The Suspended Sediment Flux in Winter in the South of Chengshantou, between the North and South Yellow Sea.

Due to the regional differences between the North and South Yellow Sea, and under the influence of winter winds, the relative changes in the coastal current and the Yellow Sea warm current will lead to the instability of the front, which will lead to the cross-front transport of sediment. Therefore, the study of sediment exchange between the North and South Yellow Sea has become an indispensable part of the study of the Yellow Sea environment. In this study, the current field and sediment concentration in the southern part of Chengshantou, a representative area of the Yellow Sea, were observed in winter in order to analyze the sediment exchange process between the North Yellow Sea and the South Yellow Sea in winter. The observation results show that in the southern sea area of Chengshantou, in winter, the current velocity does not change with the water depth when it exceeds 15 m, and the tides are regular semi-diurnal tides. When the water depth is less than 15 m, the current direction changes clockwise with the increase in the water depth. The turbidity increases rapidly when the wind direction is offshore and the bottom residual current is onshore, which may cause the sediment transported offshore under the action of wind and ocean current to settle under the obstruction of the Yellow Sea warm current, resulting in the rise of bottom turbidity. This also indicates that the change in residual current direction at different water depths may also lead to an increase in suspended sediment concentration. Based on this, in the estuarine area, the relative change in the current direction between the wind current and the coastal current may also be the cause of the change in the maximum turbidity zone.

A study on optimization of pat gene expression cassette for maize transformation.

Phosphinothricin acetyltransferase gene (pat) is an important selectable marker and also a key herbicide trait gene in several commercial products. In maize, the transformation frequency (TF) using pat as a selectable marker is the lowest among the commonly used marker options including epsps, pmi or ppo. Low pat transformation efficiency can become a major bottleneck in our ability to efficiently produce large numbers of events, especially for large molecular stack vectors with multiple trait gene cassettes. The root cause of the lower efficiency of pat in maize is not well understood and it is possible that the causes are multifaceted, including maize genotype, pat marker cassette, trait gene combinations and selection system. In this work we have identified a new variant of pat gene through codon optimization that consistently produced a higher transformation frequency (> 2x) than an old version of the pat gene that has codons optimized for expression in dicot plants. The level of PAT protein in all 16 constructs was also found multifold higher (up to 40 fold) over that of the controls. All of the T0 low copy transgenic plants generated from the 16 different constructs showed excellent tolerance to ammonium glufosinate herbicide spray tests at 4x and 8x recommended field application rates (1x = 595 g active ingredient (ai)/hectare of ammonium glufosinate) in the greenhouse.

Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize.

KEY MESSAGE: Predictive ability derived from gene expression and metabolic information was evaluated using genomic prediction methods based on datasets from a public maize panel. With the rapid development of high throughput biological technologies, information from gene expression and metabolites has received growing attention in plant genetics and breeding. In this study, we evaluated the utility of gene expression and metabolic information for genomic prediction using data obtained from a maize diversity panel. Our results show that, when used as predictor variables, gene expression levels and metabolite abundances provided reasonable predictive abilities relative to those based on genetic markers, although these values were not as large as those with genetic markers. Integrating gene expression levels and metabolite abundances with genetic markers significantly improved predictive abilities in comparison to the benchmark genomic best linear unbiased prediction model using genome-wide markers only. Predictive abilities based on gene expression and metabolites were trait-specific and were affected by the time of measurement and tissue samples as well as the number of genes and metabolites included in the model. In general, our results suggest that, rather than being conventionally used as intermediate phenotypes, gene expression and metabolic information can be used as predictors for genomic prediction and help improve genetic gains for complex traits in breeding programs.

[Evaluation indices of greenhouse gas mitigation technologies in cropland ecosystem].

