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Genomic selection or genomic prediction (GP) has increasingly become an important molecular breeding technology for crop improvement. GP aims to utilise genome-wide marker data to predict genomic breeding value for traits of economic importance. Though GP studies have been widely conducted in various crop species such as wheat and maize, its application in cotton, an essential renewable textile fibre crop, is still significantly underdeveloped. We aim to develop a new GP-based breeding system that can improve the efficiency of our cotton breeding program. This article presents a GP study on cotton fibre quality and yield traits using 1385 breeding lines from the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia) cotton breeding program which were genotyped using a high-density SNP chip that generated 12,296 informative SNPs. The aim of this study was twofold: (1) to identify the models and data sources (i.e. genomic and pedigree) that produce the highest prediction accuracies; and (2) to assess the effectiveness of GP as a selection tool in the CSIRO cotton breeding program. The prediction analyses were conducted under various scenarios using different Bayesian predictive models. Results highlighted that the model combining genomic and pedigree information resulted in the best cross validated prediction accuracies: 0.76 for fibre length, 0.65 for fibre strength, and 0.64 for lint yield. Overall, this work represents the largest scale genomic selection studies based on cotton breeding trial data. Prediction accuracies reported in our study indicate the potential of GP as a breeding tool for cotton. The study highlighted the importance of incorporating pedigree and environmental factors in GP models to optimise the prediction performance., (© 2022. Crown.)

Background: Clostridioides (clostridium) difficile infection (CDI) is the most common cause of nosocomial diarrheal disease, which has become a public health problem worldwide; gut dysbiosis plays a central role in its pathophysiology. This study conducted a bibliometric analysis of publications on gut microbiota and CDI to summarize the current status of research including research hotspots., Methods: Relevant publications from January 2004 to February 2022 were identified from the Web of Science Core Collection. Three bibliometric tools were used to perform visualization analyses., Results: A total of 1983 publications were analyzed. Annual publications increased from 11 in 2004-237 in 2021, with the US being the leading producer (47.55 % of all papers). EG Pamer had the highest average citations per article (average citations per item = 153.03, H-index = 29). Frontiers in Microbiology published the most papers. The main research foci were "fecal microbiota transplantation," "colonization resistance," and "multidrug-resistant bacteria." The keywords with the highest frequency in recent years include: gut dysbiosis, antibiotic resistance, bile-acids, 16 s sequencing, multidrug-resistant bacteria, and short chain fatty acids., Conclusions: Gut microbiota and CDI is likely to remain a prominent area of research in the foreseeable future. Current research hotspots ("fecal microbiota transplantation," "colonization resistance," and "multidrug-resistant bacteria") should receive even more attention in future studies., Competing Interests: Conflict of Interest The authors declare no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Heritable variation in traits under natural selection is a prerequisite for evolutionary response. While it is recognized that trait heritability may vary spatially and temporally depending on which environmental conditions traits are expressed under, less is known about the possibility that genetic variance contributing to the expected selection response in a given trait may vary at different stages of ontogeny. Specifically, whether different loci underlie the expression of a trait throughout development and thus providing an additional source of variation for selection to act on in the wild, is unclear. Here we show that body size, an important life-history trait, is heritable throughout ontogeny in the nine-spined stickleback ( Pungitius pungitius ). Nevertheless, both analyses of quantitative trait loci and genetic correlations across ages show that different chromosomes/loci contribute to this heritability in different ontogenic time-points. This suggests that body size can respond to selection at different stages of ontogeny but that this response is determined by different loci at different points of development. Hence, our study provides important results regarding our understanding of the genetics of ontogeny and opens an interesting avenue of research for studying age-specific genetic architecture as a source of non-parallel evolution.

Background and Aims: Early-onset colorectal cancer (CRC) is increasing in many developed countries. Type 2 diabetes mellitus has increased substantially in younger adults; however, its role in early-onset CRC remains unidentified., Methods: We conducted a claims-based nested case-control study using IBM MarketScan Commercial Database (2006-2015). Incident early-onset CRC diagnosed at ages 18-49 was identified by the International Classification of Diseases, ninth Revision, Clinical Modification diagnosis code, and the first coded diagnostic pathology date was assigned as the index date. Controls were frequency matched with cases. Type 2 diabetes, stratified by severity, was identified through International Classification of Diseases, ninth Revision, Clinical Modification using the Klabunde algorithm. Multivariable logistic regressions were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs)., Results: A total of 6001 early-onset CRC and 52,104 controls were included. Type 2 diabetes was associated with an increased risk of early-onset CRC (5.0% in cases vs 3.7% in controls; OR = 1.24, 95% CI: 1.09-1.41). The positive association was more pronounced for uncontrolled (OR = 1.37; 95% CI: 1.12-1.67) or complicated (OR = 1.59, 95% CI: 1.08-2.35) type 2 diabetes compared with controlled diabetes (OR = 1.13, 95% CI: 0.94-1.36)., Conclusion: Individuals with type 2 diabetes have a higher risk of early-onset CRC, with stronger associations for uncontrolled diabetes and complicated diabetes. The rising prevalence of type 2 diabetes among younger adults may partially contribute to the increasing incidence of early-onset CRC., (© 2022 The Authors.)

