Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products.

This perspective describes the opportunities and challenges of data-driven approaches for crop diversity management (genebanks and breeding) in the context of agricultural research for sustainable development in the Global South. Data-driven approaches build on larger volumes of data and flexible analyses that link different datasets across domains and disciplines. This can lead to more information-rich management of crop diversity, which can address the complex interactions between crop diversity, production environments, and socioeconomic heterogeneity and help to deliver more suitable portfolios of crop diversity to users with highly diverse demands. We describe recent efforts that illustrate the potential of data-driven approaches for crop diversity management. A continued investment in this area should fill remaining gaps and seize opportunities, including i) supporting genebanks to play a more active role in linking with farmers using data-driven approaches; ii) designing low-cost, appropriate technologies for phenotyping; iii) generating more and better gender and socioeconomic data; iv) designing information products to facilitate decision-making; and v) building more capacity in data science. Broad, well-coordinated policies and investments are needed to avoid fragmentation of such capacities and achieve coherence between domains and disciplines so that crop diversity management systems can become more effective in delivering benefits to farmers, consumers, and other users of crop diversity.

Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli.

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies.

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.

The genetic basis of dispersal in a vertebrate metapopulation.

Dispersal affects evolutionary processes by changing population size and genetic composition, influencing the viability and persistence of populations. Investigating which mechanisms underlie variation in dispersal phenotypes and whether populations harbour adaptive potential for dispersal is crucial to understanding the eco-evolutionary dynamics of this important trait. Here, we investigate the genetic architecture of dispersal among successfully recruited individuals in an insular metapopulation of house sparrows. We use an extensive long-term individual-based ecological data set and high-density single-nucleotide polymorphism (SNP) genotypes for over 2500 individuals. We conducted a genome-wide association study (GWAS), and found a relationship between dispersal probability and a SNP located near genes known to regulate circadian rhythm, glycogenesis and exercise performance, among other functions. However, this SNP only explained 3.8% of variance, suggesting that dispersal is a polygenic trait. We then used an animal model to estimate heritable genetic variation (σA 2 ), which composes 10% of the total variation in dispersal probability. Finally, we investigated differences in σA 2 across populations occupying ecologically relevant habitat types (farm vs. non-farm) using a genetic groups animal model. We found different adaptive potentials across habitats, with higher mean breeding value, σA 2 , and heritability for the habitat presenting lower dispersal rates, suggesting also different roles of environmental variation. Our results suggest a complex genetic architecture of dispersal and demonstrate that adaptive potential may be environment dependent in key eco-evolutionary traits. The eco-evolutionary implications of such environment dependence and consequent spatial variation are likely to become ever more important with the increased fragmentation and loss of suitable habitats for many natural populations.

Environmental context of phenotypic plasticity in flowering time in sorghum and rice.

Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.

The Garden of Forking Paths: Reinterpreting Haseman-Elston Regression for a Genotype-by-Environment Model.

Haseman-Elston regression (HE-reg) has been known as a classic tool for detecting an additive genetic variance component. However, in this study we find that HE-reg can capture GxE under certain conditions, so we derive and reinterpret the analytical solution of HE-reg. In the presence of GxE, it leads to a natural discrepancy between linkage and association results, the latter of which is not able to capture GxE if the environment is unknown. Considering linkage and association as symmetric designs, we investigate how the symmetry can and cannot hold in the absence and presence of GxE, and consequently we propose a pair of statistical tests, Symmetry Test I and Symmetry Test II, both of which can be tested using summary statistics. Test statistics, and their statistical power issues are also investigated for Symmetry Tests I and II. Increasing the number of sib pairs is important to improve statistical power for detecting GxE.

Parental Nurturance Moderates the Etiology of Youth Resilience.

