Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P < 0.05 from African ancestry GWAS meta-analysis (9241 cases and 10193 controls), then meta-analyze with European ancestry GWAS data (122977 cases and 105974 controls) from the Breast Cancer Association Consortium. The approach identifies four loci for overall breast cancer risk [1p13.3, 5q31.1, 15q24 (two independent signals), and 15q26.3] and two loci for estrogen receptor-negative disease (1q41 and 7q11.23) at genome-wide significance. Four of the index single nucleotide polymorphisms (SNPs) lie within introns of genes (KCNK2, C5orf56, SCAMP2, and SIN3A) and the other index SNPs are located close to GSTM4, AMPD2, CASTOR2, and RP11-168G16.2. Here we present risk loci with consistent direction of associations in African and European descendants. The study suggests that replication across multiple ancestry populations can help improve the understanding of breast cancer genetics and identify causal variants.

No abstract

Genome-wide association studies (GWAS) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWAS) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify novel candidate genes, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 47 tissues from the Genotype-Tissue Expression data using a multivariate adaptive shrinkage method along with association summary statistics from the Breast Cancer Association Consortium and UK Biobank data. We identified 380 genes at 129 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold (p < 2.36E-6). Of them, 29 genes were located in 11 novel regions that were at least 1Mb away from published GWAS hits. The rest of TWAS-significant genes were located in 118 known genomic loci from previous GWAS of breast cancer. After conditioning on previous GWAS index variants, we found that 22 genes located in known GWAS loci remained statistically significant. Our study maps potential target genes in more than half of known GWAS loci and discovers multiple new loci, providing new insights into breast cancer genetics.

In the past 15 years, genome-wide association studies (GWAS) have provided novel insight into the genetic architecture of various complex traits; however, this insight has been primarily focused on populations of European descent. This emphasis on European populations has led to individuals of recent African descent being grossly underrepresented in the study of genetics. With African Americans making up less than 2% of participants in neuropsychiatric GWAS, this discrepancy is magnified in diseases such as schizophrenia and bipolar disorder. In this study, we performed GWAS and the gene-based association method PrediXcan for schizophrenia (n = 2,256) and bipolar disorder (n = 1,019) in African American cohorts. In our PrediXcan analyses, we identified PRMT7 (P = 5.5 × 10−6, local false sign rate = 0.12) as significantly associated with schizophrenia following an adaptive shrinkage multiple testing adjustment. This association with schizophrenia was confirmed in the much larger, predominantly European, Psychiatric Genomics Consortium. In addition to the PRMT7 association with schizophrenia, we identified rs10168049 (P = 1.0 × 10−6) as a potential candidate locus for bipolar disorder with highly divergent allele frequencies across populations, highlighting the need for diversity in genetic studies.