BACKGROUND: Apolipoprotein E (APOE) alleles are well-established genetic risk factors for Alzheimer's disease (AD), but their effects on AD biomarkers (amyloid beta [Aβ]42/40, phosphorylated tau [p-tau]181, neurofilament light chain [NfL], and glial fibrillary acidic protein [GFAP]) may vary across populations due to ancestry-, age-, and sex-related differences. We hypothesized that these effects vary across Hispanic/Latino background groups with distinct ancestral admixture.

METHODS: We analyzed ε2 and ε4 allele associations with AD biomarkers using survey-weighted linear regression models, adjusting for demographic covariates. Secondary analyses examined genetic analysis group- and ancestry-specific effects.

RESULTS: ε4 was associated with lower Aβ42/40 and higher p-tau181and GFAP levels, but not with NfL, suggesting its role in Aβ and tau deposition and neuroinflammation. ε4 associations were stronger in those with higher European and lower African ancestry.

DISCUSSION: These findings expand on prior studies suggesting that genetic ancestry modifies APOE-associated AD risk in Hispanic/Latino populations and highlight the importance of capturing ancestry-based heterogeneity in AD biomarker research.

BACKGROUND: Sleep traits, including sleep apnoea (SA), insomnia, daytime sleepiness, and snoring, frequently co-occur with cardiometabolic diseases (CMDs), with shared genetic factors suspected to underlie these associations. However, the contribution of shared genetic determinants to these associations is not fully understood.

METHODS: We conducted a genome-wide pleiotropic association study applying sequential genetic methods to identify shared genetic variants, genes, pathways and causal associations between the four sleep traits and seven CMDs, including LDSC, high-definition likelihood analysis, colocalisation, gene-based tests, enrichment analysis and Mendelian randomisation. Next, validation of those pleiotropic variants was performed in individuals from the All of Us and MVP studies.

FINDINGS: Among 28 pairs of sleep traits and CMDs, 25 showed significant genetic correlations. Pleiotropic analysis identified 754 independent SNPs (691 unique) and 102 colocalized loci (85 unique). Among these, 47 SNPs (44 unique) were validated as significantly associated with both traits in the pairs, and notably, rs429358 (19q13.32, APOE) demonstrated pleiotropic effects across SA, insomnia and type 2 diabetes (T2D). Forty-eight annotated genes were validated by gene-based tests. Shared genes were enriched in phenotypes related to mortality and growth. Pathway analysis highlighted Cushing syndrome, hormone secretion, and cGMP-PKG, Ras and calcium signalling pathways. After adjusting for glycaemic traits and blood pressure, genetically predicted T2D increased risk of SA, sleepiness, and snoring. Conversely, SA was positively associated with heart failure and T2D independently.

INTERPRETATION: This study of sleep traits and CMDs reveals shared genetic determinants that may partially explain their epidemiologic association and suggests potential treatment targets.

FUNDING: Described in Acknowledgements.

Prediction models for cognitive aging measures have largely evaluated demographic variables and APOE carrier status in populations of European ancestry. To comprehensively assess prediction models among Hispanic/Latinos, we considered 12 models (6 predictor sets and 2 methods) for global cognitive score change (GCSC) and mild cognitive impairment (MCI) in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) (N = 5856). Based on the average mean squared error (MSE) for GCSC or average area under the curve (AUC) for MCI across 100 randomly split testing and training sets, performance was similar across models, but slightly better for the following models: the chronic conditions model and genetic model (mean MSEs = 0.2464) using gradient-boosted trees for GCSC prediction and the chronic conditions model (mean AUC = 62%) and metabolite model (mean AUC = 60%) using logistic regression for MCI prediction. Using the Shapley Additive Explanations (SHAP) method, age at baseline, time between exams, and sex were the most important predictors for GCSC, followed by diabetes and global ancestral proportions. Diabetes and the metabolite ribitol had the highest influence on prediction of MCI. Although prediction performance was not especially high and did not vary greatly across models, incorporating information on diabetes, ancestry and metabolites may help improve prediction of GCSC and MCI.

In admixed populations, formed by a mixing of two or more previously isolated populations, genomic segments can be traced to their ancestral populations ('ancestries'). Admixture mapping (AM) associates local ancestry with outcomes in admixed populations, detecting signals when causal variants differ in frequency or effect across ancestral populations. Prior work showed that adjusting for nearby GWAS-identified common variants does not fully explain some AM signals. Here, we assessed two approaches to explain the previously unexplained AM signal: (1) including sets of rare variants; (2) increasing the genomic region considered when searching for common variants. We studied these hypotheses comprehensively using a whole-genome sequencing dataset coupled with metabolomics from the Hispanic Community Health Study/Study of Latinos. From 16 genomic regions of interest, we identified 35 rare-variant sets associated with metabolite levels, of which 4 associations replicated. The locations of identified rare variant sets are either within the same gene or in proximity to adjusted common variants. Overall, our study provides new insight into the genetic architecture underlying AM signals via evaluating the contributions of common and rare variant sets. By integrating rare-variant analysis within the AM framework, our work highlights the potential interplay between variant types and establishes a novel strategy to guide future fine-mapping and causal variant identification in admixed populations. Yet the detected rare variants appear to explain only a small fraction of the AM signal, while inclusion of common variants from a larger genomic region appears to explain most of the AM signals.

Polygenic scores (PGSs) offer moderate to high prediction accuracy for complex traits, but most are developed in European ancestry cohorts, reducing their performance in populations of other ancestries. This study aimed to improve standing height prediction, a heritable and ancestry-influenced trait, in an admixed Latino cohort, the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), by modeling ancestry using principal components (PCs) alongside PGSs. SNPs were selected from a large European ancestry genome-wide association study (GWAS) using various p value thresholds, and weights were trained using traditional and penalized regression in the UK Biobank (UKB). PGSs with PCs were trained separately in the HCHS/SOL and UKB. Compared to PGSs alone, modeling PGSs with PCs moderately improved height prediction in the HCHS/SOL (squared correlation [R2] increase of ∼0.05), while mild improvements were observed in the UKB (R2 increase of ∼0.01). These results underscore the importance of incorporating genetic ancestry into predictive models for admixed populations, particularly when the trait exhibits ancestry-specific associations.