MOTIVATION: Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. RESULTS: We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. AVAILABILITY AND IMPLEMENTATION: SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc.

SUMMARY: Mendelian randomization is being utilized to assess causal effects of polygenic exposures, where many genetic instruments are subject to horizontal pleiotropy. Existing methods for detecting and correcting for horizontal pleiotropy have important assumptions that may not be fulfilled. Built upon the core gene hypothesis, we developed MR Corge for performing sensitivity analysis of Mendelian randomization. MR Corge identifies a small number of putative core instruments that are more likely to affect genes with a direct biological role in an exposure and obtains causal effect estimates based on these instruments, thereby reducing the risk of horizontal pleiotropy. Using positive and negative controls, we demonstrated that MR Corge estimates aligned with established biomedical knowledge and the results of randomized controlled trials. MR Corge may be widely applied to investigate polygenic exposure-outcome relationships. AVAILABILITY AND IMPLEMENTATION: An open-sourced R package is available at https://github.com/zhwm/MRCorge.

BACKGROUND: Third-generation sequencing platforms, such as PacBio sequencing, have been developed rapidly in recent years. PacBio sequencing generates much longer reads than the second-generation sequencing (or the next generation sequencing, NGS) technologies and it has unique sequencing error patterns. An effective read simulator is essential to evaluate and promote the development of new bioinformatics tools for PacBio sequencing data analysis. RESULTS: We developed a new PacBio Sequencing Simulator (PaSS). It can learn sequence patterns from PacBio sequencing data currently available. In addition to the distribution of read lengths and error rates, we included a context-specific sequencing error model. Compared to existing PacBio sequencing simulators such as PBSIM, LongISLND and NPBSS, PaSS performed better in many aspects. Assembly tests also suggest that reads simulated by PaSS are the most similar to experimental sequencing data. CONCLUSION: PaSS is an effective sequence simulator for PacBio sequencing. It will facilitate the evaluation and development of new analysis tools for the third-generation sequencing data.

As one of the major crops in China, the winter wheat production is directly related to the national economic development, the social stabilization and the food security. Normalized difference vegetation index (NDVI) time series has been widely used in crop identification, while most of the present researches about NDVI time series focused on moderate or low resolution remote sensing images, which affecting the accuracy of winter wheat extraction. With the successful launch of the first satellite GF-1 of China High-resolution Earth Observation System, more possibilities have been provided for construction of NDVI time series with high time resolution and high spatial resolution. Considering the peculiarity of winter wheat, which curvilinear integral of the NDVI time series different from other crops, this can be used for identification of winter wheat. So the paper present a high precision winter wheat identification method, Curvilinear integral method, which makes full use of phenology characteristics of winter wheat based on curvilinear integral of NDVI time series. This method is technologically easy and apparently effective.