Biology subjects

Wood, A. C.

Publications and source records attributed to Wood, A. C..

7 recordsLinked to original sources

bioRxiv2026

Genetic drivers of protein changes over time: Findings, considerations, and approaches in TOPMed cohorts and UK Biobank

Age is a major risk factor for many diseases, but the biological processes driving aging are heterogeneous across individuals. Efforts to untangle differences between chronological and biological age have focused on identifying age-associated markers, such as omics clocks. Many omics features, including proteins, are strongly associated with age, and genetics contribute to variance in these measures. However, few studies have identified genetic drivers of interindividual variability in omics changes over time. Using longitudinal proteomics data (Olink 3k) from the Multi-Ethnic Study of Atherosclerosis (MESA), we calculated a protein slope for each individual (n=2,007) and protein (n=2,737) across 3 visits spanning 14-18 years, then conducted a genome-wide analysis for each slope, both with and without adjusting for baseline protein level. Subsets in UK Biobank (UKB; n=948) and CARDIA (n=1,328) with longitudinal proteomics data were used for replication. We considered additional methods for modeling of protein change and variability, including linear mixed models, SNP-by-age interactions, and variance quantitative trait loci. Without baseline adjustment, only 19 proteins (20 credible sets) had a slope pQTL in MESA, with poor replication in UKB and CARDIA. With baseline adjustment, 607 proteins (698 credivle sets) had a slope pQTL and over 70% replicated in CARDIA and/or UKB; such baseline adjusted models may, however, be subject to collider bias. Longitudinal and cross-sectional interaction models identified fewer than 14 pQTLs, suggesting they were generally underpowered; but 73% of proteins with a variance pQTL also had a slope pQTL. By examining effect direction concordance, replication rate, directed acyclic graphs, and signal overlap with other models we demonstrate that many baseline-adjusted slope pQTLs may be arising due to model misspecification or regression to the mean. Overall, our results highlight considerations for modeling strategies of change phenotypes and build on understanding of potential genetic mechanisms influencing interindividual proteome changes over time.

genetics↗

bioRxiv2026

Short-term, Long-term, and Genetic Determinants of Human Plasma Proteome Variability

Plasma proteomics is increasingly used for biomarker discovery and predictive modeling, yet diurnal protein trajectories remain insufficiently characterized. In our review of recent proteomic biomarker studies, 43% of the identified biomarkers had previously been reported to display 24-h rhythmicity. We demonstrate that ignoring these short-term dynamic effects compromises the robustness of reported models predicting health outcomes. We integrated a population-scale multi-ethnic longitudinal cohort with repeated measures over 10 years, with two cohorts of healthy adults undergoing frequent plasma sampling across days under controlled circadian, sleep and food-intake conditions. This design enabled estimation of short-term intraindividual variability (ST), long-term intraindividual variability (LT), population-level variability (POP) and genetic effects (GEN) across 7,289 protein targets. ST, LT, POP, and GEN define diverse protein trajectories, including rapid dynamics, long-term change, and individual-specific signatures. Using and generalizing this framework will facilitate covariate selection, study design, biomarker prioritization, and variability-aware modeling by users of proteomic data.

genomics↗

bioRxiv2025

The Neurofibrillary Tangle Maturity Scale: A Novel Framework for Tangle Pathology Evaluation in Alzheimer's Disease

