Local genetic correlation analysis is an important tool for identifying genetic loci with shared biology across traits. Recently, Border et al. have shown that the results of these analyses are confounded by cross-trait assortative mating (xAM), leading to many false-positive findings. Here, we describe LAVA-Knock, a local genetic correlation method that builds off an existing genetic correlation method, LAVA, and augments it by generating synthetic data in a way that preserves local and long-range linkage disequilibrium (LD), allowing us to reduce the confounding induced by xAM. We show in simulations based on a realistic xAM model and in genome-wide association study (GWAS) applications for 630 trait pairs that LAVA-Knock can greatly reduce the bias due to xAM relative to LAVA. Furthermore, we show a significant positive correlation between the reduction in local genetic correlations and estimates in the literature of cross-mate phenotype correlations; in particular, pairs of traits that are known to have high cross-mate phenotype correlation values have a significantly higher reduction in the number of local genetic correlations compared with other trait pairs. A few representative examples include education and intelligence, education and alcohol consumption, and attention-deficit hyperactivity disorder and depression. These results suggest that LAVA-Knock can reduce confounding due to both short-range LD and long-range LD induced by xAM.

中文翻译：

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通过仿冒品实现的局部遗传相关性减少了由于跨性状分类交配引起的混杂


局部遗传相关性分析是识别跨性状共享生物学的遗传位点的重要工具。最近，Border 等人表明，这些分析的结果与跨性状分类交配 （xAM） 相混淆，导致许多假阳性结果。在这里，我们描述了 LAVA-Knock，这是一种局部遗传相关方法，它建立在现有的遗传相关方法 LAVA 的基础上，并通过以保留局部和远程连锁不平衡 （LD） 的方式生成合成数据来增强它，使我们能够减少 xAM 诱导的混杂。我们在基于真实 xAM 模型的模拟和 630 个性状对的全基因组关联研究 （GWAS） 应用中表明，LAVA-Knock 可以大大减少 xAM 相对于 LAVA 引起的偏差。此外，我们显示了局部遗传相关性的减少与文献中交叉配偶表型相关性的估计之间存在显着的正相关;特别是，与其他性状对相比，已知具有高交叉配对表型相关性值的性状对的局部遗传相关性数量减少量明显更高。一些代表性的例子包括教育和智力、教育和酒精消费以及注意力缺陷多动障碍和抑郁症。这些结果表明，LAVA-Knock 可以减少由 xAM 诱导的短程 LD 和长程 LD 引起的混杂。