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Background: A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling. Methods: We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined). Results: Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models. Conclusion: This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level. [ABSTRACT FROM AUTHOR]

Background: Maternal effects influence juvenile traits such as body weight and early growth in broilers. Ignoring significant maternal effects leads to reduced accuracy and inflated predicted breeding values. Including genetic and environmental direct-maternal covariances into prediction models in broilers can increase the accuracy and limit inflation of predicted breeding values better than simply adding maternal effects to the model. To test this hypothesis, we applied a model accounting for direct-maternal genetic covariance and direct-maternal environmental covariance to estimate breeding values. Results: This model, and simplified versions of it, were tested using simulated broiler populations and then was applied to a large broiler population for validation. The real population analyzed consisted of a commercial line of broilers, for which body weight at a common slaughter age was recorded for 41 selection rounds. The direct-maternal genetic covariance was negative whereas the direct-maternal environmental covariance was positive. Simulated populations were created to mimic the real population. The predictive ability of the models was assessed by cross-validation, where the validation birds were all from the last five selection rounds. Accuracy of prediction was defined as the correlation between the predicted breeding values estimated without the phenotypic records of the validation population and a predictor. The predictors were the breeding values estimated using all the phenotypic information and the phenotypes corrected for the fixed effects, and for the simulated data, the true breeding values. In the real data, adding the environmental covariance, with or without also adding the genetic covariance, increased the accuracy, or reduced deflation of breeding values compared with a model not including dam–offspring covariance. Nevertheless, in the simulated data, reduction in the inflation of breeding values was possible and was associated with a gain in accuracy of up to 6% compared with a model not including both forms of direct-maternal covariance. Conclusions: In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values. [ABSTRACT FROM AUTHOR]

Background: This paper highlights the relationships between economic weights, genetic progress, and phenotypic progress in genomic breeding programs that aim at generating genetic progress in complex, i.e., multi-trait, breeding objectives via a combination of estimated breeding values for different trait complexes. Results: Based on classical selection index theory in combination with quantitative genetic models, we provide a methodological framework for calculating expected genetic and phenotypic progress for all components of a complex breeding objective. We further provide an approach to study the sensitivity of the system to modifications, e.g. to changes in the economic weights. We propose a novel approach to derive the covariance structure of the stochastic errors of estimated breeding values from the observed correlations of estimated breeding values. We define 'realized economic weights' as those weights that would coincide with the observed composition of the genetic trend and show, how they can be calculated. The suggested methodology is illustrated with an index that aims at achieving a breeding goal composed of six trait complexes, that was applied in German Holstein cattle breeding until 2021. Conclusions: Based on the presented results, the main conclusions are (i) the composition of the observed genetic progress matches the expectations well, with predictions being slightly better when the covariance of estimation errors is taken into account; (ii) the composition of the expected phenotypic trend deviates significantly from the expected genetic trend due to the differences in trait heritabilities; and (iii) the realized economic weights derived from the observed genetic trend deviate substantially from the predefined ones, in one case even with a reversed sign. Further results highlight the implications of the change to a modified breeding goal based on the example of a new index comprising eight, partly new, trait complexes, which is used since 2021 in the German Holstein breeding program. The proposed framework and the analytical tools and software provided will be useful to define more rational and generally accepted breeding objectives in the future. [ABSTRACT FROM AUTHOR]

Background: Sesame is a rare example of non-model and minor crop for which numerous genetic loci and candidate genes underlying features of interest have been disclosed at relatively high resolution. These progresses have been achieved thanks to the applications of the genome-wide association study (GWAS) approach. GWAS has benefited from the availability of high-quality genomes, re-sequencing data from thousands of genotypes, extensive transcriptome sequencing, development of haplotype map and web-based functional databases in sesame. Results: In this paper, we reviewed the GWAS methods, the underlying statistical models and the applications for genetic discovery of important traits in sesame. A novel online database SiGeDiD (http://sigedid.ucad.sn/) has been developed to provide access to all genetic and genomic discoveries through GWAS in sesame. We also tested for the first time, applications of various new GWAS multi-locus models in sesame. Conclusions: Collectively, this work portrays steps and provides guidelines for efficient GWAS implementation in sesame, a non-model crop. [ABSTRACT FROM AUTHOR]

Background: To evaluate the association between apolipoprotein B gene polymorphism and coronary heart disease in some populations at home and abroad by means of meta-analysis.Methods: Using the strict exclusion criteria for primary screening of the literature and applying the Hardy-Weinberg equilibrium to test the genetic balance of the selected literature. The corresponding models were selected according to the results of the heterogeneity test. The Begg's test and Egger's test were used to evaluate publication bias, and meta-analysis was performed using Stata 12.0.Results: The study included twelve articles. In the literature, a total of 1596 patients with coronary heart disease and 1431 controls.Meta-analysis results showed no statistical value in the following three genetic models: allelic comparison (a vs A,P = 0.811,OR = 0.95, 95%CI = 0.62-1.46), recessive genetic models (aa vs Aa/AA, P = 0.86,OR = 0.94, 95%CI = 0.45-1.96), or dominant genetic models (aa/Aa vs AA, P = 0.73,OR = 0.92, 95%CI = 0.58-1.47). Subgroup analysis based on ethnicity showed allelic comparison (a vs A,P = 0.464,OR = 1.32, 95%CI = 0.63-2.78), recessive genetic models (aa vs Aa/AA, P = 0.422,OR = 1.52, 95%CI = 0.55-4.21), and dominant genetic models (aa/Aa vs AA, P = 0.551,OR = 1.26, 95%CI = 0.58-2.73) in Asians, allelic comparison (a vs A,P = 0.410,OR = 0.79, 95%CI = 0.45-1.39), recessive genetic models (aa vs Aa/AA, P = 0.041,OR = 0.75,95%CI = 0.57-0.99),dominant genetic models (aa/Aa vs AA, P = 0.385,OR = 0.75, 95%CI = 0.40-1.43) in Caucasian; CONCLUSION: The ApoB(apolipoprotein B) XbaI locus is not a risk factor when it comes to the development of coronary heart disease in the domestic and international populations included in this paper. In Caucasians, people carrying the aa genotype may be less susceptible to CHD (coronary heart disease). The results of recessive genetic models have to take the effect of heterogeneity and sample sizes into account. Further research may require a larger and more rigorous research design. [ABSTRACT FROM AUTHOR]

Background: Gene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms. Results: In this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse. Conclusions: Computer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1. [ABSTRACT FROM AUTHOR]

Background: To investigate the association of vitamin D receptor (VDR) gene polymorphism with caries risk in children(< 18 years). Methods: The electronic databases PubMed, Cochrane, EMBASE, Web of Science, CNKI, Cqvip, and Wanfang were searched for observational studies on the relationship between VDR single nucleotide polymorphism(SNP) and caries, including cohort, case–control, and cross-sectional studies. Quality assessment of selected studies was conducted using the Newcastle Ottawa scale. Odds ratios (OR) with 95% confidence intervals (CI) values for associations of individual VDR SNP with dental caries were calculated based on four genetic models: allelic, recessive, dominant, and over-dominant. Results: Of 79 studies considered, 10 (nine case–control and one cross-sectional) were selected for analysis; the studies involved seven VDR SNPs: ApaI(rs7975232),BsmI(rs1544410),FokI(rs2228570),TaqI(rs731236), TaqI/BglI(rs739837), FokI(rs10735810) and Cdx-2(rs11568820). Alleles C and T of FokI(rs10735810) were significantly differently distributed in the caries and caries-free groups (OR = 1.33, 95% CI: 1.30–2.30, P = 0.03), with CC + CT genotypes at this locus associated with greater risk of developing caries than the TT genotype (OR = 1.87, 95%CI: 1.15–3.04, P = 0.01). Further, TT + CC genotype at TaqI(rs731236) was associated with a 1.33-fold higher risk of caries development than the TC genotype (OR = 1.33, 95%CI: 1.06–1.67, P = 0.02). On subgroup analysis, the association between TaqI(rs731236) and caries risk was affected by dentition type, and ethnicity (permanent dentition: OR = 1.48, 95%CI: 1.07–2.03, P = 0.02; Asian: OR = 1.38, 95%CI: 1.02–1.87, P = 0.03;). Genotype distributions at BsmI(rs1544410), TaqI/BglI(rs739837), FokI(rs2228570), and ApaI(rs7975232) did not differ significantly between the caries and caries-free groups. Conclusions: Caries risk could be associated with TaqI(rs731236) and FokI(rs10735810) genotypes, and TaqI(rs731236) may be a risk factor for permanent teeth caries among Asian people. [ABSTRACT FROM AUTHOR]

