The green-brown polymorphism in Orthoptera is a prominent example of the coexistence of multiple colour variants, especially since this polymorphism is shared by many species. The processes that maintain phenotypic polymorphisms depend on the underlying genetic and developmental regulation of body colouration, but these are not well understood for Orthoptera. Here we report on the inheritance of the green-brown polymorphism in the meadow grasshopper Pseudochorthippus parallelus, a species with four discrete colour morphs that differ in the distribution of green colouration across the body. We provide the most detailed analysis of the green-brown polymorphism to date using half-sib full-sib breeding and phenotyping of 4,300 offspring. The data strongly support a simple Mendelian control of the presence/absence of green colour in different regions of the body, involving four autosomal loci, two of which are genetically linked. However, estimation of population allele and haplotype frequencies using probabilistic simulations shows weak linkage disequilibrium in the population. The contrast between pedigree and population linkage suggests the presence of long-standing allelic variation and thus corroborates that long-term balancing selection is acting. Our study confirms and extends our understanding of inheritance patterns within the Chorthippus clade, providing unprecedented insights into the number and linkage of loci involved. The results have implications for the maintenance of polymorphisms and suggest that fluctuations in the phenotypic composition of populations can be generated by the segregation of genetic variants even in the absence of fluctuating selection.

The distribution of allelic effects on traits, along with their gene-by-gene and gene-by-environment interactions, contributes to the phenotypes available for selection and the trajectories of adaptive variants. Nonetheless, uncertainty persists regarding the effect sizes underlying adaptations and the importance of genetic interactions. Herein, we aimed to investigate the genetic architecture and the epistatic and environmental interactions involving loci that contribute to multiple adaptive traits using 2 new panels of Drosophila melanogaster recombinant inbred lines (RILs). To better fit our data, we re-implemented functions from R/qtl using additive genetic models. We found 14 quantitative trait loci (QTLs) underlying melanism, wing size, song pattern, and ethanol resistance. By combining our mapping results with population genetic statistics, we identified potential new genes related to these traits. None of the detected QTLs showed clear evidence of epistasis, and our power analysis indicated that we should have seen at least 1 significant interaction if sign epistasis or strong positive epistasis played a pervasive role in trait evolution. In contrast, we did find roles for gene-by-environment interactions involving pigmentation traits. Overall, our data suggest that the genetic architecture of adaptive traits often involves alleles of detectable effect, that strong epistasis does not always play a role in adaptation, and that environmental interactions can modulate the effect size of adaptive alleles.

Disease resistance in plants can be conferred by single genes of large effect or by multiple genes each conferring incomplete resistance. The latter case, termed quantitative resistance, may be difficult for pathogens to overcome through evolution due to the low selection pressures exerted by the actions of any single gene and, for some diseases, is the only identified source of genetic resistance. We evaluated quantitative resistance to 2 diseases of maize in a biparental mapping population as well as backcrosses to both parents. Quantitative trait locus analysis shows that the genetic architecture of resistance to these diseases is characterized by several modes of gene action including additivity as well as dominance, overdominance, and epistasis. Heterosis or hybrid vigor, the improved performance of a hybrid compared with its parents, can be caused by nonadditive gene action and is fundamental to the breeding of several crops including maize. In the backcross populations and a diverse set of maize hybrids, we find heterosis for resistance in many cases and that the degree of heterosis appears to be dependent on both hybrid genotype and disease.

The courtship song of Drosophila melanogaster has long served as an excellent model system for studies of animal communication and differences in courtship song have been demonstrated among populations and between species. Here, we report that flies of African and European origin, which diverged ∼13,000 years ago, show significant genetic differentiation in the use of slow vs fast pulse song. Using a combination of quantitative trait mapping and population genetic analysis, we detected a single strong quantitative trait locus underlying this trait and we identified candidate genes that may contribute to the evolution of this trait. Song trait variation between parental strains of our recombinant inbred panel enabled detection of genomic intervals associated with 6 additional song traits, some of which include known courtship-related genes. These findings improve the prospects for further genetic insights into the evolution of reproductive behavior and the biology underlying courtship song.

A central challenge in biology is to understand how complex behaviors evolve. Given their importance to fitness, complex behavioral traits often evolve as an integrated package, but it is unclear whether suites of traits evolve through a few pleiotropic genetic changes, each affecting many behaviors, or by accumulating several changes that, when combined, give rise to an entire package of correlated traits. Typically, three-spined stickleback exhibit paternal care, a behavior that characterizes the entire Gasterosteidae family. However, an unusual "white" three-spined stickleback ecotype exhibits a suite of traits associated with the evolutionary loss of paternal care. In the white ecotype, males disperse embryos from their nests rather than care for them, build loose nests, exhibit high rates of courtship, and are relatively small in body size. These behavioral differences are apparent in stickleback reared in a common garden environment, suggesting the differences have a heritable basis. In an F2 intercross, we show that these traits are genetically uncorrelated and map to different genomic regions, suggesting that components of the white reproductive strategy segregate independently and evolved through the addition of multiple genetic changes. These results contribute to the growing body of evidence that the behavioral diversity observed in nature may often evolve by accumulating and combining alleles, each with modular effects, and show that this principle applies to a suite of behavioral traits that together form an integrated and adaptive strategy.

