Learning Gaussian Graphical Models from Correlated Data.

Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after "adjusting" for the effects of other variables and leads to more parsimonious and interpretable models. There are well established procedures to build GGMs from a sample of independent and identical distributed observations.

Polygenic prediction of body mass index and obesity through the life course and across ancestries.

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.

Multi-ancestry genome-wide association analyses incorporating SNP-by-psychosocial interactions identify novel loci for serum lipids.

Serum lipid levels, which are influenced by both genetic and environmental factors, are key determinants of cardiometabolic health and are influenced by both genetic and environmental factors. Improving our understanding of their underlying biological mechanisms can have important public health and therapeutic implications. Although psychosocial factors, including depression, anxiety, and perceived social support, are associated with serum lipid levels, it is unknown if they modify the effect of genetic loci that influence lipids.

Lipoprotein(a) and Heart Failure Among Black and White Participants in Atherosclerosis Risk in Communities Study, Framingham Offspring Study, and Multi-Ethnic Study of Atherosclerosis: The Pooling Project.

Background: This study investigated Lp(a) (lipoprotein(a)) levels with heart failure (HF) incidence overall and ejection fraction (EF) subtypes among Black and White participants in a pooled analysis of MESA (Multi-Ethnic Study of Atherosclerosis), FOS (Framingham Offspring Study), and ARIC (Atherosclerosis Risk in Communities Study).

Methods: This study was conducted among 16 771 White and Black participants in ARIC (N=10 347), MESA (N=4150), and FOS (N=2274). Baseline was time of Lp(a) measurement (ARIC Visit 4: 1996-1998; MESA Visit 1: 2000-2002; FOS Cycle 6: 1995-1998).

Multi-ancestry whole genome sequencing analysis of lean body mass.

Background: Lean body mass is a crucial physiological component of body composition. Although lean body mass has a high heritability, studies evaluating the genetic determinants of lean mass (LM) have to date been limited largely to genome-wide association studies (GWAS) and common variants. Using whole genome sequencing (WGS)-based studies, we aimed to discover novel genetic variants associated with LM in population-based cohorts with multiple ancestries.

Lipoprotein(a) and Risk of Incident Atherosclerotic Cardiovascular Disease: Impact of High-Sensitivity C-Reactive Protein and Risk Variability Among Human Clinical Subgroups.

Elevated lipoprotein(a) [Lp(a)] is associated with increased incidence of atherosclerotic cardiovascular disease (ASCVD). We aimed to assess the utility of Lp(a) as an ASCVD risk-enhancing factor, as recommended by the 2019 ACC/AHA guidelines on ASCVD primary prevention, and to determine whether C-reactive protein (CRP) modifies the association of elevated Lp(a) with ASCVD risk. Lp(a), high sensitivity CRP (hs-CRP), and other ASCVD risk factors, including blood lipids, blood pressure, diabetes status, body weight and height, and smoking, were measured in 15,933 participants (median age 61.

Diabetes Mellitus Risk Prediction in the Framingham Offspring Study and Large Population Analysis.

: Diabetes mellitus is a major cause of death and a significant risk factor for cardiovascular disease, kidney failure, neuropathy, and retinopathy. Our objectives were to develop a diabetes risk model and apply it to a large population. : Non-diabetic adults in the Framingham Offspring Study ( = 2416) were followed for 10 years for new diabetes.

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele.

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups.

A large-scale genome-wide study of gene-sleep duration interactions for blood pressure in 811,405 individuals from diverse populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discovered 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes.

Heterogeneous effects of genetic variants and traits associated with fasting insulin on cardiometabolic outcomes.

Elevated fasting insulin levels (FI), indicative of altered insulin secretion and sensitivity, may precede type 2 diabetes (T2D) and cardiovascular disease onset. In this study, we group FI-associated genetic variants based on their genetic and phenotypic similarities and identify seven clusters with distinct mechanisms contributing to elevated FI levels. Clusters fall into two types: "non-diabetogenic hyperinsulinemia," where clusters are not associated with increased T2D risk, and "diabetogenic hyperinsulinemia," where T2D associations are driven by body fat distribution, liver function, circulating lipids, or inflammation.

Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed.

Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterize 14,324 ancestrally diverse RNA-sequencing samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and integrate whole genome sequencing data to perform and expression and splicing quantitative trait locus (-/trans-e/sQTL) analyses in six tissues and cell types, most notably whole blood (N=6,454) and lung (N=1,291). We show this dataset enables greater detection of secondary cis-e/sQTL signals than was achieved in previous studies, and that secondary cis-eQTL and primary trans-eQTL signal discovery is not saturated even though eGene discovery is.

A Large-Scale Genome-wide Association Study of Blood Pressure Accounting for Gene-Depressive Symptomatology Interactions in 564,680 Individuals from Diverse Populations.

Background: Gene-environment interactions may enhance our understanding of hypertension. Our previous study highlighted the importance of considering psychosocial factors in gene discovery for blood pressure (BP) but was limited in statistical power and population diversity. To address these challenges, we conducted a multi-population genome-wide association study (GWAS) of BP accounting for gene-depressive symptomatology (DEPR) interactions in a larger and more diverse sample.

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data.

Hepatic Steatosis and Fibrosis, Cardiorespiratory Fitness, and Metabolic Mediators in the Community.

Background And Aims: Individuals with steatotic liver disease (SLD) are at high cardiovascular disease (CVD) risk, but approaches to characterise and mitigate this risk are limited. By investigating relations, and shared metabolic pathways, of hepatic steatosis/fibrosis and cardiorespiratory fitness (CRF), we sought to identify new avenues for CVD risk reduction in SLD.

Methods: In Framingham Heart Study (FHS) participants (N = 2722, age 54 ± 9 years, 53% women), vibration-controlled transient elastography (VCTE) was performed between 2016-2019 to assess hepatic steatosis (continuous attenuation parameter [CAP]) and fibrosis (liver fibrosis measure [LSM]).

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes.

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI.

Learning Gaussian Graphical Models from Correlated Data.

Gaussian Graphical Models (GGM) have been widely used in biomedical research to explore complex relationships between many variables. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways.