Genetically regulated gene expression underlies lipid traits in Hispanic cohorts

Advancements in genetic research by the Hispanic Community Health Study/Study of Latinos: A 10-year retrospective review

The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a multicenter, longitudinal cohort study designed to evaluate environmental, lifestyle, and genetic risk factors as they relate to cardiometabolic and other chronic diseases among Hispanic/Latino populations in the United States. Since the study's inception in 2008, as a result of the study's robust genetic measures, HCHS/SOL has facilitated major contributions to the field of genetic research. This 10-year retrospective review highlights the major findings for genotype-phenotype relationships and advancements in statistical methods owing to the HCHS/SOL. Furthermore, we discuss the ethical and societal challenges of genetic research, especially among Hispanic/Latino adults in the United States. Continued genetic research, ancillary study expansion, and consortia collaboration through HCHS/SOL will further drive knowledge and advancements in human genetics research.

Potential drug targets for myocardial infarction identified through Mendelian randomization analysis and Genetic colocalization

Myocardial infarction (MI) is a major cause of death and disability worldwide, but current treatments are limited by their invasiveness, side effects, and lack of efficacy. Novel drug targets for MI prevention are urgently needed. In this study, we used Mendelian randomization to identify potential therapeutic targets for MI using plasma protein quantitative trait loci as exposure variables and MI as the outcome variable. We further validated our findings using reverse causation analysis, Bayesian co-localization analysis, and external datasets. We also constructed a protein-protein interaction network to explore the relationships between the identified proteins and known MI targets. Our analysis revealed 2 proteins, LPA and APOA5, as potential drug targets for MI, with causal effects on MI risk confirmed by multiple lines of evidence. LPA and APOA5 are involved in lipid metabolism and interact with target proteins of current MI medications. We also found 4 other proteins, IL1RN, FN1, NT5C, and SEMA3C, that may have potential as drug targets but require further confirmation. Our study demonstrates the utility of Mendelian randomization and protein quantitative trait loci in discovering novel drug targets for complex diseases such as MI. It provides insights into the underlying mechanisms of MI pathology and treatment.

Transcriptome-wide association studies: recent advances in methods, applications and available databases

Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.

Frequencies of variants in genes associated with dyslipidemias identified in Costa Rican genomes

Dyslipidemias are risk factors in diseases of significant importance to public health, such as atherosclerosis, a condition that contributes to the development of cardiovascular disease. Unhealthy lifestyles, the pre-existence of diseases, and the accumulation of genetic variants in some loci contribute to the development of dyslipidemia. The genetic causality behind these diseases has been studied primarily on populations with extensive European ancestry. Only some studies have explored this topic in Costa Rica, and none have focused on identifying variants that can alter blood lipid levels and quantifying their frequency. To fill this gap, this study focused on identifying variants in 69 genes involved in lipid metabolism using genomes from two studies in Costa Rica. We contrasted the allelic frequencies with those of groups reported in the 1000 Genomes Project and gnomAD and identified potential variants that could influence the development of dyslipidemias. In total, we detected 2,600 variants in the evaluated regions. However, after various filtering steps, we obtained 18 variants that have the potential to alter the function of 16 genes, nine variants have pharmacogenomic or protective implications, eight have high risk in Variant Effect Predictor, and eight were found in other Latin American genetic studies of lipid alterations and the development of dyslipidemia. Some of these variants have been linked to changes in blood lipid levels in other global studies and databases. In future studies, we propose to confirm at least 40 variants of interest from 23 genes in a larger cohort from Costa Rica and Latin American populations to determine their relevance regarding the genetic burden for dyslipidemia. Additionally, more complex studies should arise that include diverse clinical, environmental, and genetic data from patients and controls and functional validation of the variants.

Genomic study of maternal lipid traits in early pregnancy concurs with four known adult lipid loci

BACKGROUND

Blood lipids during pregnancy are associated with cardiovascular diseases and adverse pregnancy outcomes. Genome-wide association studies (GWAS) in predominantly male European ancestry populations have identified genetic loci associated with blood lipid levels. However, the genetic architecture of blood lipids in pregnant women remains poorly understood.

OBJECTIVE

Our goal was to identify genetic loci associated with blood lipid levels among pregnant women from diverse ancestry groups and to evaluate whether previously known lipid loci in predominantly European adults are transferable to pregnant women.

METHODS

The trans-ancestry GWAS were conducted on serum levels of total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and triglycerides during first trimester among pregnant women from four population groups (608 European-, 623 African-, 552 Hispanic- and 235 East Asian-Americans) recruited in the NICHD Fetal Growth Studies cohort. The four GWAS summary statistics were combined using trans-ancestry meta-analysis approaches that account for genetic heterogeneity among populations.

RESULTS

Loci in CELSR2 and APOE were genome-wide significantly associated (p-value < 5×10-8) with total cholesterol and LDL levels. Loci near CETP and ABCA1 approached genome-wide significant association with HDL (p-value = 2.97×10-7 and 9.71×10-8, respectively). Less than 20% of previously known adult lipid loci were transferable to pregnant women.

