Gene‐ and pathway‐level analyses of iCOGS variants highlight novel signaling pathways underlying familial breast cancer susceptibility. Issue 8 (9th January 2021) - Lonjou, Christine; Eon‐Marchais, Séverine; Truong, Thérèse; Dondon, Marie‐Gabrielle; Karimi, Mojgan; Jiao, Yue; Damiola, Francesca; Barjhoux, Laure; Le Gal, Dorothée; Beauvallet, Juana; Mebirouk, Noura; Cavaciuti, Eve; et al.
Abstract: Single‐nucleotide polymorphisms (SNPs) in over 180 loci have been associated with breast cancer (BC) through genome‐wide association studies involving mostly unselected population‐based case‐control series. Some of them modify BC risk of women carrying a BRCA1 or BRCA2 ( BRCA1/2 ) mutation and may also explain BC risk variability in BC‐prone families with no BRCA1/2 mutation. Here, we assessed the contribution of SNPs of the iCOGS array in GENESIS consisting of BC cases with no BRCA1 /2 mutation and a sister with BC, and population controls. Genotyping data were available for 1281 index cases, 731 sisters with BC, 457 unaffected sisters and 1272 controls. In addition to the standard SNP‐level analysis using index cases and controls, we performed pedigree‐based association tests to capture transmission information in the sibships. We also performed gene‐ and pathway‐level analyses to maximize the power to detect associations with lower‐frequency SNPs or those with modest effect sizes. While SNP‐level analyses identified 18 loci, gene‐level analyses identified 112 genes. Furthermore, 31 Kyoto Encyclopedia of Genes and Genomes and 7 Atlas of Cancer Signaling Network pathways were highlighted (false discovery rate of 5%). Using results from the "index case‐control" analysis, we built pathway‐derived polygenic risk scores (PRS) and assessed their performance in the population‐based CECILE study and in a data set composed of GENESIS‐affected sisters and CECILE controls. Although these PRS had poor predictive value in the general population, they performed better than a PRS built using our SNP‐level findings, and we found that the joint effect of family history and PRS needs to be considered in risk prediction models. Abstract : What's new? Genetic studies have identified more than 180 single‐nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, but these studies are reaching their limits. Here, the authors evaluated SNPs in the iCOGS genotyping array using a multilevel approach, including single variant, gene, and pathway analyses. They measured the contribution of the SNPs to breast cancer in patients who have a sister with breast cancer but do not carry a BRCA1/2 mutation. They showed that a pathway‐derived polygenic risk score performed poorly in the general population, and that the best predictive model must include family history.