GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries

Epigenome-Wide Association Study of Kidney Function Identifies Trans-Ethnic and Ethnic-Specific Loci

BACKGROUND: DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach. METHODS: The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses. RESULTS: We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development. CONCLUSIONS: We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context

Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids. © 2021, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term ‘variance stratification’. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI. © 2021, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Genetic and non-genetic factors affecting the expression of COVID-19-relevant genes in the large airway epithelium

BACKGROUND: The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression. METHODS: We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants. RESULTS: We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections. CONCLUSIONS: These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

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

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids. © 2022, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci

BACKGROUND: DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach. METHODS: The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses. RESULTS: We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development. CONCLUSIONS: We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context

Clonal hematopoiesis associated with epigenetic aging and clinical outcomes

Clonal hematopoiesis of indeterminate potential (CHIP) is a common precursor state for blood cancers that most frequently occurs due to mutations in the DNA-methylation modifying enzymes DNMT3A or TET2. We used DNA-methylation array and whole-genome sequencing data from four cohorts together comprising 5522 persons to study the association between CHIP, epigenetic clocks, and health outcomes. CHIP was strongly associated with epigenetic age acceleration, defined as the residual after regressing epigenetic clock age on chronological age, in several clocks, ranging from 1.31 years (GrimAge, p < 8.6 × 10−7) to 3.08 years (EEAA, p < 3.7 × 10−18). Mutations in most CHIP genes except DNA-damage response genes were associated with increases in several measures of age acceleration. CHIP carriers with mutations in multiple genes had the largest increases in age acceleration and decrease in estimated telomere length. Finally, we found that ~40% of CHIP carriers had acceleration >0 in both Hannum and GrimAge (referred to as AgeAccelHG+). This group was at high risk of all-cause mortality (hazard ratio 2.90, p < 4.1 × 10−8) and coronary heart disease (CHD) (hazard ratio 3.24, p < 9.3 × 10−6) compared to those who were CHIP−/AgeAccelHG−. In contrast, the other ~60% of CHIP carriers who were AgeAccelHG− were not at increased risk of these outcomes. In summary, CHIP is strongly linked to age acceleration in multiple clocks, and the combination of CHIP and epigenetic aging may be used to identify a population at high risk for adverse outcomes and who may be a target for clinical interventions

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

Comparison of Proteomic Assessment Methods in Multiple Cohort Studies

Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from −0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies