Kathleen M. Cardone, S. Dudek, K. Keat, Yuki Bradford, Zinhle Cindi, Eric S. Daar, Roy Gulick, Sharon A. Riddler, Jeffrey L. Lennox, P. Sinxadi, David W. Haas, Marylyn D. Ritchie
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We explored associations with baseline CD4 T-cell count prior to ART initiation (n=4959) and change from baseline to week 48 on ART (n=3274) among treatment-naïve participants in prospective, randomized ART studies of the AIDS Clinical Trials Group. We separately examined an African-ancestry-derived and a European-ancestry-derived PGSlymph, and evaluated their performance across all participants, and also in the African and European ancestral groups separately. Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ~26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ~7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ~6-10% of variability in multivariate models (including age, sex, and PCs) but only ~1% in univariate models. In summary, a lymphocyte count PGS derived from the general population was not consistently associated with CD4 T-cell recovery on ART. Nonetheless, adjusting for clinical covariates is quite important when estimating such polygenic effects.","PeriodicalId":34954,"journal":{"name":"Pacific Symposium on Biocomputing. 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Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ~26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ~7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ~6-10% of variability in multivariate models (including age, sex, and PCs) but only ~1% in univariate models. 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引用次数: 0
摘要
获得安全有效的抗逆转录病毒疗法(ART)是全球应对艾滋病大流行的基石。在艾滋病病毒感染者中,开始接受病毒抑制性抗逆转录病毒疗法后,CD4 T 细胞的绝对恢复能力在个体间存在相当大的差异。宿主遗传学对这一变异性的贡献尚不十分清楚。由于缺乏可公开获得的 CD4 T 细胞计数汇总统计数据,我们对多基因评分的贡献进行了探讨,该评分来自可公开获得的大量普通人群绝对淋巴细胞计数汇总统计数据(PGSlymph)。我们探讨了艾滋病临床试验组(AIDS Clinical Trials Group)前瞻性随机抗逆转录病毒疗法(ART)研究中未接受过治疗的参与者中,抗逆转录病毒疗法开始前的 CD4 T 细胞计数基线(4959 人)和从基线到抗逆转录病毒疗法第 48 周的变化(3274 人)之间的关联。我们分别研究了非洲裔和欧洲裔的 PGSlymph,并评估了它们在所有参与者中的表现,以及在非洲裔和欧洲裔群体中的表现。包括 PGSlymph、基线血浆 HIV-1 RNA、年龄、性别和 15 个遗传相似性主成分 (PCs) 的多变量模型解释了基线 CD4 T 细胞计数变异性的约 26-27%,但 PGSlymph 只占这一变异性的 <1%。还包括基线 CD4 T 细胞计数的模型可解释抗逆转录病毒疗法后 CD4 T 细胞计数增加的约 7-9% 的变异性,但 PGSlymph 在这一变异性中所占比例小于 1%。在单变量分析中,PGSlymph 与 CD4 T 细胞计数的基线或变化无明显关联。在非洲血统的个体中,多变量模型中的非洲 PGSlymph 项与 CD4 T 细胞计数的变化显著相关,而在单变量模型中则不显著。当应用于普通医学生物库人群(宾夕法尼亚医学生物库)的淋巴细胞计数时,PGSlymph 在多变量模型(包括年龄、性别和 PCs)中解释了 ~6-10% 的变异,但在单变量模型中仅解释了 ~1%。总之,来自普通人群的淋巴细胞计数 PGS 与抗逆转录病毒疗法的 CD4 T 细胞恢复并不一致。然而,在估计此类多基因效应时,调整临床协变量是非常重要的。
Lymphocyte Count Derived Polygenic Score and Interindividual Variability in CD4 T-cell Recovery in Response to Antiretroviral Therapy
Access to safe and effective antiretroviral therapy (ART) is a cornerstone in the global response to the HIV pandemic. Among people living with HIV, there is considerable interindividual variability in absolute CD4 T-cell recovery following initiation of virally suppressive ART. The contribution of host genetics to this variability is not well understood. We explored the contribution of a polygenic score which was derived from large, publicly available summary statistics for absolute lymphocyte count from individuals in the general population (PGSlymph) due to a lack of publicly available summary statistics for CD4 T-cell count. We explored associations with baseline CD4 T-cell count prior to ART initiation (n=4959) and change from baseline to week 48 on ART (n=3274) among treatment-naïve participants in prospective, randomized ART studies of the AIDS Clinical Trials Group. We separately examined an African-ancestry-derived and a European-ancestry-derived PGSlymph, and evaluated their performance across all participants, and also in the African and European ancestral groups separately. Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ~26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ~7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ~6-10% of variability in multivariate models (including age, sex, and PCs) but only ~1% in univariate models. In summary, a lymphocyte count PGS derived from the general population was not consistently associated with CD4 T-cell recovery on ART. Nonetheless, adjusting for clinical covariates is quite important when estimating such polygenic effects.