CONCLUSION

This trans-ancestry GWAS meta-analysis in pregnant women identified associations that concur with four known adult lipid loci. Limited replication of known lipid-loci from predominantly European study populations to pregnant women underlines the need for genomic studies of lipids in ancestrally diverse pregnant women.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov, NCT00912132.

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations

BACKGROUND

Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed.

METHODS

To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants.

RESULTS

Our results revealed 24 suggestive signals (p < 1 × 10-4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN.

CONCLUSIONS

These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

Transcriptome prediction performance across machine learning models and diverse ancestries

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits.

Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

Population-Matched Transcriptome Prediction Increases TWAS Discovery and Replication Rate

Most genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) focus on European populations; however, these results cannot always be accurately applied to non-European populations due to genetic architecture differences. Using GWAS summary statistics in the Population Architecture using Genomics and Epidemiology study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we perform TWAS to determine gene-trait associations. We compared results using three transcriptome prediction models derived from Multi-Ethnic Study of Atherosclerosis populations: the African American and Hispanic/Latino (AFHI) model, the European (EUR) model, and the African American, Hispanic/Latino, and European (ALL) model. We identified 240 unique significant trait-associated genes. We found more significant, colocalized genes that replicate in larger cohorts when applying the AFHI model than the EUR or ALL model. Thus, TWAS with population-matched transcriptome models have more power for discovery and replication, demonstrating the need for more transcriptome studies in diverse populations.

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TWAS mechanistically extends GWAS findings in diverse populations

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Population-matched transcriptome models detect more replicable associations

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Colocalization shows GWAS variants likely act through gene expression regulation

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More GWAS and transcriptome modeling in diverse populations are needed

Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.

Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics

Inhibition of sclerostin is a therapeutic approach to lowering fracture risk in patients with osteoporosis. However, data from phase 3 randomized controlled trials (RCTs) of romosozumab, a first-in-class monoclonal antibody that inhibits sclerostin, suggest an imbalance of serious cardiovascular events, and regulatory agencies have issued marketing authorizations with warnings of cardiovascular disease. Here, we meta-analyze published and unpublished cardiovascular outcome trial data of romosozumab and investigate whether genetic variants that mimic therapeutic inhibition of sclerostin are associated with higher risk of cardiovascular disease. Meta-analysis of up to three RCTs indicated a probable higher risk of cardiovascular events with romosozumab. Scaled to the equivalent dose of romosozumab (210 milligrams per month; 0.09 grams per square centimeter of higher bone mineral density), the SOST genetic variants were associated with lower risk of fracture and osteoporosis (commensurate with the therapeutic effect of romosozumab) and with a higher risk of myocardial infarction and/or coronary revascularization and major adverse cardiovascular events. The same variants were also associated with increased risk of type 2 diabetes mellitus and higher systolic blood pressure and central adiposity. Together, our findings indicate that inhibition of sclerostin may elevate cardiovascular risk, warranting a rigorous evaluation of the cardiovascular safety of romosozumab and other sclerostin inhibitors.

Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and transcriptomic data to showcase their improved statistical power of identifying gene-trait associations while, importantly, offering further biological insights. TWAS have thus far focused on common variants as available from GWAS. Compared with common variants, the findings for or even applications to low-frequency variants are limited and their underlying role in regulating gene expression is less clear. To fill this gap, we extend TWAS to integrating whole genome sequencing data with transcriptomic data for low-frequency variants. Using the data from the Framingham Heart Study, we demonstrate that low-frequency variants play an important and universal role in predicting gene expression, which is not completely due to linkage disequilibrium with the nearby common variants. By including low-frequency variants, in addition to common variants, we increase the predictivity of gene expression for 79% of the examined genes. Incorporating this piece of functional genomic information, we perform association testing for five lipid traits in two UK10K whole genome sequencing cohorts, hypothesizing that cis-expression quantitative trait loci, including low-frequency variants, are more likely to be trait-associated. We discover that two genes, LDLR and TTC22, are genome-wide significantly associated with low-density lipoprotein cholesterol based on 3203 subjects and that the association signals are largely independent of common variants. We further demonstrate that a joint analysis of both common and low-frequency variants identifies association signals that would be missed by testing on either common variants or low-frequency variants alone.

Biological Role of Unsaturated Fatty Acid Desaturases in Health and Disease

Polyunsaturated fatty acids (PUFAs) are considered one of the most important components of cells that influence normal development and function of many organisms, both eukaryotes and prokaryotes. Unsaturated fatty acid desaturases play a crucial role in the synthesis of PUFAs, inserting additional unsaturated bonds into the acyl chain. The level of expression and activity of different types of desaturases determines profiles of PUFAs. It is well recognized that qualitative and quantitative changes in the PUFA profile, resulting from alterations in the expression and activity of fatty acid desaturases, are associated with many pathological conditions. Understanding of underlying mechanisms of fatty acid desaturase activity and their functional modification will facilitate the development of novel therapeutic strategies in diseases associated with qualitative and quantitative disorders of PUFA.