Main

Breast cancer is the leading cause of cancer-related mortality for women worldwide. Genetic susceptibility to breast cancer is known to be conferred by common variants, identified through genome-wide association studies (GWAS), together with rarer coding variants conferring higher disease risks. The latter, identified through genetic linkage or targeted sequencing studies, includes protein-truncating variants (PTVs) and some rare missense variants in ATM, BARD1, BRCA1, BRCA2, CHEK2, RAD51C, RAD51D, PALB2 and TP53 (ref. 1). However, these variants together explain less than half the familial relative risk (FRR) of breast cancer2. The contribution of rare coding variants in other genes remains largely unknown.

Here we used data from the following three large whole-exome sequencing (WES) studies, primarily of European ancestry, to assess the role of rare variants in all coding genes: the Breast Cancer Risk after Diagnostic Gene Sequencing (BRIDGES) dataset that included samples from eight studies in the Breast Cancer Association Consortium (BCAC), the PERSPECTIVE (Personalized Risk assessment for prevention and early detection of breast cancer: integration and implementation) dataset that included three BCAC studies (Supplementary Table 1) and UK Biobank (UKB). After quality control (QC; Methods), these datasets comprised 26,368 female cases and 217,673 female controls (Supplementary Table 2).

We considered the following two main categories of variants: PTVs and rare missense variants (minor allele frequency <0.001). Single-variant association tests are generally underpowered for rare variants; however, burden tests, in which variants are collapsed together, can be more powerful if the associated variants have similar effect sizes3. To further improve power, we incorporated data on family history of breast cancer (Methods)4. Association tests were conducted for all genes in which there was at least one carrier of a variant (15,616 genes for PTVs and 18,601 genes for rare missense variants).

In the PTV meta-analysis, 30 genes were associated at P < 0.001 (Supplementary Table 3 and Figs. 1 and 2). Of these, six met exome-wide significance (P < 2.5 × 10−6), of which five are known breast cancer risk genes— ATM, BRCA1, BRCA2, CHEK2 and PALB2. Associations were also identified for PTVs in MAP3K1 (P = 1.2 × 10−9). Associations at P < 1 × 10−4 were also identified for PTVs in LZTR1, ATR interacting protein (ATRIP) and the known risk gene BARD1. Of the other previously identified breast cancer susceptibility genes, associations with P < 0.01 were observed for CDH1 and RAD51D (Supplementary Table 4). Associations significant at P < 0.01 were not observed for other known susceptibility genes, but PTV frequencies were very low and the confidence limits include the previous odds ratio (OR) estimates1,5.

Fig. 1: Manhattan plot of z scores from the meta-analysis assessing the association between protein-truncating variant carriers within genes and breast cancer risk.
figure 1

The x axis is the chromosomal position, and the y axis is the meta-analyzed z score from testing H0: β = ln(OR) = 0 in the UK Biobank and BCAC datasets (two-tailed). The blue lines correspond to z = ±3.29, P = 0.001, the red lines correspond to z = ±4.71, P = 2.5 × 10−6. All labeled genes are those with P < 0.001. All P values are unadjusted for multiple testing.

Full size image
Fig. 2: Quantile–quantile plot of P values from the meta-analysis assessing the association between protein-truncating variant carriers and breast cancer risk.
figure 2

P values are from the meta-analyzed z score from testing H0: β = ln(OR) = 0 in the UKB and BCAC datasets (two-tailed). The x axis is the expected log10 P values from the null hypothesis, the y axis is the observed log10 P values. All highlighted genes have P < 0.0005 and are associated with an increased risk of breast cancer. All P values are unadjusted for multiple testing.

Full size image

There was no evidence for an excess of associations significant at P < 0.001 after allowing for the six exome-wide significant genes (Fig. 2). However, 28 of the 30 associations at P < 0.001 correspond to an increased risk, compared with ~15 that would be expected by chance. This imbalance suggests some of the other associations may be genuine.

