Disease risk estimates in V30M variant transthyretin amyloidosis (A-ATTRv) from Mallorca.

BACKGROUND: Variant transthyretin amyloidosis (A-ATTRv) is an autosomal dominant disease caused by a range of TTR gene variants which entail great phenotypical heterogeneity and penetrance. In Majorca, the A-ATTRv caused by the V30M gene variant (A-ATTRV30M) is the most common. Since asymptomatic carriers are at risk of developing the disease, estimating age of onset is vital for proper management and follow-up. Thus, the aim of this study was to estimate age-related penetrance in ATTRV30M variant carriers from Majorca. METHODS: The disease risk among carriers from ATTRV30M families from Majorca was estimated by Non-parametric survival estimation. Factors potentially involved in the disease expression, namely gender and parent of origin were also analysed. RESULTS: A total of 48 heterozygous ATTRV30M families (147 affected patients and 123 were asymptomatic carriers) were included in the analysis. Penetrance progressively increased from 6% at 30 years to 75% at 90 years of age. In contrast to other European populations, we observe a similar risk for both males and females, and no difference of risk according to the parent of origin. CONCLUSIONS: In this first study assessing the age-related penetrance of ATTRV30M variant in Majorcan families, no effect of gender or parent of origin was observed. These findings will be helpful for improving management and follow-up of TTR variant carrier individuals.

Alternative methods for H1 simulations in genome-wide association studies.

OBJECTIVE: Assessing the statistical power to detect susceptibility variants plays a critical role in genome-wide association (GWA) studies both from the prospective and retrospective point of view. Power is empirically estimated by simulating phenotypes under a disease model H1. For this purpose, the gold standard consists in simulating genotypes given the phenotypes (e.g. Hapgen). We introduce here an alternative approach for simulating phenotypes under H1 that does not require generating new genotypes for each simulation. METHODS: In order to simulate phenotypes with a fixed total number of cases and under a given disease model, we suggest 3 algorithms: (1) a simple rejection algorithm; (2) a numerical Markov chain Monte-Carlo (MCMC) approach, and (3) an exact and efficient backward sampling algorithm. In our study, we validated the 3 algorithms both on a simulated dataset and by comparing them with Hapgen on a more realistic dataset. For an application, we then conducted a simulation study on a 1000 Genomes Project dataset consisting of 629 individuals (314 cases) and 8,048 SNPs from chromosome X. We arbitrarily defined an additive disease model with two susceptibility SNPs and an epistatic effect. RESULTS: The 3 algorithms are consistent, but backward sampling is dramatically faster than the other two. Our approach also gives consistent results with Hapgen. Using our application data, we showed that our limited design requires a biological a priori to limit the investigated region. We also proved that epistatic effects can play a significant role even when simple marker statistics (e.g. trend) are used. We finally showed that the overall performance of a GWA study strongly depends on the prevalence of the disease: the larger the prevalence, the better the power. CONCLUSIONS: Our approach is a valid alternative to Hapgen-type methods; it is not only dramatically faster but has 2 main advantages: (1) there is no need for sophisticated genotype models (e.g. haplotype frequencies, or recombination rates), and (2) the choice of the disease model is completely unconstrained (number of SNPs involved, gene-environment interactions, hybrid genetic models, etc.). Our 3 algorithms are available in an R package called 'waffect' ('double-u affect', for weighted affectations).

A PCSK9 variant and familial combined hyperlipidaemia.

BACKGROUND: Our discovery in 2003 of the first mutations of PCSK9 gene causing autosomal dominant hypercholesterolaemia (ADH) shed light on an unknown factor that strongly influences the level of circulating low density lipoprotein cholesterol (LDL-C). PCSK9 gain of function mutations cause hypercholesterolaemia by a reduction of LDL receptor levels, while PCSK9 loss of function variants are associated with a reduction of LDL-C values and a decreased risk of coronary heart disease. METHODS AND RESULTS: We report an insertion of two leucines (p.L21tri also designated p.L15_L16ins2L) in the leucine stretch of the signal peptide of PCSK9 that is found in two of 25 families with familial combined hyperlipidaemia (FCHL). This mutant is associated with high total cholesterol and LDL-C values in these families and is found also in a patient with familial hypercholesterolaemia and her father. CONCLUSION: PCSK9 variants might contribute to FCHL phenotype and are to be taken into consideration in the study of this complex and multigenic disease with other genes implicated in dyslipidaemia.

