Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application.

We rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α = 0.001. Notably, when 5 % or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31 % of African Ancestry, mean age of 62, with 58 % women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on both pooled samples and within each ancestry group. Our results suggest that mtDNA-encoded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the RNR1 and RNR2 genes (p < 0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations (p < 0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.

Proteomics of left ventricular structure in the Multi-Ethnic Study of Atherosclerosis.

AIMS: Proteomic profiling offers an expansive approach to biomarker discovery and mechanistic hypothesis generation for LV remodelling, a critical component of heart failure (HF). We sought to identify plasma proteins cross-sectionally associated with left ventricular (LV) size and geometry in a diverse population-based cohort without known cardiovascular disease (CVD). METHODS AND RESULTS: Among participants of the Multi-Ethnic Study of Atherosclerosis (MESA), we quantified plasma abundances of 1305 proteins using an aptamer-based platform at exam 1 (2000-2002) and exam 5 (2010-2011) and assessed LV structure by cardiac magnetic resonance (CMR) at the same time points. We used multivariable linear regression with robust variance to assess cross-sectional associations between plasma protein abundances and LV structural characteristics at exam 1, reproduced findings in later-life at exam 5, and explored relationships of associated proteins using annotated enrichment analysis. We studied 763 participants (mean age 60 ± 10 years at exam 1; 53% female; 19% Black race; 31% Hispanic ethnicity). Following adjustment for renal function and traditional CVD risk factors, plasma levels of 3 proteins were associated with LV mass index at both time points with the same directionality (FDR < 0.05): leptin (LEP), renin (REN), and cathepsin-D (CTSD); 20 with LV end-diastolic volume index: LEP, NT-proBNP, histone-lysine N-methyltransferase (EHMT2), chordin-like protein 1 (CHRDL1), tumour necrosis factor-inducible gene 6 protein (TNFAIP6), NT-3 growth factor receptor (NTRK3), c5a anaphylatoxin (C5), neurogenic locus notch homologue protein 3 (NOTCH3), ephrin-B2 (EFNB2), osteomodulin (OMD), contactin-4 (CNTN4), gelsolin (GSN), stromal cell-derived factor 1 (CXCL12), calcineurin subunit B type 1 (PPP3R1), insulin-like growth factor 1 receptor (IGF1R), bone sialoprotein 2 (IBSP), interleukin-11 (IL-11), follistatin-related protein 1 (FSTL1), periostin (POSTN), and biglycan (BGN); and 4 with LV mass-to-volume ratio: RGM domain family member B (RGMB), transforming growth factor beta receptor type 3 (TGFBR3), ephrin-A2 (EFNA2), and cell adhesion molecule 3 (CADM3). Functional annotation implicated regulation of the PI3K-Akt pathway, bone morphogenic protein signalling, and cGMP-mediated signalling. CONCLUSIONS: We report proteomic profiling of LV size and geometry, which identified novel associations and reinforced previous findings on biomarker candidates for LV remodelling and HF. If validated, these proteins may help refine risk prediction and identify novel therapeutic targets for HF.

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations.

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1), and one locus associated with a sex-associated mutation pathway (SRGAP2C). We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

Associations of Serum GDF-15 Levels with Physical Performance, Mobility Disability, Cognition, Cardiovascular Disease, and Mortality in Older Adults.

