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Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions. Author summary: An important question in genetics is how the effects of mutations combine to alter phenotypes. Genetic interactions (epistasis) describe non-additive effects of pairs of mutations, but can also involve higher-order (three- and four-way etc.) combinations. Quantifying higher-order interactions is experimentally very challenging requiring a large number of measurements. Techniques based on deep mutational scanning (DMS) represent valuable sources of data to study epistasis. However, the best way to extract the relevant pairwise and higher-order epistatic coefficients (genetic interaction terms) from this data for the task of phenotypic prediction remains an unresolved problem. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values. Critically, this formalism currently only allows for two alleles (amino acid or nucleotide states) per sequence position, hampering applications in more biologically realistic scenarios. Here we present an extension of the Walsh-Hadamard transform that overcomes this limitation and demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic genetic landscapes. [ABSTRACT FROM AUTHOR]

Recently the 1/f signal of human electroencephalography has attracted attention, as it could potentially reveal a quantitative measure of neural excitation and inhibition in the brain, that may be relevant in a clinical setting. The purpose of this short article is to show that the 1/f signal depends on the vigilance state of the brain in both humans and mice. Therefore, proper labelling of the EEG signal is important as improper labelling may obscure disease-related changes in the 1/f signal. We demonstrate this by comparing EEG results from a longitudinal study in a genetic mouse model for synaptic dysfunction in schizophrenia and autism spectrum disorders to results from a large European cohort study with schizophrenia and mild Alzheimer's disease patients. The comparison shows when the 1/f is corrected for vigilance state there is a difference between groups, and this effect disappears when vigilance state is not corrected for. In conclusion, more attention should be paid to the vigilance state during analysis of EEG signals regardless of the species. [ABSTRACT FROM AUTHOR]

In this article I test whether students' educational expectations respond to prior academic performance and whether this responsiveness varies by socio-economic status (SES). The responsiveness of high-SES students' expectations may be lower as suggested by the compensatory advantage mechanism or higher because of alienation processes of low-SES students. However, the association between achievement and expectations may be in part spurious because of unobserved social and genetic confounders. This issue is largely ignored by previous research. Therefore, in this paper I estimate behavioral genetic twin models that take into account the possible confounding of the responsiveness of expectations to performance by unobserved genetic and social influences. While students' expectations respond to prior performance, this responsiveness is reduced by more than half once unobserved genetic and social confounders are accounted for. Also, SES differences in responsiveness to performance are completely accounted for by high-SES students' expectations being less responsive to prior levels of cognitive ability. So, this study shows the relevance of taking different types of confounding into account when studying the formation of educational expectations. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is an important health issue in elderly people. Many studies have suggested that genetic factors are important risk factors for OA, of which tumor necrosis factor-α (TNF-α) is one of the most examined genes. Moreover, several studies have investigated the relationship between TNF-α G-308A polymorphisms and OA risk, but consistent results have not been obtained. Objective: This study examines the association between TNF-α G-308A polymorphisms and knee OA. Moreover, meta-analysis and trial sequential analysis (TSA) was used to determine whether this is a susceptibility gene for knee OA. Methods: Between 2015 and 2019, 591 knee OA cases and 536 healthy controls were recruited. The Kellgren–Lawrence grading system was used to identify the knee OA cases. A meta-analysis was conducted including related studies published until 2020 from PubMed, Embase, and previous meta-analysis to improve the evidence level of the current study. The results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CI) to evaluate the effect of this polymorphism on knee OA risk. The TSA was used to estimate the sample sizes required in this issue. Results: A nonsignificant association was found between the AA genotype and knee OA [adjusted OR, 0.84; 95% CI, 0.62–1.15) in the recessive model] in the present case–control study, and analysis of other genetic models showed a similar trend. After adding the critical case–control samples for Asians, the TNF-α G-308A, AA genotype exhibited 2.57 times more risk of developing arthritis when compared with the GG + GA genotype (95% CI, 1.56–4.23), and the cumulative samples for TSA (n = 2182) were sufficient to obtain a definite conclusion. Conclusions: The results of this meta-analysis revealed that the TNF-α G-308A, AA genotype is a susceptible genotype for OA in the Asian population. This study integrated all current evidence to arrive at this conclusion, suggesting that future studies on Asians are not required. [ABSTRACT FROM AUTHOR]

Objectives: Muscle health is essential for children's physical development and future health. PPARGC1A gene encode the peroxisome proliferator-activated receptor coactivator 1α which coactivates transcription factors that control mediating skeletal muscle fiber type conversion and skeletal muscle fiber formation. The PPARGC1A rs8192678 Gly/Ser (Gly482Ser) polymorphism was associated with the regulation of skeletal muscle fibre type. This paper aims to explore the association between the PPARGC1A rs8192678 (Gly482Ser) polymorphism and muscle fitness in Chinese schoolchildren. Methods: We detected the distribution of the PPARGC1A rs8192678 (Gly482Ser) polymorphism by DNA typing of saliva samples from untrained Southern Chinese Han children aged 7–12 years. Considering that muscle studies in children cannot use invasive sampling, we analyzed the association between alleles and genotypes with high validity tests of muscle fitness assesment in children(handgrip strength, standing long jump, sit-ups and push-ups). Results: The results showed no significant differences in height, weight or body mass index between the sexes. The grip strength indicators were correlated with age in boys and height and weight in girls. Sit-ups were significantly higher in girls with the PPARGC1A Gly/Gly genotype than in boys, and handgrip strength and standing long jump were significantly lower in girls with the PPARGC1A rs8192678 (Gly482Ser) genotype than in boys. Genetic model analysis showed that the Gly482 allele had a dominant genetic effect on the Gly482 allele is hypothesized to influence the expression of type I fibers in skeletal muscle in girls, while the Ser482 allele affects on type II fibers in girls. The two alleles had little genetic effect on boys. Conclusions: The results suggested the possible association of the PPARGC1A rs8192678 (Gly482Ser) polymorphism on myofibril type-related phenotypes in Han Chinese children in southern China, with a particular impact on girls. [ABSTRACT FROM AUTHOR]

HIF1A gene polymorphisms have been confirmed the association with cancer risk through the statistical meta-analysis based on single genetic association (SGA) studies. A good number SGA studies also investigated the association of HIF1A gene with several other diseases, but no researcher yet performed statistical meta-analysis to confirm this association more accurately. Therefore, in this paper, we performed a statistical meta-analysis to draw a consensus decision about the association of HIF1A gene polymorphisms with several diseases except cancers giving the weight on large sample size. This meta-analysis was performed based on 41 SGA study's findings, where the polymorphisms rs11549465 (1772 C/T) and rs11549467 (1790 G/A) of HIF1A gene were analyzed based on 11544 and 7426 cases and 11494 and 7063 control samples, respectively. Our results showed that the 1772 C/T polymorphism is not significantly associated with overall disease risks. The 1790 G/A polymorphism was significantly associated with overall diseases under recessive model (AA vs. AG + GG), which indicates that the A allele is responsible for overall diseases though it is recessive. The subgroup analysis based on ethnicity showed the significant association of 1772 C/T polymorphism with overall disease for Caucasian population under the all genetic models, which indicates that the C allele controls overall diseases. The ethnicity subgroup showed the significant association of 1790 G/A polymorphism with overall disease for Asian population under the recessive model (AA vs. AG + GG), which indicates that the A allele is responsible for overall diseases. The subgroup analysis based on disease types showed that 1772 C/T is significantly associated with chronic obstructive pulmonary disease (COPD) under two genetic models (C vs. T and CC vs. CT + TT), skin disease under two genetic models (CC vs. TT and CC + CT vs. TT), and diabetic complications under three genetic models (C vs. T, CT vs. TT and CC + CT vs. TT), where C allele is high risk factor for skin disease and diabetic complications (since, ORs > 1), but low risk factor for COPD (since, ORs < 1). Also the 1790 G/A variant significantly associated with the subgroup of cardiovascular disease (CVD) under homozygote model, diabetic complications under allelic and homozygote models, and other disease under four genetic models, where the A is high risk factor for diabetic complications and low risk factor for CVD. Thus, this study provided more evidence that the HIF1A gene is significantly associated with COPD, CVD, skin disease and diabetic complications. These might be the severe comorbidities and risk factors for multiple cancers due to the effect of HIF1A gene and need further investigations accumulating large number of studies. [ABSTRACT FROM AUTHOR]

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort. Author summary: In recent years, there is an increased interest of estimating heritability from genome-wide SNP data in large scale cohort studies. Here, we propose the PredLMM, a computationally rapid and memory-efficient linear mixed model for heritability estimation. The proposed approach can estimate SNP heritability on Biobank-scale datasets in a fraction of time compared to the existing mixed model based approaches. Along with the extensive simulations illustrating the precision and robustness of the PredLMM, we have also estimated heritability of several anthropometric traits from the UK Biobank cohort. [ABSTRACT FROM AUTHOR]

