Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants (CCVs) in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium, and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER+ or ER−) and human ERBB2 (HER2+ or HER2−) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER− tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

Breast cancer risk is strongly associated with an intergenic region on 11q13. We have previously shown that the strongest risk-associated SNPs fall within a distal enhancer that regulates CCND1. Here, we report that, in addition to regulating CCND1, this enhancer regulates two estrogen-regulated long noncoding RNAs, CUPID1 and CUPID2. We provide evidence that the risk-associated SNPs are associated with reduced chromatin looping between the enhancer and the CUPID1 and CUPID2 bidirectional promoter. We further show that CUPID1 and CUPID2 are predominantly expressed in hormone-receptor-positive breast tumors and play a role in modulating pathway choice for the repair of double-strand breaks. These data reveal a mechanism for the involvement of this region in breast cancer.

Genome-wide association studies (GWASs) have revealed increased breast cancer risk associated with multiple genetic variants at 5p12. Here, we report the fine mapping of this locus using data from 104,660 subjects from 50 case-control studies in the Breast Cancer Association Consortium (BCAC). With data for 3,365 genotyped and imputed SNPs across a 1 Mb region (positions 44,394,495-45,364,167; NCBI build 37), we found evidence for at least three independent signals: the strongest signal, consisting of a single SNP rs10941679, was associated with risk of estrogen-receptor-positive (ER) breast cancer (per-g allele OR ER = 1.15; 95% CI 1.13-1.18; p = 8.35 × 10). After adjustment for rs10941679, we detected signal 2, consisting of 38 SNPs more strongly associated with ER-negative (ER) breast cancer (lead SNP rs6864776: per-a allele OR ER = 1.10; 95% CI 1.05-1.14; p conditional = 1.44 × 10), and a single signal 3 SNP (rs200229088: per-t allele OR ER = 1.12; 95% CI 1.09-1.15; p conditional = 1.12 × 10). Expression quantitative trait locus analysis in normal breast tissues and breast tumors showed that the g (risk) allele of rs10941679 was associated with increased expression of FGF10 and MRPS30. Functional assays demonstrated that SNP rs10941679 maps to an enhancer element that physically interacts with the FGF10 and MRPS30 promoter regions in breast cancer cell lines. FGF10 is an oncogene that binds to FGFR2 and is overexpressed in ∼10% of human breast cancers, whereas MRPS30 plays a key role in apoptosis. These data suggest that the strongest signal of association at 5p12 is mediated through coordinated activation of FGF10 and MRPS30, two candidate genes for breast cancer pathogenesis.

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Relationships between viral load, severity of illness, and transmissibility of virus are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with the control of the viral load. Neutralising antibodies correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralising antibodies. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.

Since its first identification in Scotland, over 1000 cases of unexplained pediatric hepatitis in children have been reported worldwide, including 278 cases in the UK 1 . Here we report investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator subjects, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in liver, blood, plasma or stool from 27/28 cases. We found low levels of Adenovirus (HAdV) and Human Herpesvirus 6B (HHV-6B), in 23/31 and 16/23 respectively of the cases tested. In contrast, AAV2 was infrequently detected at low titre in blood or liver from control children with HAdV, even when profoundly immunosuppressed.AAV2, HAdV and HHV-6 phylogeny excluded emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T-cells and B-lineage cells.Proteomic comparison of liver tissue from cases and healthy controls, identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins.HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and in severe cases HHV-6B, may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children.

Breast cancer genome-wide association studies (GWAS) have identified 150 genomic risk regions containing more than 13,000 credible causal variants (CCVs). The CCVs are predominantly noncoding and enriched in regulatory elements. However, the genes underlying breast cancer risk associations are largely unknown. Here, we used genetic colocalization analysis to identify loci at which gene expression could potentially explain breast cancer risk phenotypes. Using data from the Breast Cancer Association Consortium (BCAC) and quantitative trait loci (QTL) from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Project (TCGA), we identify shared genetic relationships and reveal novel associations between cancer phenotypes and effector genes.Seventeen genes, including NTN4, were identified as potential mediators of breast cancer risk. For NTN4, we showed the rs61938093 CCV at this region was located within an enhancer element that physically interacts with the NTN4 promoter, and the risk allele reduced NTN4 promoter activity. Furthermore, knockdown of NTN4 in breast cells increased cell proliferation in vitro and tumor growth in vivo. These data provide evidence linking risk-associated variation to genes that may contribute to breast cancer predisposition.The influence of common genetic variation on gene expression underlies a considerable proportion of the heritability associated with complex traits. Mapping of expression QTL (eQTL), where genetic variants are tested for association with gene expression levels, is widely used to identify genes that are regulated by trait-associated variants. Several studies have shown that eQTLs are enriched in cell types relevant to the trait of interest1; 2. For example, T cell-specific eQTLs are over-represented for autoimmune risk alleles and monocyte-specific eQTLs for Alzheimer's [MIM: 104300] and Parkinson's [MIM: 168600] disease alleles2. For breast cancer [MIM: 114480], several studies have used eQTL data from tumor and normal tissues datasets to identify candidate target genes3-6. Recent studies have also showed that breast cancer risk variants could regulate genes in cells of the tumor microenvironment, such as immune cells and fibroblasts7; 8. Because eQTLs are widespread, overlap between GWAS and eQTL signals is likely to occur by chance when using nominal significance