Genetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while being computationally efficient, tend to yield estimates of genetic correlation with reduced precision. We propose SCORE (scalable genetic correlation estimator), a randomized method of moments estimator of genetic correlation that is both scalable and accurate. SCORE obtains more precise estimates of genetic correlations relative to summary-statistic methods that can be applied at scale; it achieves a $44\text{\%}$ reduction in standard error relative to LD-score regression (LDSC) and a $20\text{\%}$ reduction relative to high-definition likelihood (HDL) (averaged over all simulations). The efficiency of SCORE enables computation of genetic correlations on the UK Biobank dataset, consisting of $\approx 300$ K individuals and $\approx 500$ K SNPs, in a few h (orders of magnitude faster than methods that analyze individual data, such as GCTA). Across 780 pairs of traits in $\mathrm{291,273}$ unrelated white British individuals in the UK Biobank, SCORE identifies significant genetic correlation between 200 additional pairs of traits over LDSC (beyond the 245 pairs identified by both).

遗传相关性是努力理解复杂性状之间关系的重要参数。当前分析个体基因型数据以估计遗传相关性的方法难以扩展到大型数据集。分析汇总数据的方法虽然计算效率高，但往往会产生遗传相关性的估计，但精度会降低。我们提出了 SCORE（可扩展遗传相关估计器），这是一种可扩展且准确的遗传相关矩估计的随机方法。SCORE 获得了相对于可大规模应用的汇总统计方法更精确的遗传相关性估计；它达到了一个$44\text{\%}$相对于 LD 评分回归 (LDSC) 和$20\text{\%}$相对于高清似然 (HDL) 的减少（所有模拟的平均值）。SCORE 的效率可以计算 UK Biobank 数据集上的遗传相关性，包括$\approx 300$K 个人和$\approx 500$K SNP，在几个小时内（比分析单个数据的方法快几个数量级，例如 GCTA）。跨越 780 对特征$\mathrm{291,273}$在英国生物库中无关的英国白人个体，SCORE 确定了 LDSC 上另外 200 对性状之间的显着遗传相关性（超出了两者确定的 245 对）。