Incorporating external information to improve sparse signal detection in rare‐variant gene‐set‐based analyses

Abstract

Gene-set analyses are used to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial, external, information concerning the likely importance of each gene within each set. For example, for traits that are under purifying selection, we would expect genes showing extensive genic constraint to be more likely to be trait associated than unconstrained genes. Here we improve gene-set analyses by incorporating such external information into a higher-criticism-based signal detection analysis. We show that when this external information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. Further, our approach is particularly powerful when the signal is sparse, that is when only a small number of genes within the set are associated with the trait. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS) and implicate a number of genesets containing SOD1 and NEK1 as well as showing enrichment of small p values for gene-sets containing known ALS genes. We implement our approach in the R package wHC. K E Y W O R D Samyotrophic lateral sclerosis, gene-set-based analysis, higher criticism, prior information, weighted p values

Ultra-Rare Genetic Variation in Relapsing Polychondritis: A Whole-Exome Sequencing Study

“…We also performed exploratory analysis to examine whether RP was associated with rare genetic variants at the pathway level. Three pathway-analysis approaches were used including Gene Set Enrichment Analysis ("GEEA preranked") (9), Sequence kernel association test ("SKAT_robust") (10), and higher criticism test (11). The input of GSEA and higher criticism test was from genes ranked by p values from the gene-level collapsing analysis.…”

Ultra-rare genetic variation in relapsing polychondritis: a whole-exome sequencing study

“…Three pathway-analysis approaches were used including Gene Set Enrichment Analysis ('GSEA preranked'), 10 Sequence kernel association test ('SKAT_robust') 11 and higher criticism test. 12 The input of GSEA and higher criticism test was from genes ranked by p values from the gene-level collapsing analysis. For the higher criticism test, both unweighted and weighted higher criticism tests were performed.…”

“…We also performed exploratory analysis to examine whether RP was associated with rare genetic variants at the pathway level. Three pathway-analysis approaches were used including Gene Set Enrichment Analysis (‘GSEA preranked’),10 Sequence kernel association test (‘SKAT_robust’)11 and higher criticism test 12. The input of GSEA and higher criticism test was from genes ranked by p values from the gene-level collapsing analysis.…”

A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity

“…We intend to update our tool with the latest data available. Future improvement of NHC method will include: (1) the employment of tissue-specific biological networks, with transcriptomic databases such as GTEx; 12,59 (2) the consideration of genes with products that are functionally complementary but do not interact physically; (3) the implementation of alternative graph theory algorithms to test computational performance further with experimental evidence; 6,14 and (4) parallel programming of our approach to reduce computing time for large cohorts. These are some of the approaches that could be followed to further improve the detection of physiological homogeneity in the midst of genetic heterogeneity, for various human diseases, whether rare or common, and infectious or otherwise.…”

“…Meanwhile, there are several methods that could search for variants in physiologically related genes from NGS data, but they need to predefine gene sets before the analysis. 12 We therefore aimed to develop a practical genome-wide computational approach connecting deleterious genetic heterogeneity with physiological homogeneity, by integrating NGS data, population genetics, predictions of mutation deleteriousness, biological interaction networks, pathway information, gene ontology annotations and statistics, in order to identify significant disease-specific genetic signals at the gene cluster level in an unbiased, efficient, and systematic manner. 6,13,14 We developed the network-based heterogeneity clustering (NHC) approach for this purpose.…”