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Regulatory mutations affecting the expression level and pattern of dosage-sensitive genes can create dosage imbalance in cells affected by the expression change. The deleterious effect of dosage imbalance is higher in homozygous and lower in heterozygous individuals. Pleiotropism of regulatory mutations is possible. In addition to the dosage-sensitive trait, the mutation may engender a dominant advantageous trait. Individuals that are heterozygous for such mutations will be fitter than the homozygotes. Pleiotropism may help explain heterosis, the vigor displayed by hybrids. Hybrids are heterozygous at many loci, causing transgressive fitness compared with homozygous parents. Adaptive evolution often involves structural variation affecting genes or cis -regulatory changes that engender novel and favorable gain-of-function gene regulation. Such mutation could result in a favorable dominant trait. At the same time, the gene product could be dosage sensitive if its change in concentration disrupts another trait. As a result, the mutant allele would display dosage-sensitive pleiotropy (DSP). By minimizing imbalance while conserving the favorable dominant effect, heterozygosity can increase fitness and result in heterosis. The properties of these alleles are consistent with evidence from multiple studies that indicate increased fitness of heterozygous regulatory mutations. DSP can help explain mysterious properties of heterosis as well as other effects of hybridization. [ABSTRACT FROM AUTHOR]

Common wall lizards (Podarcis muralis) in Italy show a striking variation in body coloration across the landscape, with highly exaggerated black and green colors in hot and dry climates and brown and white colors in cool and wet climates. Males are more intensely colored than females, and previous work has suggested that the maintenance of variation in coloration across the landscape reflects climatic effects on the strength of male–male competition, and through this sexual selection. However climatic effects on the intensity of male–male competition would need to be exceptionally strong to fully explain the geographic patterns of color variation. Thus, additional processes may contribute to the maintenance of color variation. Here we test the hypothesis that selection for green and black ornamentation in the context of male–male competition is opposed by selection against ornamentation because the genes involved in the regulation of coloration have pleiotropic effects on thermal physiology, such that ornamentation is selected against in cool climates. Field observations revealed no association between body coloration and microhabitat use or field active body temperatures. Consistent with these field data, lizards at the extreme ends of the phenotypic distribution for body coloration did not show any differences in critical minimum temperature, preferred body temperature, temperature‐dependent metabolic rate, or evaporative water loss when tested in the laboratory. Combined, these results provide no evidence that genes that underlie sexual ornamentation are selected against in cool climate because of pleiotropic effects on thermal biology. Research Highlights: Common wall lizards in Italy show a striking variation in coloration across the landscape with highly exaggerated coloration occurring in hot and dry climates.This could be because climate selects for coloration in warm climates and/or because the genes that underlie.We show that there are no differences in thermal traits between lizards with different color phenotypes providing limited evidence for the antagonistic pleiotropy hypothesis. [ABSTRACT FROM AUTHOR]

Introduction: Throughout domestication, crop plants have gone through strong genetic bottlenecks, dramatically reducing the genetic diversity in today's available germplasm. This has also reduced the diversity in traits necessary for breeders to develop improved varieties. Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. However, even with recent advances in genetic engineering we still face the rate limiting step of identifying which genes and mutations we should target to generate diversity in specific traits. Methods: Here, we apply a comparative evolutionary approach, pairing phylogenetic and expression analyses to identify potential candidate genes for diversifying soybean (Glycine max) canopy cover development via the nuclear auxin signaling gene families, while minimizing pleiotropic effects in other tissues. In soybean, rapid canopy cover development is correlated with yield and also suppresses weeds in organic cultivation. Results and discussion: We identified genes most specifically expressed during early canopy development from the TIR1/AFB auxin receptor, Aux/IAA auxin coreceptor, and ARF auxin response factor gene families in soybean, using principal component analysis. We defined Arabidopsis thaliana and model legume species orthologs for each soybean gene in these families allowing us to speculate potential soybean phenotypes based on well-characterized mutants in these model species. In future work, we aim to connect genetic and functional diversity in these candidate genes with phenotypic diversity in planta allowing for improvements in soybean rapid canopy cover, yield, and weed suppression. Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops. [ABSTRACT FROM AUTHOR]

Summary Carotenoids play essential roles in photosynthesis, photoprotection, and human health. Efforts to increase carotenoid content in several staple crops have been successful through both conventional selection and genetic engineering methods. Interestingly, in some cases, altering carotenoid content has had unexpected effects on other aspects of plant metabolism, impacting traits like sugar content, dry matter percentage, fatty acid content, stress tolerance, and phytohormone concentrations. Studies across several diverse crop species have identified negative correlations between carotenoid and starch contents, as well as positive correlations between carotenoids and soluble sugars. Collectively, these reports suggest a metabolic interaction between carotenoids and carbohydrates. We synthesize evidence pointing to four hypothesized mechanisms: (1) direct competition for precursors; (2) physical interactions in plastids; (3) influences of sugar or apocarotenoid signaling networks; and (4) nonmechanistic population or statistical sources of correlations. Though the carotenoid biosynthesis pathway is well understood, the regulation and interactions of carotenoids, especially in nonphotosynthetic tissues, remain unclear. This topic represents an underexplored interplay between primary and secondary metabolism where further research is needed. [ABSTRACT FROM AUTHOR]

Hearing difficulty (HD) is a major health burden in older adults. While ageing-related changes in the peripheral auditory system play an important role, genetic variation associated with brain structure and function could also be involved in HD predisposition. We analysed a large-scale HD genome-wide association study (GWAS; n total = 501 825, 56% females) and GWAS data related to 3935 brain imaging-derived phenotypes (IDPs) assessed in up to 33 224 individuals (52% females) using multiple MRI modalities. To investigate HD pleiotropy with brain structure and function, we conducted genetic correlation, latent causal variable, Mendelian randomization and multivariable generalized linear regression analyses. Additionally, we performed local genetic correlation and multi-trait co-localization analyses to identify genomic regions and loci implicated in the pleiotropic mechanisms shared between HD and brain IDPs. We observed a widespread genetic correlation of HD with 120 IDPs in females, 89 in males and 171 in the sex-combined analysis. The latent causal variable analysis showed that some of these genetic correlations could be due to cause-effect relationships. For seven of them, the causal effects were also confirmed by the Mendelian randomization approach: vessel volume→HD in the sex-combined analysis; hippocampus volume→HD, cerebellum grey matter volume→HD, primary visual cortex volume→HD and HD→fluctuation amplitudes of node 46 in resting-state functional MRI dimensionality 100 in females; global mean thickness→HD and HD→mean orientation dispersion index in superior corona radiata in males. The local genetic correlation analysis identified 13 pleiotropic regions between HD and these seven IDPs. We also observed a co-localization signal for the rs13026575 variant between HD, primary visual cortex volume and SPTBN1 transcriptomic regulation in females. Brain structure and function may have a role in the sex differences in HD predisposition via possible cause-effect relationships and shared regulatory mechanisms. [ABSTRACT FROM AUTHOR]

In animals, the nervous system evolved as the primary interface between multicellular organisms and the environment. As organisms became larger and more complex, the primary functions of the nervous system expanded to include the modulation and coordination of individual responsive cells via paracrine and synaptic functions as well as to monitor and maintain the organism's own internal environment. This was initially accomplished via paracrine signaling and eventually through the assembly of multicell circuits in some lineages. Cells with similar functions and centralized nervous systems have independently arisen in several lineages. We highlight the molecular mechanisms that underlie parallel diversifications of the nervous system. [ABSTRACT FROM AUTHOR]

