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BackgroundIntracranial aneurysm (IA) is a common cerebrovascular abnormality characterized by localized dilation and wall thinning in intracranial arteries, that frequently leads to fatal vascular rupture. The mechanisms underlying IA formation, growth and rupture are mostly unknown, and while increasing evidence suggest a genetic component of IA, identification of specific genes or causal molecular pathways remains largely inconclusive and only a small fraction of the risk attributable to genetics for IA in the general population.Methods:Here, we combined whole exome sequencing and identity-by -descent analyses with functional investigations to identify rare IA predisposing variants in familial forms of IA and understand their contribution to the pathophysiology of IA.ResultsWe identified two rare missense variants in theCTSOgene shared by all the affected relatives in two large pedigrees with multiple IA-affected relative.CTSOencodes for the cysteine-type papain-like cathepsin CTSO. Functional analyses revealed that CTSO acts as an extracellular protease controlling vascular smooth muscle cell migration and adhesion to the extracellular matrix. CTSO depletion, as well as expression of the two CTSO variants, which were poorly secreted, led to increase the amount of fibronectin. This effect is associated with a marked increase in VSMC stiffness which was rescued by exogenous CTSO.ConclusionsThis report identifies rareCTSOvariants in familial IA patients and suggests that the increased susceptibility to IA induced by these variants is likely related to their primary effects on the vascular tissue, and more particularly on the media layer of the wall of cerebral arteries.

The effective sample size (ESS) is a quantity estimated in genome-wide association studies (GWAS) with related individuals and/or linear mixed models used in analysis. ESS originally measured relative power in family-based GWAS and has recently become important for correcting GWAS summary statistics in post-GWAS analyses. However, existing ESS approaches have been overlooked and based on empirical estimation. This work presents an analytical form of ESS in mixed-model GWAS of quantitative traits, which is derived using the expectation of quadratic form and validated in extensive simulations. We illustrate the performance and relevance of our ESS estimator in common GWAS scenarios and analytically show that (i) family-based studies are consistently underpowered compared to studies of unrelated individuals of the same sample size; (ii) conditioning on polygenic genetic effect by linear mixed models boosts power; and (iii) power of detecting gene-environment interaction can be substantially gained or lost in family-based designs depending on exposure distribution. We further analyze UK Biobank dataset in two samples of 336,347 unrelated and 68,910 related individuals. Analysis in unrelated individuals reveals a high accuracy of our ESS estimator compared to the existing empirical approach; and analysis of related individuals suggests that the loss in effective sample size due to relatedness is at most 0.94x. Overall, we provide an analytical form of ESS for guiding GWAS designs and processing summary statistics in post-GWAS analyses.

Since their introduction in the 50’s, variance component mixed models have been widely used in many application fields. In this context, ReML estimation is by far the most popular procedure to infer the variance components of the model. Although many implementations of the ReML procedure are readily available, there is still need for computational improvements due to the ever-increasing size of the datasets to be handled, and to the complexity of the models to be adjusted. In this paper, we present a Min-Max (MM) algorithm for ReML inference and combine it with several speed-up procedures. The ReML MM algorithm we present is compared to 5 state-of-the-art publicly available algorithms used in statistical genetics. The computational performance of the different algorithms are evaluated on several datasets representing different plant breeding experimental designs. The MM algorithm ranks among the top 2 methods in almost all settings and is more versatile than many of its competitors. The MM algorithm is a promising alternative to the classical AI-ReML algorithm in the context of variance component mixed models. It is available in the MM4LMM R-package.

The problem of inferring the relatedness distribution between two individuals from biallelic marker data is considered. This problem can be cast as an estimation task in a mixture model: at each marker the latent variable is the relatedness state, and the observed variable is the genotype of the two individuals. In this model, only the prior proportions are unknown, and can be obtained via ML estimation using the EM algorithm. When the markers are biallelic and the data unphased, the identifiability of the model is known not to be guaranteed. In this article, model identifiability is investigated in the case of phased data generated from a crossing design, a classical situation in plant genetics. It is shown that identifiability can be guaranteed under some conditions on the crossing design. The adapted ML estimator is implemented in an R package called Relatedness. The performance of the ML estimator is evaluated and compared to that of the benchmark moment estimator, both on simulated and real data. Compared to its competitor, the ML estimator is shown to be more robust and to provide more realistic estimates.