Accounting for competition in genetic analysis, with particular emphasis on forest genetic trials
Tóm tắt
Available experimental evidence suggests that there are genetic differences in the abilities of trees to compete for resources, in addition to non-genetic differences due to micro-site variation. The use of indirect genetic effects within the framework of linear mixed model methodology has been proposed for estimating genetic parameters and responses to selection in the presence of genetic competition. In this context, an individual’s total breeding value reflects the effects of its direct breeding value on its own phenotype and its competitive breeding value on the phenotype of its neighbours. The present study used simulated data to investigate the relevance of accounting for competitive effects at the genetic and non-genetic levels in terms of the estimation of (co)variance components and selection response. Different experimental designs that resulted in different genetic relatedness levels within a neighbourhood and survival were other key issues examined. Variances estimated for additive genetic and residual effects tended to be biased under models that ignored genetic competition. Models that fitted competition at the genetic level only also resulted in biased (co)variance estimates for direct additive, competitive additive and residual effects. The ability to detect the correct model was reduced when relatedness within a neighbourhood was very low and survival decreased. Selection responses changed considerably between selecting on breeding value estimates from a model ignoring genetic competition and total breeding estimates using the correct model. Our results suggest that considering a genetic basis to competitive ability will be important to optimise selection programmes for genetic improvement of tree species.
Tài liệu tham khảo
Bergsma R, Kanis E, Knol EF, Bijma P (2008) The contribution of social effects to heritable variation in finishing traits of domestic pigs (Sus scrofa). Genetics 178:1559–1570
Bijma P (2010a) Fisher's fundamental theorem of inclusive fitness and the change in fitness due to natural selection when conspecifics interact. J Evol Biol 23:194–206
Bijma P (2010b) Estimating indirect genetic effects: precision of estimates and optimum designs. Genetics 186:1013–1028
Bijma P (2010c) Multilevel selection 4: modeling the relationship of indirect genetic effects and group size. Genetics 186:1029–1031
Bijma P (2011) A general definition of the heritable variation that determines the potential of a population to respond to selection. Genetics 189:1347–1359
Bijma P, Muir WM, Van Arendonk JA (2007a) Multilevel selection 1: quantitative genetics of inheritance and response to selection. Genetics 175:277–288
Bijma P, Muir WM, Ellen ED, Wolf JB, Van Arendonk JA (2007b) Multilevel selection 2: estimating the genetic parameters determining inheritance and response to selection. Genetics 175:289–299
Binkley D, Stape JL, Ryan MG (2004) Thinking about efficiency of resource use in forests. Forest Ecol Manag 193:5–16
Cannell MGR (1978) Improving per hectare forest productivity. In: Hollis CA, Squillace AE (eds) 5th North American Forest Biology Workshop. University of Florida, Gainesville, pp 120–148
Cannell MGR, Rothery P, Ford ED (1984) Competition within stands of Picea sitchensis and Pinus contorta. Ann Bot 53:349–362
Cantet RJC, Cappa EP (2008) On identifiability of (co)variance components in animal models with competition effects. J Anim Breed Genet 125:371–381
Cappa EP, Cantet RJC (2008) Direct and competition additive effects in tree breeding: Bayesian estimation from an individual tree mixed model. Silvae Genet 57:45–56
Chambers PGS, Borralho NMG, Potts BM (1996) Genetic analysis of survival in Eucalyptus globulus ssp. globulus. Silvae Genet 45:107–112
Cheng J, Janssens S, Buys N (2009) Full sib pens of pigs are not suitable to identify variance component of associative effect: a simulation study using Gibbs sampling. BMC Genet 10:9
Denison RF, Kiers ET, West SA (2003) Darwinian agriculture: when can humans find solutions beyond the reach of natural selection? Q Rev Biol 78:145–168
Donald CM (1968) The breeding of crop ideotypes. Euphytica 17:385–403
Ellen ED, Muir WM, Teuscher F, Bijma P (2007) Genetic improvement of traits affected by interactions among individuals: sib selection schemes. Genetics 176:489–499
Foster GS, Rousseau RJ, Nance WL (1998) Eastern cottonwood clonal mixing study: intergenotypic competition effects. Forest Ecol Manag 112:9–22
Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293
Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml User Guide. Release 3.0., Hemel Hempstead, HP1 1ES, UK
Griffing B (1967) Selection in reference to biological groups. I. Individual and group selection applied to populations of unordered groups. Aust J Biol Sci 20:127–139
Griffing B (1977) Selection for populations of interacting phenotypes. In: Pollak E, Kempthorne O, Bailey TB (eds) Proceedings of the International Conference on Quantitative Genetics. Iowa State University Press, Ames, pp 413–434
Grime JP (1977) Evidence for existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am Nat 111:1169–1194
Hozo SP, Djulbegovic B, Hozo I (2005) Estimating the mean and variance from the median, range, and the size of a sample. BMC Med Res Methodol 5:13
Magnussen S (1989) Effects and adjustments of competition bias in progeny trials with single-tree plots. For Sci 35:532–547
Magnussen S (1994) A method to adjust simultaneously for spatial microsite and competition effects. Can J For Res 24:985–995
Moore AJ, Brodie ED III, Wolf JB (1997) Interacting phenotypes and the evolutionary process. I. Direct and indirect genetic effects of social interactions. Evolution 51:1352–1362
Muir WM (2005) Incorporation of competitive effects in forest tree or animal breeding programs. Genetics 170:1247–1259
Muir WM, Bijma P, Chen CY, Misztal I, Wade MJ (2010) Advances in selection theory including experimental demonstrations: the theory of selection with social competition. 9th World Congress on Genetics Applied to Livestock Production (WCGALP), August 1–6, 2010
Resende MDV, Stringer JK, Cullis BR, Thompson R (2005) Joint modelling of competition and spatial variability in forest field trials. Braz J Math Stat 23:7–22
Stram DO, Lee JW (1994) Variance components testing in the longitudinal mixed effects setting. Biometrics 50:1171–1177
Stringer JK (2006) Joint modelling of spatial variability and interplot competition to improve the efficiency of plant improvement. PhD Thesis, University of Queensland, Brisbane
Stringer JK, Cullis BR, Thompson R (2011) Joint modeling of spatial variability and within-row interplot competition to increase the efficiency of plant improvement. J Agric Biol Environ Stat 16:269–281
Tuskan GA, McKinley CR (1984) The use of competition indices in advanced-generation selection. Silvae Genet 33:209–215
von Euler F, Baradat P, Lemoine B (1992) Effects of plantation density and spacing on competitive interactions among half-sib families of maritime pine. Can J For Res 22:482–489
White TL, Adams WT, Neale DB (2007) Forest genetics. CABI, Cambridge
Wolf JB, Brodie ED III, Cheverud JM, Moore AL, Wade MJ (1998) Evolutionary consequences of indirect genetic effects. Trends Ecol Evol 13:64–69