Primary small cell carcinoma of the esophagus (PSCCE) is a lethal neuroendocrine carcinoma. Previous studies proposed a genetic similarity between PSCCE and esophageal squamous cell carcinoma (ESCC) but provided little evidence for differences in clinical course and neuroendocrine differentiation. We perform whole-exome sequencing, RNA sequencing and immunohistochemistry profiling on 46 PSCCE cases. Integrated analyses enable the discovery of multiple mechanisms of RB1 disruption in 98% (45/46) of cases. The transcriptomic landscape of PSCCE closely resembles small cell lung cancer (SCLC) but differs from ESCC or esophageal adenocarcinoma (EAC). Distinct gene expression patterns regulated by ASCL1 and NEUROD1 define two molecular subtypes, PSCCE-A and PSCCE-N, which are highly similar to SCLC subtypes. A T cell excluded phenotype is widely observed in PSCCE. In conclusion, PSCCE has genomic alterations, transcriptome features and molecular subtyping highly similar to SCLC but distinct from ESCC or EAC. These observations are relevant to oncogenesis mechanisms and therapeutic vulnerability.

Comparative studies of quantitative and neutral genetic differentiation ( Q ST - F ST tests) provide means to detect adaptive population differentiation. However, Q ST - F ST tests can be overly liberal if the markers used deflate F ST below its expectation, or overly conservative if methodological biases lead to inflated F ST estimates. We investigated how marker type and filtering criteria for marker selection influence Q ST - F ST comparisons through their effects on F ST using simulations and empirical data on over 18 000 in silico genotyped microsatellites and 3.8 million single-locus polymorphism (SNP) loci from four populations of nine-spined sticklebacks ( Pungitius pungitius ). Empirical and simulated data revealed that F ST decreased with increasing marker variability, and was generally higher with SNPs than with microsatellites. The estimated baseline F ST levels were also sensitive to filtering criteria for SNPs: both minor alleles and linkage disequilibrium (LD) pruning influenced F ST estimation, as did marker ascertainment. However, in the case of stickleback data used here where Q ST is high, the choice of marker type, their genomic location, ascertainment and filtering made little difference to outcomes of Q ST - F ST tests. Nevertheless, we recommend that Q ST - F ST tests using microsatellites should discard the most variable loci, and those using SNPs should pay attention to marker ascertainment and properly account for LD before filtering SNPs. This may be especially important when level of quantitative trait differentiation is low and levels of neutral differentiation high., Competing Interests: We have no competing interests., (© 2019 The Authors.)

Quantitative trait loci (QTL) mapping of wood properties in conifer species has focused on single time point measurements or on trait means based on heterogeneous wood samples (e.g., increment cores), thus ignoring systematic within-tree trends. In this study, functional QTL mapping was performed for a set of important wood properties in increment cores from a 17-yr-old Scots pine (Pinus sylvestris L.) full-sib family with the aim of detecting wood trait QTL for general intercepts (means) and for linear slopes by increasing cambial age. Two multi-locus functional QTL analysis approaches were proposed and their performances were compared on trait datasets comprising 2 to 9 time points, 91 to 455 individual tree measurements and genotype datasets of amplified length polymorphisms (AFLP), and single nucleotide polymorphism (SNP) markers. The first method was a multilevel LASSO analysis whereby trend parameter estimation and QTL mapping were conducted consecutively; the second method was our Bayesian linear mixed model whereby trends and underlying genetic effects were estimated simultaneously. We also compared several different hypothesis testing methods under either the LASSO or the Bayesian framework to perform QTL inference. In total, five and four significant QTL were observed for the intercepts and slopes, respectively, across wood traits such as earlywood percentage, wood density, radial fiberwidth, and spiral grain angle. Four of these QTL were represented by candidate gene SNPs, thus providing promising targets for future research in QTL mapping and molecular function. Bayesian and LASSO methods both detected similar sets of QTL given datasets that comprised large numbers of individuals., (Copyright © 2014 Li et al.)