Parenting behaviors are among the most robust predictors of youth resilience to adversity. Critically, however, very few studies examining these effects have been genetically-informed, and none have considered parenting as an etiologic moderator of resilience. What's more, despite the multidimensionality of resilience, extant etiologic literature has largely focused on a single domain. The current study sought to fill these respective gaps in the literature by examining whether and how parental nurturance shapes the etiology of academic, social, and psychological resilience, respectively. We employed a unique sample of twins (N = 426 pairs; ages 6-11) exposed to moderate-to-severe levels of environmental adversity (i.e., family poverty, neighborhood poverty, community violence) from the Twin Study of Behavioral and Emotional Development in Children. As expected, parental nurturance was positively correlated with all forms of resilience. Extended univariate genotype-by-environment interaction models revealed that parental nurturance significantly moderated genetic influences on all three domains of resilience (academic resilience A1= -0.53, psychological resilience A1= -1.22, social resilience A1= -0.63; all p < .05), such that as parental nurturance increased, genetic influences on youth resilience decreased. Put another way, children experiencing high levels of parental nurturance were more resilient to disadvantage, regardless of their genetic predisposition towards resilience. In the absence of nurturing parenting, however, genetic influences played an outsized role in the origins of resilience. Such findings indicate that parental nurturance may serve as a malleable protective factor that increases youth resilience regardless of genetic influences.

Among- and within-population variation in germination response shapes ecological resilience in the Mediterranean cliff species Brassica incana Ten.

BACKGROUND AND AIMS: Understanding how plant species respond to extreme conditions is crucial for predicting their ecological resilience under climate change. Here, we aim to forecast the ecological resilience of the Mediterranean cliff species Brassica incana (Brassicaceae) by estimating population variation in germination response under novel extreme environmental conditions. METHODS: We investigated the thermal germination responses in 14 populations of B. incana by exposing seeds to temperatures within and outside conditions experienced in their local environment. Then, we quantified among- and within-population variation in germination response to extreme temperatures, estimated genotype-by-environment interactions (G × E) and tested if population performance at extreme temperatures is explained by local climate. KEY RESULTS: We found significant among-population differences in germination response, a different level of within-population variability, and different mechanisms underlying G × E patterns. Also, populations experiencing warmer temperatures in their local environment showed a better performance at both cold and hot extremes while populations experiencing colder temperatures showed a limited ability to germinate under extreme conditions. CONCLUSIONS: Our results suggest that populations experiencing warmer temperatures in their local environment have a higher potential to face future thermal extreme conditions and their role is thus crucial to promote species ecological resilience.

Multi-location evaluation of field pea in Indian climates: eco-phenological dynamics, crop-environment relationships, and identification of mega-environments.

Characterization of crop-growing environments in relation to crop's genotypic performance is crucial to harness positive genotype-by-environment interactions (GEI) in systematic breeding programs. Given that, the study aimed to delineate the impact of diverse environments on crop phenology and yield traits of dwarf-statured field pea, pinpointing location(s) favoring higher yield and distinctiveness within breeding lines. We tested twelve field pea breeding lines across twenty locations in India, covering Central Zone (CZ), North Western Plain Zone (NWPZ), North Eastern Plain Zone (NEPZ), and Northern Hill Zone (NHZ). Across these locations, maximum and minimum temperatures during flowering (TMAXF, TMINF) and reproductive period (TMAXRP, TMINRP) ranged 18.9-28.3, 3.3-18.0, 15.0-30.8, and 7.9-22.1oC, respectively. Meanwhile, notable variations in phenological and agronomic traits (coefficient of variation) were observed: flowering (31%), days to maturity (21%), reproductive period (18%), grain yield (48%), and 100-seed weight (18%). Combined ANOVA demonstrated an oversized impact of environment (81%) on yield, while genotype and GEI effects were 2% and 14%, respectively. The variables TMINF, TMINRP, and cumulative growing degree-day showed positive correlations with yield, while extended vegetative and maturity durations negatively influenced yield (p < 0.05). Additionally, linear mixed-models and PCA results explained that instability in crop phenology had significant influence on field pea yield. Seed weight was markedly varied within the locations (9.9-20.8 g) and both higher and lower seed weights were associated with lower yields (Optimal = 17.1 g). HA-GGE biplot-based on environment focus-scaling demonstrated three mega-environments and specific locations viz. Kota (CZ), SK Nagar (CZ), Raipur (CZ), Sehore (CZ), and Pantnagar (NWPZ) as the ideal testing-environments with high efficiency in selecting new genotypes with wider adaptability. The study findings highlight distinct impact of environments on crop phenology and agronomic traits of field pea (dwarf-type), hold substantial value in designing efficient field pea (dwarf-type) breeding program at mega-environment scale.

