Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.

中文翻译：

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从大规模家庭数据估计的遗传易感性改进了重度抑郁症的遗传预测、风险评分分析和基因定位


大型生物样本库样本为整合广泛的表型、家族记录和分子遗传学数据以研究复杂的性状和疾病提供了机会。我们介绍了 Pearson-Aitken 家庭遗传风险评分 （PA-FGRS），这是一种根据扩展的、年龄审查的家谱记录中的诊断模式估计疾病易感性的方法。然后，我们使用 iPSYCH2015 病例队列研究 30,949 例 MDD、39,655 例随机人群对照和 200 多万名亲属，应用该方法研究一种典型的复杂疾病，即重度抑郁症 （MDD）。我们表明，将从家庭记录估计的 PA-FGRS 负债与先证者的分子基因型相结合可以改善三条调查线。结合 PA-FGRS 责任可以提高 MDD 的分类，高于多基因评分，确定与合并症、复发和严重程度相关的 MDD 临床异质性的稳健遗传贡献，并且可以提高全基因组关联研究的效力。我们的方法灵活易用，我们的研究方法可以推广到其他数据集和其他复杂的性状和疾病。