Barley production is severely affected by drought caused by the unpredictable Mediterranean weather patterns, which include uneven rainfall and extreme temperatures. This leads to a decrease in crop yield. However, to tackle this issue, landraces and wild species are crucial sources of variation for stress adaptive traits. By incorporating these traits into improved varieties, we may see an increase in yield and stability under drought conditions. Seventy‐six quantitative traits loci (QTLs) identified traits were mapped using recombinant inbred lines (RIL) population Arta × Harmal‐2//Esp/1808‐4L, evaluated at six dry and semi‐dry areas over 3 years. The study investigated traits such as grain yield, biological yield, harvest index, kernel weight, seed per head, days to heading, kernel filling duration, growth vigour, growth habit, lodging and plant height. Numerous QTLs were discovered that are associated with various phenotypic traits related to grain yield, kernel yield, duration of filling period and days to heading. For areas with less than 250 mm/annum of rainfall, QTLs were identified on chromosome 2H for biological yield, days to heading, and kernel weight, on 1H for harvest index, and on 2H, 4H, and 5H for kernel weight. For semi‐dry areas with rainfall less than 450 mm, QTLs were found on chromosome 6H for grain yield, 2H and 5H for kernel weight, 1H and 6H for seed per head, and 2H for days to heading. Notably, these QTLs significantly explain more than 10% of phenotypic variation. The 2H chromosome was found to have the most important QTL and pleiotropic effect for yield and its components, such as kernel weight, days to heading, and biological yield. The cross Arta/Harmal was adapted, and mechanisms were developed to cope with drought stress, reflected by the significant and positive correlation of biological yield and harvest index with grain yield. Chromosomes 1H, 2H, 4H and 5H harbour more than 60% of mapped QTLs for dry areas. It is worth noting that the QTLs mentioned earlier, along with the kernel weight QTLs (QKW 1.5, QKW2.7b, QKW4.1, QKW6.7, QKW6.9), have consistently exhibited positive effects on crop yield in semi‐dry and dry areas, making them potential candidates for breeding drought‐tolerant crops. Genomic co‐localisation of the QTL for Arta/Harmal population suggested that selection for drought through linked markers can be an option for drought tolerance selection for barley in dry areas. [ABSTRACT FROM AUTHOR]

Observations of genetically repeated evolution (repeatability) in complex organisms are incongruent with the Fisher–Orr model, which implies that repeated use of the same gene should be rare when mutations are pleiotropic (i.e. affect multiple traits). When spatially divergent selection occurs in the presence of migration, mutations of large effect are more strongly favored, and hence, repeatability is more likely, but it is unclear whether this observation is limited by pleiotropy. Here, we explore this question using individual-based simulations of a two-patch model incorporating multiple quantitative traits governed by mutations with pleiotropic effects. We explore the relationship between fitness trade-offs and repeatability by varying the alignment between mutation effect and spatial variation in trait optima. While repeatability decreases with increasing trait dimensionality, trade-offs in mutation effects on traits do not strongly limit the contribution of a locus of large effect to repeated adaptation, particularly under increased migration. These results suggest that repeatability will be more pronounced for local rather than global adaptation. Whereas pleiotropy limits repeatability in a single-population model, when there is local adaptation with gene flow, repeatability can occur if some loci are able to produce alleles of large effect, even when there are pleiotropic trade-offs. [ABSTRACT FROM AUTHOR]

Advancing male age is often hypothesized to reduce both male fertility and offspring quality due to reproductive senescence. However, the effects of advancing male age on reproductive output and offspring quality are not always deleterious. For example, older fathers might buffer the effects of reproductive senescence by terminally investing in reproduction. Similarly, males that survive to reproduce at an old age might carry alleles that confer high viability (viability selection), which are then inherited by offspring, or might have high reproductive potential (selective disappearance). Differentiating these mechanisms requires an integrated experimental study of paternal survival and reproductive performance, as well as offspring quality, which is currently lacking. Using a cross-sectional study in Drosophila melanogaster , we test the effects of paternal age at conception (PAC) on paternal survival and reproductive success, and on the lifespans of sons. We discover that mating at an old age is linked with decreased future male survival, suggesting that mating-induced mortality is possibly due to old fathers being frail. We find no evidence for terminal investment and show that reproductive senescence in fathers does not onset until their late-adult life. Additionally, we find that as a father's lifespan increases, his probability of siring offspring increases for older PAC treatments only. Lastly, we show that sons born to older fathers live longer than those born to younger fathers due to viability selection. Collectively, our results suggest that advancing paternal age is not necessarily associated with deleterious effects for offspring and may even lead to older fathers producing longer-lived offspring. [ABSTRACT FROM AUTHOR]

Background and aims - Variation in flower color among individuals within plant populations has long fascinated horticulturalists and evolutionary biologists alike. Nevertheless, the diversity of genetic factors and ecological interactions that act to maintain flower-color polymorphisms over time are understood for very few species. Here, we suggest that Solanum carolinense (horsenettle) makes an excellent model system for studying the evolutionary ecology of a flower-color polymorphism, and we begin to shed insight into factors that might affect the stability of the polymorphism. Material and methods - We transplanted 24 horsenettle ramets of each of 40 field-collected genets (10 genets per each of four source fields) into a common-garden plot (in an oldfield with an existing horsenettle population). For each ramet, we visually characterized petal color and quantified production of flowers, fruits, seeds, and damage by 11 species of herbivores. Key results - Rather than varying continuously within ramets and among genets, petal color fell into three relatively discrete categories that are common in most horsenettle populations: purple, white, and mauve. In the common-garden experiment, purple-petalled morphs initiated significantly more flower buds, opened more flowers, and produced more fruits and seeds than did the mauve-petalled or white-petalled morphs. Petal-color morphs differed only slightly in resistance to a minority of the herbivore community, with no consistent advantage for color morphs across species of herbivores or types of tissues fed upon. Conclusion - Horsenettle has numerous characteristics that make it an excellent system for studying the genetics and ecology that drive the maintenance of flower-color polymorphisms. Although pleiotropic effects of flower-color on resistance to herbivory appear to be minimal in horsenettle, potential pleiotropies that differentially affect maternal and paternal reproduction, or sexual and vegetative reproduction, are worthy of further study. [ABSTRACT FROM AUTHOR]

Background and Hypothesis Schizophrenia (SCZ) and anorexia nervosa (AN) are 2 severe and highly heterogeneous disorders showing substantial familial co-aggregation. Genetic factors play a significant role in both disorders, but the shared genetic etiology between them is yet to be investigated. Study Design Using summary statistics from recent large genome-wide association studies on SCZ (N cases = 53 386) and AN (N cases = 16 992), a 2-sample Mendelian randomization analysis was conducted to explore the causal relationship between SCZ and AN. MiXeR was employed to quantify their polygenic overlap. A conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was adopted to identify loci jointly associated with both disorders. Functional annotation and enrichment analyses were performed on the shared loci. Study Results We observed a cross-trait genetic enrichment, a suggestive bidirectional causal relationship, and a considerable polygenic overlap (Dice coefficient = 62.2%) between SCZ and AN. The proportion of variants with concordant effect directions among all shared variants was 69.9%. Leveraging overlapping genetic associations, we identified 6 novel loci for AN and 33 novel loci for SCZ at condFDR <0.01. At conjFDR <0.05, we identified 10 loci jointly associated with both disorders, implicating multiple genes highly expressed in the cerebellum and pituitary and involved in synapse organization. Particularly, high expression of the shared genes was observed in the hippocampus in adolescence and orbitofrontal cortex during infancy. Conclusions This study provides novel insights into the relationship between SCZ and AN by revealing a shared genetic component and offers a window into their complex etiology. [ABSTRACT FROM AUTHOR]

Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence functions of the treatment effect, and then we construct a continuous updating estimator and establish its asymptotic properties under a many weak invalid instruments asymptotic regime by developing novel semiparametric theory. We also provide a measure of weak identification, an overidentification test, and a graphical diagnostic tool. [ABSTRACT FROM AUTHOR]

This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome‐wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High‐Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two‐sample Mendelian randomization analyses, analysis under the multiplicity‐complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co‐localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones. [ABSTRACT FROM AUTHOR]

Abstract The highly polygenic nature of human longevity renders pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between aging-related traits (ARTs), we aimed to model the additive variance in lifespan as a function of the cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores (iLGSs) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts (the Scripps Wellderly cohort and the Medical Genome Reference Bank (MRGB)) and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of iLGS, we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at a higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with iLGS highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.

代谢性脑血管病以代谢危险因素引起的脑血管损害为核心，其概念已被提出并处于早期探索阶段。代谢性脑血管病的防治重点在于控制代谢危险因素，目前临床用药策略仍是对各个代谢危险因素进行单独干预，因用药种类多和剂量大给患者带来很多困扰。因此，临床亟需一种具有多作用靶点、可协同作用的药物以减少代谢性脑血管病患者的用药负担并提高疗效。牛磺酸作为一种条件必需氨基酸，具有调节糖脂代谢、调节渗透压、稳定细胞膜、抗氧化、抗衰老等多种生物学作用，对代谢性脑血管病治疗具有潜在临床价值。本文聚焦牛磺酸多效性，探讨牛磺酸在代谢性脑血管病中的作用机制及其临床应用前景。 Abstract: The concept of cerebro-metabolic disease, which centers on cerebrovascular damage caused by metabolic risk factors, has been proposed and is in the early stages of exploration. The prevention and treatment of cerebro-metabolic disease focus on the control of metabolic risk factors. At present, the clinical drug strategy is still based on the individual intervention of each metabolic risk factor, which brings a lot of trouble to patients due to the variety of drugs used and the high dosage. Therefore, there is an urgent need for a synergistic drug with multiple targets to reduce the drug burden and improve the efficacy in patients with cerebro-metabolic disease. Taurine, as a conditionally essential amino acid, has a variety of biological effects such as regulation of glycolipid metabolism, osmotic pressure regulation, cell membrane stabilization, antioxidation, anti-aging, etc., which has potential clinical value for cerebro-metabolic disease treatment. This paper focused on the pleiotropy of taurine and discussed the mechanism of taurine in cerebro-metabolic disease and its clinical application prospects.

BackgroundMajor depressive disorder and insomnia often coexist， and the two may share a mechanism of pathogenesis and be affected by common underlying genes with strong pleiotropic effects. Previous genome-wide association studies （GWAS） mainly focused on single-gene morphological characters analysis， which impose limitations on showing possible pleiotropic effects.ObjectiveTo identify genetic loci related to insomnia and major depressive disorder， and to examine whether there are common genetic factors underlying both insomnia and depression.MethodsThe GWAS data for major depressive disorder originates from the Psychiatric Genomics Consortium （PGC）， which comprises a total of 246 363 depressive cases and 561 190 controls. The insomnia GWAS data was downloaded from Sleep Disorder Knowledge Portal， involving 1 331 010 participants. Then the conditional false discovery rate （cFDR） and conjunction cFDR （ccFDR） were utilized to identify the genetic loci associated with major depressive disorder and insomnia， and pathway enrichment analysis was performed to examine the biological functions of these loci.ResultsA significant pleiotropic effect was detected between major depressive disorder and insomnia. By leveraging pleiotropic enrichment， 21 susceptibility loci （17 novel loci） for major depressive disorder and 38 susceptibility loci （28 novel loci） for insomnia were identified with the threshold of cFDR

Abstract Background Drought adaptation is critical to many tree species persisting under climate change, however our knowledge of the genetic basis for trees to adapt to drought is limited. This knowledge gap impedes our fundamental understanding of drought response and application to forest production and conservation. To improve our understanding of the genomic determinants, architecture, and trait constraints, we assembled a reference genome and detected ~ 6.5 M variants in 432 phenotyped individuals for the foundational tree Corymbia calophylla. Results We found 273 genomic variants determining traits with moderate heritability (h 2 SNP = 0.26–0.64). Significant variants were predominantly in gene regulatory elements distributed among several haplotype blocks across all chromosomes. Furthermore, traits were constrained by frequent epistatic and pleiotropic interactions. Conclusions Our results on the genetic basis for drought traits in Corymbia calophylla have several implications for the ability to adapt to climate change: (1) drought related traits are controlled by complex genomic architectures with large haplotypes, epistatic, and pleiotropic interactions; (2) the most significant variants determining drought related traits occurred in regulatory regions; and (3) models incorporating epistatic interactions increase trait predictions. Our findings indicate that despite moderate heritability drought traits are likely constrained by complex genomic architecture potentially limiting trees response to climate change.

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms. Mendelian randomization (MR) faces challenges in mapping risk genes due to limited and tissue-specific instrumental variables. We propose mintMR, a multi-context multivariable integrative MR framework, that models molecular exposure effects across multiple tissues for each gene region and estimates multiple gene regions simultaneously, improving causal gene identification. [ABSTRACT FROM AUTHOR]

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples. We developed JASPER, a fast, robust method for multitrait association testing with a set of genetic variants. JASPER accommodates population structure and relatedness, while handling diverse trait types, making it a powerful tool for uncovering pleiotropic effects, as demonstrated in our analysis of gene expression traits in the Framingham cohort. [ABSTRACT FROM AUTHOR]

This study aims to explore the genetic architecture shared between Attention-Deficit/Hyperactivity Disorder (ADHD) and risk behavior. Based on the latest large-scale Genome-wide association studies (GWAS), we firstly employed Linkage disequilibrium score regression (LDSC) and Local Analysis of Variant Association (LAVA) to investigate the genetic correlation between risk behavior and ADHD. Then, we conducted cross-trait analysis to identified the Pleiotropic loci. Finally, bidirectional Mendelian randomization analysis (MR) was applied to examine the causal relationship. We found a significant positive genetic correlation between ADHD and risk-taking behavior (rg = 0.351, p = 6.50E-37). The cross-trait meta-analysis identified 27 significant SNPs shared between ADHD and risk behavior. The most significant locus, located near the CADM2 gene on chromosome 3, had been identified associated with this two trait (pADHD = 3.07E-05 and prisk-taking behavior = 2.47E-30). The same situation can also be observed near the FOXP2 gene on chromosome 7 (rs8180817, pmeta = 5.72E-21). We found CCDC171 gene and other genes played a significant role in ADHD and risk behavior in mRNA level. Bidirectional MR analysis found a causal relationship between them. The majority of our data sources were of European origin, which may limit the generalizability of our findings to other ethnic populations. This article reveals in depth the shared genetic structure between ADHD and risk-taking behavior, finding a significant positive genetic correlation between ADHD and risk-taking behavior. Providing insights for the future treatment and management of these two traits. • ADHD has a significant positive genetic correlation with risk-taking behavior. • ADHD and risk-taking behavior have pleiotropic loci. • GRIN2A and FES potential are genes newly discovered shared between ADHD and risk-taking behavior. • Bi-directional Mendelian randomization suggests a causal relationship between ADHD and risk-taking behavior. [ABSTRACT FROM AUTHOR]

