Most genome-wide association study (GWAS) analyses test the association between single-nucleotide polymorphisms (SNPs) and a single trait or outcome. While valuable second-step analyses of these associations (e.g., calculating genetic correlations between traits) are common, single-step multivariate analyses of GWAS data are rarely performed. This is unfortunate because multivariate analyses can reveal information which is irrevocably obscured in multi-step analysis. One simple example is the distinction between variance common to a set of measures, and variance specific to each. Neither GWAS of sum- or factor-scores, nor GWAS of the individual measures will deliver a clean picture of loci associated with each measure’s specific variance. While multivariate GWAS opens up a broad new landscape of feasible and informative analyses, its adoption has been slow, likely due to the heavy computational demands and difficulties specifying models it requires. Here we describe GW-SEM 2.0, which is designed to simplify model specification and overcome the inherent computational challenges associated with multivariate GWAS. In addition, GW-SEM 2.0 allows users to accurately model ordinal items, which are common in behavioral and psychological research, within a GWAS context. This new release enhances computational efficiency, allows users to select the fit function that is appropriate for their analyses, expands compatibility with standard genomic data formats, and outputs results for seamless reading into other standard post-GWAS processing software. To demonstrate GW-SEM’s utility, we conducted (1) a series of GWAS using three substance use frequency items from data in the UK Biobank, (2) a timing study for several predefined GWAS functions, and (3) a Type I Error rate study. Our multivariate GWAS analyses emphasize the utility of GW-SEM for identifying novel patterns of associations that vary considerably between genomic loci for specific substances, highlighting the importance of differentiating between substance-specific use behaviors and polysubstance use. The timing studies demonstrate that the analyses take a reasonable amount of time and show the cost of including additional items. The Type I Error rate study demonstrates that hypothesis tests for genetic associations with latent variable models follow the hypothesized uniform distribution. Taken together, we suggest that GW-SEM may provide substantially deeper insights into the underlying genomic architecture for multivariate behavioral and psychological systems than is currently possible with standard GWAS methods. The current release of GW-SEM 2.0 is available on CRAN (stable release) and GitHub (beta release), and tutorials are available on our github wiki (https://jpritikin.github.io/gwsem/).

大多数全基因组关联研究 (GWAS) 分析测试单核苷酸多态性 (SNP) 与单一性状或结果之间的关联。虽然对这些关联的有价值的第二步分析（例如，计算性状之间的遗传相关性）很常见，但很少对 GWAS 数据进行单步多变量分析。这是不幸的，因为多变量分析可以揭示在多步骤分析中不可逆转地模糊的信息。一个简单的例子是一组度量共有的方差与每个度量特定的方差之间的区别。无论是总分或因子得分的 GWAS，还是单个测量的 GWAS，都无法清晰地描绘出与每个测量的特定方差相关的位点。虽然多变量 GWAS 开辟了可行且信息丰富的分析的广阔新领域，它的采用速度很慢，可能是由于大量的计算需求和难以指定所需的模型。在这里，我们描述了 GW-SEM 2.0，它旨在简化模型规范并克服与多变量 GWAS 相关的固有计算挑战。此外，GW-SEM 2.0 允许用户在 GWAS 上下文中准确地对行为和心理研究中常见的有序项目进行建模。这个新版本提高了计算效率，允许用户选择适合他们分析的拟合函数，扩展与标准基因组数据格式的兼容性，并输出结果以无缝读取到其他标准的后 GWAS 处理软件。为了演示 GW-SEM 的效用，我们进行了 (1) 使用来自英国生物银行数据的三个物质使用频率项目的一系列 GWAS，(2) 几个预定义 GWAS 功能的时间研究，以及 (3) 类型 I 错误率研究。我们的多变量 GWAS 分析强调了 GW-SEM 在识别特定物质基因组位点之间差异很大的新关联模式方面的效用，突出了区分特定物质使用行为和多物质使用的重要性。时间研究表明，分析花费了合理的时间，并显示了包括附加项目的成本。I 型错误率研究表明，对潜在变量模型的遗传关联的假设检验遵循假设的均匀分布。综合起来，我们建议 GW-SEM 可以比目前使用标准 GWAS 方法更深入地了解多元行为和心理系统的潜在基因组结构。当前版本的 GW-SEM 2.0 可在 CRAN（稳定版）和 GitHub（测试版）上找到，教程可在我们的 github wiki (https://jpritikin.github.io/gwsem/) 上找到。