Symbols and Terminology in Biometry

The many biologists whose work requires statistical science must be concerned for sound management and interpretation of quantitative data. Biometricians also need increased care for literacy and clarity in speech and writing, with precise phrasing for every numerical statement. This paper illustrates common confusions that arise from inexact terminology, or from words and symbols used without adequate definition. Careless statements on quantitative relations, or on probabilities, may be ambiguous; bad practices seriously pollute scientific journals and obstruct transmission of information. Such faults can affect daily life for a modern citizen. Pedantry is unwanted, and to be dogmatic about corrective measures would be stupid. This paper suggests that biologists and biometricians should examine the practicability of a system yet to be devised for standardizing use of symbols and the generally accepted terminology for the methods, techniques, and processes of statistical analysis. The outcome should influence all that we biometricians say and do — as authors, as consultants, and as referees for journals.     

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.     

The Diagnostic Value of DNA Methylation in Leukemia: A Systematic Review and Meta-Analysis

Background

Accumulating evidence supports a role of DNA methylation in the pathogenesis of leukemia. The aim of our study was to evaluate the potential genes with aberrant DNA methylation in the prediction of leukemia risk by a comprehensive meta-analysis of the published data.

Methods

A series of meta-analyses were done among the eligible studies that were harvested after a careful filtration of the searching results from PubMed literature database. Mantel-Haenszel odds ratios and 95% confidence intervals were computed for each methylation event assuming the appropriate model.

Results

A total of 535 publications were initially retrieved from PubMed literature database. After a three-step filtration, we harvested 41 case-control articles that studied the role of gene methylation in the prediction of leukemia risk. Among the involving 30 genes, 20 genes were shown to be aberrantly methylated in the leukemia patients. A further subgroup meta-analysis by subtype of leukemia showed that CDKN2A, CDKN2B, ID4 genes were significantly hypermethylated in acute myeloid leukemia.

Conclusions

Our meta-analyses identified strong associations between a number of genes with aberrant DNA methylation and leukemia. Further studies should be required to confirm the results in the future.     

Lipid biodynamics: new perspectives [published errtum appears in Biochimie 1987 May;69(5):558

Active biological systems can be divided into five phases: the aqueous polar phase, the monolayer and/or bilayer interfacial phase, the apolar or hydrophobic phase, the solid or insoluble phase and the gaseous phase. The micellar phase is a special dispersed state of an interfacial phase. Molecules are distributed among these five phases according to their physicochemical properties. Herein is proposed a standardization in strict compliance with the CGS (cm, g, s) unit system and uses the mass/volume, mole per cm3 (mol X cm-3) chemical unit. This standardization requires a new set of symbols to clearly distinguish the concentrations in the different phases. The numerous implications of this standardization are discussed with respect to the quantitative classification of lipids based upon interphase partition coefficients, a new definition of micelles, simple models for the study of lipid biodynamic behavior and sites of action of lipid metabolism enzymes as well as determination of the physicochemical parameters of circulating lipoproteins. By compartmentalization in an aqueous polar phase, an interfacial phase comprising phospholipids and free cholesterol and an apolar phase comprising triglycerides and esterified cholesterol, this standardization will greatly simplify quantitative research on the factors regulating and disturbing cholesterol homeostasis. The notion of total cholesterol must be foresaken, since the biodynamic behavior of free cholesterol and esterified cholesterol are fundamentally different. Free cholesterol shares the fate of the interfacial phase of which it is a part, this fate being hinged on enzymatic biotransformations and/or ligand--receptor interactions. The proposed standardization gives rise to a new rationale using simple calculations and its advantage will be 2-fold: first, in the design of experimental protocols; and second, in allowing immediate and unambiguous comparison of experimental data based upon strictly defined parameters.     

Assigning statistical significance to proteotypic peptides via database searches

Querying MS/MS spectra against a database containing only proteotypic peptides reduces data analysis time due to reduction of database size. Despite the speed advantage, this search strategy is challenged by issues of statistical significance and coverage. The former requires separating systematically significant identifications from less confident identifications, while the latter arises when the underlying peptide is not present, due to single amino acid polymorphisms (SAPs) or post-translational modifications (PTMs), in the proteotypic peptide libraries searched. To address both issues simultaneously, we have extended RAId's knowledge database to include proteotypic information, utilized RAId's statistical strategy to assign statistical significance to proteotypic peptides, and modified RAId's programs to allow for consideration of proteotypic information during database searches. The extended database alleviates the coverage problem since all annotated modifications, even those that occurred within proteotypic peptides, may be considered. Taking into account the likelihoods of observation, the statistical strategy of RAId provides accurate E-value assignments regardless whether a candidate peptide is proteotypic or not. The advantage of including proteotypic information is evidenced by its superior retrieval performance when compared to regular database searches.     