Transgenerational effects of the maternal gestational environment on the post-natal performance of beef cattle: A reaction norm approach.

In tropical beef cattle production systems, animals are commonly raised on pastures, exposing them to potential stressors. The end of gestation typically overlaps with a dry period characterized by limited food availability. Late gestation is pivotal for fetal development, making it an ideal scenario for inter- and transgenerational effects of the maternal gestational environment. Intergenerational effects occur due to exposure during gestation, impacting the development of the embryo and its future germline. Transgenerational effects, however, extend beyond direct exposure to the subsequent generations. The objective of the present study was to verify these effects on the post-natal performance of zebu beef cattle. We extended the use of a reaction norm model to identify genetic variation in the animals' responses to transgenerational effects. The inter- and transgenerational effects were predominantly positive (-0.09% to 19.74%) for growth and reproductive traits, indicating improved animal performance on the phenotypic scale in more favourable maternal gestational environments. Additionally, these effects were more pronounced in the reproductive performance of females. On average, the ratio of direct additive genetic variances of the slope and intercept of the reaction norm ranged from 1.23% to 3.60% for direct and from 10.17% to 11.42% for maternal effects. Despite its relatively modest magnitude, this variation proved sufficient to prompt modifications in parameter estimates. The average percentage variation of direct heritability estimates ranged from 19.3% for scrotal circumference to 33.2% for yearling weight across the environmental descriptors evaluated. Genetic correlations between distant environments for the studied traits were generally high for direct effects and far from unity for maternal effects. Changes in EBV rankings of sires across different gestational environments were also observed. Due to the multifaceted nature of inter- and transgenerational effects of the maternal gestational environment on various traits of beef cattle raised under tropical pasture conditions, they should not be overlooked by producers and breeders. There were differences in the specific response of beef cattle to variations in the quality of the maternal gestational environment, which can be partially explained by transgenerational epigenetic inheritance. Adopting a reaction norm model to capture a portion of the additive variance induced by inter- or transgenerational effects could be an alternative for future research and animal genetic evaluations.

Niche construction and the environmental term of the price equation: How natural selection changes when organisms alter their environments.

Organisms construct their own environments and phenotypes through the adaptive processes of habitat choice, habitat construction, and phenotypic plasticity. We examine how these processes affect the dynamics of mean fitness change through the environmental change term of the Price Equation. This tends to be ignored in evolutionary theory, owing to the emphasis on the first term describing the effect of natural selection on mean fitness (the additive genetic variance for fitness of Fisher's Fundamental Theorem). Using population genetic models and the Price Equation, we show how adaptive niche constructing traits favorably alter the distribution of environments that organisms encounter and thereby increase population mean fitness. Because niche-constructing traits increase the frequency of higher-fitness environments, selection favors their evolution. Furthermore, their alteration of the actual or experienced environmental distribution creates selective feedback between niche constructing traits and other traits, especially those with genotype-by-environment interaction for fitness. By altering the distribution of experienced environments, niche constructing traits can increase the additive genetic variance for such traits. This effect accelerates the process of overall adaption to the niche-constructed environmental distribution and can contribute to the rapid refinement of alternative phenotypic adaptations to different environments. Our findings suggest that evolutionary biologists revisit and reevaluate the environmental term of the Price Equation: owing to adaptive niche construction, it contributes directly to positive change in mean fitness; its magnitude can be comparable to that of natural selection; and, when there is fitness G × E, it increases the additive genetic variance for fitness, the much-celebrated first term.

