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Genome-wide association study (GWAS) entails examining a large number of single nucleotide polymorphisms (SNPs) in a limited sample with hundreds of individuals, implying a variable selection problem in the high dimensional dataset. Although many single-locus GWAS approaches under polygenic background and population structure controls have been widely used, some significant loci fail to be detected. In this study, we used an iterative modified-sure independence screening (ISIS) approach in reducing the number of SNPs to a moderate size. Expectation-Maximization (EM)-Bayesian least absolute shrinkage and selection operator (BLASSO) was used to estimate all the selected SNP effects for true quantitative trait nucleotide (QTN) detection. This method is referred to as ISIS EM-BLASSO algorithm. Monte Carlo simulation studies validated the new method, which has the highest empirical power in QTN detection and the highest accuracy in QTN effect estimation, and it is the fastest, as compared with efficient mixed-model association (EMMA), smoothly clipped absolute deviation (SCAD), fixed and random model circulating probability unification (FarmCPU), and multi-locus random-SNP-effect mixed linear model (mrMLM). To further demonstrate the new method, six flowering time traits in Arabidopsis thaliana were re-analyzed by four methods (New method, EMMA, FarmCPU, and mrMLM). As a result, the new method identified most previously reported genes. Therefore, the new method is a good alternative for multi-locus GWAS.

Since the establishment of the mixed linear model (MLM) method for genome-wide association studies (GWAS) by Zhang et al. [...]FASTmrEMMA detected the most QTNs (29), followed by FarmCPU (19) and LASSO (12), and GEMMA detected the least QTNs (7). [...]a significant number of QTNs were found to coincide with the physical regions of the confidence intervals of reported QTLs, demonstrating the effectiveness and feasibility of multi-locus GWAS methods in RILs. [...]the estimated QTN effect is actually the average effect of allelic substitution, being a + (q − p)d. Let a + (q − p)d = 0, then d = a/(p − q).

Background Ferula L. is one of the largest and most taxonomically complicated genera as well as being an important medicinal plant resource in the family Apiaceae. To investigate the plastome features and phylogenetic relationships of Ferula and its neighboring genera Soranthus Ledeb., Schumannia Kuntze., and Talassia Korovin, we sequenced 14 complete plastomes of 12 species.  Results The size of the 14 complete chloroplast genomes ranged from 165,607 to 167,013 base pairs (bp) encoding 132 distinct genes (87 protein-coding, 37 tRNA, and 8 rRNA genes), and showed a typical quadripartite structure with a pair of inverted repeats (IR) regions. Based on comparative analysis, we found that the 14 plastomes were similar in codon usage, repeat sequence, simple sequence repeats (SSRs), and IR borders, and had significant collinearity. Based on our phylogenetic analyses, Soranthus , Schumannia , and Talassia should be considered synonymous with Ferula . Six highly divergent regions ( rps16 / trnQ-UUG , trnS-UGA / psbZ , psbH / petB , ycf1 / ndhF , rpl32 , and ycf1 ) were also detected, which may represent potential molecular markers, and combined with selective pressure analysis, the weak positive selection gene ccsA may be a discriminating DNA barcode for Ferula species. Conclusion Plastids contain abundant informative sites for resolving phylogenetic relationships. Combined with previous studies, we suggest that there is still much room for improvement in the classification of Ferula . Overall, our study provides new insights into the plastome evolution, phylogeny, and taxonomy of this genus.

Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRMLM) for GWAS. The RMLM simply treats the SNP-effect as random, but it allows a modified Bonferroni correction to be used to calculate the threshold p value for significance tests. The MRMLM is a multi-locus model including markers selected from the RMLM method with a less stringent selection criterion. Due to the multi-locus nature, no multiple test correction is needed. Simulation studies show that the MRMLM is more powerful in QTN detection and more accurate in QTN effect estimation than the RMLM, which in turn is more powerful and accurate than the EMMA. To demonstrate the new methods, we analyzed six flowering time related traits in Arabidopsis thaliana and detected more genes than previous reported using the EMMA. Therefore, the MRMLM provides an alternative for multi-locus GWAS.