Neurofibrillary tangles are dynamic neuropathologic hallmarks of Alzheimers disease with a hypothesized lifespan morphologically-defined by three maturity levels: pretangles, mature tangles, and ghost tangles. To better understand the progression of tangle pathophysiology, we characterized tangle maturity level predilection of 15 tau antibodies recognizing a broad range of linear, phosphorylation, conformational, and truncation epitopes in the hippocampus of 24 postmortem brains. We developed the tangle maturity scoring system to semi-quantitatively evaluate each tangle maturity level. Based on proportions of tangle maturity levels, we classified antibodies as "early" (mostly pretangles and mature tangles), "middling" (mature tangles with pretangles and ghost tangles), and "advanced" (mostly ghost tangles and mature tangles) tangle maturity markers. To summarize tangle maturity predilection, we developed the tangle maturity scale to integrate individual tangle maturity scores. Correlations showed stronger relationships between tangle maturity scale and subsector thickness for more advanced tangle maturity markers in CA1 and subiculum, whereas Braak tangle stage remained consistently correlated throughout markers of the tangle lifespan. To aid in scoring hippocampi, we used machine learning to recognize tangle maturity levels, which performed comparably to a domain expert and showed similar relationships by Spearman correlation. Pattern recognition software was used to assess tangle and neuritic tau burden separately, which generally correlated with Braak stage and neuronal counts. However, tangle-derived tau burden more consistently correlated with hippocampal subsector thickness. In conclusion, we developed manual and automated scoring systems to evaluate tangle maturity levels, demonstrating early 4R, phosphorylated, and oligomeric tau accumulation preceding more advanced 3R and truncated tau. Our study provides supportive evidence of disease-relevant ordering of tau posttranslational modifications in the brain, which may have implications for theragnostic development. These findings underscore the promise of computerized quantitative analyses (i.e., pathomics) for high-throughput feature extraction from whole-slide images to enhance our understanding of microscopically observed morphologic changes.

neuroscience↗

bioRxiv2024

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. We provided a set of reasonable strategies for outlier and imputation handling to process metabolite data. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole genome sequencing data from 16,359 individuals passing the quality control filters, and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus-metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2, D2HGDH, SUGP1, UTG2B17, strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

bioinformatics↗

bioRxiv2019

Type-2 diabetes with low LDL-C: genetic insights into a unique phenotype

Although hyperlipidemia is traditionally considered a risk factor for type-2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL-C increases T2D risk. We thus sought to comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that LDL-C was negatively associated with T2D (OR=0.43[0.41, 0.45] per mmol/L unit of LDL-C), despite positive associations of LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n=431,167) and the GLGC consortium (n=188,577), and T2D from the DIAGRAM consortium (n=898,130). We identified 31 loci associated with lower LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this phenotype, and 9 have previously been implicated in non-alcoholic fatty liver disease. Finally, two-sample Mendelian randomization analyses suggest that low LDL-C causes T2D, although causal interpretations are challenging due to pleiotropy. Our findings extend our current understanding of the higher T2D risk among individuals with low LDL-C, and of the underlying mechanisms, including those underlying the diabetogenic effect of LDL-C-lowering medications.

genetics↗

bioRxiv2019

Multi-ethnic analysis shows genetic risk and environmental predictors interact to influence 25(OH)D concentration and optimal vitamin D intake