Precision health is a person-centered approach to health and well-being that is operationalized through evaluating omics-level profiles and their associations with the exposome. A precision health approach addresses the challenge that "one size does not fit all" in the management of an individual's health.

The purpose of this white paper is to introduce a Precision Health Model and its application in research and clinical care.

An expert panel reviewed and synthesized the extant literature related to precision health, the current state of omics' science, and common exposome factors that influence the health/illness continuum. A case study provides the framework for the application of the Precision Health Model.

Precision health and key domains are defined and serve as the platform for the development of the Precision Health Model.

Application of the Precision Health Model will provide inclusive, equitable, person-centered research and clinical care.

The courtship song of Drosophila melanogaster has long served as an excellent model system for studies of animal communication and differences in courtship song have been demonstrated among populations and between species. Here, we report that flies of African and European origin, which diverged approximately 13,000 years ago, show significant genetic differentiation in the use of slow versus fast pulse song. Using a combination of quantitative trait mapping and population genetic analysis we detected a single strong QTL underlying this trait and we identified candidate genes that may contribute to the evolution of this trait. Song trait variation between parental strains of our recombinant inbred panel enabled detection of genomic intervals associated with six additional song traits, some of which include known courtship-related genes. These findings improve the prospects for further genetic insights into the evolution of reproductive behavior and the biology underlying courtship song.

The distribution of allelic effects on traits, along with their gene-by-gene and gene-by-environment interactions, contributes to the phenotypes available for selection and the trajectories of adaptive variants. Nonetheless, uncertainty persists regarding the effect sizes underlying adaptations and the importance of genetic interactions. Herein, we aimed to investigate the genetic architecture and the epistatic and environmental interactions involving loci that contribute to multiple adaptive traits using two new panels of Drosophila melanogaster recombinant inbred lines (RILs). To better fit our data, we re-implemented functions from R/qtl (Broman et al. 2003) using additive genetic models. We found 14 quantitative trait loci (QTL) underlying melanism, wing size, song pattern, and ethanol resistance. By combining our mapping results with population genetic statistics, we identified potential new genes related to these traits. None of the detected QTLs showed clear evidence of epistasis, and our power analysis indicated that we should have seen at least one significant interaction if sign epistasis or strong positive epistasis played a pervasive role in trait evolution. In contrast, we did find roles for gene-by-environment interactions involving pigmentation traits. Overall, our data suggest that the genetic architecture of adaptive traits often involves alleles of detectable effect, that strong epistasis does not always play a role in adaptation, and that environmental interactions can modulate the effect size of adaptive alleles.

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Over the last three decades, human genetics has gone from dissecting high-penetrance Mendelian diseases to discovering the vast and complex genetic etiology of common human diseases. In tackling this complexity, scientists have discovered the importance of numerous genetic processes - most notably functional regulatory elements - in the development and progression of these diseases. Simultaneously, scientists have increasingly used multiplex assays of variant effect to systematically phenotype the cellular consequences of millions of genetic variants. In this article, we argue that the context of genetic variants - at all scales, from other genetic variants and gene regulation to cell biology to organismal environment - are critical components of how we can employ genomics to interpret these variants, and ultimately treat these diseases. We describe approaches to extend existing experimental assays and computational approaches to examine and quantify the importance of this context, including through causal analytic approaches. Having a unified understanding of the molecular, physiological, and environmental processes governing the interpretation of genetic variants is sorely needed for the field, and this perspective argues for feasible approaches by which the combined interpretation of cellular, animal, and epidemiological data can yield that knowledge.

The elucidation of genetic architecture and molecular regulatory networks underlying complex traits remains a significant challenge in life science, largely due to the substantial background effects that arise from epistasis and gene-environment interactions. The chromosome segment substitution line (CSSL) is an ideal material for genetic and molecular dissection of complex traits due to its near-isogenic properties; yet a comprehensive analysis, from the basic identification of substitution segments to advanced regulatory network, is still insufficient. Here, we developed two cotton CSSL populations on the Gossypium hirsutum background, representing wide adaptation and high lint yield, with introgression from G. barbadense, representing superior fibre quality. We sequenced 99 CSSLs that demonstrated significant differences from G. hirsutum in fibre, and characterized 836 dynamic fibre transcriptomes in three crucial developmental stages. We developed a workflow for precise resolution of chromosomal substitution segments; the genome sequencing revealed substitutions collectively representing 87.25% of the G. barbadense genome. Together, the genomic and transcriptomic survey identified 18 novel fibre-quality-related quantitative trait loci with high genetic contributions and the comprehensive landscape of fibre development regulation. Furthermore, analysis determined unique cis-expression patterns in CSSLs to be the driving force for fibre quality alteration; building upon this, the co-expression regulatory network revealed biological relationships among the noted pathways and accurately described the molecular interactions of GhHOX3, GhRDL1 and GhEXPA1 during fibre elongation, along with reliable predictions for their interactions with GhTBA8A5. Our study will enhance more strategic employment of CSSL in crop molecular biology and breeding programmes.

