PALB2 interacts with BRCA2, and biallelic mutations in PALB2 (also known as FANCN), similar to biallelic BRCA2 mutations, cause Fanconi anemia. We identified monoallelic truncating PALB2 mutations in 10/923 individuals with familial breast cancer compared with 0/1,084 controls (P = 0.0004) and show that such mutations confer a 2.3-fold higher risk of breast cancer (95% confidence interval (c.i.) = 1.4-3.9, P = 0.0025). The results show that PALB2 is a breast cancer susceptibility gene and further demonstrate the close relationship of the Fanconi anemia-DNA repair pathway and breast cancer predisposition. PALB2 (for 'partner and localizer of BRCA2') encodes a recently discovered protein that interacts with BRCA2, is implicated in its nuclear localization and stability and is required for some functions of BRCA2 in homologous recombination and double-strand break repair1. In a paper in this issue, we show that biallelic PALB2 mutations are responsible for a subset of Fanconi anemia cases characterized by a phenotype similar to that caused by biallelic BRCA2 mutations2. Prompted by these observations, we investigated whether monoallelic PALB2 mutations confer susceptibility to breast cancer by sequencing the gene © 2007 Nature Publishing Group Correspondence should be addressed to N.R (nazneen.rahman@icr.ac.uk).. AUTHOR CONTRIBUTIONS The study was designed by N.R. and M.R.S. The molecular analyses were performed by S.S., P.K., A.R., S.R., K.S., R.B., T.C., H.J. and S.H. under the direction of N.R. The statistical analyses were performed by D.T., A.E. and L.M. under the direction of D.F.E. The familial collections were initiated by D.G.E. and D.E. and were collected by the Breast Cancer Susceptibility Collaboration (UK). The manuscript was written by N.R. and M.R.S.Note: Supplementary information is available on the Nature Genetics website. COMPETING INTERESTS STATEMENTThe authors declare that they have no competing financial interests. We identified truncating PALB2 mutations in 10/923 (1.1%) independently ascertained individuals with familial breast cancer from separate families compared with 0/1,084 (0%) controls (P = 0.0004) ( Table 1 and Fig. 1a). Nine of the PALB2 mutations were in the 908 families with female breast cancer only (1.0%). One occurred in the 15 families (6.7%) with cases of both female and male breast cancer (P = 0.15). Although this observation requires further investigation, it suggests that PALB2 mutations may confer a higher relative risk of male breast cancer than female breast cancer, and BRCA2 mutations are known to confer a high relative risk of male breast cancer3. One proband with a PALB2 mutation developed melanoma at 47 years of age in addition to breast cancer at 56 years. Apart from this individual, there were no other malignancies other than breast cancer in individuals with PALB2 mutations. Two of four first-degree affected relatives of probands with PALB2 mutations also carried a PALB2 mutation. This pattern of incomplete segregation in affected relatives is typical of s...

We identified constitutional truncating mutations of the BRCA1-interacting helicase BRIP1 in 9/1,212 individuals with breast cancer from BRCA1/BRCA2 mutation-negative families but in only 2/2,081 controls (P = 0.0030), and we estimate that BRIP1 mutations confer a relative risk of breast cancer of 2.0 (95% confidence interval = 1.2-3.2, P = 0.012). Biallelic BRIP1 mutations were recently shown to cause Fanconi anemia complementation group J. Thus, inactivating truncating mutations of BRIP1, similar to those in BRCA2, cause Fanconi anemia in biallelic carriers and confer susceptibility to breast cancer in monoallelic carriers.

We screened individuals from 443 familial breast cancer pedigrees and 521 controls for ATM sequence variants and identified 12 mutations in affected individuals and two in controls (P = 0.0047). The results demonstrate that ATM mutations that cause ataxia-telangiectasia in biallelic carriers are breast cancer susceptibility alleles in monoallelic carriers, with an estimated relative risk of 2.37 (95% confidence interval (c.i.) = 1.51-3.78, P = 0.0003). There was no evidence that other classes of ATM variant confer a risk of breast cancer.

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to play an important role in genetic susceptibility to common disease. To address this we undertook a large direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed ~19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated ~50% of all common CNVs larger than 500bp. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell-lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease, IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis, and type 1 diabetes, and TSPAN8 for type 2 diabetes, though in each case the locus had previously been identified in SNP-based studies, reflecting our observation that the majority of common CNVs which are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs which can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.