Biology subjects

Sebastiani, P.

Publications and source records attributed to Sebastiani, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Occupationally Relevant Wildfire Smoke Inhalation Impairs Nitric Oxide Signaling and Promotes Progressive Aortic Stiffening in Hypercholesterolemic Mice

BackgroundWildland firefighters experience repeated occupational exposure to wildfire smoke at high particulate matter (PM) concentrations, leading to elevated cardiovascular disease risk and hypertension prevalence. However, the pathophysiological processes linking cumulative smoke inhalation to vascular damage and blood pressure elevation remain poorly characterized. To evaluate these effects under controlled exposure conditions, we used a preclinical exposure model calibrated to match the cumulative PM burden deposited in wildland firefighter airways over 7-14 years of service. Male apolipoprotein E knockout (Apoe-/-) mice underwent whole-body inhalation of Douglas fir smoke or filtered air for 2 hours/day, 5 days/week, for 8 or 16 weeks at target PM concentrations of 40 mg/m3. ResultsProlonged smoke exposure induced sustained elevation of circulating tumor necrosis factor-alpha (TNF-), interleukin-1 beta (IL-1{beta}), and interleukin-6 (IL-6), coupled with diffused nuclear factor kappa B (NF-{kappa}B) activation throughout the aortic wall. Smoke inhalation disrupted endothelial adherens junctions, upregulated intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), and promoted monocyte recruitment to aortic tissues, concurrent with enhanced monocyte chemoattractant protein-1 (MCP-1) expression. Oxidative stress was evidenced by increased nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunit 2 (NOX2) expression, elevated superoxide levels, and endothelial nitric oxide synthase (eNOS) uncoupling in the aorta, leading to lipid peroxidation and accompanied by intimal apoptosis. These inflammatory and oxidative perturbations occurred alongside a pro-fibrotic phenotypic shift characterized by transforming growth factor beta 1 (TGF-{beta}1) upregulation, myofibroblast differentiation, and progressive collagen accumulation in medial and adventitial compartments of the aortic wall. Functionally, smoke exposure progressively impaired aortic cyclic distensibility through combined wall thickening and circumferential tissue stiffening, while severely attenuating endothelium-dependent and nitric oxide (NO)-mediated vasodilation. These functional and structural shifts culminated in elevated systolic and diastolic blood pressures. While endothelial dysfunction reached maximal impairment by 8 weeks, aortic stiffening continued to worsen through 16 weeks of exposure, demonstrating differential temporal progression of vascular damage. ConclusionsThese findings demonstrate that occupationally relevant wildfire smoke exposure produces convergent inflammatory, oxidative, and profibrotic vascular remodeling with progressive loss of arterial compliance and impaired endothelium-dependent vasodilation, underscoring potential vascular targets for cardiovascular health surveillance and risk mitigation in wildland firefighters.

pharmacology and toxicology↗

bioRxiv2026

Genetic Modifiers of Cognitive Function: Novel Genetic Associations with Memory, Executive Function, and Attention in the Long Life Family Study

Cognitive performance is central to health and quality of life. Studying the factors that sustain performance may offer insights into maintaining cognitive function into advanced ages and may inform strategies to promote healthy cognitive aging. To identify single nucleotide polymorphisms (SNPs) underlying cognitive function, we performed genome-wide association studies (GWAS) of nine neuropsychological test scores capturing performance in three cognitive domains in 2,455 participants of the Long Life Family Study (LLFS). We identified 12 variants in seven tests and three domains that reached genome-wide significance (p < 5x10-). Three rare (minor allele frequency, European population MAF<0.01) protective, intronic variants, rs190287985 (CCSER1), rs75730801 (FHOD3), and rs552842447 (LINC00508), were uniquely associated with semantic fluency, phonemic fluency and number span forward, respectively. Two rare deleterious variants, rs556333682 and rs188304645, were associated with performance on the Hopkins Verbal Learning Test-Revised (HVLT-R) learning trials and lie in ischemia-related genes SH3TC1 and RPH3A. Another variant, rs180691759, associated with HVLT-R delayed recall, was proximal to RSPO3 and linked to decreased ECHDC1 expression, implicating ischemic and unexplained Ethylmalonic acid (EMA) pathways. We compared the results with GWASs of a general cognitive factor (GCF) and reaction time (RT) in the UK Biobank and meta-analysis results of the UK Biobank, CHARGE and COGENT by Davies et al. We found that rs535509651 associates with HVLT-R delayed recall in the LLFS and nominally associates (p<0.05) with GCF, and that rs10424537 associates with Immediate Logical Memory in the LLFS and nominally associates with RT. Furthermore, we identified 5 genome-wide significant loci in Davies et al. that reached loci adjusted significance in the LLFS (GCF p<0.05/128 and RT p<0.05/39). Each locus was associated with a single cognitive domain in the LLFS. We annotated the genome-wide significant results with quantitative trait loci (QTL) analyses of whole blood transcriptomic, serum metabolomic data, and gene set enrichment analyses (GSEA) using all nominally significant transcripts (p < 0.05/12). QTL analyses discovered 1 SNP-transcript (ECHDC1, p < 3 x 10-6), no SNP-lipid (p < 2 x 10-4), and no SNP-polar metabolite (p < 2 x 10-4) associations. Gene set enrichment analyses identified three pathways at FDR < 0.05. Our findings provide insight into the domain-specific genetic architecture of cognitive function.