Background: Growth differentiation factor 15 (GDF-15) is a member of the TGFß superfamily secreted by many cell types and found at higher blood concentrations as chronological age increases (1). Given the emergence of GDF-15 as a key protein associated with aging, it is important to understand the multitude of conditions with which circulating GDF-15 is associated. Methods: We pooled data from 1,174 randomly selected Health ABC Study (Health ABC) participants and 1,503 Cardiovascular Health Study (CHS) participants to evaluate the risk of various conditions and age-related outcomes across levels of GDF-15. The primary outcomes were (1) risk of mobility disability and falls; (2) impaired cognitive function; (3) and increased risk of cardiovascular disease and total mortality. Results: The pooled study cohort had a mean age of 75.4 +/-4.4 years. Using a Bonferroni-corrected threshold, our analyses show that high levels of GDF-15 were associated with a higher risk of severe mobility disability (HR: 2.13 [1.64, 2.77]), coronary heart disease (HR: 1.47 [1.17, 1.83]), atherosclerotic cardiovascular disease (HR: 1.56 [1.22, 1.98]), heart failure (HR: 2.09 [1.66, 2.64]), and mortality (HR: 1.81 [1.53, 2.15]) when comparing the highest and lowest quartiles. For CHS participants, analysis of extreme quartiles in fully adjusted models revealed a 3.5-fold higher risk of dementia (HR: 3.50 [1.97, 6.22]). Conclusions: GDF-15 is associated with several age-related outcomes and diseases, including mobility disability, impaired physical and cognitive performance, dementia, cardiovascular disease, and mortality. Each of these findings demonstrates the importance of GDF-15 as a potential biomarker for many aging-related conditions.

Trimethylamine N-Oxide and Related Gut Microbe-Derived Metabolites and Incident Heart Failure Development in Community-Based Populations.

BACKGROUND: Growing evidence indicates that trimethylamine N-oxide, a gut microbial metabolite of dietary choline and carnitine, promotes both cardiovascular disease and chronic kidney disease risk. It remains unclear how circulating concentrations of trimethylamine N-oxide and its related dietary and gut microbe-derived metabolites (choline, betaine, carnitine, γ-butyrobetaine, and crotonobetaine) affect incident heart failure (HF). METHODS: We evaluated 11 768 participants from the Cardiovascular Health Study and the Multi-Ethnic Study of Atherosclerosis with serial measures of metabolites. Cox proportional hazard models were used to examine the associations between metabolites and incident HF, adjusted for cardiovascular disease risk factors. RESULTS: In all, 2102 cases of HF occurred over a median follow-up of 15.9 years. After adjusting for traditional risk factors, higher concentrations of trimethylamine N-oxide (hazard ratio, 1.15 [95% CI, 1.09-1.20]; P<0.001), choline (hazard ratio, 1.44 [95% CI, 1.26-1.64]; P<0.001), and crotonobetaine (hazard ratio, 1.24 [95% CI, 1.16-1.32]; P<0.001) were associated with increased risk for incident HF. After further adjustment for renal function (potential confounder or mediator), these associations did not reach Bonferroni-corrected statistical significance (P=0.01, 0.049, and 0.006, respectively). Betaine and carnitine were nominally associated with a higher incidence of HF (P<0.05). In exploratory analyses, results were similar for subtypes of HF based on left ventricular ejection fraction, and associations appeared generally stronger among Black and Hispanic/Latino versus White adults, although there were no interactions for any metabolites with race. CONCLUSIONS: In this pooled analysis of 2 well-phenotyped, diverse, community-based cohorts, circulating concentrations of gut microbe-derived metabolites such as trimethylamine N-oxide, choline, and crotonobetaine were independently associated with a higher risk of developing HF. REGISTRATION: URL: https://www.clinicaltrials.gov/; Unique identifiers: NCT00005133 and NCT00005487.

Associations of circulating T-cell subsets in carotid artery stiffness: the Multi-Ethnic Study of Atherosclerosis.

