scholarly journals Do Candidate Genes Affect the Brain’s White Matter Microstructure? Large-Scale Evaluation of 6,165 Diffusion MRI Scans

2017 ◽  
Author(s):  
Neda Jahanshad ◽  
Habib Ganjgahi ◽  
Janita Bralten ◽  
Anouk den Braber ◽  
Joshua Faskowitz ◽  
...  
Keyword(s):  
White Matter ◽  
Multiple Testing ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Multiple Testing Correction ◽  
Healthy Human ◽  
Multiple Granularity ◽  
Single Cohort

Abstract:Susceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3×10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

scholarly journals NRG1 Genetic Variant Influences the Efficacy of Androgen-Deprivation Therapy in Men with Prostate Cancer

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 528
Author(s):  
Shu-Pin Huang ◽  
Yei-Tsung Chen ◽  
Lih-Chyang Chen ◽  
Cheng-Hsueh Lee ◽  
Chao-Yuan Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Androgen Deprivation Therapy ◽  
Tyrosine Kinases ◽  
Multiple Testing ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Androgen Deprivation ◽  
Nucleotide Polymorphisms ◽  
Multiple Testing Correction ◽  
Cancer Specific Survival

Neuregulins (NRGs) activate receptor tyrosine kinases of the ErbB family, and play essential roles in the proliferation, survival, and differentiation of normal and malignant tissue cells. We hypothesized that genetic variants of NRG signalling pathway genes may influence treatment outcomes in prostate cancer. To test this hypothesis, we performed a comprehensive analysis to evaluate the associations of 459 single-nucleotide polymorphisms in 19 NRG pathway genes with cancer-specific survival (CSS), overall survival (OS), and progression-free survival (PFS) in 630 patients with prostate cancer receiving androgen-deprivation therapy (ADT). After multivariate Cox regression and multiple testing correction, we found that NRG1 rs144160282 C > T is significantly associated with worsening CSS, OS, and PFS during ADT. Further analysis showed that low expression of NRG1 is closely related to prostate cancer, as indicated by a high Gleason score, an advanced stage, and a shorter PFS rate. Meta-analysis of 16 gene expression datasets of 1,081 prostate cancer samples and 294 adjacent normal samples indicate lower NRG1 expression in the former compared with the latter (p < 0.001). These results suggest that NRG1 rs144160282 might be a prognostic predictor of the efficacy of ADT. Further studies are required to confirm the significance of NRG1 as a biomarker and therapeutic target for prostate cancer.

Changes in mild cognitive impairment and its subtypes as seen on diffusion tensor imaging

2012 ◽  
Vol 24 (9) ◽  
pp. 1483-1493 ◽  
Author(s):  
Senthil Thillainadesan ◽  
Wei Wen ◽  
Lin Zhuang ◽  
John Crawford ◽  
Nicole Kochan ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
White Matter ◽  
Multiple Testing ◽  
Diffusion Tensor ◽  
Internal Capsule ◽  
Anterior Cingulate ◽  
Corona Radiata ◽  
Amnestic Mci ◽  
Subcortical Regions ◽  
The Right

ABSTRACTBackground: Previous studies using diffusion tensor imaging (DTI) have observed microstructural abnormalities in white matter regions in both Alzheimer's disease and mild cognitive impairment (MCI). The aim of this work was to examine the abnormalities in white matter and subcortical regions of MCI and its subtypes in a large, community-dwelling older aged cohortMethods: A community-based sample of 396 individuals without dementia underwent medical assessment, neuropsychiatric testing, and neuroimaging. Of these, 158 subjects were classified as MCI and 238 as cognitively normal (controls) based on international MCI consensus criteria. Regional fractional anisotropy (FA) and mean diffusivity (MD) measures were calculated from the DTI and compared between groups. The false discovery rate correction was applied for multiple testing.Results: Subjects with MCI did not have significant differences in FA compared with controls after correction for multiple testing, but had increased MD in the right putamen, right anterior limb of the internal capsule, genu and splenium of the corpus callosum, right posterior cingulate gyrus, left superior frontal gyrus, and right and left corona radiata. When compared with controls, changes in left anterior cingulate, left superior frontal gyrus, and right corona radiata were associated with amnestic MCI (aMCI), whereas changes in the right putamen, right anterior limb of the internal capsule, and the right corona radiata were associated with non-amnestic MCI (naMCI). On logistic regression, the FA values in the left superior gyrus and MD values in the anterior cingulate distinguished aMCI from naMCI.Conclusions: MCI is associated with changes in white matter and subcortical regions as seen on DTI. Changes in some anterior brain regions distinguish aMCI from naMCI.

