doi: 10.1111/j.1469-1809.2009.00547.x European Mathematical Genetics Meeting, Munich, Germany, 14 th –15 th May 2009 Testing for genetic association in the presence of linkage Jeanine J. Houwing-Duistermaat, J. Lebrec and A. Callegaro Dept of Medical Statistics and Bioinformatics, LUMC, Netherlands j.j.houwing@lumc.nl When dense single nucleotide polymorphism arrays have been typed in families, a strategy may be to first perform linkage analysis followed by association testing in regions showing linkage. We consider data on nuclear families, where parental genotypes are available. For this design, test statistics for association in the presence of linkage have been proposed for quantitative traits based on normally distributed random effects (Abecasis et al.) and for binary (Jonasdottir et al.) and survival (Zhong et al.) data using gamma distributed random effects. We derive test statistics for general phenotypes based on the generalized linear models framework where correlation between relatives is modeled using normally distributed random effects. We extend the methods developed by Lebrec et al. for linkage analysis of general phenotypes by adding a genotypic effect to the mean. For known identical by descent (IBD) status the variance of the test statistic is computed. When uncertainty in IBD exists we propose to estimate the variance empirically. The new score statistic appears to be a weighted FBAT statistic. The performance of the new test statistic with respect to the standard FBAT statistic is studied by means of simulation. As illustration the test statistic is applied to data on Rheumatoid Arthritis from the NARAC study (GAW15, Witte et al.). From our simulations it appears that weighting by the IBD information only slightly improves the power. Improvement of power is obtained when the variance of the test-statistic is computed for situations where the IBD is known. References Abecasis, G., Cardon, L. & Cookson,W. (2000) Am J of Hum Genet 66, 279–292. Jonasdottir, G., Humphreys, K. & Palmgren, J. (2007). Genet Epidemiol 31, 528–540. Lebrec, J. & Houwelingen, H. (2007) Human Heridity 64, 5–15. Witte, J.H., Schnell, A.H., Cordell, H.J., Almasy, L. & MacCCluer, J.W. (2007) Genet Epidemiol 31, S1 Zhong, X. & Li, H. (2004). Biostatistics 5, 307–327. Bivariate linkage analysis for mapping quantitative trait loci in pedigrees: Comparison of methods and assessment of the test statistics distributions in the NEMO study Aude Saint-Pierre 1 , Brigitte Mangin 2 and Maria Martinez 1 1 INSERM U563, France; 2 INRA UR875, France The use of correlated phenotypes may increase the power to map the un- derlying quantitiative loci (QTL). One popular approach is the multivariate variance components, a generalization of the univariate variance components method (Amos, 1994; Blangero et al., 1997). Several issues make the linkage analysis of multiple phenotypes more complicated than those of univariate phenotypes. The asymptotic distributions of the multivariate variance com- ponents methods are subtle and nontrivial, mainly because of the dimension reduction of the parameter space under the null hypothesis and the non- negative constraints on some of the linkage components. Existing claims are indeed contradictory (Almasy et al., 1997, de Andrade et al., 1997; Amos et al., 2001) and some might be erroneous (Wang K, 2002; Mangin, per- sonnal communication). Further, estimation, through simulations, of the null distribution faces the problem of extensive time-consuming computations, especially in extended pedigrees and when using multiple markers. As a con- sequence, multivariate linkage studies have most often relied on assumed asymptotic distributions rather than on empirical ones. Other approaches, as Principal Component Analysis, permit to reduce the dimensionality of the data. Multivariate linkage test can be computed using the SPC test (Mangin et al., 1998), which is the sum of independent univariate tests of new pheno- types (linear combinations of raw phenotypes). In practice, however, linkage studies using data reduction techniques have often relied on the significance of the best univariate finding and ignored the multi-test problem. Here, we have applied different multivariate linkage tests to detect QTLs for Bone Mineral Density, measured at the lumbar spine and at the femoral Neck, in NEMO data (103 extended pedigrees). Extensive simulation studies were conducted to study the empirical performances of variance-components and PCA-based linkage tests. Efficient estimates of the empirical distributions were obtained by simulating 12,000 replicates of NEMO data under the null hypothesis. Our analyses highlighted the problem of interpreting nominal p-values of bivariate variance-components linkage tests: some of the sug- gested theoretical distributions resulted to be too liberal while others were found quite conservative. Our results also showed that in the NEMO data both variance components and PCA approaches have similar performances, advocating thus for the use of data-reduction techniques. References Amos (1994) “Robust Variance-Components Approach for Assessing Ge- netic Linkage in Pedigrees”. Am J Hum Genet 54, 535–543. Blangero, J. & Almasy, L. (1997) Multipoint oligogenic linkage analysis of quantitative traits. Genet Epidemiol 14, 959–964. Almasy, L., Dyer, T.D. & Blangero, J. (1997) Bivariate quantitative trait linkage analysis: pleiotropy versus co-incident linkages. Genet Epidemiol 14, 953– 958. de Andrade, M., Thiel, T.J., Yu, L. & Amos, C.I. (1997) Assessing linkage in chromosome 5 using components of variance approach: univariate versus multivariate. Genet Epidemiol 14, 773–778. Amos, C.I., de Andrade, M. & Zhu, D.K. (2001) Comparison of multivariate tests for genetic linkage. Hum Hered 51, 133–144. Wang, K. (2002) Mapping quantitative trait toci using multiple phenotypes in general pedigrees. Hum Hered 54, 57–68. Mangin, B., Thoquet, P. & Grimsley, N. (1998) Pleiotropic QTL analysis. Biometrics 54, 88–89. Comparison of HapMap reference panels for impu- tation of genotype data in genome-wide association studies Denise Brocklebank, Carl Anderson and Andrew Morris ∗ Genetic and Genomic Epidemiology Unit, Wellcome Trust Centre for Human Ge- netics, University of Oxford, Oxford, UK Genome-wide association (GWA) studies of hundreds of thousands of single nucleotide polymorphisms (SNPs) typed on thousands of individuals, such as those performed as part of the Wellcome Trust Case Control Consor- tium (Wellcome Trust Case Control Consortium, 2007), have proved to be extremely successful in identifying common variants contributing modest effects to complex human traits. However, the power of such studies can be increased through the imputation of “unobserved” SNPs which are not 658 Annals of Human Genetics (2009) 73,658–669 C  2009 The Authors Journal compilation C  2009 Blackwell Publishing Ltd/University College London