Circadian rhythms in a wide range of organisms are mediated by molecular mechanisms based on transcription-translation feedback. In this paper, we use bifurcation theory to explore mathematical models of genetic oscillators, based on Kim & Forger's interpretation of the circadian clock in mammals. At the core of their models is a negative feedback loop whereby PER proteins (PER1 and PER2) bind to and inhibit their transcriptional activator, BMAL1. For oscillations to occur, the dissociation constant of the PER:BMAL1 complex, K^d , must be ≤ 0.04 nM, which is orders of magnitude smaller than a reasonable expectation of 1–10 nM for this protein complex. We relax this constraint by two modifications to Kim & Forger's 'single negative feedback' (SNF) model: first, by introducing a multistep reaction chain for posttranscriptional modifications of Per mRNA and posttranslational phosphorylations of PER, and second, by replacing the first-order rate law for degradation of PER in the nucleus by a Michaelis-Menten rate law. These modifications increase the maximum allowable K^d to ~2 nM. In a third modification, we consider an alternative rate law for gene transcription to resolve an unrealistically large rate of Per2 transcription at very low concentrations of BMAL1. Additionally, we studied extensions of the SNF model to include a second negative feedback loop (involving REV-ERB) and a supplementary positive feedback loop (involving ROR). Contrary to Kim & Forger's observations of these extended models, we find that, with our modifications, the supplementary positive feedback loop makes the oscillations more robust than observed in the models with one or two negative feedback loops. However, all three models are similarly robust when accounting for circadian rhythms (~24 h period) with K^d ≥ 1 nM. Our results provide testable predictions for future experimental studies. Author summary: The circadian rhythm aligns bodily functions to the day/night cycle and is important for our health. The rhythm originates from an intracellular molecular clock mechanism that mediates rhythmic gene expression. It is long understood that transcriptional negative feedback with sufficient time delay is key to generating circadian oscillations. However, some of the most widely cited mathematical models for the circadian clock suffer from problems of parameter 'fragilities'. That is, sustained oscillations are possible only for physically unrealistic parameter values. A recent model by Kim & Forger nicely incorporates the inhibitory binding of PER proteins to their transcription activator BMAL1, but oscillations in the Kim-Forger model require a binding affinity between PER and BMAL1 that is orders of magnitude larger than observed binding affinities of protein complexes. To rectify this problem, we make several physiologically credible modifications to the Kim-Forger model, which allow oscillations to occur with more realistic binding affinities. The modified model is further extended to explore the potential roles of supplementary feedback loops in the mammalian clock mechanism. Ultimately, accurate models of the circadian clock will provide better predictive tools for chronotherapy and chrono-pharmacology studies. [ABSTRACT FROM AUTHOR]

Intracellular spatial heterogeneity is frequently observed in bacteria, where the chromosome occupies part of the cell's volume and a circuit's DNA often localizes within the cell. How this heterogeneity affects core processes and genetic circuits is still poorly understood. In fact, commonly used ordinary differential equation (ODE) models of genetic circuits assume a well-mixed ensemble of molecules and, as such, do not capture spatial aspects. Reaction-diffusion partial differential equation (PDE) models have been only occasionally used since they are difficult to integrate and do not provide mechanistic understanding of the effects of spatial heterogeneity. In this paper, we derive a reduced ODE model that captures spatial effects, yet has the same dimension as commonly used well-mixed models. In particular, the only difference with respect to a well-mixed ODE model is that the association rate constant of binding reactions is multiplied by a coefficient, which we refer to as the binding correction factor (BCF). The BCF depends on the size of interacting molecules and on their location when fixed in space and it is equal to unity in a well-mixed ODE model. The BCF can be used to investigate how spatial heterogeneity affects the behavior of core processes and genetic circuits. Specifically, our reduced model indicates that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome. The extent of these effects depends on the value of the BCF, which we found to be close to unity. For translation, the value of the BCF is always greater than unity, it increases with mRNA size, and, with biologically relevant parameters, is substantially larger than unity. Our model has broad validity, has the same dimension as a well-mixed model, yet it incorporates spatial heterogeneity. This simple-to-use model can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects. Author summary: A general and simple modeling framework to determine how spatial heterogeneity modulates the dynamics of gene networks is currently lacking. To this end, we provide a simple-to-use ordinary differential equation (ODE) model that can be used to both analyze and design genetic circuits while accounting for spatial intracellular effects. We apply our model to several core biological processes and determine that transcription and its regulation are more effective for genes located at the cell poles than for genes located on the chromosome and this difference increases with regulator size. For translation, we predict the effective binding between ribosomes and mRNA is higher than that predicted by a well-mixed model, and it increases with mRNA size. We provide examples where spatial effects are significant and should be considered but also where a traditional well-mixed model suffices despite severe spatial heterogeneity. Finally, we illustrate how the operation of well-known genetic circuits is impacted by spatial effects. [ABSTRACT FROM AUTHOR]

Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuable reference information for clinical treatment. This study selected the BRFSS (Behavioral Risk Factor Surveillance System) dataset, which is publicly available on the Kaggle platform, as the research object, aiming to provide a scientific basis for the early diagnosis and treatment of diabetes through advanced machine learning techniques. Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The GA-XGBoost model optimized the hyperparameters of the XGBoost model through a genetic algorithm to improve the model's predictive accuracy. Combined with the better-performing LightGBM model and random forest model, a two-layer Stacking model was constructed. This model not only outperforms single machine learning models in predictive effect but also provides a new idea and method in the field of model integration. (3) Shapley value analysis identified features that have a significant impact on the prediction of diabetes, such as age and body mass index. This analysis not only enhances the transparency of the model but also provides more precise treatment decision support for doctors and patients. In summary, this study has not only improved the accuracy of predicting the risk of diabetes by adopting advanced machine learning techniques and model integration strategies but also provided a powerful tool for the early diagnosis and personalized treatment of diabetes. [ABSTRACT FROM AUTHOR]

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier. Author summary: Targeted therapy aims to selectively target cancer cells without damaging the normal ones. Synthetic lethality is a negative genetic interaction in which alteration of both genes leads to cell death and mediates drug sensitivity. In contrast, synthetic viability is a positive genetic interaction in which gene alteration rescues the cell sickness induced by alteration in the vulnerable gene and promotes cell viability, leading to drug resistance. Hence, identifying these genetic interactions is crucial to fostering selective treatment and improving the patient's health. We have designed MAGICAL, a multi-class classifier for predicting genetic interactions, a machine-learning model that can predict SL and SV based on the network properties. We aim to address how these genetic interactions get affected when the placement of the nodes (genes) in the network changes. As genetic interaction in cancer has a key role in precision oncology/targeted therapy, this work would enable researchers to understand how these interactions foster better treatment. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis infects two billion people across the globe, and results in 8–9 million new tuberculosis (TB) cases and 1–1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWH/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB. Author summary: We investigated the genetic basis of susceptibility to Mycobacterium tuberculosis using Diversity Outbred mice, a mouse population suited for studying complex genotype-phenotype relationships. We found five new genetic loci and three known loci. Three loci associated with multiple correlated disease traits likely contain genes that are major regulators of host inflammatory responses which favor M. tuberculosis growth. Further, these three loci contain four gene candidates with single nucleotide polymorphisms that are predicted to have deleterious effects upon protein functions. [ABSTRACT FROM AUTHOR]

Objective: The Toll-like receptor (TLR) 4-mediated nuclear factor kappa B (NF-κB) signaling pathway regulates the production of inflammatory factors and plays a key role in the pathogenesis of gouty arthritis. The aim of the present study was to investigate the link among TLR4 gene polymorphisms at various loci, protein expression, and gouty arthritis susceptibility. Methods: Between 2016 and 2021, a case-control study was used to collect a total of 1207 study subjects, including 317 male patients with gouty arthritis (gout group) and 890 healthy males (control group). The association between gout susceptibility and different genetic models was analyzed by typing three loci of the TLR4 gene (rs2149356, rs2737191, and rs10759932) using a multiplex point mutation rapid assay, and the association between protein expression and gout was confirmed by measuring TLR4 protein concentrations using enzyme-linked immunosorbent assays (ELISAs). Results: In a codominant models AA and AG, the rs2737191 polymorphism in the gout group increased the risk of gout compared to the AA genotype (OR = 2.249, 95%CI 1.010~5.008), and the risk of gout was higher for those carrying the G allele compared to the A allele (OR = 2.227, 95%CI 1.006~4.932). TLR4 protein expression was different between the two groups with different locus genotypes. The differences in TLR4 protein expression between the gout group and control group were statistically significant between the following genotypes: the GG and GT genotypes of the rs2149356 polymorphism; the AA and AG genotypes of the rs2737191 polymorphism; and the TT and TC genotypes of the rs10759932 polymorphism(P<0.05). The TLR4 protein level in the gout group (19.19±3.09 ng/ml) was significantly higher than that in the control group (15.85±4.75 ng/ml). Conclusion: The AG genotype of the TLR4 gene rs2737191 polymorphism may be correlated with the development of gouty arthritis. The level of TLR4 protein expression is significantly higher in patients with gouty arthritis than in controls, and there is a correlation between high TLR4 protein expression and the development of gouty arthritis. [ABSTRACT FROM AUTHOR]

Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans. Author summary: Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans. [ABSTRACT FROM AUTHOR]

Background: Previous research has suggested that the ELMO1 gene may play a role in the development of diabetic kidney disease. Diabetic kidney disease (DKD) is a serious complication of diabetes and the leading cause of chronic kidney disease and end-stage renal disease (ESRD). Objective and rationale: This study aim was to systematically review and explore the association between ELMO1 gene polymorphisms and diabetic kidney disease. A comprehensive systematic review provides a clear conclusion and high-level evidence for the association between ELMO1 gene and DKD for future application in personalized medicine. Methods: A comprehensive search of electronic databases, per PRISMA instructions, was conducted in Scopus, EMBASE, Web of Science, and PubMed databases from 1980 to January 2023. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using appropriate models. Subgroup and sensitivity analyses were performed to explore potential sources of heterogeneity and assess the robustness of the findings. Results: A total of 5794 diabetes patients with DKD, 4886 diabetes patients without DKD, and 2023 healthy controls were included in the 17 studies that made up this systematic review. In the investigation of DM (Diabetes Mellitus) with DKD vs. DM without DKD, the susceptibility for DKD for the EMLO1 rs741301 polymorphism indicated a significant difference under the dominant, homozygote, and recessive genetic models. The susceptibility for DKD for the EMLO1 rs1345365, rs10255208, and rs7782979 polymorphisms demonstrated a significant difference under the allele genetic models in the analysis of DM with DKD vs. DM without DKD groups. There was a considerable increase in DKD risk in the Middle East when the population was stratified by the region. Conclusion: The findings of the meta-analysis show that there are a significant connection between the EMLO1 rs741301 polymorphism and DKD susceptibility in overall analyses; as well as rs1345365, rs10255208, and rs7782979 polymorphisms; especially in the Middle East region. [ABSTRACT FROM AUTHOR]

History of incarceration is associated with an excess of morbidity and mortality. While the incarceration experience itself comes with substantive health risks (e.g., injury, psychological stress, exposure to infectious disease), most individuals eventually return from prison to the general population where they will be diagnosed with the same age-related conditions that drive mortality in the non-incarcerated population but at exaggerated rates. However, the interplay between history of incarceration as a risk factor and more traditional risk factors for age-related diseases (e.g., genetic risk factors) has not been studied. Here, we focus on cognitive impairment, a hallmark of neurodegenerative conditions like Alzheimer's disease, as an age-related state that may be uniquely impacted by the confluence of environmental stressors (e.g., incarceration) and genetic risk factors. Using data from the Health and Retirement Study, we found that incarceration and APOE-ε4 genotype (i.e., the chief genetic risk factor for Alzheimer's disease) both constituted substantive risk factors for cognitive impairment in terms of overall risk and earlier onset. The observed effects were mutually independent, however, suggesting that the risk conveyed by incarceration and APOE-ε4 genotype operate across different risk pathways. Our results have implications for the study of criminal-legal contact as a public health risk factor for age-related, neurodegenerative conditions. [ABSTRACT FROM AUTHOR]

The recent development of genetic markers for Bacillus anthracis has made it possible to monitor the spread and distribution of this pathogen during and between anthrax outbreaks. In Namibia, anthrax outbreaks occur annually in the Etosha National Park (ENP) and on private game and livestock farms. We genotyped 384 B. anthracis isolates collected between 1983–2010 to identify the possible epidemiological correlations of anthrax outbreaks within and outside the ENP and to analyze genetic relationships between isolates from domestic and wild animals. The isolates came from 20 animal species and from the environment and were genotyped using a 31-marker multi-locus-VNTR-analysis (MLVA) and, in part, by twelve single nucleotide polymorphism (SNP) markers and four single nucleotide repeat (SNR) markers. A total of 37 genotypes (GT) were identified by MLVA, belonging to four SNP-groups. All GTs belonged to the A-branch in the cluster- and SNP-analyses. Thirteen GTs were found only outside the ENP, 18 only within the ENP and 6 both inside and outside. Genetic distances between isolates increased with increasing time between isolations. However, genetic distance between isolates at the beginning and end of the study period was relatively small, indicating that while the majority of GTs were only found sporadically, three genetically close GTs, accounting for more than four fifths of all the ENP isolates, appeared dominant throughout the study period. Genetic distances among isolates were significantly greater for isolates from different host species, but this effect was small, suggesting that while species-specific ecological factors may affect exposure processes, transmission cycles in different host species are still highly interrelated. The MLVA data were further used to establish a model of the probable evolution of GTs within the endemic region of the ENP. SNR-analysis was helpful in correlating an isolate with its source but did not elucidate epidemiological relationships. Author Summary: Anthrax, the disease caused by Bacillus anthracis, is a neglected zoonotic diseases in the context of its impact on poor rural and periurban communities in Africa and other less developed areas of the world. Several regions of Namibia, the Etosha National Park in particular, are well known as being endemic areas for anthrax and, together, provide a good model for the investigation of the genetic diversity of B. anthracis circulating in livestock, wildlife and humans, and surrounding environments. The application of modern molecular strain typing techniques to the analysis of genotypic diversity, as it relates to the spatial and temporal distribution of B. anthracis strains in Namibia, is described in this paper. In particular, we demonstrate how it is possible to distinguish outbreaks of the disease caused by different strains from those caused by the spread of a single strain, to trace an outbreak strain back to its possible origin, and to track the routes of transmission of an outbreak strain within and between animal populations. The data described are relevant to all those concerned with monitoring, surveillance and prevention of the spread of anthrax in endemic areas. [ABSTRACT FROM AUTHOR]

Background: Some low molecular weight heparins (LMWHs) prolong survival of cancer patients and inhibit experimental metastasis. The underlying mechanisms are still not clear but it has been suggested that LMWHs (at least in part) limit metastasis by preventing cancer cell-induced destruction of the endothelial glycocalyx. Methodology/Principal Findings: To prove or refute this hypothesis, we determined the net effects of the endothelial glycocalyx in cancer cell extravasation and we assessed the anti-metastatic effect of a clinically used LMWH in the presence and absence of an intact endothelial glycocalyx. We show that both exogenous enzymatic degradation as well as endogenous genetic modification of the endothelial glycocalyx decreased pulmonary tumor formation in a murine experimental metastasis model. Moreover, LMWH administration significantly reduced the number of pulmonary tumor foci and thus experimental metastasis both in the presence or absence of an intact endothelial glycocalyx. Conclusions: In summary, this paper shows that the net effect of the endothelial glycocalyx enhances experimental metastasis and that a LMWH does not limit experimental metastasis by a process involving the endothelial glycocalyx. [ABSTRACT FROM AUTHOR]

Background: Dengue is the most important mosquito-borne viral disease affecting humans. The only prevention measure currently available is the control of its vectors, primarily Aedes aegypti. Recent advances in genetic engineering have opened the possibility for a new range of control strategies based on genetically modified mosquitoes. Assessing the potential efficacy of genetic (and conventional) strategies requires the availability of modeling tools that accurately describe the dynamics and genetics of Ae. aegypti populations.Methodology/Principal findings: We describe in this paper a new modeling tool of Ae. aegypti population dynamics and genetics named Skeeter Buster. This model operates at the scale of individual water-filled containers for immature stages and individual properties (houses) for adults. The biology of cohorts of mosquitoes is modeled based on the algorithms used in the non-spatial Container Inhabiting Mosquitoes Simulation Model (CIMSiM). Additional features incorporated into Skeeter Buster include stochasticity, spatial structure and detailed population genetics. We observe that the stochastic modeling of individual containers in Skeeter Buster is associated with a strongly reduced temporal variation in stage-specific population densities. We show that heterogeneity in container composition of individual properties has a major impact onspatial heterogeneity in population density between properties. We detail how adult dispersal reduces this spatial heterogeneity. Finally, we present the predicted genetic structure of the population by calculating FST values and isolation by distance patterns, and examine the effects of adult dispersal and container movement between properties. Conclusions/Significance: We demonstrate that the incorporated stochasticity and level of spatial detail have major impacts on the simulated population dynamics, which could potentially impact predictions in terms of control measures. The capacity to describe population genetics confers the ability to model the outcome of genetic control methods. Skeeter Buster is therefore an important tool to model Ae. aegypti populations and the outcome of vector control measures. [ABSTRACT FROM AUTHOR]

