Racial residential segregation is associated with ambient air pollution exposure after adjustment for multilevel sociodemographic factors: Evidence from eight US-based cohorts. - Zewdie, Hiwot Y; Fahey, Carolyn A; Harrington, Anna L; Hart, Jaime E; Biggs, Mary L; McClure, Leslie A; Whitsel, Eric A; Kaufman, Joel D; Hajat, Anjum (Environ Epidemiol, 2025)
OBJECTIVE: We examined if racial residential segregation (RRS) - a fundamental cause of disease - is independently associated with air pollution after accounting for other neighborhood and individual-level sociodemographic factors, to better understand its potential role as a confounder of air pollution-health studies. METHODS: We compiled data from eight large cohorts, restricting to non-Hispanic Black and White urban-residing participants observed at least once between 1999 and 2005. We used 2000 decennial census data to derive a spatial RRS measure (divergence index) and neighborhood socioeconomic status (NSES) index for participants' residing Census tracts, in addition to participant baseline data, to examine associations between RRS and sociodemographic factors (NSES, education, race) and residential exposure to spatiotemporal model-predicted PM and NO levels. We fit random-effects meta-analysis models to pool estimates across adjusted cohort-specific multilevel models. RESULTS: Analytic sample included eligible participants in CHS (N = 3,605), MESA (4,785), REGARDS (22,649), NHS (90,415), NHSII (91,654), HPFS (32,625), WHI-OS (77,680), and WHI-CT (56,639). In adjusted univariate models, a quartile higher RRS was associated with 3.73% higher PM exposure (95% CI: 2.14%, 5.32%), and an 11.53% higher (95% CI: 10.83%, 12.22%) NO exposure on average. In fully adjusted models, higher RRS was associated with 3.25% higher PM exposure (95% CI: 1.45%, 5.05%; < 0.05) and 10.22% higher NO exposure (95% CI: 6.69%, 13.74%; < 0.001) on average. CONCLUSIONS: Our findings indicate that RRS is associated with the differential distribution of poor air quality independent of NSES or individual race, suggesting it may be a relevant confounder to be considered in future air pollution epidemiology studies.

Rare damaging CCR2 variants are associated with lower lifetime cardiovascular risk. - Georgakis, Marios K; Malik, Rainer; Bounkari, Omar El; Hasbani, Natalie R; Li, Jiang; Huffman, Jennifer E; Shakt, Gabrielle; Tack, Reinier W P; Kimball, Tamara N; Asare, Yaw; Morrison, Alanna C; Tsao, Noah L; et al. (Genome Med, 2025)
BACKGROUND: Previous work has shown a role of CCL2, a key chemokine governing monocyte trafficking, in atherosclerosis. However, it remains unknown whether targeting CCR2, the cognate receptor of CCL2, provides protection against human atherosclerotic cardiovascular disease. METHODS: Computationally predicted damaging or loss-of-function (REVEL > 0.5) variants within CCR2 were detected in whole-exome-sequencing data from 454,775 UK Biobank participants and tested for association with cardiovascular endpoints in gene-burden tests. Given the key role of CCR2 in monocyte mobilization, variants associated with lower monocyte count were prioritized for experimental validation. The response to CCL2 of human cells transfected with these variants was tested in migration and cAMP assays. Validated damaging variants were tested for association with cardiovascular endpoints, atherosclerosis burden, and vascular risk factors. Significant associations were replicated in six independent datasets (n = 1,062,595). RESULTS: Carriers of 45 predicted damaging or loss-of-function CCR2 variants (n = 787 individuals) were at lower risk of myocardial infarction and coronary artery disease. One of these variants (M249K, n = 585, 0.15% of European ancestry individuals) was associated with lower monocyte count and with both decreased downstream signaling and chemoattraction in response to CCL2. While M249K showed no association with conventional vascular risk factors, it was consistently associated with a lower risk of myocardial infarction (odds ratio [OR]: 0.66, 95% confidence interval [CI]: 0.54-0.81, p = 6.1 × 10) and coronary artery disease (OR: 0.74, 95%CI: 0.63-0.87, p = 2.9 × 10) in the UK Biobank and in six replication cohorts. In a phenome-wide association study, there was no evidence of a higher risk of infections among M249K carriers. CONCLUSIONS: Carriers of an experimentally confirmed damaging CCR2 variant are at a lower lifetime risk of myocardial infarction and coronary artery disease without carrying a higher risk of infections. Our findings provide genetic support for the translational potential of CCR2-targeting as an atheroprotective approach.
Keywords: Aged; Cardiovascular Diseases; Chemokine CCL2; Female; Genetic Predisposition to Disease; Humans; Male; Middle Aged; Monocytes; Receptors, CCR2

Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study. - Yang, Doris; Zhou, Doudou; Cai, Steven; Gan, Ziming; Pencina, Michael; Avillach, Paul; Cai, Tianxi; Hong, Chuan (JMIR Med Inform, 2025)
BACKGROUND: Cohort studies contain rich clinical data across large and diverse patient populations and are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alternative is to harmonize data from existing cohorts through multicohort studies. However, given differences in variable encoding, accurate variable harmonization is difficult. OBJECTIVE: We propose SONAR (Semantic and Distribution-Based Harmonization) as a method for harmonizing variables across cohort studies to facilitate multicohort studies. METHODS: SONAR used semantic learning from variable descriptions and distribution learning from study participant data. Our method learned an embedding vector for each variable and used pairwise cosine similarity to score the similarity between variables. This approach was built off 3 National Institutes of Health cohorts, including the Cardiovascular Health Study, the Multi-Ethnic Study of Atherosclerosis, and the Women's Health Initiative. We also used gold standard labels to further refine the embeddings in a supervised manner. RESULTS: The method was evaluated using manually curated gold standard labels from the 3 National Institutes of Health cohorts. We evaluated both the intracohort and intercohort variable harmonization performance. The supervised SONAR method outperformed existing benchmark methods for almost all intracohort and intercohort comparisons using area under the curve and top-k accuracy metrics. Notably, SONAR was able to significantly improve harmonization of concepts that were difficult for existing semantic methods to harmonize. CONCLUSIONS: SONAR achieves accurate variable harmonization within and between cohort studies by harnessing the complementary strengths of semantic learning and variable distribution learning.
Keywords: Algorithms; Cohort Studies; Female; Humans; Machine Learning; Semantics

Selected social and lifestyle correlates of brain health markers: the Cross-Cohort Collaboration Consortium. - Grasset, Leslie; Bis, Joshua C; Frenzel, Stefan; Kojis, Daniel; Simino, Jeannette; Yaqub, Amber; Beiser, Alexa; Berr, Claudine; Bressler, Jan; Bülow, Robin; DeCarli, Charles S; Fohner, Alison E; et al. (Alzheimers Dement, 2025)
INTRODUCTION: To investigate the associations of education level, marital status, and physical activity with dementia risk and brain MRI markers. METHODS: Data from six community-based samples from the Cross-Cohort Collaboration Consortium were analyzed. Self-reported education level, marital status, and physical activity at age 60 to 75 years were harmonized. Subsamples of participants with brain MRI markers at time of exposure were selected. Associations with dementia risk and cross-sectional MRI markers were meta-analyzed. RESULTS: Higher education level was associated with lower dementia risk (hazard ratio [HR] = 0.65, 95% confidence interval [CI] = 0.59; 0.72 vs low level) but not significantly with brain MRI markers. Compared with being unmarried, being married was only associated with higher total brain and hippocampal volumes. Being physically active was associated with lower dementia risk (HR = 0.73, 95% CI = 0.52; 1.04), as well as larger total brain volume and smaller white matter hyperintensity volume. DISCUSSION: This study provides further evidence regarding the contribution of education level and physical activity to dementia resilience. HIGHLIGHTS: Education level, marital status, and physical activity are thought to contribute to resilience against ADRD. We used random-effects meta-analysis to summarize results from six community-based samples from the CCC. In this cross-cohort meta-analysis, higher education level and being physically active were associated with lower risk of dementia. In cross-sectional analyses, being married was associated with larger TBV and HV, while being physically active was associated with larger TBV and lower WMHV.
Keywords: Aged; Brain; Cohort Studies; Cross-Sectional Studies; Dementia; Educational Status; Exercise; Female; Humans; Life Style; Magnetic Resonance Imaging; Male; Marital Status; Middle Aged; Risk Factors

