Biology subjects

Moon, J.-Y.

Publications and source records attributed to Moon, J.-Y..

7 recordsLinked to original sources

bioRxiv2026

ScreenDMT reveals metabolites replicably associated with sleep disorders across three human cohorts

The robust replication of scientific findings across independent studies is essential, yet it remains challenging in high-dimensional biological datasets. A critical aspect of true replication requires detecting not only the repeated presence but also the consistent direction of an effect. Traditional methods for evaluating replication often ignore effect directionality or lack adequate statistical power when adjusting for multiple comparisons. To address this, we extend the directional MaxP test (DMT) and our screening-integrated procedure, ScreenDMT, to evaluate directional replication across an arbitrary number of independent studies. We mathematically prove ScreenDMT's validity for an arbitrary number of studies when the studies' sample sizes grow unbounded. Through extensive simulation studies, we demonstrate that ScreenDMT effectively controls error rates, such as the false discovery rate (FDR), in finite sample sizes and yields higher statistical power than competing methods especially for three studies. We applied the extended ScreenDMT to untargeted blood metabolomics profiles across three diverse human cohorts - ARIC (N=1,337), MESA (N=1,874), and HCHS/SOL (N=6,180) - to discover replicable biomarkers for dichotomous insomnia, long sleep, short sleep, and excessive daytime sleepiness (EDS). We found low levels of cortisol to be replicably associated with EDS, and high levels of n-formylmethionine to be associated with long sleep. Furthermore, directional pathway analysis revealed that low levels of xanthine metabolites are replicably associated with insomnia. These convergent findings validate ScreenDMT as a powerful methodology for simultaneous multi-cohort replication and offer robust insights into the metabolic signatures underlying sleep disorders across diverse populations.

bioinformatics↗

bioRxiv2025

Sub-second Fluctuation between Top-Down and Bottom-Up Modes Distinguishes Diverse Human Brain States

Information continuously flows between regions of the human brain, exhibiting distinct patterns that dynamically shift across states of consciousness, cognitive modes, and neuropsychiatric conditions. In this study, we introduce Relative Phase Analysis (RPA), a method that leverages phase-lead/lag relationships to reveal the real-time dynamics of dominant directional patterns and their rapid transitions. We demonstrate that the human brain switches on a sub-second timescale between a top-down mode-where anterior regions drive posterior activity-and a bottom-up mode, characterized by reverse directionality. These dynamics are most pronounced during full consciousness and gradually become less distinct as awareness diminishes. Furthermore, we find from simultaneous EEG-fMRI recordings that the top-down mode is expressed when higher-order cognitive networks are more active while the bottom-up mode is expressed when sensory systems are more active. Moreover, comparisons of an attention deficit hyperactivity disorder (ADHD) inattentive cohort with typically developing individuals reveal distinct imbalances in these transition dynamics, highlighting the potential of RPA as a diagnostic biomarker. Complementing our empirical findings, a coupled-oscillator model of the structural brain network recapitulates these emergent patterns, suggesting that such directional modes and transitions may arise naturally from inter-regional neural interactions. Altogether, this study provides a framework for understanding whole-brain dynamics in real-time and identifies sub-second fluctuations in top-down versus bottom-up directionality as a fundamental mechanism underlying human information processing.

neuroscience↗

bioRxiv2024

Weighted variance component test for the integrative multi-omics analysis of microbiome data

Metabolic dysregulation and alterations have been linked to various diseases and conditions. Innovations in high-throughput technology now allow rapid profiling of the metabolome and metagenome -- often the gene content of bacterial populations -- for characterizing metabolism. Due to the small sample sizes and high dimensionality of the data, pathway analysis (wherein the effect of multiple genes or metabolites on an outcome is cumulatively assessed) of metabolomic data is commonly conducted and also represents a standard for metagenomic analysis. However, how to integrate both data types remains unclear. Recognizing that a metabolic pathway can be complementarily characterized by both metagenomics and metabolomics, we propose a weighted variance components framework to test if the joint effect of genes and metabolites in a biological pathway is associated with outcomes. The approach allows analytic p-value calculation, correlation between data types, and optimal weighting. Power simulations show that our approach often outperforms other strategies while maintaining type I error. The approach is illustrated on real data.

