Pathway Analysis Approaches for Rare and Common Variants: Insights From Genetic Analysis Workshop 18

Abstract

Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18 (GAW18), seven research groups applied pathway analysis techniques to whole genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. Annotation of genetic variants is inconsistent across databases, incomplete, and biased towards known genes. At the analysis stage, insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type 1 error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric. Interpretation of findings is further complicated by evidence in support of interactions between pathways as well as the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false positive findings and to identify specific causal variants within a given pathway. Despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed.

Familial resemblances in human whole blood transcriptome

“…Using the observed cell proportions remains the best way to remove this effect as much as possible [ 17 ]. Moreover, certain methodological considerations in pathway analysis including the fact that annotation of genetic variants is inconsistent across databases, incomplete and biased toward known genes must be noted [ 29 ]. The gold standard to establish validity of findings from pathway analysis remains the replication of results in independent studies.…”

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

“…Pathway analysis can be described as “a group of statistical methods that exploit a priori knowledge of pathways” [ 94 ]. It forms the link between ‘omics’ results and the phenotype/disease under study and provides a biological meaning to the genes and variants detected (interpretation of results).…”

“…Furthermore, it reduces the multiple-testing burden and, thus, offers a huge analysis potential. Aslibekyan et al [ 94 ] showed that in spite of the great potential of pathway analysis, there are still many obstacles to overcome (for example, due to the lack of a golden analysis standard).…”

Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants

“…For almost two decades, the class of pathway analysis methods [e.g., GSEA; ( Subramanian et al, 2005 )] have been proposed as methods for aggregating gene-based summary statistics across multiple genes within a biologically defined set (e.g., a pathway). Although many of the original pathway analysis methods were developed for use on gene-expression data, the application of these methods to sequencing data has been proposed ( Wu et al, 2010 ; Wu and Zhi, 2013 ) and applied specifically to analyze rare variants ( Aslibekyan et al, 2014 ; Moore et al, 2016 ; Richardson et al, 2016 ; Larson et al, 2017 ). These approaches first conduct gene-based tests of association and then use methods to aggregate the gene-level test statistics ( Petersen et al, 2011 ; Aslibekyan et al, 2014 ; Valcarcel et al, 2016 ).…”