Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes

OBJECTIVE

To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD.

RESULTS

Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs77142250 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16).

CONCLUSIONS

The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data

INTRODUCTION

Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.

METHODS

We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants.

RESULTS

Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses.

DISCUSSION

This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

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.

Association of Common and Rare Variants with Alzheimer's Disease in over 13,000 Diverse Individuals with Whole-Genome Sequencing from the Alzheimer's Disease Sequencing Project

Alzheimer's Disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous. Here, we investigated the association between AD and both common variants and aggregates of rare coding and noncoding variants in 13,371 individuals of diverse ancestry with whole genome sequence (WGS) data. Pooled-population analyses identified genetic variants in or near APOE, BIN1, and LINC00320 significantly associated with AD (p < 5×10-8). Population-specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and noncoding variants in this region. Finally, we observed suggestive associations (p < 5×10-5) of aggregates of rare coding rare variants in ABCA7 among non-Hispanic Whites (p=5.4×10-6), and rare noncoding variants in the promoter of TOMM40 distinct of APOE in pooled-population analyses (p=7.2×10-8). Complementary pooled-population and population-specific analyses offered unique insights into the genetic architecture of AD.

Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data

INTRODUCTION

Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci.

METHODS

We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants.

RESULTS

Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses.

DISCUSSION

This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.

Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study

Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus

BACKGROUND

Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD.

METHODS

From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D.

RESULTS

A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance (P<5.0×10-8): rs147138607 (intergenic variant between CACNA1E and ZNF648) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, P=3.6×10-9, rs11444867 (intergenic variant near HS3ST1) with HR 1.89, 95% CI 1.52 - 2.35, P=9.9×10-9, and rs335407 (intergenic variant between TFB1M and NOX3) HR 1.25, 95% CI 1.16 - 1.35, P=1.5×10-8. Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with P<0.05, and 5 were significant after Bonferroni correction (P<0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase (P=1.0×10-16).

CONCLUSIONS

The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p  < 5x10 - 8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p  < 1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR < 0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Whole Genome DNA and RNA Sequencing of Whole Blood Elucidates the Genetic Architecture of Gene Expression Underlying a Wide Range of Diseases

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis -eQTL variant-gene transcript (eGene) pairs at p <5×10 -8 (2,855,111 unique cis -eQTL variants and 15,982 unique eGenes) and 1,469,754 trans -eQTL variant-eGene pairs at p <1e-12 (526,056 unique trans -eQTL variants and 7,233 unique eGenes). In addition, 442,379 cis -eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis- eGenes are enriched for immune functions (FDR <0.05). The cis -eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Sugar-Sweetened Beverage Consumption May Modify Associations Between Genetic Variants in the CHREBP (Carbohydrate Responsive Element Binding Protein) Locus and HDL-C (High-Density Lipoprotein Cholesterol) and Triglyceride Concentrations

Supplemental Digital Content is available in the text.

Background:

ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the CHREBP locus have separately been linked to HDL-C (high-density lipoprotein cholesterol) and triglyceride concentrations. We hypothesized that SSB consumption would modify the association between genetic variants in the CHREBP locus and dyslipidemia.

Methods:

Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near CHREBP were considered. SSB consumption was estimated from validated questionnaires, and participants were grouped by their estimated intake.

Results:

In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95% CI, 1.16–3.07] mg/dL per allele; P<0.0001), but not significantly among the lowest SSB consumers (P=0.81; P_Diff <0.0001). Similar results were observed for 2 additional variants (rs35709627 and rs71556736). For triglyceride, rs55673514 was positively associated with triglyceride concentrations only among the highest SSB consumers (β, 0.06 [95% CI, 0.02–0.09] ln-mg/dL per allele, P=0.001) but not the lowest SSB consumers (P=0.84; P_Diff=0.0005).

Conclusions:

Our results identified genetic variants in the CHREBP locus that may protect against SSB-associated reductions in HDL-C and other variants that may exacerbate SSB-associated increases in triglyceride concentrations.

