Mid-life plasma proteins associated with late-life prefrailty and frailty: a proteomic analysis.

Physical frailty is a syndrome that typically manifests in later life, although the pathogenic process causing physical frailty likely begins decades earlier. To date, few studies have examined the biological signatures in mid-life associated with physical frailty later in life. Among 4,189 middle-aged participants (57.8 ± 5.0 years, 55.8% women) from the Atherosclerosis Risk in Community (ARIC) study, we evaluated the associations of 4,955 plasma proteins (log 2-transformed and standardized) measured using the SomaScan platform with their frailty status approximately 20 years later. Using multinomial logistic regression models adjusting for demographics, health behaviors, kidney function, total cholesterol, and comorbidities, 12 and 221 proteins were associated with prefrailty and frailty in later life, respectively (FDR p < 0.05). Top frailty-associated proteins included neurocan core protein (NCAN, OR = 0.66), fatty acid-binding protein heart (FABP3, OR = 1.62) and adipocyte (FABP4, OR = 1.65), as well proteins involved in the contactin-1 (CNTN1), toll-like receptor 5 (TLR5), and neurogenic locus notch homolog protein 1 (NOTCH1) signaling pathway relevant to skeletal muscle regeneration, myelination, and inflammation. Pathway analyses suggest midlife dysregulation of inflammation, metabolism, extracellular matrix, angiogenesis, and lysosomal autophagy among those at risk for late-life frailty. After further adjusting for midlife body mass index (BMI) - an established frailty risk factor - only CNTN1 (OR = 0.75) remained significantly associated with frailty. Post-hoc analyses demonstrated that the top 41 midlife frailty-associated proteins mediate 32% of the association between mid-life BMI and late-life frailty. Our findings provide new insights into frailty etiology earlier in the life course, enhancing the potential for prevention.

EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints.

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations.

Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks. We identify 389 DEGs; the top DEG, FN1, was downregulated in cases (q = 3.26 × 10-9) and encodes fibronectin which plays a role in wound healing. The top three gene expression modules implicate networks related to immune response (CEACAM5; p = 9.62 × 10-16 and CPA3; p = 2.39 × 10-14) and wound healing (FN1; p = 7.63 × 10-9). Multi-omic analysis identifies FKBP5, a co-chaperone of glucocorticoid receptor signaling known to be involved in drug response in asthma, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. This work reveals molecular dysregulation on three axes - increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response - that may play a critical role in asthma within the African Diaspora.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Session Introduction: Overcoming health disparities in precision medicine.

The following sections are included:OverviewDealing with the lack of diversity in current research datasetsDevelopment of fair machine learning algorithmsRace, genetic ancestry, and population structureConclusionAcknowledgments.

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Evaluating genomic signatures of aging in brain tissue as it relates to Alzheimer's disease.

Telomere length (TL) attrition, epigenetic age acceleration, and mitochondrial DNA copy number (mtDNAcn) decline are established hallmarks of aging. Each has been individually associated with Alzheimer's dementia, cognitive function, and pathologic Alzheimer's disease (AD). Epigenetic age and mtDNAcn have been studied in brain tissue directly but prior work on TL in brain is limited to small sample sizes and most studies have examined leukocyte TL. Importantly, TL, epigenetic age clocks, and mtDNAcn have not been studied jointly in brain tissue from an AD cohort. We examined dorsolateral prefrontal cortex (DLPFC) tissue from N = 367 participants of the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP). TL and mtDNAcn were estimated from whole genome sequencing (WGS) data and cortical clock age was computed on 347 CpG sites. We examined dementia, MCI, and level of and change in cognition, pathologic AD, and three quantitative AD traits, as well as measures of other neurodegenerative diseases and cerebrovascular diseases (CVD). We previously showed that mtDNAcn from DLPFC brain tissue was associated with clinical and pathologic features of AD. Here, we show that those associations are independent of TL. We found TL to be associated with ß-amyloid levels (beta = - 0.15, p = 0.023), hippocampal sclerosis (OR = 0.56, p = 0.0015) and cerebral atherosclerosis (OR = 1.44, p = 0.0007). We found strong associations between mtDNAcn and clinical measures of AD. The strongest associations with pathologic measures of AD were with cortical clock and there were associations of mtDNAcn with global AD pathology and tau tangles. Of the other pathologic traits, mtDNAcn was associated with hippocampal sclerosis, macroscopic infarctions and CAA and cortical clock was associated with Lewy bodies. Multi-modal age acceleration, accelerated aging on both mtDNAcn and cortical clock, had greater effect size than a single measure alone. These findings highlight for the first time that age acceleration determined on multiple genomic measures, mtDNAcn and cortical clock may have a larger effect on AD/AD related disorders (ADRD) pathogenesis than single measures.