Hepatic steatosis is known to be heritable, but its genetic basis is mostly uncharacterized. Steatosis is associated with metabolic and adiposity features; recent studies hypothesize that shared genetic effects between these traits could account for some of the unexplained heritability. This study aimed to quantify these genetic associations in a family-based sample of non-Hispanic white adults. 704 participants (18–95 years, 55.8% female) from the Fels Longitudinal Study with an MRI assessment of liver fat were included. Quantitative genetic analyses estimated the age- and sex-adjusted heritability of individual traits and the genetic correlations within trait pairs. Mean liver fat was 5.95% (SE = 0.23) and steatosis (liver fat >5.56%) was present in 29.8% of participants. Heritability (h2± SE) of steatosis was 0.72 ± 0.17 (p = 6.80e−6). All other traits including liver enzymes, fasting glucose, HOMA-IR, visceral and subcutaneous adipose tissue (VAT, SAT), body mass index, body fat percent, waist circumference, lipids and blood pressure were also heritable. Significant genetic correlations were found between liver fat and all traits except aspartate aminotransferase (AST), and among most trait pairs. Highest genetic correlations were between liver fat and HOMA-IR (0.85 ± 0.08, p = 1.73e−8), fasting glucose and ALT (0.89 ± 0.26, p = 6.68e−5), and HOMA-IR with: waist circumference (0.81 ± 0.12, p = 3.76e−6), body fat percent (0.78 ± 0.12 p = 2.42e−5) and VAT (0.73 ± 0.07, p = 6.37e−8). Common genes may exist between liver fat accumulation, metabolic features and adiposity phenotypes. • Steatosis and all other related phenotypes examined were significantly heritable. • Liver fat showed significant genetic correlation with various phenotypes. • The strongest genetic correlation of liver fat was with insulin resistance. • Glucose homeostasis phenotypes were also genetically correlated to adiposity traits. [ABSTRACT FROM AUTHOR]

Background: Drought adaptation is critical to many tree species persisting under climate change, however our knowledge of the genetic basis for trees to adapt to drought is limited. This knowledge gap impedes our fundamental understanding of drought response and application to forest production and conservation. To improve our understanding of the genomic determinants, architecture, and trait constraints, we assembled a reference genome and detected ~ 6.5 M variants in 432 phenotyped individuals for the foundational tree Corymbia calophylla. Results: We found 273 genomic variants determining traits with moderate heritability (h2SNP = 0.26–0.64). Significant variants were predominantly in gene regulatory elements distributed among several haplotype blocks across all chromosomes. Furthermore, traits were constrained by frequent epistatic and pleiotropic interactions. Conclusions: Our results on the genetic basis for drought traits in Corymbia calophylla have several implications for the ability to adapt to climate change: (1) drought related traits are controlled by complex genomic architectures with large haplotypes, epistatic, and pleiotropic interactions; (2) the most significant variants determining drought related traits occurred in regulatory regions; and (3) models incorporating epistatic interactions increase trait predictions. Our findings indicate that despite moderate heritability drought traits are likely constrained by complex genomic architecture potentially limiting trees response to climate change. [ABSTRACT FROM AUTHOR]

Context: Despite the recognition of attention deficit hyperactivity disorder (ADHD) as a multifaceted neurodevelopmental disorder, its core causes are still ambiguous. The objective of this study was to explore if the traits of circulating immune cells contribute causally to susceptibility to ADHD. Methods: By employing a unified GWAS summary data covering 731 immune traits from the GWAS Catalog (accession numbers from GCST0001391 to GCST0002121), our analysis focused on the flow cytometry of lymphocyte clusters, encompassing 3,757 Sardinians, to identify genetically expected immune cells. Furthermore, we obtained summarized GWAS statistics from the Psychiatric Genomics Consortium to evaluate the genetic forecasting of ADHD. The studies employed ADHD2019 (20,183 cases and 35,191 controls from the 2019 GWAS ADHD dataset) and ADHD2022 (38,691 cases and 275,986 controls from the 2022 GWAS ADHD dataset). Through the examination of genome-wide association signals, we identified shared genetic variances between circulating immune cells and ADHD, employing the comprehensive ADHD2022 dataset. We primarily utilized inverse variance weighted (IVW) and weighted median methods in our Mendelian randomization research and sensitivity assessments to evaluate diversity and pleiotropy. Results: After adjusting for false discovery rate (FDR), three distinct immunophenotypes were identified as associated with the risk of ADHD: CD33 in Im MDSC (OR=1.03, CI: 1.01~1.04, P=3.04×10-5, PFDR=0.015), CD8br NKT %T cell (OR=1.08, 95%CI: 1.04~1.12, P=9.33×10-5, PFDR=0.023), and CD8br NKT % lymphocyte (OR=1.08, 95%CI: 1.03~1.12, P=3.59×10-4, PFDR=0.066). Furthermore, ADHD showed no statistical effects on immunophenotypes. It's worth noting that 20 phenotypes exist where ADHD's appearance could diminish 85% of immune cells, including FSC-A in myeloid DC (β= -0.278, 95% CI: 0.616~0.931, P=0.008), CD3 in CD45RA- CD4+ (β= -0.233, 95% CI: 0.654~0.960, P=0.017), CD62L- monocyte AC (b=0.227, 95% CI: 0.038~1.518, P=0.019), CD33 in CD33br HLA DR+ CD14dim (β= -0.331, 95% CI: 0.543~0.950, P=0.020), and CD25 in CD39+ resting Treg (β=0.226, 95% CI: 1.522, P=0.022), and FSC-A in monocytes (β= -0.255, 95% CI: 0.621~0.967, P=0.234), among others. Conclusion: Studies indicate that the immune system's response influences the emergence of ADHD. The findings greatly improve our understanding of the interplay between immune responses and ADHD risk, aiding in the development of treatment strategies from an immunological perspective. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa) is a cereal crop with a starchy endosperm. Starch is composed of amylose and amylopectin. Amylose content (AC) is the principal determinant of rice quality, but varieties with similar ACs can still vary substantially in their quality. In this study, we analyzed the total AC (TAC) and its constituent fractions, the hot water-soluble amylose content (SAC) and hot water-insoluble amylose content (IAC), in two sets of related chromosome segment substitution lines of rice with a common genetic background grown in two years. We searched for quantitative trait loci (QTLs) associated with SAC, IAC, and TAC and identified one common QTL (qSAC–6, qIAC–6, and qTAC–6) on chromosome 6. Map-based cloning revealed that the gene underlying the trait associated with this common QTL is Waxy (Wx). An analysis of the colors of soluble and insoluble starch–iodine complexes and their λmax values (wavelengths at the positions of their peak absorbance values) as well as gel permeation chromatography revealed that Wx is responsible for the biosynthesis of amylose, comprising a large proportion of the soluble fractions of the SAC. Wx is also involved in the biosynthesis of long chains of amylopectin, comprising the hot water-insoluble fractions of the IAC. These findings highlight the pleiotropic effects of Wx on the SAC and IAC. This pleiotropy indicates that these traits have a positive genetic correlation. Therefore, further studies of rice quality should use rice varieties with the same Wx genotype to eliminate the pleiotropic effects of this gene, allowing the independent relationship between the SAC or IAC and rice quality to be elucidated through a multiple correlation analysis. These findings are applicable to other valuable cereal crops as well. [ABSTRACT FROM AUTHOR]