Registration:

URL: https://www.clinicaltrials.gov; Unique identifier: NCT00005133, NCT00005121, NCT00005487, and NCT00000479.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Network analysis of drug effect on triglyceride-associated DNA methylation

BACKGROUND

DNA methylation, an epigenetic modification, can be affected by environmental factors and thus regulate gene expression levels that can lead to alterations of certain phenotypes. Network analysis has been used successfully to discover gene sets that are expressed differently across multiple disease states and suggest possible pathways of disease progression. We applied this framework to compare DNA methylation levels before and after lipid-lowering medication and to identify modules that differ topologically between the two time points, revealing the association between lipid medication and these triglyceride-related methylation sites.

METHODS

We performed quality control using beta-mixture quantile normalization on 463,995 cytosine-phosphate-guanine (CpG) sites and deleted problematic sites, resulting in 423,004 probes. We identified 14,850 probes that were nominally associated with triglycerides prior to treatment and performed weighted gene correlation network analysis (WGCNA) to construct pre- and posttreatment methylation networks of these probes. We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre- and posttreatment. For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules.

RESULTS

Six triglyceride-associated modules were identified using pretreatment methylation probes. The same 3 modules were not preserved in posttreatment data using both the module-preservation and the GHD methods. Top-enriched pathways for the 3 differentially methylated modules are sphingolipid signaling pathway, proteoglycans in cancer, and metabolic pathways (p values < 0.005). One module in particular included an enrichment of lipid-related pathways among the top results.

CONCLUSIONS

The same 3 modules, which were differentially methylated between pre- and posttreatment, were identified using both WGCNA module-preservation and GHD methods. Pathway analysis revealed that triglyceride-associated modules contain groups of genes that are involved in lipid signaling and metabolism. These 3 modules may provide insight into the effect of fenofibrate on changes in triglyceride levels and these methylation sites.

Whole genome sequence analyses of brain imaging measures in the Framingham Study

Objective

We sought to identify rare variants influencing brain imaging phenotypes in the Framingham Heart Study by performing whole genome sequence association analyses within the Trans-Omics for Precision Medicine Program.

Methods

We performed association analyses of cerebral and hippocampal volumes and white matter hyperintensity (WMH) in up to 2,180 individuals by testing the association of rank-normalized residuals from mixed-effect linear regression models adjusted for sex, age, and total intracranial volume with individual variants while accounting for familial relatedness. We conducted gene-based tests for rare variants using (1) a sliding-window approach, (2) a selection of functional exonic variants, or (3) all variants.

Results

We detected new loci in 1p21 for cerebral volume (minor allele frequency [MAF] 0.005, p = 10⁻⁸) and in 16q23 for hippocampal volume (MAF 0.05, p = 2.7 × 10⁻⁸). Previously identified associations in 12q24 for hippocampal volume (rs7294919, p = 4.4 × 10⁻⁴) and in 17q25 for WMH (rs7214628, p = 2.0 × 10⁻³) were confirmed. Gene-based tests detected associations (p ≤ 2.3 × 10⁻⁶) in new loci for cerebral (5q13, 8p12, 9q31, 13q12-q13, 15q24, 17q12, 19q13) and hippocampal volumes (2p12) and WMH (3q13, 4p15) including Alzheimer disease– (UNC5D) and Parkinson disease–associated genes (GBA). Pathway analyses evidenced enrichment of associated genes in immunity, inflammation, and Alzheimer disease and Parkinson disease pathways.

Conclusions

Whole genome sequence–wide search reveals intriguing new loci associated with brain measures. Replication of novel loci is needed to confirm these findings.

Whole exome sequencing in the Framingham Heart Study identifies rare variation in HYAL2 that influences platelet aggregation

