Comparison of genotype- and haplotype-based approaches for fine-mapping of alcohol dependence using COGA data

It is generally assumed that the detection of disease susceptibility genes via fine-mapping association study is facilitated by consideration of marker haplotypes. In this study, we compared the performance of genotype-based and haplotype-based association studies using the Collaborative Study of Genetics of Alcoholism dataset, on several chromosomal regions showing evidence for linkage with ALDX1. After correction for multiple testing, the most significant results were observed with the genotype-based analyses on two regions of chromosomes 2 and 7. Interestingly, the analyses results from this dataset showed that there was no advantage of the haplotype-based analyses over genotype-based (single-locus) analyses. However, caution should be taken when generalizing these results to other chromosomal regions or to other populations

Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity

We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model

Genome-wide linkage analysis of systolic blood pressure slope using the Genetic Analysis Workshop 13 data sets

Systolic blood pressure (SBP) is an age-dependent complex trait for which both environmental and genetic factors may play a role in explaining variability among individuals. We performed a genome-wide scan of the rate of change in SBP over time on the Framingham Heart Study data and one randomly selected replicate of the simulated data from the Genetic Analysis Workshop 13. We used a variance-component model to carry out linkage analysis and a Markov chain Monte Carlo-based multiple imputation approach to recover missing information. Furthermore, we adopted two selection strategies along with the multiple imputation to deal with subjects taking antihypertensive treatment. The simulated data were used to compare these two strategies, to explore the effectiveness of the multiple imputation in recovering varying degrees of missing information, and its impact on linkage analysis results. For the Framingham data, the marker with the highest LOD score for SBP slope was found on chromosome 7. Interestingly, we found that SBP slopes were not heritable in males but were for females; the marker with the highest LOD score was found on chromosome 18. Using the simulated data, we found that handling treated subjects using the multiple imputation improved the linkage results. We conclude that multiple imputation is a promising approach in recovering missing information in longitudinal genetic studies and hence in improving subsequent linkage analyses

Genome-wide association analyses of North American Rheumatoid Arthritis Consortium and Framingham Heart Study data utilizing genome-wide linkage results

The power of genome-wide association studies can be improved by incorporating information from previous study findings, for example, results of genome-wide linkage analyses. Weighted false-discovery rate (FDR) control can incorporate genome-wide linkage scan results into the analysis of genome-wide association data by assigning single-nucleotide polymorphism (SNP) specific weights. Stratified FDR control can also be applied by stratifying the SNPs into high and low linkage strata. We applied these two FDR control methods to the data of North American Rheumatoid Arthritis Consortium (NARAC) study and the Framingham Heart Study (FHS), combining both association and linkage analysis results. For the NARAC study, we used linkage results from a previous genome scan of rheumatoid arthritis (RA) phenotype. For the FHS study, we obtained genome-wide linkage scores from the same 550 k SNP data used for the association analyses of three lipids phenotypes (HDL, LDL, TG). We confirmed some genes previously reported for association with RA and lipid phenotypes. Stratified and weighted FDR methods appear to give improved ranks to some of the replicated SNPs for the RA data, suggesting linkage scan results could provide useful information to improve genome-wide association studies

Clinical–pathologic significance of cancer stem cell marker expression in familial breast cancers

Human breast cancer cells with a CD44(+)/CD24(−/low) or ALDH1+ phenotype have been demonstrated to be enriched for cancer stem cells (CSCs) using in vitro and in vivo techniques. The aim of this study was to determine the association between CD44(+)/CD24(−/low) and ALDH1 expression with clinical–pathologic tumor characteristics, tumor molecular subtype, and survival in a well characterized collection of familial breast cancer cases. 364 familial breast cancers from the Ontario Familial Breast Cancer Registry (58 BRCA1-associated, 64 BRCA2-associated, and 242 familial non-BRCA1/2 cancers) were studied. Each tumor had a centralized pathology review performed. TMA sections of all tumors were analyzed for the expression of ER, PR, HER2, CK5, CK14, EGFR, CD44, CD24, and ALDH1. The Chi square test or Fisher’s exact test was used to analyze the marker associations with clinical–pathologic tumor variables, molecular subtype and genetic subtype. Analyses of the association of overall survival (OS) with marker status were conducted using Kaplan–Meier plots and log-rank tests. The CD44(+)/CD24(−/low) and ALDH1+ phenotypes were identified in 16% and 15% of the familial breast cancer cases, respectively, and associated with high-tumor grade, a high-mitotic count, and component features of the medullary type of breast cancer. CD44(+)/CD24(−/low) and ALDH1 expression in this series were further associated with the basal-like molecular subtype and the CD44(+)/CD24(−/low) phenotype was independently associated with BRCA1 mutational status. The currently accepted breast CSCs markers are present in a minority of familial breast cancers. Whereas the presence of these markers is correlated with several poor prognostic features and the basal-like subtype of breast cancer, they do not predict OS

The interaction of PP1 with BRCA1 and analysis of their expression in breast tumors

