Single Trait Research Articles

Differential susceptibility of Spodoptera frugiperda (Lepidoptera: Noctuidae) to single versus pyramided Bt traits in Brazilian soybean: what doesn't kill you makes you stronger?

Lepidopteran pest control in agriculture has become heavily dependent on cultivars that express Bacillus thuringiensis (Bt) toxins as 'plant-incorporated protectants'. However, populations of Spodoptera frugiperda (Smith) in Brazil appear resistant to the Bt traits currently available in commercial soybean cultivars. This study evaluated S. frugiperda life history when feeding on three different Bt soybean cultivars. Cultivars expressing Cry1Ac + Cry1F and Cry1A.105 + Cry2Ab2 + Cry1Ac Bt toxins caused 100% larval mortality in S. frugiperda. Both non-Bt and Cry1Ac-expressing soybean induced transgenerational effects that increased the survival of subsequent generations. A Cry1Ac soybean diet reduced the generation time (T) of S. frugiperda relative to non-Bt soybean, resulting in shorter generation time and more rapid population growth. The implications of these results revealed how diet can alter aspects of insect life history and biology, and have important implications for sustainable management of S. frugiperda on soybean. © 2024 Society of Chemical Industry.

Intracemetery relationships: Non-metric trait combination analysis of Maspalomas of Gran Canaria (Canary Islands, Spain).

Maspalomas is one of the most important archaeological sites in Gran Canaria, Canary Islands. The necropolis is one of the few funerary sites on the island where several the skeletons were found in anatomical position. The burials correspond to graves and cists dated between the 12th and 15th century CE. These graves and cists are clustered together in different formations across the necropolis, giving rise to a complex composition that denotes the existence of possible interpersonal relationships of the people buried there. A total of 135 calcanei and 118 tali were analysed to find non-metric traits and to test whether the clustered burials share a non-metric trait relationship. Trait combinations were formed using talus and calcaneus non-metric traits separately. The results of this study suggest that the individuals of Maspalomas showed a very high prevalence of lateral and medial talar facets, attributed to prolonged squatting position and/or walking on uneven ground. The calcaneal facet pattern (that may be aetiologically genetic) is more closely related to that observed among North-Africans or Indians than to Western Europeans. Calcaneal facet type Ib, and other genetically-determined traits, such as the extra facet extension of Posterior Facet, or the medial root of the inferior extensor retinaculum trait, either as single traits or as the combination of both traits, were significantly associated with individuals buried in different geographical areas of the necropolis defined by differences in burial structures, a finding that may suggest that genetically-linked individuals were buried in a separate area of the necropolis. The use of trait combination analysis in this study shows that the method can be applied to identify relationships among genetically or professionally related individuals that were subjected to a different burial procedure by their contemporaries.

Anuran communities increase aggregations of conspecific calls in response to aircraft noise

Abstract Noise pollution has been shown to affect wild animals in various ways, but little is known about its consequences at the community level. Investigating animals’ overall vocal responses to noise across multiple sympatric species can reveal the complex nature of noise impacts but is challenging. In this study, we employed social network analysis (SNA) to evaluate how anuran communities and populations vary their calling behaviors in response to aircraft noise. SNA of anuran communities revealed that conspecific individuals increase the aggregation of their spectral (i.e., minimum frequency, maximum frequency, and dominant frequency), temporal (call duration, call rate, and call effort), and overall spectral–temporal features as an airplane passes through. SNA of populations also revealed that anurans could increase the interindividual similarity of multiple call characteristics in response to airplane noise. Furthermore, our network analysis of multiple species and multiple call traits revealed an effect of noise in species whose calling behavior did not change in previous separate analyses of each species and single traits. This study suggests that noise pollution may change the pattern of combined acoustic properties at the community level. Our findings highlight the importance of integrated methods and theories for understanding the ecological consequences of noise pollution in future studies.

MTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test.

Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of Arabidopsis thaliana shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.

