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1. The genetic architecture of teosinte catalyzed and constrained maize domestication

   https://doi.org/10.1073/pnas.1820997116  

   Yang, Chin Jian ; Samayoa, Luis Fernando ; Bradbury, Peter J. ; Olukolu, Bode A. ; Xue, Wei ; York, Alessandra M. ; Tuholski, Michael R. ; Wang, Weidong ; Daskalska, Lora L. ; Neumeyer, Michael A. ; et al ( March 2019 , Proceedings of the National Academy of Sciences)

   The process of evolution under domestication has been studied using phylogenetics, population genetics–genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance–covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory.

    

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2. Are we underestimating the genetic variances of dimorphic traits?

   https://doi.org/10.1002/ece3.1361  

   Wolak, Matthew_E ; Roff, Derek_A ; Fairbairn, Daphne_J ( January 2015 , Ecology and Evolution)

   Populations often contain discrete classes or morphs (e.g., sexual dimorphisms, wing dimorphisms, trophic dimorphisms) characterized by distinct patterns of trait expression. In quantitative genetic analyses, the different morphs can be considered as different environments within which traits are expressed. Genetic variances and covariances can then be estimated independently for each morph or in a combined analysis. In the latter case, morphs can be considered as separate environments in a bivariate analysis or entered as fixed effects in a univariate analysis. Although a common approach, we demonstrate that the latter produces downwardly biased estimates of additive genetic variance and heritability unless the quantitative genetic architecture of the traits concerned is perfectly correlated between the morphs. This result is derived for four widely used quantitative genetic variance partitioning methods. Given that theory predicts the evolution of genotype‐by‐environment (morph) interactions as a consequence of selection favoring different trait combinations in each morph, we argue that perfect correlations between the genetic architectures of the different morphs are unlikely. A sampling of the recent literature indicates that the majority of researchers studying traits expressed in different morphs recognize this and do estimate morph‐specific quantitative genetic architecture. However, ca. 16% of the studies in our sample utilized only univariate, fixed‐effects models. We caution against this approach and recommend that it be used only if supported by evidence that the genetic architectures of the different morphs do not differ.

    

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3. Regularized regression can improve estimates of multivariate selection in the face of multicollinearity and limited data

   https://doi.org/10.1093/evlett/qrad064  

   Sztepanacz, Jacqueline_L ; Houle, David ( January 2024 , Evolution Letters)

   The breeder’s equation, Δz¯=Gβ , allows us to understand how genetics (the genetic covariance matrix, G) and the vector of linear selection gradients β interact to generate evolutionary trajectories. Estimation of β using multiple regression of trait values on relative fitness revolutionized the way we study selection in laboratory and wild populations. However, multicollinearity, or correlation of predictors, can lead to very high variances of and covariances between elements of β, posing a challenge for the interpretation of the parameter estimates. This is particularly relevant in the era of big data, where the number of predictors may approach or exceed the number of observations. A common approach to multicollinear predictors is to discard some of them, thereby losing any information that might be gained from those traits. Using simulations, we show how, on the one hand, multicollinearity can result in inaccurate estimates of selection, and, on the other, how the removal of correlated phenotypes from the analyses can provide a misguided view of the targets of selection. We show that regularized regression, which places data-validated constraints on the magnitudes of individual elements of β, can produce more accurate estimates of the total strength and direction of multivariate selection in the presence of multicollinearity and limited data, and often has little cost when multicollinearity is low. We also compare standard and regularized regression estimates of selection in a reanalysis of three published case studies, showing that regularized regression can improve fitness predictions in independent data. Our results suggest that regularized regression is a valuable tool that can be used as an important complement to traditional least-squares estimates of selection. In some cases, its use can lead to improved predictions of individual fitness, and improved estimates of the total strength and direction of multivariate selection.

    

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4. A conserved genetic architecture among populations of the maize progenitor, teosinte, was radically altered by domestication

   https://doi.org/10.1073/pnas.2112970118  

   Chen, Qiuyue ; Samayoa, Luis Fernando ; Yang, Chin Jian ; Olukolu, Bode A. ; York, Alessandra M. ; Sanchez-Gonzalez, Jose de ; Xue, Wei ; Glaubitz, Jeffrey C. ; Bradbury, Peter J. ; Romay, Maria Cinta ; et al ( October 2021 , Proceedings of the National Academy of Sciences)

   Very little is known about how domestication was constrained by the quantitative genetic architecture of crop progenitors and how quantitative genetic architecture was altered by domestication. Yang et al. [C. J. Yang et al. , Proc. Natl. Acad. Sci. U.S.A. 116, 5643–5652 (2019)] drew multiple conclusions about how genetic architecture influenced and was altered by maize domestication based on one sympatric pair of teosinte and maize populations. To test the generality of their conclusions, we assayed the structure of genetic variances, genetic correlations among traits, strength of selection during domestication, and diversity in genetic architecture within teosinte and maize. Our results confirm that additive genetic variance is decreased, while dominance genetic variance is increased, during maize domestication. The genetic correlations are moderately conserved among traits between teosinte and maize, while the genetic variance–covariance matrices ( G -matrices) of teosinte and maize are quite different, primarily due to changes in the submatrix for reproductive traits. The inferred long-term selection intensities during domestication were weak, and the neutral hypothesis was rejected for reproductive and environmental response traits, suggesting that they were targets of selection during domestication. The G -matrix of teosinte imposed considerable constraint on selection during the early domestication process, and constraint increased further along the domestication trajectory. Finally, we assayed variation among populations and observed that genetic architecture is generally conserved among populations within teosinte and maize but is radically different between teosinte and maize. While selection drove changes in essentially all traits between teosinte and maize, selection explains little of the difference in domestication traits among populations within teosinte or maize. 

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5. Social effects of territorial neighbours on the timing of spring breeding in North American red squirrels

   https://doi.org/10.1111/jeb.13437  

   Fisher, David N. ; Wilson, Alastair J. ; Boutin, Stan ; Dantzer, Ben ; Lane, Jeffrey E. ; Coltman, David W. ; Gorrell, Jamie C. ; McAdam, Andrew G. ( March 2019 , Journal of Evolutionary Biology)

   Organisms can affect one another's phenotypes when they socially interact. Indirect genetic effects occur when an individual's phenotype is affected by genes expressed in another individual. These heritable effects can enhance or reduce adaptive potential, thereby accelerating or reversing evolutionary change. Quantifying these social effects is therefore crucial for our understanding of evolution, yet estimates of indirect genetic effects in wild animals are limited to dyadic interactions. We estimated indirect phenotypic and genetic effects, and their covariance with direct effects, for the date of spring breeding in North American red squirrels (Tamiasciurus hudsonicus) living in an array of territories of varying spatial proximity. Additionally, we estimated indirect effects and the strength of selection at low and high population densities. Social effects of neighbours on the date of spring breeding were different from zero at high population densities but not at low population densities. Indirect phenotypic effects accounted for a larger amount of variation in the date of breeding than differences attributable to the among‐individual variance, suggesting social interactions are important for determining breeding dates. The genetic component to these indirect effects was however not statistically significant. We therefore showcase a powerful and flexible method that will allow researchers working in organisms with a range of social systems to estimate indirect phenotypic and genetic effects, and demonstrate the degree to which social interactions can influence phenotypes, even in a solitary species.

    

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