Dominance and multi-locus interaction.

Dominance is usually considered a constant value that describes the relative difference in fitness or phenotype between heterozygotes and the average of homozygotes at a focal polymorphic locus. However, the observed dominance can vary with the genetic background of the focal locus. Here, alleles at other loci modify the observed phenotype through position effects or dominance modifiers that are sometimes associated with pathogen resistance, lineage, sex, or mating type. Theoretical models have illustrated how variable dominance appears in the context of multi-locus interaction (epistasis). Here, we review empirical evidence for variable dominance and how the observed patterns may be captured by proposed epistatic models. We highlight how integrating epistasis and dominance is crucial for comprehensively understanding adaptation and speciation.

Epigenome-metabolism nexus in the retina: implications for aging and disease.

Intimate links between epigenome modifications and metabolites allude to a crucial role of cellular metabolism in transcriptional regulation. Retina, being a highly metabolic tissue, adapts by integrating inputs from genetic, epigenetic, and extracellular signals. Precise global epigenomic signatures guide development and homeostasis of the intricate retinal structure and function. Epigenomic and metabolic realignment are hallmarks of aging and highlight a link of the epigenome-metabolism nexus with aging-associated multifactorial traits affecting the retina, including age-related macular degeneration and glaucoma. Here, we focus on emerging principles of epigenomic and metabolic control of retinal gene regulation, with emphasis on their contribution to human disease. In addition, we discuss potential mitigation strategies involving lifestyle changes that target the epigenome-metabolome relationship for maintaining retinal function.

Estimating disease heritability from complex pedigrees allowing for ascertainment and covariates.

We propose TetraHer, a method for estimating the liability heritability of binary phenotypes. TetraHer has five key features. First, it can be applied to data from complex pedigrees that contain multiple types of relationships. Second, it can correct for ascertainment of cases or controls. Third, it can accommodate covariates. Fourth, it can model the contribution of common environment. Fifth, it produces a likelihood that can be used for significance testing. We first demonstrate the validity of TetraHer on simulated data. We then use TetraHer to estimate liability heritability for 229 codes from the tenth International Classification of Diseases (ICD-10). We identify 107 codes with significant heritability (p < 0.05/229), which can be used in future analyses for investigating the genetic architecture of human diseases.

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits.

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits.

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.

Community assembly influences plant trait economic spectra and functional trade-offs at ecosystem scales.

Plant functional traits hold the potential to greatly improve the understanding and prediction of climate impacts on ecosystems and carbon cycle feedback to climate change. Traits are commonly used to place species along a global conservative-acquisitive trade-off, yet how and if functional traits and conservative-acquisitive trade-offs scale up to mediate community and ecosystem fluxes is largely unknown. Here, we combine functional trait datasets and multibiome datasets of forest water and carbon fluxes at the species, community, and ecosystem-levels to quantify the scaling of the tradeoff between maximum flux and sensitivity to vapor pressure deficit. We find a strong conservative-acquisitive trade-off at the species scale, which weakens modestly at the community scale and largely disappears at the ecosystem scale. Functional traits, particularly plant water transport (hydraulic) traits, are strongly associated with the key dimensions of the conservative-acquisitive trade-off at community and ecosystem scales, highlighting that trait composition appears to influence community and ecosystem flux dynamics. Our findings provide a foundation for improving carbon cycle models by revealing i) that plant hydraulic traits are most strongly associated with community- and ecosystem scale flux dynamics and ii) community assembly dynamics likely need to be considered explicitly, as they give rise to ecosystem-level flux dynamics that differ substantially from trade-offs identified at the species-level.

Linking fine root lifespan to root chemical and morphological traits-A global analysis.

Fine root lifespan is a critical trait associated with contrasting root strategies of resource acquisition and protection. Yet, its position within the multidimensional "root economics space" synthesizing global root economics strategies is largely uncertain, and it is rarely represented in frameworks integrating plant trait variations. Here, we compiled the most comprehensive dataset of absorptive median root lifespan (MRL) data including 98 observations from 79 woody species using (mini-)rhizotrons across 40 sites and linked MRL to other plant traits to address questions of the regulators of MRL at large spatial scales. We demonstrate that MRL not only decreases with plant investment in root nitrogen (associated with more metabolically active tissues) but also increases with construction of larger diameter roots which is often associated with greater plant reliance on mycorrhizal symbionts. Although theories linking organ structure and function suggest that root traits should play a role in modulating MRL, we found no correlation between root traits associated with structural defense (root tissue density and specific root length) and MRL. Moreover, fine root and leaf lifespan were globally unrelated, except among evergreen species, suggesting contrasting evolutionary selection between leaves and roots facing contrasting environmental influences above vs. belowground. At large geographic scales, MRL was typically longer at sites with lower mean annual temperature and higher mean annual precipitation. Overall, this synthesis uncovered several key ecophysiological covariates and environmental drivers of MRL, highlighting broad avenues for accurate parametrization of global biogeochemical models and the understanding of ecosystem response to global climate change.

