A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations.

Although multi-parent populations (MPPs) integrate the advantages of linkage and association mapping populations in the genetic dissection of complex traits and especially combine genetic analysis with crop breeding, it is difficult to detect small-effect quantitative trait loci (QTL) for complex traits in multiparent advanced generation intercross (MAGIC), nested association mapping (NAM), and random-open-parent association mapping (ROAM) populations. To address this issue, here we proposed a multi-locus linear mixed model method, namely mppQTL, to detect QTLs, especially small-effect QTLs, in these MPPs. The new method includes two steps. The first is genome-wide scanning based on a single-locus linear mixed model; the P-values are obtained from likelihood-ratio test, the peaks of negative logarithm P-value curve are selected by group-lasso, and all the selected peaks are regarded as potential QTLs. In the second step, all the potential QTLs are placed on a multi-locus linear mixed model, all the effects are estimated using expectation-maximization empirical Bayes algorithm, and all the non-zero effect vectors are further evaluated via likelihood-ratio test for significant QTLs. In Monte Carlo simulation studies, the new method has higher power in QTL detection, lower false positive rate, lower mean absolute deviation for QTL position estimate, and lower mean squared error for the estimate of QTL size (r2) than existing methods because the new method increases the power of detecting small-effect QTLs. In real dataset analysis, the new method (19) identified five more known genes than the existing three methods (14). This study provides an effective method for detecting small-effect QTLs in any MPPs.

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies.

Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.

A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions.

Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5-3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.

The effect of several activated biochars on Cd immobilization and microbial community composition during in-situ remediation of heavy metal contaminated sediment.

Chemical activation and microwave assisted activation were adopted to modify biochar. Activated biochars were characterized by SEM, BET, FTIR, XRD and XPS. Raw biochar, activated biochars and commercial activated carbon were compared as remediation strategies for sediment from the Xiangjiang River containing 14.70 mg/kg Cd. After the treatment by activated biochar, the overlying water and pore water concentration of Cd decreased by 71% and 49%, respectively. And the threat of heavy metal along with bioavailability of Cd was depressed. Moreover, the immobilsation of Cd in sediment was related to BET surface area and the content of oxygen containing functional groups of activated biochars. Furthermore, a PCR-DGGE-based experiment was performed for the detection of microbial community. The indigenous microbial community was affected and new microbial community appeared after treat by activated biochar. Activated biochar can be used as an inexpensive and efficient in situ remediation material of sediment containing metal.

PID1 in adipocytes modulates whole-body glucose homeostasis.

The novel obesity-associated protein Phosphotyrosine Interaction Domain containing 1 (PID1) inhibits insulin-PI3K/Akt signaling pathway and insulin-stimulated glucose uptake in vitro. In this study, we generated fat tissue-specific aP2-PID1 transgenic (aP2-PID1tg) mice and PID1 knockout (PID1-/-) mice to explore how PID1 affects glucose metabolism in vivo. We observed insulin resistance and impaired insulin-PI3K/Akt signaling in aP2-PID1tg mice. Consistent with these data, the PID1-/- mice displayed improved glucose tolerance and insulin sensitivity under chow diet, with increased Akt phosphorylation in white adipose tissue (WAT). We further demonstrated that PID1 could interact with low density lipoprotein receptor-related protein 1 (LRP1) but not the insulin receptor (IR) in adipocytes, and its overexpression could lead to decreased GLUT4 level. Our results thus indentify PID1 as a critical regulator of glucose metabolism in adipocytes.

Two new phenylpropanoid glycosides from the roots of Aruncus sylvester.

Two new phenylpropanoid glycosides, 1,3,4-tri-O-(E)-caffeoyl-ß-d-glucopyranoside (1) and 1,4-di-O-(E)-caffeoyl-ß-d-glucopyranoside (2), along with four known phenylpropanoid glycosides (3-6), were isolated from the roots of Aruncus sylvester. The structures of 1 and 2 were elucidated using various spectroscopic methods. Compounds 1 and 2 displayed significant scavenging activity of 2,2-diphenyl-1-picrylhydrazyl free radicals with IC50 values of 110 and 258 µM (ascorbic acid: IC50 = 574 µM).