The genetics of quantitative traits: challenges and prospects. Nat Rev Genet. 2009;10:565-577. [PMID: 19584810 DOI: 10.1038/nrg2612]

Pleiotropy, epistasis and the genetic architecture of quantitative traits

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.

Pervasive tissue-, genetic background-, and allele-specific gene expression effects in Drosophila melanogaster

The pervasiveness of gene expression variation and its contribution to phenotypic variation and evolution is well known. This gene expression variation is context dependent, with differences in regulatory architecture often associated with intrinsic and environmental factors, and is modulated by regulatory elements that can act in cis (linked) or in trans (unlinked) relative to the genes they affect. So far, little is known about how this genetic variation affects the evolution of regulatory architecture among closely related tissues during population divergence. To address this question, we analyzed gene expression in the midgut, hindgut, and Malpighian tubule as well as microbiome composition in the two gut tissues in four Drosophila melanogaster strains and their F1 hybrids from two divergent populations: one from the derived, European range and one from the ancestral, African range. In both the transcriptome and microbiome data, we detected extensive tissue- and genetic background-specific effects, including effects of genetic background on overall tissue specificity. Tissue-specific effects were typically stronger than genetic background-specific effects, although the two gut tissues were not more similar to each other than to the Malpighian tubules. An examination of allele specific expression revealed that, while both cis and trans effects were more tissue-specific in genes expressed differentially between populations than genes with conserved expression, trans effects were more tissue-specific than cis effects. Despite there being highly variable regulatory architecture, this observation was robust across tissues and genetic backgrounds, suggesting that the expression of trans variation can be spatially fine-tuned as well as or better than cis variation during population divergence and yielding new insights into cis and trans regulatory evolution.

Epistatic interaction has the reverse effects with its constitutive quantitative trait loci

Epistasis is one of important genetic components for a quantitative trait in plant. Eshed and Zamir found negative epistatic interactions of quantitative trait loci in Tomato first. We detected that positive (negative) QTLs generated mostly negative (positive) epistatic interactions on heading date in rice, and then proposed the hypothese that QTL epistasis plays a role of homeostasis in one of our recent papers. In order to further provide additional evidence, the effects of QTLs and their epistatic effects on two quantitative traits of plant height (ph) and thousand kernel weight (tkw) were analyzed in this study. The same regularity was verified again. We detected that positive ph QTLs and negative tkw QTLs always generated reverse epistatic effects, respectively. Moreover, high-order epistatic effects were estimated on these two traits. The sum of all epistatic effects would partially neutralize the additive of constitutive QTL effects. This feature of epistsis would be the mechanism for bionts to maintain homeostasis while the obstacle for human to achieve the pyramiding breeding objectives. More evidences are still being collected to support our assumption.

Genetic variation and association of yield, yield components, and carbon storage in sorghum (Sorghum bicolor [L.] Moench) genotypes

Trait heritability and the response to selection depend on genetic variation, a prerequisite to developing sorghum varieties with desirable agronomic traits and high carbon sequestration for sustainable crop production and soil health. The present study aimed to assess the extent of genetic variability and associations among agronomic and carbon storage traits in selected sorghum genotypes to identify the best candidates for production or breeding. Fifty genotypes were evaluated at Ukulinga, Bethlehem and Silverton sites in South Africa during the 2022/23 growing season. The following agronomic and carbon storage traits were collected: days to 50% heading (DTH), days to 50% maturity (DTM), plant height (PH), total plant biomass (PB), shoot biomass (SB), root biomass (RB), root-to-shoot biomass ratio (RS), grain yield (GY), harvest index (HI), shoot carbon content (SCc), root carbon content (RCc), grain carbon content (GCc), total plant carbon stock (PCs), shoot carbon stock (SCs), root carbon stock (RCs), and root-to-shoot carbon stock ratio (RCs/SCs), and grain carbon stock (GCs). Higher genotypic coefficient of variations (GCVs) were recorded for GY at 45.92%, RB (39.24%), RCs/SCs (38.45), and RCs (34.62). Higher phenotypic coefficient of variations (PCVs) were recorded for PH (68.91%), followed by GY (51.8%), RB (50.51%), RS (41.96%), RCs/SCs (44.90%), and GCs (41.90%). High broad-sense heritability and genetic advance were recorded for HI (83.76 and 24.53%), GY (78.59 and 9.98%), PB (74.14 and 13.18%) and PCs (53.63 and 37.57%), respectively, suggesting a marked genetic contribution to the traits. Grain yield exhibited positive association with HI (r = 0.76; r = 0.79), DTH (r = 0.13; r = 0.31), PH (r = 0.1; r = 0.27), PB (r = 0.01; r = 0.02), RB (r = 0.05; r = 0.06) based on genotypic and phenotypic correlations, respectively. Further, the path analysis revealed significant positive direct effects of SB (0.607) and RB (0.456) on GY. The RS exerted a positive and significant indirect effect (0.229) on grain yield through SB. The study revealed that PB, SB, RB, RS, RCs, and RCs/SCs are the principal traits when selecting sorghum genotypes with high yield and carbon storage capacity.

Expression genome-wide association study identifies key regulatory variants enriched with metabolic and immune functions in four porcine tissues

BACKGROUND

Integration of high throughput DNA genotyping and RNA-sequencing data enables the discovery of genomic regions that regulate gene expression, known as expression quantitative trait loci (eQTL). In pigs, efforts to date have been mainly focused on purebred lines for traits with commercial relevance as such growth and meat quality. However, little is known on genetic variants and mechanisms associated with the robustness of an animal, thus its overall health status. Here, the liver, lung, spleen, and muscle transcriptomes of 100 three-way crossbred female finishers were studied, with the aim of identifying novel eQTL regulatory regions and transcription factors (TFs) associated with regulation of porcine metabolism and health-related traits.

RESULTS

An expression genome-wide association study with 535,896 genotypes and the expression of 12,680 genes in liver, 13,310 genes in lung, 12,650 genes in spleen, and 12,595 genes in muscle resulted in 4,293, 10,630, 4,533, and 6,871 eQTL regions for each of these tissues, respectively. Although only a small fraction of the eQTLs were annotated as cis-eQTLs, these presented a higher number of polymorphisms per region and significantly stronger associations with their target gene compared to trans-eQTLs. Between 20 and 115 eQTL hotspots were identified across the four tissues. Interestingly, these were all enriched for immune-related biological processes. In spleen, two TFs were identified: ERF and ZNF45, with key roles in regulation of gene expression.

CONCLUSIONS

This study provides a comprehensive analysis with more than 26,000 eQTL regions identified that are now publicly available. The genomic regions and their variants were mostly associated with tissue-specific regulatory roles. However, some shared regions provide new insights into the complex regulation of genes and their interactions that are involved with important traits related to metabolism and immunity.

Genome-wide analysis of histone modifications can contribute to the identification of candidate cis-regulatory regions in the threespine stickleback fish

BACKGROUND

Cis-regulatory mutations often underlie phenotypic evolution. However, because identifying the locations of promoters and enhancers in non-coding regions is challenging, we have fewer examples of identified causative cis-regulatory mutations that underlie naturally occurring phenotypic variations than of causative amino acid-altering mutations. Because cis-regulatory elements have epigenetic marks of specific histone modifications, we can detect cis-regulatory elements by mapping and analyzing them. Here, we investigated histone modifications and chromatin accessibility with cleavage under targets and tagmentation (CUT&Tag) and assay for transposase-accessible chromatin-sequencing (ATAC-seq).

RESULTS

Using the threespine stickleback (Gasterosteus aculeatus) as a model, we confirmed that the genes for which nearby regions showed active marks, such as H3K4me1, H3K4me3, and high chromatin accessibility, were highly expressed. In contrast, the expression levels of genes for which nearby regions showed repressive marks, such as H3K27me3, were reduced, suggesting that our chromatin analysis protocols overall worked well. Genomic regions with peaks of histone modifications showed higher nucleotide diversity within and between populations. By comparing gene expression in the gills of the marine and stream ecotypes, we identified several insertions and deletions (indels) with transposable element fragments in the candidate cis-regulatory regions.

CONCLUSIONS

Thus, mapping and analyzing histone modifications can help identify cis-regulatory elements and accelerate the identification of causative mutations in the non-coding regions underlying naturally occurring phenotypic variations.

Natural variation in starvation sensitivity maps to a point mutation in phospholipase IPLA2-VIA in Drosophila melanogaster

Resistance to starvation is a classic complex trait, where genetic and environmental variables can greatly modify an animal's ability to survive without nutrients. In this study, we investigate the genetic basis of starvation resistance using complementary quantitative and classical genetic mapping in Drosophila melanogaster. Using the Drosophila Genetics Reference Panel (DGRP) as a starting point, we queried the genetic basis of starvation sensitivity in one of the most sensitive DGRP lines. We localize a major effect locus modifying starvation resistance to the phospholipase iPLA2-VIA. This finding is consistent with the work of others which demonstrate the importance of lipid regulation in starvation stress. Furthermore, we demonstrate that iPLA2-VIA plays a role in the maintenance of sugar reserves post-starvation, which highlights a key dynamic between lipid remodeling, sugar metabolism and resistance to starvation stress.

The regulation and differentiation of regulatory T cells and their dysfunction in autoimmune diseases

The discovery of FOXP3+ regulatory T (Treg) cells as a distinct cell lineage with a central role in regulating immune responses provided a deeper understanding of self-tolerance. The transcription factor FOXP3 serves a key role in Treg cell lineage determination and maintenance, but is not sufficient to enable the full potential of Treg cell suppression, indicating that other factors orchestrate the fine-tuning of Treg cell function. Moreover, FOXP3-independent mechanisms have recently been shown to contribute to Treg cell dysfunction. FOXP3 mutations in humans cause lethal fulminant systemic autoinflammation (IPEX syndrome). However, it remains unclear to what degree Treg cell dysfunction is contributing to the pathophysiology of common autoimmune diseases. In this Review, we discuss the origins of Treg cells in the periphery and the multilayered mechanisms by which Treg cells are induced, as well as the FOXP3-dependent and FOXP3-independent cellular programmes that maintain the suppressive function of Treg cells in humans and mice. Further, we examine evidence for Treg cell dysfunction in the context of common autoimmune diseases such as multiple sclerosis, inflammatory bowel disease, systemic lupus erythematosus and rheumatoid arthritis.

Genetic and environmental interactions contribute to immune variation in rewilded mice

The relative and synergistic contributions of genetics and environment to interindividual immune response variation remain unclear, despite implications in evolutionary biology and medicine. Here we quantify interactive effects of genotype and environment on immune traits by investigating C57BL/6, 129S1 and PWK/PhJ inbred mice, rewilded in an outdoor enclosure and infected with the parasite Trichuris muris. Whereas cellular composition was shaped by interactions between genotype and environment, cytokine response heterogeneity including IFNγ concentrations was primarily driven by genotype with consequence on worm burden. In addition, we show that other traits, such as expression of CD44, were explained mostly by genetics on T cells, whereas expression of CD44 on B cells was explained more by environment across all strains. Notably, genetic differences under laboratory conditions were decreased following rewilding. These results indicate that nonheritable influences interact with genetic factors to shape immune variation and parasite burden.

Construction of a high-density linkage map and QTL detection for growth traits in South African abalone (Haliotis midae)

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.

Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses

KEY MESSAGE

Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.

A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.

Genetics of flight in spongy moths (Lymantria dispar ssp.): functionally integrated profiling of a complex invasive trait

BACKGROUND

Flight can drastically enhance dispersal capacity and is a key trait defining the potential of exotic insect species to spread and invade new habitats. The phytophagous European spongy moths (ESM, Lymantria dispar dispar) and Asian spongy moths (ASM; a multi-species group represented here by L. d. asiatica and L. d. japonica), are globally invasive species that vary in adult female flight capability-female ASM are typically flight capable, whereas female ESM are typically flightless. Genetic markers of flight capability would supply a powerful tool for flight profiling of these species at any intercepted life stage. To assess the functional complexity of spongy moth flight and to identify potential markers of flight capability, we used multiple genetic approaches aimed at capturing complementary signals of putative flight-relevant genetic divergence between ESM and ASM: reduced representation genome-wide association studies, whole genome sequence comparisons, and developmental transcriptomics. We then judged the candidacy of flight-associated genes through functional analyses aimed at addressing the proximate demands of flight and salient features of the ecological context of spongy moth flight evolution.

