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Function of cancer associated genes revealed by modern univariate and multivariate association tests.

ABSTRACT: Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in cancer, and can be indicators of their position in gene networks of cancer-driving processes. Recent studies show that gene associations are often non-linear and non-monotone. In order to obtain a more complete picture of all CNV associations, we performed omnibus univariate analysis by utilizing dCov, MIC, and HHG association tests, which are capable of detecting any type of association, including non-monotone relationships. For comparison we used Spearman and Pearson association tests, which detect only linear or monotone relationships. Application of dCov, MIC and HHG tests resulted in identification of twice as many associations compared to those found by Spearman and Pearson alone. Interestingly, most of the new associations were detected by the HHG test. Next, we utilized dCov's and HHG's ability to perform multivariate analysis. We tested for association between genes of unknown function and known cancer-related pathways. Our results indicate that multivariate analysis is much more effective than univariate analysis for the purpose of ascribing biological roles to genes of unknown function. We conclude that a combination of multivariate and univariate omnibus association tests can reveal significant information about gene networks of disease-driving processes. These methods can be applied to any large gene or pathway dataset, allowing more comprehensive analysis of biological processes.

SUBMITTER: Gorfine M

PROVIDER: S-EPMC4429101 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Function of cancer associated genes revealed by modern univariate and multivariate association tests.

Gorfine Malka M Goldstein Boaz B Fishman Alla A Heller Ruth R Heller Yair Y Lamm Ayelet T AT

PloS one 20150512 5

Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in can ...[more]

PMID: 25965968

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Project description:ObjectiveSeveral observational studies have revealed a potential relationship between menstrual reproductive factors (MRF) and osteoarthritis (OA). However, the precise causal relationship remains elusive. This study performed Mendelian randomization (MR) to provide deeper insights into this relationship.MethodsUtilizing summary statistics of genome-wide association studies (GWAS), we conducted univariate MR to estimate 2 menstrual factors (Age at menarche, AAM; Age at menopause, AMP) and 5 reproductive factors (Age at first live birth, AFB; Age at last live birth, ALB; Number of live births, NLB; Age first had sexual intercourse, AFSI; Age started oral contraceptive pill, ASOC) on OA (overall OA, OOA; knee OA, KOA and hip OA, HOA). The sample size of MRF ranged from 123846 to 406457, and the OA sample size range from 393873 to 484598. Inverse variance weighted (IVW) method was used as the primary MR analysis methods, and MR Egger, weighted median was performed as supplements. Sensitivity analysis was employed to test for heterogeneity and horizontal pleiotropy. Finally, multivariable MR was utilized to adjust for the influence of BMI on OA.ResultsAfter conducting multiple tests (P<0.0023) and adjusting for BMI, MR analysis indicated that a lower AFB will increase the risk of OOA (odds ratio [OR] = 0.97, 95% confidence interval [CI]: 0.95-0.99, P = 3.39×10-4) and KOA (OR = 0.60, 95% CI: 0.47-0.78, P = 1.07×10-4). ALB (OR = 0.61, 95% CI: 0.45-0.84, P = 2.06×10-3) and Age AFSI (OR = 0.66, 95% CI: 0.53-0.82, P = 2.42×10-4) were negatively associated with KOA. In addition, our results showed that earlier AMP adversely affected HOA (OR = 1.12, 95% CI: 1.01-1.23, P = 0.033), and earlier ASOC promote the development of OOA (OR = 0.97, 95% CI: 0.95-1.00, P = 0.032) and KOA (OR = 0.58, 95% CI: 0.40-0.84, P = 4.49×10-3). ALB (OR = 0.98, 95% CI: 0.96-1.00, P = 0.030) and AFSI (OR = 0.98, 95% CI: 0.97-0.99, P = 2.66×10-3) also showed a negative association with OOA but they all did not pass multiple tests. The effects of AAM and NLB on OA were insignificant after BMI correction.ConclusionThis research Certificates that Early AFB promotes the development of OOA, meanwhile early AFB, ALB, and AFSI are also risk factors of KOA. Reproductive factors, especially those related to birth, may have the greatest impact on KOA. It provides guidance for promoting women's appropriate age fertility and strengthening perinatal care.

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Function of cancer associated genes revealed by modern univariate and multivariate association tests.

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Function of cancer associated genes revealed by modern univariate and multivariate association tests.

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