Genome-wide association study in Chinese identifies novel loci for blood pressure and hypertension.

Hypertension is a common disorder and the leading risk factor for cardiovascular disease and premature deaths worldwide. Genome-wide association studies (GWASs) in the European population have identified multiple chromosomal regions associated with blood pressure, and the identified loci altogether explain only a small fraction of the variance for blood pressure. The differences in environmental exposures and genetic background between Chinese and European populations might suggest potential different pathways of blood pressure regulation. To identify novel genetic variants affecting blood pressure variation, we conducted a meta-analysis of GWASs of blood pressure and hypertension in 11 816 subjects followed by replication studies including 69 146 additional individuals. We identified genome-wide significant (P < 5.0 × 10(-8)) associations with blood pressure, which included variants at three new loci (CACNA1D, CYP21A2, and MED13L) and a newly discovered variant near SLC4A7. We also replicated 14 previously reported loci, 8 (CASZ1, MOV10, FGF5, CYP17A1, SOX6, ATP2B1, ALDH2, and JAG1) at genome-wide significance, and 6 (FIGN, ULK4, GUCY1A3, HFE, TBX3-TBX5, and TBX3) at a suggestive level of P = 1.81 × 10(-3) to 5.16 × 10(-8). These findings provide new mechanistic insights into the regulation of blood pressure and potential targets for treatments.

Random forest fishing: a novel approach to identifying organic group of risk factors in genome-wide association studies.

Genome-wide association studies (GWAS) has brought methodological challenges in handling massive high-dimensional data and also real opportunities for studying the joint effect of many risk factors acting in concert as an organic group. The random forest (RF) methodology is recognized by many for its potential in examining interaction effects in large data sets. However, RF is not designed to directly handle GWAS data, which typically have hundreds of thousands of single-nucleotide polymorphisms as predictor variables. We propose and evaluate a novel extension of RF, called random forest fishing (RFF), for GWAS analysis. RFF repeatedly updates a relatively small set of predictors obtained by RF tests to find globally important groups predictive of the disease phenotype, using a novel search algorithm based on genetic programming and simulated annealing. A key improvement of RFF results from the use of guidance incorporating empirical test results of genome-wide pairwise interactions. Evaluated using simulated and real GWAS data sets, RFF is shown to be effective in identifying important predictors, particularly when both marginal effects and interactions exist, and is applicable to very large GWAS data sets.

Variable set enrichment analysis in genome-wide association studies.

Complex diseases such as hypertension are inherently multifactorial and involve many factors of mild-to-minute effect sizes. A genome-wide association study (GWAS) typically tests hundreds of thousands of single-nucleotide polymorphisms (SNPs), and offers opportunity to evaluate aggregated effects of many genetic variants with effects that are too small to detect individually. The gene-set-enrichment analysis (GSEA) is a pathway-based approach that tests for such aggregated effects of genes that are linked by biological functions. A key step in GSEA is the summary statistic (gene score) used to measure the overall relevance of a gene based on all SNPs tested in the gene. Existing GSEA methods use maximum statistics sensitive to gene size and linkage equilibrium. We propose the approach of variable set enrichment analysis (VSEA) and study new gene score methods that are less dependent on gene size. The new method treats groups of variables (SNPs or other variants) as base units for summarizing gene scores and relies less on gene definition itself. The power of VSEA is analyzed by simulation studies modeling various scenarios of complex multiloci interactions. Results show that the new gene scores generally performed better, some substantially so, than existing GSEA extension to GWAS. The new methods are implemented in an R package and when applied to a real GWAS data set demonstrated its practical utility in a GWAS setting.