RESULTS

Candidate gene sets were typically non-overlapping across different genetic approaches, with only nine gene annotations shared between any pair of approaches. We detected an array of flight-relevant functional themes across gene sets that collectively suggest divergence in flight capability between European and Asian spongy moth lineages has coincided with evolutionary differentiation in multiple aspects of flight development, execution, and surrounding life history. Overall, our results indicate that spongy moth flight evolution has shaped or been influenced by a large and functionally broad network of traits.

CONCLUSIONS

Our study identified a suite of flight-associated genes in spongy moths suited to exploration of the genetic architecture and evolution of flight, or validation for flight profiling purposes. This work illustrates how complementary genetic approaches combined with phenotypically targeted functional analyses can help to characterize genetically complex traits.

Evolution of Phenotypic Variance Provides Insights into the Genetic Basis of Adaptation

Most traits are polygenic, and the contributing loci can be identified by genome-wide association studies. The genetic basis of adaptation (adaptive architecture) is, however, difficult to characterize. Here, we propose to study the adaptive architecture of traits by monitoring the evolution of their phenotypic variance during adaptation to a new environment in well-defined laboratory conditions. Extensive computer simulations show that the evolution of phenotypic variance in a replicated experimental evolution setting can distinguish between oligogenic and polygenic adaptive architectures. We compared gene expression variance in male Drosophila simulans before and after 100 generations of adaptation to a novel hot environment. The variance change in gene expression was indistinguishable for genes with and without a significant change in mean expression after 100 generations of evolution. We suggest that the majority of adaptive gene expression evolution can be explained by a polygenic architecture. We propose that tracking the evolution of phenotypic variance across generations can provide an approach to characterize the adaptive architecture.

Natural variation in the Caenorhabditis elegans egg-laying circuit modulates an intergenerational fitness trade-off

Evolutionary transitions from egg laying (oviparity) to live birth (viviparity) are common across various taxa. Many species also exhibit genetic variation in egg-laying mode or display an intermediate mode with laid eggs containing embryos at various stages of development. Understanding the mechanistic basis and fitness consequences of such variation remains experimentally challenging. Here, we report highly variable intra-uterine egg retention across 316 Caenorhabditis elegans wild strains, some exhibiting strong retention, followed by internal hatching. We identify multiple evolutionary origins of such phenotypic extremes and pinpoint underlying candidate loci. Behavioral analysis and genetic manipulation indicates that this variation arises from genetic differences in the neuromodulatory architecture of the egg-laying circuitry. We provide experimental evidence that while strong egg retention can decrease maternal fitness due to in utero hatching, it may enhance offspring protection and confer a competitive advantage. Therefore, natural variation in C. elegans egg-laying behaviour can alter an apparent trade-off between different fitness components across generations. Our findings highlight underappreciated diversity in C. elegans egg-laying behavior and shed light on its fitness consequences. This behavioral variation offers a promising model to elucidate the molecular changes in a simple neural circuit underlying evolutionary shifts between alternative egg-laying modes in invertebrates.

Selection among critically endangered landlocked salmon (Salmo salar m. sebago) families in survival and growth traits across early life stages and in different environments

Endangered wild fish populations are commonly supported by hatchery propagation. However, hatchery-reared fish experience very different selective pressures compared to their wild counterparts, potentially causing genotype-by-environment interactions (G × E) in essential fitness traits. We experimentally studied early selection in a critically endangered landlocked Atlantic salmon population, first from fertilization to the swim-up stage in a common hatchery setting, and thereafter until the age of 5 months in two contrasting rearing environments. Swim-up progeny were moved either to standard indoor hatchery tanks involving conventional husbandry or to seminatural outdoor channels providing only natural food. After the first summer, sampled survivors were assigned to their families by genotyping. Early survival until the swim-up stage was mostly determined by maternal effects, but also involved significant variation due to sires and full-sib families (potential genetic effects). High on-growing survival in hatchery tanks (88.7%) maintained a more even distribution among families (relative share 1.5%-4.2%) than the seminatural environment (0.0%-5.4%). This heterogeneity was mostly maternal, whereas no independent paternal effect occurred. Heritability estimates were high for body size traits in both environments (0.62-0.69). Genetic correlations between the environments were significantly positive for body size traits (0.67-0.69), and high body condition in hatchery was also genetically linked to rapid growth in the seminatural environment (0.54). Additive and phenotypic growth variation increased in the seminatural environment, but scaling effects probably played a less significant role for G × E, compared to re-ranking of genotypes. Our results suggest that not only maternal effects, but also genetic effects, direct selection according to the environmental conditions experienced. Consistently high genetic variation in growth implies that, despite its low overall genetic diversity and long history in captive rearing (>50 years), this landlocked Atlantic salmon population still possesses adaptive potential for response to change from hatchery rearing back to more natural conditions.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Is the genetic architecture of behavior exceptionally complex?

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.

Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance

Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.

Identification of QTLs for symbiotic nitrogen fixation and related traits in a soybean recombinant inbred line population

KEY MESSAGE

The genetic architecture of symbiotic N fixation and related traits was investigated in the field. QTLs were identified for percent N derived from the atmosphere, shoot [N] and C to N ratio. Soybean [Glycine max (L.) Merr.] is cultivated worldwide and is the most abundant source of plant-based protein. Symbiotic N2 fixation (SNF) in legumes such as soybean is of great importance; however, yields may still be limited by N in both high yielding and stressful environments. To better understand the genetic architecture of SNF and facilitate the development of high yielding cultivars and sustainable soybean production in stressful environments, a recombinant inbred line population consisting of 190 lines, developed from a cross between PI 442012A and PI 404199, was evaluated for N derived from the atmosphere (Ndfa), N concentration ([N]), and C to N ratio (C/N) in three environments. Significant genotype, environment and genotype × environment effects were observed for all three traits. A linkage map was constructed containing 3309 single nucleotide polymorphism (SNP) markers. QTL analysis was performed for additive effects of QTLs, QTL × environment interactions, and QTL × QTL interactions. Ten unique additive QTLs were identified across all traits and environments. Of these, two QTLs were detected for Ndfa and eight for C/N. Of the eight QTLs for C/N, four were also detected for [N]. Using QTL × environment analysis, six QTLs were detected, of which five were also identified in the additive QTL analysis. The QTL × QTL analysis identified four unique epistatic interactions. The results of this study may be used for genomic selection and introgression of favorable alleles for increased SNF, [N], and C/N via marker-assisted selection.

