Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease.

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

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-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program.

BACKGROUND: Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. METHODS AND FINDINGS: Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders. CONCLUSIONS: We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data.

RATIONALE: Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear. OBJECTIVES: We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program. METHODS: Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants. MEASUREMENTS AND MAIN RESULTS: In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability. CONCLUSIONS: Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.

Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program.

Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.

Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program.

Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

NetSHy: network summarization via a hybrid approach leveraging topological properties.

MOTIVATION: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS: In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION: R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association of clonal hematopoiesis with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is associated with age and smoking, but other determinants of the disease are incompletely understood. Clonal hematopoiesis of indeterminate potential (CHIP) is a common, age-related state in which somatic mutations in clonal blood populations induce aberrant inflammatory responses. Patients with CHIP have an elevated risk for cardiovascular disease, but the association of CHIP with COPD remains unclear. We analyzed whole-genome sequencing and whole-exome sequencing data to detect CHIP in 48 835 patients, of whom 8444 had moderate to very severe COPD, from four separate cohorts with COPD phenotyping and smoking history. We measured emphysema in murine models in which Tet2 was deleted in hematopoietic cells. In the COPDGene cohort, individuals with CHIP had risks of moderate-to-severe, severe, or very severe COPD that were 1.6 (adjusted 95% confidence interval [CI], 1.1-2.2) and 2.2 (adjusted 95% CI, 1.5-3.2) times greater than those for noncarriers. These findings were consistently observed in three additional cohorts and meta-analyses of all patients. CHIP was also associated with decreased FEV1% predicted in the COPDGene cohort (mean between-group differences, -5.7%; adjusted 95% CI, -8.8% to -2.6%), a finding replicated in additional cohorts. Smoke exposure was associated with a small but significant increased risk of having CHIP (odds ratio, 1.03 per 10 pack-years; 95% CI, 1.01-1.05 per 10 pack-years) in the meta-analysis of all patients. Inactivation of Tet2 in mouse hematopoietic cells exacerbated the development of emphysema and inflammation in models of cigarette smoke exposure. Somatic mutations in blood cells are associated with the development and severity of COPD, independent of age and cumulative smoke exposure.

Mendelian randomization in the multivariate general linear model framework.

Mendelian randomization (MR) is an application of instrumental variable (IV) methods to observational data in which the IV is a genetic variant. MR methods applicable to the general exponential family of distributions are currently not well characterized. We adapt a general linear model framework to the IV setting and propose a general MR method applicable to any full-rank distribution from the exponential family. Empirical bias and coverage are estimated via simulations. The proposed method is compared to several existing MR methods. Real data analyses are performed using data from the REGARDS study to estimate the potential causal effect of smoking frequency on stroke risk in African Americans. In simulations with binary variates and very weak instruments the proposed method had the lowest median [Q1 , Q3 ] bias (0.10 [-3.68 to 3.62]); compared with 2SPS (0.27 [-3.74 to 4.26]) and the Wald method (-0.69 [-1.72 to 0.35]). Low bias was observed throughout other simulation scenarios; as well as more than 90% coverage for the proposed method. In simulations with count variates, the proposed method performed comparably to 2SPS; the Wald method maintained the most consistent low bias; and 2SRI was biased towards the null. Real data analyses find no evidence for a causal effect of smoking frequency on stroke risk. The proposed MR method has low bias and acceptable coverage across a wide range of distributional scenarios and instrument strengths; and provides a more parsimonious framework for asymptotic hypothesis testing compared to existing two-stage procedures.

Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

Gene expression associations with body mass index in the Multi-Ethnic Study of Atherosclerosis.

BACKGROUND/OBJECTIVES: Obesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations. Gene expression association with BMI was assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) in four self-identified race and ethnicity (SIRE) groups to identify genes associated with obesity. SUBJECTS/METHODS: RNA-sequencing was performed on 1096 MESA participants (37.8% white, 24.3% Hispanic, 28.4% African American, and 9.5% Chinese American) and linear models were used to assess the association of expression from each gene for its effect on BMI, adjusting for age, sex, sequencing center, study site, five expression and four genetic principal components in each self-identified race group. Sample-size-weighted meta-analysis was performed to identify genes with BMI-associated expression across ancestry groups. RESULTS: Within individual SIRE groups, there were zero to three genes whose expression is significantly (p < 1.97 × 10-6) associated with BMI. Across all groups, 45 genes were identified by meta-analysis whose expression was significantly associated with BMI, explaining 29.7% of BMI variation. The 45 genes are expressed in a variety of tissues and cell types and are enriched for obesity-related processes including erythrocyte function, oxygen binding and transport, and JAK-STAT signaling. CONCLUSIONS: We have identified genes whose expression is significantly associated with obesity in a multi-ethnic cohort. We have identified novel genes associated with BMI as well as confirmed previously identified genes from earlier genetic analyses. These novel genes and their biological pathways represent new targets for understanding the biology of obesity as well as new therapeutic intervention to reduce obesity and improve global public health.