Limited intraspecific variation in drought resistance along a pronounced tropical rainfall gradient.

Assessing within-species variation in response to drought is crucial for predicting species' responses to climate change and informing restoration and conservation efforts, yet experimental data are lacking for the vast majority of tropical tree species. We assessed intraspecific variation in response to water availability across a strong rainfall gradient for 16 tropical tree species using reciprocal transplant and common garden field experiments, along with measurements of gene flow and key functional traits linked to drought resistance. Although drought resistance varies widely among species in these forests, we found little evidence for within-species variation in drought resistance. For the majority of functional traits measured, we detected no significant intraspecific variation. The few traits that did vary significantly between drier and wetter origins of the same species all showed relationships opposite to expectations based on drought stress. Furthermore, seedlings of the same species originating from drier and wetter sites performed equally well under drought conditions in the common garden experiment and at the driest transplant site. However, contrary to expectation, wetter-origin seedlings survived better than drier-origin seedlings under wetter conditions in both the reciprocal transplant and common garden experiment, potentially due to lower insect herbivory. Our study provides the most comprehensive picture to date of intraspecific variation in tropical tree species' responses to water availability. Our findings suggest that while drought plays an important role in shaping species composition across moist tropical forests, its influence on within-species variation is limited.

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study.

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes.

Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.

Deciphering Structural Traits for Thermal and Kinetic Stability across Protein Family Evolution through Ancestral Sequence Reconstruction.

Natural proteins are frequently marginally stable, and an increase in environmental temperature can easily lead to unfolding. As a result, protein engineering to improve protein stability is an area of intensive research. Nonetheless, since there is usually a high degree of structural homology between proteins from thermophilic organisms and their mesophilic counterparts, the identification of structural determinants for thermoadaptation is challenging. Moreover, in many cases, it has become clear that the success of stabilization strategies is often dependent on the evolutionary history of a protein family. In the last few years, the use of ancestral sequence reconstruction (ASR) as a tool for elucidation of the evolutionary history of functional traits of a protein family has gained strength. Here, we used ASR to trace the evolutionary pathways between mesophilic and thermophilic kinases that participate in the biosynthetic pathway of vitamin B1 in bacteria. By combining biophysics approaches, X-ray crystallography, and molecular dynamics simulations, we found that the thermal stability of these enzymes correlates with their kinetic stability, where the highest thermal/kinetic stability is given by an increase in small hydrophobic amino acids that allow a higher number of interatomic hydrophobic contacts, making this type of interaction the main support for stability in this protein architecture. The results highlight the potential benefits of using ASR to explore the evolutionary history of protein sequence and structure to identify traits responsible for the kinetic and thermal stability of any protein architecture.

Parallel Evolution at the Regulatory Base-Pair Level Contributes to Mammalian Interspecific Differences in Polygenic Traits.

Parallel evolution occurs when distinct lineages with similar ancestral states converge on a new phenotype. Parallel evolution has been well documented at the organ, gene pathway, and amino acid sequence level but in theory, it can also occur at individual nucleotides within noncoding regions. To examine the role of parallel evolution in shaping the biology of mammalian complex traits, we used data on single-nucleotide polymorphisms (SNPs) influencing human intraspecific variation to predict trait values in other species for 11 complex traits. We found that the alleles at SNP positions associated with human intraspecific height and red blood cell (RBC) count variation are associated with interspecific variation in the corresponding traits across mammals. These associations hold for deeper branches of mammalian evolution as well as between strains of collaborative cross mice. While variation in RBC count between primates uses both ancient and more recently evolved genomic regions, we found that only primate-specific elements were correlated with primate body size. We show that the SNP positions driving these signals are flanked by conserved sequences, maintain synteny with target genes, and overlap transcription factor binding sites. This work highlights the potential of conserved but tunable regulatory elements to be reused in parallel to facilitate evolutionary adaptation in mammals.