Background: Arterial stiffness measured by total pulse wave velocity (T-PWV) is associated with increased risk of multiple age-related diseases. T-PWV can be described by structural (S-PWV) and load-dependent (LD-PWV) arterial stiffening. T-cells have been associated with arterial remodeling, blood pressure, and arterial stiffness in humans and animals; however, it is unknown whether T-cells are related to S-PWV or LD-PWV. Therefore, we evaluated the cross-sectional associations of peripheral T-cell subpopulations with T-PWV, S-PWV, and LD-PWV stiffness. Methods: Peripheral blood T-cells were characterized using flow cytometry and the carotid artery was measured using B-mode ultrasound to calculate T-PWV at the baseline examination in a subset of the Multi-Ethnic Study of Atherosclerosis (MESA, n=1,984). A participant-specific exponential model was used to calculate S-PWV and LD-PWV based on elastic modulus and blood pressure gradients. The associations between five primary (p-significance<0.01) and twenty-five exploratory (p-significance<0.05) immune cell subpopulations, per 1-SD increment, and arterial stiffness measures were assessed using adjusted, linear regressions. Results: For the primary analysis, higher CD4+CD28-CD57+ T-cells were associated with higher LD-PWV (ß=0.04 m/s, p<0.01) after adjusting for co-variates. For the exploratory analysis, T-cell subpopulations that commonly shift with aging towards memory and differentiated/immunosenescent phenotypes were associated with greater T-PWV, S-PWV, and LD-PWV after adjusting for co-variates. Conclusions: In this cross-sectional study, several T-cell subpopulations commonly associated with aging were related with measures of arterial stiffness. Longitudinal studies that examine changes in T-cell subpopulations and measures of arterial stiffness are warranted.

Immune cell profiling of the ICAM1 p.K56M heart failure with preserved ejection fraction risk variant.

AIMS: Intercellular adhesion molecule-1 (ICAM-1) facilitates inflammation via leucocyte recruitment and has been implicated in heart failure with preserved ejection fraction (HFpEF). Approximately 35% of African American individuals carry a copy of an ICAM1 missense variant (rs5491; p.K56M), which is associated with an increased risk of HFpEF. The pathways by which rs5491 increases HFpEF risk are not well defined. We evaluated the circulating immune cell profile of rs5491. METHODS: Among African American individuals in the Multi-Ethnic Study of Atherosclerosis, we evaluated the associations of rs5491 with 29 circulating peripheral blood mononuclear cell subsets. The top immune cells were then related to echocardiographic measures of structure and function. RESULTS: Among 502 individuals with immune cell profiling (mean age 63 years, 51% female), 191 individuals (38%) had at least one copy of rs5491. Each additional rs5491 allele was significantly associated with higher proportions of Tc17 CD8+ cytotoxic T cells (ß = 1.34, SE = 0.45, P = 9.5 × 10-5) and Tc2 CD8+ cytotoxic T cells (ß = 1.19, SE = 0.44, P = 0.00012). There were no other associations noted between rs5491 and the remaining immune cells. A higher proportion of Tc17 cells was significantly associated with a higher left ventricular ejection fraction, E/e' average and right ventricular systolic pressure (RVSP), while a higher proportion of Tc2 cells was significantly associated with a higher RVSP. CONCLUSIONS: The ICAM1 p.K56M variant (rs5491) carries a distinct and inflammatory T-cell subset profile. These cytotoxic T cells are in turn associated with alterations in cardiac function and adverse haemodynamics later in life, thus providing insight into pathways by which rs5491 may increase the risk of HFpEF.

Proteogenomics in cerebrospinal fluid and plasma reveals new biological fingerprint of cerebral small vessel disease.

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia with no specific mechanism-based treatment. We used Mendelian randomization to combine a unique cerebrospinal fluid (CSF) and plasma pQTL resource with the latest European-ancestry GWAS of MRI-markers of cSVD (white matter hyperintensities, perivascular spaces). We describe a new biological fingerprint of 49 protein-cSVD associations, predominantly in the CSF. We implemented a multipronged follow-up, across fluids, platforms, and ancestries (Europeans and East-Asian), including testing associations of direct plasma protein measurements with MRI-cSVD. We highlight 16 proteins robustly associated in both CSF and plasma, with 24/4 proteins identified in CSF/plasma only. cSVD-proteins were enriched in extracellular matrix and immune response pathways, and in genes enriched in microglia and specific microglial states (integration with single-nucleus RNA sequencing). Immune-related proteins were associated with MRI-cSVD already at age twenty. Half of cSVD-proteins were associated with stroke, dementia, or both, and seven cSVD-proteins are targets for known drugs (used for other indications in directions compatible with beneficial therapeutic effects. This first cSVD proteogenomic signature opens new avenues for biomarker and therapeutic developments.