BackgroundVitamin D inadequacy affects almost 50% of adults in the United States and is associated with numerous adverse health effects. Vitamin D concentration [25(OH)D] is a complex trait with genetic and environmental predictors that work in tandem to influence 25(OH)D and may determine how much vitamin D intake is required to reach an optimal 25(OH)D concentration. To date, there has been little investigation into how genetics and environment interact to affect 25(OH)D.\n\nObjectiveInteractions between continuous measures of a polygenic score (PGS) and vitamin D intake (PGS*intake) or available ultra-violet (UV) radiation (PGS*UV) were evaluated separately in individuals of African or European ancestry.\n\nMethodsMega-analyses were performed using three independent cohorts (N=9,668; African ancestry n=1,099; European ancestry n=8,569). Interaction terms and joint effects (main and interaction terms) were tested using one-degree of freedom (DF) and 2-DF models, respectively. All models controlled for age, sex, body mass index (BMI), cohort, and dietary intake/available UV. Additionally, in participants achieving Institute of Medicine (IOM) vitamin D intake recommendations, 25(OH)D was evaluated by level of genetic risk of 25(OH)D deficiency.\n\nResultsThe 2-DF PGS*intake, 1-DF PGS*UV and 2-DF PGS*UV results were statistically significant in participants of European ancestry (p=3.3x10-18, 2.1x10-2, and 2.4x10-19, respectively), but not in those of African ancestry. In European-ancestry participants who reached IOM vitamin D intake guidelines, the percent of participants achieving adequate 25(OH)D (>20ng/ml) increased as genetic risk decreased (72% vs 89% in the highest vs lowest risk categories; p=0.018).\n\nConclusionsAvailable UV radiation and vitamin D intake interact with genetics to influence 25(OH)D. Individuals with higher genetic risk of deficiency may require more vitamin D exposure to maintain optimal 25(OH)D concentrations. Overall, the results showcase the importance of incorporating both environmental and genetic factors into analyses, as well as the potential for gene-environment interactions to inform personalized dosing of vitamin D.\n\nSources of SupportO_ST_ABSARICC_ST_ABSThe Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). The authors thank the staff and participants of the ARIC study for their important contributions.\n\nFunding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle).\n\nMESAMESA and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881, and DK063491.\n\nThe MESA CARe data used for the analyses described in this manuscript were obtained through Genetics (accession numbers). Funding for CARe genotyping was provided by NHLBI Contract N01-HC-65226.\n\nFunding support for the Vitamin D dataset was provided by grant HL096875\n\nWHIThe WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. This manuscript was not prepared in collaboration with investigators of the WHI, has not been reviewed and/or approved by the Womens Health Initiative (WHI), and does not necessarily reflect the opinions of the WHI investigators or the NHLBI.\n\nWHI PAGE is funded through the NHGRI Population Architecture Using Genomics and Epidemiology (PAGE) network (Grant Number U01 HG004790). Assistance with phenotype harmonization, SNP selection, data cleaning, meta-analyses, data management and dissemination, and general study coordination, was provided by the PAGE Coordinating Center (U01HG004801-01).\n\nFunding support for WHI GARNET was provided through the NHGRI Genomics and Randomized Trials Network (GARNET) (Grant Number U01 HG005152). Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GARNET Coordinating Center (U01 HG005157). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Funding support for genotyping, which was performed at the Broad Institute of MIT and Harvard, was provided by the NIH Genes, Environment and Health Initiative [GEI] (U01 HG004424). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGa Paccession phs000200.v11.p3.\n\nFunding for WHI SHARe genotyping was provided by NHLBI Contract N02-HL-64278.\n\nOtherKEH was supported by an NLM training grant to the Computation and Informatics in Biology and Medicine Training Program (NLM 5T15LM007359). Computational resources were supported by a core grant to the Center for Demography and Ecology at the University of Wisconsin-Madison (P2C HD047873).\n\nJM was supported by the Department of Ophthalmology and Visual Sciences, and by an unrestricted grant to the Department of Ophthalmology and Visual Sciences from the Research to Prevent Blindness, and by National Institutes of Health, National Eye Institute grants R01 EY016686, R01 EY025292.

genomics↗

bioRxiv2019

Ancestry-specific polygenic scores and SNP heritability of 25(OH)D in African- and European-ancestry populations

ContextVitamin D inadequacy, assessed by 25-hydroxyvitamin D [25(OH)D], affects around 50% of adults in the United States and is associated with numerous adverse health outcomes. Blood 25(OH)D concentrations are influenced by genetic factors that may determine how much vitamin D intake is required to reach optimal 25(OH)D. Despite large genome-wide association studies (GWASs), only a small portion of the genetic factors contributing to differences in 25(OH)D levels has been discovered.\n\nObjectiveTherefore, knowledge of a fuller set of genetic factors could be useful for risk prediction of 25(OH)D inadequacy, personalized vitamin D supplementation, and prevention of morbidity and mortality from deficient 25(OH)D.\n\nDesignUsing PRSice and weights from published African- and European-ancestry GWAS summary statistics, ancestry-specific polygenic scores (PGSs) were created to capture a more complete set of genetic factors.\n\nPatients or Other ParticipantsParticipants (European ancestry n=9,569, African ancestry n=2,761) came from three cohort studies.\n\nMain Outcome Measure(s)Blood concentrations of 25(OH)D.\n\nResultsThe PGS for African ancestry was derived using all input SNPs (a p-value cut-off of 1.0) and had an R2 of 0.3%; for European ancestry, the optimal PGS used a p-value cut-off of 3.5x10-4 in the target/tuning dataset and had an R2 of 1.0% in the validation cohort. Those with highest genetic risk had 25(OH)D that was 2.8-3.0 ng/ml lower than those with lowest genetic risk (p=0.0463 to 3.2x10-13), requiring an additional 467 to 500 IU of vitamin D intake to maintain equivalent 25(OH)D.\n\nConclusionsPGSs are a powerful predictive tool that could be leveraged for personalized vitamin D supplementation to prevent the negative downstream effects of 25(OH)D inadequacy.

genetics↗