Although nonparametric methods in genome-wide association studies (GWAS) are robust in quantitative trait nucleotide (QTN) detection, the absence of polygenic background control in single-marker association in genome-wide scans results in a high false positive rate. To overcome this issue, we proposed an integrated nonparametric method for multi-locus GWAS. First, a new model transformation was used to whiten the covariance matrix of polygenic matrix K and environmental noise. Using the transferred model, Kruskal–Wallis test along with least angle regression was then used to select all the markers that were potentially associated with the trait. Finally, all the selected markers were placed into multi-locus model, these effects were estimated by empirical Bayes, and all the nonzero effects were further identified by a likelihood ratio test for true QTN detection. This method, named pKWmEB, was validated by a series of Monte Carlo simulation studies. As a result, pKWmEB effectively controlled false positive rate, although a less stringent significance criterion was adopted. More importantly, pKWmEB retained the high power of Kruskal–Wallis test, and provided QTN effect estimates. To further validate pKWmEB, we re-analyzed four flowering time related traits in Arabidopsis thaliana, and detected some previously reported genes that were not identified by the other methods.

Background Rapeseed ( Brassica napus L.) and soybean ( Glycine max L.) seeds are rich in both protein and oil, which are major sources of biofuels and nutrition. Although the difference in seed oil content between soybean (~ 20%) and rapeseed (~ 40%) exists, little is known about its underlying molecular mechanism. Results An integrated omics analysis was performed in soybean, rapeseed, Arabidopsis ( Arabidopsis thaliana L. Heynh), and sesame ( Sesamum indicum L.), based on Arabidopsis acyl-lipid metabolism- and carbon metabolism-related genes. As a result, candidate genes and their transcription factors and microRNAs, along with phylogenetic analysis and co-expression network analysis of the PEPC gene family, were found to be largely associated with the difference between the two species. First, three soybean genes ( Glyma.13G148600, Glyma.13G207900 and Glyma.12G122900 ) co-expressed with GmPEPC1 are specifically enriched during seed storage protein accumulation stages, while the expression of BnPEPC1 is putatively inhibited by bna-miR169, and two genes BnSTKA and BnCKII are co-expressed with BnPEPC1 and are specifically associated with plant circadian rhythm, which are related to seed oil biosynthesis. Then, in de novo fatty acid synthesis there are rapeseed-specific genes encoding subunits β-CT ( BnaC05g37990D ) and BCCP1 ( BnaA03g06000D ) of heterogeneous ACCase, which could interfere with synthesis rate, and β-CT is positively regulated by four transcription factors ( BnaA01g37250D, BnaA02g26190D, BnaC01g01040D and BnaC07g21470D ). In triglyceride synthesis, GmLPAAT2 is putatively inhibited by three miRNAs (gma-miR171, gma-miR1516 and gma-miR5775). Finally, in rapeseed there was evidence for the expansion of gene families, CALO, OBO and STERO, related to lipid storage, and the contraction of gene families, LOX, LAH and HSI2, related to oil degradation. Conclusions The molecular mechanisms associated with differences in seed oil content provide the basis for future breeding efforts to improve seed oil content.

Bacterial cellulose (BC) and silk fibroin (SF) double-network hydrogel with high mechanical strength and biocompatibility was synthesized by using BC as the novel polymer substrate to absorb aqueous SF solution as the modifier. Fundamental physical characterizations were carried out by scanning electron microscopy, energy dispersive spectrometry, fourier transform infrared spectrometry, X-ray diffraction and X-ray photoelectron spectroscopy. Thermogravimetric and differential thermal analysis was used to investigate the stability of the hydrogel. Strength and biocompatibility of the BC/SF hydrogel for cartilage tissue engineering were evaluated by tensile strength tester and cell culture experiments. Results reveal that the BC/SF double-network hydrogel was prepared successfully, and the hydrogel could be used as a cartilage repair material in clinical application.Graphic abstract

