TY  - JOUR
AB  - INTRODUCTION: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. METHODS: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). RESULTS: Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. CONCLUSION: Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.
AU  - Brugger, M.
AU  - Lutz, M.
AU  - Müller-Nurasyid, M.
AU  - Lichtner, P.
AU  - Slater, E.P.*
AU  - Matthäi, E.*
AU  - Bartsch, D.K.*
AU  - Strauch, K.
C1  - 70605
C2  - 55595
SP  - 8-31
TI  - Joint linkage and association analysis using GENEHUNTER-MODSCORE with an application to familial pancreatic cancer.
JO  - Hum. Hered.
VL  - 89
IS  - 1
PY  - 2024
SN  - 0001-5652
ER  - 

TY  - JOUR
AU  - Katsoula, G.
AU  - Southam, L.
AU  - Steinberg, J.
AU  - Barysenska, A.
AU  - Wilkinson, J.M.*
AU  - Zeggini, E.
C1  - 59809
C2  - 49054
CY  - Allschwilerstrasse 10, Ch-4009 Basel, Switzerland
SP  - 213-213
TI  - A comprehensive transcriptional map of knee osteoarthritis.
JO  - Hum. Hered.
VL  - 84
IS  - 4-5
PB  - Karger
PY  - 2020
SN  - 0001-5652
ER  - 

TY  - JOUR
AU  - Macdonald-Dunlop, E.*
AU  - Joshi, P.*
AU  - Peters, J.*
AU  - Folkersen, L.*
AU  - Ingelsson, E.*
AU  - Timmers, P.*
AU  - Michaelsson, K.*
AU  - Gustafsson, S.*
AU  - Enroth, S.*
AU  - Johansson, A.*
AU  - Smith, G.*
AU  - Zhernakova, D.*
AU  - Siegbahn, A.*
AU  - Kalnapenkis, A.*
AU  - Eriksson, N.*
AU  - Wheeler, E.*
AU  - Fu, J.*
AU  - Franke, L.*
AU  - Hayward, C.*
AU  - Wallentin, L.*
AU  - Esko, T.*
AU  - Zeggini, E.
AU  - Teunissen, C.*
AU  - Langenberg, C.*
AU  - Hansson, O.*
AU  - Eriksson, P.*
AU  - Gyllensten, U.*
AU  - Butterworth, A.*
AU  - Mälarstig, A.*
AU  - Wilson, J.*
C1  - 59811
C2  - 49056
CY  - Allschwilerstrasse 10, Ch-4009 Basel, Switzerland
SP  - 216-217
TI  - Genomic architecture of 184 plasma proteins in 18,884 individuals: The SCALLOP consortium.
JO  - Hum. Hered.
VL  - 84
IS  - 4-5
PB  - Karger
PY  - 2020
SN  - 0001-5652
ER  - 

TY  - JOUR
AU  - Png, G.
AU  - Barysenska, A.
AU  - Tsafantakis, E.*
AU  - Karaleftheri, M.*
AU  - Dedoussis, G.*
AU  - Gilly, A.
AU  - Zeggini, E.
C1  - 59808
C2  - 49053
CY  - Allschwilerstrasse 10, Ch-4009 Basel, Switzerland
SP  - 220-220
TI  - Exploring the genetic architecture of the human neurological proteome using whole genome sequencing.
JO  - Hum. Hered.
VL  - 84
IS  - 4-5
PB  - Karger
PY  - 2020
SN  - 0001-5652
ER  - 

TY  - JOUR
AB  - BACKGROUND/AIMS: Theoretically, the trait-model parameters (disease allele frequency and penetrance function) can be estimated without bias in a MOD score linkage analysis. We aimed to practically evaluate the MOD score approach regarding its ability to provide unbiased trait-model parameters for various pedigree-type and trait-model scenarios. We further investigated the ability of the MOD score approach to detect imprinting using affected sib pairs (ASPs) and affected half-sib pairs (AHSPs) when all parental genotypes are missing. METHODS: Simulated pedigree data were analyzed using the GENEHUNTER-MODSCORE software package. Parameter estimation performance in terms of bias and variability was evaluated with regard to trait-model type and pedigree complexity. RESULTS: Generally, parameters were estimated with lower bias and variability with increasing pedigree complexity, especially for recessive and overdominant models. However, dominant and additive models could hardly be distinguished even when using 3-generation pedigrees. Imprinting could clearly be detected for mixtures of mainly ASPs and only few AHSPs with the common parent of the imprinted sex, even though no parental genotypes were available. CONCLUSION: Our results provide guidance to researchers regarding the possibility to estimate trait-model parameters by a MOD score analysis, including the degree of imprinting, with certain types of pedigrees.
AU  - Brugger, M.
AU  - Rospleszcz, S.
AU  - Strauch, K.
C1  - 52329
C2  - 43918
CY  - Basel
SP  - 103-139
TI  - Estimation of trait-model parameters in a MOD score linkage analysis.
JO  - Hum. Hered.
VL  - 82
IS  - 3-4
PB  - Karger
PY  - 2016
SN  - 0001-5652
ER  - 