Pervasive genotype-by-environment interactions shape the fitness effects of antibiotic resistance mutations.

The fitness effects of antibiotic resistance mutations are a major driver of resistance evolution. While the nutrient environment affects bacterial fitness, experimental studies of resistance typically measure fitness of mutants in a single environment only. We explored how the nutrient environment affected the fitness effects of rifampicin-resistant rpoB mutations in Escherichia coli under several conditions critical for the emergence and spread of resistance-the presence of primary or secondary antibiotic, or the absence of any antibiotic. Pervasive genotype-by-environment (GxE) interactions determined fitness in all experimental conditions, with rank order of fitness in the presence and absence of antibiotics being strongly dependent on the nutrient environment. GxE interactions also affected the magnitude and direction of collateral effects of secondary antibiotics, in some cases so drastically that a mutant that was highly sensitive in one nutrient environment exhibited cross-resistance to the same antibiotic in another. It is likely that the mutant-specific impact of rpoB mutations on the global transcriptome underpins the observed GxE interactions. The pervasive, mutant-specific GxE interactions highlight the importance of doing what is rarely done when studying the evolution and spread of resistance in experimental and clinical work: assessing fitness of antibiotic-resistant mutants across a range of relevant environments.

A predominant role of genotypic variation in both expression of sperm competition genes and paternity success in Drosophila melanogaster.

Sperm competition is a crucial aspect of male reproductive success in many species, including Drosophila melanogaster, and seminal fluid proteins (Sfps) can influence sperm competitiveness. However, the combined effect of environmental and genotypic variation on sperm competition gene expression remains poorly understood. Here, we used Drosophila Genetic Reference Panel (DGRP) inbred lines and manipulated developmental population density (i.e. larval density) to test the effects of genotype, environment and genotype-by-environment interactions (GEI) on the expression of the known sperm competition genes Sex Peptide, Acp36DE and CG9997. High larval density resulted in reduced adult body size, but expression of sperm competition genes remained unaffected. Furthermore, we found no significant GEI but genotypic effects in the expression of SP and Acp36DE. Our results also revealed GEI for relative competitive paternity success (second male paternity; P2), with genes' expression positively correlated with P2. Given the effect of genotype on the expression of genes, we conducted a genome-wide association study (GWAS) and identified polymorphisms in putative cis-regulatory elements as predominant factors regulating the expression of SP and Acp36DE. The association of genotypic variation with sperm competition outcomes, and the resilience of sperm competition genes' expression against environmental challenges, demonstrates the importance of genome variation background in reproductive fitness.

Predicting plasticity of rosette growth and metabolic fluxes in Arabidopsis thaliana.

Plants can rapidly mitigate the effects of suboptimal growth environments by phenotypic plasticity of fitness-traits. While genetic variation for phenotypic plasticity offers the means for breeding climate-resilient crop lines, accurate genomic prediction models for plasticity of fitness-related traits are still lacking. Here, we employed condition- and accession-specific metabolic models for 67 Arabidopsis thaliana accessions to dissect and predict plasticity of rosette growth to changes in nitrogen availability. We showed that specific reactions in photorespiration, linking carbon and nitrogen metabolism, as well as key pathways of central carbon metabolism exhibited substantial genetic variation for flux plasticity. We also demonstrated that, in comparison with a genomic prediction model for fresh weight (FW), genomic prediction of growth plasticity improves the predictability of FW under low nitrogen by 58.9% and by additional 15.4% when further integrating data on plasticity of metabolic fluxes. Therefore, the combination of metabolic and statistical modeling provides a stepping stone in understanding the molecular mechanisms and improving the predictability of plasticity for fitness-related traits.

Plasticity in growth is genetically variable and highly conserved across spatial scales in a Mediterranean pine.

