The geometry of phenotypic evolution in developmental hyperspace.

Charles Darwin proposed that evolution occurs primarily by natural selection, but this view has been controversial from the beginning. Two of the major opposing views have been mutationism and neutralism. Early molecular studies suggested that most amino acid substitutions in proteins are neutral or nearly neutral and the functional change of proteins occurs by a few key amino acid substitutions. This suggestion generated an intense controversy over selectionism and neutralism. This controversy is partially caused by Kimura's definition of neutrality, which was too strict (|2Ns|< or =1). If we define neutral mutations as the mutations that do not change the function of gene products appreciably, many controversies disappear because slightly deleterious and slightly advantageous mutations are engulfed by neutral mutations. The ratio of the rate of nonsynonymous nucleotide substitution to that of synonymous substitution is a useful quantity to study positive Darwinian selection operating at highly variable genetic loci, but it does not necessarily detect adaptively important codons. Previously, multigene families were thought to evolve following the model of concerted evolution, but new evidence indicates that most of them evolve by a birth-and-death process of duplicate genes. It is now clear that most phenotypic characters or genetic systems such as the adaptive immune system in vertebrates are controlled by the interaction of a number of multigene families, which are often evolutionarily related and are subject to birth-and-death evolution. Therefore, it is important to study the mechanisms of gene family interaction for understanding phenotypic evolution. Because gene duplication occurs more or less at random, phenotypic evolution contains some fortuitous elements, though the environmental factors also play an important role. The randomness of phenotypic evolution is qualitatively different from allele frequency changes by random genetic drift. However, there is some similarity between phenotypic and molecular evolution with respect to functional or environmental constraints and evolutionary rate. It appears that mutation (including gene duplication and other DNA changes) is the driving force of evolution at both the genic and the phenotypic levels.

Identifying selection on polygenic complex traits in crops and livestock is important for understanding evolution and helps prioritize important characteristics for breeding. Quantitative trait loci (QTL) that contribute to polygenic trait variation often exhibit small or infinitesimal effects. This hinders the ability to detect QTL-controlling polygenic traits because enormously high statistical power is needed for their detection. Recently, we circumvented this challenge by introducing a method to identify selection on complex traits by evaluating the relationship between genome-wide changes in allele frequency and estimates of effect size. The approach involves calculating a composite statistic across all markers that capture this relationship, followed by implementing a linkage disequilibrium-aware permutation test to evaluate if the observed pattern differs from that expected due to drift during evolution and population stratification. In this manuscript, we describe “Ghat,” an R package developed to implement this method to test for selection on polygenic traits. We demonstrate the package by applying it to test for polygenic selection on 15 published European wheat traits including yield, biomass, quality, morphological characteristics, and disease resistance traits. Moreover, we applied Ghat to different simulated populations with different breeding histories and genetic architectures. The results highlight the power of Ghat to identify selection on complex traits. The Ghat package is accessible on CRAN, the Comprehensive R Archival Network, and on GitHub.

Changes in alleles frequencies of marker loci linked to yield quantitative trait loci (QTL) were studied in 188 barley entries (landraces, old and modern cultivars) grown in six trials representing low and high yielding conditions in Spain (2004) and Syria (2004, 2005). A genome wise association analysis was performed per trial, using 811 DArT® markers of known map position. At the first stage of analysis, spatially adjusted genotypic means were created per trial by fitting mixed models. At the second stage, single QTL models were fitted with correction for population substructure, using regression models. Finally, multiple QTL models were constructed by backward selection from a regression model containing all significant markers from the single QTL analyses. In addition to the association analyses per trial, genotype by environment interaction was investigated across the six trials. Landraces seemed best adapted to low yielding environments, while old and modern entries adapted better to high yielding environments. The number of QTL and the magnitude of their effects were comparable for low and high input conditions. However, none of the QTL were found within a given bin at any chromosome in more than two of the six trials. Changes in allele frequencies of marker loci close to QTL for grain yield in landraces, old and modern barley cultivars could be attributed to selection exercised in breeding, suggesting that modern breeding may have increased frequencies of marker alleles close to QTL that favour production particularly under high yield potential environments. Moreover, these results also indicate that there may be scope for improving yield under low input systems, as breeding so far has hardly changed allele frequencies at marker loci close to QTL for low yielding conditions.

