Statistical Methods for Genome Wide Association Studies

This thesis focus on various statistical methods for analyzing Genome Wide Association data. The thesis include four papers, three of them considers the analysis of complex traits, and the last one a method for analyzing mendelian traits.Although GWAS have identified many associated regions in the genome for many com- plex diseases, there is still much of the genetic heritability that remains unexplained. The power of detecting new genetic risk variants can be improved by considering several genes in the same model.A genetic variant in the HLA region on chromosome 6 is necessary but not sufficient to develop Celiac Disease. In the first two papers we utilize this information to discover additional genetic variants. In Paper I this is done by a method which use the ’Cochran Armitage trend test’, to find a trend in allele frequencies. Simulations are used to evaluate the power of this test compared with the commonly used Pearson 1 df chisquare test and the test is then applied to a previously published Celiac Disease case-control material.In paper II the HLA information is utilized by a stratified TDT, conditioning on the HLA variants. In addition, an imputation-based version of the TDT is presented, as well as a likelihood ratio test searching for two-locus interactions by comparing the heterogeneity and epistasis models. Here the candidates for interaction analysis are chosen by a two-step approach, combining the results from the TDT and prior information from previous studies.In contrast to the approach used in paper II for identifying interactions between genes, in paper 3 we instead consider the method of performing a full Genome Wide Interaction Analysis. By examining how commonly we will find interactions without marginal effects in a GWIA we discuss what conclusions can be drawn from such findings.In the final paper we develop a program locating a region containing a causal gene for rare monogenic traits. This program can be used in large pedigrees with multiple affected cases, and discerns the causal region by coloring them according to how common they are in the population

Statistical Methods for Genome Wide Association Studies

This thesis focus on various statistical methods for analyzing Genome Wide Association data. The thesis include four papers, three of them considers the analysis of complex traits, and the last one a method for analyzing mendelian traits.Although GWAS have identified many associated regions in the genome for many com- plex diseases, there is still much of the genetic heritability that remains unexplained. The power of detecting new genetic risk variants can be improved by considering several genes in the same model.A genetic variant in the HLA region on chromosome 6 is necessary but not sufficient to develop Celiac Disease. In the first two papers we utilize this information to discover additional genetic variants. In Paper I this is done by a method which use the ’Cochran Armitage trend test’, to find a trend in allele frequencies. Simulations are used to evaluate the power of this test compared with the commonly used Pearson 1 df chisquare test and the test is then applied to a previously published Celiac Disease case-control material.In paper II the HLA information is utilized by a stratified TDT, conditioning on the HLA variants. In addition, an imputation-based version of the TDT is presented, as well as a likelihood ratio test searching for two-locus interactions by comparing the heterogeneity and epistasis models. Here the candidates for interaction analysis are chosen by a two-step approach, combining the results from the TDT and prior information from previous studies.In contrast to the approach used in paper II for identifying interactions between genes, in paper 3 we instead consider the method of performing a full Genome Wide Interaction Analysis. By examining how commonly we will find interactions without marginal effects in a GWIA we discuss what conclusions can be drawn from such findings.In the final paper we develop a program locating a region containing a causal gene for rare monogenic traits. This program can be used in large pedigrees with multiple affected cases, and discerns the causal region by coloring them according to how common they are in the population

Estimation of copy number aberrations: Comparison of exome sequencing data with SNP microarrays identifies homozygous deletions of 19q13.2 and iin neuroblastoma

In the pediatric cancer neuroblastoma, analysis of recurrent chromosomal aberrations such as loss of chromosome 1p, 11q, gain of 17q and MYCN amplification are used for patient stratification and subsequent therapy decision making. Different analysis techniques have been used for detection of segmental abnormalities, including fluorescence in situ hybridization (FISH), comparative genomic hybridization (CGH)-microarrays and multiplex ligation-dependent probe amplification (MLPA). However, as next-generation sequencing becomes available for clinical use, this technique could also be used for assessment of copy number alterations simultaneously with mutational analysis. In this study we compare genomic profiles generated through exome sequencing data with profiles generated from high resolution Affymetrix single nucleotide polymorphism (SNP) microarrays on 30 neuroblastoma tumors of different stages. Normalized coverage reads for tumors were calculated using Control-FREEC software and visualized through a web based Shiny application, prior to comparison with corresponding SNP-microarray data. The two methods show high-level agreement for breakpoints and copy number of larger segmental aberrations and numerical aneuploidies. However, several smaller gene containing deletions that could not readily be detected through the SNP-microarray analyses were identified through exome profiling, most likely due to difference between spatial distribution of microarray probes and targeted regions of the exome capture. These smaller aberrations included focal ATRX deletion in two tumors and three cases of novel deletions in chromosomal region 19q13.2 causing homozygous loss of multiple genes including the CIC (Capicua) gene. In conclusion, genomic profiles generated from normalized coverage of exome sequencing show concordance with SNP microarray generated genomic profiles. Exome sequencing is therefore a useful diagnostic tool for copy number variant (CNV) detection in neuroblastoma tumors, especially considering the combination with mutational screening. This enables detection of theranostic targets such as ALK and ATRX together with detection of significant segmental aneuploidies, such as 2p-gain, 17q-gain, 11q-deletion as well as MYCN amplification