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）已经成功地确定了我们DNA中的变化，称为遗传变异，这些变化导致我们患上许多常见人类疾病的风险，包括那些具有重大公共卫生负担的疾病，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提供信息;（ii）预测个体未来疾病的发生，称为多基因风险评分（PRS）。然而，尽管取得了成功，大多数GWAS都是在欧洲和北美的欧洲血统的白色人群中进行的。GWAS中的这种偏倚与许多疾病的全球和种族/民族分布形成鲜明对比，因此可能加剧医疗保健差异，因为来自白色欧洲血统个体的遗传发现不太可能对其他人群产生影响。例如，在白色欧洲血统个体中导致疾病的遗传变异可能在其他人群中观察不到。因此，来自白色欧洲血统GWAS的PRS对非洲、亚洲或混合血统个体的疾病风险预测不太可靠。对遗传学研究中群体偏见的关注促使GWAS最近扩展到来自更多样化祖先群体的个体。该提案的总体愿景是开发新的统计方法，用于分析多祖先GWAS，以考虑在不同人群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，并与更广泛的研究界分享，以更好地了解人群之间遗传差异对疾病的影响，从而减少医疗保健差异。不同人群共享的疾病生物学知识的提高将为与每个人相关的治疗开发机会提供信息。此外，考虑到个人祖先的PRS将提供更准确的疾病风险预测，无论其遗传背景如何。

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.