Epigenome-wide DNA Methylation Association Study of CHIP Provides Insight into Perturbed Gene Regulation.

With age, hematopoietic stem cells can acquire somatic mutations in leukemogenic genes that confer a proliferative advantage in a phenomenon termed "clonal hematopoiesis of indeterminate potential" (CHIP). How these mutations confer a proliferative advantage and result in increased risk for numerous age-related diseases remains poorly understood. We conducted a multiracial meta-analysis of epigenome-wide association studies (EWAS) of CHIP and its subtypes in four cohorts (N=8196) to elucidate the molecular mechanisms underlying CHIP and illuminate how these changes influence cardiovascular disease risk. The EWAS findings were functionally validated using human hematopoietic stem cell (HSC) models of CHIP. A total of 9615 CpGs were associated with any CHIP, 5990 with DNMT3A CHIP, 5633 with TET2 CHIP, and 6078 with ASXL1 CHIP (P <1×10-7). CpGs associated with CHIP subtypes overlapped moderately, and the genome-wide DNA methylation directions of effect were opposite for TET2 and DNMT3A CHIP, consistent with their opposing effects on global DNA methylation. There was high directional concordance between the CpGs shared from the meta-EWAS and human edited CHIP HSCs. Expression quantitative trait methylation analysis further identified transcriptomic changes associated with CHIP-associated CpGs. Causal inference analyses revealed 261 CHIP-associated CpGs associated with cardiovascular traits and all-cause mortality (FDR adjusted p-value <0.05). Taken together, our study sheds light on the epigenetic changes impacted by CHIP and their associations with age-related disease outcomes. The novel genes and pathways linked to the epigenetic features of CHIP may serve as therapeutic targets for preventing or treating CHIP-mediated diseases.

Epidemiologic Features of Recovery From SARS-CoV-2 Infection.

Importance: Persistent symptoms and disability following SARS-CoV-2 infection, known as post-COVID-19 condition or "long COVID," are frequently reported and pose a substantial personal and societal burden. Objective: To determine time to recovery following SARS-CoV-2 infection and identify factors associated with recovery by 90 days. Design, Setting, and Participants: For this prospective cohort study, standardized ascertainment of SARS-CoV-2 infection was conducted starting in April 1, 2020, across 14 ongoing National Institutes of Health-funded cohorts that have enrolled and followed participants since 1971. This report includes data collected through February 28, 2023, on adults aged 18 years or older with self-reported SARS-CoV-2 infection. Exposure: Preinfection health conditions and lifestyle factors assessed before and during the pandemic via prepandemic examinations and pandemic-era questionnaires. Main Outcomes and Measures: Probability of nonrecovery by 90 days and restricted mean recovery times were estimated using Kaplan-Meier curves, and Cox proportional hazards regression was performed to assess multivariable-adjusted associations with recovery by 90 days. Results: Of 4708 participants with self-reported SARS-CoV-2 infection (mean [SD] age, 61.3 [13.8] years; 2952 women [62.7%]), an estimated 22.5% (95% CI, 21.2%-23.7%) did not recover by 90 days post infection. Median (IQR) time to recovery was 20 (8-75) days. By 90 days post infection, there were significant differences in restricted mean recovery time according to sociodemographic, clinical, and lifestyle characteristics, particularly by acute infection severity (outpatient vs critical hospitalization, 32.9 days [95% CI, 31.9-33.9 days] vs 57.6 days [95% CI, 51.9-63.3 days]; log-rank P < .001). Recovery by 90 days post infection was associated with vaccination prior to infection (hazard ratio [HR], 1.30; 95% CI, 1.11-1.51) and infection during the sixth (Omicron variant) vs first wave (HR, 1.25; 95% CI, 1.06-1.49). These associations were mediated by reduced severity of acute infection (33.4% and 17.6%, respectively). Recovery was unfavorably associated with female sex (HR, 0.85; 95% CI, 0.79-0.92) and prepandemic clinical cardiovascular disease (HR, 0.84; 95% CI, 0.71-0.99). No significant multivariable-adjusted associations were observed for age, educational attainment, smoking history, obesity, diabetes, chronic kidney disease, asthma, chronic obstructive pulmonary disease, or elevated depressive symptoms. Results were similar for reinfections. Conclusions and Relevance: In this cohort study, more than 1 in 5 adults did not recover within 3 months of SARS-CoV-2 infection. Recovery within 3 months was less likely in women and those with preexisting cardiovascular disease and more likely in those with COVID-19 vaccination or infection during the Omicron variant wave.