TY  - JOUR
AU  - Dankowski, T.*
AU  - Buck, D.*
AU  - Andlauer, T.F.*
AU  - Antony, G.*
AU  - Bayas, A.*
AU  - Bechmann, L.*
AU  - Berthele, A.*
AU  - Bettecken, T.*
AU  - Chan, A.*
AU  - Franke, A.*
AU  - Gold, R.*
AU  - Graetz, C.*
AU  - Haas, J.*
AU  - Hecker, M.*
AU  - Herms, S.*
AU  - Hohlfeld, R.*
AU  - Infante-Duarte, C.*
AU  - Jöckel, K.-H.*
AU  - Kieseier, B.C.*
AU  - Knier, B.*
AU  - Knop, M.*
AU  - Lichtner, P.
AU  - Lieb, W.*
AU  - Lill, C.M.*
AU  - Limmroth, V.*
AU  - Linker, R.A.*
AU  - Loleit, V.*
AU  - Meuth, S.G.*
AU  - Moebus, S.*
AU  - Müller-Myhsok, B.*
AU  - Nischwitz, S.*
AU  - Noethen, M.M.*
AU  - Friedemann, P.*
AU  - Puetz, M.*
AU  - Ruck, T.*
AU  - Salmen, A.*
AU  - Stangel, M.*
AU  - Stellmann, J.*
AU  - Strauch, K.
AU  - Stuerner, K.H.*
AU  - Tackenberg, B.*
AU  - Bergh, F.T.*
AU  - Tumani, H.*
AU  - Waldenberger, M.
AU  - Weber, F.*
AU  - Wiend, H.*
AU  - Wildemann, B.*
AU  - Zettl, U.K.*
AU  - Ziemann, U.*
AU  - Zipp, F.*
AU  - Hemmer, B.*
AU  - Ziegler, A.*
C1  - 44466
C2  - 36964
CY  - Basel
SP  - 32-33
TI  - Exome array GWAS in 10,000 Germans identifies association between MUC22 and multiple sclerosis.
JO  - Hum. Hered.
VL  - 79
IS  - 1
PB  - Karger
PY  - 2015
SN  - 0001-5652
ER  - 

TY  - JOUR
AB  - OBJECTIVE: As the mode of inheritance is often unknown for complex diseases, a MOD-score analysis, in which the parametric LOD score is maximized with respect to the trait-model parameters, can be a powerful approach in genetic linkage analysis. Because the calculation of the disease-locus likelihood is the most time-consuming step in a MOD-score analysis, we aimed to optimize this part of the calculation to speed up linkage analysis using the GENEHUNTER-MODSCORE software package. METHODS: Our new algorithm is based on minimizing the effective number of inheritance vectors by collapsing them into classes. To this end, the disease-locus-likelihood contribution of each inheritance vector is represented and stored in its algebraic form as a symbolic sum of products of penetrances and disease-allele frequencies. Simulations were used to assess the speedup of our new algorithm. RESULTS: We were able to achieve speedups ranging from 1.94 to 11.52 compared to the original GENEHUNTER-MODSCORE version, with higher speedups for larger pedigrees. When calculating p values, the speedup ranged from 1.69 to 10.36. CONCLUSION: Computation times for MOD-score analysis, involving the evaluation of many tested sets of trait-model parameters and p value calculation, have been prohibitively high so far. With our new algebraic algorithm, such an analysis is now feasible within a reasonable amount of time.
AU  - Brugger, M.
AU  - Strauch, K.
C1  - 43211
C2  - 36311
CY  - Basel
SP  - 179-194
TI  - Fast linkage analysis with MOD scores using algebraic calculation.
JO  - Hum. Hered.
VL  - 78
IS  - 3-4
PB  - Karger
PY  - 2014
SN  - 0001-5652
ER  - 