An eye for a tooth

Objective: To propose a possible link between dental extraction and intra‐ocular complications. Background: Several publications in medical literature describe intra‐ocular complications after different dental procedures. Results: Retinal detachment and vitreous hemorrhage following dental extraction with an appropriate anesthesia. Conclusion: Systemic complications following dental treatments should not be neglected and proper medical treatment must be provided as soon as possible.     

PRIDE: the proteomics identifications database

The advent of high-throughput proteomics has enabled the identification of ever increasing numbers of proteins. Correspondingly, the number of publications centered on these protein identifications has increased dramatically. With the first results of the HUPO Plasma Proteome Project being analyzed and many other large-scale proteomics projects about to disseminate their data, this trend is not likely to flatten out any time soon. However, the publication mechanism of these identified proteins has lagged behind in technical terms. Often very long lists of identifications are either published directly with the article, resulting in both a voluminous and rather tedious read, or are included on the publisher's website as supplementary information. In either case, these lists are typically only provided as portable document format documents with a custom-made layout, making it practically impossible for computer programs to interpret them, let alone efficiently query them. Here we propose the proteomics identifications (PRIDE) database (http://www.ebi.ac.uk/pride) as a means to finally turn publicly available data into publicly accessible data. PRIDE offers a web-based query interface, a user-friendly data upload facility, and a documented application programming interface for direct computational access. The complete PRIDE database, source code, data, and support tools are freely available for web access or download and local installation.     

Does Publication Bias Inflate the Apparent Efficacy of Psychological Treatment for Major Depressive Disorder? A Systematic Review and Meta-Analysis of US National Institutes of Health-Funded Trials

Background

The efficacy of antidepressant medication has been shown empirically to be overestimated due to publication bias, but this has only been inferred statistically with regard to psychological treatment for depression. We assessed directly the extent of study publication bias in trials examining the efficacy of psychological treatment for depression.

Methods and Findings

We identified US National Institutes of Health grants awarded to fund randomized clinical trials comparing psychological treatment to control conditions or other treatments in patients diagnosed with major depressive disorder for the period 1972–2008, and we determined whether those grants led to publications. For studies that were not published, data were requested from investigators and included in the meta-analyses. Thirteen (23.6%) of the 55 funded grants that began trials did not result in publications, and two others never started. Among comparisons to control conditions, adding unpublished studies (Hedges’ g = 0.20; CI_95% -0.11~0.51; k = 6) to published studies (g = 0.52; 0.37~0.68; k = 20) reduced the psychotherapy effect size point estimate (g = 0.39; 0.08~0.70) by 25%. Moreover, these findings may overestimate the "true" effect of psychological treatment for depression as outcome reporting bias could not be examined quantitatively.

Conclusion

The efficacy of psychological interventions for depression has been overestimated in the published literature, just as it has been for pharmacotherapy. Both are efficacious but not to the extent that the published literature would suggest. Funding agencies and journals should archive both original protocols and raw data from treatment trials to allow the detection and correction of outcome reporting bias. Clinicians, guidelines developers, and decision makers should be aware that the published literature overestimates the effects of the predominant treatments for depression.     

Bibliography search in biology and medicine: a tutorial for Medline PubMed use

To be informed about the last publications recently published or to produce a bibliography in a given thematic field is essential for researchers in the biomedical field. If the use of Internet information searching tools such as "Google" or "Alltheweb" makes possible to discover a great part of the grey literature, bibliographic databases like Embase, Current Contents, Biosis or Medline via PubMed are essential tools to locate scientific articles. Among these bibliographic databases, Medline PubMed, thanks to its free access, is the most used. However, a correct utilization of the various functionalities proposed (thesaurus MeSH, systematization of bibliographic searches...), and consequently the quality of bibliographic researches carried out in this database, requires to master elementary knowledge which are exposed in this article.     

The meta-analysis of genome-wide association studies

The pressure to publish novel genetic associations has meant that meta-analysis has been applied to genome-wide association studies without the time for a careful consideration of the methods that are used. This review distinguishes between the use of meta-analysis to validate previously reported genetic associations and its use for gene discovery, and advocates viewing gene discovery as an exploratory screen that requires independent replication instead of treating it as the application of hundreds of thousands of statistical tests. The review considers the use of fixed and random effects meta-analyses, the investigation of between-study heterogeneity, adjustment for confounding, assessing the combined evidence and genomic control, and comments on alternative approaches that have been used in the literature.     

TableButler – a Windows based tool for processing large data tables generated with high-throughput methods

Background  

High-throughput "omics" based data analysis play emerging roles in life sciences and molecular diagnostics. This emphasizes the urgent need for user-friendly windows-based software interfaces that could process the diversity of large tab-delimited raw data files generated by these methods. Depending on the study, dozens to hundreds of these data tables are generated. Before the actual statistical or cluster analysis, these data tables have to be combined and merged to expression matrices (e.g., in case of gene expression analysis). Gene annotations as well as information concerning the samples analyzed may be appended, renewed or extended. Often additional data values shall be computed or certain features must be filtered out.     