Genomic prediction for rust resistance in pea.

Genomic selection (GS) has become an indispensable tool in modern plant breeding, particularly for complex traits. This study aimed to assess the efficacy of GS in predicting rust (Uromyces pisi) resistance in pea (Pisum sativum), using a panel of 320 pea accessions and a set of 26,045 Silico-Diversity Arrays Technology (Silico-DArT) markers. We compared the prediction abilities of different GS models and explored the impact of incorporating marker × environment (M×E) interaction as a covariate in the GBLUP (genomic best linear unbiased prediction) model. The analysis included phenotyping data from both field and controlled conditions. We assessed the predictive accuracies of different cross-validation strategies and compared the efficiency of using single traits versus a multi-trait index, based on factor analysis and ideotype-design (FAI-BLUP), which combines traits from controlled conditions. The GBLUP model, particularly when modified to include M×E interactions, consistently outperformed other models, demonstrating its suitability for traits affected by complex genotype-environment interactions (GEI). The best predictive ability (0.635) was achieved using the FAI-BLUP approach within the Bayesian Lasso (BL) model. The inclusion of M×E interactions significantly enhanced prediction accuracy across diverse environments in GBLUP models, although it did not markedly improve predictions for non-phenotyped lines. These findings underscore the variability of predictive abilities due to GEI and the effectiveness of multi-trait approaches in addressing complex traits. Overall, our study illustrates the potential of GS, especially when employing a multi-trait index like FAI-BLUP and accounting for M×E interactions, in pea breeding programs focused on rust resistance.

Convergent expansions of keystone gene families drive metabolic innovation in a major eukaryotic clade.

Many remarkable innovations have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by the same underlying gene families, while other times many different gene families are involved. Conversely, a gene family may be repeatedly recruited for a single trait or many different traits. To understand the general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic traits and gene count in 14,710 gene families from 993 species of Saccharomycotina yeasts. Using a recently developed phylogenetic approach that reduces spurious correlations, we discovered that gene family expansion and contraction was significantly linked to trait gain and loss in 45/56 (80%) of traits. While 601/746 (81%) of significant gene families were associated with only one trait, we also identified several 'keystone' gene families that were significantly associated with up to 13/56 (23%) of all traits. These results indicate that metabolic innovations in yeasts are governed by a narrow set of major genetic elements and mechanisms.

Unraveling mate choice evolution through indirect genetic effects

Abstract Attractiveness is not solely determined by a single sexual trait but rather by a combination of traits. Because the response of the chooser is based on the combination of sexual traits in the courter, variation in the chooser’s responses that are attributable to the opposite-sex courter genotypes (i.e., the indirect genetic effects [IGEs] on chooser response) can reflect genetic variation in overall attractiveness. This genetic variation can be associated with the genetic basis of other traits in both the chooser and the courter. Investigating this complex genetic architecture, including IGEs, can enhance our understanding of the evolution of mate choice. In the present study on the field cricket Gryllus bimaculatus, we estimated (1) genetic variation in overall attractiveness and (2) genetic correlations between overall attractiveness and other pre- and postcopulatory traits (e.g., male latency to sing, female latency to mount, male guarding intensity, male and female body mass, male mandible size, and testis size) within and between sexes. We revealed a genetic basis for attractiveness in both males and females. Furthermore, a genetic variance associated with female attractiveness was correlated with a genetic variance underlying larger male testes. Our findings imply that males that mate with attractive females can produce offspring that are successful in terms of precopulatory sexual selection (daughters who are attractive) and postcopulatory sexual selection (sons with an advantage in sperm competition), potentially leading to runaway sexual selection. Our study exemplifies how the incorporation of the IGE framework provides novel insights into the evolution of mate choice.