Mouse (Mus musculus) models have been heavily utilized in developmental biology research to understand mammalian embryonic development, as mice share many genetic, physiological, and developmental characteristics with humans. New explorations into the integration of temporal (stage-specific) and transcriptional (tissue-specific) data have expanded our knowledge of mouse embryo tissue-specific gene functions. To better understand the substantial impact of synonymous mutational variations in the cell-state-specific transcriptome on a tissue's codon and codon pair usage landscape, we have established a novel resource—Mouse Embryo Codon and Codon Pair Usage Tables (Mouse Embryo CoCoPUTs). This webpage not only offers codon and codon pair usage, but also GC, dinucleotide, and junction dinucleotide usage, encompassing four strains, 15 murine embryonic tissue groups, 18 Theiler stages, and 26 embryonic days. Here, we leverage Mouse Embryo CoCoPUTs and employ the use of heatmaps to depict usage changes over time and a comparison to human usage for each strain and embryonic time point, highlighting unique differences and similarities. The usage similarities found between mouse and human central nervous system data highlight the translation for projects leveraging mouse models. Data for this analysis can be directly retrieved from Mouse Embryo CoCoPUTs. This cutting-edge resource plays a crucial role in deciphering the complex interplay between usage patterns and embryonic development, offering valuable insights into variation across diverse tissues, strains, and stages. Its applications extend across multiple domains, with notable advantages for biotherapeutic development, where optimizing codon usage can enhance protein expression; one can compare strains, tissues, and mouse embryonic stages in one query. Additionally, Mouse Embryo CoCoPUTs holds great potential in the field of tissue-specific genetic engineering, providing insights for tailoring gene expression to specific tissues for targeted interventions. Furthermore, this resource may enhance our understanding of the nuanced connections between usage biases and tissue-specific gene function, contributing to the development of more accurate predictive models for genetic disorders. [ABSTRACT FROM AUTHOR]

Background: Scopoletin and umbelliferone belong to coumarins, which are plant specialized metabolites with potent and wide biological activities, the accumulation of which is induced by various environmental stresses. Coumarins have been detected in various plant species, including medicinal plants and the model organism Arabidopsis thaliana. In recent years, key role of coumarins in maintaining iron (Fe) homeostasis in plants has been demonstrated, as well as their significant impact on the rhizosphere microbiome through exudates secreted into the soil environment. Several mechanisms underlying these processes require clarification. Previously, we demonstrated that Arabidopsis is an excellent model for studying genetic variation and molecular basis of coumarin accumulation in plants. Results: Here, through targeted metabolic profiling and gene expression analysis, the gene-metabolite network of scopoletin and umbelliferone accumulation was examined in more detail in selected Arabidopsis accessions (Col-0, Est-1, Tsu-1) undergoing different culture conditions and characterized by variation in coumarin content. The highest accumulation of coumarins was detected in roots grown in vitro liquid culture. The expression of 10 phenylpropanoid genes (4CL1, 4CL2, 4CL3, CCoAOMT1, C3'H, HCT, F6'H1, F6'H2,CCR1 and CCR2) was assessed by qPCR in three genetic backgrounds, cultured in vitro and in soil, and in two types of tissues (leaves and roots). We not only detected the expected variability in gene expression and coumarin accumulation among Arabidopsis accessions, but also found interesting polymorphisms in the coding sequences of the selected genes through in silico analysis and resequencing. Conclusions: To the best of our knowledge, this is the first study comparing accumulation of simple coumarins and expression of phenylpropanoid-related genes in Arabidopsis accessions grown in soil and in liquid cultures. The large variations we detected in the content of coumarins and gene expression are genetically determined, but also tissue and culture dependent. It is particularly important considering that growing plants in liquid media is a widely used technology that provides a large amount of root tissue suitable for metabolomics. Research on differential accumulation of coumarins and related gene expression will be useful in future studies aimed at better understanding the physiological role of coumarins in roots and the surrounding environments. [ABSTRACT FROM AUTHOR]

Background: Prior research exploring the correlation between the XRCC3 Thr241Met polymorphism and the susceptibility to pancreatic cancer has yielded conflicting outcomes. To date, there has been a notable absence of studies examining this polymorphism. The primary aim of the current investigation is to elucidate the potential role of the XRCC3 Thr241Met polymorphism as a risk factor in the development of pancreatic cancer. Methods: The comprehensive literature search was meticulously conducted across primary databases, including PubMed, Embase, and CNKI (China National Knowledge Infrastructure), spanning from the inception of each database through January 2024. To synthesize the data, a meta-analysis was performed using either a fixed or random-effects model, as appropriate, to calculate the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs). Results: The analysis revealed significant associations between the XRCC3 Thr241Met polymorphism and an increased risk of pancreatic cancer. This was evidenced through various genetic model comparisons: allele contrast (T vs. C: OR = 0.77, 95% CI = 0.70–0.86, P < 0.001), homozygote comparison (TT vs. CC: OR = 0.71, 95% CI = 0.58–0.88, P = 0.001), heterozygote comparison (TC vs. CC: OR = 0.67, 95% CI = 0.52–0.87, P = 0.003), and a dominant genetic model (TT/TC vs. CC: OR = 0.68, 95% CI = 0.57–0.81, P < 0.001). Additionally, subgroup analyses based on ethnicity disclosed that these associations were particularly pronounced in the Caucasian population, with all genetic models showing significance (P < 0.05). Conclusions: The XRCC3 Thr241Met polymorphism has been identified as contributing to a reduced risk of pancreatic cancer in the Caucasian population. This finding underscores the need for further research to validate and expand upon our conclusions, emphasizing the urgency for continued investigations in this domain. [ABSTRACT FROM AUTHOR]

Background: The Cochran-Armitage trend test (CATT) is powerful in detecting association between a susceptible marker and a disease. This test, however, may suffer from a substantial loss of power when the underlying genetic model is unknown and incorrectly specified. Thus, it is useful to derive tests obtaining the plausible power against all common genetic models. For this purpose, the genetic model selection (GMS) and genetic model exclusion (GME) methods were proposed recently. Simulation results showed that GMS and GME can obtain the plausible power against three common genetic models while the overall type I error is well controlled. Results: Although GMS and GME are powerful statistically, they could be seriously affected by known confounding factors such as gender, age and race. Therefore, in this paper, via comparing the difference of Hardy-Weinberg disequilibrium coefficients between the cases and the controls within each sub-population, we propose the stratified genetic model selection (SGMS) and exclusion (SGME) methods which could eliminate the effect of confounding factors by adopting a matching framework. Our goal in this paper is to investigate the robustness of the proposed statistics and compare them with other commonly used efficiency robust tests such as MAX3 and χ² with 2 degrees of freedom (df) test in matched case-control association designs through simulation studies. Conclusion: Simulation results showed that if the mean genetic effect of the heterozygous genotype is between those of the two homozygous genotypes, then the proposed tests and MAX3 are preferred. Otherwise, χ² with 2 df test may be used. To illustrate the robust procedures, the proposed tests are applied to a real matched pair case-control etiologic study of sarcoidosis. [ABSTRACT FROM AUTHOR]

