Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration

Bi-level structured functional analysis for genome-wide association studies

Genome-wide association studies (GWAS) have led to great successes in identifying genotype-phenotype associations for complex human diseases. In such studies, the high dimensionality of single nucleotide polymorphisms (SNPs) often makes analysis difficult. Functional analysis, which interprets SNPs densely distributed in a chromosomal region as a continuous process rather than discrete observations, has emerged as a promising avenue for overcoming the high dimensionality challenges. However, the majority of the existing functional studies continue to be individual SNP based and are unable to sufficiently account for the intricate underpinning structures of SNP data. SNPs are often found in groups (e.g., genes or pathways) and have a natural group structure. Additionally, these SNP groups can be highly correlated with coordinated biological functions and interact in a network. Motivated by these unique characteristics of SNP data, we develop a novel bi-level structured functional analysis method and investigate disease-associated genetic variants at the SNP level and SNP group level simultaneously. The penalization technique is adopted for bi-level selection and also to accommodate the group-level network structure. Both the estimation and selection consistency properties are rigorously established. The superiority of the proposed method over alternatives is shown through extensive simulation studies. A type 2 diabetes SNP data application yields some biologically intriguing results.

Small Nucleolar RNAs in Pseudoexfoliation Glaucoma

Small nucleolar RNAs (snoRNAs) are small non-coding regulatory RNAs that have been investigated extensively in recent years. However, the relationship between snoRNA and glaucoma is still unknown. This study aims to analyze the levels of snoRNA expression in the aqueous humor (AH) of patients with pseudoexfoliation glaucoma (PEXG) compared to a control group and identify hypothetical snoRNA-dependent mechanisms contributing to PEXG. The AH was obtained from eighteen Caucasian patients, comprising nine PEXG and nine age-matched control patients. RNA was isolated, and a microarray system was used to determine the snoRNA expression profiles. Functional and enrichment analyses were performed. We identified seven snoRNAs, SNORD73B, SNORD58A, SNORD56, SNORA77, SNORA72, SNORA64, and SNORA32, in the AH of the PEXG and control group patients. Five snoRNAs showed statistically significantly lower expression in the PEXG group, and two snoRNAs had statistically significantly higher expression in the PEXG group compared to the control group. In addition, we identified two factors-CACNB3 for SNORA64 and TMEM63C for SNORA32, similar to PEX-related genes (CACNA1A and TMEM136). The enrichment analysis for four genes targeted by snoRNAs revealed possible mechanisms associated with glaucoma and/or PEX, but the direct role of snoRNAs in these biological processes was not proven.

Penalized partial least squares for pleiotropy

BACKGROUND

The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level.

RESULTS

Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers.

CONCLUSION

The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.