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77 records · Page 5Linked to original sources
PubMed2026

Psychological impacts of APOE genotype disclosure among Latinos in New York City: a randomized controlled trial.

INTRODUCTION: Latinos face increased Alzheimer's disease (AD) risk but are underrepresented in studies of APOE genotype disclosure. We evaluated the psychological impacts of APOE disclosure in the Información de la Enfermedad de Alzheimer para Latinos (IDEAL) study, a randomized controlled trial among Latinos in New York City. METHODS: Latino northern Manhattan residents without self-reported AD (mean age 52, 69% women, 49% college graduates) were randomized in the period August 2021 to July 2024 to receive AD risk estimates to age 85 incorporating APOE genotype, family history, and ethnicity (disclosure) or the same factors excluding APOE (non-disclosure). Bilingual genetic counselors delivered risk estimates to both groups, unmasked to randomization. Follow-up surveys were completed 6 weeks, 9 months, and 15 months after risk delivery. Primary outcomes were impact of genetic testing in AD (IGT-AD) and Impact of Event Scale-Revised (IES-R). Secondary outcomes were changes from baseline in depression, anxiety, and perceived AD threat. Analyses used intention-to-treat with multiple imputation. RESULTS: Disclosure (N = 194) and non-disclosure (N = 180) groups did not differ on IGT-AD (mean disclosure-non-disclosure difference [MD] at 6 weeks: -1.5, p = 0.14; 9 months: -1.2, p = 0.35; 15 months: -2.0, p = 0.07), IES-R (MD at 6 weeks: 0.00, p = 0.98; 9 months: 0.01, p = 0.84; 15 months: 0.03, p = 0.64), or change in secondary outcomes. Occurrence of disclosure-related adverse events was similar in the disclosure (N = 2) and non-disclosure (N = 3) groups. DISCUSSION: In this Latino cohort, APOE disclosure did not have clinically significant adverse psychological effects, addressing an important evidence gap. TRIAL REGISTRATION: ClinicalTrials.gov NCT04471779.

Aged↗
PubMed2005

Accounting for haplotype uncertainty in matched association studies: a comparison of simple and flexible techniques.

Population-based case-control studies measuring associations between haplotypes of single nucleotide polymorphisms (SNPs) are increasingly popular, in part because haplotypes of a few "tagging" SNPs may serve as surrogates for variation in relatively large sections of the genome. Due to current technological limitations, haplotypes in cases and controls must be inferred from unphased genotypic data. Using individual-specific inferred haplotypes as covariates in standard epidemiologic analyses (e.g., conditional logistic regression) is an attractive analysis strategy, as it allows adjustment for nongenetic covariates, provides omnibus and haplotype-specific tests of association, and can estimate haplotype and haplotype x environment interaction effects. In principle, some adjustment for the uncertainty in inferred haplotypes should be made. Via simulation, we compare the performance (bias and mean squared error of haplotype and haplotype x environment interaction effect estimates) of several analytic strategies using inferred haplotypes in the context of matched case-control data. These strategies include using only the most likely haplotype assignment, the expectation substitution approach described by Stram et al. ([2003b] Hum. Hered. 55:179-190) and others, and an improper version of multiple imputation. For relatively uncomplicated haplotype structures and moderate haplotype relative risks (</=2), all methods performed comparably well (small bias with appropriately-sized confidence intervals). For larger relative risks, the most likely haplotype and multiple imputation strategies showed noticeable bias towards the null; the expectation substitution strategy still performed well. When there was more uncertainty in the inferred haplotypes, the most likely and multiple imputation strategies showed even more bias towards the null, while the expectation substitution method had slightly smaller than nominal confidence intervals for larger relative risks (>/=5). An application to progesterone-receptor haplotypes and endometrial cancer further illustrates that the performance of all these methods depends on how well the observed haplotypes "tag" the unobserved causal variant.

Algorithms↗
PubMed2026

Anthropometric and cardio-metabolic trait variation and genetic associations in sub-Saharan Africa.

