The need to diversify genomic studies: Insights from Andean highlanders and Amazonians.

More globally diverse perspectives are needed in genomic studies and precision medicine practices on non-Europeans. Here, we illustrate this by discussing the distribution of clinically actionable genetic variants involved in drug response in Andean highlanders and Amazonians, considering their environment, history, genetic structure, and historical biases in the perception of biological diversity of Native Americans.

ClOneHORT: Approaches for Improved Fidelity in Generative Models of Synthetic Genomes.

Motivation: Deep generative models have the potential to overcome difficulties in sharing individual-level genomic data by producing synthetic genomes that preserve the genomic associations specific to a cohort while not violating the privacy of any individual cohort member. However, there is significant room for improvement in the fidelity and usability of existing synthetic genome approaches. Results: We demonstrate that when combined with plentiful data and with population-specific selection criteria, deep generative models can produce synthetic genomes and cohorts that closely model the original populations. Our methods improve fidelity in the site-frequency spectra and linkage disequilibrium decay and yield synthetic genomes that can be substituted in downstream local ancestry inference analysis, recreating results with .91 to .94 accuracy. Availability: The model described in this paper is freely available at github.com/rlaboulaye/clonehort .

Multiomic analysis of uterine leiomyomas in self-described Black and White women: molecular insights into health disparities.

BACKGROUND: Black women are at an increased risk of developing uterine leiomyomas and experiencing worse disease prognosis than White women. Epidemiologic and molecular factors have been identified as underlying these disparities, but there remains a paucity of deep, multiomic analysis investigating molecular differences in uterine leiomyomas from Black and White patients. OBJECTIVE: To identify molecular alterations within uterine leiomyoma tissues correlating with patient race by multiomic analyses of uterine leiomyomas collected from cohorts of Black and White women. STUDY DESIGN: We performed multiomic analysis of uterine leiomyomas from Black (42) and White (47) women undergoing hysterectomy for symptomatic uterine leiomyomata. In addition, our analysis included the application of orthogonal methods to evaluate fibroid biomechanical properties, such as second harmonic generation microscopy, uniaxial compression testing, and shear-wave ultrasonography analyses. RESULTS: We found a greater proportion of MED12 mutant uterine leiomyomas from Black women (>35% increase; Mann-Whitney U, P<.001). MED12 mutant tumors exhibited an elevated abundance of extracellular matrix proteins, including several collagen isoforms, involved in the regulation of the core matrisome. Histologic analysis of tissue fibrosis using trichrome staining and secondary harmonic generation microscopy confirmed that MED12 mutant tumors are more fibrotic than MED12 wild-type tumors. Using shear-wave ultrasonography in a prospectively collected cohort, Black patients had fibroids that were firmer than White patients, even when similar in size. In addition, these analyses uncovered ancestry-linked expression quantitative trait loci with altered allele frequencies in African and European populations correlating with differential abundance of several proteins in uterine leiomyomas independently of MED12 mutation status, including tetracoidpeptide repeat protein 38. CONCLUSION: Our study shows that Black women have a higher prevalence of uterine leiomyomas harboring mutations in MED12 and that this mutational status correlates with increased tissue fibrosis compared with wild-type uterine leiomyomas. Our study provides insights into molecular alterations correlating with racial disparities in uterine leiomyomas and improves our understanding of the molecular etiology underlying uterine leiomyoma development within these populations.

Strong positive selection biases identity-by-descent-based inferences of recent demography and population structure in Plasmodium falciparum.

Malaria genomic surveillance often estimates parasite genetic relatedness using metrics such as Identity-By-Decent (IBD), yet strong positive selection stemming from antimalarial drug resistance or other interventions may bias IBD-based estimates. In this study, we use simulations, a true IBD inference algorithm, and empirical data sets from different malaria transmission settings to investigate the extent of this bias and explore potential correction strategies. We analyze whole genome sequence data generated from 640 new and 3089 publicly available Plasmodium falciparum clinical isolates. We demonstrate that positive selection distorts IBD distributions, leading to underestimated effective population size and blurred population structure. Additionally, we discover that the removal of IBD peak regions partially restores the accuracy of IBD-based inferences, with this effect contingent on the population's background genetic relatedness and extent of inbreeding. Consequently, we advocate for selection correction for parasite populations undergoing strong, recent positive selection, particularly in high malaria transmission settings.