Association analysis identifies 65 new breast cancer risk loci.

Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Evidence for dysregulation of genome-wide recombination in oocytes with nondisjoined chromosomes 21.

In oocytes with nondisjoined chromosomes 21 due to a meiosis I (MI) error, recombination is significantly reduced along chromosome 21; several lines of evidence indicate that this contributes to the nondisjunction event. A pilot study found evidence that these oocytes also have reduced recombination genome-wide when compared with controls. This suggests that factors that act globally may be contributing to the reduced recombination on chromosome 21, and hence, the nondisjunction event. To identify the source of these factors, we examined two levels of recombination count regulation in oocytes: (i) regulation at the maternal level that leads to correlation in genome-wide recombination across her oocytes and (ii) regulation at the oocyte level that leads to correlation in recombination count among the chromosomes of an oocyte. We sought to determine whether either of these properties was altered in oocytes with an MI error. As it relates to maternal regulation, we found that both oocytes with an MI error (N = 94) and their siblings (N = 64) had reduced recombination when compared with controls (N = 2723). At the oocyte level, we found that the correlation in recombination count among the chromosomes of an oocyte is reduced in oocytes with MI errors compared with that of their siblings or controls. These results suggest that regulation at the maternal level predisposes MI error oocytes to reduced levels of recombination, but additional oocyte-specific dysregulation contributes to the nondisjunction event.

A Custom Genotyping Array Reveals Population-Level Heterogeneity for the Genetic Risks of Prostate Cancer and Other Cancers in Africa.

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array. SIGNIFICANCE: This study presents an Africa-specific genotyping array, which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

Development, Evaluation, and Implementation of a Pan-African Cancer Research Network: Men of African Descent and Carcinoma of the Prostate.

PURPOSE: Cancer of the prostate (CaP) is the leading cancer among men in sub-Saharan Africa (SSA). A substantial proportion of these men with CaP are diagnosed at late (usually incurable) stages, yet little is known about the etiology of CaP in SSA. METHODS: We established the Men of African Descent and Carcinoma of the Prostate Network, which includes seven SSA centers partnering with five US centers to study the genetics and epidemiology of CaP in SSA. We developed common data elements and instruments, regulatory infrastructure, and biosample collection, processing, and shipping protocols. We tested this infrastructure by collecting epidemiologic, medical record, and genomic data from a total of 311 patients with CaP and 218 matched controls recruited at the seven SSA centers. We extracted genomic DNA from whole blood, buffy coat, or buccal swabs from 265 participants and shipped it to the Center for Inherited Disease Research (Baltimore, MD) and the Centre for Proteomics and Genomics Research (Cape Town, South Africa), where genotypes were generated using the UK Biobank Axiom Array. RESULTS: We used common instruments for data collection and entered data into the shared database. Double-entered data from pilot participants showed a 95% to 98% concordance rate, suggesting that data can be collected, entered, and stored with a high degree of accuracy. Genotypes were obtained from 95% of tested DNA samples (100% from blood-derived DNA samples) with high concordance across laboratories. CONCLUSION: We provide approaches that can produce high-quality epidemiologic and genomic data in multicenter studies of cancer in SSA.

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10-8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC.

The development of a highly informative mouse Simple Sequence Length Polymorphism (SSLP) marker set and construction of a mouse family tree using parsimony analysis.

To identify highly informative markers for a large number of commonly employed murine crosses, we selected a subset of the extant mouse simple sequence length polymorphism (SSLP) marker set for further development. Primer pairs for 314 SSLP markers were designed and typed against 54 inbred mouse strains. We designed new PCR primer sequences for the markers selected for multiplexing using the fluorescent dyes FAM, VIC, NED, and ROX. The number of informative markers for C57BL/6J x DBA/2J is 217, with an average spacing of 6.8 centiMorgans (cM). For all other pairs of strains, the mean number of informative markers per cross is 197.0 (SD 37.8) with a mean distance between markers of 6.8 cM (SD 1.1). To confirm map positions of the 224 markers in our set that are polymorphic between Mus musculus and Mus spretus, we used The Jackson Laboratory (TJL) interspecific backcross mapping panel (TJL BSS); 168 (75%) of these markers had not been previously mapped in this cross by other investigators, adding new information to this community map resource. With this large data set, we sought to reconstruct a phylogenetic history of the laboratory mouse using Wagner parsimony analysis. Our results are largely congruent with the known history of inbred mouse strains.