Optimizing the power of genome-wide association studies by using publicly available reference samples to expand the control group.

Genome-wide association (GWA) studies have proved extremely successful in identifying novel genetic loci contributing effects to complex human diseases. In doing so, they have highlighted the fact that many potential loci of modest effect remain undetected, partly due to the need for samples consisting of many thousands of individuals. Large-scale international initiatives, such as the Wellcome Trust Case Control Consortium, the Genetic Association Information Network, and the database of genetic and phenotypic information, aim to facilitate discovery of modest-effect genes by making genome-wide data publicly available, allowing information to be combined for the purpose of pooled analysis. In principle, disease or control samples from these studies could be used to increase the power of any GWA study via judicious use as "genetically matched controls" for other traits. Here, we present the biological motivation for the problem and the theoretical potential for expanding the control group with publicly available disease or reference samples. We demonstrate that a naïve application of this strategy can greatly inflate the false-positive error rate in the presence of population structure. As a remedy, we make use of genome-wide data and model selection techniques to identify "axes" of genetic variation which are associated with disease. These axes are then included as covariates in association analysis to correct for population structure, which can result in increases in power over standard analysis of genetic information from the samples in the original GWA study.

Evaluating the effects of imputation on the power, coverage, and cost efficiency of genome-wide SNP platforms.

Genotype imputation is potentially a zero-cost method for bridging gaps in coverage and power between genotyping platforms. Here, we quantify these gains in power and coverage by using 1,376 population controls that are from the 1958 British Birth Cohort and were genotyped by the Wellcome Trust Case-Control Consortium with the Illumina HumanHap 550 and Affymetrix SNP Array 5.0 platforms. Approximately 50% of genotypes at single-nucleotide polymorphisms (SNPs) exclusively on the HumanHap 550 can be accurately imputed from direct genotypes on the SNP Array 5.0 or Illumina HumanHap 300. This roughly halves differences in coverage and power between the platforms. When the relative cost of currently available genome-wide SNP platforms is accounted for, and finances are limited but sample size is not, the highest-powered strategy in European populations is to genotype a larger number of individuals with the HumanHap 300 platform and carry out imputation. Platforms consisting of around 1 million SNPs offer poor cost efficiency for SNP association in European populations.

Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation.

BACKGROUND: Recently, it has been proposed that epigenetic variation may contribute to the risk of complex genetic diseases like cancer. We aimed to demonstrate that epigenetic changes in normal cells, collected years in advance of the first signs of morphological transformation, can predict the risk of such transformation. METHODS: We analyzed DNA methylation (DNAm) profiles of over 27,000 CpGs in cytologically normal cells of the uterine cervix from 152 women in a prospective nested case-control study. We used statistics based on differential variability to identify CpGs associated with the risk of transformation and a novel statistical algorithm called EVORA (Epigenetic Variable Outliers for Risk prediction Analysis) to make predictions. RESULTS: We observed many CpGs that were differentially variable between women who developed a non-invasive cervical neoplasia within 3 years of sample collection and those that remained disease-free. These CpGs exhibited heterogeneous outlier methylation profiles and overlapped strongly with CpGs undergoing age-associated DNA methylation changes in normal tissue. Using EVORA, we demonstrate that the risk of cervical neoplasia can be predicted in blind test sets (AUC = 0.66 (0.58 to 0.75)), and that assessment of DNAm variability allows more reliable identification of risk-associated CpGs than statistics based on differences in mean methylation levels. In independent data, EVORA showed high sensitivity and specificity to detect pre-invasive neoplasia and cervical cancer (AUC = 0.93 (0.86 to 1) and AUC = 1, respectively). CONCLUSIONS: We demonstrate that the risk of neoplastic transformation can be predicted from DNA methylation profiles in the morphologically normal cell of origin of an epithelial cancer. Having profiled only 0.1% of CpGs in the human genome, studies of wider coverage are likely to yield improved predictive and diagnostic models with the accuracy needed for clinical application. TRIAL REGISTRATION: The ARTISTIC trial is registered with the International Standard Randomised Controlled Trial Number ISRCTN25417821.

Assessment of sex-specific effects in a genome-wide association study of rheumatoid arthritis.

Rheumatoid arthritis (RA) is three times more common in females than in males, suggesting that sex may play a role in modifying genetic associations with disease. We have addressed this hypothesis by performing sex-differentiated and sex-interaction analyses of a genome-wide association study of RA in a North American population. Our results identify a number of novel associations that demonstrate strong evidence of association in both sexes combined, with no evidence of heterogeneity in risk between males and females. However, our analyses also highlight a number of associations with RA in males or females only. These signals may represent true sex-specific effects, or may reflect a lack of power to detect association in the smaller sample of males, and thus warrant further investigation.