In spite of the increasing studies on greenhouse gas (GHG) emissions mitigation technologies, there is still a lack of systematic indices for evaluation of their overall impacts in croplands. In this study, we collected all the indices relating to greenhouse gas emissions and analyzed each index following the principles of representativeness, objectivity, completeness, dominance and operability. Finally, we proposed evaluation indices for mitigation technologies based on the current situation of China. Crop yield per unit area was proposed as a constrained index, and greenhouse gas emissions intensity, defined as GHG emissions per unit of produced yield, was proposed as comprehensive index to evaluate the greenhouse effect of various croplands mitigation technologies. Calculation of GHG emissions intensity involved yield, change of soil organic carbon, direct N2O emissions, paddy CH4 emissions and direct and indirect emissions from inputs into croplands. By following these evaluation indices, the greenhouse effect of the technologies could be well evaluated, which could provide scientific basis for their further adoption.

The impact of population structure on genomic prediction in stratified populations.

Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize. Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5% of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65% improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.

Family-based association mapping in crop species.

Identification of allelic variants associated with complex traits provides molecular genetic information associated with variability upon which both artificial and natural selections are based. Family-based association mapping (FBAM) takes advantage of linkage disequilibrium among segregating progeny within crosses and among parents to provide greater power than association mapping and greater resolution than linkage mapping. Herein, we discuss the potential adaption of human family-based association tests and quantitative transmission disequilibrium tests for use in crop species. The rapid technological advancement of next generation sequencing will enable sequencing of all parents in a planned crossing design, with subsequent imputation of genotypes for all segregating progeny. These technical advancements are easily adapted to mating designs routinely used by plant breeders. Thus, FBAM has the potential to be widely adopted for discovering alleles, common and rare, underlying complex traits in crop species.

Accuracy of across-environment genome-wide prediction in maize nested association mapping populations.

Most of previous empirical studies with genome-wide prediction were focused on within-environment prediction based on a single-environment (SE) model. In this study, we evaluated accuracy improvements of across-environment prediction by using genetic and residual covariance across correlated environments. Predictions with a multienvironment (ME) model were evaluated for two corn polygenic leaf structure traits, leaf length and leaf width, based on within-population (WP) and across-population (AP) experiments using a large maize nested association mapping data set consisting of 25 populations of recombinant inbred-lines. To make our study more applicable to plant breeding, two cross-validation schemes were used by evaluating accuracies of (CV1) predicting unobserved phenotypes of untested lines and (CV2) predicting unobserved phenotypes of lines that have been evaluated in some environments but not others. We concluded that (1) genome-wide prediction provided greater prediction accuracies than traditional quantitative trait loci-based prediction in both WP and AP and provided more advantages over quantitative trait loci -based prediction for WP than for AP. (2) Prediction accuracy with ME was significantly greater than that attained by SE in CV1 and CV2, and gains with ME over SE were greater in CV2 than in CV1. These gains were also greater in WP than in AP in both CV1 and CV2. (3) Gains with ME over SE attributed to genetic correlation between environments, with little effect from residual correlation. Impacts of marker density on predictions also were investigated in this study.

[Distribution characteristics of heavy metals in soil profile of facility vegetable fields].

Taking the facility vegetable fields having been planted for 1-12 years and the adjacent wheat land in Shouguang City of Shandong Province as test objects, this paper studied the distribution characteristics of arsenic (As), cadmium (Cd), copper (Cu), zinc (Zn), chromium (Cr), and nickel (Ni) in their soil profiles (0-150 cm). With the increase of soil depth, the test heavy metals contents in the soil profiles all had a decreasing trend, and in the same soil layers, the contents were obviously higher in facility vegetable fields than in wheat land. Comparing with those in the same soil layers of wheat land, the average contents of As, Cd, Cu, Zn, Cr, and Ni in 0-20 and 120-150 cm soil layers of facility vegetable fields were 35.0%, 146.2%, 65.6%, 36.4%, and 21.5%, and 10.6%, 178.5%, 19.4%, 20.2%, 15.2%, and 9.3% higher, respectively, and the average contents of the heavy metals in 20-120 cm soil layer of facility vegetable fields were also higher in some degree. In the 0-20 cm soil layer of facility vegetable fields, the Cd, Cu, and Zn contents had significant positive correlations with planting years (P < 0.05), with the accumulation rates of Cd, Cu, and Zn being 0.027, 1.153, and 2.830 mg x kg(-1) x a(-1), respectively. In facility vegetable fields, the test six heavy metals contents were significantly positively correlated with the contents of soil organic carbon and total nitrogen, and the Cd, Cu, Zn, and Cr contents were significantly correlated with the content of soil total phosphorus (P < 0.01). Applying organic fertilizer could induce the heavy metals accumulation in the soils of facility vegetable fields to some extent.