The Characteristics of Molière’s Comedy Writing —Centred on Harpagon’s Characterisation

The works of the French classical comedian Molière are of exceptional value. In his satirical comedies, Molière often creates sensible, vivid, contemporary, and real characters through descriptions of detailed behavior and the setting of coincident and conflicts. Harpagon, for instance, has become a global synonym for miserliness due to Molière’s extreme focus on this single character trait. Critics of his time labeled Harpagon as a “flat character”, yet it is precisely this “flatness” that makes the character memorable. Molière possessed a serious spirit of realism and a democratic tendency, using comedy as a means to educate the public. Through characters like Harpagon, he exposed the bourgeoisie’s cruelty, hypocrisy, and greedy exploitation of the lower class, delivering sharp and pungent satire. Molière’s ability to craft such characters not only entertained but also conveyed powerful social critiques, solidifying his importance in literary and social history.

Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease

Abdominal obesity increases the risk for non-alcoholic fatty liver disease (NAFLD), now known as metabolic dysfunction-associated steatotic liver disease (MASLD). To elucidate the directional cell-type level biological mechanisms underlying the association between abdominal obesity and MASLD, we integrated adipose and liver single nucleus RNA-sequencing and bulk cis-expression quantitative trait locus (eQTL) data with the UK Biobank genome-wide association study (GWAS) data using colocalization. Then we used colocalized cis-eQTL variants as instrumental variables in Mendelian randomization (MR) analyses, followed by functional validation experiments on the target genes of the cis-eQTL variants. We identified 17 colocalized abdominal obesity GWAS variants, regulating 17 adipose cell-type marker genes. Incorporating these 17 variants into MR discovers a putative tissue-of-origin, cell-type-aware causal effect of abdominal obesity on MASLD consistently with multiple MR methods without significant evidence for pleiotropy or heterogeneity. Single cell data confirm the adipocyte-enriched mean expression of the 17 genes. Our cellular experiments across human adipogenesis identify risk variant -specific epigenetic and transcriptional mechanisms. Knocking down two of the 17 genes, PPP2R5A and SH3PXD2B, shows a marked decrease in adipocyte lipidation and significantly alters adipocyte function and adipogenesis regulator genes, including DGAT2, LPL, ADIPOQ, PPARG, and SREBF1. Furthermore, the 17 genes capture a characteristic MASLD expression signature in subcutaneous adipose tissue. Overall, we discover a significant cell-type level effect of abdominal obesity on MASLD and trace its biological effect to adipogenesis. NIH grants R01HG010505, R01DK132775, and R01HL170604; the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant No. 802825), Academy of Finland (Grants Nos. 333021), the Finnish Foundation for Cardiovascular Research the Sigrid Jusélius Foundation and the Jane and Aatos Erkko Foundation; American Association for the Study of Liver Diseases (AASLD) Advanced Transplant Hepatology award and NIH/NIDDK (P30DK41301) Pilot and Feasibility award; NIH/NIEHS F32 award (F32ES034668); Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2021), the Academy of Finland grant (Contract no. 138006); Academy of Finland (Grant Nos 335443, 314383, 272376 and 266286), Sigrid Jusélius Foundation, Finnish Medical Foundation, Finnish Diabetes Research Foundation, Novo Nordisk Foundation (#NNF20OC0060547, NNF17OC0027232, NNF10OC1013354) and Government Research Funds to Helsinki University Hospital; Orion Research Foundation, Maud Kuistila Foundation, Finish Medical Foundation, and University of Helsinki.

Exploring Phylogenetic Signal in Multivariate Phenotypes by Maximizing Blomberg's K.

