BACKGROUND Inherited susceptibility to common, complex diseases may be caused by rare, pathogenic variants ("monogenic") or by the cumulative effect of numerous common variants ("polygenic"). Comprehensive genome interpretation should enable assessment for both monogenic and polygenic components of inherited risk. The traditional approach requires two distinct genetic testing technologies-high coverage sequencing of known genes to detect monogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). We assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs. METHODS First, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for 3 common diseases-coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF)-calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in a cohort of 11,502 individuals of European ancestry. RESULTS We found imputation accuracy r2 values of greater than 0.90 for all ten samples-including those of African and Ashkenazi Jewish ancestry-with lcWGS data at 0.5×. GPSs calculated using lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the 3 common diseases (r2 = 0.93-0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 individuals of European ancestry, confirming odds ratios per standard deviation increment ranging 1.28 to 1.59, consistent with previous studies. CONCLUSIONS lcWGS is an alternative technology to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.

中文翻译：

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低覆盖率的全基因组测序可准确评估常见变体并计算全基因组多基因评分。

背景技术对常见的复杂疾病的遗传易感性可能是由罕见的致病性变异（“单基因”）或许多常见变异（“多基因”）的累积作用引起的。全面的基因组解释应能评估遗传风险的单基因和多基因成分。传统方法需要两种不同的基因测试技术-已知基因的高覆盖度测序（用于检测单基因变体）和全基因组基因分型阵列，然后进行插值计算全基因组多基因评分（GPS）。我们评估了使用低覆盖全基因组测序（lcWGS）作为基因分型阵列来计算GPS的替代方法的可行性和准确性。方法首先，我们对10个人的WGS数据进行了下采样和估算，以评估与已知基因型的一致性。其次，我们评估了184个样本中使用lcWGS和基因分型阵列计算的3种常见疾病-冠状动脉疾病（CAD），乳腺癌（BC）和房颤（AF）的GPS之间的相关性。第三，我们评估了基于lcWGS的基因型调用和120个已知基因型个体的GPS计算的一致性，这些个体被选择来反映不同的祖先背景。第四，我们评估了由lcWGS计算的GPS与疾病谱表在欧洲血统的11,502人的队列中的关系。结果我们发现，所有十个样本（包括非洲和阿什肯纳兹犹太血统的样本）的插补精度r2值均大于0.90，而lcWGS数据为0.5倍。使用lcWGS和基因分型阵列，然后在184位个体中进行估算的GPS与3种常见疾病中的每一种都高度相关（r2 = 0.93-0.97），且得分分布相似。使用来自120个不同祖先背景个体的lcWGS数据，我们在估算精度和GPS相关性方面发现了相似的结果。最后，我们使用lcWGS在11502个欧洲血统的个体中计算了CAD，BC和AF的GPS，确认了每标准偏差增量的比值比在1.28至1.59之间，与先前的研究一致。结论lcWGS是基因分型阵列的另一种技术，可用于常见遗传变异评估和GPS计算。lcWGS提供了可比的插补精度，同时还克服了基因分型阵列设计中变量选择所固有的确定性偏差。