Genome-wide linkage analysis for aggressive prostate cancer in Utah high risk pedigrees

posterResearch has consistently shown that genetics plays a critical role in prostate cancer (CaP) development, but the identification of CaP genes has proven to be very difficult. Hereditary prostate cancer is a complex disease involving numerous genes and variable phenotypic expression. This heterogeneity has led researchers to pursue genes associated with alternative phenotypes for CaP, such as tumor aggressiveness. Several recent linkage studies have used clinical and pathological data to define CaP aggressiveness as a qualitative trait. The International Consortium for Prostate Cancer Genetics (ICPCG) recently completed such an analysis using pooled data from 11 member institutions. This analysis required all families be small to moderate in size in order to facilitate standard linkage analysis software. Hence, although the ICPCG analysis included data from the Utah prostate cancer pedigree resource, the Utah pedigrees were not analyzed in their complete form. Specifically, pedigrees were divided and trimmed before analysis, which reduced the power of the analysis to detect predisposition loci. Here we present the results of a genome-wide scan for aggressive prostate cancer predisposition loci utilizing the full Utah pedigrees

Genetic susceptibility of prostate cancer: genome-wide screen of ""non-aggressive"" disease

posterResearch has consistently shown that genetics plays a critical role in prostate cancer (CaP) development, but the identification of CaP genes has proven to be very difficult. Hereditary prostate cancer is a complex disease believed to involve numerous genes and variable penetrance. It has been proposed that studying alternative, highly homogenous phenotypes related to CaP may be a solution for overcoming the apparent heterogeneity that has hindered the identification of susceptibility genes. Several recent studies have applied this idea to "aggressive" or "clinically significant" cases of CaP. Using the resources of the Utah Population Database, we identified two phenotypes often associated with non-aggressive disease that show significant familiality. We present those results here

SumLINK statistic for linkage analysis: application to the ICPCG pooled linkage resource

posterWe propose a novel, genome-wide, linkagebased statistic, "sumLINK," for identification of disease susceptibility loci. Our approach focuses primarily on "linked" pedigrees (those with pedigree-specific LOD ? 0.588; equivalent to unadjusted p ? 0.05) to identify regions of extreme consistency across powerful pedigrees. The sumLINK statistic is simply the sum of multipoint LOD scores for linked pedigrees at a given point in the genome. The genetic factors underlying many complex human traits are poorly understood. Linkage findings are often difficult to replicate, and localizing the genes responsible for linkage signals is challenging. We believe that focusing on individually powerful pedigrees may give the greatest opportunity to identify and localize true susceptibility loci and the underlying genes

Survey of excess familiality in prostate cancer

posterProstate cancer (PCa) is the most commonly diagnosed cancer among men, and has long been recognized to occur in familial clusters. However, identification of genes predisposing individuals to prostate cancer has been difficult. Putative PCa predisposition loci identified by genetic linkage have been reported on almost all chromosomes, but successful confirmation reports have been rare. PCa is a complex disease likely involving multiple genes and variable phenotypic expression. As a step toward understanding PCa heterogeneity, we used the resources of the Utah Population Database to review several PCa-related phenotypes for excess familiality. PCa subgroups that can be shown to have a strong familial component become candidates for linkage analysis and other genetic testing to determine the genetic basis for the observed phenotype

Comparison of linkage analysis methods for genome-wide scanning of extended pedigrees, with application to the TG/HDL-C ratio in the Framingham Heart Study

BACKGROUND: High triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C) jointly increase coronary disease risk. We performed linkage analysis for TG/HDL-C ratio in the Framingham Heart Study data as a quantitative trait, using methods implemented in LINKAGE, GENEHUNTER (GH), MCLINK, and SOLAR. Results were compared to each other and to those from a previous evaluation using SOLAR for TG/HDL-C ratio on this sample. We also investigated linked pedigrees in each region using by-pedigree analysis. RESULTS: Fourteen regions with at least suggestive linkage evidence were identified, including some that may increase and some that may decrease coronary risk. Ten of the 14 regions were identified by more than one analysis, and several of these regions were not previously detected. The best regions identified for each method were on chromosomes 2 (LOD = 2.29, MCLINK), 5 (LOD = 2.65, GH), 7 (LOD = 2.67, SOLAR), and 22 (LOD = 3.37, LINKAGE). By-pedigree multi-point LOD values in MCLINK showed linked pedigrees for all five regions, ranging from 3 linked pedigrees (chromosome 5) to 14 linked pedigrees (chromosome 7), and suggested localizations of between 9 cM and 27 cM in size. CONCLUSION: Reasonable concordance was found across analysis methods. No single method identified all regions, either by full sample LOD or with by-pedigree analysis. Concordance across methods appeared better at the pedigree level, with many regions showing by-pedigree support in MCLINK when no evidence was observed in the full sample. Thus, investigating by-pedigree linkage evidence may provide a useful tool for evaluating linkage regions

PedGenie: an analysis approach for genetic association testing in extended pedigrees and genealogies of arbitrary size

BACKGROUND: We present a general approach to perform association analyses in pedigrees of arbitrary size and structure, which also allows for a mixture of pedigree members and independent individuals to be analyzed together, to test genetic markers and qualitative or quantitative traits. Our software, PedGenie, uses Monte Carlo significance testing to provide a valid test for related individuals that can be applied to any test statistic, including transmission disequilibrium statistics. Single locus at a time, composite genotype tests, and haplotype analyses may all be performed. We illustrate the validity and functionality of PedGenie using simulated and real data sets. For the real data set, we evaluated the role of two tagging-single nucleotide polymorphisms (tSNPs) in the DNA repair gene, NBS1, and their association with female breast cancer in 462 cases and 572 controls selected to be BRCA1/2 mutation negative from 139 high-risk Utah breast cancer families. RESULTS: The results from PedGenie were shown to be valid both for accurate p-value calculations and consideration of pedigree structure in the simulated data set. A nominally significant association with breast cancer was observed with the NBS1 tSNP rs709816 for carriage of the rare allele (OR = 1.61, 95% CI = 1.10–2.35, p = 0.019). CONCLUSION: PedGenie is a flexible and valid statistical tool that is intuitively simple to understand, makes efficient use of all the data available from pedigrees without requiring trimming, and is flexible to the types of tests to which it can be applied. Further, our analyses of real data indicate NBS1 may play a role in the genetic etiology of heritable breast cancer

A cautionary note on the appropriateness of using a linkage resource for an association study

BACKGROUND: Utilizing a linkage resource for association analysis requires consideration both of the marker data used and correlations among relatives in pedigrees. We previously developed a method for association testing in pedigrees. We applied our method to 50 replicates of microsatellite data surrounding five genes involved in high-density lipoprotein (HDL) in the Genetic Analysis Workshop 13 (GAW13) simulated data and examined association with HDL as well as linkage disequilibrium (LD) between markers. RESULTS: Although no association was intentionally simulated, we found significant evidence of weak LD between microsatellite markers (flanking/~5 cM from the genes), in some but not all replicates. This level of LD compared well to that observed in the real GAW13 Framingham data. Only one region had sufficient replicates to assess power, and this was low (12.5–20.8%). More power was attained using all individuals and accounting for relationships, compared with one independent individual/pedigree, although this was not significant due to small sample sizes. Not accounting for relatedness inflated statistical significance (p < 0.0001). CONCLUSION: A correction for dependence is necessary in association studies to avoid an inflation of significance probabilities. Our results further illustrate that use of microsatellite marker data is not an effective approach for association testing