Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.

In mammalian ovaries, follicular atresia occurs periodically and destroys almost all the follicles in the ovary. Follicle-stimulating hormone (FSH) acts as the primary survival factor during follicular atresia by preventing apoptosis in granulosa cells. FoxO1 is a critical factor in promoting follicular atresia and granulosa cell apoptosis. FSH inhibits the induction of FoxO1. In this report, we investigated the role of FSH-FoxO1 pathway in mouse follicular atresia. FSH dampened stress-induced apoptosis and the expression of FoxO1 and pro-apoptosis genes in mouse granulosa cells (MGCs). In contrast, overexpression of FoxO1 inhibited the viability of MGCs and induced the expression of endogenous FoxO1. The signaling cascades involved in regulating FoxO1 activity upon FSH treatment were identified using FSH signaling antagonists. Blocking protein kinase A (PKA), phosphatidylinositol-3 kinase (PI3K) or protein kinase B (AKT) restored the upregulation of FoxO1 and apoptotic signals, which was suppressed by FSH. Moreover, inhibition of PKA or PI3K impaired FSH-induced AKT activity, but inactivation of PI3K or AKT had little effect on PKA activity in the presence of FSH. Correspondingly, constitutive activation of FoxO1 (all three AKT sites were replaced by alanines) also promoted MGC apoptosis despite FSH administration. Furthermore, both luciferase reporter assays and chromatin immunoprecipitation assays showed that FoxO1 directly bound to a FoxO-recognized element site within the FoxO1 promoter and contributed to the regulation of FoxO1 expression in response to FSH. Taken together, we propose a novel model in which FSH downregulates FoxO1-dependent apoptosis in MGCs by coordinating the PKA–PI3K–AKT–FoxO1 axis and FoxO1–FoxO1 positive feedback.

The beet armyworm, Spodoptera exigua (Hübner), is a worldwide, polyphagous agricultural pest feeding on vegetable, field, and flower crops. However, the lacking of genome architecture severely limits our understanding of its rapid adaptation and the development of efficient pest managements. We report a chromosome-level genome assembly using single-molecule real-time PacBio sequencing and Hi-C data. The final genome assembly was 446.80 Mb with a scaffold N50 of 14.36 Mb, and captured 97.9% complete arthropod Benchmarking Universal Single-Copy Orthologs (n=1,658). A total of 367 contigs were anchored to 32 pseudo-chromosomes, covering 96.18% (429.74 Mb) of the total genome length. We predicted 17,707 protein-coding genes, of which 81.69% were supported by transcriptome evidence and 97.94% matched the UniProt protein records. We also identified 867,102 (147.97 Mb/33.12%) repeats and 1,609 noncoding RNAs. Synteny analyses indicated a strong collinearity between three lepidopteran species. Our high-quality genome information provides a valuable resource for better understanding and management of the beet armyworm.

A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.

AbstractThe family Rhizobiaceae includes many genera of soil bacteria, often isolated for their association with plants. Herein, we investigate the genomic diversity of a group of Rhizobium species and unclassified strains isolated from atypical environments, including seawater, rock matrix or polluted soil. Based on whole-genome similarity and core genome phylogeny, we show that this group corresponds to the genus Pseudorhizobium. We thus reclassify Rhizobium halotolerans, R. marinum, R. flavum and R. endolithicum as P. halotolerans comb. nov., P. marinum comb. nov., P. flavum comb. nov. and R. endolithicum comb. nov., respectively, and show that P. pelagicum is a synonym of P. marinum. We also delineate a new chemolithoautotroph species, P. banfieldiae sp. nov., whose type strain is NT-26T (= DSM 106348T = CFBP 8663T). This genome-based classification was supported by a chemotaxonomic comparison, with gradual taxonomic resolution provided by fatty acid, protein and metabolic profiles. In addition, we used a phylogenetic approach to infer scenarios of duplication, horizontal transfer and loss for all genes in the Pseudorhizobium pangenome. We thus identify the key functions associated with the diversification of each species and higher clades, shedding light on the mechanisms of adaptation to their respective ecological niches. Respiratory proteins acquired at the origin of Pseudorhizobium are combined with clade-specific genes to encode different strategies for detoxification and nutrition in harsh, nutrient-poor environments. Finally, we predict diagnostic phenotypes for the distinction of P. banfieldiae from other Pseudorhizobium species, including autotrophy and sensitivity to the azo dye Congo Red, which we experimentally validated.