Evolvability: A Quantitative-Genetics Perspective

The concept of evolvability emerged in the early 1990s and soon became fashionable as a label for different streams of research in evolutionary biology. In evolutionary quantitative genetics, evolvability is defined as the ability of a population to respond to directional selection. This differs from other fields by treating evolvability as a property of populations rather than organisms or lineages and in being focused on quantification and short-term prediction rather than on macroevolution. While the term evolvability is new to quantitative genetics, many of the associated ideas and research questions have been with the field from its inception as biometry. Recent research on evolvability is more than a relabeling of old questions, however. New operational measures of evolvability have opened possibilities for understanding adaptation to rapid environmental change, assessing genetic constraints, and linking micro- and macroevolution.

Integrating genomics and multivariate evolutionary quantitative genetics: a case study of constraints on sexual selection in Drosophila serrata

In evolutionary quantitative genetics, the genetic variance–covariance matrix, G , and the vector of directional selection gradients, β , are key parameters for predicting multivariate selection responses and genetic constraints. Historically, investigations of G and β have not overlapped with those dissecting the genetic basis of quantitative traits. Thus, it remains unknown whether these parameters reflect pleiotropic effects at individual loci. Here, we integrate multivariate genome-wide association study (GWAS) with G and β estimation in a well-studied system of multivariate constraint: sexual selection on male cuticular hydrocarbons (CHCs) in Drosophila serrata . In a panel of wild-derived re-sequenced lines, we augment genome-based restricted maximum likelihood to estimate G alongside multivariate single nucleotide polymorphism (SNP) effects, detecting 532 significant associations from 1 652 276 SNPs. Constraint was evident, with β lying in a direction of G with low evolvability. Interestingly, minor frequency alleles typically increased male CHC-attractiveness suggesting opposing natural selection on β . SNP effects were significantly misaligned with the major eigenvector of G , g max , but well aligned to the second and third eigenvectors g 2 and g 3 . We discuss potential factors leading to these varied results including multivariate stabilizing selection and mutational bias. Our framework may be useful as researchers increasingly access genomic methods to study multivariate selection responses in wild populations.

493 Awardee Talk: Filling Knowledge Gaps in Quantitative Genetics Through Online Education

The genomic revolution has been compared to the industrial revolution, with caveats that it has happened faster and will have a far greater impact on our lives. Interpreting and using knowledge emanating from this revolution requires unique skills. Providing education in quantitative genetics that keeps pace with that need, particularly where expertise and funds are limited, remains challenging. One solution is sharing resources and capacities across-institutions to deliver high-quality instruction online. Beginning with 4 universities in 2007, expanding to 7 in 2012, a multi-state U.S. consortium built an online Masters-level curriculum in quantitative genetics and genomics. Sixteen courses were developed, each revised based on review by 2 academic peers and an instructional designer. Over 330 students from 34 U.S. and 5 international institutions have completed over 1,200 credit hours. Anonymous student feedback has been overwhelmingly positive. The curriculum was established with funding from two USDA-NIFA Higher Education Challenge grants. In 2015 it was integrated into AG*IDEA, a national consortium offering online courses in agriculture. A permanent infrastructure was thereby established with students earning formal academic credit. Only students matriculated at one of 19 AG*IDEA member universities can enroll directly, sadly limiting access, especially to international students. A potential constraint of online instruction is a disconnect with students. In some courses, a blended-learning format has been introduced with a weekly virtual recitation session. To increase engagement, an experiential learning opportunity also is offered. This entails a web-based simulation game—CyberSheep—where students apply genetic principles to a virtual breeding cooperative. Additionally, CyberSheep is typically played by 400 undergraduate students at 5 U.S. universities each academic term, contributing to their learning of animal genetics. Outcomes of these initiatives demonstrate that online training can be an effective tool to fill knowledge gaps in quantitative genetics, with opportunity to reach a wider audience.

Decomposing phenotypic skew and its effects on the predicted response to strong selection

The major frameworks for predicting evolutionary change assume that a phenotype's underlying genetic and environmental components are normally distributed. However, the predictions of these frameworks may no longer hold if distributions are skewed. Despite this, phenotypic skew has never been decomposed, meaning the fundamental assumptions of quantitative genetics remain untested. Here, we demonstrate that the substantial phenotypic skew in the body size of juvenile blue tits (Cyanistes caeruleus) is driven by environmental factors. Although skew had little impact on our predictions of selection response in this case, our results highlight the impact of skew on the estimation of inheritance and selection. Specifically, the non-linear parent-offspring regressions induced by skew, alongside selective disappearance, can strongly bias estimates of heritability. The ubiquity of skew and strong directional selection on juvenile body size implies that heritability is commonly overestimated, which may in part explain the discrepancy between predicted and observed trait evolution.

The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to Indirect Genetic Effects dominates heritable variation and response to selection

Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently we investigate the population level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.

The microbiome extends host evolutionary potential

The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.

Integrating genomics and multivariate evolutionary quantitative genetics: A case study of multivariate constraints on sexual selection in Drosophila serrata

In evolutionary quantitative genetics, the genetic variance-covariance matrix, G, and the vector of directional selection gradients, β , are key parameters for predicting multivariate selection responses and genetic constraints. Historically, investigations of G and β have not overlapped with those dissecting the genetic basis of quantitative traits. Thus, it remains unknown whether these parameters reflect pleiotropic effects at individual loci. Here, we integrate multivariate GWAS with G and β estimation in a well-studied system of multivariate constraint; sexual selection on male cuticular hydrocarbons (CHCs) in Drosophila serrata. In a panel of wild-derived resequenced lines, we augment genome-based REML, (GREML) to estimate G alongside multivariate SNP effects, detecting 532 significant associations from 1,652,276 SNPs. Constraint was evident, with β lying in a direction of G with low evolvability. Interestingly, minor frequency alleles typically increased male CHC-attractiveness suggesting opposing natural selection on β. SNP effects were significantly misaligned with the major eigenvector of G, gmax, but well aligned to the second and third eigenvectors g2 and g3. We discuss potential factors leading to these varied results including multivariate stabilising selection and mutational bias. Our framework may be useful as researchers increasingly access genomic methods to study multivariate selection responses in wild populations.