The northern pike Esox lucius is a freshwater fish with low genetic diversity but ecological success throughout the Northern Hemisphere. Here, we generate an annotated chromosome-level genome assembly of 941 Mbp in length with 25 chromosome-length scaffolds. We then genotype 47 northern pike from Alaska through New Jersey at a genome-wide scale and characterize a striking decrease in genetic diversity along the sampling range. Individuals west of the North American Continental Divide have substantially higher diversity than those to the east (e.g. Interior Alaska and St. Lawrence River have on average 181 and 64K heterozygous SNPs per individual, or a heterozygous SNP every 5.2 and 14.6 kbp, respectively). Individuals clustered within each population with strong support, with numerous private alleles observed within each population. Evidence for recent population expansion was observed for a Manitoba hatchery and the St. Lawrence population (Tajima's D = -1.07 and -1.30, respectively). Several chromosomes have large regions with elevated diversity, including LG24, which holds amhby, the ancestral sex determining gene. As expected amhby was largely male-specific in Alaska and the Yukon and absent southeast to these populations, but we document some amhby(-) males in Alaska and amhby(+) males in the Columbia River, providing evidence for a patchwork of presence of this system in the western region. These results support the theory that northern pike recolonized North America from refugia in Alaska and expanded following deglaciation from west to east, with probable founder effects resulting in loss of both neutral and functional diversity (e.g. amhby).

Haliotis midae is one of the most important molluscs in South African commercial aquaculture. In this study, a high-resolution integrated linkage map was constructed, and QTL identified using 2b-RADseq for genotyping SNPs in three families. The final integrated linkage map was composed by merging the individual family maps, resulting in 3290 informative SNPs mapping to 18 linkage groups, conforming to the known haploid chromosome number for H. midae. The total map spanned 1798.25 cM with an average marker interval of 0.55 cM, representing a genome coverage of 98.76%. QTL analysis, across all three families, resulted in a total of five QTL identified for growth-related traits, shell width, shell length, and total body weight. For shell width and total body weight, one QTL was identified for each trait respectively, whilst three QTL were identified for shell length. The identified QTL respectively explained between 7.20% and 11.40% of the observed phenotypic variance. All three traits were significantly correlated (r = 0.862-0.970; p < 0.01) and shared overlapping QTL. The QTL for growth traits were mapped back to the H. midae draft genome and BLAST searches revealed the identity of candidate genes, such as egf-1, megf10, megf6, tnx, sevp1, kcp, notch1, and scube2 with possible functional roles in H. midae growth. The constructed high-density linkage map and mapped QTL have given valuable insights regarding the genetic architecture of growth-related traits and will be important genetic resources for marker-assisted selection. It remains, however, important to validate causal variants through linkage disequilibrium fine mapping in future.

This study evaluated 290 recombinant inbred lines (RILs) of the nested association mapping (NAM) population from the UK. The population derived from 24 families, where a common parent was "Paragon," one of the UK's spring wheat cultivar standards. All genotypes were tested in two regions of Kazakhstan at the Kazakh Research Institute of Agriculture and Plant Industry (KRIAPI, Almaty region, Southeast Kazakhstan, 2019-2022 years) and Alexandr Barayev Scientific-Production Center for Grain Farming (SPCGF, Shortandy, Akmola region, Northern Kazakhstan, 2019-2022 years). The studied traits consisted of plant adaptation-related traits, including heading date (HD, days), seed maturation date (SMD, days), plant height (PH, cm), and peduncle length (PL, cm). In addition, the yield per m2 was analyzed in both regions. Based on a field evaluation of the population in northern and southeastern Kazakhstan and using 10,448 polymorphic SNP (single-nucleotide polymorphism) markers, the genome-wide association study (GWAS) allowed for detecting 74 QTLs in four studied agronomic traits (HD, SMD, PH, and PL). The literature survey suggested that 16 of the 74 QTLs identified in our study had also been detected in previous QTL mapping studies and GWASs for all studied traits. The results will be used for further studies related to the adaptation and productivity of wheat in breeding projects for higher grain productivity.

The success of genome-wide association studies (GWASs) in uncovering genetic variants associated with complex eye diseases has paved the way for the development of risk prediction approaches based on disease genetics. Derived from GWAS data, polygenic risk scores (PRSs) have been emerging as a promising indicator of an individual's genetic liability to disease. In this review, we recap the current progress of PRS development and utility across a range of common eye diseases. While illustrating the prediction accuracy of PRSs and their valuable role in risk stratification for certain eye diseases, we also address PRSs' uncertain implementation in clinical settings at this stage, particularly in circumstances where limited treatment options are available. Finally, we discuss obstacles in translating PRSs into practice, including barriers to clinical impact, issues when working with different ancestry groups, and communicating risk scores, as well as projections for future improvements.

