Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.

中文翻译：

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弹性网络在具有自适应阈值选择的共享多基因性检测中的潜在应用

目前的研究表明，成百上千个具有小到适度效应的单核苷酸多态性 (SNP) 促成了许多疾病的遗传基础，这种现象被称为多基因性。此外，许多此类疾病表现出多基因重叠，其中风险等位基因在相关遗传位点上共享。检测两个表型之间多基因重叠的简单策略是基于对单变量进行排序p-来自两项全基因组关联研究（GWAS）的值。尽管Lasso和弹性网络等高维变量选择策略已在其他GWAS分析设置中使用，但它们尚未用于检测共享多基因性。在本文中，我们说明了如何利用弹性网络，以多基因分数作为因变量，并在选择惩罚参数时进行适当的调整，来检测涉及共享多基因性的 SNP 子集。我们提供理论来更好地理解我们的方法，并使用合成数据集说明它们的实用性。给出了在各种情况下将弹性网络方法与排序方法进行比较的广泛模拟的结果。模拟研究的结果表明，当 SNP 之间的相关性较高时，弹性网络方法之一更为优越。最后，我们将这些方法应用于两个真实数据集，以进一步说明这些方法的功能、局限性和差异。