Common variants in HSPB7 and FRMD4B associated with advanced heart failure.

BACKGROUND: Heart failure results from abnormalities in multiple biological processes that contribute to cardiac dysfunction. We tested the hypothesis that inherited variation in genes of known importance to cardiovascular biology would thus contribute to heart failure risk. METHODS AND RESULTS: We used the ITMAT/Broad/CARe cardiovascular single-nucleotide polymorphism array to screen referral populations of patients with advanced heart failure for variants in approximately 2000 genes of predicted importance to cardiovascular biology. Our design was a 2-stage case-control study. In stage 1, genotypes in Caucasian patients with heart failure (n=1590; ejection fraction, 32+/-16%) were compared with those in unaffected controls (n=577; ejection fraction, 67+/-8%) who were recruited from the same referral centers. Associations were tested for independent replication in stage 2 (308 cases and 2314 controls). Two intronic single-nucleotide polymorphisms showed replicated associations with all-cause heart failure as follows: rs1739843 in HSPB7 (combined P=3.09x10(-6)) and rs6787362 in FRMD4B (P=6.09x10(-6)). For both single-nucleotide polymorphisms, the minor allele was protective. In subgroup analyses, rs1739843 associated with both ischemic and nonischemic heart failure, whereas rs6787362 associated principally with ischemic heart failure. Linkage disequilibrium surrounding rs1739843 suggested that the causal variant resides in a region containing HSPB7 and a neighboring gene, CLCNKA, whereas the causal variant near rs6787362 is probably within FRMD4B. Allele frequencies for these single-nucleotide polymorphisms were substantially different in African Americans (635 cases and 714 controls) and showed no association with heart failure in this population. CONCLUSIONS: Our findings identify regions containing HSPB7 and FRMD4B as novel susceptibility loci for advanced heart failure. More broadly, in an era of genome-wide association studies, we demonstrate how knowledge of candidate genes can be leveraged as a complementary strategy to discern the genetics of complex disorders.

Concept, design and implementation of a cardiovascular gene-centric 50 k SNP array for large-scale genomic association studies.

A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a "cosmopolitan" tagging approach to capture the genetic diversity across approximately 2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.