Recent studies have revealed the importance of feedbacks between contemporary rapid evolution (i.e. evolution that occurs through changes in allele frequencies) and ecological dynamics. Despite its inherent interdisciplinary nature, however, studies on eco-evolutionary feedbacks have been mostly ecological and tended to focus on adaptation at the phenotypic level without considering the genetic architecture of evolutionary processes. In empirical studies, researchers have often compared ecological dynamics when the focal species under selection has a single genotype with dynamics when it has multiple genotypes. In theoretical studies, common approaches are models of quantitative traits where mean trait values change adaptively along the fitness gradient and Mendelian traits with two alleles at a single locus. On the other hand, it is well known that genetic architecture can affect short-term evolutionary dynamics in population genetics. Indeed, recent theoretical studies have demonstrated that genetic architecture (e.g. the number of loci, linkage disequilibrium and ploidy) matters in eco-evolutionary dynamics (e.g. evolutionary rescue where rapid evolution prevents extinction and population cycles driven by (co)evolution). I propose that theoretical approaches will promote the synthesis of functional genomics and eco-evolutionary dynamics through models that combine population genetics and ecology as well as nonlinear time-series analyses using emerging big data.This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Selection and random genetic drift are the two main forces affecting the selection response of recurrent selection (RS) programs by changes in allele frequencies. Therefore, detailed knowledge on allele frequency changes attributable to these forces is of fundamental importance for assessing RS programs. The objectives of our study were to (1) estimate the number, position, and genetic effect of quantitative trait loci (QTL) for selection index and its components in the base populations, (2) determine changes in allele frequencies of QTL regions due to the effects of random genetic drift and selection, and (3) predict allele frequency changes by using QTL results and compare these predictions with observed values. We performed QTL analyses, based on restriction fragment length polymorphisms (RFLPs) and simple sequence repeats (SSRs), in 274 F(2:3) lines of cross KW1265 x D146 (A x B) and 133 F(3:4) lines of cross D145 x KW1292 (C x D) originating from two European flint maize populations. Four (A x B) and seven (C x D) cycles of RS were analyzed with SSRs for significant allele frequency changes due to selection. Several QTL regions for selection index were detected with simple and composite interval mapping. In some of them, flanking markers showed a significant allele frequency change after the first and the final selection cycles. The correlation between observed and predicted allele frequencies was significant only in A x B. We attribute these observations mainly to (1) the high dependence of the power of QTL detection on the population size and (2) the occurrence of undetectable QTL in repulsion phase. Assessment of allele frequency changes in RS programs can be used to detect marker alleles linked to QTL regions under selection pressure.

Blood pressure (BP) in any human population exhibits as a continuous variable that fits a bell-shaped curve. Hypertensive individuals are those whose BP is maintained at one extreme of the curve and above a defined cutoff. Despite progress made in identifying the mechanisms underlying certain rare monogenic forms of hypertension,1,2 the etiology and pathogenesis of essential hypertension remain poorly understood. Because existing human populations are genetically heterogeneous, and because environmental factors impacting on the pathogenesis of hypertension cannot be controlled in a given population, it is difficult to identify the molecular mechanisms that transduce the sequela of essential hypertension via direct human studies.3 To alleviate the drawbacks of human investigations, animal models, especially inbred rodents, have been developed and experimentally manipulated to identify quantitative trait loci (QTLs) for BP, because major confounding environmental factors, such as diet and genetic background, can be systematically controlled. Once identified in animal models, the molecular basis may be translated into physiological understandings of essential hypertension in humans. It is with this expectation that efforts have been launched to identify the molecular basis of BP QTLs in animal models. Because the identification of individual QTLs is primarily based on their chromosome locations unbiased by, or unrestricted to, their physiological roles, positional cloning is believed to be the most efficient strategy. Before we embark on discussions regarding QTL discovery, a definition is in order. Semantic arguments abound as to exactly what a QTL, that is, a locus,4 entails. Is it 1 gene or a collection of genes? As genetic mapping progresses from a large chromosome segment to an interval of submegabase, several regions initially thought to contain 1 BP QTL5 appear to harbor >1 in each of them,6–10 whereas several other regions turned out to harbor 1 QTL as expected.11,12 …

