Input of Cd from agriculture phosphate fertilizer application in China during 2006-2016.

Phosphate fertilizer applications are an important source of soil Cd in China. However, the input of Cd from phosphate fertilizer has always been neglected in China because of its low content. In this paper, we calculated the Cd input from phosphate fertilizer in China during 2006-2016. According to the data, the total phosphate fertilizer consumption and agriculture application rate tended to decrease after 2014. In 2016, the phosphate fertilizer application rate ranged from 12.14 to 99.38 kg/ha with a mean value of 42.70 kg/ha, and excessive fertilizer application occurred in Xinjiang, Henan, and Hubei Provinces. The Cd content in phosphate fertilizer was 0.75 mg/kg based on 1222 samples. The national Cd input from phosphate fertilizer was estimated to be 10.52 tons in 2016, with DAP/MAP being the largest contributor, accounting for 83.31% of the total input. These findings demonstrate the necessity of performing assessments to regulate the utilization of phosphate fertilizer in China, especially in Henan and Xinjiang Provinces.

LEP: A Statistical Method Integrating Individual-Level and Summary-Level Data of the Same Trait From Different Populations.

Statistical approaches for integrating multiple data sets in genome-wide association studies (GWASs) are increasingly important. Proper utilization of more relevant information is expected to improve statistical efficiency in the analysis. Among these approaches, LEP was proposed for joint analysis of individual-level data and summary-level data in the same population by leveraging pleiotropy. The key idea of LEP is to explore correlation of the association status among different data sets while accounting for the heterogeneity. In this commentary, we show that LEP is applicable to integrate individual-level data and summary-level data of the same trait from different populations, providing new insights into the genetic architecture of different populations.

Assessing heavy metal pollution in paddy soil from coal mining area, Anhui, China.

Heavy metal pollution in agricultural soil has negative impact on crop quality and eventually on human health. A total of 24 top soil samples were collected from paddy field near the Zhangji Coal Mine in Huainan City, Anhui Province. Seven heavy metals (Cu, Zn, As, Cr, Cd, Pb, and Ni) were selected to evaluate the pollution status through total content and chemical speciation, geo-accumulation index (Igeo), and risk assessment code (RAC) and investigate leaching behavior of heavy metals under simulated rainfall. The results of present study indicated that mining activities were responsible for elevated Cu and Cd in surrounding paddy soil. Based on the results of chemical speciation, most heavy metals were associated with the residual fraction, and the environmental risk of heavy metals in soil was sequenced as Pb > Cd > Ni > As > Zn > Cu > Cr. It revealed that Pb in soil would pose a higher environmental risk due to its higher reducible fraction, then followed by Cd, Ni, As, and Zn, which would pose a medium risk. The result of simulated rainfall leaching analysis showed that heavy metals could be categorized into two groups: concentrations of Cu, Ni, Cd, Zn, and Cr in the leachates displayed a continuous decrease tendency with the increase in accumulative simulated rain volume; whereas leachable tendency of As and Pb was enhanced with increasing leaching time and rain volume. Generally, the leaching percentage of heavy metals followed the sequence of As > Zn > Ni > Cd > Cr > Cu > Pb. More attention should be paid to the higher environmental risk of Pb and higher leaching percentage of As with regard to ecosystem safety and human health.

Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy.

MOTIVATION: A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area. RESULTS: In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraging Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn's disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (±0.58%) to 68.33% (±0.32%) using about 195 000 variants. AVAILABILITY AND IMPLEMENTATION: The LEP software is available at https://github.com/daviddaigithub/LEP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies.

BACKGROUND: To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding light on the genetic architecture of complex disease. The polygenicity of complex diseases is a widely accepted phenomenon through which a vast number of risk variants, each with a modest individual effect, collectively contribute to the heritability of complex diseases. This imposes a major challenge on fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larger sample size is required to detect individual risk variants with weak/moderate effects. Meanwhile, accumulating evidence suggests that different complex diseases can share genetic risk variants, a phenomenon known as pleiotropy. RESULTS: In this study, we propose a statistical framework for Leveraging Pleiotropic effects in large-scale GWAS data (LPG). LPG utilizes a variational Bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient and scalable for genome-wide-scale analysis. To demonstrate the advantages of LPG over existing methods that do not leverage pleiotropy, we conducted extensive simulation studies and applied LPG to analyze two pairs of disorders (Crohn's disease and Type 1 diabetes, as well as rheumatoid arthritis and Type 1 diabetes). The results indicate that by levelaging pleiotropy, LPG can improve the power of prioritization of risk variants and the accuracy of risk prediction. CONCLUSIONS: Our methodology provides a novel and efficient tool to detect pleiotropy among GWAS data for multiple traits/diseases collected from different studies. The software is available at https://github.com/Shufeyangyi2015310117/LPG .

