Methods for evaluating gene expression from Affymetrix microarray datasets

<p>Abstract</p> <p>Background</p> <p>Affymetrix high density oligonucleotide expression arrays are widely used across all fields of biological research for measuring genome-wide gene expression. An important step in processing oligonucleotide microarray data is to produce a single value for the gene expression level of an RNA transcript using one of a growing number of statistical methods. The challenge for the researcher is to decide on the most appropriate method to use to address a specific biological question with a given dataset. Although several research efforts have focused on assessing performance of a few methods in evaluating gene expression from RNA hybridization experiments with different datasets, the relative merits of the methods currently available in the literature for evaluating genome-wide gene expression from Affymetrix microarray data collected from real biological experiments remain actively debated.</p> <p>Results</p> <p>The present study reports a comprehensive survey of the performance of all seven commonly used methods in evaluating genome-wide gene expression from a well-designed experiment using Affymetrix microarrays. The experiment profiled eight genetically divergent barley cultivars each with three biological replicates. The dataset so obtained confers a balanced and idealized structure for the present analysis. The methods were evaluated on their sensitivity for detecting differentially expressed genes, reproducibility of expression values across replicates, and consistency in calling differentially expressed genes. The number of genes detected as differentially expressed among methods differed by a factor of two or more at a given false discovery rate (FDR) level. Moreover, we propose the use of genes containing single feature polymorphisms (SFPs) as an empirical test for comparison among methods for the ability to detect true differential gene expression on the basis that SFPs largely correspond to <it>cis</it>-acting expression regulators. The PDNN method demonstrated superiority over all other methods in every comparison, whilst the default Affymetrix MAS5.0 method was clearly inferior.</p> <p>Conclusion</p> <p>A comprehensive assessment of seven commonly used data extraction methods based on an extensive barley Affymetrix gene expression dataset has shown that the PDNN method has superior performance for the detection of differentially expressed genes.</p

A Robust Statistical Method for Association-Based eQTL Analysis

Background: It has been well established that theoretical kernel for recently surging genome-wide association study (GWAS) is statistical inference of linkage disequilibrium (LD) between a tested genetic marker and a putative locus affecting a disease trait. However, LD analysis is vulnerable to several confounding factors of which population stratification is the most prominent. Whilst many methods have been proposed to correct for the influence either through predicting the structure parameters or correcting inflation in the test statistic due to the stratification, these may not be feasible or may impose further statistical problems in practical implementation. Methodology: We propose here a novel statistical method to control spurious LD in GWAS from population structure by incorporating a control marker into testing for significance of genetic association of a polymorphic marker with phenotypic variation of a complex trait. The method avoids the need of structure prediction which may be infeasible or inadequate in practice and accounts properly for a varying effect of population stratification on different regions of the genome under study. Utility and statistical properties of the new method were tested through an intensive computer simulation study and an association-based genome-wide mapping of expression quantitative trait loci in genetically divergent human populations. Results/Conclusions: The analyses show that the new method confers an improved statistical power for detecting genuin

Additional file 1 of Risks of digestive diseases in long COVID: evidence from a population-based cohort study

Additional file 1: Figure S1. Directed Acyclic Graphs (DAG) for covariate selection. Figure S2. Flow chart of eligible participants’ selection. Figure S3. Distribution of follow-up time in the contemporary cohort (A) and the historical cohort (B). Figure S4. Hazard ratio of digestive outcomes in COVID-19 group and the contemporary comparison by severity of COVID-19. Table S1. Respiratory support treatments definition. Table S2. Outcome ascertainment. Table S3. The numbers (percentages) of participants with missing covariates. Table S4. Baseline characteristics of COVID-19 group and contemporary comparisons before weighting. Table S5. Hazard ratio of digestive outcomes in COVID-19 group and the contemporary comparison at different follow-up times. Table S6. Baseline characteristics of COVID-19, contemporary comparisons by severity of COVID-19 before weighting. Table S7. Baseline characteristics of COVID-19, contemporary comparisons by severity of COVID-19 after weighting. Table S8. Baseline characteristics of COVID-19 group and contemporary comparisons by status of SARS-CoV reinfection before weighting. Table S9. Baseline characteristics of COVID-19 group and contemporary comparisons by severity of SARS-CoV reinfection after weighting. Table S10. Hazard ratio of digestive outcomes in the reinfected group, single SARS-CoV-2 infection group, and non-infected comparisons. Table S11. Hazard ratio of digestive outcomes in reinfected group and single SARS-CoV-2 infection group in head-to-head comparison. Table S12. Baseline characteristics of COVID-19 group and contemporary comparisons in the sensitive analysis restricting to the period before vaccination was available before weighting. Table S13. Baseline characteristics of COVID-19 group and contemporary comparisons in the sensitive analysis restricting to the period before vaccination was available after weighting. Table S14. Hazard ratio of digestive outcomes in COVID-19 group and contemporary and historical comparisons in subgroups in the sensitive analysis restricting to the period before vaccination was available. Table S15. Hazard ratio of digestive outcomes in COVID-19 group compared to the contemporary and historical comparisons by pooling estimates across all five imputed datasets. Table S16. Hazard ratio of digestive outcomes compared with contemporary and historical comparisons in subgroups. Table S17. Hazard ratio of digestive outcomes in COVID-19 group, the contemporary and historical comparison by sex. Table S18. Baseline characteristics of COVID-19 group and historical comparisons before weighting. Table S19. Baseline characteristics of COVID-19 group and historical comparisons after weighting. Table S20. Baseline characteristics of COVID-19 group and historical comparisons by severity of COVID-19 before weighting. Table S21. Baseline characteristics of COVID-19 group and historical comparisons by severity of COVID-19 after weighting. Table S22. Baseline characteristics of COVID-19 group and historical comparisons in the sensitive analysis restricting to the period before vaccination was available before weighting. Table S23. Baseline characteristics of COVID-19 group and historical comparisons in the sensitive analysis restricting to the period before vaccination was available after weighting. Table S24. Hazard ratio of digestive outcomes in COVID-19 group and the historical comparison by severity of COVID-19

Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork.

BackgroundA typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. Their predictive power is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date has been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community.DescriptionUsing a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of legacy trait data and entered them into the GeneNetwork http://www.genenetwork.org. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, may interact to condition more complex biological phenotypes (higher-order traits). Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring them.ConclusionBy integrating barley genotypic, phenotypic and mRNA abundance data sets directly within GeneNetwork's analytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome sequence, we prove the importance of the concept and practice of modular development and interoperability of software engineering for biological data sets