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Within biology, there have been long-standing goals to understand how traits impact fitness, determine the degree of adaptation, and predict responses to selection. One key step in answering these questions is to study the mode of gene action or genetic architecture of traits. The genetic architecture underlying a trait will ultimately determine whether selection can lead to a change in the phenotype. Theoretical and empirical research have shown that additive architectures are most responsive to selection. The genus Solanum offers a unique system to quantify the genetic architecture of traits. Crosses between Solanum pennellii and S. lycopersicum, which have evolved unique adaptive traits for very different environments, offer an opportunity to investigate the genetic architecture of a variety of morphological traits that often are not variable within species. We generated cohorts between strains of these two Solanum species and collected phenotypic data for eight morphological traits. The genetic architectures underlying these traits were estimated using an information-theoretic approach to line cross analysis. By estimating the genetic architectures of these traits, we were able to show a key role for maternal and epistatic effects and infer the accessibility of these traits to selection.

Soybean is a major source of protein and edible oil worldwide. Originating from the Huang-Huai-Hai region, which has a temperate climate, soybean has adapted to a wide latitudinal gradient across China. However, the genetic mechanisms responsible for the widespread latitudinal adaptation in soybean, as well as the genetic basis, adaptive differentiation, and evolutionary implications of theses natural alleles, are currently lacking in comprehensive understanding. In this study, we examined the genetic variations of fourteen major gene loci controlling flowering and maturity in 103 wild species, 1048 landraces, and 1747 cultivated species. We found that E1, E3, FT2a, J, Tof11, Tof16, and Tof18 were favoured during soybean improvement and selection, which explained 75.5% of the flowering time phenotypic variation. These genetic variation was significantly associated with differences in latitude via the LFMM algorithm. Haplotype network and geographic distribution analysis suggested that gene combinations were associated with flowering time diversity contributed to the expansion of soybean, with more HapA clustering together when soybean moved to latitudes beyond 35°N. The geographical evolution model was developed to accurately predict the suitable planting zone for soybean varieties. Collectively, by integrating knowledge from genomics and haplotype classification, it was revealed that distinct gene combinations improve the adaptation of cultivated soybeans to different latitudes. This study provides insight into the genetic basis underlying the environmental adaptation of soybean accessions, which could contribute to a better understanding of the domestication history of soybean and facilitate soybean climate-smart molecular breeding for various environments.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

Leaf tensile resistance, a leaf's ability to withstand pulling forces, is an important determinant of plant ecological strategies. One potential driver of leaf tensile resistance is growing season length. When growing seasons are long, strong leaves, which often require more time and resources to construct than weak leaves, may be more advantageous than when growing seasons are short. Growing season length and other ecological conditions may also impact the morphological traits that underlie leaf tensile resistance.

To understand variation in leaf tensile resistance, we measured size-dependent leaf strength and size-independent leaf toughness in diverse genotypes of the widespread perennial grass Panicum virgatum (switchgrass) in a common garden. We then used quantitative genetic approaches to estimate the heritability of leaf tensile resistance and whether there were genetic correlations between leaf tensile resistance and other morphological traits.

Leaf tensile resistance was positively associated with aboveground biomass (a proxy for fitness). Moreover, both measures of leaf tensile resistance exhibited high heritability and were positively genetically correlated with leaf lamina thickness and leaf mass per area (LMA). Leaf tensile resistance also increased with the growing season length in the habitat of origin, and this effect was mediated by both LMA and leaf thickness.

Differences in growing season length may promote selection for different leaf lifespans and may explain existing variation in leaf tensile resistance in P. virgatum. In addition, the high heritability of leaf tensile resistance suggests that P. virgatum will be able to respond to climate change as growing seasons lengthen.

The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Estimates of narrow sense heritability derived from genomic data that contain related individuals may be biased due to the within-family effects such as dominance, epistasis and common environmental factors. However, for many wild populations, removal of related individuals from the data would result in small sample sizes. In 2013, Zaitlen et al. proposed a method to estimate heritability in populations that include close relatives by simultaneously fitting an identity-by-state (IBS) genomic relatedness matrix (GRM) and an identity-by-descent (IBD) GRM. The IBD GRM is identical to the IBS GRM, except relatedness estimates below a specified threshold are set to 0. We applied this method to a sample of 8557 wild Soay sheep from St. Kilda, with genotypic information for 419,281 single nucleotide polymorphisms. We aimed to see how this method would partition heritability into population-level (IBS) and family-associated (IBD) variance for a range of genetic architectures, and so we focused on a mixture of polygenic and monogenic traits. We also implemented a variant of the model in which the IBD GRM was replaced by a GRM constructed from SNPs with low minor allele frequency to examine whether any additive genetic variance is captured by rare alleles. Whilst the inclusion of the IBD GRM did not significantly improve the fit of the model for the monogenic traits, it improved the fit for some of the polygenic traits, suggesting that dominance, epistasis and/or common environment not already captured by the non-genetic random effects fitted in our models may influence these traits.

Are traits with high levels of plasticity more complex in their genetic architecture, as they can be modulated by numerous different environmental inputs? Many authors have assumed that behavioral traits, in part because they are highly plastic, have an exceptionally complex genetic basis. We quantitatively summarized data from 31 genome-wide association studies (GWAS) and 87 traits in Drosophila melanogaster and found no evidence that behavioral traits have fundamental differences in the number of single-nucleotide polymorphisms or the significance or effect size of those associations, compared with nonbehavioral (morphological or physiological) traits. We suggest the assertion that behavioral traits are inherently more complex on a genetic basis compared with other types of traits should not be assumed true, and merits further investigation.

Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.

The process of speciation generates biodiversity. According to the null model of speciation, barriers between populations arise in allopatry, where, prior to biology, geography imposes barriers to gene flow. On the other hand, sympatric speciation requires that the process of speciation happen in the absence of a geographical barrier, where the members of the population have no spatial, temporal barriers. Several attempts have been made to theoretically identify the conditions in which speciation can occur in sympatry. However, these efforts suffer from several limitations. We propose a model for sympatric speciation based on adaptation for resource utilization. We use a genetics-based model to investigate the relative roles of prezygotic and postzygotic barriers, from the context of ecological disruptive selection, sexual selection, and genetic architecture, in causing and maintaining sympatric speciation. Our results show that sexual selection that acts on secondary sexual traits does not play any role in the process of speciation in sympatry and that assortative mating based on an ecologically relevant trait forces the population to show an adaptive response. We also demonstrate that understanding the genetic architecture of the trait under ecological selection is very important and that it is not required for the strength of ecological disruptive selection to be very high in order for speciation to occur in sympatry. Our results provide an insight into the kind of scenarios in which sympatric speciation can be demonstrated in the lab.

Herbivore pressure can vary across the range of a species, resulting in different defensive strategies. If herbivory is greater at lower latitudes, plants may be better defended there, potentially driving a latitudinal gradient in defense. However, relationships that manifest across the entire range of a species may be confounded by differences within genetic subpopulations, which may obscure the drivers of these latitudinal gradients.

We grew plants of the widespread perennial grass Panicum virgatum in a common garden that included genotypes from three genetic subpopulations spanning an 18.5° latitudinal gradient. We then assessed defensive strategies of these plants by measuring two physical resistance traits-leaf mass per area (LMA) and leaf ash, a proxy for silica-and multiple measures of herbivory by caterpillars of the generalist herbivore fall armyworm (Spodoptera frugiperda).

Across all genetic subpopulations, low-latitude plants experienced less herbivory than high-latitude plants. Within genetic subpopulations, however, this relationship was inconsistent-the most widely distributed and phenotypically variable subpopulation (Atlantic) exhibited more consistent latitudinal trends than either of the other two subpopulations. The two physical resistance traits, LMA and leaf ash, were both highly heritable and positively associated with resistance to different measures of herbivory across all subpopulations, indicating their importance in defense against herbivores. Again, however, these relationships were inconsistent within subpopulations.

Defensive gradients that occur across the entire species range may not arise within localized subpopulations. Thus, identifying the drivers of latitudinal gradients in herbivory defense may depend on adequately sampling the diversity within a species.

Non-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows.

Including non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95-69.59% of the total genetic variance.

Including non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

Maize has great importance in South Sudan as the most cultivated cereal after sorghum; however, numerous challenges are encountered in its production. To raise maize production, it is critical to exploit the wealth of its genetic variation for grain yield enhancement.

This study aimed to conduct association analysis to identify specific simple sequence repeat (SSR) markers associated with quantitative agronomic traits.

Genetic variation and population structure were investigated among 31 maize accessions by association analysis using 50 SSR markers and seven quantitative agronomic traits.

The genotypes exhibited abundant genetic variation, and 418 alleles were detected with an average of 8.4 alleles per locus. The average genetic diversity, major allele frequency, and polymorphic information content were 0.754, 0.373, and 0.725, respectively. The population structure based on 50 SSR markers divided the maize accessions into two main groups and an admixed group without considering their descent. Association analysis was performed using a general linear model (Q GLM) and a mixed linear model (Q + K MLM). Q GLM detected 44 trait-marker associations involving 23 SSR markers. Q + K MLM detected four marker-trait associations involving three SSR markers (umc2286, umc1303, umc1429) associated with days to tasseling, days to silking, leaf length, and leaf width.

The detected significant SSR markers related to agronomic traits could be useful for future genetic studies. Additionally, markers affecting several agronomic traits and overlapped SSR markers require further testing on a wide range of genotypes prior to their consideration as candidate markers for marker assisted selection for South Sudan maize improvement.

In dioecious populations, the sexes employ divergent reproductive strategies to maximize fitness and, as a result, genetic variants can affect fitness differently in males and females. Moreover, recent studies have highlighted an important role of the mating environment in shaping the strength and direction of sex-specific selection. Here, we measure adult fitness for each sex of 357 lines from the Drosophila Synthetic Population Resource in two different mating environments. We analyze the data using three different approaches to gain insight into the sex-specific genetic architecture for fitness: classical quantitative genetics, genomic associations, and a mutational burden approach. The quantitative genetics analysis finds that on average segregating genetic variation in this population has concordant fitness effects both across the sexes and across mating environments. We do not find specific genomic regions with strong associations with either sexually antagonistic (SA) or sexually concordant (SC) fitness effects, yet there is modest evidence of an excess of genomic regions with weak associations, with both SA and SC fitness effects. Our examination of mutational burden indicates stronger selection against indels and loss-of-function variants in females than in males.

