MOTIVATION Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual-SNP analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple testing correction burden. On the other hand, multiple-SNP analyses (e.g., gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype-phenotype association. RESULTS In this paper, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and noninformative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS data set gains new additional insights into the genetic control of complex traits. AVAILABILITY R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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使用置换辅助调整，在具有套索的GWAS中确定遗传变异的优先级。

动机大规模的全基因组关联研究（GWAS）已导致鉴定与许多复杂性状和疾病相关的广泛遗传变异。尽管取得了成功，但目前大多数GWAS中采用的个体SNP分析方法仍可能受到限制，因为从生物学上讲解表型的全面遗传结构通常很简单，并且由于繁重的多重测试校正负担，统计上的能力不足。另一方面，多重SNP分析（例如，基于基因或基于区域的SNP集分析）通常更强大，可以检查一组SNP对目标表型的联合作用。然而，当前的多SNP方法只能在SNP集水平上得出总体结论，而不能直接告知SNP集中的哪些SNP在驱动总体基因型与表型的关联。结果在本文中，我们提出了在套索（Plasso）中的一种新的排列辅助调整程序，以在GWAS的联合多SNP回归模型中识别与表型相关的SNP。套索的调整参数确定收缩量，这对于变量选择的执行至关重要。在拟议的普拉索程序中，我们首先生成与表型不相关的伪SNP排列。然后，精心选择套索调整参数以分离真实信号SNP和非信息伪SNP。我们通过仿真来说明plasso，以证明其优于现有方法的性能，将plasso应用于真实的GWAS数据集，对复杂性状的遗传控制有了新的认识。可通过https://github.com/xyz5074/plasso获得实现拟议方法的可用性R代码。补充信息补充数据可从Bioinformatics在线获得。