Objective: Gestational diabetes mellitus (GDM) is a growing public health concern that has not been extensively studied. Numerous studies have indicated that a variant (rs8050136) of the fat mass-associated gene, FTO, is associated with both GDM and Type 2 diabetes mellitus(T2DM). We conducted a meta-analysis on the association between the FTO single nucleotide polymorphism (SNP) rs8050136 and T2DM, followed by a case-control study on the association of the said SNP and GDM in a sample of Bangladeshi women. Method: A total of 25 studies were selected after exploring various databases and search engines, which were assessed using the Newcastle-Ottawa Scale (NOS). The MetaGenyo web tool was used to conduct this meta-analysis. A case-control study was performed on 218 GDM patients and 284 controls to observe any association between FTO rs8050136 and GDM. Genotyping was performed using the tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS) method, and statistical analyses were performed using various statistical softwares. Results: In the meta-analysis 26231 cases and 43839 controls were examined. Pooled association analyses revealed a statistically significant relationship between the FTO rs8050136 polymorphism and an elevated risk of T2DM under all genetic models (P<0.05). In the case-control study, synergistic analyses of the SNP and gravida with GDM revealed a significant (P<0.01) association with an increase in odds by 1.6 to 2.4 folds in multigravida and decrease in odds by 2 folds in primigravida. A positive family history of diabetes and the minor allele of this SNP collectively increased the risk of developing GDM by many-fold (1.8 to 2.7 folds). However, after accounting for family history of diabetes and gravidity, analyses showed no significant association with GDM. Conclusion: Our meta-analysis revealed a significant association between SNP rs8050136 of FTO with T2DM, and this variant was substantially associated with an increased risk of GDM in a sample of Bangladeshi multigravida women. [ABSTRACT FROM AUTHOR]

The multifactorial pathogenesis of severe malaria is partly attributed to host genes, such as those encoding cytokines involved in complex inflammatory reactions, namely tumor necrosis factor-alpha (TNF-α). However, the relationship between TNF-α -308G >A gene polymorphism (rs1800629) and the severity of Plasmodium falciparum (P. falciparum) malaria remains unclear, which prompts a meta-analysis to obtain more precise estimates. The present meta-analysis aimed to better understand this correlation and provide insight into its association in populations with different ethnicities. Literature search outcomes included eight eligible articles in which TNF-α -308G >A polymorphism was determined in uncomplicated malaria (UM) and severe malaria (SM) of P. falciparum as represented in the case and control groups. Pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated in standard homozygous, recessive, dominant, and codominant genetic models. Subgroup analysis was based on ethnicity, i.e., Africans and Asians. The analyses included overall and the modified outcomes; the latter was obtained without the studies that deviated from the Hardy-Weinberg Equilibrium. The significant data also underwent sensitivity treatment but not publication bias tests because the number of studies was less than ten. Interaction tests were applied to differential outcomes between the subgroups. Overall and HWE-compliant analyses showed no significant association between the TNF-α -308G >A polymorphism and susceptibility to P. falciparum SM (ORs = 1.10–1.52, 95%CIs = 0.68–2.79; Pa = 0.24–0.68). Stratification by ethnicity revealed that two significant associations were found only in the Asians favoring SM for dominant (OR = 1.95, 95% CI = 1.06–3.61, Pa = 0.03) and codominant (OR = 1.83, 95% CI = 1.15–2.92, Pa = 0.01) under the random-effects model, but not among the African populations. The two significant Asian associations were improved with a test of interaction with P-value of0.02–0.03. The significant core outcomes were robust. Results of the meta-analysis suggest that TNF-α -308G >A polymorphism might affect the risk of P. falciparum SM, particularly in individuals of Asian descent. This supports ethnicity as one of the dependent factors of the TNF-α -308G >A association with the clinical severity of malaria. Further large and well-designed genetic studies are needed to confirm this conclusion. Author summary: Host genetic factors play important role in the development and progress of severe malaria due to Plasmodium falciparum infection. One of the key factors is the abnormality in the gene that encodes cytokines, particularly tumor necrosis factor-alpha (TNF-α), which are involved in complex inflammatory reactions. However, the relationship between the abnormality of this gene and malaria severity remains unclear. The present meta-analysis aimed to better understand this correlation and provide insight into its association in populations with different ethnicities. The analyses showed no significant association. Stratification by ethnicity revealed that two significant associations were found only in the Asians favoring SM for dominant and codominant, but not among the African populations. Results of the meta-analysis suggest that TNF-α -308G >A might affect the risk of P. falciparum SM, particularly in individuals of Asian descent. This supports ethnicity as one of the dependent factors of the association between the abnormality of this gene and clinical severity of malaria. Further large and well-designed genetic studies are needed to confirm this conclusion. [ABSTRACT FROM AUTHOR]

Genetic feedback loops can be used by cells to regulate internal processes or to keep track of time. It is often thought that, for a genetic circuit to display self-sustained oscillations, a degree of cooperativity is needed in the binding and unbinding of actor species. This cooperativity is usually modeled using a Hill function, regardless of the actual promoter architecture. Furthermore, genetic circuits do not operate in isolation and often transcription factors are shared between different promoters. In this work we show how mathematical modelling of genetic feedback loops can be facilitated with a mechanistic fold-change function that takes into account the titration effect caused by competing binding sites for transcription factors. The model shows how the titration effect facilitates self-sustained oscillations in a minimal genetic feedback loop: a gene that produces its own repressor directly without cooperative transcription factor binding. The use of delay-differential equations leads to a stability contour that predicts whether a genetic feedback loop will show self-sustained oscillations, even when taking the bursty nature of transcription into account. Author summary: Cells keep track of time by genetic feedback loops—transcription factor proteins that repress their own production and whose availability oscillates regularly in time. For oscillations to occur, the amount of transcription factor that is produced should be extremely sensitive to the amount of transcription factor that is present in the cell. In some genetic circuits, sensitivity comes from the fact that multiple transcription factors need to bind to the gene simultaneously to successfully inhibit its production, but it is also possible to generate this sensitivity when multiple binding sites compete for the same pool of transcription factors. In this work we show how to mathematically model this competition effect, and predict under which circumstances stable oscillations occur. [ABSTRACT FROM AUTHOR]

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest. Author summary: An increasing number of diseases previously thought to be caused by a mutation in a single gene are now being considered as involving several variants in a small number of genes (i.e. "oligogenic"). There is a limited number of dedicated bioinformatic tools to study such oligogenic causes of diseases. These include so called aggregation tests. Yet, an important challenge is to select the right aggregation test among the various ones that have been developed, as each suffers from different limitations. We have computationally compared 59 aggregation methods to explore their limitations. We found that combining 36 of them results in a more robust method, which we baptized "Excalibur". It can handle a wider range of hypotheses and case-control studies than any of the single methods, while reducing the number of false positive results. Excalibur also provides a comprehensive elucidation of the underlying genetic architecture pertaining to each genomic region under investigation. Thus, it provides a user-friendly, and statistically sound platform to study oligogenic inheritance with the increasing amount of available genetic data. [ABSTRACT FROM AUTHOR]

Although numerous studies have suggested the association between TNF-α-308G/A polymorphism and susceptibility to obstructive sleep apnea (OSA), the results remained controversial and ambiguous. We performed the present meta-analysis to derive a more precise estimation.The PubMed, Embase, Cochrane library, Web of Science, China National Knowledge Infrastructure, Wanfang databases, and Weipu databases (until January 8, 2022) were accessed to retrieve relevant articles. Pooled odds ratios (ORs) with 95% confidence interval (95% CI) were calculated using the STATA statistical software.Totally, fourteen studies involving 2595 cases and 2579 controls were enrolled in this meta-analysis. Pooled results demonstrated significant association between TNF-α-308G/A polymorphism and OSA risk for the overall population(allele model:OR = 1.87 [1.47, 2.38] (n = 14), dominant model: OR = 1.88[1.48, 2.39] (n = 14), recessive model:OR = 2.83 [2.00, 4.00] (n = 11), homozygous model:OR = 3.30 [2.32, 4.68] (n = 11), and heterozygous model:OR = 1.67 [1.36, 2.06] (n = 14); P<0.001, respectively).Subgroup analysis showed that in both Caucasians and Asians, the A allele conferred increased risk to OSA compared to the G allele (Caucasians: OR = 1.40[1.03, 1.90] (n = 5), P = 0.033, Asians: OR = 2.30 [1.62, 3.26] (n = 9), P< 0.001). In subgroup analysis restricted to hospital-based individuals, significant association between TNF-α-308G/A polymorphism and OSA risk was identified under each genetic model. Whereas, in population-based individuals, increased risk of OSA were only found in homozygous model (OR = 2.19[1.23, 3.90] (n = 3), P = 0.008) and recessive model (OR = 1.77 [1.00, 3.13] (n = 3), P = 0.048). There was a substantial between-study heterogeneity (I2 = 69.10%) across studies which was explained by source of control participants (P = 0.036) by meta-regression. The results of leave-one-out meta-analysis and publication bias suggested the reliability and stability of our results.This meta-analysis suggested that TNF-α-308A allele may be a risk factor for the development of OSA. However, large scale,multi-center and well-designed case-control studies are needed in the future. [ABSTRACT FROM AUTHOR]