Pacific abalone is a high-value, commercially important marine invertebrate. It shows low growth as well as individual and yearly growth variation in aquaculture. Marker-assisted selection breeding could potentially resolve the problem of low and variable growth and increase genetic gain. Expression of quantitative trait loci (QTLs) for growth-related traits, viz., body weight, shell length, and shell width were analyzed at the first, second, and third year of age using an F1 cross population. A total of 37 chromosome-wide QTLs were identified in linkage groups 01, 02, 03, 04, 06, 07, 08, 10, 11, 12, and 13 at different ages. None of the QTLs detected at any one age were expressed in all three age groups. This result suggests that growth-related traits at different ages are influenced by different QTLs in each year. However, multiple-trait QTLs (where one QTL affects all three traits) were detected each year that are also age-specific. Eleven multiple-trait QTLs were detected at different ages: two QTLs in the first year; two QTLs in the second year; and seven QTLs in the third year. As abalone hatcheries use three-year-old abalone for breeding, QTL-linked markers that were detected at the third year of age could potentially be used in marker-assisted selection breeding programs.

Maize serves as a crucial nutrient reservoir for a significant portion of the global population. However, to effectively address the growing world population's hidden hunger, it is essential to focus on two key aspects: biofortification of maize and improving its yield potential through advanced breeding techniques. Moreover, the coordination of multiple targets within a single breeding program poses a complex challenge. This study compiled mapping studies conducted over the past decade, identifying quantitative trait loci associated with grain quality and yield related traits in maize. Meta-QTL analysis of 2,974 QTLs for 169 component traits (associated with quality and yield related traits) revealed 68 MQTLs across different genetic backgrounds and environments. Most of these MQTLs were further validated using the data from genome-wide association studies (GWAS). Further, ten MQTLs, referred to as breeding-friendly MQTLs (BF-MQTLs), with a significant phenotypic variation explained over 10% and confidence interval less than 2 Mb, were shortlisted. BF-MQTLs were further used to identify potential candidate genes, including 59 genes encoding important proteins/products involved in essential metabolic pathways. Five BF-MQTLs associated with both quality and yield traits were also recommended to be utilized in future breeding programs. Synteny analysis with wheat and rice genomes revealed conserved regions across the genomes, indicating these hotspot regions as validated targets for developing biofortified, high-yielding maize varieties in future breeding programs. After validation, the identified candidate genes can also be utilized to effectively model the plant architecture and enhance desirable quality traits through various approaches such as marker-assisted breeding, genetic engineering, and genome editing.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation, are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants of the genes that control this trait is of high importance if we want to better comprehend how this trait evolves in natural populations. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource (DSPR) as a model system. First, we used quantitative genetics and Quantitative Trait Loci (QTL) mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to 1) alter tissue-specific gene expression and 2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.

Fusarium head blight (FHB) is a highly destructive fungal disease of wheat to which host resistance is quantitatively inherited and largely influenced by the environment. Resistance to FHB has been associated with taller height and later maturity; however, a further understanding of these relationships is needed. An association mapping panel (AMP) of 192 predominantly Canadian spring wheat was genotyped with the wheat 90K single-nucleotide polymorphism (SNP) array. The AMP was assessed for FHB incidence (INC), severity (SEV) and index (IND), days to anthesis (DTA), and plant height (PLHT) between 2015 and 2017 at three Canadian FHB-inoculated nurseries. Seven multi-environment trial (MET) datasets were deployed in a genome-wide association study (GWAS) using a single-locus mixed linear model (MLM) and a multi-locus random SNP-effect mixed linear model (mrMLM). MLM detected four quantitative trait nucleotides (QTNs) for INC on chromosomes 2D and 3D and for SEV and IND on chromosome 3B. Further, mrMLM identified 291 QTNs: 50 (INC), 72 (SEV), 90 (IND), 41 (DTA), and 38 (PLHT). At two or more environments, 17 QTNs for FHB, DTA, and PLHT were detected. Of these 17, 12 QTNs were pleiotropic for FHB traits, DTA, and PLHT on chromosomes 1A, 1D, 2D, 3B, 5A, 6B, 7A, and 7B; two QTNs for DTA were detected on chromosomes 1B and 7A; and three PLHT QTNs were located on chromosomes 4B and 6B. The 1B DTA QTN and the three pleiotropic QTNs on chromosomes 1A, 3B, and 6B are potentially identical to corresponding quantitative trait loci (QTLs) in durum wheat. Further, the 3B pleiotropic QTN for FHB INC, SEV, and IND co-locates with TraesCS3B02G024900 within the Fhb1 region on chromosome 3B and is ~3 Mb from a cloned Fhb1 candidate gene TaHRC. While the PLHT QTN on chromosome 6B is putatively novel, the 1B DTA QTN co-locates with a disease resistance protein located ~10 Mb from a Flowering Locus T1-like gene TaFT3-B1, and the 7A DTA QTN is ~5 Mb away from a maturity QTL QMat.dms-7A.3 of another study. GWAS and QTN candidate genes enabled the characterization of FHB resistance in relation to DTA and PLHT. This approach should eventually generate additional and reliable trait-specific markers for breeding selection, in addition to providing useful information for FHB trait discovery.