genetics↗

bioRxiv2025

hypeR-GEM: connecting metabolite signatures to enzyme-coding genes via genome-scale metabolic models

Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. While well-established tools exist for transcriptomics and other omics layers, the development of robust enrichment resources for metabolomics remains comparatively limited. To address this gap, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites annotation, demonstrating hypeR-GEMs practical utility in a real-world use case.

bioinformatics↗

bioRxiv2025

IPSC-based modeling of resiliency in centenarians reveals longevity-specific signatures

Centenarians represent a human model of resilience to age-related decline, yet resiliency mechanisms remain elusive. Here, we establish an induced pluripotent stem cell (iPSC)-based platform to interrogate resilience signatures in centenarians. IPSC-derived neurons from centenarians exhibit transcriptional programs promoting synaptic integrity, calcium homeostasis, and cholesterol biosynthesis, while suppressing proteostatic stress pathways. Functionally, these neurons maintain stable calcium dynamics, reduced baseline mitochondrial activity, and energy-efficient homeostasis. Upon challenge, centenarian-derived neurons mount a robust stress response, in contrast to attenuated responses in non-centenarian controls. This resilience signature parallels adaptations in long-lived mammals and aligns with healthy brain aging, while showing erosion in Alzheimers disease and cancer. Our platform provides a scalable human model for dissecting resilience biology offering a framework to extend healthspan and mitigate age-related decline.

molecular biology↗

bioRxiv2025

METABOLOMIC SIGNATURES OF EXTREME OLD AGE: FINDINGS FROM THE NEW ENGLAND CENTENARIAN STUDY

The New England Centenarian Study (NECS) provides a unique resource for the study of extreme human longevity (EL). To gain insight into biological pathways related to EL, chronological age and survival, we used an untargeted serum metabolomic approach (> 1,400 metabolites) in 213 NECS participants, followed by integration of our findings with metabolomic data from four additional studies. Compared to their offspring and matched controls, EL individuals exhibited a distinct metabolic profile characterized by higher levels of primary and secondary bile acids - most notably chenodeoxycholic acid (CDCA) and lithocholic acid (LCA) - higher levels of biliverdin and bilirubin, and stable levels of selected steroids. Notably, elevated levels of both bile acids and steroids were associated with lower mortality. Several metabolites associated with age and survival were inversely associated with metabolite ratios related to NAD+ production and/or levels (tryptophan/kynurenine, cortisone/cortisol), gut bacterial metabolism (ergothioneine/ trimethylamine N-oxide, aspartate/quinolinate), and oxidative stress (methionine/methionine sulfoxide), implicating these pathways in aging and/or longevity. We further developed a metabolomic clock predictive of biological age, with age deviations significantly associated with mortality risk. Key metabolites predictive of biological aging, such as taurine and citrate, were not captured by traditional age analyses, pointing to their potential role as biomarkers for healthy aging. These results highlight metabolic pathways that may be targeted to promote metabolic resilience and healthy aging.

