A meta-analysis and polygenic score study identifies novel genetic markers for waist-hip ratio in African populations.

Objective: Understanding the genetic underpinnings of anthropometric traits in diverse populations is crucial for gaining insights into their biological mechanisms and potential implications for health.

Methods: We conducted a genome-wide association study, meta-analysis, and gene set analysis of waist-hip ratio (WHR), WHR adjusted for BMI (WHRadjBMI), waist circumference, BMI, and height using the African Collaborative Center for Microbiome and Genomics Research (ACCME) cohort (n = ~11,000) for discovery and polygenic score target analyses and the Africa America Diabetes Mellitus (AADM) study (n = ~5200) for replication and polygenic score validation. We generated and compared polygenic scores from European, African, Afro-Caribbean, and multiethnic ancestry populations.

Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations.

Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks.

An approach to identify gene-environment interactions and reveal new biological insight in complex traits.

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework.

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians.

Background: Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

Genome, HLA and polygenic risk score analyses for prevalent and persistent cervical human papillomavirus (HPV) infections.

Genetic variants that underlie susceptibility to cervical high-risk human papillomavirus (hrHPV) infections are largely unknown. We conducted discovery genome-wide association studies (GWAS), replication, meta-analysis and colocalization, generated polygenic risk scores (PRS) and examined the association of classical HLA alleles and cervical hrHPV infections in a cohort of over 10,000 women. We identified genome-wide significant variants for prevalent hrHPV around LDB2 and for persistent hrHPV near TPTE2, SMAD2, and CDH12, which code for proteins that are significantly expressed in the human endocervix.

Genes, environment, and African ancestry in cardiometabolic disorders.

The past two decades have been characterized by a substantial global increase in cardiometabolic diseases, but the prevalence and incidence of these diseases and related traits differ across populations. African ancestry populations are among the most affected yet least included in research. Populations of African descent manifest significant genetic and environmental diversity and this under-representation is a missed opportunity for discovery and could exacerbate existing health disparities and curtail equitable implementation of precision medicine.

Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases.

Ancestral and environmental patterns in the association between triglycerides and other cardiometabolic risk factors.

Background: West Africans and African Americans with substantial (∼80%) West African ancestry are characterized by low levels of triglycerides (TG) compared to East Africans and Europeans. The impact of these varying TG levels on other cardiometabolic risk factors is unclear. We compared the strength of association between TG with hypertension, blood pressure, BMI, waist circumference, type 2 diabetes (T2D), and fasting glucose across West African (WA), East African (EA), and European (EU) ancestry populations residing in three vastly different environmental settings: sub-Saharan Africa, United States, and Europe.

2022 ASHG presidential address-One human race: Billions of genomes.

This article is based on the address given by the author at the 2022 meeting of The American Society of Human Genetics (ASHG) in Los Angeles, California. The video of the original address can be found at the ASHG website.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.

Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches.

Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption.

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging.

Semiparametric partial linear modeling of risk factors for ear infections: the Early Childhood Longitudinal Study.

The Early Childhood Longitudinal Study-Kindergarten Class of 2010-2011 (ECLS-K:2011) ascertained timing of ear infections within age specified intervals and parent's/caregiver's report of medically diagnosed hearing loss. In this nationally representative, school-based sample of children followed from kindergarten entry through fifth grade, academic performance in reading, mathematics, and science was assessed longitudinally. Prior investigations of this ECLS-K:2011 cohort showed that age has a non-linear, monotonically increasing functional relationship with academic performance.