Reducing Epistasis and Pleiotropy Can Avoid the Survival of the Flattest Tragedy

This study investigates whether reducing epistasis and pleiotropy enhances mutational robustness in evolutionary adaptation, utilizing an indirect encoded model within the "survival of the flattest" (SoF) fitness landscape. By simulating genetic variations and their phenotypic consequences, we explore organisms' adaptive mechanisms to maintain positions on higher, narrower evolutionary peaks amidst environmental and genetic pressures. Our results reveal that organisms can indeed sustain their advantageous positions by minimizing the complexity of genetic interactions-specifically, by reducing the levels of epistasis and pleiotropy. This finding suggests a counterintuitive strategy for evolutionary stability: simpler genetic architectures, characterized by fewer gene interactions and multifunctional genes, confer a survival advantage by enhancing mutational robustness. This study contributes to our understanding of the genetic underpinnings of adaptability and robustness, challenging traditional views that equate complexity with fitness in dynamic environments.

A concerted neuron-astrocyte program declines in ageing and schizophrenia

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA sequencing to analyse the prefrontal cortex of 191 human donors aged 22-97 years, including healthy individuals and people with schizophrenia. Latent-factor analysis of these data revealed that, in people whose cortical neurons more strongly expressed genes encoding synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the synaptic neuron and astrocyte program (SNAP). In schizophrenia and ageing-two conditions that involve declines in cognitive flexibility and plasticity1,2-cells divested from SNAP: astrocytes, glutamatergic (excitatory) neurons and GABAergic (inhibitory) neurons all showed reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy people of similar age, may underlie many aspects of normal human interindividual differences and may be an important point of convergence for multiple kinds of pathophysiology.

Humane genomics education can reduce racism

Moving instruction "beyond Mendel" can counter inaccurate, essentialist views.

A large-effect fitness trade-off across environments is explained by a single mutation affecting cold acclimation

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Mining genic resources regulating nitrogen-use efficiency based on integrative biological analyses and their breeding applications in maize and other crops

Nitrogen (N) is an essential factor for limiting crop yields, and cultivation of crops with low nitrogen-use efficiency (NUE) exhibits increasing environmental and ecological risks. Hence, it is crucial to mine valuable NUE improvement genes, which is very important to develop and breed new crop varieties with high NUE in sustainable agriculture system. Quantitative trait locus (QTL) and genome-wide association study (GWAS) analysis are the most common methods for dissecting genetic variations underlying complex traits. In addition, with the advancement of biotechnology, multi-omics technologies can be used to accelerate the process of exploring genetic variations. In this study, we integrate the substantial data of QTLs, quantitative trait nucleotides (QTNs) from GWAS, and multi-omics data including transcriptome, proteome, and metabolome and further analyze their interactions to predict some NUE-related candidate genes. We also provide the genic resources for NUE improvement among maize, rice, wheat, and sorghum by homologous alignment and collinearity analysis. Furthermore, we propose to utilize the knowledge gained from classical cases to provide the frameworks for improving NUE and breeding N-efficient varieties through integrated genomics, systems biology, and modern breeding technologies.

Genome-wide analyses based on a novel donkey 40K liquid chip reveal the gene responsible for coat color diversity in Chinese Dezhou donkey

Dezhou donkey is one of the representative local breeds in China, which is mainly divided into two strains: Sanfen and Wutou. There are obvious differences in coat color between the two strains. The former shows light points around the eyes, around the muzzle and under the belly, while the latter is completely solid black. In this study, genome-wide association analysis was performed for the differences in coat color traits between the Sanfen (n = 97) and Wutou (n = 108) strains using a novel donkey 40K liquid chip developed based on GenoBaits technology, to identify genomic regions and causal genes that could explain this variation. We also used FST and The cross-population composite likelihood ratio test (XPCLR) analyses to explore selected regions related to coat color differences. We identified one significant region on chromosome 15, with the most significant SNP located within the agouti signaling protein (ASIP) gene. At the same time, both FST and XPCLR methods detected the same selected region on chromosome 15, and ASIP was the gene with the strongest signal. ASIP and melanocortin 1 receptor (MC1R) control the ratio of eumelanin to pheomelanin through their protein activity. They are deeply involved in the process of melanosome organation and melanogenesis, thus affecting mammals' coat color variation. We used a range of genome-wide approach to identify the genetic basis of coat color variation in Dezhou donkeys. The results provide a supplement to the color variation study in Chinese donkeys at the genome-wide level, and preliminarily verified the reliability of the Molbreeding Donkey No. 1 40K liquid chip.

Concerted neuron-astrocyte gene expression declines in aging and schizophrenia

Human brains vary across people and over time; such variation is not yet understood in cellular terms. Here we describe a striking relationship between people's cortical neurons and cortical astrocytes. We used single-nucleus RNA-seq to analyze the prefrontal cortex of 191 human donors ages 22-97 years, including healthy individuals and persons with schizophrenia. Latent-factor analysis of these data revealed that in persons whose cortical neurons more strongly expressed genes for synaptic components, cortical astrocytes more strongly expressed distinct genes with synaptic functions and genes for synthesizing cholesterol, an astrocyte-supplied component of synaptic membranes. We call this relationship the Synaptic Neuron-and-Astrocyte Program (SNAP). In schizophrenia and aging - two conditions that involve declines in cognitive flexibility and plasticity 1,2 - cells had divested from SNAP: astrocytes, glutamatergic (excitatory) neurons, and GABAergic (inhibitory) neurons all reduced SNAP expression to corresponding degrees. The distinct astrocytic and neuronal components of SNAP both involved genes in which genetic risk factors for schizophrenia were strongly concentrated. SNAP, which varies quantitatively even among healthy persons of similar age, may underlie many aspects of normal human interindividual differences and be an important point of convergence for multiple kinds of pathophysiology.

Genome-Wide Network Analysis of Above- and Below-Ground Co-growth in Populus euphratica

Tree growth is the consequence of developmental interactions between above- and below-ground compartments. However, a comprehensive view of the genetic architecture of growth as a cohesive whole is poorly understood. We propose a systems biology approach for mapping growth trajectories in genome-wide association studies viewing growth as a complex (phenotypic) system in which above- and below-ground components (or traits) interact with each other to mediate systems behavior. We further assume that trait-trait interactions are controlled by a genetic system composed of many different interactive genes and integrate the Lotka-Volterra predator-prey model to dissect phenotypic and genetic systems into pleiotropic and epistatic interaction components by which the detailed genetic mechanism of above- and below-ground co-growth can be charted. We apply the approach to analyze linkage mapping data of Populus euphratica, which is the only tree species that can grow in the desert, and characterize several loci that govern how above- and below-ground growth is cooperated or competed over development. We reconstruct multilayer and multiplex genetic interactome networks for the developmental trajectories of each trait and their developmental covariation. Many significant loci and epistatic effects detected can be annotated to candidate genes for growth and developmental processes. The results from our model may potentially be useful for marker-assisted selection and genetic editing in applied tree breeding programs. The model provides a general tool to characterize a complete picture of pleiotropic and epistatic genetic architecture in growth traits in forest trees and any other organisms.

QTL epistasis plays a role of homeostasis on heading date in rice

If there was no gene interaction, the gene aggregation effect would increase infinitely with the increase of gene number. Epistasis avoids the endless accumulation of gene effects, playing a role of homeostasis. To confirm the role, QTL epistases were analyzed by four single-segment substitution lines with heading date QTLs in this paper. We found that QTLs of three positive effects and one negative effect generated 62.5% negative dual QTL epistatic effects and 57.7% positive triple QTL epistatic effects, forming the relationship "positive QTLs-negative one order interactions-positive two order interactions". In this way, the aggregation effect of QTLs was partially neutralized by the opposite epistatic effect sum. There also were two exceptions, QTL OsMADS50 and gene Hd3a-2 were always with consistent effect directions with their epistases, implying they could be employed in pyramiding breeding with different objectives. This study elucidated the mechanism of epistatic interactions among four QTLs and provided valuable genetic resources for improving heading date in rice.

Ameliorating the effects of multiple stresses on agronomic traits in crops: modern biotechnological and omics approaches

While global climate change poses a significant environmental threat to agriculture, the increasing population is another big challenge to food security. To address this, developing crop varieties with increased productivity and tolerance to biotic and abiotic stresses is crucial. Breeders must identify traits to ensure higher and consistent yields under inconsistent environmental challenges, possess resilience against emerging biotic and abiotic stresses and satisfy customer demands for safer and more nutritious meals. With the advent of omics-based technologies, molecular tools are now integrated with breeding to understand the molecular genetics of genotype-based traits and develop better climate-smart crops. The rapid development of omics technologies offers an opportunity to generate novel datasets for crop species. Identifying genes and pathways responsible for significant agronomic traits has been made possible by integrating omics data with genetic and phenotypic information. This paper discusses the importance and use of omics-based strategies, including genomics, transcriptomics, proteomics and phenomics, for agricultural and horticultural crop improvement, which aligns with developing better adaptability in these crop species to the changing climate conditions.

Selection on the promoter regions plays an important role in complex traits during duck domestication

BACKGROUND

Identifying the key factors that underlie complex traits during domestication is a great challenge for evolutionary and biological studies. In addition to the protein-coding region differences caused by variants, a large number of variants are located in the noncoding regions containing multiple types of regulatory elements. However, the roles of accumulated variants in gene regulatory elements during duck domestication and economic trait improvement are poorly understood.

RESULTS

We constructed a genomics, transcriptomics, and epigenomics map of the duck genome and assessed the evolutionary forces that have been in play across the whole genome during domestication. In total, 304 (42.94%) gene promoters have been specifically selected in Pekin duck among all selected genes. Joint multi-omics analysis reveals that 218 genes (72.01%) with selected promoters are located in open and active chromatin, and 267 genes (87.83%) with selected promoters were highly and differentially expressed in domestic trait-related tissues. One important candidate gene ELOVL3, with a strong signature of differentiation on the core promoter region, is known to regulate fatty acid elongation. Functional experiments showed that the nearly fixed variants in the top selected ELOVL3 promoter in Pekin duck decreased binding ability with HLF and increased gene expression, with the overexpression of ELOVL3 able to increase lipid deposition and unsaturated fatty acid enrichment.

CONCLUSIONS

This study presents genome resequencing, RNA-Seq, Hi-C, and ATAC-Seq data of mallard and Pekin duck, showing that selection of the gene promoter region plays an important role in gene expression and phenotypic changes during domestication and highlights that the variants of the ELOVL3 promoter may have multiple effects on fat and long-chain fatty acid content in ducks.

Epigenetic modifications regulate cultivar-specific root development and metabolic adaptation to nitrogen availability in wheat

The breeding of crops with improved nitrogen use efficiency (NUE) is crucial for sustainable agriculture, but the involvement of epigenetic modifications remains unexplored. Here, we analyze the chromatin landscapes of two wheat cultivars (KN9204 and J411) that differ in NUE under varied nitrogen conditions. The expression of nitrogen metabolism genes is closely linked to variation in histone modification instead of differences in DNA sequence. Epigenetic modifications exhibit clear cultivar-specificity, which likely contributes to distinct agronomic traits. Additionally, low nitrogen (LN) induces H3K27ac and H3K27me3 to significantly enhance root growth in KN9204, while remarkably inducing NRT2 in J411. Evidence from histone deacetylase inhibitor treatment and transgenic plants with loss function of H3K27me3 methyltransferase shows that changes in epigenetic modifications could alter the strategy preference for root development or nitrogen uptake in response to LN. Here, we show the importance of epigenetic regulation in mediating cultivar-specific adaptation to LN in wheat.