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities.

Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

Epidemiologic association and shared genetic architecture between cataract and hearing difficulties among middle-aged and older adults.

Age-related cataract and hearing difficulties are major sensory disorders that often co-exist in the global-wide elderly and have a tangible influence on the quality of life. However, the epidemiologic association between cataract and hearing difficulties remains unexplored, while little is known about whether the two share their genetic etiology. We first investigated the clinical association between cataract and hearing difficulties using the UK Biobank covering 502,543 individuals. Both unmatched analysis (adjusted for confounders) and a matched analysis (one control matched for each patient with cataract according to confounding factors) were undertaken and confirmed that cataract was associated with hearing difficulties (OR, 2.12; 95% CI, 1.98-2.27; OR, 2.03; 95% CI, 1.86-2.23, respectively). Furthermore, we explored and quantified the shared genetic architecture of these two complex sensory disorders at the common variant level using the bivariate causal mixture model (MiXeR) and conditional/conjunctional false discovery rate method based on the largest available genome-wide association studies of cataract (N = 585,243) and hearing difficulties (N = 323,978). Despite detecting only a negligible genetic correlation, we observe polygenic overlap between cataract and hearing difficulties and identify 6 shared loci with mixed directions of effects. Follow-up analysis of the shared loci implicates candidate genes QKI, STK17A, TYR, NSF, and TCF4 likely contribute to the pathophysiology of cataracts and hearing difficulties. In conclusion, this study demonstrates the presence of epidemiologic association between cataract and hearing difficulties and provides new insights into the shared genetic architecture of these two disorders at the common variant level.

Childhood adiposity underlies numerous adult brain traits commonly attributed to midlife obesity.

Obese adults are often reported to have smaller brain volumes than their non-obese peers. Whether this represents evidence of accelerations in obesity-driven atrophy or is instead a legacy of developmental differences established earlier in the lifespan remains unclear. This study aimed to investigate whether early-life differences in adiposity explain differences in numerous adult brain traits commonly attributed to mid-life obesity. We utilised a two-sample lifecourse Mendelian randomization study in 37,501 adults recruited to UK Biobank (UKB) imaging centers from 2014, with secondary analyses in 6,996 children assessed in the Adolescent Brain Cognitive Development Study (ABCD) recruited from 2018. Exposures were genetic variants for childhood (266 variants) and adult (470 variants) adiposity derived from a GWAS of 407,741 UKB participants. Primary outcomes were adult total brain volume; grey matter volume, thickness, and surface area; white matter volume and hyperintensities; and hippocampus, amygdala, and thalamus volumes at mean age 55 in UKB. Secondary outcomes were equivalent childhood measures collected at mean age 10 in ABCD. In UKB, individuals who were genetically-predicted to have had higher levels of adiposity in childhood were found to have multiple smaller adult brain volumes relative to intracranial volume (e.g. z-score difference in normalised brain volume per category increase in adiposity [95%CI] = -0.20 [-0.28, -0.12]; p = 4 × 10-6). These effect sizes remained essentially unchanged after accounting for birthweight or current adult obesity in multivariable models, whereas most observed adult effects attenuated towards null (e.g. adult z-score [95%CI] for total volume = 0.06 [-0.05,0.17]; p = 0.3). Observational analyses in ABCD showed a similar pattern of changes already present in those with a high BMI by age 10 (z-score [95%CI] = -0.10 [-0.13, -0.07]; p = 8 × 10-13), with follow-up genetic risk score analyses providing some evidence for a causal effect already at this early age. Sensitivity analyses revealed that many of these effects were likely due to the persistence of larger head sizes established in those who gained excess weight in childhood (childhood z-score [95%CI] for intracranial volume = 0.14 [0.05,0.23]; p = 0.002), rather than smaller brain sizes per se. Our data suggest that persistence of early-life developmental differences across the lifecourse may underlie numerous neuroimaging traits commonly attributed to obesity-related atrophy in later life.

Enhanced neural synchronization during social communications between dyads with high autistic traits.