TY  - JOUR
AB  - Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.
AU  - Freytag, S.*
AU  - Manitz, J.*
AU  - Schlather, M.*
AU  - Kneib, T.*
AU  - Amos, C.I.*
AU  - Risch, A.*
AU  - Chang-Claude, J.*
AU  - Heinrich, J.
AU  - Bickeböller, H.*
C1  - 29163
C2  - 31675
CY  - Basel
SP  - 64-75
TI  - A network-based kernel kachine test for the identification of risk pathways in genome-wide association studies.
JO  - Hum. Hered.
VL  - 76
IS  - 2
PB  - Karger
PY  - 2014
SN  - 0001-5652
ER  - 

TY  - JOUR
AU  - Fuchs, C.
C1  - 31118
C2  - 34159
CY  - Basel
SP  - 100-101
TI  - Estimation of cell-to-cell regulatory heterogeneities from cell populations.
JO  - Hum. Hered.
VL  - 76
IS  - 2
PB  - Karger
PY  - 2013
SN  - 0001-5652
ER  - 

TY  - JOUR
AU  - Sharapov, S.*
AU  - Tsepilov, Y.*
AU  - Ried, J.S.
AU  - Strauch, K.
AU  - Gieger, C.
AU  - Aulchenko, Y.*
C1  - 31117
C2  - 34299
CY  - Basel
SP  - 94-95
TI  - Genome-wide environmental sensitivity analysis of human metabolomics data.
JO  - Hum. Hered.
VL  - 76
IS  - 2
PB  - Karger
PY  - 2013
SN  - 0001-5652
ER  - 

TY  - JOUR
AU  - Tsepilov, Y.A.*
AU  - Shin, S.*
AU  - Soranzo, N.*
AU  - Spector, T.*
AU  - Strauch, K.
AU  - Gieger, C.
AU  - Aulchenko, Y.*
AU  - Ried, J.S.
C1  - 31119
C2  - 34298
CY  - Basel
SP  - 94
TI  - Genome-wide scan of metabolomics data using non-additive intra-locus models.
JO  - Hum. Hered.
VL  - 76
IS  - 2
PB  - Karger
PY  - 2013
SN  - 0001-5652
ER  - 

TY  - JOUR
AB  - Objective: We present a parametric method for linkage analysis of quantitative phenotypes. The method provides a test for linkage as well as an estimate of different phenotype parameters. We have implemented our new method in the program GENEHUNTER-QMOD and evaluated its properties by performing simulations. Methods: The phenotype is modeled as a normally distributed variable, with a separate distribution for each genotype. Parameter estimates are obtained by maximizing the LOD score over the normal distribution parameters with a gradient-based optimization called PGRAD method. Results: The PGRAD method has lower power to detect linkage than the variance components analysis (VCA) in case of a normal distribution and small pedigrees. However, it outperforms the VCA and Haseman-Elston regression for extended pedigrees, nonrandomly ascertained data and non-normally distributed phenotypes. Here, the higher power even goes along with conservativeness, while the VCA has an inflated type I error. Parameter estimation tends to underestimate residual variances but performs better for expectation values of the phenotype distributions. Conclusion: With GENEHUNTER-QMOD, a powerful new tool is provided to explicitly model quantitative phenotypes in the context of linkage analysis. It is freely available at http://www.helmholtz-muenchen.de/genepi/downloads.
AU  - Künzel, T.*
AU  - Strauch, K.
C1  - 10651
C2  - 30443
SP  - 208-219
TI  - Parameter estimation and quantitative parametric linkage analysis with GENEHUNTER-QMOD.
JO  - Hum. Hered.
VL  - 73
IS  - 4
PB  - Karger
PY  - 2012
SN  - 0001-5652
ER  - 

TY  - JOUR
AB  - Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.
AU  - Ma, J.Z.*
AU  - Xiao, F.F.*
AU  - Xiong, M.*
AU  - Andrew, A.S.*
AU  - Brenner, H.*
AU  - Duell, E.J.*
AU  - Haugen, A.*
AU  - Hoggart, C.*
AU  - Hung, R.J.*
AU  - Lazarus, P.*
AU  - Liu, C.L.*
AU  - Matsuo, K.*
AU  - Mayordomok, J.I.*
AU  - Schwartz, A.G.*
AU  - Staratschek-Jox, A.*
AU  - Wichmann, H.-E.
AU  - Yang, P.*
AU  - Amos, C.I.*
C1  - 10650
C2  - 30444
SP  - 185-194
TI  - Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer.
JO  - Hum. Hered.
VL  - 73
IS  - 4
PB  - Karger
PY  - 2012
SN  - 0001-5652
ER  - 