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.

Multi-ancestry polygenic risk scores for venous thromboembolism.

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality, with large disparities in incidence rates between Black and White Americans. Polygenic risk scores (PRSs) limited to variants discovered in genome-wide association studies in European-ancestry samples can identify European-ancestry individuals at high risk of VTE. However, there is limited evidence on whether high-dimensional PRS constructed using more sophisticated methods and more diverse training data can enhance the predictive ability and their utility across diverse populations. We developed PRSs for VTE using summary statistics from the International Network against Venous Thrombosis (INVENT) consortium genome-wide association studies meta-analyses of European- (71 771 cases and 1 059 740 controls) and African-ancestry samples (7482 cases and 129 975 controls). We used LDpred2 and PRS-CSx to construct ancestry-specific and multi-ancestry PRSs and evaluated their performance in an independent European- (6781 cases and 103 016 controls) and African-ancestry sample (1385 cases and 12 569 controls). Multi-ancestry PRSs with weights tuned in European-ancestry samples slightly outperformed ancestry-specific PRSs in European-ancestry test samples (e.g. the area under the receiver operating curve [AUC] was 0.609 for PRS-CSx_combinedEUR and 0.608 for PRS-CSxEUR [P = 0.00029]). Multi-ancestry PRSs with weights tuned in African-ancestry samples also outperformed ancestry-specific PRSs in African-ancestry test samples (PRS-CSxAFR: AUC = 0.58, PRS-CSx_combined AFR: AUC = 0.59), although this difference was not statistically significant (P = 0.34). The highest fifth percentile of the best-performing PRS was associated with 1.9-fold and 1.68-fold increased risk for VTE among European- and African-ancestry subjects, respectively, relative to those in the middle stratum. These findings suggest that the multi-ancestry PRS might be used to improve performance across diverse populations to identify individuals at highest risk for VTE.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

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.

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.

A plasma protein-based risk score to predict hip fractures.

As there are effective treatments to reduce hip fractures, identification of patients at high risk of hip fracture is important to inform efficient intervention strategies. To obtain a new tool for hip fracture prediction, we developed a protein-based risk score in the Cardiovascular Health Study using an aptamer-based proteomic platform. The proteomic risk score predicted incident hip fractures and improved hip fracture discrimination in two Trøndelag Health Study validation cohorts using the same aptamer-based platform. When transferred to an antibody-based proteomic platform in a UK Biobank validation cohort, the proteomic risk score was strongly associated with hip fractures (hazard ratio per s.d. increase, 1.64; 95% confidence interval 1.53-1.77). The proteomic risk score, but not available polygenic risk scores for fractures or bone mineral density, improved the C-index beyond the fracture risk assessment tool (FRAX), which integrates information from clinical risk factors (C-index, FRAX 0.735 versus FRAX + proteomic risk score 0.776). The developed proteomic risk score constitutes a new tool for stratifying patients according to hip fracture risk; however, its improvement in hip fracture discrimination is modest and its clinical utility beyond FRAX with information on femoral neck bone mineral density remains to be determined.