NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Kirkeveien 166, 0424, Oslo, Norway.
Department of Cognitive Science, University of California San Diego, La Jolla, San Diego, CA, 92093, USA.
Hum Genet. 2020 Jan;139(1):85-94. doi: 10.1007/s00439-019-02060-2. Epub 2019 Sep 13.
In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.
近年来，全基因组关联研究 (GWAS) 的样本量变得更大，统计能力得到了提高，并且发现了数千个与性状相关的变异体，为复杂人类性状和疾病的遗传病因提供了新的见解。然而，大多数复杂表型的多基因结构的很大一部分仍然未被发现。我们在这里回顾条件错误发现率 (condFDR) 方法，这是一种用于分析 GWAS 汇总数据的无模型策略，该方法提高了现有 GWAS 的产量，并提供了遗传重叠的新发现，包括精神、心血管和神经障碍以及心理和认知特征。condFDR 方法的灵感来自经验贝叶斯方法，并利用辅助遗传信息来提高发现单核苷酸多态性 (SNP) 的统计能力。跨性状 condFDR 策略分析单独的 GWAS 数据，并利用重叠 SNP 关联，即跨性状富集，来增加与性状相关的 SNP 的发现。condFDR 方法的扩展到联合 FDR(conjFDR) 可以识别两种表型之间的共享基因组位置。conjFDR 方法允许检测共享基因组关联，而无需考虑表型之间的遗传相关性，这通常会揭示共享位置之间混合的拮抗和激动作用。本综述提供了 condFDR 和其他相关跨性状分析工具之间的方法比较，并展示了 condFDR 分析如何为复杂表型之间的遗传关系提供新的见解。