systems biology↗

bioRxiv2025

Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies

Although aging is a universal event, some individuals are able to achieve extreme longevity. The Long-Life Family Study (LLFS) enrolls participants from families enriched with long-lived individuals, serves as a valuable dataset for studying ageing phenotypes and identify potential intervention targets. We analyzed the association between age at blood draw and 16,284 RNAseq-based blood transcriptomic data from 2,167 LLFS participants with ages ranging from 18 to 107, replicated the results in the Integrative Longevity Omics Study (ILO) dataset of 20,884 RNAseq-based blood transcriptomic data from 419 participants, with ages ranging from 60 to 108, and further compared our findings to a published reference aging signature. We identified 4,227 transcripts increasing and 4,044 transcripts decreasing with age, and enrichment analysis revealed age-related upregulation of inflammatory and senescence-related pathways, and downregulation of MYC and Wnt/{beta}-catenin targets, among others. Further, a subset of transcripts showed age associations unique to the longevity-enriched cohorts (LLFS and ILO). We also identified 314 transcripts significantly associated with mortality risk and found that pro-survival gene sets included NK cell-mediated cytotoxicity and GPCR signaling. Finally, increased transcriptomic age predicted using transcriptomic clock was strongly associated with increased mortality. In summary, this study identified robust transcriptomic signatures of aging and mortality in a longevity-enriched population, highlighting key biological pathways such as immune modulation, inflammation, and senescence. Authors notesThis manuscript has been peer-reviewed and accepted by GeroScience (Springer). This bioRxiv article reflects the published version, incorporating revisions made in response to reviewers comments. The main content, results, and conclusions remain unchanged from the previous version, while the Discussion section has been expanded to further address the functional annotation and clinical relevance of the identified markers. Copy of the acceptance letterThe editors are pleased to inform you that your manuscript, JAAA-D-25-01946R1 entitled "Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies" has been accepted for publication in GeroScience as an Original Article. The editors commend you on your outstanding contribution to the journal. Your manuscript can be published online ahead of print within approximately two weeks.

molecular biology↗

bioRxiv2025

Increased Genetic Protection Against Alzheimer's Disease in Centenarians

We constructed a polygenic protective score specific to Alzheimers disease (AD PPS) based on the current literature among the participants enrolled in five studies of healthy aging and extreme longevity in the US, Europe, and Asia. This AD PPS did not include variants on Apolipoprotein E (APOE) gene. Comparisons of AD PPS in different data sets of healthy agers and centenarians showed that centenarians have stronger genetic protection against AD compared to individuals without familial longevity. The current study also shows evidence that this genetic protection increases with increasingly older ages in centenarians (centenarians who died before reaching age 105 years, semi-supercentenarians who reached age 105 to 109 years, and supercentenarians who reached age 110 years and older). However, the genetic protection was of modest size: the average increase in AD PPS was approximately one additional protective allele per 5 years of gained lifetime. Additionally, we show that the higher AD PPS was associated with better cognitive function and decreased mortality. Taken together, this analysis suggests that individuals who achieve the most extreme ages, on average, have the greatest protection against AD. This finding is robust to different genetic backgrounds with important implications for universal applicability of therapeutics that target this AD PPS.

genetics↗

bioRxiv2025

Robust Serum Proteomic Signatures of APOE2

We previously identified a signature of 16 serum proteins that highlighted a role of the e2 allele of APOE in lipid regulation via apolipoprotein B (APOB) and apolipoprotein E (APOE), and in inflammation. The serum proteins were profiled using the aptamer-based Somalogic technology. Here, we validate and expand the serum protein signature of APOE using a combination of mass-spectrometry, ELISA, Luminex, antibody-based Olink proteomics, and blood transcriptomics. We replicate the association between APOB and the e2 allele of APOE, we correct the pattern of association between APOE genotypes and serum level of APOE, and we detect new associations between APOE genotypes and the complex of apolipoproteins APOC1, APOC4, APOC2, APOC3, APOE, APOF and APOL1. In addition, we discover 13 new proteins that correlate with APOE genotypes. This extended signature includes granule proteins CAMP, CTSG, DEFA3, and MPO secreted from neutrophils and points to olfactomedin 4 (OLFM4) as a new target for the prevention of Alzheimers disease.

genetics↗

bioRxiv2025

Integrative analysis across metagenomic taxonomic classifiers: A case study of the gut microbiome in aging and longevity

