第809回千葉医学会例会・第一外科教室談話会 64.

<p>RR, relative risk.</p

Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association-0

<p><b>Copyright information:</b></p><p>Taken from "Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S55-S55.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866728.</p><p></p>e band, block 2: late time window – 1–2.5 Hz wave band, block 3: early time window – 3–7 Hz wave band) are connected. Within each block measures are taken at different locations on the skull. The dotted line represents the mean of all measures. Blue, ERP-M taking into account structure + magnitude: measures of block 1 are much larger than eothers (block 2 and 3), independent from location on the skull. Red, ERP-P taking into account structure: measures of the central and parietal midline channel are larger than those of the frontal channels, this pattern is congruent across blocks

Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association-2

<p><b>Copyright information:</b></p><p>Taken from "Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S55-S55.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866728.</p><p></p

Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association-1

<p><b>Copyright information:</b></p><p>Taken from "Surrogate phenotype definition for alcohol use disorders: a genome-wide search for linkage and association"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S55-S55.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866728.</p><p></p

DataSheet_1_A genome-wide association study on hematopoietic stem cell transplantation reveals novel genomic loci associated with transplant outcomes.docx

IntroductionData on genomic susceptibility for adverse outcomes after hematopoietic stem cell transplantation (HSCT) for recipients are scarce.MethodsWe performed a genome wide association study (GWAS) to identify genes associated with survival/mortality, relapse, and severe graft-versus-host disease (sGvHD), fitting proportional hazard and subdistributional models to data of n=1,392 recipients of European ancestry from three centres.ResultsThe single nucleotide polymorphism (SNP) rs17154454, intronic to the neuronal growth guidant semaphorin 3C gene (SEMA3C), was genome-wide significantly associated with event-free survival (p=7.0x10-8) and sGvHD (p=7.5x10-8). Further associations were detected for SNPs in the Paxillin gene (PXN) with death without prior relapse or sGvHD, as well as for SNPs of the Plasmacytoma Variant Translocation 1 gene (PVT1, a long non-coding RNA gene), the Melanocortin 5 Receptor (MC5R) gene and the WW Domain Containing Oxidoreductase gene (WWOX), all associated with the occurrence of sGvHD. Functional considerations support the observed associations.DiscussionThus, new genes were identified, potentially influencing the outcome of HSCT.</p

DataSheet_2_A genome-wide association study on hematopoietic stem cell transplantation reveals novel genomic loci associated with transplant outcomes.docx

IntroductionData on genomic susceptibility for adverse outcomes after hematopoietic stem cell transplantation (HSCT) for recipients are scarce.MethodsWe performed a genome wide association study (GWAS) to identify genes associated with survival/mortality, relapse, and severe graft-versus-host disease (sGvHD), fitting proportional hazard and subdistributional models to data of n=1,392 recipients of European ancestry from three centres.ResultsThe single nucleotide polymorphism (SNP) rs17154454, intronic to the neuronal growth guidant semaphorin 3C gene (SEMA3C), was genome-wide significantly associated with event-free survival (p=7.0x10-8) and sGvHD (p=7.5x10-8). Further associations were detected for SNPs in the Paxillin gene (PXN) with death without prior relapse or sGvHD, as well as for SNPs of the Plasmacytoma Variant Translocation 1 gene (PVT1, a long non-coding RNA gene), the Melanocortin 5 Receptor (MC5R) gene and the WW Domain Containing Oxidoreductase gene (WWOX), all associated with the occurrence of sGvHD. Functional considerations support the observed associations.DiscussionThus, new genes were identified, potentially influencing the outcome of HSCT.</p

DataSheet1_Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.docx

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.</p

Comparison of study designs, selected epidemiologic variables, genotyping platforms and results.

†<p>After implementing data quality measures described in methods.</p

Comparison of odds ratios for acetylcholine receptor pathway showing.

<p>A) the most significant SNP for each gene used in Central Europe-Toronto analysis and odds ratios for same SNPs for Germany MD Anderson); B) the most significant SNP assigned to each gene in either data set (i.e., the actual SNPs used in pathway analyses in the two data sets). Chromosome number (Chr) and genes for both graphs are shown on left. (Central Europe – Toronto SNPs: solid fill, Germany MD Anderson matching SNPs: no fill; Germany MD Anderson top SNP (differing from Central Europe-Toronto): grey fill). A) Reference allele same in both Central Europe-Toronto and Germany-MD Anderson but chosen to show positive association for Central Europe-Toronto. B) Reference allele always chosen to show positive association. <i>CHRNA5</i> is excluded as SNPs are identical to those representing <i>CHRNA3</i>. Odds ratios adjusted for age, sex and country of study.</p

Number of significant pathway associations (using FDR< = 0.05) for Central Europe-Toronto (CETO) and Germany-MD Anderson (GRMD) by pathway analysis method.

<p>Number of significant pathway associations (using FDR< = 0.05) for Central Europe-Toronto (CETO) and Germany-MD Anderson (GRMD) by pathway analysis method.</p