Two-locus genome-wide linkage scan for prostate cancer susceptibility genes with an interaction effect

The Relevance of Genomic Signatures at Adhesion GPCR Loci in Humans

Adhesion G protein-coupled receptors (aGPCRs) have a long evolutionary history dating back to very basal unicellular eukaryotes. Almost every vertebrate is equipped with a set of different aGPCRs. Genomic sequence data of several hundred extinct and extant species allows for reconstruction of aGPCR phylogeny in vertebrates and non-vertebrates in general but also provides a detailed view into the recent evolutionary history of human aGPCRs. Mining these sequence sources with bioinformatic tools can unveil many facets of formerly unappreciated aGPCR functions. In this review, we extracted such information from the literature and open public sources and provide insights into the history of aGPCR in humans. This includes comprehensive analyses of signatures of selection, variability of human aGPCR genes, and quantitative traits at human aGPCR loci. As indicated by a large number of genome-wide genotype-phenotype association studies, variations in aGPCR contribute to specific human phenotypes. Our survey demonstrates that aGPCRs are significantly involved in adaptation processes, phenotype variations, and diseases in humans.

Genetic heterogeneity in Finnish hereditary prostate cancer using ordered subset analysis

Prostate cancer (PrCa) is the most common male cancer in developed countries and the second most common cause of cancer death after lung cancer. We recently reported a genome-wide linkage scan in 69 Finnish hereditary PrCa (HPC) families, which replicated the HPC9 locus on 17q21-q22 and identified a locus on 2q37. The aim of this study was to identify and to detect other loci linked to HPC. Here we used ordered subset analysis (OSA), conditioned on nonparametric linkage to these loci to detect other loci linked to HPC in subsets of families, but not the overall sample. We analyzed the families based on their evidence for linkage to chromosome 2, chromosome 17 and a maximum score using the strongest evidence of linkage from either of the two loci. Significant linkage to a 5-cM linkage interval with a peak OSA nonparametric allele-sharing LOD score of 4.876 on Xq26.3-q27 (ΔLOD=3.193, empirical P=0.009) was observed in a subset of 41 families weakly linked to 2q37, overlapping the HPCX1 locus. Two peaks that were novel to the analysis combining linkage evidence from both primary loci were identified; 18q12.1-q12.2 (OSA LOD=2.541, ΔLOD=1.651, P=0.03) and 22q11.1-q11.21 (OSA LOD=2.395, ΔLOD=2.36, P=0.006), which is close to HPC6. Using OSA allows us to find additional loci linked to HPC in subsets of families, and underlines the complex genetic heterogeneity of HPC even in highly aggregated families.

Mitochondrial haplogroups and polymorphisms reveal no association with sporadic prostate cancer in a southern European population

BACKGROUND

It is known that mitochondria play an important role in certain cancers (prostate, renal, breast, or colorectal) and coronary disease. These organelles play an essential role in apoptosis and the production of reactive oxygen species; in addition, mtDNA also reveals the history of populations and ancient human migration. All these events and variations in the mitochondrial genome are thought to cause some cancers, including prostate cancer, and also help us to group individuals into common origin groups. The aim of the present study is to analyze the different haplogroups and variations in the sequence in the mitochondrial genome of a southern European population consisting of subjects affected (n = 239) and non-affected (n = 150) by sporadic prostate cancer.

METHODOLOGY AND PRINCIPAL FINDINGS

Using primer extension analysis and DNA sequencing, we identified the nine major European haplogroups and CR polymorphisms. The frequencies of the haplogroups did not differ between patients and control cohorts, whereas the CR polymorphism T16356C was significantly higher in patients with PC compared to the controls (p = 0.029). PSA, staging, and Gleason score were associated with none of the nine major European haplogroups. The CR polymorphisms G16129A (p = 0.007) and T16224C (p = 0.022) were significantly associated with Gleason score, whereas T16311C (p = 0.046) was linked with T-stage.

CONCLUSIONS AND SIGNIFICANCE

Our results do not suggest that mtDNA haplogroups could be involved in sporadic prostate cancer etiology and pathogenesis as previous studies performed in middle Europe population. Although some significant associations have been obtained in studying CR polymorphisms, further studies should be performed to validate these results.

Global prostate cancer incidence and the migration, settlement, and admixture history of the Northern Europeans

The most salient feature of prostate cancer is its striking ethnic disparity. High incidences of the disease are documented in two ethnic groups: descendents of the Northern Europeans and African Americans. Other groups, including native Africans, are much less susceptible to the disease. Given that many risk factors may contribute to carcinogenesis, an etiological cause for the ethnic disparity remains to be defined. By analyzing the global prostate cancer incidence data, we found that distribution of prostate cancer incidence coincides with the migration and settlement history of Northern Europeans. The incidences in other ethnic groups correlate to the settlement history and extent of admixture of the Europeans. This study suggests that prostate cancer has been spread by the transmission of a genetic susceptibility that resides in the Northern European genome.

