From Interaction to Co-Association —A Fisher r-To-z Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

Yuan, Zhongshang; Liu, Hong; Zhang, Xiaoshuai; Li, Fangyu; Zhao, Jinghua; Zhang, Furen; Xue, Fuzhong

doi:10.1371/journal.pone.0070774

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…This transformation was done to r D and r C , that is, Z D = ½(ln(1 + r D ) - ln(1 - r D )), Z C = ½(ln(1 + r C ) - ln(1 - r C )). [ 23 ] The KCCU statistic [ 24 ] was taken as a measure of haplotype-based gene–gene interaction in the IFG+DM and NFPG groups, which can be defined as …”

Section: Resultsmentioning

confidence: 99%

Haplotype-based interaction of the PPARGC1A and UCP1 genes is associated with impaired fasting glucose or type 2 diabetes mellitus

Pei

Liu²,

Cai³

et al. 2017

Medicine

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The aim of this study is to evaluate the effect of single-nucleotide polymorphisms (SNPs) of the PPARGC1A and UCP1 genes on impaired fasting glucose (IFG) or type 2 diabetes mellitus (T2DM) and the haplotype-based interaction between these genes.A cross-sectional study was conducted by cluster sampling in Henan province, China. Based on the level of fasting plasma glucose (FPG) and the history of T2DM, the participants were divided into 2 groups; 83 individuals were in the IFG+DM group (those with IFG or T2DM) and 445 individuals were in the NFPG group (those with normal FPG). Kernel canonical correlation analysis (KCCA), a haplotype-based gene-gene interaction method, which can increase the biological interpretability and extract nonlinear characteristics of SNPs, was used to analyze the correlation and interaction between PPARGC1A and UCP1 genes.The age, BMI, total cholesterol and triglycerides were statistically different between 2 groups (P ≤ .001). Haplotype analysis showed no significant difference in frequency distribution between the 2 groups when the PPARGC1A or UCP1 gene was tested (P > .05). KCCA analysis showed that the maximum kernel canonical correlation coefficient of the PPARGC1A and UCP1 genes was 0.9977 and 0.9995 in the IFG+DM and NPFG groups, respectively. A haplotype-based gene–gene interaction was observed significantly (U = −6.28, P < .001), indicating the possibility of an interaction between haplotype AAG of the PPARGC1A gene and haplotypes CTCG (odds ratio [OR] = 1.745, 95% confidence interval [95% CI] 1.069–2.847) and CTCA (OR = 0.239, 95% CI 0.060–0.958) of the UCP1 gene.Haplotype-based interaction between the PPARGC1A and UCP1 genes is associated with IFG or T2DM among residents in Henan, China.

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Section: Resultsmentioning

confidence: 99%

Haplotype-based interaction of the PPARGC1A and UCP1 genes is associated with impaired fasting glucose or type 2 diabetes mellitus

Pei

Liu²,

Cai³

et al. 2017

Medicine

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“…The BOLD signal values of each TR in active blocks were subjected to correlation analysis between pairs of ROIs. For further statistical analysis, a Fisher r-to-z transformation was performed to improve the normality of the correlation coefficients (Yuan et al, 2013 ). For each network connection, a separate 2 (dominant eye, non-dominant eye) by 2 (subject group) mixed design ANOVA was used to determine the strength of the correlation, with simple effect analyses used in the event of significant interactions.…”

Section: Methodsmentioning

confidence: 99%

Impaired Activation of Visual Attention Network for Motion Salience Is Accompanied by Reduced Functional Connectivity between Frontal Eye Fields and Visual Cortex in Strabismic Amblyopia

Wang

Liang

Laycock

et al. 2017

Front. Hum. Neurosci.

