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Our paper on the use of simulation to help improve analysis validation and results reproducibility.

Lotterhos KE, Moore JH, Stapleton AE. Analysis validation has been neglected in the Age of Reproducibility. PLoS Biol. 2018 Dec 10;16(12):e3000070. [PubMed] [PLoS Biology]

Abstract

Increasingly complex statistical models are being used for the analysis of biological data. Recent commentary has focused on the ability to compute the same outcome for a given dataset (reproducibility). We argue that a reproducible statistical analysis is not necessarily valid because of unique patterns of nonindependence in every biological dataset. We advocate that analyses should be evaluated with known-truth simulations that capture biological reality, a process we call "analysis validation." We review the process of validation and suggest criteria that a validation project should meet. We find that different fields of science have historically failed to meet all criteria, and we suggest ways to implement meaningful validation in training and practice.

Our new paper with Dr. Kristel van Steen on approaches for improving evidence for statistical interactions.

Van Steen K, Moore JH. How to increase our belief in discovered statistical interactions via large-scale association studies? Hum Genet. 2019 [PubMed] [Human Genetics]


Abstract

The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these.

This editorial is in response to some claims that an observed linear relationship between relative pair trait correlation and IBD genetic sharing is indicative of a simple additive genetic architecture dominated by independent genetic effects. As we show here, you could observe this pattern under a genetic architecture dominated by epistasis.

Moore JH, Mackay TFC, Williams SM. Testing the assumptions of parametric linear models: the need for biological data mining in disciplines such as human genetics. BioData Min. 2019 Feb 11;12:6. [PubMed] [BioData Mining]

Abstract

All data science methods have specific assumptions that are made in order for their inferences to be valid. Some assumptions impact statistical significance testing and some influence the models themselves. For example, a fundamental assumption of linear regression is that the relationship between the independent and dependent variables is additive such that a unit increase in one leads to a unit increase in the other with some error that can be modeled using a normal distribution. The presence of a nonlinear relationship between the variables violates this assumption and can lead to inaccurate inferences. We demonstrate this here using a simple example from human genetics and then end with some thoughts about the role of biological data mining in revealing nonlinear relationships between variables.

Our paper on how to prepare next-gen scientists for big data is out. We outline here a curriculum focused on precision medicine, data science, and artificial intelligence.

Moore JH, Boland MR, Camara PG, Chervitz H, Gonzalez G, Himes BE, Kim D, Mowery DL, Ritchie MD, Shen L, Urbanowicz RJ, Holmes JH. Preparing next-generation scientists for biomedical big data: artificial intelligence approaches. Per Med. 2019 [PubMed] [PerMed]

Abstract

Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.