Abstract
Failures of the additive infinitesimal model continue to provide incentive to study other modes of gene action, in particular, epistasis. Epistasis can be modeled as a QTL by genetic background interaction. Association mapping models lend themselves to fitting such an interaction because they often include both main marker and genetic background factors. In this study, I review a model that fits the QTL by background interaction as an added random effect in the now standard mixed model framework of association analyses. The model is applied to four-generation pedigrees where the objective is to predict the genotypic values of fourth-generation individuals that have not been phenotyped. In particular, I look at how well epistatic effects are estimated under two levels of inbreeding. Interaction detection power was 8% and 65% for pedigrees of 240 randomly mated individuals when the interaction generated 6% and 20% of the phenotypic variance, respectively. Power increased to 21% and 94% for these conditions when evaluated individuals were inbred by selfing four times. The interaction variance was estimated in an unbiased way under both levels of inbreeding, but its mean squared error was reduced by 40% to 70% when estimated in inbred relative to randomly mated individuals. The performance of the epistatic model was also enhanced relative to the additive model by inbreeding. These results are promising for the application of the model to typically self-pollinating crops such as wheat and soybean.
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Abbreviations
- IBD:
-
Identity by descent
- QTL:
-
Quantitative trait locus/loci
References
Arbelbide M, Bernardo R (2006) Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat. Theor Appl Genet 112:885–890
Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224
Boer MP, ter Braak CJF, Jansen RC (2002) A penalized likelihood method for mapping epistatic quantitative trait loci with one-dimensional genome searches. Genetics 162:951–960
Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177
Bryant EH, Meffert LM (1993) The effect of serial founder-flush cycles on quantitative genetic variation in the housefly. Heredity 70:122–129
Carlborg O, Jacobsson L, Ahgren P, Siegel P, Andersson L (2006) Epistasis and the release of genetic variation during long-term selection. Nat Genet 38:418–420
Charcosset A, Causse M, Moreau L, Gallais A (1994) Investigation into the effect of genetic background on QTL expression using three connected maize recombinant inbred lines (RIL) populations. In: v. Ooijen JW, Jansen J (eds) Biometrics in plant breeding: applications of molecular markers. CPRO-DLO, Wageningen, The Netherlands, pp 75–84
Cheverud JM, Routman EJ (1996) Epistasis as a source of increased additive genetic variance at population bottlenecks. Evolution 50:1042–1051
Cheverud JM, Vaughn TT, Pletscher S, King-Ellison K, Bailiff J, Adams E, Erickson C, Bonislawski A (1999) Epistasis and the evolution of additive genetic variance in populations that pass through a bottleneck. Evolution 53:1009–1018
Cooper M, Podlich DW, Micallef KP, Smith OS, Jensen NM, Chapman SC (2002) Complexity, quantitative traits and plant breeding: A role for simulation modeling in the genetic improvement of crops. In: Kang MS (ed) Quantitative genetics, genomics and plant breeding. CABI Publishing, Wallingford, U.K., pp 143–166
Frey KJ, Holland JB (1999) Nine cycles of recurrent selection for increased groat-oil content in oat. Crop Sci 39:1636–1641
Goodnight CJ (1987) On the effect of founder events on epistatic genetic variance. Evolution 41:80–91
Goodnight CJ (2004) Gene interaction and Selection. In: Lamkey KR (ed) Long-term selection: maize. plant breeding reviews, Vol. 24. John Wiley & Sons, Inc., Hoboken, New Jersey, pp 269–292
Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Applied Genet 38:226–231
Jannink J-L (2003) Selection dynamics and limits under additive-by-additive epistatic gene action. Crop Sci 43:489–497
Jannink J-L (2007) Identifying quantitative trait locus by genetic background interactions in association studies. Genetics 176:553–561
Jannink J-L, Jansen RC (2001) Mapping epistatic QTL with one-dimensional genome searches. Genetics 157:445–454
Jannink J-L, Bink MCAM, Jansen RC (2001) Using complex plant pedigrees to map valuable genes. Trends Plant Sci 6:337–342
Kennedy BW, Quinton M, van Arendonk JAM (1992) Estimation of effects of single genes on quantitative traits. J Anim Sci 70:2000–2012
Kianian SF, Egli MA, Phillips RL, Rines HW, Somers DA, Gengenbach BG, Webster FH, Livingston SM, Groh S, LS OD, Sorrells ME, Wesenberg DM, Stuthman DD, Fulcher RG (1999) Association of a major groat oil content QTL and an acetyl-CoA carboxylase gene in oat. Theor Appl Genet 98:884–894
Kraakman ATW, Niks RE, Van den Berg PMMM, Stam P, Van Eeuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168:435–446
Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, Sunderland, MA
Parisseaux B, Bernardo R (2004) In silico mapping of quantitative trait loci in maize. Theor Appl Genet 109:508–514
Podlich DW, Cooper M (1998) QU-GENE: a platform for quantitative analysis of genetic models. Bioinformatics 14:632–653
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959
Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28:286–289
Weir BS, Anderson AD, Hepler AB (2006) Genetic relatedness analysis: modern data and new challenges. Nat Rev Genet 7:771–780
Whittaker JC, Thompson R, Denham MC (2000) Marker-assisted selection using ridge regression. Genet Res 75:249–252
Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES IV (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208
Acknowledgments
This research was supported by USDA-NRI grant number 2003-35300-13202. I thank Fred van Eeuwijk, Jerko Gunjaca and other organizers of the 2006 EUCARPIA Biometrics Section Meeting for an excellent conference.
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Jannink, JL. QTL × genetic background interaction: predicting inbred progeny value. Euphytica 161, 61–69 (2008). https://doi.org/10.1007/s10681-007-9509-0
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DOI: https://doi.org/10.1007/s10681-007-9509-0