Phenotypic plasticity is a main mechanism for sessile organisms to cope with changing environments. Plasticity is genetically based and can evolve under natural selection so that populations within a species show distinct phenotypic responses to environment. An important question that remains elusive is whether the intraspecific variation in plasticity at different spatial scales is independent from each other. To test whether variation in plasticity to macro- and micro-environmental variation is related among each other, we used growth data of 25 Pinus pinaster populations established in seven field common gardens in NW Spain. Phenotypic plasticity to macro-environmental variation was estimated across test sites while plasticity to micro-environmental variation was estimated by using semivariography and kriging for modeling within-site heterogeneity. We provide empirical evidence of among-population variation in the magnitude of plastic responses to both micro- and macro-environmental variation. Importantly, we found that such responses were positively correlated across spatial scales. Selection for plasticity at one scale of environmental variation may impact the expression of plasticity at other scales, having important consequences on the ability of populations to buffer climate change. These results improve our understanding of the ecological drivers underlying the expression of phenotypic plasticity.

Relationships between phenotypic plasticity and epigenetic variation in two Caribbean Acropora corals.

The plastic ability for a range of phenotypes to be exhibited by the same genotype allows organisms to respond to environmental variation and may modulate fitness in novel environments. Differing capacities for phenotypic plasticity within a population, apparent as genotype by environment interactions (GxE), can therefore have both ecological and evolutionary implications. Epigenetic gene regulation alters gene function in response to environmental cues without changes to the underlying genetic sequence and likely mediates phenotypic variation. DNA methylation is currently the most well described epigenetic mechanism and is related to transcriptional homeostasis in invertebrates. However, evidence quantitatively linking variation in DNA methylation with that of phenotype is lacking in some taxa, including reef-building corals. In this study, spatial and seasonal environmental variation in Bonaire, Caribbean Netherlands was utilized to assess relationships between physiology and DNA methylation profiles within genetic clones across different genotypes of Acropora cervicornis and A. palmata corals. The physiology of both species was highly influenced by environmental variation compared to the effect of genotype. GxE effects on phenotype were only apparent in A. cervicornis. DNA methylation in both species differed between genotypes and seasons and epigenetic variation was significantly related to coral physiological metrics. Furthermore, plastic shifts in physiology across seasons were significantly positively correlated with shifts in DNA methylation profiles in both species. These results highlight the dynamic influence of environmental conditions and genetic constraints on the physiology of two important Caribbean coral species. Additionally, this study provides quantitative support for the role of epigenetic DNA methylation in mediating phenotypic plasticity in invertebrates.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

A warmer environment can reduce sociability in an ectotherm.

The costs and benefits of being social vary with environmental conditions, so individuals must weigh the balance between these trade-offs in response to changes in the environment. Temperature is a salient environmental factor that may play a key role in altering the costs and benefits of sociality through its effects on food availability, predator abundance, and other ecological parameters. In ectotherms, changes in temperature also have direct effects on physiological traits linked to social behaviour, such as metabolic rate and locomotor performance. In light of climate change, it is therefore important to understand the potential effects of temperature on sociality. Here, we took the advantage of a 'natural experiment' of threespine sticklebacks from contrasting thermal environments in Iceland: geothermally warmed water bodies (warm habitats) and adjacent ambient-temperature water bodies (cold habitats) that were either linked (sympatric) or physically distinct (allopatric). We first measured the sociability of wild-caught adult fish from warm and cold habitats after acclimation to a low and a high temperature. At both acclimation temperatures, fish from the allopatric warm habitat were less social than those from the allopatric cold habitat, whereas fish from sympatric warm and cold habitats showed no differences in sociability. To determine whether differences in sociability between thermal habitats in the allopatric population were heritable, we used a common garden breeding design where individuals from the warm and the cold habitat were reared at a low or high temperature for two generations. We found that sociability was indeed heritable but also influenced by rearing temperature, suggesting that thermal conditions during early life can play an important role in influencing social behaviour in adulthood. By providing the first evidence for a causal effect of rearing temperature on social behaviour, our study provides novel insights into how a warming world may influence sociality in animal populations.

Importance of genetic architecture in marker selection decisions for genomic prediction.

KEY MESSAGE: We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait. Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.