Biology subjects

Hendricks, A. E.

Publications and source records attributed to Hendricks, A. E..

11 recordsLinked to original sources

bioRxiv2026

IgA Targeting in the Infant Gut Is Modulated by Diet and Increasingly Directed Towards Persistent Species

BackgroundIgA is the dominant antibody in the human gut and a key regulator of host-microbe interactions. Infants begin to produce IgA at around 6 months old and receive large quantities of IgA via human milk, but technical limitations have prevented species-level characterization of IgA binding in early life. This has left basic knowledge gaps about which species are targeted by IgA in infancy, and how modifiable lifestyle factors like breastfeeding and complementary feeding impact IgA targeting. ResultsHere we adapt Metagenomic Immunoglobulin Sequencing (MIg-Seq) for low-biomass infant fecal samples and apply this optimized protocol to 32 longitudinal samples from 16 infants enrolled in the MINT trial, a four-arm randomized controlled trial comparing meat-based, dairy-based, plant-based, and reference complementary feeding patterns, with fecal sampling at 6 and 12 months (pre and post intervention). Infant IgA targeting mirrors adults at the phylum level, with both age groups showing significantly higher IgA targeting of Pseudomonadota and lower targeting of Bacteroidota relative to other phyla. During the substantial microbiome compositional shifts noted between 6 and 12 months, IgA targeting is significantly more stable than the microbiome itself. Among persistent colonizers, IgA targeting strengthens significantly from 6 to 12 months, with the most pronounced effect observed for Bifidobacterium, a finding robust across all dietary arms and feeding modes. The feeding arm to which infants were enrolled was not significantly associated with IgA binding, but several nutrient-specific associations were discovered. Animal-derived nutrients, particularly cholesterol, are strongly positively correlated with IgA targeting of Bifidobacterium longum, while plant-derived carotenoids are positively associated with IgA targeting of Flavonifractor plautii and Ruminococcus gnavus. ConclusionsThis study introduces an experimental and computational framework for species-level IgA profiling in the infant gut. The progressive strengthening of IgA targeting of Bifidobacterium and other beneficial persistent colonizers suggests a role for IgA in reinforcing beneficial microbes during infancy. The nutrient-specific dietary effects on IgA targeting reveal the immunological consequences of the complementary feeding period, and highlight a contrast between animal-versus plant-based diets. Together, these findings point to early nutritional interventions and IgA-based therapeutics as promising tools for promoting healthy immune-microbiome development.

microbiology↗

bioRxiv2026

RAREsim2: Flexible simulation of rare variant genetic data using real haplotypes

SummaryRealistic simulated data is critical for advancing methodological development and optimizing study design in genetics research. However, many genetic simulation tools are unable to replicate the distribution of rare variants or incorporate key genetic information, such as functional annotations and linkage disequilibrium. RAREsim, an accurate rare-variant simulation algorithm that uses real genetic haplotypes, was developed to address these limitations. Here, we introduce RAREsim2, an update that provides both streamlined software and new functionalities for simulating individual-level differences (e.g., cases vs controls, technological or batch effects) and variant-level differences to represent a variety of causal models. We demonstrate RAREsim2s utility with three rare variant association methods (Burden, SKAT, and SKAT-O) across several simulation scenarios: causal variants only in cases, causal variants in both cases and controls, and no causal variants. Type I Error was maintained and the optimal test matched previously known patterns: Burden performed best given a large amount of causal variants with the same direction of effect; SKAT performed best given causal variants with opposite directions of effect. SKAT-O was powerful across all simulation scenarios. We highlight RAREsim2s capabilities to simulate various genetic ancestries (African, East Asian, Non-Finnish European, and South Asian), gene sizes (~20-80 functional rare variants per gene), strengths of association (20%, 40%, 60% functional variants), and proportions of risk variants (1, 0.75, 0.5). Importantly, real genetic regions can be simulated to include known variant functions and disease associations. Ultimately, RAREsim2 offers additional flexibility and ease in simulating a multitude of realistic genetic scenarios. Availability and implementationThe RAREsim2 python package is available at https://github.com/Hendricks-Research-Team/RAREsim2 and the code for the example demonstration is available at https://github.com/JessMurphy/RAREsim2_demo. Contactjessica.murphy@cuanschutz.edu

bioinformatics↗

bioRxiv2024

Network-based representation learning reveals the impact of time and diet on the gut microbial and metabolomic environment of infants