Angiotensin-convertingenzyme 2 (ACE2) has dual functions, regulating cardiovascular physiology and serving as the receptor for coronaviruses. Bats, the only true flying mammals and natural viral reservoirs, have evolved positive alterations in traits related to both functions of ACE2. This suggests significant evolutionary changes in ACE2 during bat evolution. To test this hypothesis, we examine the selection pressure in ACE2 along the ancestral branch of all bats (AncBat-ACE2), where powered flight and bat-coronavirus coevolution occurred, and detect a positive selection signature. To assess the functional effects of positive selection, we resurrect AncBat-ACE2 and its mutant (AncBat-ACE2-mut) created by replacing the positively selected sites. Compared to AncBat-ACE2-mut, AncBat-ACE2 exhibits stronger enzymatic activity, enhances mice's performance in exercise fatigue, and shows lower affinity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our findings indicate the functional pleiotropy of positive selection in the ancient ACE2 of bats, providing an alternative hypothesis for the evolutionary origin of bats' defense against coronaviruses. [ABSTRACT FROM AUTHOR]

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time. We introduce shaPRS, a polygenic risk score (PRS) pre-processing method that improves predictive performance of PRSs by leveraging the genetic overlap between traits or ancestries. Importantly, shaPRS requires only GWAS summary statistics of two partially correlated traits or ancestries and is agnostic with respect to the method used to generate the PRSs. [ABSTRACT FROM AUTHOR]

Key Message: In current study candidate gene (261 genes) based association mapping on 144 pigeonpea accessions for flowering time and related traits and 29 MTAs producing eight superior haplotypes were identified. In the current study, we have conducted an association analysis for flowering-associated traits in a diverse pigeonpea mini-core collection comprising 144 accessions using the SNP data of 261 flowering-related genes. In total, 13,449 SNPs were detected in the current study, which ranged from 743 (ICP10228) to 1469 (ICP6668) among the individuals. The nucleotide diversity (0.28) and Watterson estimates (0.34) reflected substantial diversity, while Tajima’s D (−0.70) indicated the abundance of rare alleles in the collection. A total of 29 marker trait associations (MTAs) were identified, among which 19 were unique to days to first flowering (DOF) and/or days to fifty percent flowering (DFF), 9 to plant height (PH), and 1 to determinate (Det) growth habit using 3 years of phenotypic data. Among these MTAs, six were common to DOF and/or DFF, and four were common to DOF/DFF along with the PH, reflecting their pleiotropic action. These 29 MTAs spanned 25 genes, among which 10 genes clustered in the protein–protein network analysis, indicating their concerted involvement in floral induction. Furthermore, we identified eight haplotypes, four of which regulate late flowering, while the remaining four regulate early flowering using the MTAs. Interestingly, haplotypes conferring late flowering (H001, H002, and H008) were found to be taller, while those involved in early flowering (H003) were shorter in height. The expression pattern of these genes, as inferred from the transcriptome data, also underpinned their involvement in floral induction. The haplotypes identified will be highly useful to the pigeonpea breeding community for haplotype-based breeding. [ABSTRACT FROM AUTHOR]

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples – the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks. [ABSTRACT FROM AUTHOR]

The relationship between rheumatoid arthritis (RA) and early onset atherosclerosis is well depicted, each with an important inflammatory component. Glycoprotein acetyls (GlycA), a novel biomarker of inflammation, may play a role in the manifestation of these two inflammatory conditions. The present study examined a potential mediating role of GlycA within the RA–atherosclerosis relationship to determine whether it accounts for the excess risk of cardiovascular disease over that posed by lipid risk factors. The UK Biobank dataset was acquired to establish associations among RA, atherosclerosis, GlycA, and major lipid factors: total cholesterol (TC), high- and low-density lipoprotein (HDL, LDL) cholesterol, and triglycerides (TGs). Genome-wide association study summary statistics were collected from various resources to perform genetic analyses. Causality among variables was tested using Mendelian Randomization (MR) analysis. Genes of interest were identified using colocalization analysis and gene enrichment analysis. MR results appeared to indicate that the genetic relationship between GlycA and RA and also between RA and atherosclerosis was explained by horizontal pleiotropy (p-value = 0.001 and <0.001, respectively), while GlycA may causally predict atherosclerosis (p-value = 0.017). Colocalization analysis revealed several functionally relevant genes shared between GlycA and all the variables assessed. Two loci were apparent in all relationships tested and included the HLA region as well as SLC22A1. GlycA appears to mediate the RA–atherosclerosis relationship through several possible pathways. GlycA, although pleiotropically related to RA, appears to causally predict atherosclerosis. Thus, GlycA is suggested as a significant factor in the etiology of atherosclerosis development in RA. [ABSTRACT FROM AUTHOR]

Dissecting the molecular basis of adaptation remains elusive despite our ability to sequence genomes and transcriptomes. At present, most genomic research on selection focusses on signatures of selective sweeps in patterns of heterozygosity. Other research has studied changes in patterns of gene expression in evolving populations but has not usually identified the genetic changes causing these shifts in expression. Here we attempt to go beyond these approaches by using machine learning tools to explore interactions between the genome, transcriptome, and life-history phenotypes in two groups of 10 experimentally evolved Drosophila populations subjected to selection for opposing life history patterns. Our findings indicate that genomic and transcriptomic data have comparable power for predicting phenotypic characters. Looking at the relationships between the genome and the transcriptome, we find that the expression of individual transcripts is influenced by many sites across the genome that are differentiated between the two types of populations. We find that single-nucleotide polymorphisms (SNPs), transposable elements, and indels are powerful predictors of gene expression. Collectively, our results suggest that the genomic architecture of adaptation is highly polygenic with extensive pleiotropy. [ABSTRACT FROM AUTHOR]

Cowpeas (Vigna unguiculata L. Walp) have been credible constituents of nutritious food and forage in human and animal diets since the Neolithic era. The modern technique of Diversity Array Technology (DArTseq) is both cost-effective and rapid in producing thousands of high-throughputs, genotyped, single nucleotide polymorphisms (SNPs) in wide-genomic analyses of genetic diversity. The aim of this study was to assess the heterogeneity in cowpea genotypes using DArTseq-derived SNPs. A total of 92 cowpea genotypes were selected, and their fourteen-day-old leaves were freeze-dried for five days. DNA was extracted using the CTAB protocol, genotyped using DArTseq, and analysed using DArTsoft14. A total of 33,920 DArTseq-derived SNPs were recalled for filtering analysis, with a final total of 16,960 SNPs. The analyses were computed using vcfR, poppr, and ape in R Studio v1.2.5001-3 software. The heatmap revealed that the TVU 9596 (SB26), Orelu (SB72), 90K-284-2 (SB55), RV 403 (SB17), and RV 498 (SB16) genotypes were heterogenous. The mean values for polymorphic information content, observed heterozygosity, expected heterozygosity, major allele frequency, and the inbreeding coefficient were 0.345, 0.386, 0.345, 0.729, and 0.113, respectively. Moreover, they validated the diversity of the evaluated cowpea genotypes, which could be used for potential breeding programmes and management of cowpea germplasm. [ABSTRACT FROM AUTHOR]