BackgroundThe genetic architecture of a quantitative trait influences the phenotypic response to natural or artificial selection. One of the main objectives of genetic mapping studies is to identify the genetic factors underlying complex traits and understand how they contribute to phenotypic expression. Presently, we are good at identifying and locating individual loci with large effects, but there is a void in describing more complex genetic architectures. Although large networks of connected genes have been reported, there is an almost complete lack of information on how polymorphisms in these networks contribute to phenotypic variation and change. To date, most of our understanding comes from theoretical, model-based studies, and it remains difficult to assess how realistic their conclusions are as they lack empirical support.ResultsA previous study provided evidence that nearly half of the difference in eight-week body weight between two divergently selected lines of chickens was a result of four loci organized in a 'radial' network (one central locus interacting with three 'radial' loci that, in turn, only interacted with the central locus). Here, we study the relationship between phenotypic change and genetic polymorphism in this empirically detected network. We use a model-free approach to study, through individual-based simulations, the dynamic properties of this polymorphic and epistatic genetic architecture. The study provides new insights to how epistasis can modify the selection response, buffer and reveal effects of major loci leading to a progressive release of genetic variation. We also illustrate the difficulty of predicting genetic architecture from observed selection response, and discuss mechanisms that might lead to misleading conclusions on underlying genetic architectures from quantitative trait locus (QTL) experiments in selected populations.ConclusionConsidering both molecular (QTL) and phenotypic (selection response) data, as suggested in this work, provides additional insights into the genetic mechanisms involved in the response to selection. Such dissection of genetic architectures and in-depth studies of their ability to contribute to short- or long-term selection response represents an important step towards a better understanding of the genetic bases of complex traits and, consequently, of the evolutionary properties of populations.

BackgroundGenetic selection improves a population by increasing the frequency of favorable alleles. Understanding and monitoring allele frequency changes is, therefore, important to obtain more insight into the long-term effects of selection. This study aimed to investigate changes in allele frequencies and in results of genome-wide association studies (GWAS), and how those two are related to each other. This was studied in two maternal pig lines where selection was based on a broad selection index. Genotypes and phenotypes were available from 2015 to 2021.ResultsSeveral large changes in allele frequencies over the years were observed in both lines. The largest allele frequency changes were not larger than expected under drift based on gene dropping simulations, but the average allele frequency change was larger with selection. Moreover, several significant regions were found in the GWAS for the traits under selection, but those regions did not overlap with regions with larger allele frequency changes. No significant GWAS regions were found for the selection index in both lines, which included multiple traits, indicating that the index is affected by many loci of small effect. Additionally, many significant regions showed pleiotropic, and often antagonistic, associations with other traits under selection. This reduces the selection pressure on those regions, which can explain why those regions are still segregating, although the traits have been under selection for several generations. Across the years, only small changes in Manhattan plots were found, indicating that the genetic architecture was reasonably constant.ConclusionsNo significant GWAS regions were found for any of the traits under selection among the regions with the largest changes in allele frequency, and the correlation between significance level of marker associations and changes in allele frequency over one generation was close to zero for all traits. Moreover, the largest changes in allele frequency could be explained by drift and were not necessarily a result of selection. This is probably because selection acted on a broad index for which no significant GWAS regions were found. Our results show that selecting on a broad index spreads the selection pressure across the genome, thereby limiting allele frequency changes.

Baltic and Arctic ecotypes of the marine midge Clunio emerged from a recent adaptive radiation, which is based on standing genetic variation at many loci involved in time-keeping and nervous system development.

Baltic and Arctic ecotypes of the marine midge Clunio emerged from a recent adaptive radiation, which is based on standing genetic variation at many loci involved in time-keeping and nervous system development.

