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      Analysis of Xq27-28 linkage in the international consortium for prostate cancer genetics (ICPCG) families

      research-article
      1 , 7 , 32 , , 1 , 2 , 1 , 3 , 4 , 5 , 5 , 6 , 5 , 1 , 7 , 8 , 7 , 9 , 7 , 10 , 11 , 12 , 13 , 11 , 13 , 11 , 12 , 13 , 11 , 12 , 13 , 11 , 14 , 11 , 15 , 11 , 15 , 11 , 16 , 11 , 17 , 11 , 16 , 11 , 16 , 11 , 18 , 19 , 20 , 21 , 19 , 20 , 21 , 42 , 19 , 22 , 4 , 23 , 24 , 23 , 25 , 23 , 26 , 23 , 24 , 23 , 25 , 27 , 27 , 27 , 28 , 28 , 28 , 29 , 30 , 29 , 31 , 32 , 33 , 34 , 32 , 33 , 34 , 35 , 36 , 37 , 35 , 37 , 35 , 37 , 38 , 38 , 39 , 40 , 40 , 40 , 41 , 27
      BMC Medical Genetics
      BioMed Central

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          Abstract

          Background

          Genetic variants are likely to contribute to a portion of prostate cancer risk. Full elucidation of the genetic etiology of prostate cancer is difficult because of incomplete penetrance and genetic and phenotypic heterogeneity. Current evidence suggests that genetic linkage to prostate cancer has been found on several chromosomes including the X; however, identification of causative genes has been elusive.

          Methods

          Parametric and non-parametric linkage analyses were performed using 26 microsatellite markers in each of 11 groups of multiple-case prostate cancer families from the International Consortium for Prostate Cancer Genetics (ICPCG). Meta-analyses of the resultant family-specific linkage statistics across the entire 1,323 families and in several predefined subsets were then performed.

          Results

          Meta-analyses of linkage statistics resulted in a maximum parametric heterogeneity lod score (HLOD) of 1.28, and an allele-sharing lod score (LOD) of 2.0 in favor of linkage to Xq27-q28 at 138 cM. In subset analyses, families with average age at onset less than 65 years exhibited a maximum HLOD of 1.8 (at 138 cM) versus a maximum regional HLOD of only 0.32 in families with average age at onset of 65 years or older. Surprisingly, the subset of families with only 2–3 affected men and some evidence of male-to-male transmission of prostate cancer gave the strongest evidence of linkage to the region (HLOD = 3.24, 134 cM). For this subset, the HLOD was slightly increased (HLOD = 3.47 at 134 cM) when families used in the original published report of linkage to Xq27-28 were excluded.

          Conclusions

          Although there was not strong support for linkage to the Xq27-28 region in the complete set of families, the subset of families with earlier age at onset exhibited more evidence of linkage than families with later onset of disease. A subset of families with 2–3 affected individuals and with some evidence of male to male disease transmission showed stronger linkage signals. Our results suggest that the genetic basis for prostate cancer in our families is much more complex than a single susceptibility locus on the X chromosome, and that future explorations of the Xq27-28 region should focus on the subset of families identified here with the strongest evidence of linkage to this region.

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          Most cited references57

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          A high-resolution recombination map of the human genome.

          Determination of recombination rates across the human genome has been constrained by the limited resolution and accuracy of existing genetic maps and the draft genome sequence. We have genotyped 5,136 microsatellite markers for 146 families, with a total of 1,257 meiotic events, to build a high-resolution genetic map meant to: (i) improve the genetic order of polymorphic markers; (ii) improve the precision of estimates of genetic distances; (iii) correct portions of the sequence assembly and SNP map of the human genome; and (iv) build a map of recombination rates. Recombination rates are significantly correlated with both cytogenetic structures (staining intensity of G bands) and sequence (GC content, CpG motifs and poly(A)/poly(T) stretches). Maternal and paternal chromosomes show many differences in locations of recombination maxima. We detected systematic differences in recombination rates between mothers and between gametes from the same mother, suggesting that there is some underlying component determined by both genetic and environmental factors that affects maternal recombination rates.
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            Genome-wide association study of prostate cancer identifies a second risk locus at 8q24.