scholarly journals Aberrant White Matter Networks Mediate Cognitive Impairment in Patients with Silent Lacunar Infarcts in Basal Ganglia Territory

2015 ◽  
Vol 35 (9) ◽  
pp. 1426-1434 ◽  
Author(s):  
Jinfu Tang ◽  
Suyu Zhong ◽  
Yaojing Chen ◽  
Kewei Chen ◽  
Junying Zhang ◽  
...  
Keyword(s):  
White Matter ◽  
Basal Ganglia ◽  
Cognitive Deficits ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Lacunar Infarcts ◽  
Healthy Elderly ◽  
Brain White Matter ◽  
Diffusion Tensor Imaging Tractography

Silent lacunar infarcts, which are present in over 20% of healthy elderly individuals, are associated with subtle deficits in cognitive functions. However, it remains largely unclear how these silent brain infarcts lead to cognitive deficits and even dementia. Here, we used diffusion tensor imaging tractography and graph theory to examine the topological organization of white matter networks in 27 patients with silent lacunar infarcts in the basal ganglia territory and 30 healthy controls. A whole-brain white matter network was constructed for each subject, where the graph nodes represented brain regions and the edges represented interregional white matter tracts. Compared with the controls, the patients exhibited a significant reduction in local efficiency and global efficiency. In addition, a total of eighteen brain regions showed significantly reduced nodal efficiency in patients. Intriguingly, nodal efficiency–behavior associations were significantly different between the two groups. The present findings provide new aspects into our understanding of silent infarcts that even small lesions in subcortical brain regions may affect large-scale cortical white matter network, as such may be the link between subcortical silent infarcts and the associated cognitive impairments. Our findings highlight the need for network-level neuroimaging assessment and more medical care for individuals with silent subcortical infarcts.

scholarly journals Vitamin D-Related Genes, Blood Vitamin D Levels and Colorectal Cancer Risk in Western European Populations

Nutrients ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1954 ◽  
Author(s):  
Veronika Fedirko ◽  
Hannah Mandle ◽  
Wanzhe Zhu ◽  
David Hughes ◽  
Afshan Siddiq ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Vitamin D ◽  
Multiple Testing ◽  
A Priori ◽  
Nucleotide Polymorphisms ◽  
Multiple Testing Correction ◽  
Vitamin D Metabolism ◽  
Gene Pathway ◽  
Vitamin D Levels ◽  
Large Populations

Higher circulating 25-hydroxyvitamin D levels (25(OH)D) have been found to be associated with lower risk for colorectal cancer (CRC) in prospective studies. Whether this association is modified by genetic variation in genes related to vitamin D metabolism and action has not been well studied in humans. We investigated 1307 functional and tagging single-nucleotide polymorphisms (SNPs; individually, and by gene/pathway) in 86 vitamin D-related genes in 1420 incident CRC cases matched to controls from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. We also evaluated the association between these SNPs and circulating 25(OH)D in a subset of controls. We confirmed previously reported CRC risk associations between SNPs in the VDR, GC, and CYP27B1 genes. We also identified additional associations with 25(OH)D, as well as CRC risk, and several potentially novel SNPs in genes related to vitamin D transport and action (LRP2, CUBN, NCOA7, and HDAC9). However, none of these SNPs were statistically significant after Benjamini–Hochberg (BH) multiple testing correction. When assessed by a priori defined functional pathways, tumor growth factor β (TGFβ) signaling was associated with CRC risk (P ≤ 0.001), with most statistically significant genes being SMAD7 (PBH = 0.008) and SMAD3 (PBH = 0.008), and 18 SNPs in the vitamin D receptor (VDR) binding sites (P = 0.036). The 25(OH)D-gene pathway analysis suggested that genetic variants in the genes related to VDR complex formation and transcriptional activity are associated with CRC depending on 25(OH)D levels (interaction P = 0.041). Additional studies in large populations and consortia, especially with measured circulating 25(OH)D, are needed to confirm our findings.

