Diversity in EWAS: current state, challenges, and solutions

Here, we report a lack of diversity in epigenome-wide association studies (EWAS) and DNA methylation (DNAm) data, discuss current challenges, and propose solutions for EWAS and DNAm research in diverse populations. The strategies we propose include fostering community involvement, new data generation, and cost-effective approaches such as locus-specific analysis and ancestry variable region analysis

Polymorphisms in estrogen-metabolizing and estrogen receptor genes and the risk of developing breast cancer among a cohort of women with benign breast disease

BACKGROUND: A cohort study was conducted to examine the role of genetic polymorphisms in three estrogen metabolizing enzymes (COMT, CYP1A1, CYP1B1) and the two estrogen receptors (ESR1, ESR2) in the progression of benign breast disease (BBD) to breast cancer. METHODS: Among participants in an ongoing cohort study, 1438 Caucasian women had a breast biopsy for BBD and were successfully genotyped for at least one of the polymorphisms examined in this study. Genotypes were determined using DNA extracted from blood specimens collected in 1989. Incident cases of breast cancer occurring subsequent to BBD diagnosis up to 2003 were identified through cancer registries. RESULTS: Among all participants, the ESR2 *5772G allele was associated with a significant decrease in the risk of breast cancer among women with BBD (Odds Ratio (OR) 0.38; 95% Confidence Interval (CI) 0.15, 0.96). Compared to the reference wild-type genotypes, marginally significant associations with the development of breast cancer were observed between carriers of the variant ESR1 – 104062T allele (OR 0.70, 95% CI 0.45, 1.09), the variant ESR2 *38A allele (OR 1.40; 95% CI 0.88, 2.25), and the variant CYP1B1 453Ser allele (OR 1.48, 95% CI 0.95, 2.32). CONCLUSION: The results indicate that specific polymorphisms in the CYP1B1, ESR1, and ESR2 genes may play a role in progression of BBD to breast cancer among Caucasian women. Although additional studies are needed to confirm or refute our findings, these results suggest that genetic markers may aid in the identification of women who are at risk for progression of BBD to cancer

Aspirin, ibuprofen, and reduced risk of advanced colorectal adenoma incidence and recurrence and colorectal cancer in the PLCO Cancer Screening Trial

BACKGROUND: Studying the differential impact of aspirin and other nonsteroidal anti-inflammatory drugs across the stages of colorectal neoplasia from early adenoma to cancer is critical for understanding the benefits of these widely used drugs. METHODS: With 13 years of follow-up, the authors prospectively evaluated the association between aspirin and ibuprofen use and incident distal adenoma (1221 cases), recurrent adenoma (862 cases), and incident colorectal cancer (CRC; 2826 cases) among men and women in the population-based Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. With multivariable-adjusted models, odds ratio (ORs) and 95% confidence intervals (CIs) for adenoma incidence and recurrence and hazard ratios (HRs) and 95% CIs for incident CRC were determined. RESULTS: The authors observed a significantly reduced risk of incident adenoma with ibuprofen use (≥30 vs \u3c4 pills per month: OR, 0.76 [95% CI, 0.60-0.95]; P(trend) = .04), particularly advanced adenoma (OR, 0.48 [95% CI, 0.28-0.83]; P(trend) = .005). Among those with a previous adenoma detected through screening, aspirin use was associated with a decreased risk of advanced recurrent adenoma (≥30 vs \u3c4 pills per month: OR, 0.56 [95% CI, 0.36-0.87]; P(trend) = 0.006). Both aspirin (HR, 0.88 [95% CI, 0.81-0.96]; P(trend) \u3c.0001) and ibuprofen use (HR, 0.81 [95% CI, 0.70-0.93); P(trend) = 0.003) ≥30 versus \u3c4 pills per month were significantly associated with reduced CRC risk. CONCLUSIONS: In this large prospective study with long-term follow-up, a beneficial role for not only aspirin, but also ibuprofen, in preventing advanced adenoma and curbing progression to recurrence and cancer among older adults was observed

Improved Imputation of Common and Uncommon Single Nucleotide Polymorphisms (SNPs) with a New Reference Set

Statistical imputation of genotype data is an important technique for analysis of genome-wide association studies (GWAS). We have built a reference dataset to improve imputation accuracy for studies of individuals of primarily European descent using genotype data from the Hap1, Omni1, and Omni2.5 human SNP arrays (Illumina). Our dataset contains 2.5-3.1 million variants for 930 European, 157 Asian, and 162 African/African-American individuals. Imputation accuracy of European data from Hap660 or OmniExpress array content, measured by the proportion of variants imputed with R^2^>0.8, improved by 34%, 23% and 12% for variants with MAF of 3%, 5% and 10%, respectively, compared to imputation using publicly available data from 1,000 Genomes and International HapMap projects. The improved accuracy with the use of the new dataset could increase the power for GWAS by as much as 8% relative to genotyping all variants. This reference dataset is available to the scientific community through the NCBI dbGaP portal. Future versions will include additional genotype data as well as non-European populations

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.

Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome-wide association study data to identify single-nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P-value threshold. However, P-values do not account for differences in power, whereas Bayes' factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P-values of a decreasing type I error rate as study size increases for single-disease associations. Consequently, the overlap analysis of traits from different-sized studies encounters issues in fair P-value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P-values, particularly in low-power scenarios. Calibration tables between BFs and P-values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P-values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis

Leveraging population admixture to characterize the heritability of complex traits.

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2)

Two Novel Susceptibility Loci for Prostate Cancer in Men of African Ancestry

Prostate cancer incidence is 1.6-fold higher in African Americans than in other populations. The risk factors that drive this disparity are unknown and potentially consist of social, environmental, and genetic influences. To investigate the genetic basis of prostate cancer in men of African ancestry, we performed a genome-wide association meta-analysis using two-sided statistical tests in 10 202 case subjects and 10 810 control subjects. We identified novel signals on chromosomes 13q34 and 22q12, with the risk-associated alleles found only in men of African ancestry (13q34: rs75823044, risk allele frequency = 2.2%, odds ratio [OR] = 1.55, 95% confidence interval [CI] = 1.37 to 1.76, P = 6.10 × 10−12; 22q12.1: rs78554043, risk allele frequency = 1.5%, OR = 1.62, 95% CI = 1.39 to 1.89, P = 7.50 × 10−10). At 13q34, the signal is located 5’ of the gene IRS2 and 3’ of a long noncoding RNA, while at 22q12 the candidate functional allele is a missense variant in the CHEK2 gene. These findings provide further support for the role of ancestry-specific germline variation in contributing to population differences in prostate cancer risk

Genetic Variation in Base Excision Repair Pathway Genes, Pesticide Exposure, and Prostate Cancer Risk

Background: Previous research indicates increased prostate cancer risk for pesticide applicators and pesticide manufacturing workers. Although underlying mechanisms are unknown, evidence suggests a role of oxidative DNA damage