Late-life plasma proteins associated with prevalent and incident frailty: A proteomic analysis.

Proteomic approaches have unique advantages in the identification of biological pathways that influence physical frailty, a multifactorial geriatric syndrome predictive of adverse health outcomes in older adults. To date, proteomic studies of frailty are scarce, and few evaluated prefrailty as a separate state or examined predictors of incident frailty. Using plasma proteins measured by 4955 SOMAmers in the Atherosclerosis Risk in Community study, we identified 134 and 179 proteins cross-sectionally associated with prefrailty and frailty, respectively, after Bonferroni correction (p < 1 × 10-5 ) among 3838 older adults aged ≥65 years, adjusting for demographic and physiologic factors and chronic diseases. Among them, 23 (17%) and 82 (46%) were replicated in the Cardiovascular Health Study using the same models (FDR p < 0.05). Notably, higher odds of prefrailty and frailty were observed with higher levels of growth differentiation factor 15 (GDF15; pprefrailty = 1 × 10-15 , pfrailty = 2 × 10-19 ), transgelin (TAGLN; pprefrailty = 2 × 10-12 , pfrailty = 6 × 10-22 ), and insulin-like growth factor-binding protein 2 (IGFBP2; pprefrailty = 5 × 10-15 , pfrailty = 1 × 10-15 ) and with a lower level of growth hormone receptor (GHR, pprefrailty = 3 × 10-16 , pfrailty = 2 × 10-18 ). Longitudinally, we identified 4 proteins associated with incident frailty (p < 1 × 10-5 ). Higher levels of triggering receptor expressed on myeloid cells 1 (TREM1), TAGLN, and heart and adipocyte fatty-acid binding proteins predicted incident frailty. Differentially regulated proteins were enriched in pathways and upstream regulators related to lipid metabolism, angiogenesis, inflammation, and cell senescence. Our findings provide a set of plasma proteins and biological mechanisms that were dysregulated in both the prodromal and the clinical stage of frailty, offering new insights into frailty etiology and targets for intervention.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.

Age at Menopause, Leukocyte Telomere Length, and Coronary Artery Disease in Postmenopausal Women.

BACKGROUND: Premature menopause is a risk factor for accelerated cardiovascular aging, but underlying mechanisms remain incompletely understood. This study investigated the role of leukocyte telomere length (LTL), a marker of cellular aging and genomic instability, in the association of premature menopause with cardiovascular disease. METHODS: Participants from the UK Biobank and Women's Health Initiative with complete reproductive history and LTL measurements were included. Primary analyses tested the association between age at menopause and LTL using multivariable-adjusted linear regression. Secondary analyses stratified women by history of gynecologic surgery. Mendelian randomization was used to infer causal relationships between LTL and age at natural menopause. Multivariable-adjusted Cox regression and mediation analyses tested the joint associations of premature menopause and LTL with incident coronary artery disease. RESULTS: This study included 130 254 postmenopausal women (UK Biobank: n=122 224; Women's Health Initiative: n=8030), of whom 4809 (3.7%) had experienced menopause before age 40. Earlier menopause was associated with shorter LTL (meta-analyzed ß=-0.02 SD/5 years of earlier menopause [95% CI, -0.02 to -0.01]; P=7.2×10-12). This association was stronger and significant in both cohorts for women with natural/spontaneous menopause (meta-analyzed ß=-0.04 SD/5 years of earlier menopause [95% CI, -0.04 to -0.03]; P<2.2×10-16) and was independent of hormone therapy use. Mendelian randomization supported a causal association of shorter genetically predicted LTL with earlier age at natural menopause. LTL and age at menopause were independently associated with incident coronary artery disease, and mediation analyses indicated small but significant mediation effects of LTL in the association of menopausal age with coronary artery disease. CONCLUSIONS: Earlier age at menopause is associated with shorter LTL, especially among women with natural menopause. Accelerated telomere shortening may contribute to the heightened cardiovascular risk associated with premature menopause.

Structural variation across 138,134 samples in the TOPMed consortium.

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.