Biology subjects

O'Connell, J. R.

Publications and source records attributed to O'Connell, J. R..

15 recordsLinked to original sources

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↗

bioRxiv2021

Clonal hematopoiesis is driven by aberrant activation of TCL1A

A diverse set of driver genes, such as regulators of DNA methylation, RNA splicing, and chromatin remodeling, have been associated with pre-malignant clonal expansion of hematopoietic stem cells (HSCs). The factors mediating expansion of these mutant clones remain largely unknown, partially due to a paucity of large cohorts with longitudinal blood sampling. To circumvent this limitation, we developed and validated a method to infer clonal expansion rate from single timepoint data called PACER (passenger-approximated clonal expansion rate). Applying PACER to 5,071 persons with clonal hematopoiesis accurately recapitulated the known fitness effects due to different driver mutations. A genome-wide association study of PACER revealed that a common inherited polymorphism in the TCL1A promoter was associated with slower clonal expansion. Those carrying two copies of this protective allele had up to 80% reduced odds of having driver mutations in TET2, ASXL1, SF3B1, SRSF2, and JAK2, but not DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 by CRISPR editing led to aberrant expression of TCL1A and expansion of HSCs in vitro. These effects were abrogated in HSCs from donors carrying the protective TCL1A allele. Our results indicate that the fitness advantage of multiple common driver genes in clonal hematopoiesis is mediated through TCL1A activation. PACER is an approach that can be widely applied to uncover genetic and environmental determinants of pre-malignant clonal expansion in blood and other tissues.

genetics↗

bioRxiv2021

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

genetics↗

bioRxiv2021

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

genetics↗

bioRxiv2021

Leveraging a founder population to identify novel rare-population genetic determinants of lipidome

Identifying the genetic determinants of inter-individual variation in lipid species (lipidome) may provide deeper understanding and new insight into the mechanistic effect of complex lipidomic pathways in CVD risk and progression beyond simple traditional lipids. Previous studies have been largely population based and thus only powered to discover associations with common genetic variants. Founder populations represent a powerful resource to accelerate discovery of novel biology associated with rare population alleles that have risen to higher frequency due to genetic drift. We performed a GWAS of 355 lipid species in 650 individuals from the Old Order Amish founder population including 127 lipid species not previously tested. We report for the first time the lipid species associated with two rare-population but Amish-enriched lipid variants: APOB_rs5742904 and APOC3_rs76353203. We also identified novel associations for 3 rare-population Amish-enriched loci with several sphingolipids and with proposed potential functional/causal variant in each locus including GLPTD2_rs536055318, CERS5_rs771033566, and AKNA_rs531892793. We replicated 7 previously known common loci including novel associations with two sterols: androstenediol with UGT locus on chromosome 2 and estriol with SLC22A8/A24 locus on chromosome 11. Our results show the power of founder populations to discover novel biology due to genetic drift that can increase the frequency of an allele from only few copies in large sample cohorts such as the UK Biobank to dozens of copies in sample size as small as 650.

genetics↗

bioRxiv2021

An Omics Analysis Search and Information System (OASIS) for Enabling Biological Discovery in the Old Order Amish

The "Omics Analysis Search and Information System" (OASIS), developed at the University of Maryland School of Medicine, enables discovery by allowing researchers to mine results from genome wide association studies (GWAS). When interesting signals are found, the research can immediately ask follow-up questions and get answers in real-time. OASIS provides this unique capability with a web-based, scientist-friendly search system and a variety of real-time analysis tools (linkage disequilibrium calculations, conditional analysis, and direct variant comparison) plus on-demand visualizations (boxplots, histograms, LocusZoom & Haploview plots, and pedigree charts). Because OASIS uses a web-based user interface, an understanding of programming or the UNIX operating system is not required. The OASIS application has been used to enable discovery from whole-exome, whole-genome, metabolome, transcriptome and methylome association results for Old Order Amish studies at the University of Maryland School of Medicine.

bioinformatics↗

bioRxiv2021

Identification of Novel Genetic Susceptibility Loci for Thoracic and Abdominal Aortic Aneurysms Via Genome-Wide Association Study Using the UK Biobank Cohort

BackgroundThoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) are known to have a strong genetic component. Methods and ResultsIn a genome-wide association study (GWAS) using the UK Biobank, we analyzed the genomes of 1,363 individuals with AAA compared to 27,260 age, ancestry, and sex-matched controls (1:20 case:control study design). A similar analysis was repeated for 435 individuals with TAA compared to 8,700 controls. Polymorphism with minor allele frequency (MAF) >0.5% were evaluated. We identified novel loci near LINC01021, ATOH8 and JAK2 genes that achieved genome-wide significance for AAA (p-value <5x10-8), in addition to three known loci. For TAA, three novel loci in CTNNA3, FRMD6 and MBP achieved genome-wide significance. There was no overlap in the genes associated with AAAs and TAAs. Additionally, we identified a linkage group of high-frequency variants (MAFs [~]10%) encompassing FBN1, the causal gene for Marfan syndrome, which was associated with TAA. In Finngen PheWeb, this FBN1 haplotype was associated with aortic dissection. Finally, we found that baseline bradycardia was associated with TAA, but not AAA. ConclusionsOur GWAS found that AAA and TAA were associated with distinct sets of genes, suggesting distinct underlying genetic architecture. We also found association between baseline bradycardia and TAA. These findings, including JAK2 association, offer plausible mechanistic and therapeutic insights. We also found a common FBN1 linkage group that is associated with TAA and aortic dissection in patients who do not have Marfan syndrome. These FBN1 variants suggest shared pathophysiology between Marfan disease and sporadic TAA. Condensed AbstractIn genome-wide association study (GWAS) of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using UK Biobank database, we found 3 novel loci associated with TAA, and 3 novel loci associated AAA. We also found significant association between baseline bradycardia and TAA. These findings, including JAK2 association, offer plausible mechanistic and therapeutic insights. Additionally, we identified a common FBN1 linkage group associated with TAA in patients who do not have Marfan syndrome. In the FinnGen cohort, this haplotype is associated with aortic dissection. These results suggest a shared pathophysiology between Marfan disease and sporadic TAA. Study LimitationsAs with any GWAS study, the discovery of novel loci associated with aortopathies does not prove functional causality, and the findings described herein needs to be validated by analysis of other databases, ideally in a patient population of more diverse genetic origins than the UK Biobank. The use of the ICD10 codes to classify disease carriers and noncarriers in a population cohort may not be the most accurate assessment of prevalence of aortopathies. The association between baseline bradycardia and TAA does not take into account the concurrent use of medications that may impact heart rate. HighlightsO_LIIdentification of 3 novel AAA-associated loci near LINC01021, ATOH8 and JAK2 genes. C_LIO_LIIdentification of 3 novel TAA-associated loci near CTNNA3, FRMD6 and MBP genes. C_LIO_LIIdentification of a linkage group of common FBN1 variants associated with non-syndromic TAA in the UK Biobank and with aortic dissection in the FinnGen cohort, strengthening the evidence for a shared pathophysiology between Marfan disease and nonsyndromic aortopathy. C_LIO_LIAssociation between baseline bradycardia and TAA but not AAA. C_LI

genomics↗

bioRxiv2020

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups using 1 degree of freedom (1df) interaction and 2df joint tests. Primary multi-ancestry analyses in 62,969 individuals in stage 1 identified 3 novel loci that were replicated in an additional 59,296 individuals in stage 2, including rs7955964 (FIGNL2/ANKRD33) showing significant 1df interactions with long sleep duration and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) showing significant 1df interactions with short sleep duration (Pint < 5x10-8). Secondary ancestry-specific two-stage analyses and combined stage 1 and 2 analyses additionally identified 23 novel loci that need external replication, including 3 and 5 loci showing significant 1df interactions with long and short sleep duration, respectively (Pint < 5x10-8). Multiple genes mapped to our 26 novel loci have known functions in sleep-wake regulation, nervous and cardiometabolic systems. We also identified new gene by long sleep interactions near five known BP loci ([≤]1Mb) including NME7, FAM208A, MKLN1, CEP164, and RGL3/ELAVL3 (Pint < 5x10-8). This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

genomics↗

bioRxiv2020

Population Stratification at the Phenotypic Variance level and Implication for the Analysis of Whole Genome Sequencing Data from Multiple Studies

In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term variance stratification. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We also illustrate the variance stratification problem, its solutions, and a corresponding diagnostic procedure in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.

genetics↗

bioRxiv2019

Genetic and functional evidence relates a missense variant in B4GALT1 to lower LDL-C and fibrinogen

Increased LDL-cholesterol (LDL-C) and fibrinogen are independent risk factors for cardiovascular disease (CVD). We identified novel associations between an Amish-enriched missense variant (p.Asn352Ser) in a functional domain of beta-1,4-galactosyltransferase 1 (B4GALT1) and 13.5 mg/dl lower LDL-C (p=1.6E-15), and 26 mg/dl lower plasma fibrinogen (p= 9.8E-05). N-linked glycan profiling found p.Asn352Ser to be associated (p-values from 1.4E-06 to 1.0E-17) with decreased glycosylation of glycoproteins including: fibrinogen, ApoB100, immunoglobulin G (IgG), and transferrin. In vitro assays found that the mutant (352Ser) protein had 50% lower galactosyltransferase activity compared to wild type (352Asn) protein. Knockdown of b4galt1 in zebrafish embryos resulted in significantly lower LDL-C compared to control, which was fully rescued by co-expression of 352Asn human B4GALT1 mRNA but only partially rescued by co-expression of 352Ser human B4GALT1 mRNA. Our findings establish B4GALT1 as a novel gene associated with lower LDL-C and fibrinogen and suggest that targeted modulation of protein glycosylation may represent a therapeutic approach to decrease CVD risk.

genetics↗

bioRxiv2019

Genome Sequencing Unveils a New Regulatory Landscape of Platelet Reactivity

Exaggerated platelet aggregation at the site of vascular injury is the underlying pathophysiology of thrombotic diseases. Here, we conduct the largest whole genome sequencing (WGS) effort to uncover the genetic determinants of platelet aggregation. Leveraging 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) individuals deeply phenotyped for platelet aggregation, we identify 18 loci using single-variant approaches. This includes the novel RGS18 locus encoding a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with eQTL signatures for RGS18 expression in platelets. A gene-based approach focusing on deleterious coding variants identifies the SVEP1 gene, previously shown to be associated with coronary artery disease, as a novel determinant of platelet aggregation. Finally, in an integrative approach leveraging epigenetic data on megakaryocytes, we find strong association between rare variants mapping to a super enhancer region for PEAR1. This is a novel finding implicating the importance of rare variants with regulatory potential in a previously documented GWAS-identified locus.

genomics↗

bioRxiv2019

De novo mutations across 1,465 diverse genomes reveal novel mutational insights and reductions in the Amish founder population.

de novo Mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) program, we directly estimate and analyze DNM counts, rates, and spectra from 1,465 trios across an array of diverse human populations. Using the resulting call set of 86,865 single nucleotide DNMs, we find a significant positive correlation between local recombination rate and local DNM rate, which together can explain up to 35.5% of the genome-wide variation in population level rare genetic variation from 41K unrelated TOPMed samples. While genome-wide heterozygosity does correlate weakly with DNM count, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, interestingly, we do find significantly fewer DNMs in Amish individuals compared with other Europeans, even after accounting for parental age and sequencing center. Specifically, we find significant reductions in the number of T[->]C mutations in the Amish, which seems to underpin their overall reduction in DNMs. Finally, we calculate near-zero estimates of narrow sense heritability (h2), which suggest that variation in DNM rate is significantly shaped by non-additive genetic effects and/or the environment, and that a less mutagenic environment may be responsible for the reduced DNM rate in the Amish. SignificanceHere we provide one of the largest and most diverse human de novo mutation (DNM) call sets to date, and use it to quantify the genome-wide relationship between local mutation rate and population-level rare genetic variation. While we demonstrate that the human single nucleotide mutation rate is similar across numerous human ancestries and populations, we also discover a reduced mutation rate in the Amish founder population, which shows that mutation rates can shift rapidly. Finally, we find that variation in mutation rates is not heritable, which suggests that the environment may influence mutation rates more significantly than previously realized.

genetics↗

bioRxiv2018

Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole genome sequencing studies

With advances in Whole Genome Sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and Sequence Kernel Association Test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally-efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-Set Mixed Model Association Tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institutes Trans-Omics for Precision Medicine (TOPMed) program. SMMAT tests share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be only fit once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMAT tests correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

genetics↗

bioRxiv2018

A Fully-Adjusted Two-Stage Procedure for Rank Normalization in Genetic Association Studies

When testing genotype-phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. While it has been shown that applying a rank-normalization transformation to trait values before testing may improve these statistical properties, the factor driving them is not the trait distribution itself, but its residual distribution after regression on both covariates and genotype. Because genotype is expected to have a small effect (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then secondly use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice no further adjustment is done in the second stage. Here, we show that this widely-used approach can lead to tests with undesirable statistical properties, due to both a combination of a mis-specified mean-variance relationship, and remaining covariate associations between the rank-normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome-wide and whole-genome sequencing association studies. We further propose and evaluate an alternative fully-adjusted two-stage approach that adjusts for covariates both when residuals are obtained, and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.

genetics↗

bioRxiv2017

Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

Deep-coverage whole genome sequencing at the population level is now feasible and offers potential advantages for locus discovery, particularly in the analysis rare mutations in non-coding regions. Here, we performed whole genome sequencing in 16,324 participants from four ancestries at mean depth >29X and analyzed correlations of genotypes with four quantitative traits - plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. We conducted a discovery analysis including common or rare variants in coding as well as non-coding regions and developed a framework to interpret genome sequence for dyslipidemia risk. Common variant association yielded loci previously described with the exception of a few variants not captured earlier by arrays or imputation. In coding sequence, rare variant association yielded known Mendelian dyslipidemia genes and, in non-coding sequence, we detected no rare variant association signals after application of four approaches to aggregate variants in non-coding regions. We developed a new, genome-wide polygenic score for LDL-C and observed that a high polygenic score conferred similar effect size to a monogenic mutation (~30 mg/dl higher LDL-C for each); however, among those with extremely high LDL-C, a high polygenic score was considerably more prevalent than a monogenic mutation (23% versus 2% of participants, respectively).

genomics↗