Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA 92122, USA.
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
Science. 2023 Jun 2;380(6648):eabo1131. doi: 10.1126/science.abo1131.
We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ~10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared with common-variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.
我们对 454712 个外显子组进行了研究，以寻找与广泛的复杂特征和常见疾病相关的基因，结果发现，全基因组关联研究中所涉及的基因中的罕见、外显率高的突变比同一基因中的常见变异体具有大 10 倍的影响。因此，通过少数几个罕见的外显突变体来识别表型极端且患有严重早发性疾病风险最高的个体，比通过具有微弱效应的许多常见变体的共同作用更好。通过将与表型相关的基因中的罕见变体组合成一个统一的遗传风险模型，我们展示了与常见变体多基因风险评分相比，在不同全球人群中的可移植性更优，从而大大提高了基于遗传的风险预测的临床实用性。
