Explore the relationship between genetic variations and phenotypes with bayesian approaches

Genome-wide association studies (GWAS) have had great success in identifying human genetic variants associated with human traits. With recent developments in high throughput biology, immense amount of data have been generated, thus calling for novel statistical and computational approaches to be developed and draw biological meaningful conclusions. A current direction for GWAS method development has been to use Bayesian approaches, where prior beliefs of variant effects are incorporated into test statistics, to boost the power to detect real associations. With previous success in developing Bayesian-based GWAS method for single phenotype, in this work the Bayesian idea is extended to multiple phenotypes, aiming at developing a method that detects pleiotropic genome-wide associations. Alongside with the method development, analytical simulations were also performed to investigate into the possible power gain by using such Bayesian approaches, as well as to understand how different factors influence the behavior of Bayesian-based GWAS methods. Many variants are pleiotropic, and discovery of these variants could help reveal disease mechanisms, suggest new therapeutic options. Therefore, we developed a pleiotropic GWAS method based on Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO), which learns pleiotropy using identified associations to discover additional associations with shared patterns. SAPPHO was applied on two sets of real data: 1. Atherosclerosis Risk in Communities (ARIC) study of 8,000 individuals, whose gold-standard associations were provided by meta-analysis of 40,000 to 100,000 individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium; 2. Cancer phenotypes from UK Biobank project, consisting several hundred to 15,000 individuals, with gold-standard obtained from GWAS catalog. For both data sets, SAPPHO was able to detect additional associations that were not detected with the conventional univariate test, and boost power when different variants follow the same association patterns. Bayesian approaches boost power for GWAS through alleviating burdens from multiple hypothesis testings, which is usually on the scale of thousands to millions. Intuitively, by making use of prior probabilities that bias favored sets thought to be enriched for significant findings, power for detecting true associations could be increased. Therefore, an analytical study was conducted here to see theoretically to what extent power could gain by using such approaches, and how does this gain depend on different factors. By calculating test power assuming perfect knowledge of a prior distribution, the population size increase required to provided the same boost without a prior was obtained, and it is shown that population size is exponentially more important than prior, providing a rigorous proof for the lack of use for prior-based GWAS methods

Proteomic analysis of elite soybean Jidou17 and its parents using iTRAQ-based quantitative approaches

BACKGROUND: Derived from Hobbit as the female parent and Zao5241 as the male parent, the elite soybean cultivar Jidou17 is significantly higher yielding and shows enhanced qualities and stronger resistance to non-biological stress than its parents. The purpose of this study is to understand the difference in protein expression patterns between Jidou17 and its parental strains and to evaluate the parental contributions to its elite traits. RESULTS: Leaves (14 days old) from Jidou17 and its parental cultivars were analysed for differential expressed proteins using an iTRAQ-based (isobaric tags for relative and absolute quantitation) method. A total of 1269 proteins was detected, with 141 and 181 proteins in Jidou17 differing from its female and male parent, respectively. Functional classification and an enrichment analysis based on biological functions, biological processes, and cellular components revealed that all the differential proteins fell into many functional categories but that the number of proteins varied greatly for the different categories, with enrichment in specific categories. A pathway analysis indicated that the differentiated proteins were mainly classified into the ribosome assembly pathway. Protein expression clustering results showed that the expression profiles between Jidou17 and its female parent Hobbit were more similar than those between Jidou17 and its male parent Zao5241 and between the two parental strains. Therefore, the female parent Hobbit contributed more to the Jidou17 genotype. CONCLUSIONS: This study applied a proven technique to study proteomics in 14-day-old soybean leaves and explored the depth and breadth of soybean protein research. The results provide new data for further understanding the mechanisms of elite cultivar development

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Passive Heave Compensator Design and Numerical Simulation for Strand Jack during Lift Operation in Deep Water

