The Biorepository and Integrative Genomics resource for inclusive genomics: insights from a diverse pediatric and admixed cohort.

Silvia Buonaiuto, Franco Marsico, Akram Mohammed, Lokesh K Chinthala, Ernestine K Amos Abanyie, Regeneron Genetics Center, Pjotr Prins, Kyobeni Mozhui, Robert J Rooney, Robert W Williams, Robert L Davis, Terri H Finkel, Chester W Brown, Vincenza Colonna
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Abstract

The Biorepository and Integrative Genomics (BIG) Initiative in Tennessee has developed a pioneering resource to address gaps in genomic research by linking genomic, phenotypic, and environmental data from a diverse Mid-South population, including underrepresented groups. We analyzed 13,152 exomes from BIG and found significant genetic diversity, with 50% of participants inferred to have non-European or several types of admixed ancestry. Ancestry within the BIG cohort is stratified, with distinct geographic and demographic patterns, as African ancestry is more common in urban areas, while European ancestry is more common in suburban regions. We observe ancestry-specific rates of novel genetic variants, which are enriched for functional or clinical relevance. Disease prevalence analysis linked ancestry and environmental factors, showing higher odds ratios for asthma and obesity in minority groups, particularly in the urban area. Finally, we observe discrepancies between self-reported race and genetic ancestry, with related individuals self-identifying in differing racial categories. These findings underscore the limitations of race as a biomedical variable. BIG has proven to be an effective model for community-centered precision medicine. We integrated genomics education, and fostered great trust among the contributing communities. Future goals include cohort expansion, and enhanced genomic analysis, to ensure equitable healthcare outcomes.

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包含基因组学的生物储存库和整合基因组学资源:来自不同儿科和混合队列的见解。
田纳西州的生物储藏库和综合基因组学(BIG)计划开发了一种开创性的资源,通过将来自不同中南部人口(包括代表性不足的群体)的基因组、表型和环境数据联系起来,解决基因组研究中的空白。我们分析了来自BIG的13152个基因组,发现了显著的遗传多样性,其中50%的参与者推断具有非欧洲血统或几种混合血统。BIG队列中的祖先是分层的,具有独特的地理和人口模式,因为非洲血统在城市地区更常见,而欧洲血统在郊区更常见。我们观察到新的遗传变异的祖先特异性率,这是丰富的功能或临床相关性。疾病流行分析将祖先和环境因素联系起来,显示少数群体,特别是城市地区,哮喘和肥胖的优势比更高。最后,我们观察到自我报告的种族和遗传祖先之间的差异,相关个体在不同的种族类别中自我认同。这些发现强调了种族作为生物医学变量的局限性。BIG已被证明是以社区为中心的精准医疗的有效模式。我们整合了基因组学教育,并在贡献社区之间培养了极大的信任。未来的目标包括扩大队列和加强基因组分析,以确保公平的医疗保健结果。
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