Identification of Las2, a major modifier gene affecting the Pas1 mouse lung tumor susceptibility locus.

Lung cancer is the leading cause of cancer death worldwide. Here, we describe a genome-wide association study of chemically induced lung tumorigenesis on 593 mice from 21 inbred strains using 115,904 genotyped and 1,952,918 imputed single nucleotide polymorphisms (SNPs). Using a genetic background-controlled genome search, we identified a novel lung tumor susceptibility gene Las2 (Lung adenoma susceptibility 2) on distal chromosome 18. Las2 showed strong association with resistance to tumor induction (rs30245983; P = 1.87 x 10(-9)) as well as epistatic interactions (P = 1.71 x 10(-3)) with the pulmonary adenoma susceptibility 1 locus, a major locus affecting mouse lung tumor development (rs13459098, P = 5.64 x 10(-27)). Sequencing analysis revealed four nonsynonymous SNPs and two insertions/deletions in the susceptible allele of Las2, resulting in the loss of tumor suppressor activities in both cell colony formation and nude mouse tumorigenicity assays. Deletion of LAS2 was observed in approximately 40% of human lung adenocarcinomas, implying that loss of function of LAS2 may be a key step for lung tumorigenesis.

Fine mapping of chromosome 6q23-25 region in familial lung cancer families reveals RGS17 as a likely candidate gene.

PURPOSE: We have previously mapped a major susceptibility locus influencing familial lung cancer risk to chromosome 6q23-25. However, the causal gene at this locus remains undetermined. In this study, we further refined this locus to identify a single candidate gene, by fine mapping using microsatellite markers and association studies using high-density single nucleotide polymorphisms (SNP). EXPERIMENTAL DESIGN: Six multigenerational families with five or more affected members were chosen for fine-mapping the 6q linkage region using microsatellite markers. For association mapping, we genotyped 24 6q-linked cases and 72 unrelated noncancer controls from the Genetic Epidemiology of Lung Cancer Consortium resources using the Affymetrix 500K chipset. Significant associations were validated in two independent familial lung cancer populations: 226 familial lung cases and 313 controls from the Genetic Epidemiology of Lung Cancer Consortium, and 154 familial cases and 325 controls from Mayo Clinic. Each familial case was chosen from one high-risk lung cancer family that has three or more affected members. RESULTS: A region-wide scan across 6q23-25 found significant association between lung cancer susceptibility and three single nucleotide polymorphisms in the first intron of the RGS17 gene. This association was further confirmed in two independent familial lung cancer populations. By quantitative real-time PCR analysis of matched tumor and normal human tissues, we found that RGS17 transcript accumulation is highly and consistently increased in sporadic lung cancers. Human lung tumor cell proliferation and tumorigenesis in nude mice are inhibited upon knockdown of RGS17 levels. CONCLUSION: RGS17 is a major candidate for the familial lung cancer susceptibility locus on chromosome 6q23-25.

Familial aggregation of common sequence variants on 15q24-25.1 in lung cancer.

Three recent genome-wide association studies identified associations between markers in the chromosomal region 15q24-25.1 and the risk of lung cancer. We conducted a genome-wide association analysis to investigate associations between single-nucleotide polymorphisms (SNPs) and the risk of lung cancer, in which we used blood DNA from 194 case patients with familial lung cancer and 219 cancer-free control subjects. We identified associations between common sequence variants at 15q24-25.1 (that spanned LOC123688 [a hypothetical gene], PSMA4, CHRNA3, CHRNA5, and CHRNB4) and lung cancer. The risk of lung cancer was more than fivefold higher among those subjects who had both a family history of lung cancer and two copies of high-risk alleles rs8034191 (odds ratio [OR] = 7.20, 95% confidence interval [CI] = 2.21 to 23.37) or rs1051730 (OR = 5.67, CI = 2.21 to 14.60, both of which were located in the 15q24-25.1 locus, than among control subjects. Thus, further research to elucidate causal variants in the 15q24-25.1 locus that are associated with lung cancer is warranted.

