A new scoring system for the diagnosis of BRCA1/2 associated breast-ovarian cancer predisposition.

International audienceCriteria have been proposed for genetic testing of breast and ovarian cancer susceptibility genes BRCA1 and BRCA2. Using simulations, this study evaluates the efficiency (sensitivity, positive predictive value [PPV] and specificity) of the various criteria used in France. The efficiency of the criteria published in 1998, which are largely used, is not optimal. We show that some extensions of these criteria provide an increase in sensitivity with a low decrease in specificity and PPV. The study shows that scoring systems (Manchester, Eisinger) have similar efficiency that may be improved. In this aim, we propose a new scoring system that takes into account unaffected individuals and kinship coefficients between family members. This system increases sensitivity without affecting PPV and specificity. Finally, we propose a two-step procedure with a large screening by the physician for recommending genetic counselling, followed by a more stringent selection by the geneticist for prescribing genetic testing. This procedure would result in an increase of genetic counselling activity but would allow the identification of almost 80% of mutation carriers among affected individuals, with a mutation detection rate of 15% and a specificity of 88%

Power comparison of different methods to detect genetic effects and gene-environment interactions

Identifying gene-environment (G × E) interactions has become a crucial issue in the past decades. Different methods have been proposed to test for G × E interactions in the framework of linkage or association testing. However, their respective performances have rarely been compared. Using Genetic Analysis Workshop 15 simulated data, we compared the power of four methods: one based on affected sib pairs that tests for linkage and interaction (the mean interaction test) and three methods that test for association and/or interaction: a case-control test, a case-only test, and a log-linear approach based on case-parent trios. Results show that for the particular model of interaction between tobacco use and Locus B simulated here, the mean interaction test has poor power to detect either the genetic effect or the interaction. The association studies, i.e., the log-linear-modeling approach and the case-control method, are more powerful to detect the genetic effect (power of 78% and 95%, respectively) and taking into account interaction moderately increases the power (increase of 9% and 3%, respectively). The case-only design exhibits a 95% power to detect G × E interaction but the type I error rate is increased

Genome-wide linkage screen for testicular germ cell tumour susceptibility loci

A family history of disease is a strong risk factor for testicular germ cell tumour (TGCT). In order to identify the location of putative TGCT susceptibility gene(s) we conducted a linkage search in 237 pedigrees with two or more cases of TGCT. One hundred and seventy-nine pedigrees were evaluated genome-wide with an average inter-marker distance of 10 cM. An additional 58 pedigrees were used to more intensively investigate several genomic regions of interest. Genetic linkage analysis was performed with the ALLEGRO software using two model-based parametric analyses and a non-parametric analysis. Six genomic regions on chromosomes 2p23, 3p12, 3q26, 12p13-q21, 18q21-q23 and Xq27 showed heterogeneity LOD (HLOD) scores of greater than 1, with a maximum HLOD of 1.94 at 3q26. Genome-wide simulation studies indicate that the observed number of HLOD peaks greater than one does not differ significantly from that expected by chance. A TGCT locus at Xq27 has been previously reported. Of the 237 pedigrees examined in this study, 66 were previously unstudied at Xq27, no evidence for linkage to this region was observed in this new pedigree set. Overall, the results indicate that no single major locus can account for the majority of the familial aggregation of TGCT, and suggests that multiple susceptibility loci with weak effects contribute to the diseas

Estimating penetrance from family data using a retrospective likelihood when ascertainment depends on genotype and age of onset.: Estimating penetrance from family data

International audienceIn diseases caused by deleterious gene mutations, knowledge of age-specific cumulative risks is necessary for medical management of mutation carriers. When pedigrees are ascertained through several affected persons, ascertainment bias can be corrected by using a retrospective likelihood. This likelihood is a function of the genotypes of pedigree members given their phenotypes and provides unbiased estimates of penetrance without modeling the selection process, provided that selection is independent of genotypes. However, since mutation testing is offered only to relatives of mutation carriers, the genotypes of family members are available only in mutated families and selection does depend on genotype. In the present study, we quantified the bias due to selection on genotype using simulations. We found that this bias depended on the true penetrance value: the lower the penetrance, the higher the bias (risk by age 80 estimated to be 46% for a true penetrance value of 20%). When age of onset is added to the selection criteria, as usually done, we showed that the bias was even higher. We modified the conditioning in the retrospective likelihood, what we call "genotype restricted likelihood" (GRL). Using simulations, we show that this method provided unbiased parameter estimates under all the selection designs considered

A nonparametric method for penetrance function estimation.

International audienceIn diseases caused by a deleterious gene mutation, knowledge of age-specific cumulative risks is necessary for medical management of mutation carriers. When pedigrees are ascertained through at least one affected individual, ascertainment bias can be corrected by using a parametric method such as the Proband's phenotype Exclusion Likelihood, or PEL, that uses a survival analysis approach based on the Weibull model. This paper proposes a nonparametric method for penetrance function estimation that corrects for ascertainment on at least one affected: the Index Discarding EuclideAn Likelihood or IDEAL. IDEAL is compared with PEL, using family samples simulated from a Weibull distribution and under alternative models. We show that, under Weibull assumption and asymptotic conditions, IDEAL and PEL both provide unbiased risk estimates. However, when the true risk function deviates from a Weibull distribution, we show that the PEL might provide biased estimates while IDEAL remains unbiased

Parent-of-origin effect in Transthyretin related amyloid polyneuropathy

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