Predation plays a role in preventing the evolution of ever more complicated sexual displays, because such displays often increase an individual's predation risk. Sexual selection theory, however, omits a key feature of predation in modeling costs to sexually selected traits: Predation is density dependent. As a result of this density dependence, predator–prey dynamics should feed back into the evolution of sexual displays, which, in turn, feeds back into predator–prey dynamics. Here, we develop both population and quantitative genetic models of sexual selection that explicitly link the evolution of sexual displays with predator–prey dynamics. Our primary result is that predation can drive eco-evolutionary cycles in sexually selected traits. We also show that mechanistically modeling the cost to sexual displays as predation leads to novel outcomes such as the maintenance of polymorphism in sexual displays and alters ecological dynamics by muting prey cycles. These results suggest predation as a potential mechanism to maintain variation in sexual displays and underscore that short-term studies of sexual display evolution may not accurately predict long-run dynamics. Further, they demonstrate that a common verbal model (that predation limits sexual displays) with widespread empirical support can result in unappreciated, complex dynamics due to the density-dependent nature of predation. It's long been known that predation acts as a major constraint on the evolution of sexual displays, but the implications of interactions between predator-prey dynamics and sexual selection are less well explored; here, population and quantitative genetic models of sexual selection reveal that predation can drive eco-evolutionary cycles in sexually selected traits. [ABSTRACT FROM AUTHOR]

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers. [ABSTRACT FROM AUTHOR]

Identifications of novel genetic signals conferring susceptibility to human complex diseases is pivotal to the disease diagnosis, prevention, and treatment. Genetic association study is a powerful tool to discover candidate genetic signals that contribute to diseases, through statistical tests for correlation between the disease status and genetic variations in study samples. In such studies with a case-control design, a standard practice is to perform the Cochran-Armitage (CA) trend test under an additive genetic model, which suffers from power loss when the model assumption is wrong. The Jonckheere-Terpstra (JT) trend test is an alternative method to evaluate association in a nonparametric way. This study compares the power of the JT trend test and the CA trend test in various scenarios, including different sample sizes (200–2000), minor allele frequencies (0.05–0.4), and underlying modes of inheritance (dominant genetic model to recessive genetic model). By simulation and real data analysis, it is shown that in general the JT trend test has higher, similar, and lower power than the CA trend test when the underlying mode of inheritance is dominant, additive, and recessive, respectively; when the sample size is small and the minor allele frequency is low, the JT trend test outperforms the CA trend test across the spectrum of genetic models. In sum, the JT trend test is a valuable alternative to the CA trend test under certain circumstances with higher statistical power, which could lead to better detection of genetic signals to human diseases and finer dissection of their genetic architecture. [ABSTRACT FROM AUTHOR]

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS. Author summary: Genome-wide association studies have identified tens of thousands of genetic variants associated with complex traits. An ever increasing number of associated variants are shown to affect multiple traits, a phenomenon known as pleiotropy. We propose a method that leverages this genetic architecture and uses summary statistics to perform an omnibus association test between one genetic variant and a set of traits. Simulations show that the method properly controls for type-I errors and increases statistical power. In addition to a powerful omnibus test, we also enable a per trait interpretation of the associations by extending the m-value framework to account for the correlation structure between traits. This framework enables a significant increase in the identification of per trait effects. [ABSTRACT FROM AUTHOR]

CRISPR gene drives could revolutionize the control of infectious diseases by accelerating the spread of engineered traits that limit parasite transmission in wild populations. Gene drive technology in mollusks has received little attention despite the role of freshwater snails as hosts of parasitic flukes causing 200 million annual cases of schistosomiasis. A successful drive in snails must overcome self-fertilization, a common feature of host snails which could prevents a drive's spread. Here we developed a novel population genetic model accounting for snails' mixed mating and population dynamics, susceptibility to parasite infection regulated by multiple alleles, fitness differences between genotypes, and a range of drive characteristics. We integrated this model with an epidemiological model of schistosomiasis transmission to show that a snail population modification drive targeting immunity to infection can be hindered by a variety of biological and ecological factors; yet under a range of conditions, disease reduction achieved by chemotherapy treatment of the human population can be maintained with a drive. Alone a drive modifying snail immunity could achieve significant disease reduction in humans several years after release. These results indicate that gene drives, in coordination with existing public health measures, may become a useful tool to reduce schistosomiasis burden in selected transmission settings with effective CRISPR construct design and evaluation of the genetic and ecological landscape. Author summary: CRISPR gene drives can propagate engineered traits in vectors, like mosquitoes, to curb transmission of infectious diseases. Here we explore whether gene drive technology can also be used in molluscs to control schistosomiasis, a debilitating neglected tropical disease requiring freshwater snails as intermediate hosts. Unlike mosquitoes, these snail species can reproduce by self-fertilization, which could disable gene drive inheritance. Despite this limitation, our mathematical model identifies conditions in which drive immunity in snails can spread and, when complemented with mass Praziquantel treatment, achieves sustained disease reduction. Modeling that integrates genetic designs with ecological conditions and public health interventions is critical to safely leverage a powerful technology like gene drive. [ABSTRACT FROM AUTHOR]

Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19—such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events—suggest new directions for improving upon existing population genetic models. [ABSTRACT FROM AUTHOR]

The sea cucumber Holothuria (Metriatyla) scabra, known as sandfish, is a high-value tropical echinoderm central to the global bêche-de-mer (BDM) trade. This species has been heavily exploited across its natural range, with overharvesting and ineffective fishery management leaving stocks in the Pacific region heavily depleted. In Fiji, sandfish stocks have not recovered since a 1988 harvest ban, with surveys reporting declining populations and recruitment failure. Therefore, to inform fishery management policy for the wild sandfish resource and to guide hatchery-based restocking efforts, a high-resolution genomic audit of Fijian populations was carried out. A total of 6,896 selectively-neutral and 186 putatively-adaptive genome-wide SNPs (DArTseq) together with an independent oceanographic particle dispersal model were used to investigate genetic structure, diversity, signatures of selection, relatedness and connectivity in six wild populations. Three genetically distinct populations were identified with shallow but significant differentiation (average Fst = 0.034, p≤0.05), comprising (1) Lakeba island (Lau archipelago), (2) Macuata (Vanua Levu), and (3) individuals from Yasawa, Ra, Serua island and Kadavu comprising the final unit. Small reductions in allelic diversity were observed in marginal populations in eastern Fiji (overall mean A = 1.956 vs. Lau, A = 1.912 and Macuata, A = 1.939). Signatures of putative local adaptation were also discovered in individuals from Lakeba island, suggesting that they be managed as a discrete unit. An isolation-by-distance model of genetic structure for Fijian sandfish is apparent, with population fragmentation occurring towards the east. Hatchery-based production of juveniles is promising for stock replenishment, however great care is required during broodstock source population selection and juvenile releases into source areas only. The successful use of genomic data here has the potential to be applied to other sea cucumber species in Fiji, and other regions involved in the global BDM trade. While preliminary insights into the genetic structure and connectivity of sandfish in Fiji have been obtained, further local, regional and distribution-wide investigations are required to better inform conservation efforts, wild stock management and hatchery-based restocking interventions for this valuable invertebrate. [ABSTRACT FROM AUTHOR]

The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell. Author summary: A central goal of systems biology is to understand how biomolecules cooperate through direct and indirect relationships. However, indirect relationships have been notoriously hard to quantify. In protein biophysics, there is a long tradition of quantifying indirect allosteric relationships between amino acids using elastic network models (ENMs). Here, we show that ENMs can be adapted to build global dynamic models of the cell and quantify, unbiasedly for the first time, how much individual biomolecules indirectly influence each other. We illustrate how this approach can be used to infer signaling pathways using budding yeast as a proof of concept. We also show that the global dynamic architecture of genetic networks may have evolved to maintain a high potential for indirect cooperative relationships. In budding yeast, human and fission yeast, we identify specific clusters of genes with key roles in distributing (effectors) or receiving (sensors) biological information through indirect relationships. We propose that elastic network modeling of cellular networks is a promising avenue for tackling current challenges in systems biology. [ABSTRACT FROM AUTHOR]

