45 research outputs found

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

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    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

    Strategies for selection of subjects for sequencing after detection of a linkage peak

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    Linkage analysis has the potential to localize disease genes of interest, but the choice of which subjects to select for follow-up sequencing after identifying a linkage peak might influence the ability to find a disease gene. We compare nine different strategies for selection of subjects for follow-up sequencing using sequence data from the Genetic Analysis Workshop 17. We found that our more selective strategies, which included methods to identify case subjects more likely to be affected by genetic causes, out-performed sequencing all case and control subjects in linked pedigrees and required sequencing fewer individuals. We found that using genotype data from population control subjects had a higher benefit-cost ratio than sequencing control subjects selected as being the opposite extreme of the case subjects. We conclude that choosing case subjects for sequencing based on more selective strategies can be reliable and cost-effective

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

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    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

    No evidence of BRCA2 mutations in chromosome 13q-linked Utah high-risk prostate cancer pedigrees

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    <p>Abstract</p> <p>Background</p> <p>Germline mutations in the <it>BRCA2 </it>gene have been suggested to account for about 5% of familial prostate cancer; mutations have been reported in 2% of early onset (i.e., ≤ 55 years) prostate cancer cases and a segregating founder mutation has been identified in Iceland (999del5). However, the role of <it>BRCA2 </it>in high risk prostate cancer pedigrees remains unclear.</p> <p>Findings</p> <p>We examined the potential involvement of <it>BRCA2 </it>in a set offive high-risk prostate cancer pedigrees in which all prostate cases were no more distantly related than two meioses from another case, and the resulting cluster contained at least four prostate cancer cases. We selected these five pedigrees from a larger dataset of 59 high-risk prostate cancer pedigrees analyzed in a genome-wide linkage screen. Selected pedigrees showed at least nominal linkage evidence to the <it>BRCA2 </it>region on chromosome 13q. We mutation screened all coding regions and intron/exon boundaries of the <it>BRCA2 </it>gene in the youngest prostate cancer case who carried the linked 13q segregating haplotype, as well as in a distantly related haplotype carrier to confirm any segregation. We observed no known protein truncating <it>BRCA2 </it>deleterious mutations. We identified one non-segregating <it>BRCA2 </it>variant of uncertain significance, one non-segregating intronic variant not previously reported, and a number of polymorphisms.</p> <p>Conclusion</p> <p>In this set of high-risk prostate cancer pedigrees with at least nominal linkage evidence to <it>BRCA2</it>, we saw no evidence for segregating <it>BRCA2 </it>protein truncating mutations in heritable prostate cancer.</p

    Genome-wide linkage using the Social Responsiveness Scale in Utah autism pedigrees

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    <p>Abstract</p> <p>Background</p> <p>Autism Spectrum Disorder<b>s </b>(ASD) are phenotypically heterogeneous, characterized by impairments in the development of communication and social behaviour and the presence of repetitive behaviour and restricted interests. Dissecting the genetic complexity of ASD may require phenotypic data reflecting more detail than is offered by a categorical clinical diagnosis. Such data are available from the Social Responsiveness Scale (SRS) which is a continuous, quantitative measure of social ability giving scores that range from significant impairment to above average ability.</p> <p>Methods</p> <p>We present genome-wide results for 64 multiplex and extended families ranging from two to nine generations. SRS scores were available from 518 genotyped pedigree subjects, including affected and unaffected relatives. Genotypes from the Illumina 6 k single nucleotide polymorphism panel were provided by the Center for Inherited Disease Research. Quantitative and qualitative analyses were done using MCLINK, a software package that uses Markov chain Monte Carlo (MCMC) methods to perform multilocus linkage analysis on large extended pedigrees.</p> <p>Results</p> <p>When analysed as a qualitative trait, linkage occurred in the same locations as in our previous affected-only genome scan of these families, with findings on chromosomes 7q31.1-q32.3 [heterogeneity logarithm of the odds (HLOD) = 2.91], 15q13.3 (HLOD = 3.64), and 13q12.3 (HLOD = 2.23). Additional positive qualitative results were seen on chromosomes 6 and 10 in regions that may be of interest for other neuropsychiatric disorders. When analysed as a quantitative trait, results replicated a peak found in an independent sample using quantitative SRS scores on chromosome 11p15.1-p15.4 (HLOD = 2.77). Additional positive quantitative results were seen on chromosomes 7, 9, and 19.</p> <p>Conclusions</p> <p>The SRS linkage peaks reported here substantially overlap with peaks found in our previous affected-only genome scan of clinical diagnosis. In addition, we replicated a previous SRS peak in an independent sample. These results suggest the SRS is a robust and useful phenotype measure for genetic linkage studies of ASD. Finally, analyses of SRS scores revealed linkage peaks overlapping with evidence from other studies of neuropsychiatric diseases. The information available from the SRS itself may, therefore, reveal locations for autism susceptibility genes that would not otherwise be detected.</p

    PedGenie: meta genetic association testing in mixed family and case-control designs

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    <p>Abstract</p> <p>Background-</p> <p>PedGenie software, introduced in 2006, includes genetic association testing of cases and controls that may be independent or related (nuclear families or extended pedigrees) or mixtures thereof using Monte Carlo significance testing. Our aim is to demonstrate that PedGenie, a unique and flexible analysis tool freely available in Genie 2.4 software, is significantly enhanced by incorporating meta statistics for detecting genetic association with disease using data across multiple study groups.</p> <p>Methods-</p> <p>Meta statistics (chi-squared tests, odds ratios, and confidence intervals) were calculated using formal Cochran-Mantel-Haenszel techniques. Simulated data from unrelated individuals and individuals in families were used to illustrate meta tests and their empirically-derived p-values and confidence intervals are accurate, precise, and for independent designs match those provided by standard statistical software.</p> <p>Results-</p> <p>PedGenie yields accurate Monte Carlo p-values for meta analysis of data across multiple studies, based on validation testing using pedigree, nuclear family, and case-control data simulated under both the null and alternative hypotheses of a genotype-phenotype association.</p> <p>Conclusion-</p> <p>PedGenie allows valid combined analysis of data from mixtures of pedigree-based and case-control resources. Added meta capabilities provide new avenues for association analysis, including pedigree resources from large consortia and multi-center studies.</p
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