Traditional genome-wide association studies (GWASs) usually focus on single-marker analysis, which only accesses marginal effects. Pathway analysis, on the other hand, considers biological pathway-gene-marker hierarchical structure and, therefore, provides additional insights into the genetic architecture underlining complex diseases. Recently, a number of methods for pathway analysis have been proposed to assess the significance of a biological pathway from a collection of single-nucleotide polymorphisms. In a study led by Dr. Nianjun Liu, associate professor in the department of biostatistics, section on statistical genetics at the University of Alabama at Birmingham, a novel approach for pathway analysis was put forth that assesses the effects of genes using the sequence kernel association test and the effects of pathways using an extended adaptive rank truncated product statistic. Co-investigators include department colleagues Mr. Qi Yan, doctoral student; Dr. Hemant K. Tiwari, professor; Dr. Nengjun Yi, professor; Dr. Xiang-Yang Lou, associate professor; and Dr. Xiangqin Cui, associate professor.

[Photo: Dr. Nianjun Liu]

It has been increasingly recognized that complex diseases are caused by both common and rare variants. This study proposed a new weighting scheme for genetic variants across the whole allelic frequency spectrum to be analyzed together without any form of frequency cutoff for defining rare variants. The proposed approach is flexible. It is applicable to both binary and continuous traits, and incorporating covariates is easy. Furthermore, it can be readily applied to GWAS data, exome-sequencing data, and deep resequencing data from either population-based or family-based studies.

The researchers evaluated the new approach on data simulated under comprehensive scenarios and discovered that it has the highest power in most of the scenarios while maintaining the correct type I error rate. They also applied their proposed methodology to data from a study of the association between bipolar disorder and candidate pathways from Wellcome Trust Case Control Consortium (WTCCC)—which was established to “exploit progress in understanding of patterns of human genome sequence variation along with advances in high-throughput genotyping technologies, and to explore the utility, design, and analyses” of GWASs—to show its utility. “Kernel-Machine Testing Coupled with a Rank-Truncation Method for Genetic Pathway Analysis” was published in the May issue of the journal Genetic Epidemiology.

Read more: http://onlinelibrary.wiley.com/doi/10.1002/gepi.21813/abstract