Despite calls for the development of consensus methods, most analyses of shotgun metagenomics data for microbiome studies use a single taxonomic classifier. In this study, we compare inferences from two broadly used classifiers, MetaPhlAn4 (marker-gene-based) and Kraken2 (k-mer-based), applied to stool metagenomic samples from participants in the Integrative Longevity Omics study to measure associations of taxonomic diversity and relative abundance with age, replicating analyses in an independent cohort. We also introduce consensus and meta-analytic approaches to compare and integrate results from multiple classifiers. While many results are consistent across the two classifiers, we find classifier-specific inferences that would be lost when using one classifier alone. When using a correlated meta-analysis approach across classifiers, differential abundance analysis captures more age-associated taxa, including 17 taxa robustly age-associated across cohorts. This study emphasizes the value of employing multiple classifiers and recommends novel approaches that facilitate the integration of results from multiple methodologies.

bioinformatics↗

bioRxiv2024

APOE Genotype and Biological Age Impact Inter-Omic Associations Related to Bioenergetics

Apolipoprotein E (APOE) modifies human aging; specifically, the {varepsilon}2 and {varepsilon}4 alleles are among the strongest genetic predictors of longevity and Alzheimers disease (AD) risk, respectively. However, detailed mechanisms for their influence on aging remain unclear. Herein, we analyzed inter-omic, context-dependent association patterns across APOE genotypes, sex, and health axes in 2,229 community-dwelling individuals to test APOE genotypes for variation in metabolites and metabolite-associations tied to a previously-validated metric of biological aging (BA) based on blood biomarkers. Our analysis, supported by validation in an independent cohort, identified top APOE-associated plasma metabolites as diacylglycerols, which were increased in {varepsilon}2-carriers and trended higher in {varepsilon}4-carriers compared to {varepsilon}3-homozygotes, despite the known opposing aging effects of the allele variants. Omics association patterns of {varepsilon}2-carriers and increased biological age were also counter-intuitively similar, displaying increased associations between insulin resistance markers and energy-generating pathway metabolites. These results provide an atlas of APOE-related omic associations and support the involvement of bioenergetic pathways in mediating the impact of APOE on aging.

systems biology↗

bioRxiv2024

Cross-platform proteomics signatures of extreme old age

In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.

bioinformatics↗

bioRxiv2024

Learning Gaussian Graphical Models from Correlated Data

Gaussian Graphical Models (GGM) have been widely used in biomedical research to explore complex relationships between many variables. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a Bootstrap algorithm to infer GGM from correlated data. We use extensive simulations of correlated data from family-based studies to show that the Bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from a family-based study known as the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well in this real example.

bioinformatics↗

bioRxiv2024

A longevity-specific bank of induced pluripotent stem cells from centenarians and their offspring

Centenarians provide a unique lens through which to study longevity, healthy aging, and resiliency. Moreover, models of human aging and resilience to disease that allow for the testing of potential interventions are virtually non-existent. We obtained and characterized over 50 centenarian and offspring peripheral blood samples including those connected to functional independence data highlighting resistance to disability and cognitive impairment. Targeted methylation arrays were used in molecular aging clocks to compare and contrast differences between biological and chronological age in these specialized subjects. Isolated peripheral blood mononuclear cells (PBMCs) were then successfully reprogrammed into high-quality induced pluripotent stem cell (iPSC) lines which were functionally characterized for pluripotency, genomic stability, and the ability to undergo directed differentiation. The result of this work is a one-of-a-kind resource for studies of human longevity and resilience that can fuel the discovery and validation of novel therapeutics for aging-related disease.

molecular biology↗

bioRxiv2024

yQTL Pipeline: a structured computational workflow for large scale quantitative trait loci discovery and downstream visualization

1Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. In order to facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the y indicates the dependent quantitative variable being modeled. Prior to genome-wide association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. Upon uploading the result file, it can generate Manhattan plots of user-selected phenotype traits and trait-QTL connection networks based on user-specified p-value thresholds. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs cumulatively associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from [~]90 min to [~]26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTL-Pipeline.

bioinformatics↗

bioRxiv2023

A Simple Strategy for Identifying Conserved Features across Non-independent Omics Studies

