Breast cancer risk is influenced by rare coding variants in susceptibility genes such as BRCA1 and many common, mainly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. We report results from a genome-wide association study (GWAS) 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 ancestry1. We identified 65 new loci associated with overall breast cancer at p<5x10-8. The majority of credible risk SNPs in the new 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 SNPs 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 utility of genetic risk scores for individualized screening and prevention.

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease1. 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 14% 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.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brainspecific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins. Huntington disease (HD)1 is an inherited neurodegenerative disorder characterized by progressive cognitive decline and psychiatric and movement symptoms. The cause of the disease is the expansion of trinucleotide (CAG) repeats in the coding region of the htt gene that translates into a polyglutamine tract in the huntingtin protein (1). Currently no treatment has been shown to delay the onset of the disease or slow its progression in patients. To speed assessment of therapies in clinical trials, it is critical to identify biological markers that can accurately monitor disease progression.Several genomics and proteomics approaches to identifying biomarkers for HD have been undertaken previously. Genomics studies have determined the molecular phenotype of human HD brain (2) and different tissues of HD mouse models at the mRNA level (3-6). Proteomics approaches have been applied to brain tissues of HD mouse models and humans to identify candidate markers (7-9). Blood plasma in particular has received considerable attention recently because of its ready accessibility clinically (10, 11). The candidate protein biomarkers identified in the blood proteomics studies are largely known inflammatory markers. Because HD is regarded primarily as a neurodegenerative disease, it is not entirely clear how directly general markers of neuroinflammation relate to the pathophysiology of HD, although astrocytosis and microgliosis (12) are prominent components of HD in its mid-to late stages (13). Another concern regarding markers discovered primarily in blood is that the blood-brain barrier may restrict brain proteins from entering plasma, and so plasma candidates may not directly reflect HD progression in the brain.Cerebrospinal fluid (CSF) is a more relevant biomaterial for biomarker discovery because it is proximal to the brain; it occupies the subarachnoid space of the central nervous system and the ventricular system around and inside the b...

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