Scientific progress heavily relies on rigorous research, adherence to scientific standards, and transparent reporting. Animal models play a crucial role in advancing biomedical research, especially in the field of gene therapy. Animal models are vital tools in preclinical research, allowing scientists to predict outcomes and understand complex biological processes. The selection of appropriate animal models is critical, considering factors such as physiological and pathophysiological similarities, availability, and ethical considerations. Animal models continue to be indispensable tools in preclinical gene therapy research. Advancements in genetic engineering and model selection have improved the fidelity and relevance of these models. As gene therapy research progresses, careful consideration of animal models and transparent reporting will contribute to the development of effective therapies for various genetic disorders and diseases. This comprehensive review explores the use of animal models in preclinical gene therapy studies for approved products up to September 2023. The study encompasses 47 approved gene therapy products, with a focus on preclinical trials. This comprehensive analysis serves as a valuable reference for researchers in the gene therapy field, aiding in the selection of suitable animal models for their preclinical investigations. [ABSTRACT FROM AUTHOR]

Background: Cyprinidae, the largest fish family, encompasses approximately 367 genera and 3006 species. While they exhibit remarkable adaptability to diverse aquatic environments, it is exceptionally rare to find them in seawater, with the Far Eastern daces being of few exceptions. Therefore, the Far Eastern daces serve as a valuable model for studying the genetic mechanisms underlying seawater adaptation in Cyprinidae. Results: Here, we sequenced the chromosome-level genomes of two Far Eastern daces (Pseudaspius brandtii and P. hakonensis), the two known cyprinid fishes found in seawater, and performed comparative genomic analyses to investigate their genetic mechanism of seawater adaptation. Demographic history reconstruction of the two species reveals that their population dynamics are correlated with the glacial-interglacial cycles and sea level changes. Genomic analyses identified Pseudaspius-specific genetic innovations related to seawater adaptation, including positively selected genes, rapidly evolving genes, and conserved non-coding elements (CNEs). Functional assays of Pseudaspius-specific variants of the prolactin (prl) gene showed enhanced cell adaptation to greater osmolarity. Functional assays of Pseudaspius specific CNEs near atg7 and usp45 genes suggest that they exhibit higher promoter activity and significantly induced at high osmolarity. Conclusions: Our results reveal the genome-wide evidence for the evolutionary adaptation of cyprinid fishes to seawater, offering valuable insights into the molecular mechanisms supporting the survival of migratory fish in marine environments. These findings are significant as they contribute to our understanding of how cyprinid fishes navigate and thrive in diverse aquatic habitats, providing useful implications for the conservation and management of marine ecosystems. [ABSTRACT FROM AUTHOR]

Background: Barley (H. vulgare L.) is an important cereal crop cultivated across various climates globally. Barley and its ancestor (H. vulgare subsp. spontaneum) are an economically valuable model for genetic research and improvement. Drought, among various abiotic stresses, is a substantial threat to agriculture due to its unpredictable nature and significant impact on crop yield. Results: This study was conducted in both greenhouse and laboratory settings. Prior to the study, wild barley accessions were pre-selected based on their sensitivity or tolerance to drought as determined from fieldwork in the 2020–2021 and 2021–2022 cropping seasons. The effects of three levels of drought stress were evaluated (control, 90–95% field capacity [FC]; mild stress, 50–55% FC; and severe stress, 25–30% FC). Several parameters were assessed, including seedling and root growth, enzymatic activity (CAT, SOD, POD), soluble protein levels, chlorophyll content, carotenoids, abaxial and adaxial stomatal density and dimensions, and relative gene expression of Dhn1, SOD, POD, and CAT. Drought stress significantly increased enzyme activities, especially at 25–30% FC, and more in the tolerant genotype. On the other hand, sensitive genotypes showed a notable increase in stomatal density. Under drought stress, there was a general decline in seedling and root growth, protein content, chlorophyll and carotenoids, and stomatal dimensions. Importantly, gene expression analysis revealed that Dhn1, SOD, POD, and CAT were upregulated under drought, with the highest expression levels observed in the drought-tolerant genotype under severe stress conditions (25–30% FC). Conclusions: Our investigation highlights the distinct morphological, physiological, biochemical, and gene-expression profiles of drought-resistant and drought-sensitive wild barley genotypes under varying degrees of drought. [ABSTRACT FROM AUTHOR]

Background: Single nucleotide polymorphisms (SNPs) in the nucleotide-binding domain leucine-rich repeat protein-3 (NLRP3) gene are reported to be linked to many inflammatory disorders. However, uncertainty persists over the associations between these SNPs and susceptibilities to chronic osteomyelitis (COM). This study aimed to investigate potential relationships between NLRP3 gene SNPs and the risks of developing COM in a Chinese Han cohort. Methods: The four tag SNPs of the NLRP3 gene were genotyped in a total of 428 COM patients and 368 healthy controlsusing the SNapShot technique. The genotype distribution, mutant allele frequency, and the four genetic models (dominant, recessive, homozygous, and heterozygous) of the four SNPs were compared between the two groups. Results: A significant association was found between rs10754558 polymorphism and the probability of COM occurence by the heterozygous model (P = 0.037, odds ratio [OR] = 1.541, 95% confidence interval [CI] = 1.025–2.319), indicating that rs10754558 may be associated with a higher risk of developing COM.In addition, possible relationship was found between rs7525979 polymorphism and the risk of COM development by the outcomes of homozygous (P = 0.073, OR = 0.453, 95% CI = 0.187–1.097) and recessive (P = 0.093, OR = 0.478, 95% CI = 0.198–1.151) models, though no statistical differences were obtained. Conclusions: Outcomes of the present study showed, for the first time, that rs10754558 polymorphism of the NLRP3 gene may increase the risk of COM development in this Chinese Han population, with genotype CG as a risk factor. Nonetheless, this conclusion requires verification from further studies with a larger sample size. [ABSTRACT FROM AUTHOR]

Background: Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. Results: Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. Conclusions: LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver. [ABSTRACT FROM AUTHOR]

Background: Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets. Methods: We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent. Results: We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment. Conclusions: Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: This study aimed to screen and validate noise-induced hearing loss (NIHL) associated single nucleotide polymorphisms (SNPs), construct genetic risk prediction models, and evaluate higher-order gene-gene, gene-environment interactions for NIHL in Chinese population. Methods: First, 83 cases and 83 controls were recruited and 60 candidate SNPs were genotyped. Then SNPs with promising results were validated in another case-control study (153 cases and 252 controls). NIHL-associated SNPs were identified by logistic regression analysis, and a genetic risk model was constructed based on the genetic risk score (GRS), and classification and regression tree (CART) analysis was used to evaluate interactions among gene-gene and gene-environment. Results: Six SNPs in five genes were significantly associated with NIHL risk (p < 0.05). A positive dose-response relationship was found between GRS values and NIHL risk. CART analysis indicated that strongest interaction was among subjects with age ≥ 45 years and cumulative noise exposure ≥ 95 [dB(A)·years], without personal protective equipment, and carried GJB2 rs3751385 (AA/AB) and FAS rs1468063 (AA/AB) (OR = 10.038, 95% CI = 2.770, 47.792), compared with the referent group. CDH23, FAS, GJB2, PTPRN2 and SIK3 may be NIHL susceptibility genes. Conclusion: GRS values may be utilized in the evaluation of the cumulative effect of genetic risk for NIHL based on NIHL-associated SNPs. Gene-gene, gene-environment interaction patterns play an important role in the incidence of NIHL. [ABSTRACT FROM AUTHOR]

