Browsing by Subject "single nucleotide polymorphisms"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Genetic variants of PDGF signaling pathway genes predict cutaneous melanoma survival
    (Impact Journals, 2017-08-14) Li, Hong; Wang, Yanru; Liu, Hongliang; Shi, Qiong; Li, Hongyu; Wu, Wenting; Zhu, Dakai; Amos, Christopher I.; Fang, Shenying; Lee, Jeffrey E.; Li, Yi; Han, Jiali; Wei, Qingyi; Epidemiology, School of Public Health
    To investigate whether genetic variants of platelet-derived growth factor (PDGF) signaling pathway genes are associated with survival of cutaneous melanoma (CM) patients, we assessed associations of single-nucleotide polymorphisms in PDGF pathway with melanoma-specific survival in 858 CM patients of M.D. Anderson Cancer Center (MDACC). Additional data of 409 cases from Harvard University were also included for further analysis. We identified 13 SNPs in four genes (COL6A3, NCK2, COL5A1 and PRKCD) with a nominal P < 0.05 and false discovery rate (FDR) < 0.2 in MDACC dataset. Based on linkage disequilibrium, functional prediction and minor allele frequency, a representative SNP in each gene was selected. In the meta-analysis using MDACC and Harvard datasets, there were two SNPs associated with poor survival of CM patients: rs6707820 C>T in NCK2 (HR = 1.87, 95% CI = 1.35-2.59, Pmeta = 1.53E-5); and rs2306574 T>C in PRKCD (HR = 1.73, 95% CI = 1.33-2.24, Pmeta = 4.56E-6). Moreover, CM patients in MDACC with combined risk genotypes of these two loci had markedly poorer survival (HR = 2.47, 95% CI = 1.58-3.84, P < 0.001). Genetic variants of rs6707820 C>T in NCK2 and rs2306574 T>C in PRKCD of the PDGF signaling pathway may be biomarkers for melanoma survival.
  • Thumbnail Image
    Item
    Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification
    (Oxford, 2020-07-13) Du, Lei; Liu, Fang; Liu, Kefei; Yao, Xiaohui; Risacher, Shannon L; Han, Junwei; Guo, Lei; Saykin, Andrew J; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Motivation Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype–phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype–phenotype associations. Results In this article, we propose a new joint multitask learning method, named MT–SCCALR, which absorbs the merits of both SCCA and logistic regression. MT–SCCALR learns genotype–phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype–phenotype pattern. Meanwhile, MT–SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT–SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype–phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders.