JY, Wang YJ, Wu XL et al (2007) A comparative study on segregation analysis and QTL mapping of quantitative traits in plants–with a case in soybean. Front Agric China 1:1–7

    Article  Google Scholar 

  • Grupe A, Germer S, Usuka J et al (2001) In silico mapping of complex disease-related traits in mice. Science 292:1915–1918

    Article  PubMed  CAS  Google Scholar 

  • Guo Y, Weller P, Farrell E et al (2006) In silico pharmcogenetics of warfarin metabolism. Nat Biotechnol 24:531–536

    Article  PubMed  CAS  Google Scholar 

  • Guo Y, Lu P, Farrell E et al (2007) In silico and in vitro pharmacogenetic analysis in mice. Proc Natl Acad Sci USA 104:17735–17740

    Article  PubMed  CAS  Google Scholar 

  • Haldane JBS, Smith CAB (1947) A new estimate of the linkage between the genes for colour-blindness and haemophilia in man. Ann Eugen 14:10–31

    PubMed  CAS  Google Scholar 

  • Haseman JK, Elston RC (1972) The investigation of linkage between a quantitative trait and a marker locus. Behav Genet 2:3–19

    Article  PubMed  CAS  Google Scholar 

  • He XH, Zhang Y-M (2008) Mapping epistatic quantitative trait loci underlying endosperm traits using all markers on the entire genome in a random hybridization design. Heredity 101:39–47

    Article  PubMed  CAS  Google Scholar 

  • Iwata H, Uga Y, Yoshioka Y et al (2007) Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among Oryza sativa L. germplasms. Theor Appl Genet 114:1437–1449

    Article  PubMed  Google Scholar 

  • Jiang CJ, Zeng ZB (1997) Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101:47–56

    Article  PubMed  CAS  Google Scholar 

  • Lander ES, Green P (1987) Construction of multilocus genetic linkage maps in humans. Proc Natl Acad Sci USA 84:2363–2367

    Article  PubMed  CAS  Google Scholar 

  • Liang DY, Liao G, Lighthall GK et al (2006a) Genetic variants of the P-glycoprotein gene Abcb1b modulate opioid-induced hyperalgesia, tolerance and dependence. Pharmacogenet Genomics 16:825–835

    Article  PubMed  CAS  Google Scholar 

  • Liang DY, Liao G, Wang J et al (2006b) A genetic analysis of opioid-induced hyperalgesia in mice. Anesthesiology 104:1054–1062

    Article  PubMed  CAS  Google Scholar 

  • Liao G, Wang J, Guo J et al (2004) In silico genetics: identification of a functional element regulating H2-Eα gene expression. Science 306:690–695

    Article  PubMed  CAS  Google Scholar 

  • Lorieux MB, Perrier GX, Gonzalez de Leon et al (1995a) Maximum likelihood models for mapping genetic markers showing segregation distortion. 1. Backcross population. Theor Appl Genet 90:73–80

    Google Scholar 

  • Lorieux M, Perrier X, Goffinet B et al (1995b) Maximum likelihood models for mapping genetic markers showing segregation distortion. 2. F2 population. Theor Appl Genet 90:81–89

    Google Scholar 

  • Luo L, Zhang Y-M, Xu S (2005) A quantitative genetics model for viability selection. Heredity 94:347–355

    Article  PubMed  CAS  Google Scholar 

  • McClurg P, Janes J, Wu C et al (2007) Genome-wide association analysis in diverse inbred mice: power and population structure. Genetics 176:675–683

    Article  PubMed  CAS  Google Scholar 

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    PubMed  CAS  Google Scholar 

  • Morton NE (1955) Sequential tests for the detection of linkage. Am J Hum Genet 7:277–318

    PubMed  CAS  Google Scholar 

  • Ott J (1976) A computer program for linkage analysis of general human pedigrees. Am J Hum Genet 28:528–529

    PubMed  CAS  Google Scholar 

  • Piepho HP (2001) A quick method for computing approximate thresholds for quantitative trait loci detection. Genetics 157:425–432

    PubMed  CAS  Google Scholar 

  • Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65:220–228

    Article  PubMed  CAS  Google Scholar 

  • Remington DL, Ungerer MC, Purugganan MD (2001) Map-based cloning of quantitative trait loci: progress and prospects. Genet Res 78:213–218

    Article  PubMed  CAS  Google Scholar 

  • Sen S, Churchill G (2001) A statistical framework for quantitative trait mapping. Genetics 159:371–387

    PubMed  CAS  Google Scholar 

  • Wang H, Zhang Y-M, Li XM et al (2005) Bayesian shrinkage estimation of quantitative trait loci parameters. Genetics 170:465–480

    Article  PubMed  CAS  Google Scholar 

  • Xu S (2002) QTL analysis in plants. In: Camp N, Cox A (eds) Quantitative trait loci: methods and protocols. Humana Press, Totowa, NJ, pp 283–310

    Chapter  Google Scholar 

  • Xu S (2003) Estimating polygenic effects using markers of the entire genome. Genetics 163:789–801

    PubMed  CAS  Google Scholar 

  • Yang R, Xu S (2007) Bayesian shrinkage analysis of quantitative trait loci for dynamic traits. Genetics 176:1169–1185

    Article  PubMed  CAS  Google Scholar 

  • Yu J, Buckler ES (2006) Genetic association mapping and genome organization of maize. Curr Opin Biotechnol 17:155–160

    PubMed  CAS  Google Scholar 

  • Yu JM, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    Article  PubMed  CAS  Google Scholar 

  • Zhang Y-M (2006) Advances on methods for mapping QTL in plant. Chin Sci Bull 51:2809–2818

    Article  CAS  Google Scholar 

  • Zhang Y-M, Xu S (2004) Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progeny. Genetics 166:1981–1993

    Article  PubMed  CAS  Google Scholar 

  • Zhang Y-M, Xu S (2005a) A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity 95:96–104

    Article  PubMed  CAS  Google Scholar 

  • Zhang Y-M, Xu S (2005b) Advanced statistical methods for detecting multiple quantitative trait loci. Recent Res Dev Genet Breed 2:1–23

    Google Scholar 

  • Zhang Y-M, Gai JY, Yang YH (2003) The EIM algorithm in the joint segregation analysis of quantitative traits. Genet Res 81:157–163

    Article  PubMed  Google Scholar 

  • Zhang Y-M, Mao YC, Xie CQ et al (2005) Mapping QTL using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.). Genetics 169:2267–2275

    Article  PubMed  CAS  Google Scholar 

  • Zhang Y-M, Lü HY, Yao LL (2008) Multiple quantitative trait loci Haseman-Elston regression using all markers on the genome. Theor Appl Genet doi:10.1007/s00122-008-0809-0 (in press)

  • Zhu C, Zhang Y-M (2007) An EM algorithm for mapping segregation distortion loci. BMC Genet 8:82

    Article  PubMed  CAS  Google Scholar 

  • Zhu C, Wang C, Zhang Y-M (2007a) Modeling segregation distortion for viability selection I. Reconstruction of linkage maps with distorted markers. Theor Appl Genet 114:295–305

    Article  PubMed  Google Scholar 

  • Zhu C, Wang F, Wang J et al (2007b) Reconstruction of linkage maps in the distorted segregation populations of backcross, doubled haploid and recombinant inbred lines. Chin Sci Bull 52:1648–1653

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to the Associate Editor Prof Zhao-Bang Zeng, and anonymous reviewers for their constructive comments and suggestions that significantly improve the presentation of the manuscript, and to all the developers that participate in the works in the paper, including Prof Shizhong Xu and my Ph D student Hai-Yan Lü for QTL mapping, Prof Junyi Gai and Dr Jiankang Wang for segregation analysis, and Dr Chengsong Zhu (postdoctorate) for genetic linkage map reconstruction. This work was supported in part by: 973 program (2006CB101708), the National Natural Science Foundation of China (30671333), NCET (NCET-05-0489), 863 program (2006 AA10Z1E5), Jiangsu Natural Science Foundation (SBK20081976), the project from the Ministry of Agriculture in China (M200814) and the 111 project (B08025) to YMZ.

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Correspondence to Yuan-Ming Zhang.

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Zhang, YM., Gai, J. Methodologies for segregation analysis and QTL mapping in plants. Genetica 136, 311–318 (2009). https://doi.org/10.1007/s10709-008-9313-3

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