Background: The association between TCF7L2 and CAPN10 gene polymorphisms and gestational diabetes mellitus (GDM) has been explored in diverse populations across different geographical regions. Yet, most of these studies have been confined to a limited number of loci, resulting in inconsistent findings. In this study, we conducted a comprehensive review of published literature to identify studies examining the relationship between TCF7L2 and CAPN10 gene polymorphisms and the incidence of GDM in various populations. We specifically focused on five loci that were extensively reported in a large number of publications and performed a meta-analysis. Methods: We prioritized the selection of SNPs with well-documented correlations established in existing literature on GDM. We searched eight Chinese and English databases: Cochrane, Elton B. Stephens. Company (EBSCO), Embase, Scopus, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal Database and retrieved all relevant articles published between the inception of the database and July 2022. The Newcastle Ottawa Scale (NOS) was used to evaluate the selected articles, and the odds ratio (OR) was used as the combined effect size index to determine the association between genotypes, alleles, and GDM using different genetic models. Heterogeneity between the studies was quantified and the I2 value calculated. Due to large heterogeneities between different ethnic groups, subgroup analysis was used to explore the correlation between genetic polymorphisms and the incidence of GDM in the different populations. The stability of the results was assessed using sensitivity analysis. Begg's and Egger's tests were used to assess publication bias. Results: A total of 39 articles reporting data on 8,795 cases and 16,290 controls were included in the analysis. The frequency of the rs7901695 genotype was statistically significant between cases and controls in the European population (OR = 0.72, 95% CI: 0.65–0.86) and the American population (OR = 0.61, 95% CI: 0.48–0.77). The frequencies of rs12255372, rs7901695, rs290487, and rs2975760 alleles were also considerably different between the cases and controls in the populations analyzed. Conclusions: rs7903146, rs12255372, rs7901695, rs290487, and rs2975760 were associated with the incidence of GDM in different populations. [ABSTRACT FROM AUTHOR]

Genomic dissection of genetic effects on desirable traits and the subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additive and non-additive effects (dominance, imprinting and epistasis) were evaluated for nine valuable traits related to growth, wood physical and chemical properties, and competitive ability using three pedigree-based and four Genomics-based Best Linear Unbiased Predictions (GBLUP) models and used to determine the genetic model. The predictive ability (PA) of two genomic prediction methods, GBLUP and Reproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classified into two types based on different quantitative genetic architectures: for type I, including wood chemical properties and Pilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive ability and acoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominance and imprinting showed low to moderate contributions (< 36.26%). GBLUP was more suitable for traits of type I (PAs = 0.37–0.39 vs. 0.14–0.25), and RKHS was more suitable for traits of type II (PAs = 0.23–0.37 vs. 0.07–0.23). Non-additive effects make no meaningful contribution to the enhancement of PA of GBLUP method for all traits. These findings enhance our current understanding of the architecture of quantitative traits and lay the foundation for the development of genomic selection strategies in Japanese larch. [ABSTRACT FROM AUTHOR]

Genetic pleiotropy refers to the simultaneous association of a gene with multiple phenotypes. It is widely distributed in the whole genome and can help to understand the common genetic mechanism of diseases or traits. In this study, a multivariate response best-subset selection (MRBSS) model based pleiotropic association analysis method is proposed. Different from the traditional genetic association model, the high-dimensional genotypic data are viewed as response variables while the multiple phenotypic data as predictor variables. Moreover, the response best-subset selection procedure is converted into an 0-1 integer optimization problem by introducing a separation parameter and a tuning parameter. Furthermore, the model parameters are estimated by using the curve search under the modified Bayesian information criterion. Simulation experiments show that the proposed method MRBSS remarkably reduces the computational time, obtains higher statistical power under most of the considered scenarios, and controls the type I error rate at a low level. The application studies in the datasets of maize yield traits and pig lipid traits further verifies the effectiveness. [ABSTRACT FROM AUTHOR]

Background: Genomic sequencing in congenital heart disease (CHD) patients often discovers novel genetic variants, which are classified as variants of uncertain significance (VUS). Functional analysis of each VUS is required in specialised laboratories, to determine whether the VUS is disease causative or not, leading to lengthy diagnostic delays. We investigated stem cell cardiac disease modelling and transcriptomics for the purpose of genetic variant classification using a GATA4 (p.Arg283Cys) VUS in a patient with CHD. Methods: We performed high efficiency CRISPR gene editing with homology directed repair in induced pluripotent stem cells (iPSCs), followed by rapid clonal selection with amplicon sequencing. Genetic variant and healthy matched control cells were compared using cardiomyocyte disease modelling and transcriptomics. Results: Genetic variant and healthy cardiomyocytes similarly expressed Troponin T (cTNNT), and GATA4. Transcriptomics analysis of cardiomyocyte differentiation identified changes consistent with the patient's clinical human phenotype ontology terms. Further, transcriptomics revealed changes in calcium signalling, and cardiomyocyte adrenergic signalling in the variant cells. Functional testing demonstrated, altered action potentials in GATA4 genetic variant cardiomyocytes were consistent with patient cardiac abnormalities. Conclusions: This work provides in vivo functional studies supportive of a damaging effect on the gene or gene product. Furthermore, we demonstrate the utility of iPSCs, CRISPR gene editing and cardiac disease modelling for genetic variant interpretation. The method can readily be applied to other genetic variants in GATA4 or other genes in cardiac disease, providing a centralised assessment pathway for patient genetic variant interpretation. [ABSTRACT FROM AUTHOR]

Background Current robust association tests for case-control genome-wide association study (GWAS) data are mainly based on the assumption of some specific genetic models. Due to the richness of the genetic models, this assumption may not be appropriate. Therefore, robust but powerful association approaches are desirable. Results In this paper, we propose a new approach to testing for the association between the genotype and phenotype for case–control GWAS. This method assumes a generalized genetic model and is based on the selected disease allele to obtain a p-value from the more powerful one-sided test. Through a comprehensive simulation study we assess the performance of the new test by comparing it with existing methods. Some real data applications are also used to illustrate the use of the proposed test. Conclusions Based on the simulation results and real data application, the proposed test is powerful and robust. [ABSTRACT FROM AUTHOR]

Background: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. Results: In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. Conclusions: In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data. [ABSTRACT FROM AUTHOR]

Computing the edit distance between two genomes under certain operations is a basic problem in the study of genome evolution. The double-cut-and-join (DCJ) model has formed the basis for most algorithmic research on rearrangements over the last few years. The edit distance under the DCJ model can be easily computed for genomes without duplicate genes. In this paper, we study the edit distance for genomes with duplicate genes under a model that includes DCJ operations, insertions and deletions. We prove that computing the edit distance is equivalent to finding the optimal cycle decomposition of the corresponding adjacency graph, and give an approximation algorithm with an approximation ratio of 1.5 + L. [ABSTRACT FROM AUTHOR]

Background: In designing genome-wide association (GWA) studies it is important to calculate statistical power. General statistical power calculation procedures for quantitative measures often require information concerning summary statistics of distributions such as mean and variance. However, with genetic studies, the effect size of quantitative traits is traditionally expressed as heritability, a quantity defined as the amount of phenotypic variation in the population that can be ascribed to the genetic variants among individuals. Heritability is hard to transform into summary statistics. Therefore, general power calculation procedures cannot be used directly in GWA studies. The development of appropriate statistical methods and a user-friendly software package to address this problem would be welcomed. Results: This paper presents GWAPower, a statistical software package of power calculation designed for GWA studies with quantitative traits, where genetic effect is defined as heritability. Based on several popular one-degree-of- freedom genetic models, this method avoids the need to specify the non-centrality parameter of the F-distribution under the alternative hypothesis. Therefore, it can use heritability information directly without approximation. In GWAPower, the power calculation can be easily adjusted for adding covariates and linkage disequilibrium information. An example is provided to illustrate GWAPower, followed by discussions. Conclusions: GWAPower is a user-friendly free software package for calculating statistical power based on heritability in GWA studies with quantitative traits. The software is freely available at: http://dl.dropbox.com/u/10502931/GWAPower.zip. [ABSTRACT FROM AUTHOR]

