Müller Stephanie J, Schurz Haiko, Tromp Gerard, van der Spuy Gian D, Hoal Eileen G, van Helden Paul D, Owusu-Dabo Ellis, Meyer Christian G, Muntau Birgit, Thye Thorsten, Niemann Stefan, Warren Robin M, Streicher Elizabeth, Möller Marlo, Kinnear Craig
DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; South African Tuberculosis Bioinformatics Initiative (SATBBI), Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa.
Genomics. 2021 Jul;113(4):1802-1815. doi: 10.1016/j.ygeno.2021.04.024. Epub 2021 Apr 20.
Despite decades of research and advancements in diagnostics and treatment, tuberculosis remains a major public health concern. New computational methods are needed to interrogate the intersection of host- and bacterial genomes. Paired host genotype datum and infecting bacterial isolate information were analysed for associations using a multinomial logistic regression framework implemented in SNPTest. A cohort of 853 admixed South African participants and a Ghanaian cohort of 1359 participants were included. Two directly genotyped variants, namely rs529920 and rs41472447, were identified in the Ghanaian cohort as being statistically significantly associated with risk for infection with strains of different members of the MTBC. Thus, a multinomial logistic regression using paired host-pathogen data may prove valuable for investigating the complex relationships driving infectious disease.
尽管在诊断和治疗方面经过了数十年的研究和进步，但结核病仍然是一个主要的公共卫生问题。需要新的计算方法来研究宿主基因组和细菌基因组的交叉点。使用SNPTest中实现的多项逻辑回归框架分析配对的宿主基因型数据和感染细菌分离株信息之间的关联。纳入了一组853名南非混血参与者和一组1359名加纳参与者。在加纳队列中，两个直接基因分型的变体，即rs529920和rs41472447，被确定与感染结核分枝杆菌复合群不同成员菌株的风险在统计学上显著相关。因此，使用配对的宿主-病原体数据进行多项逻辑回归可能对研究驱动传染病的复杂关系很有价值。