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Strabismic amblyopia is now acknowledged to be more than a simple loss of acuity and to involve alterations in visually driven attention, though whether this applies to both stimulus-driven and goal-directed attention has not been explored. Hence we investigated monocular threshold performance during a motion salience-driven attention task involving detection of a coherent dot motion target in one of four quadrants in adult controls and those with strabismic amblyopia. Psychophysical motion thresholds were impaired for the strabismic amblyopic eye, requiring longer inspection time and consequently slower target speed for detection compared to the fellow eye or control eyes. We compared fMRI activation and functional connectivity between four ROIs of the occipital-parieto-frontal visual attention network [primary visual cortex (V1), motion sensitive area V5, intraparietal sulcus (IPS) and frontal eye fields (FEF)], during a suprathreshold version of the motion-driven attention task, and also a simple goal-directed task, requiring voluntary saccades to targets randomly appearing along a horizontal line. Activation was compared when viewed monocularly by controls and the amblyopic and its fellow eye in strabismics. BOLD activation was weaker in IPS, FEF and V5 for both tasks when viewing through the amblyopic eye compared to viewing through the fellow eye or control participants' non-dominant eye. No difference in V1 activation was seen between the amblyopic and fellow eye, nor between the two eyes of control participants during the motion salience task, though V1 activation was significantly less through the amblyopic eye than through the fellow eye and control group non-dominant eye viewing during the voluntary saccade task. Functional correlations of ROIs within the attention network were impaired through the amblyopic eye during the motion salience task, whereas this was not the case during the voluntary saccade task. Specifically, FEF showed reduced functional connectivity with visual cortical nodes during the motion salience task through the amblyopic eye, despite suprathreshold detection performance. This suggests that the reduced ability of the amblyopic eye to activate the frontal components of the attention networks may help explain the aberrant control of visual attention and eye movements in amblyopes.

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“…Our group has proposed the concept of gene-gene co-association which refers to the extent to which the joint effects of two genes differs from the main effects of each gene in previous studies [ 8 – 11 ]. The distinction between gene-gene co-association and interaction has been theoretically clarified from the causal diagram perspective [ 9 ], and various simulations have also been conducted to confirm its reasonability, especially for two highly correlated genes. Specifically, taking 2 SNPs as an example (Fig.…”

Section: Introductionmentioning

confidence: 99%

“…1 ), the main effects of SNP1 and SNP2 are supposed to be β 1 and β 2 respectively and the correlation coefficient between them is r . The total effects of SNP1 and SNP2 are denoted as β 1 + β 2 + β 3 + r ( β 1 + β 2 ) and the term β 3 + r ( β 1 + β 2 ) represents the co-association where the traditional interaction β 3 is only one part of co-association [ 9 ]. Actually, gene-gene co-association is essentially used to capture the joint effects attributed to the correlation r ( β 1 + β 2 ), which has usually been neglected in traditional regression model.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several methods have been proposed to test gene-gene co-association, such as the statistics based on SNP-level Fisher r-to-z transformation [ 9 ], canonical correlation analysis (CCU) [ 8 ], kernel canonical correlation analysis (KCCU) [ 11 ] and partial least squares path modeling (PLSPM) [ 10 ]. SNP-level Fisher r-to-z transformation-based statistics, though having acceptable false positive rates and computation burden, fail to fully utilize the LD information between markers and true causal SNPs in one gene or region, leading to lower statistical power.…”

Section: Introductionmentioning

confidence: 99%

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A powerful score-based test statistic for detecting gene-gene co-association

Yuan

et al. 2016

BMC Genet

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Background: The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Results: Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ 2 ) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. Conclusions: SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.

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From Interaction to Co-Association —A Fisher r-To-z Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

Cited by 7 publications

References 27 publications

Haplotype-based interaction of the PPARGC1A and UCP1 genes is associated with impaired fasting glucose or type 2 diabetes mellitus

Haplotype-based interaction of the PPARGC1A and UCP1 genes is associated with impaired fasting glucose or type 2 diabetes mellitus

Impaired Activation of Visual Attention Network for Motion Salience Is Accompanied by Reduced Functional Connectivity between Frontal Eye Fields and Visual Cortex in Strabismic Amblyopia

A powerful score-based test statistic for detecting gene-gene co-association

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