A note on allelic tests in case-control association studies.

This paper reconsiders the relevant contribution of Sasieni in the validity of allele-based tests in case-control genetic association studies. In particular, the author clearly demonstrates that the classical chi-square test applied to allelic contingency tables is biased when the combined case-control population is not in Hardy-Weinberg equilibrium. As an alternative, he suggests using the Cochran-Armitage test for trends by basing his argument on the fact that these two tests are asymptotically equivalent at the Hardy-Weinberg equilibrium. However he only demonstrates the equality of the statistics when the observed genotypic proportions are strictly in equilibrium--which does not formally imply the suggested, and often accepted, asymptotic behavior. In this short communication, we complement this contribution by providing the proof that allelic and trend statistics are asymptotically equivalent under the conditions mentioned above. In addition, since the 'biased' allelic test is still widely used in the literature, we briefly discuss the different alternatives that have been subsequently developed, based on Sasieni's conclusions.

Computing power in case-control association studies through the use of quadratic approximations: application to meta-statistics.

In the framework of case-control studies many different test statistics are available to measure the association of a marker with a given disease. Nevertheless, choosing one particular statistic can lead to very different conclusions. In the absence of a consensus for this choice, a tempting option is to evaluate the power of these different statistics prior to make any decision. We review the available methods dedicated to power computation and assess their respective reliability in treating a wide range of tests on a wide range of alternative models. Considering Monte-Carlo, non-central chi-square and Delta-Method estimates, we evaluate empirical, asymptotic and numerical approaches. Additionally we introduce the use of the Delta-Method, extended to order 2, intended to provide better results than the traditional order-1 Delta-Method. Supplementary data can be found at: http://stat.genopole.cnrs.fr/software/dm2.

A fast, unbiased and exact allelic test for case-control association studies.

Association studies are traditionally performed in the case-control framework. As a first step in the analysis process, comparing allele frequencies using the Pearson's chi-square statistic is often invoked. However such an approach assumes the independence of alleles under the hypothesis of no association, which may not always be the case. Consequently this method introduces a bias that deviates the expected type I error-rate. In this article we first propose an unbiased and exact test as an alternative to the biased allelic test. Available data require to perform thousands of such tests so we focused on its fast execution. Since the biased allelic test is still widely used in the community, we illustrate its pitfalls in the context of genome-wide association studies and particularly in the case of low-level tests. Finally, we compare the unbiased and exact test with the Cochran-Armitage test for trend and show it perfoms similarly in terms of power. The fast, unbiased and exact allelic test code is available in R, C++ and Perl at: http://stat.genopole.cnrs.fr/software/fueatest.

LD-SPatt: large deviations statistics for patterns on Markov chains.

Statistics on Markov chains are widely used for the study of patterns in biological sequences. Statistics on these models can be done through several approaches. Central limit theorem (CLT) producing Gaussian approximations are one of the most popular ones. Unfortunately, in order to find a pattern of interest, these methods have to deal with tail distribution events where CLT is especially bad. In this paper, we propose a new approach based on the large deviations theory to assess pattern statistics. We first recall theoretical results for empiric mean (level 1) as well as empiric distribution (level 2) large deviations on Markov chains. Then, we present the applications of these results focusing on numerical issues. LD-SPatt is the name of GPL software implementing these algorithms. We compare this approach to several existing ones in terms of complexity and reliability and show that the large deviations are more reliable than the Gaussian approximations in absolute values as well as in terms of ranking and are at least as reliable as compound Poisson approximations. We then finally discuss some further possible improvements and applications of this new method.

SPA: Simple web tool to assess statistical significance of DNA patterns.

Many statistical methods and programs are available to compute the significance of a given DNA pattern in a genome sequence. In this paper, after outlining the mathematical background of this problem, we present SPA (Statistic for PAtterns), an expert system with a simple web interface designed to be applied to two of these methods (large deviation approximations and exact computations using simple recurrences). A few results are presented, leading to a comparison between the two methods and to a simple decision rule in the choice of that to be used. Finally, future developments of SPA are discussed. This tool is available at the following address: http://stat.genopole.cnrs.fr/SPA/.