Hervé Perdry

Estimating cancer risk in HNPCC by the GRL method

Hereditary nonpolyposis colorectal cancer (HNPCC) is an autosomal dominant syndrome caused by germline mutations of the mismatch repair (MMR) genes. Only a few studies have taken into account the selection of families tested for these mutations in estimating colorectal cancer (CRC) risk in carriers. They found much lower estimates of CRC risks than previous ones, but these estimates lacked precision despite the large number of families. The aim of this study was to evaluate the efficiency of the ‘genotype restricted likelihood’ (GRL) method that provides unbiased estimates of risks whatever the ascertainment process of families, and to estimate CRC and endometrial cancer risk for carriers of the MMR genes. Efficiency of the GRL method was evaluated using simulations. Risks were estimated from a sample of 36 families diagnosed with HNPCC and carrying a mutation of MSH2 or MLH1, ascertained through a cancer family clinic in Lyon (France). The efficiency of the GRL method was found to be strongly dependent on the proportion of family members tested. By age 70 years, CRC risk was estimated at 47% (95% confidence interval: 12–98%) for men and 33% (95% confidence interval: 24–54%) for women. The endometrial cancer risk was only 14% (confidence interval: 6–20%). As methods allowing for the selection of families lack efficiency, large-scale family studies should be undertaken and data should be pooled to provide reliable and precise estimates of risks for an optimal familial management.

Comorbidity and metabolic context are crucial factors determining neurological sequelae of hypoglycaemia

Aim  To determine risk factors for neurological sequelae following hypoglycemia.

Determination of the real effect of genes identified in GWAS: The example of IL2RA in multiple sclerosis

Genome-wide association studies (GWAS), although efficient to detect genes involved in complex diseases, are not designed to measure the real effect of the genes. This is illustrated here by the example of IL2RA in multiple sclerosis (MS). Association between IL2RA and MS is clearly established, although the functional variation is still unknown: the effect of IL2RA might be better described by several SNPs than by a single one. This study investigates whether a pair of SNPs better explains the observed linkage and association data than a single SNP. In total, 522 trio families and 244 affected sib-pairs were typed for 26 IL2RA SNPs. For each SNP and pairs of SNPs, the phased genotypes of patients and controls were compared to determine the SNP set offering the best risk discrimination. Consistency between the genotype risks provided by the retained set and the identical by descent allele sharing in affected sib-pairs was assessed. After controlling for multiple testing, the set of SNPs rs2256774 and rs3118470, provides the best discrimination between the case and control genotype distributions (P-corrected=0.009). The relative risk between the least and most at-risk genotypes is 3.54 with a 95% confidence interval of [2.14–5.94]. Furthermore, the linkage information provided by the allele sharing between affected sibs is consistent with the retained set (P=0.80) but rejects the SNP reported in the literature (P=0.006). Establishing a valid modeling of a disease gene is essential to test its potential interaction with other genes and to reconstruct the pathophysiological pathways.

Estimating penetrance from multiple case families with predisposing mutations: Extension of the "genotype-restricted likelihood" (GRL) method

Some diseases are due to germline mutations in predisposing genes, such as cancer family syndromes. Precise estimation of the age-specific cumulative risk (penetrance) for mutation carriers is essential for defining prevention strategies. The genotype-restricted likelihood (GRL) method is aimed at estimating penetrance from multiple case families with such a mutation. In this paper, we proposed an extension of the GRL to account for multiple trait disease and to allow for a parent-of-origin effect. Using simulations of pedigrees, we studied the properties of this method and the effect of departures from underlying hypotheses, misspecification of disease incidence in the general population or misspecification of the index case, and penetrance heterogeneity. In contrast with the previous version of the GRL, accounting for multiple trait disease allowed unbiased estimation of penetrance. We also showed that accounting for a parent-of-origin effect allowed a powerful test for detecting this effect. We found that the GRL method was robust to misspecification of disease incidence in the population, but that misspecification of the index case induced a bias in some situations for which we proposed efficient corrections. When ignoring heterogeneity, the penetrance estimate was biased toward that of the highest risk individuals. A homogeneity test performed by stratifying the families according to the number of affected members was shown to have low power and seems useless for detecting such heterogeneity. These extensions are essential to better estimate the risk of diseases and to provide valid recommendations for the management of patients.

Nouveau système de score pour le diagnostic des prédispositions aux cancers du sein et de l’ovaire associées à BRCA1/2

Criteria 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%.

Could inbred cases identified in GWAS data succeed in detecting rare recessive variants where affected sib-pairs have failed?