Ningxiang pig is a breed renowned for its exceptional meat quality, but it possesses suboptimal carcass traits. To elucidate the genetic architecture of meat quality and carcass traits in Ningxiang pigs, we assessed heritability and executed a genome-wide association study (GWAS) concerning carcass length, backfat thickness, meat color parameters (L.LD, a.LD, b.LD), and pH at two postmortem intervals (45 min and 24 h) within a Ningxiang pig population. Heritability estimates ranged from moderate to high (0.30~0.80) for carcass traits and from low to high (0.11~0.48) for meat quality traits. We identified 21 significant SNPs, the majority of which were situated within previously documented QTL regions. Furthermore, the GRM4 gene emerged as a pleiotropic gene that correlated with carcass length and backfat thickness. The ADGRF1, FKBP5, and PRIM2 genes were associated with carcass length, while the NIPBL gene was linked to backfat thickness. These genes hold the potential for use in selective breeding programs targeting carcass traits in Ningxiang pigs.

Rapid species radiations provide insight into the process of speciation and diversification. The radiation of Chrysoperla carnea-group lacewings seems to be driven, at least in part, by their species-specific pre-mating vibrational duets. We associated genetic markers from across the genome with courtship song period in the offspring of a laboratory cross between Chrysoperla plorabunda and Chrysoperla adamsi, two species primarily differentiated by their mating songs. Two genomic regions were strongly associated with the song period phenotype. Large regions of chromosomes one and two were associated with song phenotype, as fewer recombination events occurred on these chromosomes relative to the other autosomes. Candidate genes were identified by functional annotation of proteins from the C. carnea reference genome. The majority of genes that are associated with vibrational courtship signals in other insects were found within QTL for lacewing song phenotype. Together these findings suggest that decreased recombination may be acting to keep together loci important to reproductive isolation between these species. Using wild-caught individuals from both species, we identified signals of genomic divergence across the genome. We identified several candidate genes both in song-associated regions and near divergence outliers including nonA, fruitless, paralytic, period, and doublesex. Together these findings bring us one step closer to identifying the genomic basis of a mating song trait critical to the maintenance of species boundaries in green lacewings.

In vertebrates, mainly single genes with an allele ratio of 1:1 trigger sex-determination (SD), leading to initial equal sex-ratios. Such genes are designated master-key regulators (MKRs) and are frequently associated with DNA structural variations, such as copy-number variation and null-alleles. Most MKR knowledge comes from fish, especially cichlids, which serve as a genetic model for SD. We list 14 MKRs, of which dmrt1 has been identified in taxonomically distant species such as birds and fish. The identification of MKRs with known involvement in SD, such as amh and fshr, indicates that a common network drives SD. We illustrate a network that affects estrogen/androgen equilibrium, suggesting that structural variation may exert over-expression of the gene and thus form an MKR. However, the reason why certain factors constitute MKRs, whereas others do not is unclear. The limited number of conserved MKRs suggests that their heterologous sequences could be used as targets in future searches for MKRs of additional species. Sex-specific mortality, sex reversal, the role of temperature in SD, and multigenic SD are examined, claiming that these phenomena are often consequences of artificial hybridization. We discuss the essentiality of taxonomic authentication of species to validate purebred origin before MKR searches.

Wheat is an important staple crop since its proteins contribute to human and animal nutrition and are important for its end-use quality. However, wheat proteins can also cause adverse human reactions for a large number of people. We performed a genome wide association study (GWAS) on 114 proteins quantified by LC-MS-based proteomics and expressed in an environmentally stable manner in 148 wheat cultivars with a heritability > 0.6. For 54 proteins, we detected quantitative trait loci (QTL) that exceeded the Bonferroni-corrected significance threshold and explained 17.3−84.5% of the genotypic variance. Proteins in the same family often clustered at a very close chromosomal position or the potential homeolog. Major QTLs were found for four well-known glutenin and gliadin subunits, and the QTL segregation pattern in the protein encoding the high molecular weight glutenin subunit Dx5 could be confirmed by SDS gel-electrophoresis. For nine potential allergenic proteins, large QTLs could be identified, and their measured allele frequencies open the possibility to select for low protein abundance by markers as long as their relevance for human health has been conclusively demonstrated. A potential allergen was introduced in the beginning of 1980s that may be linked to the cluster of resistance genes introgressed on chromosome 2AS from Triticum ventricosum. The reported sequence information for the 54 major QTLs can be used to design efficient markers for future wheat breeding.