This article presents the concept of Antagonistic Pathogenic Pleiotropy, in which an abnormality that causes a specific pathology can simultaneously reduce other morbidities through unrelated mechanisms, resulting in the pathology causing less morbidity or mortality than expected. The concept is illustrated by the case of essential thrombocythemia (ET). Patients with ET have substantially elevated platelets and are therefore expected to have increased thrombotic events leading to reduced life expectancy. However, patients with ET do not have reduced life expectancy. A possible explanation is that elevated platelets produce higher levels of platelet factor 4 (PF4), which has been found to reduce age-associated decline in immune and cognitive function in mice and has been suggested as a treatment for age-associated illness. The benefit of elevated PF4 is hypothesized to balance the increased morbidity from hematological causes. Searches for other indications where a well-defined pathology is not associated with concomitant reduction in overall mortality may be a route to identifying factors that could protect against, prevent, or treat chronic disease. [ABSTRACT FROM AUTHOR]

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Coronary artery disease (CAD) is a common comorbidity of type 2 diabetes mellitus (T2DM). However, the pathophysiology connecting these two phenotypes remains to be further understood. Combined analysis in multi-ethnic populations can help contribute to deepening our understanding of biological mechanisms caused by shared genetic loci. We applied genetic correlation analysis and then performed conditional and joint association analyses in Chinese, Japanese, and European populations to identify the genetic variants jointly associated with CAD and T2DM. Next, the associations between genes and the two traits were also explored. Finally, fine-mapping and functional enrichment analysis were employed to identify the potential causal variants and pathways. Genetic correlation results indicated significant genetic overlap between CAD and T2DM in the three populations. Over 10,000 shared signals were identified, and 587 were shared by East Asian and European populations. Fifty-six novel shared genes were found to have significant effects on both CAD and T2DM. Most loci were fine-mapped to plausible causal variant sets. Several similarities and differences of the involved genes in GO terms and KEGG pathways were revealed across East Asian and European populations. These findings highlight the importance of immunoregulation, neuroregulation, heart development, and the regulation of glucose metabolism in shared etiological mechanisms between CAD and T2DM. [ABSTRACT FROM AUTHOR]

IntroductionThroughout domestication, crop plants have gone through strong genetic bottlenecks, dramatically reducing the genetic diversity in today’s available germplasm. This has also reduced the diversity in traits necessary for breeders to develop improved varieties. Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. However, even with recent advances in genetic engineering we still face the rate limiting step of identifying which genes and mutations we should target to generate diversity in specific traits.MethodsHere, we apply a comparative evolutionary approach, pairing phylogenetic and expression analyses to identify potential candidate genes for diversifying soybean (Glycine max) canopy cover development via the nuclear auxin signaling gene families, while minimizing pleiotropic effects in other tissues. In soybean, rapid canopy cover development is correlated with yield and also suppresses weeds in organic cultivation.Results and discussionWe identified genes most specifically expressed during early canopy development from the TIR1/AFB auxin receptor, Aux/IAA auxin co-receptor, and ARF auxin response factor gene families in soybean, using principal component analysis. We defined Arabidopsis thaliana and model legume species orthologs for each soybean gene in these families allowing us to speculate potential soybean phenotypes based on well-characterized mutants in these model species. In future work, we aim to connect genetic and functional diversity in these candidate genes with phenotypic diversity in planta allowing for improvements in soybean rapid canopy cover, yield, and weed suppression. Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops.

Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks.

Abstract Background Heat stress (HS) poses significant threats to the sustainability of livestock production. Genetically improving heat tolerance could enhance animal welfare and minimize production losses during HS events. Measuring phenotypic indicators of HS response and understanding their genetic background are crucial steps to optimize breeding schemes for improved climatic resilience. The identification of genomic regions and candidate genes influencing the traits of interest, including variants with pleiotropic effects, enables the refinement of genotyping panels used to perform genomic prediction of breeding values and contributes to unraveling the biological mechanisms influencing heat stress response. Therefore, the main objectives of this study were to identify genomic regions, candidate genes, and potential pleiotropic variants significantly associated with indicators of HS response in lactating sows using imputed whole-genome sequence (WGS) data. Phenotypic records for 18 traits and genomic information from 1,645 lactating sows were available for the study. The genotypes from the PorcineSNP50K panel containing 50,703 single nucleotide polymorphisms (SNPs) were imputed to WGS and after quality control, 1,622 animals and 7,065,922 SNPs were included in the analyses. Results A total of 1,388 unique SNPs located on sixteen chromosomes were found to be associated with 11 traits. Twenty gene ontology terms and 11 biological pathways were shown to be associated with variability in ear skin temperature, shoulder skin temperature, rump skin temperature, tail skin temperature, respiration rate, panting score, vaginal temperature automatically measured every 10 min, vaginal temperature measured at 0800 h, hair density score, body condition score, and ear area. Seven, five, six, two, seven, 15, and 14 genes with potential pleiotropic effects were identified for indicators of skin temperature, vaginal temperature, animal temperature, respiration rate, thermoregulatory traits, anatomical traits, and all traits, respectively. Conclusions Physiological and anatomical indicators of HS response in lactating sows are heritable but highly polygenic. The candidate genes found are associated with important gene ontology terms and biological pathways related to heat shock protein activities, immune response, and cellular oxidative stress. Many of the candidate genes with pleiotropic effects are involved in catalytic activities to reduce cell damage from oxidative stress and cellular mechanisms related to immune response.

Summary: The aim of this study was to scan phenotypes in adulthood associated with polygenic risk scores (PRS) for childhood cancers with well-articulated genetic architectures—acute lymphoblastic leukemia (ALL), Ewing sarcoma, and neuroblastoma—to examine genetic pleiotropy. Furthermore, we aimed to determine which SNPs could drive associations. Per-SNP summary statistics were extracted for PRS calculation. Participants with white British ancestry were exclusively included for analyses. SNPs were queried from the UK Biobank genotype imputation data. Records from the cancer registry, death registry, and inpatient diagnoses were abstracted for phenome-wide scans. Firth logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) alongside corresponding p values, adjusting for age at recruitment and sex. A total of 244,332 unrelated white British participants were included. We observed a significant association between ALL-PRS and ALL (OR: 1.20e+24, 95% CI: 9.08e+14–1.60e+33). In addition, we observed a significant association between high-risk neuroblastoma PRS and nonrheumatic aortic valve disorders (OR: 43.9, 95% CI: 7.42–260). There were no significant phenotype associations with Ewing sarcoma and neuroblastoma PRS. Regarding individual SNPs, rs17607816 increased the risk of ALL (OR: 6.40, 95% CI: 3.26–12.57). For high-risk neuroblastoma, rs80059929 elevated the risk of atrioventricular block (OR: 3.04, 95% CI: 1.85–4.99). Our findings suggest that individuals with genetic susceptibility to ALL may face a lifelong risk for developing ALL, along with a genetic pleiotropic association between high-risk neuroblastoma and circulatory diseases.