BackgroundWhile studies have explored differences in gut microbiome development for infant liquid diets (breastmilk, formula), little is known about the impact of complementary foods on infant gut microbiome development. Here, we investigated how different protein-rich foods (i.e., meat vs. dairy) affect fecal metagenomics and metabolomics during early complementary feeding from 5-12 months in U.S. formula-fed infants from a randomized controlled feeding trial. ResultsWe used a novel network representation learning approach to model the time-dependent, complex interactions between microbiome features, metabolite compounds, and diet. We then used the embedded space to detect features associated with age and diet type and found the meat diet group was enriched with microbial genes encoding amino acid, nucleic acid, and carbohydrate metabolism. Compared to a more traditional differential abundance analysis, which analyzes features independently and found no significant diet associations, network node embedding represents the infant samples, microbiome features, and metabolites in a single transformed space revealing otherwise undetected associations between infant diet and the gut microbiome. ConclusionsOur findings generate new hypotheses regarding the interplay between complementary feeding practices, microbial-metabolic interactions, and infant physiological outcomes. This work highlights the impact of complementary foods on infant gut microbiome development and the potential of using network representation learning to integrate multi-omic data, allowing for greater insight into complex diet, microbial, and metabolite interactions.

microbiology↗

bioRxiv2024

CCAFE: Estimating Case and Control Allele Frequencies from GWAS Summary Statistics

Methods involving summary statistics in genetics can be quite powerful but can be limited in utility. For instance, many post-hoc analyses of disease studies require case and control allele frequencies (AFs), which are not always published. We present two frameworks to derive case and control AFs from GWAS summary statistics using the odds ratio, case and control sample sizes, and either the total (case and control aggregated) AF or standard error (SE). In simulations and real data, derivations of case and controls AFs using total AF is highly accurate across all settings (e.g., minor AF, condition prevalence). Conversely, derivations using SE underestimate common variant AFs (e.g. minor allele frequency >0.3) in the presence of covariates. We develop an adjustment using gnomAD AFs as a proxy for true AFs, which reduces the bias when using SE. While estimating case and control AFs using the total AF is preferred due to its high accuracy, estimating from the SE can be used more broadly since SE can be derived from p-values and beta estimates, which are commonly provided. The methods provided here expand the utility of publicly available genetic summary statistics and promote the reusability of genomic data. The R package CCAFE, with implementations of both methods, is freely available on Bioconductor and GitHub.

genomics↗

bioRxiv2024

Characterizing substructure via mixture modeling of genetic similarity in large-scale summary statistics

Genetic summary data are broadly accessible and highly useful including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into groups masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted substructure limits summary data usability, especially for understudied or admixed populations. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and identifies potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse publicly available summary data resulting in improved and more equitable research.

genomics↗

bioRxiv2021

RAREsim: A simulation method for very rare genetic variants

Identification of rare variant associations is crucial to fully characterize the genetic architecture of complex traits and diseases. Essential in this process is the evaluation of novel methods in simulated data that mirrors the distribution of rare variants and haplotype structure in real data. Additionally, importing real variant annotation enables in silico comparison of methods that focus on putative causal variants, such as rare variant association tests, and polygenic scoring methods. Existing simulation methods are either unable to employ real variant annotation or severely under- or over-estimate the number of singletons and doubletons reducing the ability to generalize simulation results to real studies. We present RAREsim, a flexible and accurate rare variant simulation algorithm. Using parameters and haplotypes derived from real sequencing data, RAREsim efficiently simulates the expected variant distribution and enables real variant annotations. We highlight RAREsims utility across various genetic regions, sample sizes, ancestries, and variant classes.

genetics↗

bioRxiv2021

Summix: A method for detecting and adjusting for population structure in genetic summary data

Publicly available genetic summary data have high utility in research and the clinic including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. While several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies from summary data. Using continental reference ancestry, African (AFR), Non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v2.1 exome and genome groups and subgroups finding heterogeneous continental ancestry for several groups including African/African American ([~]84% AFR, [~]14% EUR) and American/Latinx ([~]4% AFR, [~]5% EAS, [~]43% EUR, [~]46% IAM). Compared to the unadjusted gnomAD AFs, Summixs ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities.

genetics↗

bioRxiv2020

Genome-wide Copy Number Variations in a Large Cohort of Bantu African Children

BackgroundCopy number variations (CNVs) account for a substantial proportion of inter-individual genomic variation. However, a majority of genomic variation studies have focused on single-nucleotide variations (SNVs), with limited genome-wide analysis of CNVs in large cohorts, especially in populations that are under-represented in genetic studies including people of African descent. ResultsIn this study, we carried out a genome-wide analysis in > 3400 healthy Bantu Africans from Tanzania using high density (> 2.5 million probes) genotyping arrays. We identified over 400000 CNVs larger than 1 kilobase (kb), for an average of 120 CNVs (SE = 2.57) per individual. We detected 866 large CNVs ([≥] 300 kb), some of which overlapped genomic regions previously associated with multiple congenital anomaly syndromes, including Prader-Willi/Angelman syndrome (Type1) and 22q11.2 deletion syndrome. Furthermore, several of the common CNVs seen in our cohort ([≥] 5%) overlap genes previously associated with developmental disorders. ConclusionThese findings may help refine the phenotypic outcomes and penetrance of variations affecting genes and genomic regions previously implicated in diseases. Our study provides one of the largest datasets of CNVs from individuals of African ancestry, enabling improved clinical evaluation and disease association of CNVs observed in research and clinical studies in African populations.