Common human cancer genes discovered by integrated gene-expression analysis.

BACKGROUND: Microarray technology enables a standardized, objective assessment of oncological diagnosis and prognosis. However, such studies are typically specific to certain cancer types, and the results have limited use due to inadequate validation in large patient cohorts. Discovery of genes commonly regulated in cancer may have an important implication in understanding the common molecular mechanism of cancer. METHODS AND FINDINGS: We described an integrated gene-expression analysis of 2,186 samples from 39 studies to identify and validate a cancer type-independent gene signature that can identify cancer patients for a wide variety of human malignancies. The commonness of gene expression in 20 types of common cancer was assessed in 20 training datasets. The discriminative power of a signature defined by these common cancer genes was evaluated in the other 19 independent datasets including novel cancer types. QRT-PCR and tissue microarray were used to validate commonly regulated genes in multiple cancer types. We identified 187 genes dysregulated in nearly all cancerous tissue samples. The 187-gene signature can robustly predict cancer versus normal status for a wide variety of human malignancies with an overall accuracy of 92.6%. We further refined our signature to 28 genes confirmed by QRT-PCR. The refined signature still achieved 80% accuracy of classifying samples from mixed cancer types. This signature performs well in the prediction of novel cancer types that were not represented in training datasets. We also identified three biological pathways including glycolysis, cell cycle checkpoint II and plk3 pathways in which most genes are systematically up-regulated in many types of cancer. CONCLUSIONS: The identified signature has captured essential transcriptional features of neoplastic transformation and progression in general. These findings will help to elucidate the common molecular mechanism of cancer, and provide new insights into cancer diagnostics, prognostics and therapy.

Enhanced lung tumor development in tobacco smoke-exposed p53 transgenic and Kras2 heterozygous deficient mice.

A/J mice bearing either a mutation in the p53 gene or a Kras2 heterozygous deficiency were investigated for their susceptibility to tobacco smoke-induced lung tumorigenesis. Transgenic mice and their wild-type littermates were exposed to mainstream tobacco smoke (MS) for 5 mo, followed by 4 mo of recovery in filtered air. In sham (filtered air) groups, p53 transgenic mice did not exhibit a higher tumor multiplicity but did exhibit larger tumors, with tumor load increased 3.6-fold, when compared with wild-type mice. With exposure to MS, tumor multiplicity was increased 60% but there was a strikingly increased tumor load (15.9-fold) in p53 transgenic mice. Increased tumor load (5.3-fold) but not tumor multiplicity was seen in MS-exposed Kras2 heterozygous deficient mice. Interestingly, MS exposure did not increase benzo[a]pyrene-induced lung tumorigenesis when MS exposure was initiated after BaP treatment. These results indicate that a p53 mutation or loss of a Kras2 allele increases susceptibility to MS-induced lung tumor development.

Large-scale in silico mapping of complex quantitative traits in inbred mice.

Understanding the genetic basis of common disease and disease-related quantitative traits will aid in the development of diagnostics and therapeutics. The processs of gene discovery can be sped up by rapid and effective integration of well-defined mouse genome and phenome data resources. We describe here an in silico gene-discovery strategy through genome-wide association (GWA) scans in inbred mice with a wide range of genetic variation. We identified 937 quantitative trait loci (QTLs) from a survey of 173 mouse phenotypes, which include models of human disease (atherosclerosis, cardiovascular disease, cancer and obesity) as well as behavioral, hematological, immunological, metabolic, and neurological traits. 67% of QTLs were refined into genomic regions <0.5 Mb with approximately 40-fold increase in mapping precision as compared with classical linkage analysis. This makes for more efficient identification of the genes that underlie disease. We have identified two QTL genes, Adam12 and Cdh2, as causal genetic variants for atherogenic diet-induced obesity. Our findings demonstrate that GWA analysis in mice has the potential to resolve multiple tightly linked QTLs and achieve single-gene resolution. These high-resolution QTL data can serve as a primary resource for positional cloning and gene identification in the research community.

