A key aim for current genome‐wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole‐genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost‐effective GWAS designs given current sequencing and array costs. For populations that are well‐represented in existing reference panels, we find that array genotyping alone is cost‐effective and well‐powered to detect common‐ and rare‐variant associations. For poorly represented populations, sequencing a subset of participants is often most cost‐effective, and can substantially increase imputation quality and GWAS power.

中文翻译：

---

GWAS中的测序和归因：具有成本效益的策略，可提高跨不同人群的能力和基因组覆盖范围。

当前的全基因组关联研究（GWAS）的主要目的是询问整个人类群体潜在的人类特征（包括稀有变异）的遗传变异的全谱。深度全基因组测序是完全捕获遗传变异的金标准，但对于大样本量而言仍然昂贵。阵列基因分型询问了一组稀疏的变体，可用作基因型插补的支架以捕获更广泛的变体。但是，归因质量主要取决于参考样本的大小和与目标人群的遗传距离。在这里，我们考虑对GWAS参与者的子集进行排序，并使用包括已测序GWAS参与者和外部参考面板的参考面板来估算其余部分。我们研究了混合人群（非洲和拉丁美洲裔）和欧洲人群分离株（撒丁岛和芬兰人）的测序参与者数量如何影响插补质量和GWAS能力，并根据当前的测序和阵列成本，确定了功能强大且具有成本效益的GWAS设计。对于在现有参考面板中具有良好代表性的人群，我们发现仅阵列基因分型是具有成本效益的，并且具有检测常见和稀有变异关联的强大能力。对于代表性不强的人群，对参与者的子集进行排序通常最具成本效益，并且可以大大提高插补质量和GWAS功能。鉴于当前的测序和阵列成本，具有成本效益的GWAS设计。对于在现有参考面板中具有良好代表性的人群，我们发现仅阵列基因分型是具有成本效益的，并且具有检测常见和稀有变异关联的强大能力。对于代表性不足的人群，对参与者的子集进行测序通常最具成本效益，并且可以显着提高插补质量和GWAS功能。鉴于当前的测序和阵列成本，具有成本效益的GWAS设计。对于在现有参考面板中具有良好代表性的人群，我们发现仅阵列基因分型是具有成本效益的，并且具有检测常见和稀有变异关联的强大能力。对于代表性不强的人群，对参与者的子集进行排序通常最具成本效益，并且可以大大提高插补质量和GWAS功能。