Boosting grapevine breeding for climate-smart viticulture: from genetic resources to predictive genomics

The multifaceted nature of climate change is increasing the urgency to select resilient grapevine varieties, or generate new, fitter cultivars, to withstand a multitude of new challenging conditions. The attainment of this goal is hindered by the limiting pace of traditional breeding approaches, which require decades to result in new selections. On the other hand, marker-assisted breeding has proved useful when it comes to traits governed by one or few genes with great effects on the phenotype, but its efficacy is still restricted for complex traits controlled by many loci. On these premises, innovative strategies are emerging which could help guide selection, taking advantage of the genetic diversity within the Vitis genus in its entirety. Multiple germplasm collections are also available as a source of genetic material for the introgression of alleles of interest via adapted and pioneering transformation protocols, which present themselves as promising tools for future applications on a notably recalcitrant species such as grapevine. Genome editing intersects both these strategies, not only by being an alternative to obtain focused changes in a relatively rapid way, but also by supporting a fine-tuning of new genotypes developed with other methods. A review on the state of the art concerning the available genetic resources and the possibilities of use of innovative techniques in aid of selection is presented here to support the production of climate-smart grapevine genotypes.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Uncovering the genetic architecture of parallel evolution

Identifying the genetic architecture underlying adaptive traits is exceptionally challenging in natural populations. This is because associations between traits not only mask the targets of selection but also create correlated patterns of genomic divergence that hinder our ability to isolate causal genetic effects. Here, we examine the repeated evolution of components of the auxin pathway that have contributed to the replicated loss of gravitropism (i.e. the ability of a plant to bend in response to gravity) in multiple populations of the Senecio lautus species complex in Australia. We use a powerful approach which combines parallel population genomics with association mapping in a Multiparent Advanced Generation Inter-Cross (MAGIC) population to break down genetic and trait correlations to reveal how adaptive traits evolve during replicated evolution. We sequenced auxin and shoot gravitropism-related gene regions in 80 individuals from six natural populations (three parallel divergence events) and 133 individuals from a MAGIC population derived from two of the recently diverged natural populations. We show that artificial tail selection on gravitropism in the MAGIC population recreates patterns of parallel divergence in the auxin pathway in the natural populations. We reveal a set of 55 auxin gene regions that have evolved repeatedly during the evolution of the species, of which 50 are directly associated with gravitropism divergence in the MAGIC population. Our work creates a strong link between patterns of genomic divergence and trait variation contributing to replicated evolution by natural selection, paving the way to understand the origin and maintenance of adaptations in natural populations.

Blunted Glucocorticoid Responsiveness to Stress Causes Behavioral and Biological Alterations That Lead to Posttraumatic Stress Disorder Vulnerability

BACKGROUND

Understanding why only a subset of trauma-exposed individuals develop posttraumatic stress disorder is critical for advancing clinical strategies. A few behavioral (deficits in fear extinction) and biological (blunted glucocorticoid levels, small hippocampal size, and rapid-eye-movement sleep [REMS] disturbances) traits have been identified as potential vulnerability factors. However, whether and to what extent these traits are interrelated and whether one of them could causally engender the others are not known.

METHODS

In a genetically selected rat model of reduced corticosterone responsiveness to stress, we explored posttraumatic stress disorder-related biobehavioral traits using ex vivo magnetic resonance imaging, cued fear conditioning, and polysomnographic recordings combined with in vivo photometric measurements.

RESULTS

We showed that genetic selection for blunted glucocorticoid responsiveness led to a correlated multitrait response, including impaired fear extinction (observed in males but not in females), small hippocampal volume, and REMS disturbances, supporting their interrelatedness. Fear extinction deficits and concomitant disruptions in REMS could be normalized through postextinction corticosterone administration, causally implicating glucocorticoid deficiency in two core posttraumatic stress disorder-related risk factors and manifestations. Furthermore, reduced REMS was accompanied by higher norepinephrine levels in the hippocampal dentate gyrus that were also reversed by postextinction corticosterone treatment.

CONCLUSIONS

Our results indicate a predominant role for glucocorticoid deficiency over the contribution of reduced hippocampal volume in engendering both REMS alterations and associated deficits in fear extinction consolidation, and they causally implicate blunted glucocorticoids in sustaining neurophysiological disturbances that lead to fear extinction deficits.

Discovering useful genetic variation in the seed parent gene pool for sorghum improvement

Multi-parent populations contain valuable genetic material for dissecting complex, quantitative traits and provide a unique opportunity to capture multi-allelic variation compared to the biparental populations. A multi-parent advanced generation inter-cross (MAGIC) B-line (MBL) population composed of 708 F6 recombinant inbred lines (RILs), was recently developed from four diverse founders. These selected founders strategically represented the four most prevalent botanical races (kafir, guinea, durra, and caudatum) to capture a significant source of genetic variation to study the quantitative traits in grain sorghum [Sorghum bicolor (L.) Moench]. MBL was phenotyped at two field locations for seven yield-influencing traits: panicle type (PT), days to anthesis (DTA), plant height (PH), grain yield (GY), 1000-grain weight (TGW), tiller number per meter (TN) and yield per panicle (YPP). High phenotypic variation was observed for all the quantitative traits, with broad-sense heritabilities ranging from 0.34 (TN) to 0.84 (PH). The entire population was genotyped using Diversity Arrays Technology (DArTseq), and 8,800 single nucleotide polymorphisms (SNPs) were generated. A set of polymorphic, quality-filtered markers (3,751 SNPs) and phenotypic data were used for genome-wide association studies (GWAS). We identified 52 marker-trait associations (MTAs) for the seven traits using BLUPs generated from replicated plots in two locations. We also identified desirable allelic combinations based on the plant height loci (Dw1, Dw2, and Dw3), which influences yield related traits. Additionally, two novel MTAs were identified each on Chr1 and Chr7 for yield traits independent of dwarfing genes. We further performed a multi-variate adaptive shrinkage analysis and 15 MTAs with pleiotropic effect were identified. The five best performing MBL progenies were selected carrying desirable allelic combinations. Since the MBL population was designed to capture significant diversity for maintainer line (B-line) accessions, these progenies can serve as valuable resources to develop superior sorghum hybrids after validation of their general combining abilities via crossing with elite pollinators. Further, newly identified desirable allelic combinations can be used to enrich the maintainer germplasm lines through marker-assisted backcross breeding.