Plasma neurofilament light chain concentrations are elevated in youth-onset type 2 diabetes and associated with neuropathy.

BACKGROUND AND AIMS: The lack of easily measurable biomarkers remains a challenge in executing clinical trials for diabetic neuropathy (DN). Plasma Neurofilament light chain (NFL) concentration is a promising biomarker in immune-mediated neuropathies. Longitudinal studies evaluating NFL in DN have not been performed. METHODS: A nested case-control study was performed on participants with youth-onset type 2 diabetes enrolled in the prospective Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study. Plasma NFL concentrations were measured at 4-year intervals from 2008 to 2020 in 50 participants who developed DN and 50 participants with type 2 diabetes who did not develop DN. RESULTS: NFL concentrations were similar in the DN and no DN groups at the first assessment. Concentrations were higher in DN participants at all subsequent assessment periods (all p < .01). NFL concentrations increased over time in both groups, with higher degrees of change in DN participants (interaction p = .045). A doubling of the NFL value at Assessment 2 in those without DN increased the odds of ultimate DN outcome by an estimated ratio of 2.86 (95% CI: [1.30, 6.33], p = .0046). At the final study visit, positive Spearman correlations (controlled for age, sex, diabetes duration, and BMI) were observed between NFL and HbA1c (0.48, p < .0001), total cholesterol (0.25, p = .018), and low-density lipoprotein (LDL (0.30, p = .0037)). Negative correlations were observed with measures of heart rate variability (-0.42 to -0.46, p = <.0001). INTERPRETATION: The findings that NFL concentrations are elevated in individuals with youth-onset type 2 diabetes, and increase more rapidly in those who develop DN, suggest that NFL could be a valuable biomarker for DN.

Pulmonary Function and Blood DNA Methylation: A Multiancestry Epigenome-Wide Association Meta-analysis.

Rationale: Methylation integrates factors present at birth and modifiable across the lifespan that can influence pulmonary function. Studies are limited in scope and replication. Objectives: To conduct large-scale epigenome-wide meta-analyses of blood DNA methylation and pulmonary function. Methods: Twelve cohorts analyzed associations of methylation at cytosine-phosphate-guanine probes (CpGs), using Illumina 450K or EPIC/850K arrays, with FEV1, FVC, and FEV1/FVC. We performed multiancestry epigenome-wide meta-analyses (total of 17,503 individuals; 14,761 European, 2,549 African, and 193 Hispanic/Latino ancestries) and interpreted results using integrative epigenomics. Measurements and Main Results: We identified 1,267 CpGs (1,042 genes) differentially methylated (false discovery rate, <0.025) in relation to FEV1, FVC, or FEV1/FVC, including 1,240 novel and 73 also related to chronic obstructive pulmonary disease (1,787 cases). We found 294 CpGs unique to European or African ancestry and 395 CpGs unique to never or ever smokers. The majority of significant CpGs correlated with nearby gene expression in blood. Findings were enriched in key regulatory elements for gene function, including accessible chromatin elements, in both blood and lung. Sixty-nine implicated genes are targets of investigational or approved drugs. One example novel gene highlighted by integrative epigenomic and druggable target analysis is TNFRSF4. Mendelian randomization and colocalization analyses suggest that epigenome-wide association study signals capture causal regulatory genomic loci. Conclusions: We identified numerous novel loci differentially methylated in relation to pulmonary function; few were detected in large genome-wide association studies. Integrative analyses highlight functional relevance and potential therapeutic targets. This comprehensive discovery of potentially modifiable, novel lung function loci expands knowledge gained from genetic studies, providing insights into lung pathogenesis.

Alpha Globin Gene Copy Number Is Associated with Prevalent Chronic Kidney Disease and Incident End-Stage Kidney Disease among Black Americans.

BACKGROUND: α-Globin is expressed in endothelial cells of resistance arteries, where it limits endothelial nitric oxide signaling and enhances α-adrenergic-mediated vasoconstriction. α-Globin gene (HBA) copy number is variable in people of African descent and other populations worldwide. Given the protective effect of nitric oxide in the kidney, we hypothesized that HBA copy number would be associated with kidney disease risk. METHODS: Community-dwelling Black Americans aged ≥45 years old were enrolled in a national longitudinal cohort from 2003 through 2007. HBA copy number was measured using droplet digital PCR. The prevalence ratio (PR) of CKD and the relative risk (RR) of incident reduced eGFR were calculated using modified Poisson multivariable regression. The hazard ratio (HR) of incident ESKD was calculated using Cox proportional hazards multivariable regression. RESULTS: Among 9908 participants, HBA copy number varied from 2 to 6. In analyses adjusted for demographic, clinical, and genetic risk factors, a one-copy increase in HBA was associated with 14% greater prevalence of CKD (PR, 1.14; 95% CI, 1.07 to 1.21; P<0.0001). While HBA copy number was not associated with incident reduced eGFR (RR, 1.06; 95% CI, 0.94 to 1.19; P=0.38), the hazard of incident ESKD was 32% higher for each additional copy of HBA (HR, 1.32; 95% CI, 1.09 to 1.61; P=0.005). CONCLUSIONS: Increasing HBA copy number was associated with a greater prevalence of CKD and incidence of ESKD in a national longitudinal cohort of Black Americans.