genomics↗

bioRxiv2020

Accessible Analysis of Longitudinal Data with Linear Mixed Effects Models

Longitudinal mouse models are commonly used to study possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe the linear mixed effects (LME) model and how to use it for longitudinal mice studies. We re-analyze a dataset published by Blanton et al (Science 2016) that modeled mice growth trajectories after microbiome implantation from nourished or malnourished children. We compare the fit and stability of different parameterizations of ANOVA and LME models; most models found the nourished vs. malnourished growth trajectories differed significantly. We show through simulation that the results from the two-way ANOVA and LME models are not always consistent. Incorrectly modeling correlated data can result in increased rates of false positives or false negatives, supporting the need to model correlated data correctly. We provide an interactive Shiny App to enable accessible and appropriate analysis of longitudinal data using LME models.

bioinformatics↗

bioRxiv2018

Analysis of >30,000 abstracts suggests higher false discovery rates for oncology journals, especially those with low impact factors

BackgroundRecently, there has been increasing concern about the replicability, or lack thereof, of published research. An especially high rate of false discoveries has been reported in some areas motivating the creation of resource-intensive collaborations to estimate the replication rate of published research by repeating a large number of studies. The substantial amount of resources required by these replication projects limits the number of studies that can be repeated and consequently the generalizability of the findings.\n\nMethods and findingsIn 2013, Jager and Leek developed a method to estimate the empirical false discovery rate from journal abstracts and applied their method to five high profile journals. Here, we use the relative efficiency of Jager and Leeks method to gather p-values from over 30,000 abstracts and to subsequently estimate the false discovery rate for 94 journals over a five-year time span. We model the empirical false discovery rate by journal subject area (cancer or general medicine), impact factor, and Open Access status. We find that the empirical false discovery rate is higher for cancer vs. general medicine journals (p = 5.14E-6). Within cancer journals, we find that this relationship is further modified by journal impact factor where a lower journal impact factor is associated with a higher empirical false discovery rates (p = 0.012, 95% CI: -0.010, -0.001). We find no significant differences, on average, in the false discovery rate for Open Access vs closed access journals (p = 0.256, 95% CI: -0.014, 0.051).\n\nConclusionsWe find evidence of a higher false discovery rate in cancer journals compared to general medicine journals, especially those with a lower journal impact factor. For cancer journals, a lower journal impact factor of one point is associated with a 0.006 increase in the empirical false discovery rate, on average. For a false discovery rate of 0.05, this would result in over a 10% increase to 0.056. Conversely, we find no significant evidence of a higher false discovery rate, on average, for Open Access vs. closed access journals from InCites. Our results provide identify areas of research that may need of additional scrutiny and support to facilitate replicable science. Given our publicly available R code and data, others can complete a broad assessment of the empirical false discovery rate across other subject areas and characteristics of published research.

scientific communication and education↗

bioRxiv2018

ProxECAT: Proxy External Controls Association Test. A new case-control gene region association test using allele frequencies from public controls

A primary goal of the recent investment in sequencing is to detect novel genetic associations in health and disease improving the development of treatments and playing a critical role in precision medicine. While this investment has resulted in an enormous number of sequenced genomes, individual studies of complex traits are often smaller and underpowered to detect rare variant genetic associations. Existing genetic resources such as the Exome Aggregation Consortium (>60,000 exomes) and the Genome Aggregation Database (~140,000 sequenced samples) could be used as controls in these studies. Fully utilizing these and other existing sequencing resources has the potential to increase power and could be especially useful in studies where resources to sequence additional samples are limited. However, to date, these large, publicly available genetic resources remain underutilized, or even misused, in large part due to the lack of statistical methods that can appropriately use this summary level data. We present a new method to incorporate external controls in case-control analysis called ProxECAT (Proxy External Controls Association Test). ProxECAT estimates enrichment of rare variants within a gene region using internally sequenced cases and external controls. We evaluated ProxECAT in simulations and empirical analyses of obesity cases using both low-depth of coverage (7x) whole-genome sequenced controls and ExAC as controls. We find that ProxECAT maintains the expected type I error rate with increased power as the number of external controls increases. With an accompanying R package, ProxECAT enables the use of publicly available allele frequencies as external controls in case-control analysis.

genetics↗