Due to the hierarchical structure of the tree of life, closely related species often resemble each other more than distantly related species; a pattern termed phylogenetic signal. Numerous univariate statistics have been proposed as measures of phylogenetic signal for single phenotypic traits, but the study of phylogenetic signal for multivariate data, as is common in modern biology, remains challenging. Here we introduce a new method to explore phylogenetic signal in multivariate phenotypes. Our approach decomposes the data into linear combinations with maximal (or minimal) phylogenetic signal, as measured by Blomberg's K. The loading vectors of these phylogenetic components or K-components can be biologically interpreted, and scatterplots of the scores can be used as a low-dimensional ordination of the data that maximally (or minimally) preserves phylogenetic signal. We present algebraic and statistical properties, along with two new summary statistics, KA and KG, of phylogenetic signal in multivariate data. Simulation studies showed that KA and KG have higher statistical power than the previously suggested statistic Kmult, especially if phylogenetic signal is low or concentrated in a few trait dimensions. In two empirical applications to vertebrate cranial shape (crocodyliforms and papionins), we found statistically significant phylogenetic signal concentrated in a few trait dimensions. The finding that phylogenetic signal can be highly variable across the dimensions of multivariate phenotypes has important implications for current maximum likelihood approaches to phylogenetic signal in multivariate data.

Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of mid-infrared spectra traits for animal breeding

With the rapid development of animal phenomics and deep phenotyping, we can obtain thousands of traditional (but also molecular) phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the mega-scale linear mixed model (MegaLMM), a factor model-based approach that is able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding value (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of GEBV (U) prediction with TT models and MegaLMM. We used a total of 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits (average fat percentage [AFP], average methane production [ACH4], and average SCS [ASCS]) collected on 3,302 first-parity Holstein cows. The 3,421 milk MIR wavepoint traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNPs was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for AFP (0.51 vs. 0.93), ACH4 (0.30 vs. 0.86), and ASCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions for AFP (0.59 vs. 0.86), ACH4 (0.47 vs. 0.78), and ASCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., ACH4).

Empowering cotton breeding programs through the strategic exploration and exploitation of phenotypic diversity of genetic resources under climate change conditions

Plant phenotype assessment approaches have facilitated crop improvement in recent years by providing opportunities for the dissection of complex nature quantitative traits. Fiber quality and yield are significant economic traits with a complex nature mainly due to interactions between its genetic architecture and the surrounding environment. These economic traits showed stagnant performance in recent decades due to eroded genetic diversity via the selection process. It necessitates exploiting conserved germplasm resources to rebuild genetic diversity within cotton cultivars. Compared to the conventional univariate selection methods using a single trait at a time, the multivariate analysis extends its selection to multiple variables for the selection of genotypes. In evaluating a core collection of cotton over two years using various phenotyping methods, multivariate analysis, particularly principal component analysis (PCA), was employed to assess genetic diversity. The PCA analysis demonstrated substantial variability of 39.83% and 45.37% across two years, highlighting a significant impact on yield and fiber quality attributes. The cumulative eigenvalues of principal components exceeding 80% supported the classification of accessions into six distinct groups based on their trait variability. Both PCA and cluster analysis successfully categorized accessions into six groups, indicating high diversity and suggesting their potential utility in breeding programs. The diverse genotypes identified in this study would be potential resources for variety development and accelerated breeding programs, particularly focusing on stress resilience under climate change scenarios. By utilizing these diverse genotypes, there is an opportunity to enhance breeding strategies and accelerate the development of improved cotton varieties with enhanced yield and fiber quality attributes. The significance of our study lies in its demonstration of the importance of genetic diversity in improving yield and fiber quality attributes, thus underscoring its relevance to cotton breeding programs.

A near-complete dataset of plant growth form, life history, and woodiness for all Australian plants

Tabular records of plant-trait data are essential for diverse research purposes. Here we present scorings for a trio of core plant traits, plant growth form, woodiness and life history, for nearly all (>99%) accepted taxon concepts included in the Australian Plant Census (APC). This dataset is predominantly derived from Australia’s state and national floras, supplemented by the taxonomic literature and diverse web resources. In total, 29,993 species and infraspecific taxa were scored for plant growth form, 30,279 for woodiness and 30,056 for life history, with taxa scored as displaying a single or multiple trait values, as appropriate. We provide sample R code that shows how to access and interrogate the dataset. This resource will enable rapid assessment of plant responses to disturbance events and new biogeographic analyses of trait distributions, better understandings of evolutionary trajectories, and ecological strategies.