            Recently, common variants on human chromosome 8q24 were found to be associated with prostate cancer risk. While conducting a genome-wide association study in the Cancer Genetic Markers of Susceptibility project with 550,000 SNPs in a nested case-control study (1,172 cases and 1,157 controls of European origin), we identified a new association at 8q24 with an independent effect on prostate cancer susceptibility. The most significant signal is 70 kb centromeric to the previously reported SNP, rs1447295, but shows little evidence of linkage disequilibrium with it. A combined analysis with four additional studies (total: 4,296 cases and 4,299 controls) confirms association with prostate cancer for rs6983267 in the centromeric locus (P = 9.42 x 10(-13); heterozygote odds ratio (OR): 1.26, 95% confidence interval (c.i.): 1.13-1.41; homozygote OR: 1.58, 95% c.i.: 1.40-1.78). Each SNP remained significant in a joint analysis after adjusting for the other (rs1447295 P = 1.41 x 10(-11); rs6983267 P = 6.62 x 10(-10)). These observations, combined with compelling evidence for a recombination hotspot between the two markers, indicate the presence of at least two independent loci within 8q24 that contribute to prostate cancer in men of European ancestry. We estimate that the population attributable risk of the new locus, marked by rs6983267, is higher than the locus marked by rs1447295 (21% versus 9%).
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              Parametric and nonparametric linkage analysis: a unified multipoint approach.

              In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.
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                Author and article information

                Contributors
                Journal
                BMC Med Genet
                BMC Med. Genet
                BMC Medical Genetics
                BioMed Central
                1471-2350
                2012
                19 June 2012
                : 13
                : 46
                Affiliations
                [1 ]Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, 21224, USA
                [2 ]Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
                [3 ]Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA
                [4 ]Data Coordinating Center for the ICPCG and Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, USA
                [5 ]University of Utah ICPCG Group and Division of Genetic Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
                [6 ]George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, USA
                [7 ]African American Hereditary Prostate Cancer ICPCG Group, Phoenix, AZ, USA
                [8 ]Fox Chase Cancer Center, Philadelphia, PA, USA
                [9 ]Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA
                [10 ]Translational Genomics Research Institute, Genetic Basis of Human Disease Research Division, Phoenix, AZ, USA
                [11 ]ACTANE consortium
                [12 ]Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
                [13 ]Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, The University of Melbourne, Melbourne, Australia
                [14 ]Program in Cancer Genetics, McGill University, Montreal, QC, Canada
                [15 ]Department of Medical Genetics, Oslo University Hospital, The Norwegian Radium Hospital, Oslo,Norway
                [16 ]Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Surrey, UK
                [17 ]Cancer Research UK Genetic Epidemiology Unit, Cambridge, UK
                [18 ]Division of Medical Genetics, University of Washington Medical Center, Seattle, WA, USA
                [19 ]BC/CA/HI ICPCG Group, Stanford, CA, USA
                [20 ]Department of Health Research and Policy, Stanford School of Medicine, Stanford, CA, USA
                [21 ]Stanford Cancer Institute, Stanford School of Medicine, Stanford, CA, USA
                [22 ]Department of Urology and Department of Biochemistry and Molecular Biology, University of Southern California, Los Ageles, CA, USA
                [23 ]FHCRC ICPCG Group, Seattle, WA, USA
                [24 ]Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, USA
                [25 ]Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
                [26 ]Institute for Systems Biology, Seattle, WA, USA
                [27 ]Johns Hopkins University ICPCG Group and Department of Urology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
                [28 ]Mayo Clinic, Rochester, MN, USA
                [29 ]University of Michigan ICPCG Group, Ann Arbor, MI, USA
                [30 ]Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
                [31 ]University of Michigan, Ann Arbor, MI, USA
                [32 ]University of Tampere ICPCG Group, Tampere, Finland
                [33 ]Institute of Biomedical Technology, University of Tampere, Tampere, Finland
                [34 ]Centre for Laboratory Medicine and Department of Urology, Tampere University Hospital, Tampere, Finland
                [35 ]University of Ulm ICPCG Group, Ulm, Germany
                [36 ]Dept of Urology, University of Ulm, Ulm, Germany
                [37 ]Institute of Human Genetics, University of Ulm, Ulm, Germany
                [38 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
                [39 ]Oncologic Centre, Umeå University, Umeå, Sweden
                [40 ]CeRePP ICPCG Group, 75020, Paris, France
                [41 ]Hopital Tenon, Assistance Publique-Hopitaux de Paris, 75020, Paris, France
                [42 ]Cancer Prevention Institute of California
                Article
                1471-2350-13-46
                10.1186/1471-2350-13-46
                3495053
                22712434
                15893e0e-94c4-46e8-907d-4e962a9cbddf
                Copyright ©2012 Bailey-Wilson et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 July 2011
                : 30 April 2012
                Categories
                Research Article

                Genetics
                Genetics

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