META-GSA: Combining Findings from Gene-Set Analyses across Several Genome-Wide Association Studies

From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies?

Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.

Conducting gene set tests in meta-analyses of transcriptome expression data

Research synthesis, eg, by meta-analysis, is more and more considered in the area of high-dimensional data from molecular research such as gene and protein expression data, especially because most studies and experiments are performed with very small sample sizes. In contrast to most clinical and epidemiological trials, raw data are often available for high-dimensional expression data. Therefore, direct data merging followed by a joint analysis of selected studies can be an alternative to meta-analysis by P value or effect-size merging or, more generally spoken, the merging of results. While several methods for meta-analysis of differential expression studies have been proposed, meta-analysis of gene set tests has very rarely been considered, although gene set tests are standard in the analysis of individual gene expression studies. We compare in this work the different strategies of research synthesis of gene set tests, in particularly the "early merging" of data cleaned from batch effects versus the "late merging" of individual results. In simulation studies and in examples of manipulated real-world data, we found that in most scenarios, the early merging has a higher sensitivity of detecting a gene set enrichment than the late merging. However, in scenarios with few studies, large batch effect, moderate and large sample sizes of late merging are more sensitive than early merging.

Gene-set meta-analysis of lung cancer identifies pathway related to systemic lupus erythematosus

INTRODUCTION

Gene-set analysis (GSA) is an approach using the results of single-marker genome-wide association studies when investigating pathways as a whole with respect to the genetic basis of a disease.

METHODS

We performed a meta-analysis of seven GSAs for lung cancer, applying the method META-GSA. Overall, the information taken from 11,365 cases and 22,505 controls from within the TRICL/ILCCO consortia was used to investigate a total of 234 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

RESULTS

META-GSA reveals the systemic lupus erythematosus KEGG pathway hsa05322, driven by the gene region 6p21-22, as also implicated in lung cancer (p = 0.0306). This gene region is known to be associated with squamous cell lung carcinoma. The most important genes driving the significance of this pathway belong to the genomic areas HIST1-H4L, -1BN, -2BN, -H2AK, -H4K and C2/C4A/C4B. Within these areas, the markers most significantly associated with LC are rs13194781 (located within HIST12BN) and rs1270942 (located between C2 and C4A).

CONCLUSIONS

We have discovered a pathway currently marked as specific to systemic lupus erythematosus as being significantly implicated in lung cancer. The gene region 6p21-22 in this pathway appears to be more extensively associated with lung cancer than previously assumed. Given wide-stretched linkage disequilibrium to the area APOM/BAG6/MSH5, there is currently simply not enough information or evidence to conclude whether the potential pleiotropy of lung cancer and systemic lupus erythematosus is spurious, biological, or mediated. Further research into this pathway and gene region will be necessary.