bioinformatics↗

bioRxiv2022

Inter-regional delays fluctuate in the human cerebral cortex

The flow of information between cortical regions depends on the excitability at each site, which is reflected in fluctuating field potentials. It remains uncertain how global changes in field potentials affect the latency and strength of cortico-cortical couplings. Therefore, we measured changes in oscillations and inter-regional couplings by recording intracranially from the human cerebral cortex. As participants listened to an auditory narrative, global increases in low-frequency (4-14 Hz) power were associated with stronger and more delayed inter-regional couplings. Conversely, increases in broadband high-frequency power were associated with weaker coupling and zero lag. In network oscillator models, these changes in cortico-cortical latency can be generated by varying the effective influence of inter-regional projections relative to intra-regional dynamics. Altogether, low-frequency oscillations appear to modulate information flow across the human cerebral cortex, as they covary with the timing of peak excitability between regions, and this process may be regulated by nonspecific ascending projections.

neuroscience↗

bioRxiv2021

Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity

Understanding human genetic influences on the gut microbiota helps elucidate the mechanisms by which genetics affects health outcomes. We propose a novel approach, the covariate-adjusted kernel RV (KRV) framework, to map genetic variants associated with microbiome beta-diversity, which focuses on overall shifts in the microbiota. The proposed KRV framework improves statistical power by capturing intrinsic structure within the genetic and microbiome data while reducing the multiple-testing burden. We apply the covariate-adjusted KRV test to the Hispanic Community Health Study/Study of Latinos in a genome-wide association analysis (first gene-level, then variant-level) for microbiome beta-diversity. We have identified an immunity-related gene, IL23R, reported in previous association studies and discovered 3 other novel genes, 2 of which are involved in immune functions or autoimmune disorders. Our findings highlight the value of the KRV as a powerful microbiome GWAS approach and support an important role of immunity-related genes in shaping the gut microbiome composition.

genomics↗

bioRxiv2020

find-tfbs: a tool to identify functional non-coding variants associated with complex human traits using open chromatin maps and phased whole-genome sequences

MotivationWhole-genome DNA sequencing (WGS) enables the discovery of non-coding variants, but tools are lacking to prioritize the subset that functionally impacts human phenotypes. DNA sequence variants that disrupt or create transcription factor binding sites (TFBS) can modulate gene expression. find-tfbs efficiently scans phased WGS in large cohorts to identify and count TFBSs in regulatory sequences. This information can then be used in association testing to find putatively functional non-coding variants associated with complex human diseases or traits. ResultsWe applied find-tfbs to discover functional non-coding variants associated with hematological traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) WGS dataset (Nmax=44,709). We identified >2000 associations at P<1x10-9, implicating specific blood cell-types, transcription factors and causal genes. The vast majority of these associations are captured by variants identified in large genome-wide association studies (GWAS) for blood-cell traits. find-tfbs is computationally efficient and robust, allowing for the rapid identification of non-coding variants associated with multiple human phenotypes in very large sample size. Availabilityhttps://github.com/Helkafen/find-tfbs and https://github.com/Helkafen/find-tfbs-demo Contactssebastian.meric.de.bellefon@umontreal.ca and guillaume.lettre@umontreal.ca Supplementary informationSupplementary data are available.

genomics↗

bioRxiv2020

Genetics of human gut microbiome composition

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 out of 410 genera were detected in more than 95% samples. A genome-wide association study (GWAS) of host genetic variation in relation to microbial taxa identified 31 loci affecting microbiome at a genome-wide significant (P<5x10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (GWAS signal P=1.28x10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95x10-10<P<5x10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome has causal effects in ulcerative colitis and rheumatoid arthritis.

genetics↗