Understanding the relationship between natural selection and phenotypic variation has been a long-standing challenge in human population genetics. With the emergence of biobank-scale datasets, along with new statistical metrics to approximate strength of purifying selection at the variant level, it is now possible to correlate a proxy of individual relative fitness with a range of medical phenotypes. We calculated a per-individual deleterious load score by summing the total number of derived alleles per individual after incorporating a weight that approximates strength of purifying selection. We assessed four methods for the weight, including GERP, phyloP, CADD, and fitcons. By quantitatively tracking each of these scores with the site frequency spectrum, we identified phyloP as the most appropriate weight. The phyloP-weighted load score was then calculated across 15,129,142 variants in 335,161 individuals from the UK Biobank and tested for association on 1,380 medical phenotypes. After accounting for multiple test correction, we observed a strong association of the load score amongst coding sites only on 27 traits including body mass, adiposity and metabolic rate. We further observed that the association signals were driven by common variants (derived allele frequency > 5%) with high phyloP score (phyloP > 2). Finally, through permutation analyses, we showed that the load score amongst coding sites had an excess of nominally significant associations on many medical phenotypes. These results suggest a broad impact of deleterious load on medical phenotypes and highlight the deleterious load score as a tool to disentangle the complex relationship between natural selection and medical phenotypes.

中文翻译：

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探索英国生物库中每个人对1380种医学表型的纯化选择的总效应

理解自然选择与表型变异之间的关系一直是人类遗传学的一项长期挑战。随着生物库规模数据集的出现，以及新的统计指标，可以近似估计变体水平上的纯化选择强度，现在可以将个体相对适应性的代理与一系列医学表型相关联。我们在合并了近似于纯化选择强度的权重后，通过对每个个体的衍生等位基因总数求和，计算了每个个体的有害负荷得分。我们评估了四种减肥方法，包括GERP，phyloP，CADD和fitcons。通过用站点频谱定量跟踪每个分数，我们确定phyloP是最合适的权重。然后，对来自UK Biobank的335,161个人中的15,129,142个变体计算phyloP加权负荷评分，并测试了1,380种医学表型的关联性。在考虑了多次测试校正后，我们发现仅在体重，肥胖和代谢率等27个性状上，编码位点之间的负荷评分之间存在很强的联系。我们进一步观察到关联信号是由具有高phyloP得分（phyloP> 2）的常见变异（衍生的等位基因频率> 5％）驱动的。最后，通过排列分析，我们表明编码位点之间的负荷得分在许多医学表型上具有名义上显着的关联。