EGFR-T790M is a rare lung cancer susceptibility allele with enhanced kinase activity.

The use of tyrosine kinase inhibitors (TKI) has yielded great success in treatment of lung adenocarcinomas. However, patients who develop resistance to TKI treatment often acquire a somatic resistance mutation (T790M) located in the catalytic cleft of the epidermal growth factor receptor (EGFR) enzyme. Recently, a report describing EGFR-T790M as a germ-line mutation suggested that this mutation may be associated with inherited susceptibility to lung cancer. Contrary to previous reports, our analysis indicates that the T790M mutation confers increased Y992 and Y1068 phosphorylation levels. In a human bronchial epithelial cell line, overexpression of EGFR-T790M displayed a growth advantage over wild-type (WT) EGFR. We also screened 237 lung cancer family probands, in addition to 45 bronchoalveolar tumors, and found that none of them contained the EGFR-T790M mutation. Our observations show that EGFR-T790M provides a proliferative advantage with respect to WT EGFR and suggest that the enhanced kinase activity of this mutant is the basis for rare cases of inherited susceptibility to lung cancer.

Identification of a novel tumor suppressor gene p34 on human chromosome 6q25.1.

In this study, we observed loss of heterozygosity (LOH) in human chromosomal fragment 6q25.1 in sporadic lung cancer patients. LOH was observed in 65% of the 26 lung tumors examined and was narrowed down to a 2.2-Mb region. Single-nucleotide polymorphism (SNP) analysis of genes located within this region identified a candidate gene, termed p34. This gene, also designated as ZC3H12D, C6orf95, FLJ46041, or dJ281H8.1, carries an A/G nonsynonymous SNP at codon 106, which alters the amino acid from lysine to arginine. Nearly 73% of heterozygous lung cancer tissues with LOH and the A/G SNP also exhibited loss of the A allele. In vitro clonogenic and in vivo nude mouse studies showed that overexpression of the A allele exerts tumor suppressor function compared with the G allele. p34 is located within a recently mapped human lung cancer susceptibility locus, and association of the p34 A/G SNP was tested among these families. No significant association between the less frequent G allele and lung cancer susceptibility was found. Our results suggest that p34 may be a novel tumor suppressor gene involved in sporadic lung cancer but it seems not to be the candidate familial lung cancer susceptibility gene linked to chromosomal region 6q23-25.

Candidate lung tumor susceptibility genes identified through whole-genome association analyses in inbred mice.

We performed a whole-genome association analysis of lung tumor susceptibility using dense SNP maps ( approximately 1 SNP per 20 kb) in inbred mice. We reproduced the pulmonary adenoma susceptibility 1 (Pas1) locus identified in previous linkage studies and further narrowed this quantitative trait locus (QTL) to a region of less than 0.5 Mb in which at least two genes, Kras2 (Kirsten rat sarcoma oncogene 2) and Casc1 (cancer susceptibility candidate 1; also known as Las1), are strong candidates. Casc1 knockout mouse tumor bioassays showed that Casc1-deficient mice were susceptible to chemical induction of lung tumors. We also found three more genetic loci for lung adenoma development. Analysis of one of these candidate loci identified a previously uncharacterized gene Lasc1, bearing a nonsynonymous substitution (D102E). We found that the Lasc1 Glu102 allele preferentially promotes lung tumor cell growth. Our findings demonstrate the prospects for using dense SNP maps in laboratory mice to refine previous QTL regions and identify genetic determinants of complex traits.

Five loci, SLT1 to SLT5, controlling the susceptibility to spontaneously occurring lung cancer in mice.

