The advent of high-throughput technology is generating unprecedented amounts of genotypic data that are being used in association analyses for multiple phenotypes. A single project may involve genotyping many genes with several variants (such as single nucleotide polymorphisms [SNPs]) per gene and analyzing each variant in relation to numerous phenotypes. In turn, each phenotype-SNP pair may be subjected to multiple genetic models and subgroup analyses. Hundreds of statistical tests may be performed for a single SNP, thereby complicating interpretation of results and inhibiting identification of patterns of association within a vast sea of data. Ultra-dense genome scans using 300,000 to 1,000,000 SNPs [1-3]will require efficient methods for analysis and presentation of results.
We are currently studying common SNPs in 200 candidate genes to test associations with alterations in echocardiographic phenotypes in participants from NHLBI's Framingham Heart Study. For each SNP, 144 statistical tests are performed: genotypes are analyzed with regard to six phenotypes (left ventricular [LV] mass, LV internal dimension, LV wall thickness, left atrial dimension, aortic dimension) through four genetic models (general, dominant, additive, recessive), with two levels of covariate adjustment (age and sex; age, sex and multiple additional covariates) in three samples (pooled sexes, men, women). Planned analyses of 1500 SNPs will generate nearly one quarter of a million statistical tests. Further details can be found on the CardioGenomics website [4].
As analyses commenced, it became obvious that we needed summary methods of data distillation and presentation to highlight findings of potential importance and to identify patterns of association, such as associations limited to one of multiple phenotypes, or associations limited to one sex. Therefore, we developed an approach that displays strengths of statistical associations at a glance, and that makes supporting data available easily via graphs accessed by a mouse click.
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