In this paper, the passive heave compensator for the strand jack lifting system is studied. An analytical model is developed, which considers the nonlinear characteristic of the compensator stiffness thus as to predict its response under different parameters accurately. This analytical model helps to find the feasible gas volume of the compensator. The comparative analysis is carried out to analyze the influence of key design parameters on the dynamic response of the compensator. In order to evaluate the effectiveness of the compensator, a coupling model of the strand jack lifting system is derived. The compensator efficiency is evaluated in terms of the lifted structure displacement and the strand dynamic tension. The numerical simulations are performed to evaluate the effectiveness of the compensator. Numerical results show that the compensator is able to significantly decrease the tension variation in the strands and the motion of the lifted structure

Priors, population sizes, and power in genome-wide hypothesis tests

Abstract Background Genome-wide tests, including genome-wide association studies (GWAS) of germ-line genetic variants, driver tests of cancer somatic mutations, and transcriptome-wide association tests of RNAseq data, carry a high multiple testing burden. This burden can be overcome by enrolling larger cohorts or alleviated by using prior biological knowledge to favor some hypotheses over others. Here we compare these two methods in terms of their abilities to boost the power of hypothesis testing. Results We provide a quantitative estimate for progress in cohort sizes and present a theoretical analysis of the power of oracular hard priors: priors that select a subset of hypotheses for testing, with an oracular guarantee that all true positives are within the tested subset. This theory demonstrates that for GWAS, strong priors that limit testing to 100–1000 genes provide less power than typical annual 20–40% increases in cohort sizes. Furthermore, non-oracular priors that exclude even a small fraction of true positives from the tested set can perform worse than not using a prior at all. Conclusion Our results provide a theoretical explanation for the continued dominance of simple, unbiased univariate hypothesis tests for GWAS: if a statistical question can be answered by larger cohort sizes, it should be answered by larger cohort sizes rather than by more complicated biased methods involving priors. We suggest that priors are better suited for non-statistical aspects of biology, such as pathway structure and causality, that are not yet easily captured by standard hypothesis tests

A Novel Three-SPR Parallel Platform for Vessel Wave Compensation

A wave compensation platform based on 3-SPR parallel platform is designed for marine ships with a dynamic positioning system. It can compensate for the heave, rolling, and pitching movement of a vessel under level 4 sea state. The forward kinematics of the mechanism is used to draw the central point position workspace and the attitude workspace of the moving deck of the compensation platform. The compensation effects of the 3-RPS parallel compensation platform and the 3-SPR parallel compensation platform are compared, and the feasibility and superiority of the compensation scheme using the 3-SPR parallel compensation platform are proved. To lower the working height of the upper deck of the compensation platform and reduce the extension range of the support legs, the structure of the compensation platform is optimized, and a novel 3-SPR parallel platform is designed. Finally, a simulation model was established. Using the inverse kinematic model as a compensation movement solver which can online calculate the length of branch legs based on the measured heaving, rolling, and pitching values of vessels, the compensation effect of the new structure under a certain sea state is simulated. The result demonstrated the efficiency of the ship motion decoupling movement of the newly designed compensation platform and proved the competence of it

Characteristics of stable carbon isotopic composition of shale gas

A type Ⅱ kerogen with low thermal maturity was adopted to perform hydrocarbon generation pyrolysis experiments in a vacuum (Micro-Scale Sealed Vessel) system at the heating rates of 2 °C/h and 20 °C/h. The stable carbon isotopic compositions of gas hydrocarbons were measured to investigate their evolving characteristics and the possible reasons for isotope reversal. The δ13C values of methane became more negative with the increasing pyrolysis temperatures until it reached the lightest point, after which they became more positive. Meanwhile, the δ13C values of ethane and propane showed a positive trend with elevating pyrolysis temperatures. The carbon isotopic compositions of shale gasses were mainly determined by the type of parent organic matter, thermal evolutionary extent, and gas migration in shale systems. Our experiments and study proved that the isotope reversal shouldn't occur in a pure thermogenic gas reservoir, it must be involved with some other geochemical process/es; although mechanisms responsible for the reversal are still vague. Carbon isotopic composition of the Fayetteville and Barnett shale gas demonstrated that the isotope reversal was likely involved with water–gas reaction and Fischer-Tropsch synthesis during its generation