Harnessing the power of diverse populations to empower clinical translation of genome-wide association studies of common human disease

全基因组关联研究(GWAS)已经成功地识别了我们DNA的变化，这些变化被称为基因变异，这些变化导致我们患上许多常见的人类疾病的风险，包括那些具有重大公共健康负担的疾病，如癌症、心血管疾病和糖尿病。这一成功为利用遗传学对人类健康产生重大积极影响提供了令人兴奋的机会：(I)提高我们对疾病生物学的理解，从而为潜在的新治疗方法提供信息；以及(Ii)预测个体未来的疾病发生，称为多基因风险评分(PR)。然而，尽管取得了这一成功，但大多数GWA都是在欧洲和北美的欧洲血统的白人群体中进行的。Gwas的这种偏见与许多疾病的全球和种族/民族分布形成了鲜明对比，因此可能会加剧医疗保健差距，因为来自欧洲白人血统个人的基因发现不太可能对其他人口群体产生同样的影响。例如，在欧洲白人血统的个体中导致疾病的基因变异可能在其他种群中没有观察到。因此，来自欧洲白人血统的PR对非洲、亚洲或混合血统的个体的疾病风险预测不太可靠。遗传研究中对群体偏见的担忧促使最近将GWAS扩展到来自更多不同祖先群体的个体。这项提议的总体愿景是开发新的统计方法来分析多血统GWAs，以考虑到在不同人口群体中观察到的遗传差异。这些方法将被应用到用户友好的软件工具中，并将与更广泛的研究社区共享，以更好地了解人口群体之间的遗传差异对疾病的影响，以减少医疗保健差异。不同人群之间共享的疾病生物学知识的改善将提供与每个人相关的治疗发展机会。此外，考虑到个人血统的预测将提供更准确的疾病风险预测，而不考虑他们的遗传背景。

Genome-wide association studies (GWAS) have been successful in identifying changes in our DNA, referred to as genetic variants, that contribute to our risk of developing many common human diseases, including those that have major public health burden, such as cancers, cardiovascular disease, and diabetes. This success offers exciting opportunities to use genetics for a significant positive impact on human health by: (i) improving our understanding of the biology of disease, thereby informing potential novel treatments; and (ii) predicting the future occurrence of disease in an individual, referred to as polygenic risk scores (PRS). However, despite this success, most GWAS have been performed in white populations of European ancestry, in Europe and North America. This bias in GWAS is in stark contrast to the global and racial/ethnic distribution of many diseases and may therefore exacerbate healthcare disparities because genetic findings from white European ancestry individuals are unlikely to be as impactful in other population groups. For example, the genetic variants that cause disease in white European ancestry individuals might not be observed in other population groups. Consequently, PRS derived from white European ancestry GWAS provide less reliable prediction of disease risk into individuals of African, Asian, or mixed ancestry. The concerns over population bias in genetic studies have prompted a recent expansion of GWAS into individuals from more diverse ancestry groups. The overall vision of this proposal is to develop novel statistical methods for the analysis of multi-ancestry GWAS that allow for the genetic differences observed across diverse population groups. These methods will be implemented into user-friendly software tools that will be shared with the wider research community to provide a better understanding of the impact on disease of genetic differences between population groups to reduce healthcare disparities. Improved knowledge of disease biology that is shared across diverse populations will inform treatment development opportunities that will be relevant to everyone. Furthermore, PRS that take account of the ancestry of an individual will provide more accurate prediction of disease risk, irrespective of their genetic background.