Lung function impairment and risk of incident heart failure: the NHLBI Pooled Cohorts Study.

AIMS: The aim is to evaluate associations of lung function impairment with risk of incident heart failure (HF). METHODS AND RESULTS: Data were pooled across eight US population-based cohorts that enrolled participants from 1987 to 2004. Participants with self-reported baseline cardiovascular disease were excluded. Spirometry was used to define obstructive [forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) <0.70] or restrictive (FEV1/FVC ≥0.70, FVC <80%) lung physiology. The incident HF was defined as hospitalization or death caused by HF. In a sub-set, HF events were sub-classified as HF with reduced ejection fraction (HFrEF; EF <50%) or preserved EF (HFpEF; EF ≥50%). The Fine-Gray proportional sub-distribution hazards models were adjusted for sociodemographic factors, smoking, and cardiovascular risk factors. In models of incident HF sub-types, HFrEF, HFpEF, and non-HF mortality were treated as competing risks. Among 31 677 adults, there were 3344 incident HF events over a median follow-up of 21.0 years. Of 2066 classifiable HF events, 1030 were classified as HFrEF and 1036 as HFpEF. Obstructive [adjusted hazard ratio (HR) 1.17, 95% confidence interval (CI) 1.07-1.27] and restrictive physiology (adjusted HR 1.43, 95% CI 1.27-1.62) were associated with incident HF. Obstructive and restrictive ventilatory defects were associated with HFpEF but not HFrEF. The magnitude of the association between restrictive physiology and HFpEF was similar to associations with hypertension, diabetes, and smoking. CONCLUSION: Lung function impairment was associated with increased risk of incident HF, and particularly incident HFpEF, independent of and to a similar extent as major known cardiovascular risk factors.

Mendelian randomization study of diabetes and dementia in the Million Veteran Program.

INTRODUCTION: Diabetes and dementia are diseases of high health-care burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases. METHODS: We conducted a one-sample Mendelian randomization (MR) analysis in the US Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data. RESULTS: For each standard deviation increase in genetically predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: odds ratio [OR] = 1.07 [1.05-1.08], P = 3.40E-18; vascular: OR = 1.11 [1.07-1.15], P = 3.63E-09, Alzheimer's disease [AD]: OR = 1.06 [1.02-1.09], P = 6.84E-04) and non-Hispanic Black participants (all-cause: OR = 1.06 [1.02-1.10], P = 3.66E-03, vascular: OR = 1.11 [1.04-1.19], P = 2.20E-03, AD: OR = 1.12 [1.02-1.23], P = 1.60E-02) but not in Hispanic participants (all P > 0.05). DISCUSSION: We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies using two-sample MR techniques.

Whole-Genome Sequencing Association Analyses of Stroke and Its Subtypes in Ancestrally Diverse Populations From Trans-Omics for Precision Medicine Project.

BACKGROUND AND PURPOSE: Stroke is the leading cause of death and long-term disability worldwide. Previous genome-wide association studies identified 51 loci associated with stroke (mostly ischemic) and its subtypes among predominantly European populations. Using whole-genome sequencing in ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) Program, we aimed to identify novel variants, especially low-frequency or ancestry-specific variants, associated with all stroke, ischemic stroke and its subtypes (large artery, cardioembolic, and small vessel), and hemorrhagic stroke and its subtypes (intracerebral and subarachnoid). METHODS: Whole-genome sequencing data were available for 6833 stroke cases and 27 116 controls, including 22 315 European, 7877 Black, 2616 Hispanic/Latino, 850 Asian, 54 Native American, and 237 other ancestry participants. In TOPMed, we performed single variant association analysis examining 40 million common variants and aggregated association analysis focusing on rare variants. We also combined TOPMed European populations with over 28 000 additional European participants from the UK BioBank genome-wide array data through meta-analysis. RESULTS: In the single variant association analysis in TOPMed, we identified one novel locus 13q33 for large artery at whole-genome-wide significance (P<5.00×10-9) and 4 novel loci at genome-wide significance (P<5.00×10-8), all of which need confirmation in independent studies. Lead variants in all 5 loci are low-frequency but are more common in non-European populations. An aggregation of synonymous rare variants within the gene C6orf26 demonstrated suggestive evidence of association for hemorrhagic stroke (P<3.11×10-6). By meta-analyzing European ancestry samples in TOPMed and UK BioBank, we replicated several previously reported stroke loci including PITX2, HDAC9, ZFHX3, and LRCH1. CONCLUSIONS: We represent the first association analysis for stroke and its subtypes using whole-genome sequencing data from ancestrally diverse populations. While our findings suggest the potential benefits of combining whole-genome sequencing data with populations of diverse genetic backgrounds to identify possible low-frequency or ancestry-specific variants, they also highlight the need to increase genome coverage and sample sizes.

Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology.

Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10-7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r2 > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10-4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.