Identifying hypothetical genetic influences on complex disease phenotypes

BACKGROUND

Statistical interactions between disease-associated loci of complex genetic diseases suggest that genes from these regions are involved in a common mechanism impacting, or impacted by, the disease. The computational problem we address is to discover relationships among genes from these interacting regions that may explain the observed statistical interaction and the role of these genes in the disease phenotype.

RESULTS

We describe a heuristic algorithm for generating hypothetical gene relationships from loci associated with a complex disease phenotype. This approach, called Prioritizing Disease Genes by Analysis of Common Elements (PDG-ACE), mines biomedical keywords from text descriptions of genes and uses them to relate genes close to disease-associated loci. A keyword common to, and significantly over-represented in, a pair of gene descriptions may represent a preliminary hypothesis about the biological relationship between the genes, and suggest the role the genes play in the disease phenotype.

CONCLUSION

Our experimentation shows that the approach finds previously published relationships, while failing to find relationships that don't exist. The results also indicate that the approach is robust to differences in keyword vocabulary. We outline a brief case study in which results from a recently published Type 2 Diabetes association study are used to identify potential hypotheses.

The role of the BRCA2 gene in susceptibility to prostate cancer revisited

Prostate cancer is a genetically complex disease with multiple predisposing factors affecting presentation, progression, and outcome. Epidemiologic studies have long shown an aggregation of breast and prostate cancer in some families. More recently, studies have reported an apparent excess of prostate cancer cases among BRCA2 mutation-carrying families. Additionally, population-based screens of early-onset prostate cancer patients have suggested that the prevalence of deleterious BRCA2 mutations in this group is 1% to 2%, imparting a significantly increased risk of the disease compared with noncarrier cases. However, studies of high-risk prostate cancer families suggest that BRCA2 plays at most a minimal role in these individuals, highlighting the potential genetic heterogeneity of the disease. In this commentary, we review the current literature and hypotheses surrounding the relationship between BRCA2 mutations and susceptibility to prostate cancer and speculate on the potential for involvement of additional genes.

A two-dimensional genome scan for rheumatoid arthritis susceptibility loci

We performed a genome-wide search for pairs of susceptibility loci that jointly contribute to rheumatoid arthritis in families recruited by the North American Rheumatoid Arthritis Consortium. A complete two-dimensional (2D) non-parametric linkage scan was carried out using 380 autosomal microsatellite markers in 511 families. At each 2D peak we obtained the most likely underlying genetic model explaining the two-locus effects, defining epistasis as a departure from an additive or a multiplicative two-locus penetrance function. The highest peak in the surface identified an epistatic interaction between loci 6p21 and 16p12 (two-locus lod score = 18.02, epistasis P < 0.012). Significant and suggestive two-locus effects were also obtained for region 6p21 in combination with loci 18q21, 8p23, 1q41, and 6p22, while the highest 2D peaks excluding region 6p21 were observed at locus pairs 8p23-18q21 and 1p21-18q21. The 2D peaks were further examined using combined microsatellite and single-nucleotide polymorphism (SNP) marker genotypes in 744 families. The two-locus evidence for linkage increased for region pairs 6p21-18q12, 6p21-16p12, 6p21-8p23, 1q41-6p21, and 6p21-6p22, but decreased for pairs of regions that did not include locus 6p21. In conclusion, we obtained evidence for multi-locus interactions in rheumatoid arthritis that are mediated by the major susceptibility locus at 6p21.

Fine mapping of familial prostate cancer families narrows the interval for a susceptibility locus on chromosome 22q12.3 to 1.36 Mb

Genetic studies suggest that hereditary prostate cancer is a genetically heterogeneous disease with multiple contributing loci. Studies of high-risk prostate cancer families selected for aggressive disease, analysis of large multigenerational families, and a meta-analysis from the International Consortium for Prostate Cancer Genetics (ICPCG), all highlight chromosome 22q12.3 as a susceptibility locus with strong statistical significance. Recently, two publications have narrowed the 22q12.3 locus to a 2.18 Mb interval using 54 high-risk families from the ICPCG collaboration, as defined by three recombination events on either side of the locus. In this paper, we present the results from fine mapping studies at 22q12.3 using both haplotype and recombination data from 42 high-risk families contributed from the Mayo Clinic and the Prostate Cancer Genetic Research Study (PROGRESS) mapping studies. No clear consensus interval is present when all families are used. However, in the subset of 14 families with >/=5 affected men per family, a 2.53-Mb shared consensus segment that overlaps with the previously published interval is identified. Combining these results with data from the earlier ICPCG study reduces the three-recombination interval at 22q12.3 to approximately 1.36 Mb.

