Christine Fischer

Background: Risk prediction models are widely used in clinical genetic counselling. Despite their frequent use, the genetic risk models BOADICEA, BRCAPRO, IBIS and extended Claus model (eCLAUS), used to estimate BRCA1/2 mutation carrier probabilities, have never been comparatively evaluated in a large sample from central Europe. Additionally, a novel version of BOADICEA that incorporates tumour pathology information has not yet been validated. Patients and methods: Using data from 7352 German families we estimated BRCA1/2 carrier probabilities under each model and compared their discrimination and calibration. The incremental value of using pathology information in BOADICEA was assessed in a subsample of 4928 pedigrees with available data on breast tumour molecular markers oestrogen receptor, progesterone receptor and human epidermal growth factor 2. Results: BRCAPRO (area under receiver operating characteristic curve (AUC)=0.80 (95% CI 0.78 to 0.81)) and BOADICEA (AUC=0.79 (0.78–0.80)), had significantly higher diagnostic accuracy than IBIS and eCLAUS (pBRCA1/2 mutations. However, model calibration has to be improved for this population. eCLAUS should not be used for estimating mutation carrier probabilities in clinical settings. Whenever possible, breast tumour molecular marker information should be taken into account.

Aitkin recently proposed an integrated Bayesian/likelihood approach that he claims is general and simple. We have applied this method, which does not rely on informative prior probabilities or large-sample results, to investigate the evidence of association between disease and the 16 variants in the KDR gene provided by Genetic Analysis Workshop 17. Based on the likelihood of logistic regression models and considering noninformative uniform prior probabilities on the coefficients of the explanatory variables, we used a random walk Metropolis algorithm to simulate the distributions of deviance and deviance difference. The distribution of probability values and the distribution of the proportions of positive deviance differences showed different locations, but the direction of the shift depended on the genetic factor. For the variant with the highest minor allele frequency and for any rare variant, standard logistic regression showed a higher power than the novel approach. For the two variants with the strongest effects on Q1 under a type I error rate of 1%, the integrated approach showed a higher power than standard logistic regression. The advantages and limitations of the integrated Bayesian/likelihood approach should be investigated using additional regions and considering alternative regression models and collapsing methods.

Fischer C, Engel C, Sutter C, Zachariae S, Schmutzler R, Meindl A, Heidemann S, Grimm T, Goecke TO, Debatin I, Horn D, Wieacker P, Gadzicki D, Becker K, Schäfer D, Stock F, Voigtländer T, on behalf of the German Consortium for Hereditary Breast and Ovarian Cancer. BRCA1/2 testing: uptake, phenocopies, and strategies to improve detection rates in initially negative families.In families with clustering of breast and ovarian cancer, molecular testing of the major susceptibility genes BRCA1/2 helps to identify patients with disease mutations and healthy persons at high risk who can participate in targeted intervention programs. We investigated 5559 families from the German Consortium for Hereditary Breast and Ovarian Cancer included between 1997 and 2008 and treated under clinical routine conditions. In each family an index patient/person had been screened for deleterious mutations in BRCA1/2. Healthy relatives agreed to predictive testing in 888 of 1520 BRCA1/2 mutation‐positive families (58%). Of 2646 eligible unaffected first‐degree relatives 1143 decided to be tested (43%). In 325 families with BRCA1/2‐positive index patients one related BC/OC patient was tested and 39 (12.0%; 95% confidence interval: 8.7–16.0%) discrepant cases found. A second related individual was screened in 163 of 3388 (4.9%) families with BRCA1/2‐negative index patient and in eight families a BRCA1/2 mutation was found. In BRCA1/2 mutation‐positive families, BC/OC patients lacking the familial mutation have to be expected at a rather high rate. In families with BRCA1/2‐negative index patient we recommend a second screening if another patient with a high probability of carrying a BRCA1/2 mutation is available.

Two intervals of sequence identity at the tips of X and Y chromosomes, the human pseudoautosomal regions PAR1 and PAR2, have drawn interest from researchers in human genetics, cytogenetics, and evolutionary biology. However, they have been widely ignored in linkage and association studies. The pseudoautosomal regions (PARs) pair and recombine during meiosis like autosomes, but the recombination activity in PAR1 is extremely different between sexes. In men, it exhibits the highest recombination frequencies of the genome. Conflicting genetic maps of this region have been estimated by using three-generation pedigrees, sperm typing, and by using haplotypes from single nucleotide polymorphisms. Male genetic map lengths in the literature vary, and linkage disequilibrium has not been analyzed in detail. We review existing tools like genetic and physical maps, linkage disequilibrium methods, linkage and association analysis, implemented statistical methods, and their suitability for PARs. For multipoint linkage analysis, sex specificity must be indicated twice, first using sex-specific maps, and second by considering the sex-specific pseudoautosomal inheritance pattern. Currently, microsatellite panels and single nucleotide polymorphism chips do not contain sufficient numbers of markers in PAR1 and PAR2. The number of markers in PAR1, needed in indirect association studies, should be much larger than for autosomal regions alike in size, since linkage disequilibrium is very low. For genome-wide studies, it is essential to include pseudoautosomal markers since such expensive studies cannot afford to oversee pseudoautosomal linkage or association. This drawback could be solved with a sufficient number of markers, statistical methods that are adopted for the PARs, and their integration into softwares.

Measuring the association of haplotype similarities with phenotype similarities has been used to develop statistical tests of genetic association. Previously, we applied the general approach of Mantel statistics to correlate genetic and phenotype similarity, where genetic similarity was defined by the number of intervals flanked by markers identical by state for pairs of haplotypes. Here we investigated in the case-control study design the effect on power of the Mantel statistics for five different measures of genetic similarity based on haplotypes: 1) the number of shared intervals, 2) the physical length of the shared intervals, 3) the genetic length of the shared intervals in centimorgans, 4) the genetic length of the shared intervals in linkage disequilibrium units (LDU) and 5) Yu's measure that attaches more weight to the sharing of rare than common alleles. With prior knowledge of the answers of Genetic Analysis Workshop 15 Problem 3, we analyzed the simulated data sets in two genomic regions surrounding the disease loci on chromosomes 6 and 18. For the dense map on chromosome 6, all methods showed a very high power of comparable magnitude. For chromosome 18, we observed a power between 19% and 99% at the pointwise 5% significance level using 1000 cases and 1000 controls for all methods except Yu's measure. While it yielded a much lower power, Yu's measure had 80% power around the disease locus.

Zusammenfassung: Wir konzentrieren uns auf Risikoberechnungen bei familiärem Brust- und Eierstockkrebs, da diese ein wichtiges Thema in der genetischen Beratung darstellen. Ratsuchende mit Mutationen in einem der beiden Gene BRCA1 oder BRCA2 haben eine sehr hohe Erkrankungswahrscheinlichkeit. Ist kein Test möglich oder dieser negativ, basiert die Entscheidung über eine intensivierte Vorsorge oder prophylaktische Maßnahmen auf Risikoberechnungen. Wir beschreiben einige der zugrunde liegenden Modelle sowie Untersuchungen zu deren vergleichender Validierung. Nur in einer prospektiven Studie wurden mehrere Modelle zur Berechnung der Brustkrebserkrankungswahrscheinlichkeit verglichen. Das einzige Modell in der Studie, das BRCA1/2, zusätzliche genetische Faktoren und einige weitere Risikofaktoren berücksichtigt (IBIS), zeigte die beste Vorhersagekraft. Es wurden nicht alle neueren Modelle berücksichtigt (kein BOADICEA) und keine neueren Risikofaktoren einbezogen.