False discovery is an ever-present concern in omics research, especially for burgeoning technologies with unvetted specificity of their biomolecular measurements, as such unknowns obscure the ability to characterize biologically informative features from studies performed with any single platform. Accordingly, performing replication studies of the same samples using different omics platforms is a viable strategy for identifying high-confidence molecular associations that are conserved across studies. However, an important caveat of replication studies that include the same samples is that they are inherently non-independent, leading to overestimating conservation if studies are treated otherwise. Strategies for accounting for such inter-study dependencies have been proposed for meta-analysis methods devised to increase statistical power to detect molecular associations in one or more studies. Still, they are not immediately suited for identifying conserved molecular associations across multiple studies. Here, we present a unifying strategy for performing inter-study conservation analysis as an alternative to meta-analysis strategies for aggregating summary statistical results of shared features across complementary studies while accounting for inter-study dependency. This method, which we call "adjusted maximum p-value" (AdjMaxP), is easy to implement with inter-study dependency and conservation estimated directly from the p-values from each studys molecular feature-level association testing results. Through simulation-based assessment, we demonstrate AdjMaxPs improved performance for accurately identifying conserved features over a related meta-analysis strategy for non-independent studies. AdjMaxP offers an easily implementable strategy for improving the precision of analyses for biomarker discovery from cross-platform omics study designs, thereby facilitating the adoption of such protocols for robust inference from emerging omics technologies.

bioinformatics↗

bioRxiv2023

Bayesian Differential Analysis of Cell Type Proportions

The analysis of cell type proportions in a biological sample should account for the compositional nature of the data but most analyses ignore this characteristic with the risk of producing misleading conclusions. The recent method scCODA appropriately incorporates these constraints by using a Bayesian Multinomial-Dirichlet model that requires a reference cell type to normalize the distribution of all cell types. However, a reference cell type that is stable across biological conditions may not always be available. Here, we present an approach that uses a Bayesian multinomial regression for the analysis of single cell distribution data without the need for a reference cell type. We show an implementation example using the rjags package within the R software.

bioinformatics↗

bioRxiv2022

Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity

Age-related changes in immune cell composition and functionality are associated with multimorbidity and mortality. However, many centenarians delay the onset of aging-related disease suggesting the presence of elite immunity that remains highly functional at extreme old age. To identify immune-specific patterns of aging and extreme human longevity, we analyzed novel single cell profiles from the peripheral blood mononuclear cells (PBMCs) of 7 centenarians (mean age 106) and publicly available single cell RNA-sequencing (scRNA-seq) datasets that included an additional 7 centenarians as well as 52 people at younger ages (20-89 years). The analysis confirmed known shifts in the ratio of lymphocytes to myeloid cells, and noncytotoxic to cytotoxic cell distributions with aging, but also identified significant shifts from CD4+ T cell to B cell populations in centenarians suggesting a history of exposure to natural and environmental immunogens. Our transcriptional analysis identified cell type signatures specific to exceptional longevity that included genes with age-related changes (e.g., increased expression of STK17A, a gene known to be involved in DNA damage response) as well as genes expressed uniquely in centenarians PBMCs (e.g., S100A4, part of the S100 protein family studied in age-related disease and connected to longevity and metabolic regulation). Collectively, these data suggest that centenarians harbor unique, highly functional immune systems that have successfully adapted to a history of insults allowing for the achievement of exceptional longevity.

immunology↗

bioRxiv2022

Rare Genetic Variants Correlate with Better Processing Speed

We conducted a genome-wide association study (GWAS) of Digit Symbol Substitution Test (DSST) scores administered in 4207 family members of the Long Life Family Study (LLFS). Genotype data were imputed to the HRC panel of 64,940 haplotypes resulting in ~15M genetic variants with quality score > 0.7. The results were replicated using genetic data imputed to the 1000 Genomes phase 3 reference panel from two Danish twin cohorts: the study of Middle Aged Danish Twins and the Longitudinal Study of Aging Danish Twins. The GWAS in LLFS discovered 20 rare genetic variants (minor allele frequency (MAF) < 1.0%) that reached genome-wide significance (p-value < 5x10-8). Among these, 18 variants had large protective effects on the processing speed, including rs7623455, rs9821776, rs9821587, rs78704059 on chromosome 3, which were replicated in the combined Danish twin cohort. These SNPs are located in/near two genes, THRB and RARB, that belonged to thyroid hormone receptors family that may influence speed of metabolism and cognitive aging. The gene-level tests in LLFS confirmed that these two genes are associated with processing speed.

genetics↗