Breast cancer (BC) has a significant heritable component but the genetic contribution remains unresolved in the majority of high-risk BC families. This study aims to investigate the monogenic causes underlying the familial aggregation of BC beyond BRCA1 and BRCA2, including the identification of new predisposing genes. A total of 11,511 non-BRCA familial BC cases and population-matched cancer-free female controls in the BEACCON study were investigated in two sequencing phases: 1303 candidate genes in up to 3892 cases and controls, followed by validation of 145 shortlisted genes in an additional 7619 subjects. The coding regions and exon-intron boundaries of all candidate genes and 14 previously proposed BC genes were sequenced using custom designed sequencing panels. Pedigree and pathology data were analysed to identify genotype-specific associations. The contribution of ATM, PALB2 and CHEK2 to BC predisposition was confirmed, but not RAD50 and NBN. An overall excess of loss-of-function (LoF) (OR 1.27, p = 9.05 × 10) and missense (OR 1.27, p = 3.96 × 10) variants was observed in the cases for the 145 candidate genes. Leading candidates harbored LoF variants with observed ORs of 2-4 and individually accounted for no more than 0.79% of the cases. New genes proposed by this study include NTHL1, WRN, PARP2, CTH and CDK9. The new candidate BC predisposition genes identified in BEACCON indicate that much of the remaining genetic causes of high-risk BC families are due to genes in which pathogenic variants are both very rare and convey only low to moderate risk.
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http://dx.doi.org/10.1038/s41523-021-00279-9 | DOI Listing |
Mol Plant Microbe Interact
January 2025
USDA ARS, Horticultural Crops Research Laboratory, 3420 NW Orchard Ave., Corvallis, Oregon, United States, 97330;
Members of the genus are responsible for many important diseases in agricultural and natural ecosystems. causes devastating diseases of oak, and tanoak stands in US forests and larch in the UK. The four evolutionary lineages involved express different virulence phenotypes on plant hosts, and characterization of gene content is foundational to understanding the basis for these differences.
View Article and Find Full Text PDFPLoS One
January 2025
School of Public Health, Anhui University of Science and Technology, Hefei, China.
A number of studies demonstrate the therapeutic effectiveness of Radix Bupleuri (RB) and Hedysarum Multijugum Maxim (HMM) in treating liver fibrosis, but the exact molecular mechanisms remain unclear. This study aims to explore the mechanism of RB-HMM drug pairs in treating liver fibrosis by using network pharmacology, bioinformatics, molecular docking, molecular dynamics simulation technology and in vitro experiments. Totally, 155 intersection targets between RB-HMM and liver fibrosis were identified.
View Article and Find Full Text PDFPlant J
January 2025
College of Horticulture, Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
The traditional Chinese medicinal plant Prunella vulgaris contains numerous triterpene saponin metabolites, notably ursolic and oleanolic acid saponins, which have significant pharmacological values. Despite their importance, the genes responsible for synthesizing these triterpene saponins in P. vulgaris remain unidentified.
View Article and Find Full Text PDFPlant Genome
March 2025
Department of Soil, Plant and Food Sciences, Genetics and Plant Breeding Section, University of Bari Aldo Moro, Bari, Italy.
Wheat breeders are constantly looking for genes and alleles that increase grain yield. One key strategy is finding new genetic resources in the wild and domesticated gene pools of related species with genes affecting grain size. This study explored a natural population of Triticum turgidum (L.
View Article and Find Full Text PDFProbl Endokrinol (Mosk)
January 2024
Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.
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