Causal Genetic Loci for a Motivated Behavior Spectrum Harbor Psychiatric Risk Genes

Behavioral diversity is critical for population fitness. Individual differences in risk-taking are observed across species, but underlying genetic mechanisms and conservation are largely unknown. We examined dark avoidance in larval zebrafish, a motivated behavior reflecting an approach-avoidance conflict. Brain-wide calcium imaging revealed significant neural activity differences between approach-inclined versus avoidance-inclined individuals. We used a population of ∼6,000 to perform the first genome-wide association study (GWAS) in zebrafish, which identified 34 genomic regions harboring many genes that are involved in synaptic transmission and human psychiatric diseases. We used CRISPR to study several causal genes: serotonin receptor-1b ( htr1b ), nitric oxide synthase-1 ( nos1 ), and stress-induced phosphoprotein-1 ( stip1 ). We further identified 52 conserved elements containing 66 GWAS significant variants. One encoded an exonic regulatory element that influenced tissue-specific nos1 expression. Together, these findings reveal new genetic loci and establish a powerful, scalable animal system to probe mechanisms underlying motivation, a critical dimension of psychiatric diseases.

Pervasive under-dominance in gene expression underlying emergent growth trajectories in Arabidopsis thaliana hybrids

BACKGROUND

Complex traits, such as growth and fitness, are typically controlled by a very large number of variants, which can interact in both additive and non-additive fashion. In an attempt to gauge the relative importance of both types of genetic interactions, we turn to hybrids, which provide a facile means for creating many novel allele combinations.

RESULTS

We focus on the interaction between alleles of the same locus, i.e., dominance, and perform a transcriptomic study involving 141 random crosses between different accessions of the plant model species Arabidopsis thaliana. Additivity is rare, consistently observed for only about 300 genes enriched for roles in stress response and cell death. Regulatory rare-allele burden affects the expression level of these genes but does not correlate with F1 rosette size. Non-additive, dominant gene expression in F1 hybrids is much more common, with the vast majority of genes (over 90%) being expressed below the parental average. Unlike in the additive genes, regulatory rare-allele burden in the dominant gene set is strongly correlated with F1 rosette size, even though it only mildly covaries with the expression level of these genes.

CONCLUSIONS

Our study underscores under-dominance as the predominant gene action associated with emergence of rosette growth trajectories in the A. thaliana hybrid model. Our work lays the foundation for understanding molecular mechanisms and evolutionary forces that lead to dominance complementation of rare regulatory alleles.

Evaluation of loci to predict ear morphology using two SNaPshot assays

Human ear morphology prediction with SNP-based genotypes is growing in forensic DNA phenotyping and is scarcely explored in Pakistan as a part of EVCs (externally visible characteristics). The ear morphology prediction assays with 21 SNPs were assessed for their potential utility in forensic identification of population. The SNaPshot™ multiplex chemistries, capillary electrophoresis methods and GeneMapper™ software were used for obtaining genotypic data. A total of 33 ear phenotypes were categorized with digital photographs of 300 volunteers. SHEsis software was applied to make LD plot. Ordinal and multinomial logistic regression was implemented for association testing. Multinomial logistic regression was executed to construct the prediction model in 90% training and 10% testing subjects. Several influential SNPs for ear phenotypic variation were found in association testing. The model based on genetic markers predicted ear phenotypes with moderate to good predictive accuracies demonstrated with the area under curve (AUC), sensitivity and specificity of predicted phenotypes. As an additional EVC, the estimated ear phenotypic profiles have the possibility of determining the human ear morphology differences in unknown biological samples found in crimes that do not result in a criminal database hit. Furthermore, this can help in facial reconstruction and act as an investigational lead.

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

Genetics of yellow-orange color variation in a pair of sympatric sulphur butterflies

Continuous color polymorphisms can serve as a tractable model for the genetic and developmental architecture of traits. Here we investigated continuous color variation in Colias eurytheme and Colias philodice, two species of sulphur butterflies that hybridize in sympatry. Using quantitative trait locus (QTL) analysis and high-throughput color quantification, we found two interacting large-effect loci affecting orange-to-yellow chromaticity. Knockouts of red Malpighian tubules (red), likely involved in endosomal maturation, result in depigmented wing scales. Additionally, the transcription factor bric-a-brac can act as a modulator of orange pigmentation. We also describe the QTL architecture of other continuously varying traits, together supporting a large-X effect model where the genetic control of species-defining traits is enriched on sex chromosomes. This study sheds light on the range of possible genetic architectures that can underpin a continuously varying trait and illustrates the power of using automated measurement to score phenotypes that are not always conspicuous to the human eye.

Exome-based new allele-specific PCR markers and transferability for sodicity tolerance in bread wheat (Triticum aestivum L.)

Targeted exome-based genotype by sequencing (t-GBS), a sequencing technology that tags SNPs and haplotypes in gene-rich regions was used in previous genome-wide association studies (GWAS) for sodicity tolerance in bread wheat. Thirty-nine novel SNPs including 18 haplotypes for yield and yield-components were identified. The present study aimed at developing SNP-derived markers by precisely locating new SNPs on ~180 bp allelic sequence of t-GBS, marker validation, and SNP functional characterization based on its exonic location. We identified unknown locations of significant SNPs/haplotypes by aligning allelic sequences on to IWGSC RefSeqv1.0 on respective chromosomes. Eighteen out of the target 39 SNP locations fulfilled the criteria for producing PCR markers, among which only eight produced polymorphic signals. These eight markers associated with yield, plants m-2, heads m-2, and harvest index, including a pleiotropic marker for yield, harvest index, and grains/head were validated for its amplification efficiency and phenotypic effects in focused identification germplasm strategy (FIGS) wheat set and a doubled haploid (DH) population (Scepter/IG107116). The phenotypic variation explained by these markers are in the range of 4.1-37.6 in the FIGS population. High throughput PCR-based genotyping using new markers and association with phenotypes in FIGS wheat set and DH population validated the effect of functional SNP on closely associated genes-calcineurin B-like- and dirigent protein, basic helix-loop-helix (BHLH-), plant homeodomain (PHD-) and helix-turn-helix myeloblastosis (HTH myb) type -transcription factor. Further, genome-wide SNP annotation using SnpEff tool confirmed that these SNPs are in gene regulatory regions (upstream, 3'-UTR, and intron) modifying gene expression and protein-coding. This integrated approach of marker design for t-GBS alleles, SNP functional annotation, and high-throughput genotyping of functional SNP offers translation solutions across crops and complex traits in crop improvement programs.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Combining transcriptomics and metabolomics to identify key response genes for aluminum toxicity in the root system of Brassica napus L. seedlings

