ORCID Profile
0000-0001-6209-0177
Current Organisation
University of St Andrews
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Publisher: Cold Spring Harbor Laboratory
Date: 04-07-2017
DOI: 10.1101/159210
Abstract: Linear mixed effects models are frequently used for estimating quantitative genetic parameters, including the heritability, of traits of interest. Heritability is an important metric, because it acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change. As a quantity of biological interest, it is important that the denominator, the phenotypic variance, actually reflects the amount of phenotypic variance in the relevant ecological stetting. The current practice of quantifying heritability from mixed effects models frequently deprives the heritability of variance explained by fixed effects (often leading to upward-bias) and it has been suggested to omit fixed effects when estimating heritabilities. We advocate an alternative option of fitting complex models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of heritabilities. The approach is easily implemented (an ex le is provided) and allows corrections for the estimation of heritability, such as the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We explore the complications arising depending on the nature of the covariates included as fixed effects (e.g. biological or experimental origin, characteristics of biological covariates). Furthermore, we discuss fixed effects in non-linear and generalized linear models when fixed effects. In these cases, the variance parameters depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.
Publisher: Public Library of Science (PLoS)
Date: 05-11-2019
Publisher: American Association for the Advancement of Science (AAAS)
Date: 27-05-2022
Abstract: The rate of adaptive evolution, the contribution of selection to genetic changes that increase mean fitness, is determined by the additive genetic variance in in idual relative fitness. To date, there are few robust estimates of this parameter for natural populations, and it is therefore unclear whether adaptive evolution can play a meaningful role in short-term population dynamics. We developed and applied quantitative genetic methods to long-term datasets from 19 wild bird and mammal populations and found that, while estimates vary between populations, additive genetic variance in relative fitness is often substantial and, on average, twice that of previous estimates. We show that these rates of contemporary adaptive evolution can affect population dynamics and hence that natural selection has the potential to partly mitigate effects of current environmental change.
Publisher: Oxford University Press (OUP)
Date: 11-2016
DOI: 10.1534/GENETICS.115.186536
Abstract: Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population.
Publisher: Elsevier BV
Date: 05-2021
Publisher: American Association for the Advancement of Science (AAAS)
Date: 03-03-2017
Abstract: Climate change will fundamentally alter many aspects of the natural world. To understand how species may adapt to this change, we must understand which aspects of the changing climate exert the most powerful selective forces. Siepielski et al. looked at studies of selection across species and regions and found that, across biomes, the strongest sources of selection were precipitation and transpiration changes. Importantly, local and regional climate change explained patterns of selection much more than did global change. Science , this issue p. 959
Publisher: Wiley
Date: 15-02-2018
DOI: 10.1111/JEB.13232
Abstract: Linear mixed-effects models are frequently used for estimating quantitative genetic parameters, including the heritability, as well as the repeatability, of traits. Heritability acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change, whereas repeatability informs us about the in idual consistency of phenotypic traits. As quantities of biological interest, it is important that the denominator, the phenotypic variance in both cases, reflects the amount of phenotypic variance in the relevant ecological setting. The current practice of quantifying heritabilities and repeatabilities from mixed-effects models frequently deprives their denominator of variance explained by fixed effects (often leading to upward bias of heritabilities and repeatabilities), and it has been suggested to omit fixed effects when estimating heritabilities in particular. We advocate an alternative option of fitting models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of the phenotypic variance. The approach is easily implemented and allows optimizing the estimation of phenotypic variance, for ex le by the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We address the estimation and interpretation of heritabilities in situations in which potential covariates are themselves heritable traits of the organism. Furthermore, we discuss complications that arise in generalized and nonlinear mixed models with fixed effects. In these cases, the variance parameters on the data scale depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 26-01-2018
Abstract: The comment by Myers-Smith and Myers focuses on three main points: (i) the lack of a mechanistic explanation for climate-selection relationships, (ii) the appropriateness of the climate data used in our analysis, and (iii) our focus on estimating climate-selection relationships across (rather than within) taxonomic groups. We address these critiques in our response.
Publisher: Cold Spring Harbor Laboratory
Date: 17-09-2020
DOI: 10.1101/2020.09.16.299685
Abstract: The paradox of stasis – the unexpectedly slow evolution of heritable traits under direct selection – has been widely documented in the last few decades. This paradox is often particularly acute for body size, which is often heritable and where positive associations of size and fitness are frequently identified, but constraints to the evolution of larger body sizes are often not obvious. Here, we identify a trade-off between survival and size-dependent reproduction in Soay sheep ( Ovis aries ), contributes to selection against large body size. Using recently developed theory on non-linear developmental systems, then decompose total selection of ewe lamb mass along different causal paths to fitness. Larger lambs are more likely to become pregnant, which has a large viability cost. After controlling for this pathway, however, the association between lamb mass and subsequent lifetime fitness is positive. Thus this trade-off does not fully explain stasis of size in tis population, but it does substantially reduce the strength of positive directional selection of size that would otherwise occur. While selection currently favours reduced probability of early pregnancy, largely irrespective of body size, it is likely that the occurrence of early pregnancy could result from adaptation to conditions during a recent period during which population density was much lower.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Michael Morrissey.