Biology subjects

Kunert-Graf, J. M.

Publications and source records attributed to Kunert-Graf, J. M..

3 recordsLinked to original sources

bioRxiv2020

Allele Frequency Mismatches and Apparent Mismappings in UK Biobank SNP Data

We report here some anomalies discovered in the minor allele frequencies (MAFs) and some likely mismappings found in our analyses of UK Biobank dataset (UKB) and several other databases. We compared the MAFs present in the UKB to those measured in two other UK studies, ALSPAC and TwinsUK, and found a large set of SNPs for which the UKB MAFs are inconsistent. Additionally, even after accounting for population structure effects and other possible causes of spurious correlations, we found many SNPs that appear to be in interchromosomal linkage. Analyzing these interchromosomal linkages carefully, we found that they are all associated with identical sequences on different chromosomes, implying that these SNPs are simply mismapped. Some (but certainly not all) of the MAF disagreements appear to be the result of these mismappings. Our results, including lists of SNPs with inconsistent MAFs and/or apparent interchromosomal linkage, are freely available to download at: http://kunertgraf.com/data/biobank.html

genomics↗

bioRxiv2019

Towards an information theory of quantitative genetics

Quantitative genetics has evolved dramatically in the past century, and the proliferation of genetic data, in quantity as well as type, enables the characterization of complex interactions and mechanisms beyond the scope of its theoretical foundations. In this paper, we argue that revisiting the framework for analysis is important and we begin to lay the foundations of an alternative formulation of quantitative genetics based on information theory. Information theory can provide sensitive and unbiased measures of statistical dependencies among variables, and it provides a natural mathematical language for an alternative view of quantitative genetics. In previous work we examined the information content of discrete functions and applied this approach and methods to the analysis of genetic data. In this paper we present a framework built around a set of relationships that both unifies the information measures for the discrete functions and uses them to express key quantitative genetic relationships. Information theory measures of variable interdependency are used to identify significant interactions, and a general approach is described for inferring functional relationships within genotype and phenotype data. We present information-based measures of the genetic quantities: penetrance, heritability and degrees of statistical epistasis. Our scope here includes the consideration of both two- and three-variable dependencies and independently segregating variants, which captures additive effects, genetic interactions, and two phenotype pleiotropy. This formalism and the theoretical approach naturally applies to higher multi-variable interactions and complex dependencies, and can be adapted to account for population structure, linkage and non-randomly segregating markers. This paper thus focuses on presenting the initial groundwork for a full formulation of quantitative genetics based on information theory.

genetics↗

bioRxiv2018

Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

neuroscience↗