Prospective evaluation of candidate SNPs of VEGF/VEGFR pathway in metastatic colorectal cancer (mCRC) patients (pts) treated with first-line FOLFIRI plus bevacizumab (BV).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 3518-3518 ◽  
Author(s):  
Chiara Cremolini ◽  
Fotios Loupakis ◽  
Dongyun Yang ◽  
Lisa Salvatore ◽  
Wu Zhang ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Germ Line ◽  
Univariate Analysis ◽  
Candidate Snps ◽  
Multiple Testing Correction ◽  
First Line ◽  
Historical Cohort ◽  
Predictive Role ◽  
Post Hoc

3518 Background: Despite several attempts, no predictors of benefit from BV have been so far identified. We reported the association of VEGF rs833061 T/T to a worse outcome in mCRC pts treated with first-line FOLFIRI+BV, but not in a historical cohort treated with FOLFIRI. Recent post-hoc analyses on randomized trials suggested the potential prognostic and/or predictive role of other VEGF and VEGFR1/2 SNPs. Methods: Moving from our retrospective finding, we designed a prospective validation trial in mCRC pts treated with first-line FOLFIRI+ BV to detect a HR for PFS of 1.7 for VEGF rs833061 T/T compared to C- variants. With two-sided a=0.05 and b=0.20, 199 events were required. Accrual was faster than expected and in the meanwhile promising results about other SNPs were reported and we therefore included a confirmatory analysis of VEGF rs699946 A/G, VEGFR1 rs9582036 A/C and rs7993418 A/G, VEGFR2 rs11133360 C/T, rs12505758 C/T and rs2305948 C/T and EPAS rs4145836 A/G SNPs. Germ-line DNA was extracted from peripheral blood. SNPs were analyzed by PCR and sequencing. Results: Four-hundred-twenty-four pts were included. At a median follow up of 24 months, median PFS was 10.5 months. At the univariate analysis, no differences in PFS according to VEGF rs833061 C/T variants were observed (p=0.38). Among analyzed SNPs, only VEGFR2 rs12505758 C- variants (n=118) were associated to shorter PFS compared to TT (n=306) (HR: 1.40 [95%CI: 1.07-1.84], p=0.015). In the multivariate model, this association retained significance (HR: 1.402 [95%CI:1.079-1.822], p=0.012), that was lost by applying multiple testing correction. Conclusions: This prospective experience failed to validate the hypothesized predictive impact of VEGF rs833061 variants. Also other previous retrospective findings on different candidate SNPs were not confirmed. Only VEGFR2 rs12505758 variants, whose prognostic rather than predictive impact was previously reported, correlated with PFS. We suggest that future studies on biomarkers of benefit from BV should look at the complexity of tumoral angiogenesis at different levels and not only from the genetic perspective.

scholarly journals Euclidean distance as a measure to distinguish ventral and dorsal white matter connectivity in the human brain

2016 ◽  
Author(s):  
Philipp Kellmeyer ◽  
Magnus-Sebastian Vry
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Cognitive Processing ◽  
Euclidean Distance ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Large Body ◽  
Coordinate Space ◽  
Fiber Tractography

AbstractFiber tractography based on diffusion tensor imaging (DTI) has become an important research tool for investigating the anatomical connectivity between brain regions in vivo. Combining DTI with functional magnetic resonance imaging (fMRI) allows for the mapping of structural and functional architecture of large-scale networks for cognitive processing. This line of research has shown that ventral and dorsal fiber pathways subserve different aspects of bottom-up- and top-down processing in the human brain.Here, we investigate the feasibility and applicability of Euclidean distance as a simple geometric measure to differentiate ventral and dorsal long-range white matter fiber pathways tween parietal and inferior frontal cortical regions, employing a body of studies that used probabilistic tractography.We show that ventral pathways between parietal and inferior frontal cortex have on average a significantly longer Euclidean distance in 3D-coordinate space than dorsal pathways. We argue that Euclidean distance could provide a simple measure and potentially a boundary value to assess patterns of connectivity in fMRI studies. This would allow for a much broader assessment of general patterns of ventral and dorsal large-scale fiber connectivity for different cognitive operations in the large body of existing fMRI studies lacking additional DTI data.