Background: Quest for understanding the nature of mechanisms governing the life span of clonal organisms lasts for several decades. Phylogenetic evidence for recent origins of most clones is usually interpreted as proof that clones suffer from gradual age-dependent fitness decay (e.g. Muller's ratchet). However, we have shown that a neutral drift can also qualitatively explain the observed distribution of clonal ages. This finding was followed by several attempts to distinguish the effects of neutral and non-neutral processes. Most recently, Neiman et al. 2009 (Ann N Y Acad Sci.:1168:185-200.) reviewed the distribution of asexual lineage ages estimated from a diverse array of taxa and concluded that neutral processes alone may not explain the observed data. Moreover, the authors inferred that similar types of mechanisms determine maximum asexual lineage ages in all asexual taxa. In this paper we review recent methods for distinguishing the effects of neutral and non-neutral processes and point at methodological problems related with them. Results and Discussion: We found that contemporary analyses based on phylogenetic data are inadequate to provide any clear-cut answer about the nature and generality of processes affecting evolution of clones. As an alternative approach, we demonstrate that sequence variability in asexual populations is suitable to detect agedependent selection against clonal lineages. We found that asexual taxa with relatively old clonal lineages are characterised by progressively stronger deviations from neutrality. Conclusions: Our results demonstrate that some type of age-dependent selection against clones is generally operational in asexual animals, which cover a wide taxonomic range spanning from flatworms to vertebrates. However, we also found a notable difference between the data distribution predicted by available models of sequence evolution and those observed in empirical data. These findings point at the possibility that processes affecting clonal evolution differ from those described in recent studies, suggesting that theoretical models of asexual populations must evolve to address this problem in detail. Reviewers: This article was reviewed by Isa Schön (nominated by John Logsdon), Arcady Mushegian and Timothy G. Barraclough (nominated by Laurence Hurst). [ABSTRACT FROM AUTHOR]

Background: The cost efficient two-stage design is often used in genome-wide association studies (GWASs) in searching for genetic loci underlying the susceptibility for complex diseases. Replication-based analysis, which considers data from each stage separately, often suffers from loss of efficiency. Joint test that combines data from both stages has been proposed and widely used to improve efficiency. However, existing joint analyses are based on test statistics derived under an assumed genetic model, and thus might not have robust performance when the assumed genetic model is not appropriate. Results: In this paper, we propose joint analyses based on two robust tests, MERT and MAX3, for GWASs under a two-stage design. We developed computationally efficient procedures and formulas for significant level evaluation and power calculation. The performances of the proposed approaches are investigated through the extensive simulation studies and a real example. Numerical results show that the joint analysis based on the MAX3 test statistic has the best overall performance. Conclusions: MAX3 joint analysis is the most robust procedure among the considered joint analyses, and we recommend using it in a two-stage genome-wide association study. [ABSTRACT FROM AUTHOR]

A highly conserved signaling pathway involving insulin-like growth factor 1 (IGF1), and a cascade of intracellular components that mediate its effects, plays a major role in the regulation of skeletal muscle growth. A central component in this cascade is the kinase Akt, also called protein kinase B (PKB), which controls both protein synthesis, via the kinases mammalian target of rapamycin (mTOR) and glycogen synthase kinase 3β (GSK3β), and protein degradation, via the transcription factors of the FoxO family. In this paper, we review the composition and function of this pathway in skeletal muscle fibers, focusing on evidence obtained in vivo by transgenic and knockout models and by muscle transient transfection experiments. Although this pathway is essential for muscle growth during development and regeneration, its role in adult muscle response to mechanical load is less clear. A full understanding of the operation of this pathway could help to design molecularly targeted therapeutics aimed at preventing muscle wasting, which occurs in a variety of pathologic contexts and in the course of aging. [ABSTRACT FROM AUTHOR]

Background: Cerebral stroke (CS) is the leading cause of death in China, and a complex disease caused by both alterable risk factors and genetic factors. This study intended to investigate the association of MMP3, MMP14, and MMP25 single nucleotide polymorphisms (SNPs) with CS risk in a Chinese Han population. Methods: A total of 1,348 Han Chinese were recruited in this case-control study. Four candidate loci including rs520540 A/G and rs679620 T/C of MMP3, rs2236302 G/C of MMP14, and rs10431961 T/C of MMP25 were successfully screened. The correlation between the four SNPs and CS risk was assessed by logistic regression analysis. The results were analyzed by false-positive report probability (FPRP) for chance or significance. The interactions between four SNPs associated with CS risk were assessed by multifactor dimensionality reduction (MDR). Results: rs520540 A/G and rs679620 C/T SNP in MMP3 were associated with risk of CS in allele, codominant, dominant and log-additive models. Ischemic stroke risk were significantly lower in carriers with rs520540-A allele and rs679620-T allele than those with G/G or C/C genotypes. However, rs520540-A allele and rs679620-T allele were associated with higher risk of hemorrhagic stroke. Stratified analysis showed that these two SNPs were associated with reduced risk of CS in aged < 55 years, non-smoking and non-drinking participants, and rs679620 SNP also reduced CS risk in male participants. The levels of uric acid, high-density lipoprotein cholesterol, and eosinophil were different among patients with different genotypes of rs520540 and rs679620. No statistically significant association was found between MMP14 rs2236302 G/C or MMP25 rs10431961 T/C with CS even after stratification by stroke subtypes, age, gender as well as smoking and drinking conditions in all the genetic models. Conclusion: MMP3 rs520540 A/G and rs679620 C/T polymorphisms were associated with CS risk in the Chinese Han population, which provides useful information for the prevention and diagnosis of CS. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR), one of the leading causes of visual impairment and blindness worldwide, is one of the major microvascular complications in diabetes mellitus (DM). Globally, DR prevalence among DM patients is 25%, and 6% have vision-threatening problems among them. With the higher incidence of DM globally, more DR cases are expected to be seen in the future. In order to comprehend the pathophysiological mechanism of DR in humans and discover potential novel substances for the treatment of DR, investigations are typically conducted using various experimental models. Among the experimental models, in vivo models have contributed significantly to understanding DR pathogenesis. There are several types of in vivo models for DR research, which include chemical-induced, surgical-induced, diet-induced, and genetic models. Similarly, for the in vitro models, there are several cell types that are utilised in DR research, such as retinal endothelial cells, Müller cells, and glial cells. With the advancement of DR research, it is essential to have a comprehensive update on the various experimental models utilised to mimic DR environment. This review provides the update on the in vitro, in vivo, and ex vivo models used in DR research, focusing on their features, advantages, and limitations. [ABSTRACT FROM AUTHOR]

Background: Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability (h2) of the microbiome—the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample. Results: We derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates. Conclusions: We discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. DUhQwzbuthLiGAmYiSRiRd Video Abstract [ABSTRACT FROM AUTHOR]

Background: Two polymorphisms, rs6165 and rs6166 located in the intracellular domain of FSHR has been reported to affect folliculogenesis, steroidogenesis and oocyte maturation. Several studies have highlighted the role of FSHR polymorphisms in PCOS but the findings are conflicting. A meta-analysis was carried out to decipher the emerging perspectives. Methodology: A comprehensive literature search was made using PubMed, PCOSkb, and Google Scholar. New Ottawa Scale has been utilized to evaluate the quality of each article. To evaluate the strength of association under different genetic models of rs6165 and rs6166 polymorphisms, odds ratio with a 95% confidence interval (CI) was calculated. Results: A total of 20 articles were selected for the present study. In pooled analysis and after the stratification by ethnicity, polymorphism rs6165 remains unrelated to the onset of PCOS. Besides, rs6166 exhibits significant protection in the Indian population under recessive, additive, and allele models (OR = 0.7, CI: 0.54–0.9, p = 0.006, OR = 0.65, CI: 0.48–0.89, p = 0.006, OR = 0.82, CI: 0.7–0.95, p = 0.01, respectively) and low to moderate risk in the Caucasian population under allele model (OR = 1.17, CI: 1.04–1.32, p = 0.01). Conclusion: This meta-analysis suggests that GG genotype of rs6166 provides protection against PCOS, in a population-specific manner. [ABSTRACT FROM AUTHOR]