Sequencing in over 50,000 cases identifies coding and structural variation underlying atrial fibrillation risk. - Choi, Seung Hoan; Jurgens, Sean J; Xiao, Ling; Hill, Matthew C; Haggerty, Christopher M; Sveinbjörnsson, Garðar; Morrill, Valerie N; Marston, Nicholas A; Weng, Lu-Chen; Pirruccello, James P; Arnar, David O; Gudbjartsson, Daniel Fannar; et al. (Nat Genet, 2025)
Atrial fibrillation (AF) is a prevalent and morbid abnormality of the heart rhythm with a strong genetic component. Here, we meta-analyzed genome and exome sequencing data from 36 studies that included 52,416 AF cases and 277,762 controls. In burden tests of rare coding variation, we identified novel associations between AF and the genes MYBPC3, LMNA, PKP2, FAM189A2 and KDM5B. We further identified associations between AF and rare structural variants owing to deletions in CTNNA3 and duplications of GATA4. We broadly replicated our findings in independent samples from MyCode, deCODE and UK Biobank. Finally, we found that CRISPR knockout of KDM5B in stem-cell-derived atrial cardiomyocytes led to a shortening of the action potential duration and widespread transcriptomic dysregulation of genes relevant to atrial homeostasis and conduction. Our results highlight the contribution of rare coding and structural variants to AF, including genetic links between AF and cardiomyopathies, and expand our understanding of the rare variant architecture for this common arrhythmia.
Keywords: Atrial Fibrillation; Case-Control Studies; Exome Sequencing; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Humans; Myocytes, Cardiac; Risk Factors

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies. - Li, Xihao; Chen, Han; Selvaraj, Margaret Sunitha; Van Buren, Eric; Zhou, Hufeng; Wang, Yuxuan; Sun, Ryan; McCaw, Zachary R; Yu, Zhi; Jiang, Min-Zhi; DiCorpo, Daniel; Gaynor, Sheila M; et al. (Nat Comput Sci, 2025)
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. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.
Keywords: Genetic Variation; Genome, Human; Genome-Wide Association Study; Humans; Lipids; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Whole Genome Sequencing

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele. - Zhang, Xinruo; Brody, Jennifer A; Graff, Mariaelisa; Highland, Heather M; Chami, Nathalie; Xu, Hanfei; Wang, Zhe; Ferrier, Kendra R; Chittoor, Geetha; Josyula, Navya Shilpa; Meyer, Mariah; Gupta, Shreyash; et al. (Nat Commun, 2025)
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. We discovered 18 BMI-associated signals (P < 5 × 10), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.
Keywords: Adult; African People; Alleles; Body Mass Index; Female; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Male; Middle Aged; Obesity; Polymorphism, Single Nucleotide; Population Groups; Whole Genome Sequencing

Whole Genome Variable Number Tandem Repeat Analysis in Alzheimer Disease. - Heath, Alesha; McNerney, M Windy; Yesavage, Jerome (Neurol Genet, 2025)
BACKGROUND AND OBJECTIVES: Investigation into different allelic variants may yield new associative genes to predict late-onset Alzheimer disease (LOAD). Variable number tandem repeats (VNTRs) are important polymorphic components of the genome; however, they have been previously overlooked because of their complex genotyping. New software can now determine differing lengths of VNTRs; however, this has not been tested in a large case-control population. METHODS: We used VNTRseek to genotype over 200,000 tandem repeats in 9,501 cases and controls from the Alzheimer's Disease Sequencing Project. We first identified limiting factors of this analysis and then examined the association of VNTRs with AD diagnosis in a subset of non-Hispanic White participants. RESULTS: We found that VNTRs were highly associated with areas of the genome with a high number of previously identified variants. From our case-control analysis, we identified 9 VNTRs with a repeat allele length associated with LOAD including VNTRs on DSC3, NR2E3, CCNY, PKP4, GRAP, and MAP6. DISCUSSION: We were able to show the feasibility of this new type of analysis in large-scale whole-genome sequencing data and identify promising VNTRs that are associated with LOAD.