Genetic architecture of circulating lipid levels.

Serum concentrations of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs) and total cholesterol (TC) are important heritable risk factors for cardiovascular disease. Although genome-wide association studies (GWASs) of circulating lipid levels have identified numerous loci, a substantial portion of the heritability of these traits remains unexplained. Evidence of unexplained genetic variance can be detected by combining multiple independent markers into additive genetic risk scores. Such polygenic scores, constructed using results from the ENGAGE Consortium GWAS on serum lipids, were applied to predict lipid levels in an independent population-based study, the Rotterdam Study-II (RS-II). We additionally tested for evidence of a shared genetic basis for different lipid phenotypes. Finally, the polygenic score approach was used to identify an alternative genome-wide significance threshold before pathway analysis and those results were compared with those based on the classical genome-wide significance threshold. Our study provides evidence suggesting that many loci influencing circulating lipid levels remain undiscovered. Cross-prediction models suggested a small overlap between the polygenic backgrounds involved in determining LDL-C, HDL-C and TG levels. Pathway analysis utilizing the best polygenic score for TC uncovered extra information compared with using only genome-wide significant loci. These results suggest that the genetic architecture of circulating lipids involves a number of undiscovered variants with very small effects, and that increasing GWAS sample sizes will enable the identification of novel variants that regulate lipid levels.

A combined analysis of genome-wide association studies in breast cancer.

In an attempt to identify common disease susceptibility alleles for breast cancer, we performed a combined analysis of three genome-wide association studies (GWAS), involving 2,702 women of European ancestry with invasive breast cancer and 5,726 controls. Tests for association were performed for 285,984 SNPs. Evidence for association with SNPs in genes in specific pathways was assessed using a permutation-based approach. We confirmed associations with loci reported by previous GWAS on 1p11.2, 2q35, 3p, 5p12, 8q24, 10q23.13, 14q24.1 and 16q. Six SNPs with the strongest signals of association with breast cancer, and which have not been reported previously, were typed in two further studies; however, none of the associations could be confirmed. Suggestive evidence for an excess of associations was found for genes involved in the regulation of actin cytoskeleton, glycan degradation, alpha-linolenic acid metabolism, circadian rhythm, hematopoietic cell lineage and drug metabolism. Androgen and oestrogen metabolism, a pathway previously found to be associated with the development of postmenopausal breast cancer, was marginally significant (P = 0.051 [unadjusted]). These results suggest that further analysis of SNPs in these pathways may identify associations that would be difficult to detect through agnostic single SNP analyses. More effort focused in these aspects of oncology can potentially open up promising avenues for the understanding of breast cancer and its prevention.

A genome-wide association scan on estrogen receptor-negative breast cancer.

INTRODUCTION: Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk. METHODS: We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases. RESULTS: Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer shares a polygenic basis to disease with ER-positive breast cancer. CONCLUSIONS: ER-negative breast cancer is a distinct breast cancer subtype that merits independent analyses. Given the clinical importance of this phenotype and the likelihood that genetic effect sizes are small, greater sample sizes and further studies are required to understand the etiology of ER-negative breast cancers.