{'fraction_non_alphanumeric': 0.025423728813559324, 'fraction_numerical': 0.002824858757062147, 'mean_word_length': 5.529953917050691, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Current status of pathway analysis in genome-wide association study.

Since the first publication in 2005, the genome-wide association study (GWAS) strategy has contributed significantly to the understanding of the mechanisms of human genetic diseases. Integrations of statistical methods and systematic biology are important means to explore the GWAS data. Pathway analysis establishes the importance of genetic variants from GWAS and provides insights into their biological significance. It is conducive in correlating the genetic variants, which have only small but interactive changes, to their importance in the biological pathways. At present, pathway analysis has been widely applied to studies of GWAS data, with relatively good results. In the meantime, various analytical methods are being developed and adapted for research on more types of complex data. In this review, we summarize the statistical methods of pathway analysis on GWAS data, and divide them into non-kernel methods and kernel methods. The non-kernel methods include gene set enrichment analysis (GSEA) and hierarchical Bayes prioritization (HBP) analysis, while kernel methods include linear kernel (LIN), identity-by-status kernel (IBS) and powered exponential kernel. We have summarized the calculation principles and features of these statistical methods to provide insights for further developments of new algorithms in GWAS research.