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.

Genetic Architecture and Clinical Outcomes of Combined Lipid Disturbances.

Dyslipoproteinemia often involves simultaneous derangements of multiple lipid traits. We aimed to evaluate the phenotypic and genetic characteristics of combined lipid disturbances in a general population-based cohort. Among UK Biobank participants without prevalent coronary artery disease, we used blood lipid and apolipoprotein B concentrations to ascribe individuals into 1 of 6 reproducible and mutually exclusive dyslipoproteinemia subtypes. Incident coronary artery disease risk was estimated for each subtype using Cox proportional hazards models. Phenome-wide analyses and genome-wide association studies were performed for each subtype, followed by in silico causal gene prioritization and heritability analyses. Additionally, the prevalence of disruptive variants in causal genes for Mendelian lipid disorders was assessed using whole-exome sequence data. Among 450 636 UK Biobank participants: 63 (0.01%) had chylomicronemia; 40 005 (8.9%) had hypercholesterolemia; 94 785 (21.0%) had combined hyperlipidemia; 13 998 (3.1%) had remnant hypercholesterolemia; 110 389 (24.5%) had hypertriglyceridemia; and 49 (0.01%) had mixed hypertriglyceridemia and hypercholesterolemia. Over a median (interquartile range) follow-up of 11.1 (10.4-11.8) years, incident coronary artery disease risk varied across subtypes, with combined hyperlipidemia exhibiting the largest hazard (hazard ratio, 1.92 [95% CI, 1.84-2.01]; P=2×10-16), even when accounting for non-HDL-C (hazard ratio, 1.45 [95% CI, 1.30-1.60]; P=2.6×10-12). Genome-wide association studies revealed 250 loci significantly associated with dyslipoproteinemia subtypes, of which 72 (28.8%) were not detected in prior single lipid trait genome-wide association studies. Mendelian lipid variant carriers were rare (2.0%) among individuals with dyslipoproteinemia, but polygenic heritability was high, ranging from 23% for remnant hypercholesterolemia to 54% for combined hyperlipidemia. Simultaneous assessment of multiple lipid derangements revealed nuanced differences in coronary artery disease risk and genetic architectures across dyslipoproteinemia subtypes. These findings highlight the importance of looking beyond single lipid traits to better understand combined lipid and lipoprotein phenotypes and implications for disease risk.

Unraveling community assembly in temperate desert ecosystems: Insights from multi‐seasonal surveys along the expressway

AbstractFor temperate desert ecosystems, understanding the maintenance mechanisms of biodiversity has become a central concern of policymakers, practitioners, and academicians, especially in the context of human disturbances. However, most of the past desert research is based on a single survey and single functional traits or phylogeny, and may lead to incomplete estimates of community assembly. To assess climate factors, soil factors, and human interference in the processes of dispersal assembly, filtering, and limiting similarity in community assembly, we selected 50 sites along the Beijing‐Xinjiang Expressway and conducted plant surveys at sites 0.5, 1.0, and 1.5 km away from the expressway twice, one in spring and one in autumn, and collected 31 functional traits and phylogenetic information of 74 species. We found that incomplete detection can lead to bias in biodiversity estimation due to seasonal surveys. Road disturbance at different distances had no significant effect on species composition and community assembly processes. The trade‐off between dispersal limitation and environmental filtering is the process of maintaining desert plant diversity. Precipitation directly determined the dispersal limitation and environmental filtering. Soil pH mediated the effect of temperature on biotic interaction in the dimension of phylogeny, while soil pH and organic carbon acted as intermediate factors affecting biotic interaction in functional traits. Our findings suggest that climate change, notably precipitation, has a more pronounced impact on desert plant communities than human disturbances. A multifaceted approach considering functional traits and phylogeny across seasons is essential for understanding species coexistence in desert ecosystems.