Autism is characterized by atypical social communication styles. To investigate whether individuals with high autistic traits could still have effective social communication among each other, we compared the behavioral patterns and communication quality within 64 dyads of college students paired with both high, both low, and mixed high-low (HL) autistic traits, with their gender matched. Results revealed that the high-high (HH) autistic dyads exhibited atypical behavioral patterns during conversations, including reduced mutual gaze, communicational turns, and emotional sharing compared with the low-low and/or HL autistic dyads. However, the HH autistic dyads displayed enhanced interpersonal neural synchronization during social communications measured by functional near-infrared spectroscopy, suggesting an effective communication style. Besides, they also provided more positive subjective evaluations of the conversations. These findings highlight the potential for alternative pathways to effectively communicate with the autistic community, contribute to a deeper understanding of how high autistic traits influence social communication dynamics among autistic individuals, and provide important insights for the clinical practices for supporting autistic people.

Genomics insights into flowering and floral pattern formation: regional duplication and seasonal pattern of gene expression in Camellia.

BACKGROUND: The formation and domestication of ornamental traits are influenced by various aspects, such as the recognition of esthetic values and cultural traditions. Camellia japonica is widely appreciated and domesticated around the world mainly due to its rich variations in ornamental traits. Ornamental camellias have a diverse range of resources, including different bud variations from Camellia spp. as well as inter- and intra- specific hybridization. Despite research on the formation of ornamental traits, a basic understanding of their genetics and genomics is still lacking. RESULTS: Here, we report the chromosomal-level reference genome of C. japonica through combining multiple DNA-sequencing technologies and obtain a high-density genetic linkage map of 4255 markers by sequencing 98 interspecific F1 hybrids between C. japonica and C. chekiangoleosa. We identify two whole-genome duplication events in C. japonica: one is a shared ancient γ event, and the other is revealed to be specific to genus Camellia. Based on the micro-collinearity analysis, we find large-scale segmental duplication of chromosome 8, resulting to two copies of the AGAMOUS loci, which may play a key role in the domestication of floral shapes. To explore the regulatory mechanisms of seasonal flowering, we have analyzed year-round gene expression patterns of C. japonica and C. azalea-a sister plant of continuous flowering that has been widely used for cross breeding. Through comparative analyses of gene co-expression networks and annual gene expression patterns, we show that annual expression rhythms of some important regulators of seasonal growth and development, including GIGANTEA and CONSTANS of the photoperiod pathway, have been disrupted in C. azalea. Furthermore, we reveal that the distinctive expression patterns of FLOWERING LOCUS T can be correlated with the seasonal activities of flowering and flushing. We demonstrate that the regulatory module involved in GIGANTEA, CONSTANS, and FLOWERING LOCUS T is central to achieve seasonality. CONCLUSIONS: Through the genomic and comparative genomics characterizations of ornamental Camellia spp., we propose that duplication of chromosomal segments as well as the establishment of gene expression patterns has played a key role in the formation of ornamental traits (e.g., flower shape, flowering time). This work provides a valuable genomic platform for understanding the molecular basis of ornamental traits.

A variant in long noncoding RNA NORSF affects granulosa cells response to transcription factor RFX7.

NORSF is a nuclear long noncoding RNA (lncRNA) that contributes to the follicular atresia and restrains 17ß-estradiol (E2) release by granulosa cells (GCs). Importantly, it is also a potential candidate gene in the quantitative trait locus (QTLs) for sow fertility traits. We identified NORSF as a candidate (causal) gene affecting sow fertility traits. A novel G-A variant was discovered at -478 nt of the NORSF promoter and termed as g.-478G>A. Association analysis revealed that this variant was associated with sow fertility traits (e.g., the total number of piglets born, the total number of piglets born alive, and the number of healthy piglets). Mechanistically, the g.-478G>A variant reduced the binding activity of the NORSF promoter to its transcription activator regulatory factor X7 (RFX7), leading to decreased NORSF promoter activity and transcription levels in sow GCs (sGCs), and weakened inhibitory effects on the transcription of CYP19A1, which encodes a rate-limiting enzyme for E2 synthesis and E2 release by sGCs. In addition, RFX7 is transcriptionally activated by P53, which restrains E2 release from sGCs via the RFX7/NORSF/CYP19A1 pathway. These findings indicate that the lncRNA NORSF is a causal gene in QTLs for sow fertility traits and define the P53/NORSF/CYP19A1 pathway as a new signaling pathway affecting sow reproduction, which provides a new target for improving female fertility.