Current status and environment impact of direct straw return in China's cropland - A review.

With the development of grain production technologies and improvement of rural living standard, the production and utilization of straw have significantly been changed in China. More than 1 billion tones of straw are produced per year, and vast amount of them are discarded without effective utilization, leading various environmental and social impacts. Straw return is an effective approach of the straw utilization that has been greatly recommended by government and scientists in China. This paper discussed the current status of the straw return in China. Specifically, the production and models of straw return were explored and their environmental impacts were extensively evaluated. It was concluded that straw could be positively effective on the improvement of the soil quality and the grain production. However, it appeared that the straw return also had several neglect negative effects, implying that further research and assessment on the returned straw are required before its large-scale promotion in China.

LSMM: a statistical approach to integrating functional annotations with genome-wide association studies.

Motivation: Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still two major challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, accumulating evidence from GWAS suggests the polygenicity of complex traits, i.e. a complex trait is often affected by many variants with small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Results: The availability of functional annotation data enables us to address the above challenges. In this study, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWAS of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. Availability and implementation: The LSMM software is available at https://github.com/mingjingsi/LSMM. Supplementary information: Supplementary data are available at Bioinformatics online.

IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies.

MOTIVATION: Results from genome-wide association studies (GWAS) suggest that a complex phenotype is often affected by many variants with small effects, known as 'polygenicity'. Tens of thousands of samples are often required to ensure statistical power of identifying these variants with small effects. However, it is often the case that a research group can only get approval for the access to individual-level genotype data with a limited sample size (e.g. a few hundreds or thousands). Meanwhile, summary statistics generated using single-variant-based analysis are becoming publicly available. The sample sizes associated with the summary statistics datasets are usually quite large. How to make the most efficient use of existing abundant data resources largely remains an open question. RESULTS: In this study, we propose a statistical approach, IGESS, to increasing statistical power of identifying risk variants and improving accuracy of risk prediction by i ntegrating individual level ge notype data and s ummary s tatistics. An efficient algorithm based on variational inference is developed to handle the genome-wide analysis. Through comprehensive simulation studies, we demonstrated the advantages of IGESS over the methods which take either individual-level data or summary statistics data as input. We applied IGESS to perform integrative analysis of Crohns Disease from WTCCC and summary statistics from other studies. IGESS was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.2% ( ±0.4% ) to 69.4% ( ±0.1% ) using about 240 000 variants. AVAILABILITY AND IMPLEMENTATION: The IGESS software is available at https://github.com/daviddaigithub/IGESS . CONTACT: zbxu@xjtu.edu.cn or xwan@comp.hkbu.edu.hk or eeyang@hkbu.edu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[Establishment of a genetic transformation system for Penicillium brevicompactum to produce mycophenolic acid].

OBJECTIVE: We established a genetic transformation system for Penicillium brevicompactum to produce mycophenolic acid. METHODS: We developed protoplast transformation methods mediated by Polyethylene glycol, using phleomycin resistance gene (Sh ble) as a dominant selection marker. RESULT: The frequency of transformation was up to 2 - 3 transformants per microg DNA; analysis of the transformants by PCR showed that the foreign DNA had been integrated into the host genome. The transformants retained stable after generation. CONCLUSION: The establishment of the genetic transformation system of Penicillium brevicompactum could serve as the basis for the research of molecular biology and the breeding of gene engineering of the fungus.

Fast and efficient strategies for model selection of Gaussian support vector machine.

Two strategies for selecting the kernel parameter (sigma) and the penalty coefficient (C) of Gaussian support vector machines (SVMs) are suggested in this paper. Based on viewing the model parameter selection problem as a recognition problem in visual systems, a direct parameter setting formula for the kernel parameter is derived through finding a visual scale at which the global and local structures of the given data set can be preserved in the feature space, and the difference between the two structures can be maximized. In addition, we propose a heuristic algorithm for the selection of the penalty coefficient through identifying the classification extent of a training datum in the implementation process of the sequential minimal optimization (SMO) procedure, which is a well-developed and commonly used algorithm in SVM training. We then evaluate the suggested strategies with a series of experiments on 13 benchmark problems and three real-world data sets, as compared with the traditional 5-cross validation (5-CV) method and the recently developed radius-margin bound (RM) method. The evaluation shows that in terms of efficiency and generalization capabilities, the new strategies outperform the current methods, and the performance is uniform and stable.