A traits-based approach to assess aquaculture’s contributions to food, climate change, and biodiversity goals

Aquaculture has the potential to support a sustainable and equitable food system in line with the United Nations Sustainable Development Goals (SDG) on food security, climate change, and biodiversity (FCB). Biological diversity amongst aquaculture organisms can drive diverse contributions to such goals. Existing studies have assessed the performance of a limited number of taxa in the general context of improving aquaculture production, but few explicitly consider the biological attributes of farmed aquatic taxa at the FCB nexus. Through a systematic literature review, we identify key traits associated with FCB and evaluate the potential of aquaculture to contribute to FCB goals using a fuzzy logic model. The majority of identified traits are associated with food security, and two-thirds of traits linked with food security are also associated with climate change or biodiversity, revealing potential co-benefits of optimizing a single trait. Correlations between FCB indices further suggest that challenges and opportunities in aquaculture are intertwined across FCB goals, but low mean FCB scores suggest that the focus of aquaculture research and development on food production is insufficient to address food security, much less climate or biodiversity issues. As expected, production-maximizing traits (absolute fecundity, the von Bertalanffy growth function coefficient K, macronutrient density, maximum size, and trophic level as a proxy for feed efficiency) highly influence a species’ FCB potential, but so do species preferences for environmental conditions (tolerance to phosphates, nitrates, and pH levels, as well as latitudinal and geographic ranges). Many highly farmed species that are typically associated with food security, especially finfish, score poorly for food, climate, and biodiversity potential. Algae and mollusc species tend to perform well across FCB indices, revealing the importance of non-fish species in achieving FCB goals and potential synergies in integrated multi-trophic aquaculture systems. Overall, this study provides decision-makers with a biologically informed assessment of desirable aquaculture traits and species while illuminating possible strategies to increase support for FCB goals. Our findings can be used as a foundation for studying the socio-economic opportunities and barriers for aquaculture transitions to develop equitable pathways toward FCB-positive aquaculture across nuanced regional contexts.

An investigation of the impact of imbalance on the analysis of the US crop variety evaluation program data

AbstractMulti‐environment trial data from many crop variety evaluation programs are imbalanced because only a subset of varieties is selected for the following year, which leads to missing variety by year. Inspired by the US National Cotton Variety Test trial, we conducted new simulation studies to investigate selection processes that differ from the existing literature. The followings are our four main contributions. First, we adopted a framework that utilizes a logistic regression to generate imbalanced data that follow missing completely at random, missing at random, or missing not at random (MNAR). Second, our selection process can depend on multiple traits, whereas all existing studies only used a single trait for selection. Third, besides variance components (VCs), long‐term trends that reflect genetic and non‐genetic development are of interest since the simulated data span over 30 years. Last, we evaluated the prediction accuracy for variety's overall and location‐specific performance. The results show that the VC and long‐term trends estimations are the worst under MNAR using the single trait for selection. Compared to VC, the long‐term trends estimation is more influenced by the missing mechanism and missing rate. However, the prediction accuracy for variety's performance is mainly driven by the missing rate and is less sensitive to the selection process. If ignoring the genetic and non‐genetic long‐term trends, both estimation and prediction will deteriorate. More testing years would improve estimation and prediction, despite a higher missing rate.

Single Trait versus Multi-Trait Opinion Leadership Models

The superdiffuser model identifies a particular kind of opinion leader, those with three different traits beneficial to opinion leadership: connectedness, persuasiveness, and mavenness. Two other models of opinion leadership (strength of personality and generalized opinion leader) focus on a single trait conceptually including elements of both the persuader and connector traits. A nomological network of the superdiffuser model’s connector trait was developed and used to assess the extent to which the two other opinion leader models are distinct from the connector’s nomological network. Competing hypotheses were derived from the single trait and the superdiffuser multi-trait models. A survey (N = 700) was conducted to test these claims. The balance of the evidence was in favor of the superdiffuser model hypotheses with some important exceptions.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.