To detect fully penetrant rare recessive variants that could constitute Mendelian subentities of complex diseases, we propose a novel strategy, the HBD-GWAS strategy, which can be applied to genome-wide association study (GWAS) data. This strategy first involves the identification of inbred individuals among cases using the genome-wide SNP data and then focuses on these inbred affected individuals and searches for genomic regions of shared homozygosity by descent that could harbor rare recessive disease-causing variants. In this second step, analogous to homozygosity mapping, a heterogeneity lod-score, HFLOD, is computed to quantify the evidence of linkage provided by the data. In this paper, we evaluate this strategy theoretically under different scenarios and compare its performances with those of linkage analysis using affected sib-pair (ASP) data. If cases affected by these Mendelian subentities are not enriched in the sample of cases, the HBD-GWAS strategy has almost no power to detect them, unless they explain an important part of the disease prevalence. The HBD-GWAS strategy outperforms the ASP linkage strategy only in a very limited number of situations where there exists a strong allelic heterogeneity. When several rare recessive variants within the same gene are involved, the ASP design indeed often fails to detect the gene, whereas, by focusing on inbred individuals using the HBD-GWAS strategy, the gene might be detected provided very large samples of cases are available.

Using Affected Sib-Pairs to Uncover Rare Disease Variants

Objective: We propose a new test for rare variant mapping, based on an affected sib-pair sample and a control sample. In each sib-pair, only the index case needs to be sequenced, and the number of alleles shared identical-by-descent between the sibs is used as complementary information. The test makes use of both association and linkage information. We compare this test to the Armitage test on case-control data, with cases either from the general population of cases or from a sample of cases having an affected sib. Methods: A score test based on the likelihood in a multiplicative risk model is proposed. Its power is estimated by simulations and compared to Armitage tests power. Results: The affected sib-pairs design allows a tremendous gain of power over the case-control design (from 1 to 99% for a moderate sample size and relative risk values around 3, at an α level of 10-11). When cases are ascertained in a sample of cases having an affected sib, the use of linkage information in our test allows a gain of power of more than 20% in certain situations. Conclusion: We demonstrate the interest in using familial data and both association and linkage information for rare variant mapping.

An ordered subset approach to including covariates in the transmission disequilibrium test

Clinical heterogeneity of a disease may reflect an underlying genetic heterogeneity, which may hinder the detection of trait loci. Consequently, many statistical methods have been developed that allow for the detection of linkage and/or association signals in the presence of heterogeneity.This report describes the work of two parallel investigations into similar approaches to ordered subset analysis, based on an observed covariate, in the framework of family-based association analysis using Genetic Analysis Workshop 15 simulated data.With an appropriate choice of covariate, both approaches allow detection of two loci that are undetectable by the classical transmission-disequilibrium test. For a third locus, detectable by the classical transmission-disequilibrium test, a substantial increase of power of detection is shown.

A new scoring system in cancer genetics: application to criteria for BRCA1 and BRCA2 mutation screening

Background In hereditary forms of cancer due to mutations of genes such as BRCA1 and BRCA2, methods have been proposed to predict the presence of a mutation in a family. Methods Relying on carriage probability computation is the most predictive, but scores are a good proxy and avoid using computer software. An empirical method, the Manchester scoring system, has been elaborated for BRCA1 and BRCA2 mutation identification. We propose a general scoring system based on a transformation of the carriage probability. Up to an approximation, the transformed carriage probability becomes an additive score. We applied this new scoring system to the diagnosis of BRCA1-associated and BRCA2-associated breast–ovarian cancer predisposition. Using simulations, its performance was evaluated and compared with that of the Manchester scoring system and of the exact probability. Finally, the score system was used on a sample of 4563 families screened for BRCA1 and BRCA2 mutations. Results The performance of the new scoring system was superior to the Manchester scoring system, but the probability computation remained the most predictive. The better performance of the new scoring system was attributed to accounting for unaffected family members and for the degree of kinship of relatives with the proband. Conclusions The new scoring system has a theoretical basis and may be applied to any cancer family syndrome and, more generally, to any disease with monogenic subentities, in which the causal gene mutations have been identified. It will be easily modified when additional predictive factors are found.

Accuracy of heritability estimations in presence of hidden population stratification

The heritability of a trait is the proportion of its variance explained by genetic factors; it has historically been estimated using familial data. However, new methods have appeared for estimating heritabilities using genomewide data from unrelated individuals. A drawback of this strategy is that population stratification can bias the estimates. Indeed, an environmental factor associated with the phenotype may differ among population subgroups. This factor being associated both with the phenotype and the genetic variation in the population would be a confounder. A common solution consists in adjusting on the first Principal Components (PCs) of the genomic data. We study this procedure on simulated data and on 6000 individuals from the Three-City Study. We analyse the geographical coordinates of the birth cities, which are not genetically determined, but the heritability of which should be overestimated due to population stratification. We also analyse various anthropometric traits. The procedure fails to correct the bias in geographical coordinates heritability estimates. The heritability estimates of the anthropometric traits are affected by the inclusion of the first PC, but not by the following PCs, contrarily to geographical coordinates. We recommend to be cautious with heritability estimates obtained from a large population.