By integrating QTL mapping, transcriptomics and metabolomics, 138 hub genes were identified in rapeseed root response to aluminum stress and mainly involved in metabolism of lipids, carbohydrates and secondary metabolites. Aluminum (Al) toxicity has become one of the important abiotic stress factors in areas with acid soil, which hinders the absorption of water and nutrients by roots, and consequently retards the growth of crops. A deeper understanding of the stress-response mechanism of Brassica napus may allow us to identify the tolerance gene(s) and use this information in breeding-resistant crop varieties. In this study, a population of 138 recombinant inbred lines (RILs) was subjected to aluminum stress, and QTL (quantitative trait locus) mapping was used to preliminarily locate quantitative trait loci related to aluminum stress. Root tissues from seedlings of an aluminum-resistant (R) line and an aluminum-sensitive (S) line from the RIL population were harvested for transcriptome sequencing and metabolome determination. By combining the data on quantitative trait genes (QTGs), differentially expressed genes (DEGs), and differentially accumulated metabolites (DAMs), key candidate genes related to aluminum tolerance in rapeseed were determined. The results showed that there were 3186 QTGs in the RIL population, 14,232 DEGs and 457 DAMs in the comparison between R and S lines. Lastly, 138 hub genes were selected to have a strong positive or negative correlation with 30 important metabolites (|R|≥ 0.95). These genes were mainly involved in the metabolism of lipids, carbohydrates and secondary metabolites in response to Al toxicity stress. In summary, this study provides an effective method for screening key genes by combining QTLs, transcriptome sequencing and metabolomic analysis, but also lists key genes for exploring the molecular mechanism of Al tolerance in rapeseed seedling roots.

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

Multi-parental fungal mapping population study to detect genomic regions associated with Pyrenophora teres f. teres virulence

In recent years multi-parental mapping populations (MPPs) have been widely adopted in many crops to detect quantitative trait loci (QTLs) as this method can compensate for the limitations of QTL analyses using bi-parental mapping populations. Here we report the first multi-parental nested association mapping (MP-NAM) population study used to detect genomic regions associated with host-pathogenic interactions. MP-NAM QTL analyses were conducted on 399 Pyrenophora teres f. teres individuals using biallelic, cross-specific and parental QTL effect models. A bi-parental QTL mapping study was also conducted to compare the power of QTL detection between bi-parental and MP-NAM populations. Using MP-NAM with 399 individuals detected a maximum of eight QTLs with a single QTL effect model whilst only a maximum of five QTLs were detected with an individual bi-parental mapping population of 100 individuals. When reducing the number of isolates in the MP-NAM to 200 individuals the number of QTLs detected remained the same for the MP-NAM population. This study confirms that MPPs such as MP-NAM populations can be successfully used in detecting QTLs in haploid fungal pathogens and that the power of QTL detection with MPPs is greater than with bi-parental mapping populations.

Omics approaches in effective selection and generation of potential plants for phytoremediation of heavy metal from contaminated resources

Soil and water pollution, rapid industrialization, contaminated irrigation-water, increased waste-production and surge in agricultural land leads to the accumulation of Heavy Metals (HM) with time. HM contamination has raised concern over the past years and new remediation strategies are required to deal with it. HM-contaminated soil is often used for the production of food, which makes a gateway for toxic metals into the food-chain, thereby affecting food security and human health. To avoid HM-toxicity, decontamination of important resources is essential. Therefore, exploring phytoremediation for the removal, decomposition and detoxification of hazardous metals from HM-contaminated sites is of great significance. Hyper-accumulator plants can efficiently remove HMs. However, despite many hyper-accumulator plant species, there is a research gap in the studies of phytotechnology. Hence biotechnological efforts advocating omics studies i.e. genomics, transcriptomics, proteomics, metabolomics and phenomics are in order, the purpose being to select and enhance a plant's potential for the process of phytoremediation to be more effective. There is a need to study newly developed high-efficiency hyper-accumulator plants as HM-decontaminator candidates for phytoremediation and phytomining. Therefore, this review focuses on various strategies and bio-technological methods for the removal of HM contaminants from sites, with emphasis on the advancement of phytoremediation, along with applications in cleaning up various toxic pollutants.

Single-gene resolution of diversity-driven overyielding in plant genotype mixtures

In plant communities, diversity often increases productivity and functioning, but the specific underlying drivers are difficult to identify. Most ecological theories attribute positive diversity effects to complementary niches occupied by different species or genotypes. However, the specific nature of niche complementarity often remains unclear, including how it is expressed in terms of trait differences between plants. Here, we use a gene-centred approach to study positive diversity effects in mixtures of natural Arabidopsis thaliana genotypes. Using two orthogonal genetic mapping approaches, we find that between-plant allelic differences at the AtSUC8 locus are strongly associated with mixture overyielding. AtSUC8 encodes a proton-sucrose symporter and is expressed in root tissues. Genetic variation in AtSUC8 affects the biochemical activities of protein variants and natural variation at this locus is associated with different sensitivities of root growth to changes in substrate pH. We thus speculate that - in the particular case studied here - evolutionary divergence along an edaphic gradient resulted in the niche complementarity between genotypes that now drives overyielding in mixtures. Identifying genes important for ecosystem functioning may ultimately allow linking ecological processes to evolutionary drivers, help identify traits underlying positive diversity effects, and facilitate the development of high-performance crop variety mixtures.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.