The genetics of complex traits in Africa has been historically understudied, which can contribute to healthcare inequalities. Here, we present observations of 27 anthropometric, cardiovascular, and blood biomarker measurements across 2,124 individuals from sub-Saharan Africa for whom we also have dense genotype data. First, we identified trait values that differ significantly across populations and subsistence lifestyles (e.g., hemoglobin levels and height). We then identified traits with high degrees of sexual dimorphism (e.g., weight and grip strength). ADMIXTURE analyses revealed substantial population structure in our dataset, and many of the phenotypes studied here are correlated with genetic ancestry components, particularly skin color and body size traits. A variance partitioning approach further revealed traits in which much of the SNP heritability is due to polymorphisms that also contribute to differences between ancestry components. Following genomic imputation, we performed genome-wide association studies (GWASs) for all 27 traits and identified >100 independent autosomal SNPs with genome-wide significant associations for at least one trait (p < 5 &#xd7; 10-8). Many of these trait-associated variants are rare outside of Africa (minor-allele frequency [MAF] < 1%). We found that 100 kb windows surrounding the top GWAS hits from our African-ancestry cohort were enriched for trait associations in an identically sized European cohort and vice versa. We performed a more detailed analysis of height prediction from genetic data, finding that genome-wide admixture proportions predict height in Africans better than polygenic predictors based on large-scale European height GWASs.

Female↗
PubMed2026

Pan-genomics and multi-omics for deciphering genetic variation and accelerating genetic improvement in ruminant livestock.

Livestock reference genomes have transformed the discovery of variants associated with production, reproduction, health, and environmental adaptation. Nevertheless, a single linear reference represents only one mosaic haplotype and incompletely captures sequence diversity within a species, particularly structural variants, copy-number changes, repeat-rich regions, and breed-specific sequences. Pangenomes address this limitation by integrating multiple high-quality assemblies or population-scale variants into a unified sequence or graph representation. Concurrently, multi-omics approaches connect genomic variation with transcriptomic, epigenomic, manuscriptproteomic, metabolomic, and microbiome responses, thereby improving biological interpretation of genotype-phenotype relationships. This review synthesizes recent progress in livestock pangenomics and multi-omics, with emphasis on cattle, goats, sheep, water buffalo, and chickens. It describes advances in long-read and haplotype-resolved sequencing, graph construction, structural-variant discovery and genotyping, functional annotation, and integrative analysis. Recent pangenome studies have uncovered substantial non-reference sequence, reduced reference bias, identified breed- and population-specific structural variants, and resolved candidate variants underlying pigmentation, body size, tail morphology, cashmere production, altitude adaptation, and other economically relevant traits. However, translation into routine breeding remains constrained by uneven population representation, inconsistent structural-variant definitions, limited functional annotation, computational demands, and insufficient validation across environments. Future progress will depend on diverse near-complete assemblies, graph-aware imputation and genomic prediction, long-read transcriptomics, single-cell and spatial omics, rigorous causal validation, and open, interoperable resources. Together, these developments can support more accurate, resilient, and biologically informed livestock improvement. Importantly, current dairy-cattle evidence indicates that pangenome-derived structural variants can substantially improve variant discovery and functional interpretation while yielding only marginal average gains in routine genomic prediction, favoring targeted augmentation rather than wholesale replacement of established SNP-based evaluations.

Animals↗
PubMed2024

Genetics of Latin American Diversity Project: Insights into population genetics and association studies in admixed groups in the Americas.

Latin Americans are underrepresented in genetic studies, increasing disparities in personalized genomic medicine. Despite available genetic data from thousands of Latin Americans, accessing and navigating the bureaucratic hurdles for consent or access remains challenging. To address this, we introduce the Genetics of Latin American Diversity (GLAD) Project, compiling genome-wide information from 53,738 Latin Americans across 39 studies representing 46 geographical regions. Through GLAD, we identified heterogeneous ancestry composition and recent gene flow across the Americas. Additionally, we developed GLAD-match, a simulated annealing-based algorithm, to match the genetic background of external samples to our database, sharing summary statistics (i.e., allele and haplotype frequencies) without transferring individual-level genotypes. Finally, we demonstrate the potential of GLAD as a critical resource for evaluating statistical genetic software in the presence of admixture. By providing this resource, we promote genomic research in Latin Americans and contribute to the promises of personalized medicine to more people.

Humans↗