<p>Abstract</p> <p>Background</p> <p>The breast cancer susceptibility gene, <it>BRCA1</it>, is implicated in multiple cellular processes including DNA repair, the transactivation of genes, and the ubiquitination of proteins; however its precise functions remain to be fully understood. Identification and characterization of BRCA1 protein interactions may help to further elucidate the function and regulation of BRCA1. Additionally, detection of changes in the expression levels of <it>BRCA1 </it>and its interacting proteins in primary human breast tumors may further illuminate their role in the development of breast cancer.</p> <p>Methods</p> <p>We performed a yeast two-hybrid study to identify proteins that interact with exon11 of BRCA1 and identified Protein Phosphatase 1β (PP1β), an isoform of the serine threonine phosphatase, PP1. GST-pull down and co-immunoprecipitation assays were performed to further characterize this interaction. Additionally, Real-Time PCR was utilized to determine the expression of <it>BRCA1</it>, <it>PP1</it>α, β and γ in primary human breast tumors and normal breast tissue to identify alterations in the expression of these genes in breast cancer.</p> <p>Results</p> <p>PP1 and BRCA1 co-immunoprecipitate and the region within BRCA1 as well as the specific PP1 interacting domain mediating this interaction were identified. Following mRNA expression analysis, we identified low levels of <it>BRCA1 </it>and variable levels of <it>PP1</it>α and β in primary sporadic human breast tumors. Furthermore, BRCA1, <it>PP1</it>β and PP1γ were significantly higher in normal tissue specimens (BRCA1 p = 0.01, <it>PP1</it>β: p = 0.03, <it>PP1</it>γ, p = 1.9 × 10^-6) compared to sporadic breast tumor samples. Interestingly, we also identified that ER negative tumors are associated with low levels of <it>PP1</it>α expression.</p> <p>Conclusion</p> <p>The identification and characterization of the interaction of BRCA1 with PP1 and detection of changes in the expression of <it>PP1 </it>and genes encoding other BRCA1 associated proteins identifies important genetic pathways that may be significant to breast tumorigenesis. Alterations in the expression of genes, particularly phosphatases that operate in association with BRCA1, could negatively affect the function of BRCA1 or BRCA1 associated proteins, contributing to the development of breast cancer.</p

Expression profiling of familial breast cancers demonstrates higher expression of FGFR2 in BRCA2-associated tumors

BackgroundBRCA1- and BRCA2-associated tumors appear to have distinct molecular signatures. BRCA1-associated tumors are predominantly basal-like cancers, whereas BRCA2-associated tumors have a predominant luminal-like phenotype. These two molecular signatures reflect in part the two cell types found in the terminal duct lobular unit of the breast. To elucidate novel genes involved in these two spectra of breast tumorigenesis we performed global gene expression analysis on breast tumors from germline BRCA1 and BRCA2 mutation carriers. Methodology Breast tumor RNAs from 7 BRCA1 and 6 BRCA2 mutation carriers were profiled using UHN human 19K cDNA microarrays. Supervised univariate analyses were conducted to identify genes differentially expressed between BRCA1 and BRCA2-associated tumors. Selected discriminatory genes were validated using real time reverse transcription polymerase chain reaction in the tumor RNAs, and/or by immunohistochemistry (IHC) or by in situ hybridization (ISH) on tissue microarrays (TMAs) containing an independent set of 58 BRCA1 and 64 BRCA2-associated tumors. Results Genes more highly expressed in BRCA1-associated tumors included stathmin, osteopontin, TGFβ2 and Jagged 1 in addition to genes previously identified as characteristic of basal-like breast cancers. BRCA2-associated cancers were characterized by the higher relative expression of FGF1 and FGFR2. FGFR2 protein was also more highly expressed in BRCA2-associated cancers (P = 0.004). SignificanceBRCA1-associated tumours demonstrated increased expression of component genes of the Notch and TGFβ pathways whereas the higher expression of FGFR2 and FGF1 in BRCA2-associated cancers suggests the existence of an autocrine stimulatory loop

An efficient Monte Carlo method for the computation of likelihoods in genetic linkage analysis

grantor: University of TorontoIn genetic linkage analysis, the aim is to locate genes contributing to a trait, by analysing the evidence for cosegregation of the trait with known genetic markers within a pedigree. This is done by testing for linkage with known markers for candidate genes. In any genetic model, the trait of interest is modelled against some genotypic values or parameters and, by using statistical methods, evidence of linkage is assessed and the model parameters are estimated. The common approach to statistical inference in genetics is the likelihood function. Since only the phenotypes of the individuals are observed, calculation of likelihoods involves consideration of all compatible configurations of genotypes. The number of compatible genotypic configurations on a pedigree is immense. It increases as the size of the pedigree and the number of markers increase. Thus many of the likelihoods are computationally infeasible. Where exact likelihoods cannot be computed, Monte Carlo estimates of likelihoods may provide a satisfactory alternative. Two basic approaches are contemplated in genetic linkage analysis. One is the Simple Random Sampling and the other is Gibbs Sampling. In the simple random sampling without conditioning on data, the likelihood is estimated by simulating genotypes from its genotype distribution under the model and averaging the values of the penetrance probabilities, probabilities of data given genotypes, for the realized values of genotypes. This does not work well since the realized genotypes are almost certain to be inconsistent with data, phenotypes. In this thesis, we investigate the feasibility of a new simple random Monte Carlo method for calculating the likelihood. In our method, the sampling of genotypes is done without conditioning on data. A form of importance sampling is used to calculate the likelihood. The efficiency of this approach is studied both theoretically and empirically. Our method is developed for Mendelian traits mapped against a map of linked markers. The method is applied to simple and complex pedigrees. Comparative studies of linkage analyses are performed at the end that compare our method to other methods that have been used.Ph.D

Fine-mapping results from family-based association tests with SNP genotype and SNP haplotypes for region 1 of chromosome 2, 7, and 11

<p><b>Copyright information:</b></p><p>Taken from "Comparison of genotype- and haplotype-based approaches for fine-mapping of alcohol dependence using COGA data"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S65-S65.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866717.</p><p></p> -Values are adjusted for multiple testing by FDR. Mean LD values for each 3-SNPs window are also shown

Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity

Abstract We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model