Background: The breeding value of a crossbred individual can be expressed as the sum of the contributions from each of the contributing pure breeds. In theory, the breeding value should account for segregation between breeds, which results from the difference in the mean contribution of loci between breeds, which in turn is caused by differences in allele frequencies between breeds. However, with multiple generations of crossbreeding, how to account for breed segregation in genomic models that split the breeding value of crossbreds based on breed origin of alleles (BOA) is not known. Furthermore, local breed proportions (LBP) have been modelled based on BOA and is a concept related to breed segregation. The objectives of this study were to explore the theoretical background of the effect of LBP and how it relates to breed segregation and to investigate how to incorporate breed segregation (co)variance in genomic BOA models. Results: We showed that LBP effects result from the difference in the mean contribution of loci between breeds in an additive genetic model, i.e. breed segregation effects. We found that the (co)variance structure for BS effects in genomic BOA models does not lead to relationship matrices that are positive semi-definite in all cases. However, by setting one breed as a reference breed, a valid (co)variance structure can be constructed by including LBP effects for all other breeds and assuming them to be correlated. We successfully estimated variance components for a genomic BOA model with LBP effects in a simulated example. Conclusions: Breed segregation effects and LBP effects are two alternative ways to account for the contribution of differences in the mean effects of loci between breeds. When the covariance between LBP effects across breeds is included in the model, a valid (co)variance structure for LBP effects can be constructed by setting one breed as reference breed and fitting an LBP effect for each of the other breeds. [ABSTRACT FROM AUTHOR]

Background: Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. Results: We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. Conclusions: The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis is a complex disorder most of which is 'sporadic' of unknown origin but approximately 10% is familial, arising from single mutations in any of more than 30 genes. Thus, there are more than 30 familial ALS subtypes, with different, often unknown, molecular pathologies leading to a complex constellation of clinical phenotypes. We have mouse models for many genetic forms of the disorder, but these do not, on their own, necessarily show us the key pathological pathways at work in human patients. To date, we have no models for the 90% of ALS that is 'sporadic'. Potential therapies have been developed mainly using a limited set of mouse models, and through lack of alternatives, in the past these have been tested on patients regardless of aetiology. Cancer researchers have undertaken therapy development with similar challenges; they have responded by producing complex mouse models that have transformed understanding of pathological processes, and they have implemented patient stratification in multi-centre trials, leading to the effective translation of basic research findings to the clinic. ALS researchers have successfully adopted this combined approach, and now to increase our understanding of key disease pathologies, and our rate of progress for moving from mouse models to mechanism to ALS therapies we need more, innovative, complex mouse models to address specific questions. [ABSTRACT FROM AUTHOR]

Background: Migraine is a complex neurological disorder that is characterized by a "lower threshold of neuronal hyperexcitability" with distinctive periodicity and complex vascular dysfunction. Genetic factors have impacted incredibly on the susceptibility of migraine and one such example is the TNF-α 308G > A. Aim: Therefore, we aim to provide a glimpse of the association of the TNF-α 308G > A risk on the susceptibility of migraine. Method: The pooled odds ratio with the associated 95% of confidence interval were calculated using different genetic models. Heterogeneity was accessed by using Cochran's Q Test and I2 statistics and Begg's and Egger's tests were used for finding the publication bias, tests were two-sided, and a p-value of < 0.05 was considered statistically significant. The Trial Sequential Analysis with Meta-regression Analysis were also utilized to find out the sample size requirement for meta-analysis to avoid type I error and source of heterogeneity respectively. Result: A total of 13 studies with cases: 7193 and controls: 23,091 were included and after using different genetic models, no overall association with migraine and its clinical subtype migraine with aura was observed (Allele model "OR: 1.28, 95% C.I. [0.96–1.69] and OR: 0.99,95% C.I. [0.69–1.42]) respectively. Interestingly, after sub-grouping using the "ethnicity criteria" in the migraine group, it was observed that the allelic genetic model and the dominant model were found to be significantly associated with the Asian ethnic group (OR: 1.79, 95% C.I. [1.13–2.84], and OR: 1.85, 95% C.I. [1.0927; 3.1580]. Conclusion: In conclusion, the present meta-analysis has provided evidence that 308G > A increases the risk of migraine only in the Asian population. [ABSTRACT FROM AUTHOR]

Multi-trait single step genetic evaluation is increasingly facing the situation of having more individuals with genotypes than markers within each genotype. This creates a situation where the genomic relationship matrix (G ) is not of full rank and its inversion is algebraically impossible. Recently, the SS-T-BLUP method was proposed as a modified version of the single step equations, providing an elegant way to circumvent the inversion of the G and therefore accommodate the situation described. SS-T-BLUP uses the Woodbury matrix identity, thus it requires an add-on matrix, which is usually the covariance matrix of the residual polygenic effet. In this paper, we examine the application of SS-T-BLUP to a large-scale multi-trait Australian Angus beef cattle dataset using the full BREEDPLAN single step genetic evaluation model and compare the results to the application of two different methods of using G in a single step model. Results clearly show that SS-T-BLUP outperforms other single step formulations in terms of computational speed and avoids approximation of the inverse of G . [ABSTRACT FROM AUTHOR]

Background: There is large individual variation in both clinical presentation and progression between Parkinson's disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting. Methods: Replicating across three large Parkinson's cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson's study (n = 1807) and Parkinson's Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5–10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors ("phenotypic axes"). Results: When applied to disease severity at diagnosis, the most influential of three phenotypic axes "Axis 1" was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer's disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer's disease genetic risk, and in contrast to Parkinson's disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer's disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson's disease genetics did not. Conclusions: We identify universal axes of Parkinson's disease phenotypic variation which reveal that Parkinson's patients with high concomitant genetic risk for Alzheimer's disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia. [ABSTRACT FROM AUTHOR]

Background: Despite numerous molecular and computational advances, roughly half of patients with a rare disease remain undiagnosed after exome or genome sequencing. A particularly challenging barrier to diagnosis is identifying variants that cause deleterious alternative splicing at intronic or exonic loci outside of canonical donor or acceptor splice sites. Results: Several existing tools predict the likelihood that a genetic variant causes alternative splicing. We sought to extend such methods by developing a new metric that aids in discerning whether a genetic variant leads to deleterious alternative splicing. Our metric combines genetic variation in the Genome Aggregate Database with alternative splicing predictions from SpliceAI to compare observed and expected levels of splice-altering genetic variation. We infer genic regions with significantly less splice-altering variation than expected to be constrained. The resulting model of regional splicing constraint captures differential splicing constraint across gene and exon categories, and the most constrained genic regions are enriched for pathogenic splice-altering variants. Building from this model, we developed ConSpliceML. This ensemble machine learning approach combines regional splicing constraint with multiple per-nucleotide alternative splicing scores to guide the prediction of deleterious splicing variants in protein-coding genes. ConSpliceML more accurately distinguishes deleterious and benign splicing variants than state-of-the-art splicing prediction methods, especially in "cryptic" splicing regions beyond canonical donor or acceptor splice sites. Conclusion: Integrating a model of genetic constraint with annotations from existing alternative splicing tools allows ConSpliceML to prioritize potentially deleterious splice-altering variants in studies of rare human diseases. [ABSTRACT FROM AUTHOR]