Inhibition of platelet reactivity is a common therapeutic strategy in secondary prevention of cardiovascular disease. Genetic and environmental factors influence inter-individual variation in platelet reactivity. Identifying genes that contribute to platelet reactivity can reveal new biological mechanisms and possible therapeutic targets. Here, we examined rare coding variation to identify genes associated with platelet reactivity in a population-based cohort. To do so, we performed whole exome sequencing in the Framingham Heart Study and conducted single variant and gene-based association tests against platelet reactivity to collagen, adenosine diphosphate (ADP), and epinephrine agonists in up to 1,211 individuals. Single variant tests revealed no significant associations (p<1.44×10^-7), though we observed a suggestive association with previously implicated MRVI1 (rs11042902, p = 1.95×10^-7). Using gene-based association tests of rare and low-frequency variants, we found significant associations of HYAL2 with increased ADP-induced aggregation (p = 1.07×10^-7) and GSTZ1 with increased epinephrine-induced aggregation (p = 1.62×10^-6). HYAL2 also showed suggestive associations with epinephrine-induced aggregation (p = 2.64×10^-5). The rare variants in the HYAL2 gene-based association included a missense variant (N357S) at a known N-glycosylation site and a nonsense variant (Q406*) that removes a glycophosphatidylinositol (GPI) anchor from the resulting protein. These variants suggest that improper membrane trafficking of HYAL2 influences platelet reactivity. We also observed suggestive associations of AR (p = 7.39×10^-6) and MAPRE1 (p = 7.26×10^-6) with ADP-induced reactivity. Our study demonstrates that gene-based tests and other grouping strategies of rare variants are powerful approaches to detect associations in population-based analyses of complex traits not detected by single variant tests and possible new genetic influences on platelet reactivity.

Association of genetic variations and gene expression in a family-based study

Background

Expression quantitative trait locus (eQTL) maps are considered a valuable resource in studying complex diseases. The availability of gene expression data from the Genetic Analysis Workshop 19 (GAW19) provides a great opportunity to investigate the association of gene expression with genetic variants in blood.

Methods

A total of 267 samples with gene expression and whole genome sequencing data were employed in this study. We used linear mixed models with genetic random effects along with a permutation procedure to create an eQTL map. The eQTL map was further tested in terms of functional implication, including the enrichment in disease-related variants and in regulatory regions.

Results

We identified 22,869 significant eQTLs from the GAW19 data set. These eQTLs were highly enriched with genetic loci associated with blood pressure and DNase hypersensitive regions. In addition, the majority of genes associated with eQTLs showed moderate to high heritability (h² > 0.4).

Conclusions

We successfully created an eQTL map from the GAW19 data set. Our study indicated that the eQTLs were enriched within regulatory regions, and tended to have relatively high heritability.

Hypoglycemia risk and glucose variability indices derived from routine self-monitoring of blood glucose are related to laboratory measures of insulin sensitivity and epinephrine counterregulation

BACKGROUND

the widely held assumptions that in type 1 diabetes glucose variability may correlate with insulin sensitivity and impaired epinephrine counterregulation have not been studied directly. Here we investigate possible relationships between outpatient measures of glucose variability and risk for hypoglycemia with physiological characteristics: insulin sensitivity and hypoglycemia counterregulation.

METHODS

thirty-four subjects with type 1 diabetes (14 women, 20 men; 37 ± 2.1 years old; glycosylated hemoglobin [HbA1c], 7.6  ±  0.21%) performed self-monitoring of blood glucose (SMBG) for a month, followed by an inpatient hyperinsulinemic euglycemic and hypoglycemic clamp. SMBG field data were used to calculate measures of glucose variability and risk of hypoglycemia, while the clamp procedure was used to evaluate insulin sensitivity and epinephrine response during induced hypoglycemia. Spearman partial correlations adjusted for age, duration of diabetes, body mass index, gender, and HbA1c were used to assess the relationship between the field indices of glucose variability and the physiological characteristics of diabetes.

RESULTS

two glucose variability measures correlated positively (P < 0.01) with insulin sensitivity: the Average Daily Risk Range (ADRR) (ρ = 0.5) and the Glycemic Lability Index (ρ = 0.48). The Low Blood Glucose Index, a measure of the risk for hypoglycemia, and the ADRR correlated negatively with maximum epinephrine response during hypoglycemia: ρ = -0.46, P < 0.01 and ρ = -0.4, P = 0.03, respectively.

CONCLUSIONS

higher insulin sensitivity and lower epinephrine response during hypoglycemia are related to increased glucose variability (as quantified by the ADRR), irrespective of HbA1c and other patient characteristics. Lower epinephrine relates to risk for hypoglycemia as well.