BackgroundLittle is known about the genetic architecture of economically important traits in Brown Swiss cattle because only few genome-wide association studies (GWAS) have been carried out in this breed. Moreover, most GWAS have been performed for single traits, thus not providing detailed insights into potentially existing pleiotropic effects of trait-associated loci.ResultsTo compile a comprehensive catalogue of large-effect quantitative trait loci (QTL) segregating in Brown Swiss cattle, we carried out association tests between partially imputed genotypes at 598,016 SNPs and daughter-derived phenotypes for more than 50 economically important traits, including milk production, growth and carcass quality, body conformation, reproduction and calving traits in 4578 artificial insemination bulls from two cohorts of Brown Swiss cattle (Austrian-German and Swiss populations). Across-cohort multi-trait meta-analyses of the results from the single-trait GWAS revealed 25 quantitative trait loci (QTL; P < 8.36 × 10− 8) for economically relevant traits on 17 Bos taurus autosomes (BTA). Evidence of pleiotropy was detected at five QTL located on BTA5, 6, 17, 21 and 25. Of these, two QTL at BTA6:90,486,780 and BTA25:1,455,150 affect a diverse range of economically important traits, including traits related to body conformation, calving, longevity and milking speed. Furthermore, the QTL at BTA6:90,486,780 seems to be a target of ongoing selection as evidenced by an integrated haplotype score of 2.49 and significant changes in allele frequency over the past 25 years, whereas either no or only weak evidence of selection was detected at all other QTL.ConclusionsOur findings provide a comprehensive overview of QTL segregating in Brown Swiss cattle. Detected QTL explain between 2 and 10% of the variation in the estimated breeding values and thus may be considered as the most important QTL segregating in the Brown Swiss cattle breed. Multi-trait association testing boosts the power to detect pleiotropic QTL and assesses the full spectrum of phenotypes that are affected by trait-associated variants.

With many molecular markers in many species, research efforts in quantitative genetics have focused on dissecting these traits and understanding the importance of factors such as correlated response due to hitchhiking or pleiotropy. Here, in an examination of long-term selection experiments in mice, the evidence strongly supports the primary importance of hitchhiking on the coat colour loci brown and dilute in mice selected for high weight gain. First, the amount of observed change in coat colour allele frequency could not be explained by genetic drift alone, implying that selection was of high importance. Second, the allele frequency changes included reversals in the direction change, but there were still positive correlations in the early generations with differences in weight gain between the phenotypes. Third, the correlation between the change in allele frequencies and phenotypic difference in weight gain declined over time, consistent with the decay expected from linkage associations. Fourth, the changes at both loci in a short-term selection experiment for low weight gain were in the opposite direction than the changes in the contemporaneous related population selected for high weight gain.

Domestic animals have a sufficiently old history (thousands of generations) to allow evolution of phenotypes, but also a sufficiently young history (approximately 10,000 years) to allow powerful genetic dissection of phenotypic diversity. Domestic animals are therefore a unique resource for exploring genotype-phenotype relationships. Quantitative Trait Locus (QTL) mapping has been very successful in domestic animals but the identification of Quantitative Trait Mutations (QTMs) has been hard although a few prominent success histories have been reported. Genome-wide association analysis is now emerging as a powerful method for high-resolution mapping of loci controlling phenotypic traits in domestic animals. In two recent proof-of-principle studies we have used this approach to identify the mutations underlying two monogenic trait loci in dogs, white spotting and the hair ridge in Ridgeback dogs. In each case, we used only about 10 cases and 10 controls and mapped the locus to a region of about one mega base pair. In both cases the underlying mutations were non-coding underscoring the significance of regulatory mutations as a source for phenotypic diversity. Furthermore, we were able to shed light on the evolution of the allelic series at the white spotting (S) locus in dogs which encodes the microphthalmia-associated transcription factor (MITF). Our data showed that the three variant alleles described at this locus (Irish spotting, piebald and extreme white) do not represent three independent mutations but rather different combinations of a set of regulatory mutations affecting MITF expression. This is an excellent illustration of how the characterization of alleles selected during animal domestication contributes to an improved understanding of genotype-phenotype relationships.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from , to in time ) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.