A series of linkage studies was previously conducted to identify quantitative trait loci associated with chemically induced lung tumors. However, little is known of genetic susceptibility to spontaneously occurring lung tumorigenesis (SLT) in mice. In this study, we did a whole-genome linkage disequilibrium analysis for susceptibility to SLT in mice using approximately 135,900 single-nucleotide polymorphisms (SNPs) from the Roche and Genomic Institute of the Novartis Research Foundation SNP databases. A common set of 13 mouse strains was used, including 10 resistant strains (129X1/SvJ, AKR/J, C3H/HeJ, C57BL/6J, DBA/2J, NZB/BlnJ, CAST/EiJ, SPRET/EiJ, SM/J, and LP/J) and 3 susceptible strains (A/J, BALB/cJ, and NZW/LaCJ). Fisher exact test was used to assess the association between individual SNPs and susceptibility to SLT. Five regions, SLT1 to SLT5, were mapped on chromosomes 6, 7, 8, 19, and X, respectively. SLT1 to SLT5 showed a significant association with SLT under the empirical threshold (P < or = 0.004) derived from permutation tests. SNP versus SNP association tests indicated that these SLT regions were unlikely to be caused by population substructure. Thus, SLT1 to SLT5 seem to be novel loci controlling the susceptibility to spontaneously occurring lung cancer in mice. Our results provide, for the first time, an insight into the genetic control of spontaneously occurring lung tumorigenesis.

Pooled analysis of loss of heterozygosity in breast cancer: a genome scan provides comparative evidence for multiple tumor suppressors and identifies novel candidate regions.

Somatic loss of heterozygosity (LOH) has been widely reported in breast cancer as a means of identifying putative tumor-suppressor genes. However, individual studies have rarely spanned more than a single chromosome, and the varying criteria used to declare LOH complicate efforts to formally differentiate regions of consistent versus sporadic (random) loss. We report here the compilation of an extensive database from 151 published LOH studies of breast cancer, with summary data from >15,000 tumors and primary allelotypes from >4,300 tumors. Allelic loss was evaluated at 1,168 marker loci, with large variation in the density of informative observations across the genome. Using studies in which primary allelotype information was available, we employed a likelihood-based approach with a formal chromosomal instability and selection model. The approach seeks direct evidence for preferential loss at each locus compared with nearby loci, accounts for heterogeneity across studies, and enables the direct comparison of candidate regions across the genome. Striking preferential loss was observed (in descending order of significance) in specific regions of chromosomes 7q, 16q, 13q, 17p, 8p, 21q, 3p, 18q, 2q, and 19p, as well as other regions, in many cases coinciding with previously identified candidate genes or known fragile sites. Many of these observations were not possible from any single LOH study, and our results suggest that many previously reported LOH results are not systematic or reproducible. Our approach provides a comparative framework for further investigation of regions exhibiting LOH and identifies broad genomic regions for which there exist few data.

Nonparametric disequilibrium mapping of functional sites using haplotypes of multiple tightly linked single-nucleotide polymorphism markers.

As the speed and efficiency of genotyping single-nucleotide polymorphisms (SNPs) increase, using the SNP map, it becomes possible to evaluate the extent to which a common haplotype contributes to the risk of disease. In this study we propose a new procedure for mapping functional sites or regions of a candidate gene of interest using multiple linked SNPs. Based on a case-parent trio family design, we use expectation-maximization (EM) algorithm-derived haplotype frequency estimates of multiple tightly linked SNPs from both unambiguous and ambiguous families to construct a contingency statistic S for linkage disequilibrium (LD) analysis. In the procedure, a moving-window scan for functional SNP sites or regions can cover an unlimited number of loci except for the limitation of computer storage. Within a window, all possible widths of haplotypes are utilized to find the maximum statistic S* for each site (or locus). Furthermore, this method can be applied to regional or genome-wide scanning for determining linkage disequilibrium using SNPs. The sensitivity of the proposed procedure was examined on the simulated data set from the Genetic Analysis Workshop (GAW) 12. Compared with the conventional and generalized TDT methods, our procedure is more flexible and powerful.