Background: Cluster of differentiation 147 (CD147) overexpression plays a key role in the proliferation, differentiation, invasion, metastasis, and prognosis of hepatocellular carcinoma (HCC). The aim of this study was to explore the relationship between rs6757 and the HCC risk in the South Chinese population, and the functional significance of rs6757 by affecting the efficacy of microRNA-3976 (miR-3976) binding to the CD147 3′-UTR. Methods: We performed a retrospective case-control study to analyze the association between rs6757 and the risk of HCC. We chose candidate microRNAs with the potential of interacting with rs6757 through a series of silico analyses. A luciferase reporter gene assay was implemented to detect the binding extent of microRNAs to each polymorphic allele of rs6757. Results: An obvious association between rs6757 and the risk of HCC was detected in C vs. T (OR = 1.826, 95% CI [1.263–2.642]), CC vs. TT (OR = 4.513, 95% CI [1.510–13.489]), dominant genetic model (OR = 1.824, 95% CI [1.120–2.965]), and recessive genetic model (OR = 3.765, 95% CI [1.286–11.020]). Bioinformatics analysis indicated that miR-3976 binding sites containing the rs6757-T allele had lower free energies than those with the C allele, the lower free energies, the higher affinities. Luciferase activity was remarkably decreased by miR-3976 binding to the CD147 3′-UTR bearing rs6757 T allele, which could be reversed by miR-3976 inhibitors. Furthermore, miR-3976 reduced the luciferase expression in a manner of dose-dependent when cotransfected with constructs with the CD147-TT-pSICHECK2. Conclusions: The research we have done suggests that rs6757 confers the CD147 allele-specific translational suppression by miR-3976, which provides a theoretical basis for antineoplastic therapy targeting CD147. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is one of the most important neurodegenerative disorders in elderly people. Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are found in a large proportion of the patients with sporadic and familial PD. Mutations can occur at different locations in the LRRK2. Patients with LRRK2 ROC-COR mutations face an increased risk of typical motor symptoms of PD, along with cognitive decline. An animal model with a monogenic LRRK2 gene mutation is a suitable model for exploring the pathophysiology of PD and identifying potential drug therapies. However, the effect of homozygous (HOM) LRRK2 in PD pathophysiology is unclear. Methods: We established human LRRK2 (hLRRK2) R1441G HOM transgenic (Tg) mice to explore the phenotype and pathological features that are associated with hLRRK2 R1441G Tg mouse models and discuss the potential clinical relevance. The open field test (OFT) was performed to examine motor and nonmotor behaviors. A CatWalk analysis system was used to study gait function. [18F]FDOPA PET was used to investigate functional changes in the nigrostriatal pathway in vivo. Transmission electron microscopy was used to examine the morphological changes in mitochondria and lysosomes in the substantia nigra. Results: The R1441G HOM Tg mice demonstrated gait disturbance and exhibited less anxiety-related behavior and exploratory behavior than mice with hLRRK2 at 12 months old. Additionally, [18F]FDOPA PET showed a reduction in FDOPA uptake in the striatum of the HOM Tg mice. Notably, there was significant lysosome and autophagosome accumulation in the cytoplasm of dopaminergic neurons in R1441G hemizygous (HEM) and HOM mice. Moreover, it was observed using transmission electron microscopy (TEM) that the mitochondria of R1441G Tg mice were smaller than those of hLRRK2 mice. Conclusion: This animal provides a novel HOM hLRRK2 R1441G Tg mouse model that reproduces some phenotype of Parkinsonism in terms of both motor and behavioral dysfunction. There is an increased level of mitochondrial fission and no change in the fusion process in the group of HOM hLRRK2 R1441G Tg mouse. This mutant animal model of PD might be used to study the mechanisms of mitochondrial dysfunction and explore potential new drug targets. [ABSTRACT FROM AUTHOR]

Background Whether the single nucleotide polymorphism (SNP) Lys751Gln of xeroderma pigmentosum group D(XPD) gene increases susceptibility to head and neck cancer (HNC) is controversial and undetermined. Therefore, we conducted this meta-analysis to systematically assess the possible association between them. Methods The OVID, Medline, Embase, Pubmed, Web of Science databases were searched to identify the eligible studies. The odds ratio (OR) with 95% confidence interval (95%CI) were used to assess the strength of association. Results A total of 11,443 subjects from eighteen studies were subjected to meta-analysis. Overall, XPD Lys751Gln polymorphism had no association with increased HNC risk under all five genetic models (P > 0.05). In the subgroup analysis by ethnicity and source of controls, still no significant association was found under five genetic models (P > 0.05). In the subgroup analysis by cancer type, XPD Lys751Gln polymorphism had statistically significant association with elevated laryngeal cancer (LC) and nasopharyngeal cancer (NPC) risk under heterozygous comparison and dominant model (P<0.05) and borderline significantly increased risk was found under allele contrast for LC and NPC. Carriers of Lys allele and Lys/Lys genotype may be associated with elevated LC and NPC risk. Conclusions There is overall lack of association between XPD Lys751Gln polymorphism and HNC risk under all five genetic models and still no significant association was found in the subgroup analysis by ethnicity and source of controls. However, XPD Lys751Gln polymorphism was significantly associated with susceptibility to LC and NPC and the Lys allele and Lys/Lys genotype of XPD Lys751Gln polymorphism may be a risk factor for LC and NPC. However, relatively modest sample sizes were included in this meta-analysis and studies with large sample sizes and representative population are warranted to further clarify this finding. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/5628716106316015. [ABSTRACT FROM AUTHOR]

Background: In animal genetics, linear mixed models are used to deal with genetic and environmental effects. The variance and covariance terms of these models are usually estimated by restricted maximum likelihood (REML), which provides unbiased estimators. A strong hypothesis of REML estimation is the multi-normality of the response variables. However, in practice, even if the marginal distributions of each phenotype are normal, the multi-normality assumption may be violated by non-normality of the cross-sectional dependence structure, that is to say when the copula of the multivariate distribution is not Gaussian. This study uses simulations to evaluate the impact of copula miss-specification in a bivariate animal model on REML estimations of variance components. Result: Bivariate phenotypes were simulated for populations undergoing selection, considering different copulas for the dependence structure between the error components. Two multi-trait situations were considered: two phenotypes were measured on the selection candidates, or only one phenotype was measured on the selection candidates. Three generations with random selection and five generations with truncation selection based on estimated breeding values were simulated. When selection was performed at random, no significant differences were observed between the REML estimations of variance components and the true parameters even for the non-Gaussian distributions. For the truncation selections, when two phenotypes were measured on candidates, biases were systematically observed in the variance components for high residual dependence in the case of non-Gaussian distributions, especially in the case of a heavy-tailed or asymmetric distribution when the two traits were measured. Conversely, when only one phenotype was measured on candidates, no difference was observed between the Gaussian and non-Gaussian distributions in REML estimations. Conclusions: This study confirms that REML can be used by geneticists to evaluate breeding values in the multivariate case even if the multivariate phenotypes deviate from normality in the situation of random selection or if one trait is not measured for the candidate under selection. Nevertheless, when the two traits are measured, the violation of the normality assumption may lead to non-negligible biases in the REML estimations of the variance-covariance components. [ABSTRACT FROM AUTHOR]

