Minimum Information about a Genotyping Experiment (MIGEN)

Techniques, procedures, and applications in host genetic analysis

This chapter overviews genetic techniques' fundamentals and methodological features, including different approaches, analyses, and applications that have contributed to advancing health and disease. The aim is to describe laboratory methodologies and analyses employed to understand the genetic landscape of different biological contexts, from conventional techniques to cutting-edge technologies. Besides describing detailed aspects of the polymerase chain reaction (PCR) and derived types as one of the principles for many novel techniques, we also discuss microarray analysis, next-generation sequencing, and genome editing technologies such as transcription activator-like effector nucleases (TALENs) and the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) systems. These techniques study several phenotypes, ranging from autoimmune disorders to viral diseases. The significance of integrating diverse genetic methodologies and tools to understand host genetics comprehensively and addressing the ethical, legal, and social implications (ELSI) associated with using genetic information is highlighted. Overall, the methods, procedures, and applications in host genetic analysis provided in this chapter furnish researchers and practitioners with a roadmap for navigating the dynamic landscape of host-genome interactions.

MiSet RFC Standards: Defining a Universal Minimum Set of Standards Required for Reproducibility and Rigor in Research Flow Cytometry Experiments

Poor adherence to best practices, insufficient training, and pressure to produce data quickly may lead to publications of suboptimal biomedical research flow cytometry data, which contributes to the body of irreproducible research findings. In addition, documentation of compliance with best flow cytometry practices for submission, visualization, and publication of flow cytometry data is currently endorsed by very few scientific journals, which is particularly concerning as numerous peer-reviewed flow cytometry publications emphasize instrumentation, experimental design, and data analysis as important sources of variability. Guidelines and resources for adequate reporting, annotation and deposition of flow cytometry experiments are provided by MIFlowCyt and the FlowRepository database, and comprehensive expert recommendations covering principles and techniques of field-specific flow cytometry applications have been published. To facilitate the integration of quality-defining parameters into manuscript and grant submission and publication requirements across biomedical fields that rely on the use of flow-cytometry-based techniques, a single comprehensive yet easily and universally applicable document is needed. To produce such a list of gold-standard parameters that assess whether a research flow cytometry experiment has been planned, conducted, interpreted, and reported at the highest standard, a new initiative defining the minimum set of standards a robust and rigorous research flow experiment must fulfill (MiSet RFC Standards) was proposed at CYTO 2019. MiSet RFC Standards will integrate and simplify existing resources to provide a universal benchmark a flow cytometry experiment can easily be measured against. The goal of MiSET RFC Standards is its integration into peer-review and publication procedures through partnership with stakeholders, journals and publishers in biomedical and translational research. This article introduces the aims and anticipated timeline and discusses strategies for interdisciplinary consensus and implementation. A single-resource broadly applicable guideline will harmonize standards across different fields of biomedical research and lead to publication of more robust research findings. © 2019 International Society for Advancement of Cytometry.

Propelling the paradigm shift from reductionism to systems nutrition

The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

Bayesian systems-based genetic association analysis with effect strength estimation and omic wide interpretation: a case study in rheumatoid arthritis

Rich dependency structures are often formed in genetic association studies between the phenotypic, clinical, and environmental descriptors. These descriptors may not be standardized, and may encompass various disease definitions and clinical endpoints which are only weakly influenced by various (e.g., genetic) factors. Such loosely defined complex intermediate clinical phenotypes are typically used in follow-up candidate gene association studies, e.g., after genome-wide analysis, to deepen the understanding of the associations and to estimate effect strength. This chapter discusses a solid methodology, which is useful in such a scenario, by using probabilistic graphical models, namely, Bayesian networks in the Bayesian statistical framework. This method offers systematically scalable, comprehensive hierarchical hypotheses about multivariate relevance. We discuss its workflow: from data engineering to semantic publication of the results. We overview the construction, visualization, and interpretation of complex hypotheses related to the structural analysis of relevance. Furthermore, we illustrate the use of a dependency model-based relevance measure, which takes into account the structural properties of the model, for quantifying the effect strength. Finally, we discuss the "interpretational" or translational challenge of a genetic association study, with a focus on the fusion of heterogeneous omic knowledge to reintegrate the results into a genome-wide context.