The house mouse or Mus musculus has become a premier mammalian model for genetic research due to its genetic and physiological similarities to humans. It brought mechanistic insights into numerous human diseases and has been routinely used to assess drug efficiency and toxicity, as well as to predict patient responses. To facilitate molecular mechanism studies in mouse, we present the Mouse Interactome Database (MID, Version 1), which includes 155,887 putative functional associations between mouse protein-coding genes inferred from functional association evidence integrated from 9 public databases. These putative functional associations are expected to cover 19.32% of all mouse protein interactions, and 26.02% of these function associations may represent protein interactions. On top of MID, we developed a gene set linkage analysis (GSLA) web tool to annotate potential functional impacts from observed differentially expressed genes. Two case studies show that the MID/GSLA system provided precise and informative annotations that other widely used gene set annotation tools, such as PANTHER and DAVID, did not. Both MID and GSLA are accessible through the website http://mouse.biomedtzc.cn. [ABSTRACT FROM AUTHOR]

Susceptibility to sporadic Parkinson's disease (PD) is thought to be influenced by both genetic and environmental factors and their interaction with each other. Statistical models including multiple variants in axon guidance pathway genes have recently been purported to be capable of predicting PD risk, survival free of the disease and age at disease onset; however the specific models have not undergone independent validation. Here we tested the best proposed risk panel of 23 single nucleotide polymorphisms (SNPs) in two PD sample sets, with a total of 525 cases and 518 controls. By single marker analysis, only one marker was significantly associated with PD risk in one of our sample sets (rs6692804: P = 0.03). Multimarker analysis using the reported model found a mild association in one sample set (two sided P = 0.049, odds ratio for each score change = 1.07) but no significance in the other (two sided P = 0.98, odds ratio = 1), a stark contrast to the reported strong association with PD risk (P = 4.64610-38, odds ratio as high as 90.8). Following a procedure similar to that used to build the reported model, simulated multi-marker models containing SNPs from randomly chosen genes in a genome wide PD dataset produced P-values that were highly significant and indistinguishable from similar models where disease status was permuted (3.13610-23 to 4.90610-64), demonstrating the potential for overfitting in the model building process. Together, these results challenge the robustness of the reported panel of genetic markers to predict PD risk in particular and a role of the axon guidance pathway in PD genetics in general. [ABSTRACT FROM AUTHOR]

Chondroitin/dermatan sulfate (CS/DS) proteoglycans are indispensable for animal development and homeostasis but the large number of enzymes involved in their biosynthesis have made CS/DS function a challenging problem to study genetically. In our study, we generated loss-of-function alleles in zebrafish genes encoding CS/DS biosynthetic enzymes and characterized the effect on development in single and double mutants. Homozygous mutants in chsy1, csgalnact1a, csgalnat2, chpfa, ust and chst7, respectively, develop to adults. However, csgalnact1a-/- fish develop distinct craniofacial defects while the chsy1-/- skeletal phenotype is milder and the remaining mutants display no gross morphological abnormalities. These results suggest a high redundancy for the CS/DS biosynthetic enzymes and to further reduce CS/DS biosynthesis we combined mutant alleles. The craniofacial phenotype is further enhanced in csgalnact1a-/-;chsy1-/- adults and csgalnact1a-/-;csgalnact2-/- larvae. While csgalnact1a-/-;csgalnact2-/- was the most affected allele combination in our study, CS/DS is still not completely abolished. Transcriptome analysis of chsy1-/-, csgalnact1a-/-and csgalnact1a-/-;csgalnact2-/- larvae revealed that the expression had changed in a similar way in the three mutant lines but no differential expression was found in any of fifty GAG biosynthesis enzymes identified. Thus, zebrafish larvae do not increase transcription of GAG biosynthesis genes as a consequence of decreased CS/DS biosynthesis. The new zebrafish lines develop phenotypes similar to clinical characteristics of several human congenital disorders making the mutants potentially useful to study disease mechanisms and treatment. Author summary: The components of the extracellular matrix are crucial for interactions and communication between cells during animal development and disease progression. One major component of the extracellular matrix is chondroitin sulfate/dermatan sulfate (CS/DS) proteoglycans, which support and modify cell functions and tissue homeostasis. The biosynthesis of CS/DS is complex and no genetic models have been developed to specifically reduce CS/DS in the zebrafish model organism. We have used CRISPR/Cas9 technology to knock out key CS/DS biosynthesis genes. We find that knocking out single genes rarely causes major effects on zebrafish morphology and viability, but by combining several knockout alleles we could observe malformations in the zebrafish craniofacial skeleton. In addition, one combination of alleles was embryonic lethal. Our findings describe the role of CS/DS in the development of the head skeleton and give insights in the regulation of genes involved in CS/DS biosynthesis. The zebrafish mutants generated in this study can be used as tools to further study human diseases caused by mutations in CS/DS biosynthesis enzymes. [ABSTRACT FROM AUTHOR]

Background and purpose: This study aimed to analyze the gender-specific association of the filaggrin (FLG) gene polymorphisms with atopic dermatitis (AD) in Caucasians from the central region of Russia. Methods: The study sample consisted of 906 female (including 474 patients with AD and 432 controls) and 406 male (such as 226 patients with AD and 180 controls) participants. Genotyping of ten polymorphisms of the FLG gene was done. The logistic regression was used to analyze the associations. A total of 125 SNPs (seven AD-associated SNPs and 118 proxy SNPs, r2≥0.8) FLG gene were used for the in silico functional annotation analysis in the females. Results: Significant associations were identified between seven SNPs of the FLG gene (rs12130219, rs61816761, rs558269137, rs12144049, rs3126085, rs471144, rs6661961) and AD in females: rs12144049 was associated independent individually (for allele C OR = 1.71, 95%Сl 1.19–2.46, рperm = 0.004 and OR = 1.76, 95%Сl 1.18–2.63, рperm = 0.006 according to the additive and dominant genetic models, respectively) and seven SNPs of the FLG gene within 14 haplotypes. Haplotype GGT [rs61816761-rs3126085-rs12144049] showed the strongest association (OR = 0.55, рperm = 0.001). No association between the analyzed SNPs and AD was determined in the male group. The subsequent bioinformatic analysis predicted the SNPs of the FLG gene that possessed epigenetic and non-synonymous effects, were involved in the control of gene expression and alternative splicing of genes that contribute to AD pathophysiology. Conclusion: Polymorphisms of the FLG gene are associated with AD in females but not in males in the Caucasian population of Central Russia. [ABSTRACT FROM AUTHOR]

Background: Many studies among different ethnic populations suggested that angiotensin converting enzyme (ACE) gene polymorphisms were associated with susceptibility to Alzheimer's disease (AD). However, the results remained inconclusive. In the present meta-analysis, we aimed to clarify the effect of ACE polymorphisms on AD risk using all available relevant data. Methods: Systemic literature searches were performed using PubMed, Embase, Alzgene and China National Knowledge Infrastructure (CNKI). Relevant data were abstracted according to predefined criteria. Results: Totally, 82 independent cohorts from 65 studies were included, focusing on five candidate polymorphisms. For rs1799752 polymorphism, in overall analyses, the insertion (I) allele conferred increased risk to AD compared to the deletion (D) allele (I vs. D: OR = 1.091, 95% CI = 1.007–1.181, p = 0.032); while the I carriers showed increased AD susceptibility compared with the D homozygotes (II + ID vs. DD: OR = 1.131, 95% CI = 1.008–1.270, p = 0.036). However, none of the positive results passed FDR adjustment. In subgroup analysis restricted to late-onset individuals, the associations between rs1799752 polymorphism and AD risk were identified using allelic comparison (OR = 1.154, 95% CI = 1.028–1.295, p = 0.015, FDR = 0.020), homozygotes comparison, dominant model and recessive model (II vs. ID + DD: OR = 1.272, 95% CI = 1.120–1.444, p < 0.001, FDR < 0.001). Nevertheless, no significant association could be revealed after excluding studies not in accordance with Hardy-Weinberg equilibrium (HWE). In North Europeans, but not in East Asians, the I allele demonstrated increased AD susceptibility compared to the D allele (OR = 1.096, 95% CI = 1.021–1.178, p = 0.012, FDR = 0.039). After excluding HWE-deviated cohorts, significant associations were also revealed under homozygotes comparison, additive model (ID vs. DD: OR = 1.266, 95% CI = 1.045–1.534, p = 0.016, FDR = 0.024) and dominant model (II + ID vs. DD: OR = 1.197, 95% CI = 1.062–1.350, p = 0.003, FDR = 0.018) in North Europeans. With regard to rs1800764 polymorphism, significant associations were identified particularly in subgroup of European descent under allelic comparison (T vs. C: OR = 1.063, 95% CI = 1.008–1.120, p = 0.023, FDR = 0.046), additive model and dominant model (TT + TC vs. CC: OR = 1.116, 95% CI = 1.018–1.222, p = 0.019, FDR = 0.046). But after excluding studies not satisfying HWE, all these associations disappeared. No significant associations were detected for rs4343, rs4291 and rs4309 polymorphisms in any genetic model. Conclusions: Our results suggested the significant but modest associations between rs1799752 polymorphism and risk to AD in North Europeans. While rs4343, rs4291 and rs4309 polymorphisms are unlikely to be major factors in AD development in our research. [ABSTRACT FROM AUTHOR]

Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias. Author summary: There is growing evidence that genome-wide association studies capture associations relating to environmental factors, such as indirect effects from parental genotypes. Within-family models such as sibling comparisons can be used to disentangles these different sources of association but are limited by the paucity of sibling data in large biobanks. Within-spouse pair models are a potentially tractable model because spouses share environmental factors in adulthood and may also share early-life environmental factors. Here, we evaluated the application of within-spouse models in genetic association studies, specifically considering assortative mating, a phenomenon whereby individuals may select a phenotypically similar partner. We found that within-spouse pair models can detect genuine confounding in genetic association estimates but are potentially susceptible to collider bias induced by comparing assorted pairs. Within-spouse pair estimates could be useful when combining evidence from different study designs. [ABSTRACT FROM AUTHOR]

Great gerbil (Rhombomys opimus Lichtenstein, 1823) is distributed in Central Asia and some parts of the Middle East. It is widely found in central and northeast parts of Iran with two distinct genetic lineages: R. o. sodalis in the northern slopes of the Elburz Mountains and R. o. sargadensis in the southern slopes. This large rodent acts as the main host of natural focal diseases. No study has surveyed the ecological niche of the lineages and how their distribution might be influenced by different climatic variables. To examine the distribution patterns of this murid rodent, we aimed to determine the habitat preferences and effects of environmental variables on the ecological niche. Using a species distribution approach for modeling of regional niche specialization, suitable habitats predicted for R. o. sodalis were mainly located in Golestan province in northern Iran, along the northern slope of Elburz, while R. o. sargadensis, showed great potential distribution along the southern slope of Elburz and around the Kavir Desert and the Lut Desert. Despite the widest potential distribution of R. o. sargadensis from northeast to northwest and through Central Iran, the geographic range of R. o. sodalis was smaller and mostly confined to Golestan province. The results support the presence of the two genetic lineages of Rhombomys in Iran and confirm that there is no significant niche overlap between the two subspecies. Furthermore, it provided several perspectives for future taxonomic studies and prevention hygiene programs for public health. [ABSTRACT FROM AUTHOR]

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action. Author summary: Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes. [ABSTRACT FROM AUTHOR]

Evolution by natural selection occurs when the frequencies of genetic variants change because individuals differ in Darwinian fitness components such as survival or reproductive success. Differential fitness has been demonstrated in field studies of many organisms, but it remains unclear how well we can quantitatively predict allele frequency changes from fitness measurements. Here, we characterize natural selection on millions of Single Nucleotide Polymorphisms (SNPs) across the genome of the annual plant Mimulus guttatus. We use fitness estimates to calibrate population genetic models that effectively predict allele frequency changes into the next generation. Hundreds of SNPs experienced "male selection" in 2013 with one allele at each SNP elevated in frequency among successful male gametes relative to the entire population of adults. In the following generation, allele frequencies at these SNPs consistently shifted in the predicted direction. A second year of study revealed that SNPs had effects on both viability and reproductive success with pervasive trade-offs between fitness components. SNPs favored by male selection were, on average, detrimental to survival. These trade-offs (antagonistic pleiotropy and temporal fluctuations in fitness) may be essential to the long-term maintenance of alleles. Despite the challenges of measuring selection in the wild, the strong correlation between predicted and observed allele frequency changes suggests that population genetic models have a much greater role to play in forward-time prediction of evolutionary change. Author summary: For the last 100 years, population geneticists have been deriving equations for Δp, the change in allele frequency caused by mutation, selection, migration, and genetic drift. These equations are seldom used directly, to match a prediction for Δp to an observation of Δp. Here, we apply genomic sequencing technologies to samples from natural populations, obtaining millions of observations of Δp. We estimate natural selection on SNPs in a natural population of yellow monkeyflowers and find extensive evidence for selection through differential male success. We use SNP-specific fitness estimates to calibrate a population genetic model that predicts observed Δp into the next generation, and find that when male selection favored one nucleotide, that nucleotide increased in frequency in the next generation. Since neither observed nor predicted Δp are generally large in magnitude, we developed a method called "haplotype matching" to improve prediction accuracy. The method leverages intensive whole genome sequencing of a reference panel (187 individuals) to infer selection in thousands of field individuals sequenced at much lower coverage. This method proved essential to accurately predicting Δp in this experiment and further development may facilitate population genetic prediction in the future. [ABSTRACT FROM AUTHOR]

Curiosity-driven, basic biological research "...performed without thought of practical ends..." establishes fundamental conceptual frameworks for future technological and medical breakthroughs. Traditionally, curiosity-driven research in biological sciences has utilized experimental organisms chosen for their tractability and suitability for studying the question of interest. This approach leverages the diversity of life to uncover working solutions (adaptations) to problems encountered by living things, and evolutionary context as to the extent to which these solutions may be generalized to other species. Despite the well-documented success of this approach, funding portfolios of United States granting agencies are increasingly filled with studies on a few species for which cutting-edge molecular tools are available (genetic model organisms). While this narrow focus may be justified for biomedically-focused funding bodies such as the National Institutes of Health, it is critical that robust federal support for curiosity-driven research using diverse experimental organisms be maintained by agencies such as the National Science Foundation. Using the disciplines of neurobiology and behavioral research as an example, this study finds that NSF grant awards have declined in association with a decrease in the proportion of grants funded for experimental, rather than genetic model organism research. The decline in use of experimental organisms in the literature mirrors but predates the shift grant funding. Today's dominance of genetic model organisms was thus initiated by researchers themselves and/or by publication peer review and editorial preferences, and was further reinforced by pressure from granting agencies, academic employers, and the scientific community. [ABSTRACT FROM AUTHOR]

The predominance of sexual reproduction in eukaryotes remains paradoxical in evolutionary theory. Of the hypotheses proposed to resolve this paradox, the 'Red Queen hypothesis' emphasises the potential of antagonistic interactions to cause fluctuating selection, which favours the evolution and maintenance of sex. Whereas empirical and theoretical developments have focused on host-parasite interactions, the premises of the Red Queen theory apply equally well to any type of antagonistic interactions. Recently, it has been suggested that early multicellular organisms with basic anticancer defences were presumably plagued by antagonistic interactions with transmissible cancers and that this could have played a pivotal role in the evolution of sex. Here, we dissect this argument using a population genetic model. One fundamental aspect distinguishing transmissible cancers from other parasites is the continual production of cancerous cell lines from hosts' own tissues. We show that this influx dampens fluctuating selection and therefore makes the evolution of sex more difficult than in standard Red Queen models. Although coevolutionary cycling can remain sufficient to select for sex under some parameter regions of our model, we show that the size of those regions shrinks once we account for epidemiological constraints. Altogether, our results suggest that horizontal transmission of cancerous cells is unlikely to cause fluctuating selection favouring sexual reproduction. Nonetheless, we confirm that vertical transmission of cancerous cells can promote the evolution of sex through a separate mechanism, known as similarity selection, that does not depend on coevolutionary fluctuations. Recently, it has been suggested that early multicellular organisms with basic anticancer defenses were plagued by antagonistic interactions with transmissible cancers, and that this could have played a pivotal role in the evolution of sex. Here, mathematical models show that coevolution between multicellular organisms and transmissible cancers does not easily favour sexual reproduction. [ABSTRACT FROM AUTHOR]

Background: The association between polymorphisms in vitamin D-binding protein (DBP) gene and the risk of multiple sclerosis (MS) and type 1 diabetes mellitus (T1DM) has been investigated in many studies, but the studies showed controversial results. The rationale for this meta-analysis was to determine whether DBP polymorphisms increases the risk of MS and T1DM by pooling data. Methods: Potentially relevant studies were searched using GWAS Catalog, PubMed, Embase, CNKI and WANFANG databases up to November 2019. The pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were performed to estimate the associations in a fixed-effects or random-effects model. Results: A total of 13 studies were enrolled in this meta-analysis, including eight studies for MS and five for T1DM. The overall results showed that there was no significant association of DBP rs7041 and rs4588 polymorphisms with the risk of MS and T1DM under any genetic model. Similarly, subgroup analysis by ethnicity revealed that no significant association of rs7041 and rs4588 polymorphisms with the risk of MS and T1DM was observed in white or non-white racial groups. Conclusions: This meta-analysis provides evidence that DBP rs7041 and rs4588 polymorphisms may not be associated with an increased risk in MS and T1DM. However, these findings need further validation by larger-scale epidemiological studies and genome-wide association studies (GWASs) in different populations. [ABSTRACT FROM AUTHOR]

