Biomedical subjects

Christopher D Brown

Publications and source records attributed to Christopher D Brown.

5 recordsLinked to original sources

PubMed2025

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.

Genome-wide association studies (GWAS) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over two million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipids GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic QTL regulatory mechanism datasets whilst considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals, while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.

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PubMed2005

Framework for multivariate selectivity analysis, part I: theoretical and practical merits.

A number of definitions of multivariate selectivity have been proposed in the literature. Arguably, the one that enjoys the greatest chemometric attention has been the net analyte signal (NAS) based definitions of Lorber and Zinn. Recent works have suggested that similar inference can be made for inverse least-squares calibration methods (e.g., principal components regression). However, the properties of inverse calibration methods are markedly different than classical methods, so in many practical cases involving inverse models classically derived figures of merit cannot be transparently interpreted. In Part I of this work, we discuss a selectivity framework that is theoretically consistent regardless of the calibration method. Importantly, it is also experimentally measurable, either through controlled selectivity experiments, or through analysis on opportunistically acquired sample measurements. It is statistically advantageous to use the former if such control is achievable. Selectivity is defined to be a function of the change in predicted analyte concentration that will result from a change in the concentration of an interferant, an approach consistent with traditional definitions of analytical selectivity and National Committee for Clinical Laboratory Standards recommendations for interference testing. Unlike the NAS-based definition of selectivity, the definition discussed herein is relevant to only a particular analyte-interferant pair. The theoretical and experimental aspects of this approach are illustrated with simulated data herein and in Part II of this paper, which investigates several experimental near-infrared data sets.

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PubMed2005

Framework for multivariate selectivity analysis, part II: experimental applications.

In Part I of this paper, a framework for multivariate selectivity was introduced that is both calculable from first principles and experimentally tractable. In this part, we employ the proposed selectivity framework for analyzing both in vitro and in vivo near-infrared experimental data. Two in vitro data sets are used to compare different methods for estimating selectivity and to demonstrate the benefits obtained from validation data with expanded interferant concentration ranges. The in vitro data also demonstrate that the experimentally estimated selectivities provide insights into the properties of the calibration models that are difficult or impossible to infer by other means. The merits of the proposed selectivity function are further demonstrated using a complex in vivo application: the noninvasive measurement of ethanol in humans. Results indicate that in vivo calibration model sensitivity, selectivity, and concentration correlations can be systematically interrogated using the proposed selectivity framework and judicious use of experimental measurements. These analyses not only provide selectivity and sensitivity information, but also the variance components of the total MSEP, which is invaluable information for both method development and analytical method characterization.

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PubMed2004

Noncoding regulatory sequences of Ciona exhibit strong correspondence between evolutionary constraint and functional importance.

We show that sequence comparisons at different levels of resolution can efficiently guide functional analyses of regulatory regions in the ascidians Ciona savignyi and Ciona intestinalis. Sequence alignments of several tissue-specific genes guided discovery of minimal regulatory regions that are active in whole-embryo reporter assays. Using the Troponin I (TnI) locus as a case study, we show that more refined local sequence analyses can then be used to reveal functional substructure within a regulatory region. A high-resolution saturation mutagenesis in conjunction with comparative sequence analyses defined essential sequence elements within the TnI regulatory region. Finally, we found a significant, quantitative relationship between function and sequence divergence of noncoding functional elements. This work demonstrates the power of comparative sequence analysis between the two Ciona species for guiding gene regulatory experiments.

Animals↗

PubMed2004

Discordance between net analyte signal theory and practical multivariate calibration.

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

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