We present unresolved questions in plant abiotic stress biology as posed by 15 research groups with expertise spanning eco-physiology to cell and molecular biology. Common themes of these questions include the need to better understand how plants detect water availability, temperature, salinity, and rising carbon dioxide (CO2) levels; how environmental signals interface with endogenous signaling and development (e.g. circadian clock and flowering time); and how this integrated signaling controls downstream responses (e.g. stomatal regulation, proline metabolism, and growth versus defense balance). The plasma membrane comes up frequently as a site of key signaling and transport events (e.g. mechanosensing and lipid-derived signaling, aquaporins). Adaptation to water extremes and rising CO2 affects hydraulic architecture and transpiration, as well as root and shoot growth and morphology, in ways not fully understood. Environmental adaptation involves tradeoffs that limit ecological distribution and crop resilience in the face of changing and increasingly unpredictable environments. Exploration of plant diversity within and among species can help us know which of these tradeoffs represent fundamental limits and which ones can be circumvented by bringing new trait combinations together. Better defining what constitutes beneficial stress resistance in different contexts and making connections between genes and phenotypes, and between laboratory and field observations, are overarching challenges.

Identifying the genes responsible for quantitative traits remains a major challenge. We previously found a major QTL on chromosome 4 affecting several innate fear behavioral traits obtained by an open-field test in an F2 population between White Leghorn and Nagoya breeds of chickens (Gallus gallus). Here, an integrated approach of transcriptome, haplotype frequency, and association analyses was used to identify candidate genes for the QTL in phenotypically extreme individuals selected from the same segregating F2 population as that used in the initial QTL analysis. QTL mapping for the first principal component, which summarizes the variances of all affected behavioral traits in the F2 population, revealed the behavioral QTL located at 14-35 Mb on chromosome 4 with 333 genes. After RNA-seq analysis using two pooled RNAs from extreme F2 individuals, real-time qPCR analysis in the two parental breeds and their F1 individuals greatly reduced the number of candidate genes in the QTL interval from 333 to 16 genes. Haplotype frequency analysis in the two extreme F2 groups further reduced the number of candidate genes from 16 to 11. After comparing gene expression in the two extreme groups, a conditional correlation analysis of diplotypes between gene expression and phenotype of extreme individuals revealed that NPY5R and LOC101749214 genes were strong candidate genes for innate fear behavior. This study illustrates how the integrated approach can identify candidate genes more rapidly than fine mapping of the initial QTL interval and provides new information for studying the genetic basis of innate fear behavior in chickens.

Growth is an economically important trait in bighead carp and other aquaculture species that affects production efficiency. Interestingly, the head of the bighead carp has a high market value in China; therefore, it is important to study the genetic bases of both growth and body shape traits. A genome-wide association study was performed based on 2b-RAD sequencing of 776 individuals to identify SNPs associated with growth and body shape traits, including body weight, body length, body height, and deheaded body length. In total, 26 significant and 19 suggestive SNPs were identified, and more than half of these significant SNPs were clustered in LG16. Two LGs (LG16 and LG21) contained QTLs associated with body weight. Fourteen SNPs of LG16 and two LG21 SNPs were found to be associated with body length. For body height, 12 significantly associated SNPs were identified in LG16. Additionally, 12 SNPs of LG16 and 3 SNPs of LG21 were found to be associated with deheaded body length. Forty-three genes were significantly or suggestively associated with body shape/growth traits based on GWAS results, 18 of which were candidate genes for all BW, BL, BH, and DBL traits. One of these genes, fndc5b, was selected for further analyses. Association analysis revealed that one SNP (g.245 C > T) in the introns of fndc5b was significantly associated with growth-related traits in growth-extreme samples. The mRNA levels of fndc5b in the brains of the lightweight group were significantly higher than those of the heavy-weight group. This study helps to reveal the genetic structure of growth and body development in fish and provides candidate genes for future molecular marker-assisted selection for fast growth and better body conformation in bighead carp.

Genetic dissection of highly polygenic traits is a challenge, in part due to the power necessary to confidently identify loci with minor effects. Experimental crosses are valuable resources for mapping such traits. Traditionally, genome-wide analyses of experimental crosses have targeted major loci using data from a single generation (often the F₂) with individuals from later generations being generated for replication and fine-mapping. Here, we aim to confidently identify minor-effect loci contributing to the highly polygenic basis of the long-term, bi-directional selection responses for 56-d body weight in the Virginia body weight chicken lines. To achieve this, a strategy was developed to make use of data from all generations (F₂-F₁₈) of the advanced intercross line, developed by crossing the low and high selected lines after 40 generations of selection. A cost-efficient low-coverage sequencing based approach was used to obtain high-confidence genotypes in 1Mb bins across 99.3% of the chicken genome for >3,300 intercross individuals. In total, 12 genome-wide significant, and 30 additional suggestive QTL reaching a 10% FDR threshold, were mapped for 56-d body weight. Only 2 of these QTL reached genome-wide significance in earlier analyses of the F₂ generation. The minor-effect QTL mapped here were generally due to an overall increase in power by integrating data across generations, with contributions from increased genome-coverage and improved marker information content. The 12 significant QTL explain >37% of the difference between the parental lines, three times more than 2 previously reported significant QTL. The 42 significant and suggestive QTL together explain >80%. Making integrated use of all available samples from multiple generations in experimental crosses are economically feasible using the low-cost, sequencing-based genotyping strategies outlined here. Our empirical results illustrate the value of this strategy for mapping novel minor-effect loci contributing to complex traits to provide a more confident, comprehensive view of the individual loci that form the genetic basis of the highly polygenic, long-term selection responses for 56-d body weight in the Virginia body weight chicken lines.