Statistical tests for Rare Variants Data Rare Variants in Human Genetic Diseases: Comparison of Association Statistical Tests

Abstract : Many genome wide-association studies (GWAS) have been performed to assess genetic factors involved in complex diseases. However, most of the associated common variants explain only a small proportion of the complex diseases genetic architecture. This empirical observation and the fast development of whole-genome sequencing led the research community to reconsider the importance of rare variants for identifying association with complex disease. From a statistical point of view, research about rare variants imposes to take in account the problem of their rare occurrence and their individually small contributions to the disease susceptibility. This is the reason why a large part of the existing statistical methods implemented for analyzing common variants do not work for rare variants, leading to the development of many specific new tests. Most of the methods allowing to detect the association between rare variants and diseases consist on collapsing or pooling multiple rare variants together in a single or some “supervariant(s)”. Collective frequencies will then be reasonably high to test the computed collapsed genotype variable. Currently a lot of statistical methods exist for rare variants, including among others pooled association tests (Cohort Allelic Sums Test (CAST), Combined Multivariate and Collapsing (CMC), Weighted Sum Statistic (WSS), Replication-Based Test (RBT), Variable Threshold approach (VT)), methods based on model selection (C-alpha test, …) and methods based on kernel (KBAC, SKAT). Our study will focus on the comparison and the evaluation of these existing statistical methods. The main objective of this study is to learn about methods, analyzing advantages and limitations, and to draw up a warning guide of use of rare variants tests. To investigate their performance, we will both work on simulated data, reflecting various possible real scenarios (independent or correlated rare mutations; different effects of both rare and common variants, …) but also on real sequencing data consisting on 200 patients with a diagnosis of Brugada Syndrome (BrS) and 1000 available exome data of the cohort from the project UK10K, as controls. Comparison will be made on robustness and statistical power of each test in the simulation set-ups but also in the practical set-up. We will propose an analytical framework in order to find the best way to combine rare variant tests and detect true positive association.
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Poster
International Biometric Conference, Jul 2014, Florence, Italy
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Lise Bellanger, Elodie Persyn, Floriane Simonet, Richard Redon, Jean-Jacques Schott, et al.. Statistical tests for Rare Variants Data Rare Variants in Human Genetic Diseases: Comparison of Association Statistical Tests. International Biometric Conference, Jul 2014, Florence, Italy. 〈hal-01160576〉

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