scholarly journals Alterations in sensorimotor and mesiotemporal cortices and diffuse white matter changes in primary progressive multiple sclerosis detected by adiabatic relaxometry

2020 ◽  
Author(s):  
Pavel Filip ◽  
Michal Dufek ◽  
Silvia Mangia ◽  
Shalom Michaeli ◽  
Martin Bares ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Neuronal Loss ◽  
Progressive Multiple Sclerosis ◽  
Primary Progressive Multiple Sclerosis ◽  
Primary Progressive ◽  
T1 And T2

Abstract Background: The research of primary progressive multiple sclerosis (PPMS) has not been able to capitalize on recent progresses in advanced MRI protocols searching for disease-specific microstructural changes. Methods: Conventional free precession T1 and T2, and rotating frame adiabatic T1ρ and T2ρ maps in combination with diffusion weighted parameters were acquired in 13 PPMS patients and 13 age and sex-matched controls.Results: T1ρ, a marker of crucial relevance for PPMS due to its sensitivity to neuronal loss, revealed large-scale changes in mesiotemporal structures, sensorimotor cortex and cingulate, in combination with diffuse alterations in the white matter and cerebellum. T2ρ, particularly sensitive to local tissue background gradients and thus indicator of iron accumulation, concurred with similar topography of damage, but of lower extent. Moreover, these adiabatic protocols completely dwarfed the outcomes of both conventional T1 and T2 maps and diffusion tensor/kurtosis approaches –methods previously implicated in the MRI research of PPMS.Conclusion: This study introduces adiabatic T1ρ and T2ρ as elegant markers confirming large-scale cortical grey matter, cerebellar and white matter alterations in PPMS invisible to other in vivo biomarkers.

scholarly journals Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

2020 ◽  
Vol 4 ◽  
pp. 142
Author(s):  
Paul A. Thompson ◽  
Dorothy V. M. Bishop ◽  
Else Eising ◽  
Simon E. Fisher ◽  
Dianne F. Newbury
Keyword(s):  
Structural Equation ◽  
Multiple Testing ◽  
Association Studies ◽  
Component Analysis ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Association Analyses ◽  
Generalized Structured Component Analysis ◽  
Replication Studies ◽  
Candidate Gene Association

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

scholarly journals Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Sean M. Burnard ◽  
Rodney A. Lea ◽  
Miles Benton ◽  
David Eccles ◽  
Daniel W. Kennedy ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Complex Traits ◽  
Multiple Testing ◽  
Large Scale ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Elastic Net ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

scholarly journals Generalized Structured Component Analysis in candidate gene association studies: applications and limitations

2019 ◽  
Vol 4 ◽  
pp. 142 ◽  
Author(s):  
Paul A. Thompson ◽  
Dorothy V. M. Bishop ◽  
Else Eising ◽  
Simon E. Fisher ◽  
Dianne F. Newbury
Keyword(s):  
Structural Equation ◽  
Multiple Testing ◽  
Association Studies ◽  
Component Analysis ◽  
Candidate Snps ◽  
Nucleotide Polymorphisms ◽  
Association Analyses ◽  
Generalized Structured Component Analysis ◽  
Replication Studies ◽  
Candidate Gene Association

Background: Generalized Structured Component Analysis (GSCA) is a component-based alternative to traditional covariance-based structural equation modelling. This method has previously been applied to test for association between candidate genes and clinical phenotypes, contrasting with traditional genetic association analyses that adopt univariate testing of many individual single nucleotide polymorphisms (SNPs) with correction for multiple testing. Methods: We first evaluate the ability of the GSCA method to replicate two previous findings from a genetics association study of developmental language disorders. We then present the results of a simulation study to test the validity of the GSCA method under more restrictive data conditions, using smaller sample sizes and larger numbers of SNPs than have previously been investigated. Finally, we compare GSCA performance against univariate association analysis conducted using PLINK v1.9. Results: Results from simulations show that power to detect effects depends not just on sample size, but also on the ratio of SNPs with effect to number of SNPs tested within a gene. Inclusion of many SNPs in a model dilutes true effects. Conclusions: We propose that GSCA is a useful method for replication studies, when candidate SNPs have been identified, but should not be used for exploratory analysis.

Sign in / Sign up

Export Citation Format

Share Document