Background and aims Desert mosses, which are important stabilizers in desert ecosystems, are distributed patchily as biological soil crusts in arid lands. Patch size and microhabitats of moss are affected by human activities in deserts, such as grazing, oil exploration, and reclamation. Moss patches as a microecosystem, is a functional unit. It is not clear how patch size of moss influences the stoichiometry of moss and soil. We examined the effects of moss patches of different sizes on moss and soil stoichiometry, and moss growth strategies in different microhabitats. Methods The dominant moss ( Syntrichia caninervis Mitt.) of biological soil crusts in the Gurbantunggut Desert was selected to study carbon (C), nitrogen (N), and phosphorus (P) contents in moss patches. Soil inside and under the moss patches was analyzed from open areas and under the canopy of living shrubs. The C, N, and P stoichiometry of the moss and soil, and soil enzyme activities were measured. Results The effects of patch size on C, N, and P characteristics were microhabitat dependent. Moss N content in above-ground parts, moss C, N, and P contents in below-ground parts, and sucrase activity in soil under moss patches significantly increased with increase in patch size in open areas. Under the canopy of living shrubs, patch size had no significant influence on moss C, N, and P contents, soil nutrient contents, and soil enzyme activities. Patch size effects were stronger for moss and soil C, N, and P characteristics in open areas than those under the canopy of living shrubs. Moss N and P contents in above-ground parts were significantly higher than those in below-ground parts, whereas moss C:N and C:P ratios in above-ground parts were significantly lower than those in below-ground parts. Moss C, N, and P stoichiometry was weakly correlated with soil nutrient contents and enzyme activities. Structural equation modeling showed that the models for C, N, and P cycling differed between open areas and under the canopy of living shrubs. The effects of patch size on the multifunctionality of the moss microecosystem were regulated by microhabitats. Conclusions Increase in patch size benefits moss growth more in open areas than under the canopy of living shrubs. Shrubs provide a protected environment for moss plants and drive moss growth. Above-ground parts of mosses host more functions essential for growth and photosynthesis than below-ground parts of moss patches in a temperate desert. Patch size accounted for positive effects of moss stoichiometry in open areas. Patch size may influence the ecological function of moss patches. Shrubs dominantly drive moss growth compared with patch size effects.

Background Salt stress significantly reduces soybean yield. To improve salt tolerance in soybean, it is important to mine the genes associated with salt tolerance traits. Results Salt tolerance traits of 286 soybean accessions were measured four times between 2009 and 2015. The results were associated with 740,754 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) using three-variance-component multi-locus random-SNP-effect mixed linear model (3VmrMLM). As a result, eight salt tolerance genes ( GmCHX1 , GsPRX9 , Gm5PTase8 , GmWRKY , GmCHX20a , GmNHX1 , GmSK1 , and GmLEA2-1 ) near 179 significant and 79 suggested QTNs and two salt tolerance genes ( GmWRKY49 and GmSK1 ) near 45 significant and 14 suggested QEIs were associated with salt tolerance index traits in previous studies. Six candidate genes and three gene-by-environment interactions (GEIs) were predicted to be associated with these index traits. Analysis of four salt tolerance related traits under control and salt treatments revealed six genes associated with salt tolerance ( GmHDA13 , GmPHO1 , GmERF5 , GmNAC06 , GmbZIP132 , and GmHsp90s ) around 166 QEIs were verified in previous studies. Five candidate GEIs were confirmed to be associated with salt stress by at least one haplotype analysis. The elite molecular modules of seven candidate genes with selection signs were extracted from wild soybean, and these genes could be applied to soybean molecular breeding. Two of these genes, Glyma06g04840 and Glyma07g18150 , were confirmed by qRT-PCR and are expected to be key players in responding to salt stress. Conclusions Around the QTNs and QEIs identified in this study, 16 known genes, 6 candidate genes, and 8 candidate GEIs were found to be associated with soybean salt tolerance, of which Glyma07g18150 was further confirmed by qRT-PCR.

Although seed oil content and its fatty acid compositions in soybean were affected by environment, QTN-by-environment (QEIs) and gene-by-environment interactions (GEIs) were rarely reported in genome-wide association studies. The 3VmrMLM method was used to associate the trait phenotypes, measured in five to seven environments, of 286 soybean accessions with 106,013 SNPs for detecting QTNs and QEIs. Seven oil metabolism genes ( , and ) around 598 QTNs and one oil metabolism gene GmFATB2B around 54 QEIs were verified in previous studies; 76 candidate genes and 66 candidate GEIs were predicted to be associated with these traits, in which 5 genes around QEIs were verified in other species to participate in oil metabolism, and had differential expression across environments. These genes were found to be related to soybean seed oil content in haplotype analysis. In addition, most candidate GEIs were co-expressed with drought response genes in co-expression network, and three KEGG pathways which respond to drought were enriched under drought stress rather than control condition; six candidate genes were hub genes in the co-expression networks under drought stress. The above results indicated that GEIs, together with drought response genes in co-expression network, may respond to drought, and play important roles in regulating seed oil-related traits together with oil metabolism genes. These results provide important information for genetic basis, molecular mechanisms, and soybean breeding for seed oil-related traits.