Objective: The present study aimed to determine whether the polymorphisms at rs2241766 and rs1501299 on the ADIPOQ gene were related to the susceptibility of type 2 diabetes mellitus (T2DM). Methods: Eight databases, PubMed, GWAS, Embase, Lochrane, Ebsco, CNKI (Chinese National Knowledge Infrastructure), VIP (Viper Database) and ChinaInfo were searched, and a meta-analysis of susceptibility was conducted between SNP45, SNP276 polymorphisms and T2DM. Furthermore, HWE test was conducted to assess the genetic balance of the study, evaluate the quality of Newcastle–Ottawa quality assessment scale (NOS), and establishing allelic, dominant, recessive, heterozygous, and homozygous gene models. Results: This meta-analysis included 53 articles, encompassing 9285 cases with rs2241766 and 14156 controls and 7747 cases with rs1501299 and 10607 controls. For the rs2241766 locus, a significant correlation was found in the three models by the subgroup analysis. Western Asians: dominant gene model (TT + TG vs. GG, P = 0.01); heterozygous gene model (TG vs. GG, P = 0.02); homozygous gene model (TT vs. GG, P = 0.01). South Asians: dominant gene model (TT + TG vs. GG, P = 0.004); heterozygous gene model (TG vs. GG, P = 0.009); homozygous gene model (TT vs. GG, P = 0.005). However, no statistically significant correlation was established among the five genetic models for rs1501299 locus. Conclusion: The findings of the present study indicated that the T allele of rs2241766 polymorphism is the susceptibility locus of T2DM in the West Asian population, but has a protective effect in the South Asian population, albeit further studies are needed in other populations. Also, no association was found between the ADIPOQ rs1501299 polymorphism and T2DM. [ABSTRACT FROM AUTHOR]

A mesoscale network model is proposed for the development of spike and wave discharges (SWDs) in the cortico-thalamo-cortical (C-T-C) circuit. It is based on experimental findings in two genetic models of childhood absence epilepsy–rats of WAG/Rij and GAERS strains. The model is organized hierarchically into two levels (brain structures and individual neurons) and composed of compartments for representation of somatosensory cortex, reticular and ventroposteriomedial thalamic nuclei. The cortex and the two thalamic compartments contain excitatory and inhibitory connections between four populations of neurons. Two connected subnetworks both including relevant parts of a C-T-C network responsible for SWD generation are modelled: a smaller subnetwork for the focal area in which the SWD generation can take place, and a larger subnetwork for surrounding areas which can be only passively involved into SWDs, but which is mostly responsible for normal brain activity. This assumption allows modeling of both normal and SWD activity as a dynamical system (no noise is necessary), providing reproducibility of results and allowing future analysis by means of theory of dynamical system theories. The model is able to reproduce most time-frequency changes in EEG activity accompanying the transition from normal to epileptiform activity and back. Three different mechanisms of SWD initiation reported previously in experimental studies were successfully reproduced in the model. The model incorporates also a separate mechanism for the maintenance of SWDs based on coupling analysis from experimental data. Finally, the model reproduces the possibility to stop ongoing SWDs with high frequency electrical stimulation, as described in the literature. [ABSTRACT FROM AUTHOR]

Cryptic genetic variation could arise from, for example, Gene-by-Gene (G-by-G) or Gene-by-Environment (G-by-E) interactions. The underlying molecular mechanisms and how they influence allelic effects and the genetic variance of complex traits is largely unclear. Here, we empirically explored the role of environmentally influenced epistasis on the suppression and release of cryptic variation by reanalysing a dataset of 4,390 haploid yeast segregants phenotyped on 20 different media. The focus was on 130 epistatic loci, each contributing to segregant growth in at least one environment and that together explained most (69–100%) of the narrow sense heritability of growth in the individual environments. We revealed that the epistatic growth network reorganised upon environmental changes to alter the estimated marginal (additive) effects of the individual loci, how multi-locus interactions contributed to individual segregant growth and the level of expressed genetic variance in growth. The estimated additive effects varied most across environments for loci that were highly interactive network hubs in some environments but had few or no interactors in other environments, resulting in changes in total genetic variance across environments. This environmentally dependent epistasis was thus an important mechanism for the suppression and release of cryptic variation in this population. Our findings increase the understanding of the complex genetic mechanisms leading to cryptic variation in populations, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits in breeding and evolution. Author summary: Many biological traits are polygenic, with complex interplay between underlying genes and the surrounding environment. As a result, individuals with the same allele might have distinctive phenotypes due to differences in the polygenic background and/or the environment. Such differences often create additional genetic variation that is highly relevant to quantitative and evolutionary genetics by limiting our ability to accurately predict the phenotypes in medical or agricultural applications and providing opportunities for long term evolution. Previously, yeast growth regulating genes were found to be organised in large interacting networks. Here, we found that these networks were reorganised upon environmental changes, and that this resulted in altered effect sizes of individual genes, and how the whole network contributed to growth and the level of total genetic variance, providing a basis for future studies on the genetic maintenance of trait robustness and development of genetic models for studying and predicting selection responses for quantitative traits. [ABSTRACT FROM AUTHOR]

The turning points of housing prices play a significant role in the real estate market and economy. However, because multiple factors impact the market, the prediction of the turning points of housing prices faces significant challenges. To solve this problem, in this study, a historical data-based model that incorporates a multi-population genetic algorithm with elitism into the log-periodic power law model is proposed. This model overcomes the weaknesses of multivariate and univariate methods that it does not require any external factors while achieving excellent interpretations. We applied the model to the case study collected from housing prices in Wuhan, China, from December 2016 to October 2018. To verify its reliability, we compared the results of the proposed model to those of the log-periodic power law model optimized by the standard genetic algorithm and simulated annealing, the results of which indicate that the proposed model performs best in terms of prediction. Efficiently predicting and analyzing the housing prices will help the government promulgate effective policies for regulating the real estate market and protect home buyers. [ABSTRACT FROM AUTHOR]

Random regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Weak to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.870.38-0.80). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations. [ABSTRACT FROM AUTHOR]

Perturbation of synapse development underlies many inherited neurodevelopmental disorders including intellectual disability (ID). Diverse mutations on the human TBC1D24 gene are strongly associated with epilepsy and ID. However, the physiological function of TBC1D24 in the brain is not well understood, and there is a lack of genetic mouse model that mimics TBC1D24 loss-of-function for the study of animal behaviors. Here we report that TBC1D24 is present at the postsynaptic sites of excitatory synapses, where it is required for the maintenance of dendritic spines through inhibition of the small GTPase ARF6. Mice subjected to viral-mediated knockdown of TBC1D24 in the adult hippocampus display dendritic spine loss, deficits in contextual fear memory, as well as abnormal behaviors including hyperactivity and increased anxiety. Interestingly, we show that the protein stability of TBC1D24 is diminished by the disease-associated missense mutation that leads to F251L amino acid substitution. We further generate the F251L knock-in mice, and the homozygous mutants show increased neuronal excitability, spontaneous seizure and pre-mature death. Moreover, the heterozygous F251L knock-in mice survive into adulthood but display dendritic spine defects and impaired memory. Our findings therefore uncover a previously uncharacterized postsynaptic function of TBC1D24, and suggest that impaired dendritic spine maintenance contributes to the pathophysiology of individuals harboring TBC1D24 gene mutations. The F251L knock-in mice represent a useful animal model for investigation of the mechanistic link between TBC1D24 loss-of-function and neurodevelopmental disorders. Author summary: Neurons in the brain process and store information via the specialized structures called synapses. Excitatory synapses are located on the dendritic structures called dendritic spines. Despite its small size, each dendritic spine contains a highly complicated signaling network consisting of enormous number of proteins. It is believed that dysfunction of these proteins will affect synaptic transmission and causes cognitive deficits that underlie neurodevelopmental disorders such as intellectual disability (ID). Recently many novel ID-associated gene mutations have been identified. The next crucial step will be to delineate how the gene products function normally in the brain, which will lead to better understanding of the pathophysiology of ID. Human genetic studies have linked numerous mutations on TBC1D24, a gene whose function is not well-defined, to epilepsy and intellectual disability. Here we show that TBC1D24 is localized at excitatory synapses of neurons in the hippocampus, a brain region that is critical to learning and memory. By knocking down TBC1D24 expression through RNA interference or generation of transgenic mice harboring an ID-associated Tbc1d24 missense mutation that destabilizes the protein, we demonstrate that TBC1D24 deficiency impairs the maintenance of dendritic spines in the brain as well as memory formation. This study indicates that synaptic defects underlie the cognitive deficits of individuals carrying TBC1D24 gene mutations. [ABSTRACT FROM AUTHOR]