TY  - JOUR
AB  - Objective: Genome-wide association studies have identified robust associations between single nucleotide polymorphisms and complex traits. As the proportion of phenotypic variance explained is still limited for most of the traits, larger and larger meta-analyses are being conducted to detect additional associations. Here we investigate the impact of the study design and the underlying assumption about the true genetic effect in a bimodal mixture situation on the power to detect associations. Methods: We performed simulations of quantitative phenotypes analysed by standard linear regression and dichotomized case-control data sets from the extremes of the quantitative trait analysed by standard logistic regression. Results: Using linear regression, markers with an effect in the extremes of the traits were almost undetectable, whereas analysing extremes by case-control design had superior power even for much smaller sample sizes. Two real data examples are provided to support our theoretical findings and to explore our mixture and parameter assumption. Conclusions: Our findings support the idea to re-analyse the available meta-analysis data sets to detect new loci in the extremes. Moreover, our investigation offers an explanation for discrepant findings when analysing quantitative traits in the general population and in the extremes.
AU  - Pütter, C.*
AU  - Pechlivanis, S.*
AU  - Nöthen, M.M.*
AU  - Jöckel, K.-H.*
AU  - Wichmann, H.-E.
AU  - Scherag, A.*
C1  - 6364
C2  - 29253
SP  - 173-181
TI  - Missing heritability in the tails of quantitative traits? A simulation study on the impact of slightly altered true genetic models.
JO  - Hum. Hered.
VL  - 72
IS  - 3
PB  - Karger
PY  - 2011
SN  - 0001-5652
ER  - 

TY  - JOUR
AB  - Objective: To evaluate the relevance and necessity to account for the effects of population substructure on association studies under a case-control design in central Europe, we analysed three samples drawn from different geographic areas of Germany. Two of the three samples, POPGEN (n = 720) and SHIP (n = 709), are from north and north-east Germany, respectively, and one sample, KORA (n = 730), is from southern Germany. Methods: Population genetic differentiation was measured by classical F-statistics for different marker sets, either consisting of genome-wide selected coding SNPs located in functional genes, or consisting of selectively neutral SNPs from 'genomic deserts'. Quantitative estimates of the degree of stratification were performed comparing the genomic control approach [Devlin B, Roeder K: Biometrics 1999;55:997-1004], structured association [Pritchard JK, Stephens M, Donnelly P: Genetics 2000;155:945-959] and sophisticated methods like random forests [Breiman L: Machine Learning 2001;45:5-32]. Results: F-statistics showed that there exists a low genetic differentiation between the samples along a north-south gradient within Germany (FST(KORA/POPGEN): 1.7·10-4; FST(KORA/SHIP): 5.4·10 -4; FST(POPGEN/SHIP): -1.3·10-5). Conclusion: Although the FST-values are very small, indicating a minor degree of population structure, and are too low to be detectable from methods without using prior information of subpopulation membership, such as STRUCTURE [Pritchard JK, Stephens M, Donnelly P: Genetics 2000;155:945-959], they may be a possible source for confounding due to population stratification. Copyright © 2006 S. Karger AG.
AU  - Steffens, M.*
AU  - Lamina, C.
AU  - Illig, T.
AU  - Bettecken, T.
AU  - Vogler, R.*
AU  - Entz, P.*
AU  - Suk, E.K.*
AU  - Toliat, M.R.*
AU  - Klopp, N.
AU  - Caliebe, A.*
AU  - König, I.R.*
AU  - Köhler, K.*
AU  - Lüdemann, J.*
AU  - Diaz, Lacava, A.*
AU  - Fimmers, R.*
AU  - Lichtner, P.
AU  - Ziegler, A.*
AU  - Wolf, A.*
AU  - Krawczak, M.*
AU  - Nürnberg, P.*
AU  - Hampe, J.*
AU  - Schreiber, S.*
AU  - Meitinger, T.
AU  - Wichmann, H.-E.
AU  - Roeder, K.*
AU  - Wienker, T.F.*
AU  - Baur, M.P.*
C1  - 3868
C2  - 24056
SP  - 20-29
TI  - SNP-based analysis of genetic substructure in the Germany population.
JO  - Hum. Hered.
VL  - 62
IS  - 1
PY  - 2006
SN  - 0001-5652
ER  -