Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical ...properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.
The dystonias are a group of disorders characterized by excessive contraction of muscles leading to abnormal involuntary movements. The clinical manifestations are very heterogeneous, with numerous ...distinct syndromes. The etiologies for dystonia are also heterogeneous with idiopathic, acquired, and inherited forms. Technological advances in genetics over the past two decades have led to a rapid growth in the number of genes associated with dystonia. These genes encode proteins with very diverse biological functions. This review focusses on genes that have contributed to understanding shared biological pathways relevant to specific subgroups of dystonia syndromes. Although many potential shared biological pathways have been proposed, the ones addressed here include defects in dopamine signaling, mitochondrial dysfunction and energy maintenance, toxic accumulation of heavy metals in the brain, and calcium channels and abnormal calcium homeostasis. Elucidation of these and other shared pathways is important for understanding the biological basis for dystonia and for designing novel experimental therapeutics that have the broadest potential for multiple types of dystonia.
Previous studies have shown reduced cardiovascular risk with increasing high-density lipoprotein cholesterol (HDL-C) levels. However, recent data in the general population have shown increased risk ...of adverse outcomes at very high concentrations of HDL-C. Thus, we aimed to study the gender-specific relation between very high HDL-C levels (>80, >100 mg/100 ml) and adverse cardiovascular outcomes and the genetic basis in the general population enrolled in the United Kingdom Biobank. A total of 415,416 participants enrolled in the United Kingdom Biobank without coronary artery disease were included in this prospective cohort study, with a median follow-up of 9 years. A high HDL-C level >80 mg/100 ml was associated with increased risk of all-cause death (Hazard ratio HR 1.11, confidence interval CI 1.03 to 1.20, p = 0.005) and cardiovascular death (HR 1.24, CI 1.05 to 1.46, p = 0.01) after adjustment for age, gender, race, body mass index, hypertension, smoking, triglycerides, LDL-C, stroke history, heart attack history, diabetes, eGFR, and frequent alcohol use (defined as ≥3 times/week) using Cox proportional hazard and Fine and Gray's subdistribution hazard models, respectively. In gender-stratified analyses, such associations were only observed in men (all-cause death HR 1.79, CI 1.59 to 2.02, p <0.0001; cardiovascular death HR 1.92, CI 1.52 to 2.42, p <0.0001), but not in women (all-cause death HR 0.97, CI 0.88 to 1.06, p = 0.50; cardiovascular death HR 1.04, CI 0.83 to 1.31, p = 0.70). The findings persisted after adjusting for the genetic risk score comprised of known HDL-C–associated single nucleotide polymorphisms. Very high HDL-C levels are associated with an increased risk of all-cause death and cardiovascular death among men but not in women in the general population free of coronary artery disease.
Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic ...biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.
Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple—even distinct—traits. ...Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10−8) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10−7) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.
Previous studies have shown lower cardiovascular risk with higher high-density lipoprotein cholesterol (HDL-C) levels. However, recent data in the general population have shown increased risk of ...adverse outcomes at very high HDL-C concentrations.
To study the association between very high HDL-C levels (>80 mg/dL) and mortality in patients with coronary artery disease (CAD) and to investigate the association of known HDL-C genotypes with high HDL-C level outcomes.
This prospective, multicenter, cohort study, conducted from 2006 to present in the UK and from 2003 to present in Atlanta, Georgia, recruited patients with CAD from the UK Biobank (UKB) and the Emory Cardiovascular Biobank (EmCAB), respectively. Patients without confirmed CAD were excluded from the study. Data analyses were conducted from May 10, 2020, to April 28, 2021.
High HDL-C levels (>80 mg/dL).
The primary outcome was all-cause death. The secondary outcome was cardiovascular death.
A total of 14 478 participants (mean SD age, 62.1 5.8 years; 11 034 men 76.2%) from the UKB and 5467 participants (mean SD age, 63.8 12.3 years; 3632 men 66.4%) from the EmCAB were included in the study. Over a median follow-up of 8.9 (IQR, 8.0-9.7) years in the UKB and 6.7 (IQR, 4.0-10.8) years in the EmCAB, a U-shaped association with outcomes was observed with higher risk in those with both low and very high HDL-C levels compared with those with midrange values. Very high HDL-C levels (>80 mg/dL) were associated with increased risk of all-cause death (hazard ratio HR, 1.96; 95% CI, 1.42-2.71; P < .001) and cardiovascular death (HR, 1.71; 95% CI, 1.09-2.68; P = .02) compared with those with HDL-C levels in the range of 40 to 60 mg/dL in the UKB after adjustment for confounding factors. These results were replicated in the EmCAB. These associations persisted after adjustment for the HDL-C genetic risk score within the UKB. Sensitivity analyses demonstrated that the risk of all-cause mortality in the very high HDL-C group was higher among men than women in the UKB (HR, 2.63; 95% CI, 1.75-3.95; P < .001 vs HR, 1.39; 95% CI, 0.82-2.35; P = .23).
Results of this cohort study suggest that very high HDL-C levels are paradoxically associated with higher mortality risk in individuals with CAD. This association was independent of the common polymorphisms associated with high HDL-C levels.
Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores ...to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPS
, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPS
strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPS
was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPS
demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPS
for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.
The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of US military veterans. Here we genotyped 312,571 ...MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least one blood lipid measurement, including 57,332 black and 24,743 Hispanic participants, we tested up to around 32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total n > 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes) and PDE3B (triglycerides and coronary disease).
Millions of genetic variants have been assessed for their effects on the trait of interest in genome-wide association studies (GWAS). The complex traits are affected by a set of inter-related genes. ...However, the typical GWAS only examine the association of a single genetic variant at a time. The individual effects of a complex trait are usually small, and the simple sum of these individual effects may not reflect the holistic effect of the genetic system. High-throughput methods enable genomic studies to produce a large amount of data to expand the knowledge base of the biological systems. Biological networks and pathways are built to represent the functional or physical connectivity among genes. Integrated with GWAS data, the network- and pathway-based methods complement the approach of single genetic variant analysis, and may improve the power to identify trait-associated genes. Taking advantage of the biological knowledge, these approaches are valuable to interpret the functional role of the genetic variants, and to further understand the molecular mechanism influencing the traits. The network- and pathway-based methods have demonstrated their utilities, and will be increasingly important to address a number of challenges facing the mainstream GWAS.
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