BackgroundHearing problems (HP) in adults are common and are associated with several comorbid conditions. Its prevalence increases with age, reflecting the cumulative effect of environmental factors and genetic predisposition. Although several risk loci have been already identified, HP biology and epidemiology are still insufficiently investigated by large-scale genetic studies.MethodsLeveraging the UK Biobank, the Nurses' Health Studies (I and II), the Health Professionals Follow-up Study, and the Million Veteran Program, we conducted a comprehensive genome-wide investigation of HP in 748,668 adult participants (discovery N = 501,825; replication N = 226,043; cross-ancestry replication N = 20,800). We leveraged the GWAS findings to characterize HP polygenic architecture, exploring sex differences, polygenic risk across ancestries, tissue-specific transcriptomic regulation, cause-effect relationships with genetically correlated traits, and gene interactions with HP environmental risk factors.ResultsWe identified 54 risk loci and demonstrated that HP polygenic risk is shared across ancestry groups. Our transcriptomic regulation analysis highlighted the potential role of the central nervous system in HP pathogenesis. The sex-stratified analyses showed several additional associations related to peripheral hormonally regulated tissues reflecting a potential role of estrogen in hearing function. This evidence was supported by the multivariate interaction analysis that showed how genes involved in brain development interact with sex, noise pollution, and tobacco smoking in relation to their HP associations. Additionally, the genetically informed causal inference analysis showed that HP is linked to many physical and mental health outcomes.ConclusionsThe results provide many novel insights into the biology and epidemiology of HP in adults. Our sex-specific analyses and transcriptomic associations highlighted molecular pathways that may be targeted for drug development or repurposing. Additionally, the potential causal relationships identified may support novel preventive screening programs to identify individuals at risk.

Summary Increasing crop yield depends on selecting and utilizing pleiotropic genes/alleles to improve multiple yield‐related traits (YRTs) during crop breeding. However, synergistic improvement of YRTs is challenging due to the trade‐offs between YRTs in breeding practices. Here, the favourable haplotypes of the TaCYP78A family are identified by analysing allelic variations in 1571 wheat accessions worldwide, demonstrating the selection and utilization of pleiotropic genes to improve yield and related traits during wheat breeding. The TaCYP78A family members, including TaCYP78A3, TaCYP78A5, TaCYP78A16, and TaCYP78A17, are organ size regulators expressed in multiple organs, and their allelic variations associated with various YRTs. However, due to the trade‐offs between YRTs, knockdown or overexpression of TaCYP78A family members does not directly increase yield. Favourable haplotypes of the TaCYP78A family, namely A3/5/16/17Ap‐Hap II, optimize the expression levels of TaCYP78A3/5/16/17‐A across different wheat organs to overcome trade‐offs and improve multiple YRTs. Different favourable haplotypes have both complementary and specific functions in improving YRTs, and their aggregation in cultivars under strong artificial selection greatly increase yield, even under various planting environments and densities. These findings provide new support and valuable genetic resources for molecular breeding of wheat and other crops in the era of Breeding 4.0. [ABSTRACT FROM AUTHOR]

Tobacco (Nicotiana tabacum L.) use is the leading cause of preventable death, due to deleterious chemical components and smoke from tobacco products, and therefore reducing harmful chemical components in tobacco is one of the crucial tobacco breeding targets. However, due to complexity of tobacco smoke and unavailability of high-density genetic maps, the genetic architecture of representative hazardous smoke has not been fully dissected. The present study aimed to explore the genetic architecture of nine hazardous component traits of mainstream smoke through QTL mapping using 271 recombinant inbred lines (RILs) derived from K326 and Y3 in multiple environments. The analysis of genotype and genotype by environment interaction (GE) revealed substantially greater heritability over 95% contributed mostly by GE interaction effects. We also observed strong genetic correlations among most studied hazardous smoke traits, with the highest correlation coefficient of 0.84 between carbon monoxide and crotonaldehyde. Based on a published high-density genetic map, a total of 19 novel QTLs were detected for eight traits using a full QTL model, of which 17 QTLs showed significant additive effects, six showed significant additive-by-environment interaction effects, and one pair showed significant epistasis-by-environment interaction effect. Bioinformatics analysis of sequence in QTL region predicted six genes as candidates for four traits, of which Nt21g04598.1, Nt21g04600.1, and Nt21g04601.1 had pleiotropic effects on PHE and TAR. [ABSTRACT FROM AUTHOR]

Background: The existence of chronic pain increases susceptibility to virus and is now widely acknowledged as a prominent feature recognized as a major manifestation of long-term coronavirus disease 2019 (COVID-19) infection. Given the ongoing COVID-19 pandemic, it is imperative to explore the genetic associations between chronic pain and predisposition to COVID-19. Methods: We conducted genetic analysis at the single nucleotide polymorphism (SNP), gene, and molecular levels using summary statistics of genome-wide association study (GWAS) and analyzed the drug targets by summary data-based Mendelian randomization analysis (SMR) to alleviate the multi-site chronic pain in COVID-19. Additionally, we performed a latent causal variable (LCV) method to investigate the causal relationship between chronic pain and susceptibility to COVID-19. Results: The cross-trait meta-analysis identified 19 significant SNPs shared between COVID-19 and chronic pain. Coloc analysis indicated that the posterior probability of association (PPH4) for three loci was above 70% in both critical COVID-19 and COVID-19, with the corresponding top three SNPs being rs13135092, rs7588831, and rs13135092. A total of 482 significant overlapped genes were detected from MAGMA and CPASSOC results. Additionally, the gene ANAPC4 was identified as a potential drug target for treating chronic pain (P=7.66E-05) in COVID-19 (P=8.23E-03). Tissue enrichment analysis highlighted that the amygdala (P=7.81E-04) and prefrontal cortex (P=8.19E-05) as pivotal in regulating chronic pain of critical COVID-19. KEGG pathway enrichment further revealed the enrichment of pleiotropic genes in both COVID-19 (P=3.20E-03,Padjust=4.77E-02,hsa05171) and neurotrophic pathways (P=9.03E-04,Padjust =2.55E-02,hsa04621). Finally, the latent causal variable (LCV) model was applied to find the genetic component of critical COVID-19 was causal for multi-site chronic pain (P=0.015), with a genetic causality proportion (GCP) of was 0.60. Conclusions: In this study, we identified several functional genes and underscored the pivotal role of the inflammatory system in the correlation between the paired traits. Notably, heat shock proteins emerged as potential objective biomarkers for chronic pain symptoms in individuals with COVID-19. Additionally, the ubiquitin system might play a role in mediating the impact of COVID-19 on chronic pain. These findings contribute to a more comprehensive understanding of the pleiotropy between COVID-19 and chronic pain, offering insights for therapeutic trials. [ABSTRACT FROM AUTHOR]

Background Major depressive disorder and insomnia often coexist, and the two may share a mechanism of pathogenesis and be affected by common underlying genes with strong pleiotropic effects. Previous genome-wide association studies (GWAS) mainly focused on single-gene morphological characters analysis, which impose limitations on showing possible pleiotropic effects. Objective To identify genetic loci related to insomnia and major depressive disorder, and to examine whether there are common genetic factors underlying both insomnia and depression. Methods The GWAS data for major depressive disorder originates from the Psychiatric Genomics Consortium (PGC), which comprises a total of 246 363 depressive cases and 561 190 controls. The insomnia GWAS data was downloaded from Sleep Disorder Knowledge Portal, involving 1 331 010 participants. Then the conditional false discovery rate (cFDR) and conjunction cFDR (ccFDR) were utilized to identify the genetic loci associated with major depressive disorder and insomnia, and pathway enrichment analysis was performed to examine the biological functions of these loci. Results A significant pleiotropic effect was detected between major depressive disorder and insomnia. By leveraging pleiotropic enrichment, 21 susceptibility loci (17 novel loci) for major depressive disorder and 38 susceptibility loci (28 novel loci) for insomnia were identified with the threshold of cFDR<0. 01. A total of 16 pleiotropic loci (15 novel loci) related to both major depressive disorder and insomnia were identified with the threshold of ccFDR<0. 05. pathway enrichment analysis indicated that the susceptibility loci were mainly enriched in synaptic transmission pathway, such as postsynaptic density (GO: 0014069, P=4. 91E-04, FDR=4. 84E-03), and regulation of postsynaptic membrane neurotransmitter receptor levels (GO: 0099072, P=5. 11E-04, FDR=1. 69E-02). Conclusion A significant pleiotropic enrichment is found between major depressive disorder and insomnia, and the comorbidity mechanism is related to synaptic transmission. [ABSTRACT FROM AUTHOR]

After the loss of a trait, theory predicts that the molecular machinery underlying its phenotypic expression should decay. Yet, empirical evidence is contrasting. Here, we test the hypotheses that (i) the molecular ground plan of a lost trait could persist due to pleiotropic effects on other traits and (ii) that gene co-expression network architecture could constrain individual gene expression. Our testing ground has been the Bacillus stick insect species complex, which contains close relatives that are either bisexual or parthenogenetic. After the identification of genes expressed in male reproductive tissues in a bisexual species, we investigated their gene co-expression network structure in two parthenogenetic species. We found that gene co-expression within the male gonads was partially preserved in parthenogens. Furthermore, parthenogens did not show relaxed selection on genes upregulated in male gonads in the bisexual species. As these genes were mostly expressed in female gonads, this preservation could be driven by pleiotropic interactions and an ongoing role in female reproduction. Connectivity within the network also played a key role, with highly connected—and more pleiotropic—genes within male gonad also having a gonad-biased expression in parthenogens. Our findings provide novel insight into the mechanisms which could underlie the production of rare males in parthenogenetic lineages; more generally, they provide an example of the cryptic persistence of a lost trait molecular architecture, driven by gene pleiotropy on other traits and within their co-expression network. [ABSTRACT FROM AUTHOR]

There is still a limited understanding of the dynamics contributing to the comorbidity of COVID-19 and anxiety outcomes. To dissect the pleiotropic mechanisms contributing to COVID-19/anxiety comorbidity, we used genome-wide data from UK Biobank (up to 420,531 participants), FinnGen Project (up to 329,077 participants), Million Veteran Program (175,163 participants), and COVID-19 Host Genetics Initiative (up to 122,616 cases and 2,475,240 controls). Specifically, we assessed global and local genetic correlation and genetically inferred effects linking COVID-19 outcomes (infection, hospitalization, and severe respiratory symptoms) to anxiety disorders and symptoms. We observed a strong genetic correlation of anxiety disorder with COVID-19 positive status (rg = 0.35, p = 2×10−4) and COVID-19 hospitalization (rg = 0.31, p = 7.2×10−4). Among anxiety symptoms, "Tense, sore, or aching muscles during worst period of anxiety" was genetically correlated with COVID-19 positive status (rg = 0.33, p = 0.001), while "Frequent trouble falling or staying asleep during worst period of anxiety" was genetically correlated with COVID-19 hospitalization (rg = 0.24, p = 0.004). Through a latent causal variable analysis, we observed that COVID-19 outcomes have statistically significant genetic causality proportion (gcp) on anxiety symptoms (e.g., COVID-19 positive status→"Recent easy annoyance or irritability" │gcp│ = 0.18, p = 6.72×10−17). Conversely, anxiety disorders appear to have a possible causal effect on COVID-19 (│gcp│ = 0.38, p = 3.17×10−9). Additionally, we also identified multiple loci with evidence of local genetic correlation between anxiety and COVID-19. These appear to be related to genetic effects shared with lung function, brain morphology, alcohol and tobacco use, and hematologic parameters. This study provided insights into the pleiotropic mechanisms linking COVID-19 and anxiety outcomes, suggesting differences between dynamics related to anxiety disorders and those related to anxiety symptoms. • COVID-19 has been associated with increased anxiety symptoms. • Individuals with anxiety disorders have a higher risk of severe COVID-19 outcomes. • Possible causal relationships link COVID-19 and anxiety. • Specific genomic regions could contribute to COVID-19 association with anxiety. [ABSTRACT FROM AUTHOR]

Domains of unknown function (DUFs), which are deposited in the protein family database (Pfam), are protein domains with conserved amino acid sequences and uncharacterized functions. Proteins with the same DUF were classified as DUF families. Although DUF families are generally not essential for the survival of plants, they play roles in plant development and adaptation. Characterizing the functions of DUFs is important for deciphering biological puzzles. DUFs were generally studied through forward and reverse genetics. Some novelty approaches, especially the determination of crystal structures and interaction partners of the DUFs, should attract more attention. This review described the identification of DUF genes by genome-wide and transcriptome-wide analyses, summarized the function of DUF-containing proteins, and addressed the prospects for future studies in DUFs in plants. [ABSTRACT FROM AUTHOR]

To gain insights into the role of brain structure and function on anxiety (ANX), we conducted a genetically informed investigation leveraging information from ANX genome-wide association studies available from the UK Biobank (n = 380,379), the FinnGen Program (n = 290,361), and the Million Veteran Program (n = 175,163) together with UK Biobank genome-wide data (n = 33,224) related to 3935 brain imaging–derived phenotypes (IDPs). A genetic correlation analysis between ANX and brain IDPs was performed using linkage disequilibrium score regression. To investigate ANX–brain associations, a 2-sample Mendelian randomization was performed considering multiple methods and sensitivity analyses. A subsequent multivariable Mendelian randomization was conducted to distinguish between direct and indirect effects. Finally, a generalized linear model was used to explore the associations of brain IDPs with ANX symptoms. After false discovery rate correction (q <.05), we identified 41 brain IDPs genetically correlated with ANX without heterogeneity among the datasets investigated (i.e., UK Biobank, FinnGen, and Million Veteran Program). Six of these IDPs showed genetically inferred causal effects on ANX. In the subsequent multivariable Mendelian randomization analysis, reduced area of the right posterior middle cingulate gyrus (β = −0.09, p = 8.01 × 10−4) and reduced gray matter volume of the right anterior superior temporal gyrus (β = −0.09, p = 1.55 × 10−3) had direct effects on ANX. In the ANX symptom-level analysis, the right posterior middle cingulate gyrus was negatively associated with "tense, sore, or aching muscles during the worst period of anxiety" (β = −0.13, p = 8.26 × 10−6). This study identified genetically inferred effects that are generalizable across large cohorts, thereby contributing to our understanding of how changes in brain structure and function can lead to ANX. [ABSTRACT FROM AUTHOR]