We performed additional analyses by age and (within the BCAC dataset) the following disease subtypes: estrogen receptor (ER)+ or ER−, progesterone receptor (PR)+ or PR− and triple-negative disease. When restricting the age of cases to <50 years, 40 genes were associated (all with increased risk) at P < 0.001, suggesting an enrichment of associations in this age group (Supplementary Table 5), MGAT5 met exome-wide significance, in addition to BRCA2, BRCA1, CHEK2, PALB2, ATM and MAP3K1. In the subtype-specific analyses (Supplementary Table 6), the expected associations for known genes were observed, importantly, the higher OR for ER− and triple-negative disease for BRCA1 and higher OR for ER+ disease for CHEK2, but no other genes were associated with subtype-specific disease at exome-wide significance.

For the rare missense variant meta-analysis, 28 genes had a P < 0.001, 18 of which corresponded to an increased risk of breast cancer (Supplementary Table 7 and Extended Data Figs. 1 and 2) compared to 14 expected by chance. Only CHEK2 met exome-wide significance (P = 7.0 × 10−19). Associations with P < 1 × 10−4 were also observed for rare missense variants in SAMHD1, HCN2, CLIC6 and ACTL8.

We next considered missense variants predicted deleterious combined with PTVs. We defined deleterious missense variants using Combined Annotation Dependent Depletion (CADD score > 20) (ref. 6) and Helix (Helix score > 0.5) (ref. 7). When using CADD, 33 genes had a P < 0.001, 22 of which corresponded to an increased risk of breast cancer (Supplementary Table 8 and Extended Data Figs. 3 and 4). Six genes met exome-wide significance, including the following known five risk genes: CHEK2 (P = 2.8 × 10−66), BRCA2 (P = 7.2 × 10−44), PALB2 (P = 4.5 × 10−26), ATM (P = 3.3 × 10−21) and BRCA1 (P = 1.6 × 10−17), together with CDKN2A (P = 8.3 × 10−7). Associations with P < 1 × 10−4 were also observed for SAMHD1, MRPL27, EXOC4 and PPP1R3B. When instead defining deleterious rare missense variants using Helix and combining with PTVs, 29 genes had a P < 0.001, 25 of which corresponded to an increased risk of breast cancer (Supplementary Table 9). Only the known five genes met exome-wide significance. Associations with P < 1 × 10−4 were also observed for LZTR1, MAP3K1, DCLK1, MDM4, STX3 and ATRIP.

Notably, of the genes with P < 1 × 10−4, MAP3K1, LZTR1, ATRIP, CDKN2A and SAMHD1 have prior evidence of being tumor suppressor genes (TSGs). MAP3K1 is a stress-induced serine/threonine kinase that activates the extracellular signal-regulated kinase (ERK) and Jun N-terminal kinase (JNK) pathways by phosphorylation of MAP2K1 and MAP2K4 (refs. 8,9). Inactivating variants in MAP3K1 are one of the commonest somatic driver events in breast tumors10,11. MAP3K1 is also a well-established breast cancer GWAS locus12; at least three independent signals have been identified mapping to regulatory regions with MAP3K1 expression as the likely target13,14. To evaluate whether the MAP3K1 PTV association we observed was driven by the GWAS associations, or vice-versa, we fitted logistic regression models to UKB data in which the PTV burden variable and the lead GWAS SNPs (SNP1: rs62355902, SNP2: rs984113 and SNP3: rs112497245) were considered jointly (Supplementary Table 10). In the model with all variables, the OR associated with carrying a PTV (OR = 4.95 (2.27, 10.82)) was similar to the unadjusted OR. Similarly, the ORs for each of the SNPs were similar to the ORs without adjustment for PTVs. This suggests that the PTV burden and GWAS associations are independent and reflect the distinct effects of inactivating coding alterations and regulatory variants.

