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Hokanson, J.

Publications and source records attributed to Hokanson, J..

3 recordsLinked to original sources

The influence of unmeasured confounding on the MR Steiger approach

The Mendelian Randomization (MR) Steiger approach is used to determine the direction of a possible causal effect between two phenotypes [1]. For two phenotypes, denoted phenotype 1 and 2, the MR Steiger approach is composed of two parts: (1) MR is performed for a set of single nucleotide polymorphisms (SNPs) that serve as instrumental variables for phenotype 1 and (2) the difference of two correlations, the correlation between the SNPs and phenotype 1 and the correlation between the SNPs and phenotype 2, is calculated. These two parts are then used to determine the direction of a possible causal effect between the two phenotypes. The original MR Steiger paper [1] shows that unmeasured confounding of the two phenotypes affects the validity of the MR Steiger approach, but does not elucidate as to how this occurs. In particular, it was argued that if the magnitude of the observational variance explained between the two phenotypes is above 0.2, the MR Steiger method may return the incorrect causal direction due to unmeasured confounding. This may initially seem surprising since unmeasured confounding does not induce spurious associations between the SNP and phenotype 2, as we demonstrate using directed acyclic graphs. In this note, we show that this is because unmeasured confounding may rescale the magnitude of a non-zero association, and thereby distort the comparison of the correlation between the SNP and phenotype 2 and the correlation between the SNP and phenotype 1. We will end with a number of cautionary remarks on the MR Steiger method, which are partly motivated by this and mentioned in the original MR Steiger paper [1].

genetics↗

An open source platform for analyzing and sharing worm behavior data

Animal behavior is increasingly being recorded in systematic imaging studies that generate large data sets. To maximize the usefulness of these data there is a need for improved resources for analyzing and sharing behavior data that will encourage re-analysis and method development by computational scientists1. However, unlike genomic or protein structural data, there are no widely used standards for behavior data. It is therefore desirable to make the data available in a relatively raw form so that different investigators can use their own representations and derive their own features. For computational ethology to approach the level of maturity of other areas of bioinformatics, we need to address at least three challenges: storing and accessing video files, defining flexible data formats to facilitate data sharing, and making software to read, write, browse, and analyze the data. We have developed an open resource to begin addressing these challenges using worm tracking as a model.

animal behavior and cognition↗

Real-time decoding of bladder pressure from pelvic nerve activity

Real time algorithms for decoding physiological signals from peripheral nerve recordings form an important component of closed loop bioelectronic medicine (electroceutical) systems. As a feasibility demonstration, we considered the problem of decoding bladder pressure from pelvic nerve electroneurograms. We extracted power spectral density of the nerve signal across a band optimised for Shannon Mutual Information, followed by linearization via piece-wise linear regression, and finally decoded signal reconstruction through optimal linear filtering. We demonstrate robust and effective reconstruction of bladder pressure, both prior to and following pharmacological manipulation.

neuroscience↗