How to perform meaningful estimates of genetic effects

Abstract

Although the genotype-phenotype map plays a central role both in Quantitative and Evolutionary Genetics, the formalization of a completely general and satisfactory model of genetic effects, particularly accounting for epistasis, remains a theoretical challenge. Here, we use a two-locus genetic system in simulated populations with epistasis to show the convenience of using a recently developed model, NOIA, to perform estimates of genetic effects and the decomposition of the genetic variance that are orthogonal even under deviations from the Hardy-Weinberg proportions. We develop the theory for how to use this model in interval mapping of quantitative trait loci using Halley-Knott regressions, and we analyze a real data set to illustrate the advantage of using this approach in practice. In this example, we show that departures from the Hardy-Weinberg proportions that are expected by sampling alone substantially alter the orthogonal estimates of genetic effects when other statistical models, like F2 or G2A, are used instead of NOIA. Finally, for the first time from real data, we provide estimates of functional genetic effects as sets of effects of natural allele substitutions in a particular genotype, which enriches the debate on the interpretation of genetic effects as implemented both in functional and in statistical models. We also discuss further implementations leading to a completely general genotype-phenotype map.

Figure 1. Effects of departures from the HWP on genetic effects.

The genetic effects were obtained using the F₂, G2A and NOIA models in a two locus genetic system that was simulated in nine F₂ populations with departures from HWP ranging from zero to 97% (see text for details).

Figure 2. Effects of departures from the HWP on the variance components.

The variance decomposition was performed for the same cases as in Figure 1. V_P is the phenotypic variance, which (in absence of environmental variance) is equal to V_G, the genetic variance. V_A is the additive variance, V_D is the dominance variance and V_I is the epistatic (interaction) variance.