Background: The objective of the present study was to investigate how variation in the faecal microbial composition is associated with variation in average daily gain (ADG), backfat thickness (BFT), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI), using data from two experimental pig lines that were divergent for feed efficiency. Estimates of microbiability were obtained by a Bayesian approach using animal mixed models. Microbiome-wide association analyses (MWAS) were conducted by single-operational taxonomic units (OTU) regression and by back-solving solutions of best linear unbiased prediction using a microbiome covariance matrix. In addition, accuracy of microbiome predictions of phenotypes using the microbiome covariance matrix was evaluated. Results: Estimates of heritability ranged from 0.31 ± 0.13 for FCR to 0.51 ± 0.10 for BFT. Estimates of microbiability were lower than those of heritability for all traits and were 0.11 ± 0.09 for RFI, 0.20 ± 0.11 for FCR, 0.04 ± 0.03 for DFI, 0.03 ± 0.03 for ADG, and 0.02 ± 0.03 for BFT. Bivariate analyses showed a high microbial correlation of 0.70 ± 0.34 between RFI and FCR. The two approaches used for MWAS showed similar results. Overall, eight OTU with significant or suggestive effects on the five traits were identified. They belonged to the genera and families that are mainly involved in producing short-chain fatty acids and digestive enzymes. Prediction accuracy of phenotypes using a full model including the genetic and microbiota components ranged from 0.60 ± 0.19 to 0.78 ± 0.05. Similar accuracies of predictions of the microbial component were observed using models that did or did not include an additive animal effect, suggesting no interaction with the genetic effect. Conclusions: Our results showed substantial associations of the faecal microbiome with feed efficiency related traits but negligible effects with growth traits. Microbiome data incorporated as a covariance matrix can be used to predict phenotypes of animals that do not (yet) have phenotypic information. Connecting breeding environment between training sets and predicted populations could be necessary to obtain reliable microbiome predictions. [ABSTRACT FROM AUTHOR]

Epilepsy has been extensively studied as a common neurological disease. Efforts have been made on rodent and other animal models to reveal the pathogenic mechanisms of epilepsy and develop new drugs for treatment. However, the features of current epilepsy models cannot fully mimic different types of epilepsy in humans, hence non-human primate models of epilepsy are required. The common marmoset (Callithrix jacchus) is a New World monkey that is widely used to study brain function. Here, we present a natural marmoset model of generalized epilepsy. In this unique marmoset family, generalized epilepsy was successfully induced by handling operations in some individuals. We mapped the marmoset family with handling-sensitive epilepsy and found that the epileptic phenotype can be inherited. These marmosets were more sensitive to the epilepsy inducers pentylenetetrazol. Using electrocorticogram (ECoG) recordings, we detected epileptiform discharge in marmosets with a history of seizures. In summary, we report a family of marmosets with generalized seizures induced by handling operations. This epileptic marmoset family provides insights to better understand the mechanism of generalized epilepsy and helps to develop new therapeutic methods. [ABSTRACT FROM AUTHOR]

Background: Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.Methods: We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.Results: We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.Conclusions: Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed. [ABSTRACT FROM AUTHOR]

Aim: The association of polymorphisms in the three genes of SOCS3, JAK2 and STAT3 with genetic susceptibility to type 2 diabetes mellitus (T2DM) was explored, and its interaction with environmental factors such as hypertension and triglycerides was analyzed. Methods: The Hardy–Weinberg balance test was used to analyze the random balance of genes in the population. The analysis of the association of SNPs with T2DM was performed using Pearson's chi-square test. Haplotype frequency distribution, SNPs-SNPs interaction and environmental factors were analyzed by chi-square test and logistic regression. Results: The genotype distribution of SNPs rs2280148 of the SOCS3 gene was statistically significant. The allele frequency distribution of SNPs (rs4969168/rs2280148) was statistically different. After covariate correction, the SOCS3 gene locus (rs4969168) showed an association with T2DM in additive model, while the rs2280148 locus showed an association with T2DM in all three models. The locus (rs10974914/rs10815157) allele and genotype frequency distribution of JAK2 were statistically significant. After covariate correction, two SNPs in the gene showed association with T2DM in both additive and recessive models. The distribution of genotype frequencies of SNPs rs1053005 locus in gene STAT3 was statistically significant between the two groups. In recessive genetic models, rs1053005 locus polymorphisms was associated with T2DM. Haplotype S3 (G G)/S 4 (G T) of the SOCS3 gene as well as haplotype J2 (A G)/J 3 (G C) of the JAK2 gene were closely associated with T2DM. There was an interaction between SNPs rs4969168 and SNPs rs2280148 in the SOCS3 gene. There was an interaction between the SOCS3, JAK2 and STAT3 genes and hypertension/triglycerides. Conclusion: The SOCS3 and JAK2 genes may be associated with T2DM in the Chinese population, in which SNPs carrying the A allele (rs4969168)/G allele (rs2280148)/C allele (rs10815157) have a reduced risk of T2DM. Haplotype S3 (G G)/S 4 (G T) of the SOCS3 gene and haplotype J2 (A G)/J 3 (G C) of the JAK2 gene may be influencing factor for T2DM. The interaction between SNPs rs4969168 and SNPs rs2280148 increases the risk of T2DM. Hypertension and triglycerides may interact with SNPs of T2DM susceptibility genes. [ABSTRACT FROM AUTHOR]

Background: In the comparative genomics field, one of the goals is to estimate a sequence of genetic changes capable of transforming a genome into another. Genome rearrangement events are mutations that can alter the genetic content or the arrangement of elements from the genome. Reversal and transposition are two of the most studied genome rearrangement events. A reversal inverts a segment of a genome while a transposition swaps two consecutive segments. Initial studies in the area considered only the order of the genes. Recent works have incorporated other genetic information in the model. In particular, the information regarding the size of intergenic regions, which are structures between each pair of genes and in the extremities of a linear genome. Results and conclusions: In this work, we investigate the sorting by intergenic reversals and transpositions problem on genomes sharing the same set of genes, considering the cases where the orientation of genes is known and unknown. Besides, we explored a variant of the problem, which generalizes the transposition event. As a result, we present an approximation algorithm that guarantees an approximation factor of 4 for both cases considering the reversal and transposition (classic definition) events, an improvement from the 4.5-approximation previously known for the scenario where the orientation of the genes is unknown. We also present a 3-approximation algorithm by incorporating the generalized transposition event, and we propose a greedy strategy to improve the performance of the algorithms. We performed practical tests adopting simulated data which indicated that the algorithms, in both cases, tend to perform better when compared with the best-known algorithms for the problem. Lastly, we conducted experiments using real genomes to demonstrate the applicability of the algorithms. [ABSTRACT FROM AUTHOR]

Background: Quinoa (Chenopodium quinoa) is a high-value grain known for its excellent nutritional balance. It is an allotetraploid species (AABB, 2n = 4x = 36) formed by the hybridization between AA and BB genome diploid (2n = 2x = 18) species. This study reports genetic studies in Chenopodium ficifolium as a potential B genome diploid model system to simplify the genetic studies of quinoa including gene identification and marker-assisted breeding. Results: Portsmouth, New Hampshire and Quebec City, Quebec accessions of C. ficifolium were used to develop an F2 population segregating for agronomically relevant traits including flowering time, plant height, the number of branches, branch angle, and internode length. Marker-trait associations were identified for the FLOWERING LOCUS T-LIKE 1 (FTL1) marker gene, where the alternate alleles (A1/A2) were segregating among the F2 generation plants in association with flowering time, plant height, and the number of branches. There was a strong correlation of the flowering time trait with both plant height and the number of branches. Thus, a possible multifaceted functional role for FTL1 may be considered. The parental Portsmouth and Quebec City accessions were homozygous for the alternate FTL1 alleles, which were found to be substantially diverged. SNPs were identified in the FTL1 coding sequence that could have some functional significance in relation to the observed trait variation. Conclusion: These results draw further attention to the possible functional roles of the FTL1 locus in Chenopodium and justify continued exploration of C. ficifolium as a potential diploid model system for the genetic study of quinoa. We expect our findings to aid in quinoa breeding as well as to any studies related to the Chenopodium genus. [ABSTRACT FROM AUTHOR]