Department of Mathematics and Statistic, Laval University, Québec, QC G1V 0A6, Canada.
CERVO Brain Research Centre, Québec, QC G1E 1T2, Canada.
HGG Adv. 2023 May 20;4(3):100209. doi: 10.1016/j.xhgg.2023.100209. eCollection 2023 Jul 13.
Genetic correlations between human traits and disorders such as schizophrenia (SZ) and bipolar disorder (BD) diagnoses are well established. Improved prediction of individual traits has been obtained by combining predictors of multiple genetically correlated traits derived from summary statistics produced by genome-wide association studies, compared with single trait predictors. We extend this idea to penalized regression on summary statistics in Multivariate Lassosum, expressing regression coefficients for the multiple traits on single nucleotide polymorphisms (SNPs) as correlated random effects, similarly to multi-trait summary statistic best linear unbiased predictors (MT-SBLUPs). We also allow the SNP contributions to genetic covariance and heritability to depend on genomic annotations. We conducted simulations with two dichotomous traits having polygenic architecture similar to SZ and BD, using genotypes from 29,330 subjects from the CARTaGENE cohort. Multivariate Lassosum produced polygenic risk scores (PRSs) more strongly correlated with the true genetic risk predictor and had better discrimination power between affected and non-affected subjects than previously published sparse multi-trait (PANPRS) and univariate (Lassosum, sparse LDpred2, and the standard clumping and thresholding) methods in most simulation settings. Application of Multivariate Lassosum to predict SZ, BD, and related psychiatric traits in the Eastern Quebec SZ and BD kindred study revealed associations with every trait stronger than those obtained with univariate sparse PRSs, particularly when heritability and genetic covariance depended on genomic annotations. Multivariate Lassosum thus appears promising to improve prediction of genetically correlated traits with summary statistics for a selected subset of SNPs.
人类特征与精神分裂症 (SZ) 和双相情感障碍 (BD) 等疾病之间的遗传相关性已得到充分证实。通过结合来自全基因组关联研究的汇总统计数据中多个遗传相关特征的预测因子，与单一特征预测因子相比，可以更好地预测个体特征。我们将这一思想扩展到多变量 Lassosum 中的汇总统计量的惩罚回归中，将多个特征的回归系数表示为单核苷酸多态性 (SNP) 的相关随机效应，类似于多特征汇总统计最佳线性无偏预测值 (MT-SBLUPs)。我们还允许 SNP 对遗传协方差和遗传率的贡献取决于基因组注释。我们使用来自 CARTaGENE 队列的 29330 名受试者的基因型，对具有类似于 SZ 和 BD 的多基因结构的两个二分特征进行了模拟。多变量 Lassosum 生成的多基因风险评分 (PRS) 与真实遗传风险预测因子的相关性更强，并且在大多数模拟设置中，与以前发表的稀疏多特征 (PANPRS) 和单变量 (Lassosum、稀疏 LDpred2 和标准聚类和阈值) 方法相比，在区分受影响和未受影响的受试者方面具有更好的判别能力。多变量 Lassosum 在东魁北克 SZ 和 BD 家族研究中预测 SZ、BD 和相关精神特征的应用表明，与每个特征的关联都比使用单变量稀疏 PRS 更强，特别是当遗传率和遗传协方差取决于基因组注释时。因此，多变量 Lassosum 似乎有望通过汇总统计数据为选定的 SNP 子集来提高遗传相关特征的预测能力。