Issues in predicting protein function from sequence

Identifying homologues, defined as genes that arose from a common evolutionary ancestor, is often a relatively straightforward task, thanks to recent advances made in estimating the statistical significance of sequence similarities found from database searches. The extent by which homologues possess similarities in function, however, is less amenable to statistical analysis. Consequently, predicting function by homology is a qualitative, rather than quantitative, process and requires particular care to be taken. This review focuses on the various approaches that have been developed to predict function from the scale of the atom to that of the organism. Similarities in homologues' functions differ considerably at each of these different scales and also vary for different domain families. It is argued that due attention should be paid to all available clues to function, including orthologue identification, conservation of particular residue types, and the co-occurrence of domains in proteins. Pitfalls in database searching methods arising from amino acid compositional bias and database size effects are also discussed.     

Individual variation in cognitive performance: developmental and evolutionary perspectives

Animal cognition experiments frequently reveal striking individual variation but rarely consider its causes and largely ignore its potential consequences. Studies often focus on a subset of high-performing subjects, sometimes viewing evidence from a single individual as sufficient to demonstrate the cognitive capacity of a species. We argue that the emphasis on demonstrating species-level cognitive capacities detracts from the value of individual variation in understanding cognitive development and evolution. We consider developmental and evolutionary interpretations of individual variation and use meta-analyses of data from published studies to examine predictors of individual performance. We show that reliance on small sample sizes precludes robust conclusions about individual abilities as well as inter- and intraspecific differences. We advocate standardization of experimental protocols and pooling of data between laboratories to improve statistical rigour. Our analyses show that cognitive performance is influenced by age, sex, rearing conditions and previous experience. These effects limit the validity of comparative analyses unless developmental histories are taken into account, and complicate attempts to understand how cognitive traits are expressed and selected under natural conditions. Further understanding of cognitive evolution requires efforts to elucidate the heritability of cognitive traits and establish whether elevated cognitive performance confers fitness advantages in nature.     

Determining Causality and Controlling Disease is Based on Collaborative Research involving Multidisciplinary Approaches

Understanding the causes of infectious disease to facilitate better control requires observational and experimental studies. Often these must be conducted at many scales such as at the molecular, cellular, organism, and population level. Studies need to consider both intrinsic and extrinsic factors affecting the pathogen/host interaction. They also require a combination of study methods covered by disciplines such as pathology, epidemiology, microbiology, and ecology. Therefore, it is important that disciplines work together when designing and conducting studies. Finally, we need to integrate and interpret data across levels and disciplines to better formulate control strategies. This requires another group of specialists with broad cross-disciplinary training in epidemiology and an ability to readily work with others.     

A Bayesian approach for constructing genetic maps when markers are miscoded

The advent of molecular markers has created opportunities for a better understanding of quantitative inheritance and for developing novel strategies for genetic improvement of agricultural species, using information on quantitative trait loci (QTL). A QTL analysis relies on accurate genetic marker maps. At present, most statistical methods used for map construction ignore the fact that molecular data may be read with error. Often, however, there is ambiguity about some marker genotypes. A Bayesian MCMC approach for inferences about a genetic marker map when random miscoding of genotypes occurs is presented, and simulated and real data sets are analyzed. The results suggest that unless there is strong reason to believe that genotypes are ascertained without error, the proposed approach provides more reliable inference on the genetic map.     

How ecologists define drought,and why we should do better

Drought, widely studied as an important driver of ecosystem dynamics, is predicted to increase in frequency and severity globally. To study drought, ecologists must define or at least operationalize what constitutes a drought. How this is accomplished in practice is unclear, particularly given that climatologists have long struggled to agree on definitions of drought, beyond general variants of “an abnormal deficiency of water.” We conducted a literature review of ecological drought studies (564 papers) to assess how ecologists describe and study drought. We found that ecologists characterize drought in a wide variety of ways (reduced precipitation, low soil moisture, reduced streamflow, etc.), but relatively few publications (~32%) explicitly define what are, and are not, drought conditions. More troubling, a surprising number of papers (~30%) simply equated “dry conditions” with “drought” and provided little characterization of the drought conditions studied. For a subset of these, we calculated Standardized Precipitation Evapotranspiration Index values for the reported drought periods. We found that while almost 90% of the studies were conducted under conditions quantifiable as slightly to extremely drier than average, ~50% were within the range of normal climatic variability. We conclude that the current state of the ecological drought literature hinders synthesis and our ability to draw broad ecological inferences because drought is often declared but is not explicitly defined or well characterized. We suggest that future drought publications provide at least one of the following: (a) the climatic context of the drought period based on long‐term records; (b) standardized climatic index values; (c) published metrics from drought‐monitoring organizations; (d) a quantitative definition of what the authors consider to be drought conditions for their system. With more detailed and consistent quantification of drought conditions, comparisons among studies can be more rigorous, increasing our understanding of the ecological effects of drought.