The power of two-locus affected sib-pair linkage analysis to detect interacting disease loci

It has been shown that two-locus linkage analysis can, for some two-locus disease models, be used to detect effects at disease loci that do not reach significance in a genome scan. However, few examples exist where two-locus linkage has been successfully used to map genes. We study the possible gain in power of affected sib-pair nonparametric two-locus linkage analysis for two-locus models which fulfil the two-locus triangle constraints. Using a new parameterization of the two-locus joint identity-by-descent sharing probabilities we can, for fixed marginal sharing at both of two unlinked disease loci, derive a two-locus distribution such that the power of a two-locus analysis is maximized. In a simulation study we look at two test statistics, the two-locus maximum likelihood score and the correlation between nonparametric linkage scores, and study power as a function of marginal sharing. We show that in a best-case scenario two-locus linkage can have considerable power to detect pairs of interacting loci if there is a moderate increase in allele sharing at one of the two loci, even if there is a very small increase in allele sharing at the other locus. But we also show that the power to detect interacting loci in a two-locus analysis decreases as the marginal sharing at the two loci decreases and for any distribution with a small increase in allele sharing at both loci the power of a two-locus analysis is always low.

Affected relative pairs and simultaneous search for two-locus linkage in the presence of epistasis

It is commonly believed that multiple interacting genes increase the susceptibility of genetically complex diseases, yet few linkage analyses of human diseases scan for more than one locus at a time. To overcome some of the statistical and computational limitations of a simultaneous search for two disease susceptibility loci in the presence of epistasis, we developed new score statistics to simultaneously scan for two disease susceptibility loci in pedigree data. These model-free score statistics are based on developments for model-free maximum lod scores, which in turn are based on variance components for indicators of disease status. To overcome reduced power caused by many parameters in the general two-locus model, we impose constraints on ratios of variance components, much like those used for robust single-locus linkage statistics (e.g., minimax constraints). The resulting three-degree of freedom score statistic, constrained as a one-sided multivariate test, can be computed rapidly, making simultaneous search feasible for human genetic linkage studies. Furthermore, using recent developments to rapidly compute simulation P-values for score statistics, it is feasible to empirically evaluate the statistical significance of the proposed score statistics. Application of these methods to two large studies of the genetic linkage of prostate cancer illuminates their strengths and limitations. The results provide weak suggestions for linkage of several pairs of chromosomal regions (chromosome pairs 1-21, 3-13, 5-9, and 14-19), all of which showed stronger linkage signals when the score statistics accounted for epistasis. These novel score statistics should prove useful for linkage studies of other complex human diseases.

Localization of a Prostate Cancer Predisposition Gene to an 880-kb Region on Chromosome 22q12.3 in Utah High-Risk Pedigrees

Chromosome 22q has become recently a region of interest for prostate cancer. We identified previously a logarithm of odds (LOD) of 2.42 at chromosome 22q12.3. Additionally, this region has been noted by eight other studies, with linkage evidence ranging from LOD of 1.50 to 3.57. Here, we do fine mapping and localization of the region using a pedigree-specific recombinant mapping approach in 14 informative, high-risk Utah pedigrees. These 14 pedigrees were chosen because they were either "linked" or "haplotype-sharing" pedigrees or both. "Linked" pedigrees were those with significant pedigree-specific linkage evidence (LOD, >0.588; P < 0.05) to the 22q12.3 region, regardless of the number of prostate cancer cases sharing the segregating haplotype. "Haplotype-sharing" pedigrees were those with at least five prostate cancer cases sharing a segregating haplotype in the 22q12.3 region, regardless of the linkage evidence. In each pedigree, the most likely haplotype configuration (in addition to the multipoint LOD graph for linked pedigrees) was used to infer the position of recombinant events and delimit the segregating chromosomal segment in each pedigree. These pedigree-specific chromosomal segments were then overlaid to form a consensus recombinant map across all 14 pedigrees. Using this method, we identified a 881,538-bp interval at 22q12.3, between D22S1265 and D22S277, which is the most likely region that contains the 22q prostate cancer predisposition gene. The unique Utah extended high-risk pedigree resource allows this powerful localization approach in pedigrees with evidence for segregating predisposition to prostate cancer. We are mutation screening candidate genes in this region to identify specific genetic variants segregating in these pedigrees.