ATRIP codes for a DNA damage response protein that forms a complex with ATR. ATR–ATRIP is involved in the process that activates checkpoint signaling when single-stranded DNA is detected following the processing of DNA double-stranded breaks or stalled replication forks15,16. LZTR1 codes for a protein found in the Golgi apparatus17. Germline mutations in LZTR1 have been associated with schwannomatosis, a rare tumor predisposition syndrome18,19. CDKN2A also codes for tumor suppressor proteins, including p16(INK4A) and p14(ARF) (ref. 20). CDKN2A is a known melanoma21 and pancreatic cancer susceptibility gene and is an important TSG altered in a wide variety of tumors, including breast cancer22. There have been some previous suggestions that deleterious germline CDKN2A is associated with breast cancer risk23,24. SAMHD1 promotes the degradation of nascent DNA at stalled replication forks, limiting the release of single-stranded DNA25. SAMHD1 also encodes dNTPase that protects cells from viral infections26 and is frequently mutated in multiple tumor types, including breast cancer. Furthermore, damaging germline variants in SAMHD1 have recently been associated with delayed age at natural menopause and increased all-cause cancer risk27. MDM4 encodes a p53 repressor overexpressed in a variety of tumors28 and is also a GWAS locus for triple-negative breast cancer13,29.

Pathology information was available for cases in the BCAC dataset. We tabulated pathology characteristics for carriers of variants in genes with P < 1 × 10−4 in the meta-analysis of PTVs or the meta-analysis of predicted deleterious (CADD) rare missense variants combined with PTVs (Supplementary Tables 11 and 12). These data suggest a slightly higher proportion of mixed lobular and ductal tumors for LZTR1 PTV carriers and MRPL27 deleterious rare missense variant or PTV carriers. There was a slightly higher proportion diagnosed >50 years for ATRIP PTV carriers and a higher proportion of HER2+ tumors for EXOC4 deleterious rare missense variant or PTV carriers. However, the number of carriers is small in each case.

We performed Gene Set Enrichment Analysis (GSEA) based on the PTV associations for pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG), BioCarta and Reactome. We did this for all genes including and excluding BRCA1, BRCA2, ATM, CHEK2 and PALB2. When including the five genes, 28 pathways had a false discovery rate (q) < 0.05 (Supplementary Table 13). Of these, all but one (Reactome peptide hormone biosynthesis) include BRCA1 or BRCA2. The top pathway was Reactome DNA double-strand break repair. After excluding the five genes (Supplementary Table 14), the strongest enrichment was for the BioCarta NFKB and CD40 pathways (which contain MAP3K1), Reactome DNA double-strand break response and Reactome hormone peptide biosynthesis (all q < 0.10).

To evaluate the overall contribution of PTVs to the FRR, we fitted models to the effect size using an empirical Bayes approach. We used whole-genome sequencing data in UKB to estimate the missing contribution due to large rearrangements. Under the assumption of an exponentially distributed effect size, the estimated proportion of risk genes (a) was 0.0047 with a median OR of 1.38. Under this model, an estimated 10.61% of the FRR would be explained by all PTVs, of which 9.64% would be due to the five genes BRCA1, BRCA2, ATM, CHEK2 and PALB2 and 0.97% due to all other genes combined with MAP3K1 contributing 0.14% (Supplementary Table 15). Only the six genes reaching exome-wide significance for PTVs had a posterior probability of association >0.90. We repeated these analyses using the subsets of genes including breast cancer driver genes and target genes of GWAS signals identified in ref. 13, the list of cancer predisposition genes identified in ref. 30, Catalogue Of Somatic Mutations In Cancer (COSMIC) TSGs31 and the top pathways identified by GSEA (Supplementary Table 16). The largest contributions to the FRR, after excluding the five known genes, were for the BioCarta CD40 pathway (0.657%, n = 16, a = 0.628) and COSMIC TSGs (0.639%, n = 320, a = 0.196). Thus, these results suggest that the majority of the remaining risk genes are TSGs.

These results demonstrate that large exome sequencing studies, combined with efficient burden analyses, can identify additional breast cancer susceptibility genes. The excess of positive associations at P < 0.001 indicates that further genes should be identifiable through large datasets—the heritability analyses suggest the number of associated genes might be of the order of 90, with the majority of these being TSGs. Although the estimated risks associated with the new genes, in particular MAP3K1 PTVs, would be large enough to be of clinical relevance, the effect sizes might be over-estimated due to the ‘winner’s curse’32. Thus, further replication in larger datasets will also be necessary to provide more precise estimates for variants in the new genes, to define the set of variants in these genes associated with breast cancer, the clinic-pathological characteristics of tumors in variant carriers and the combined effects of pathogenic variants and other risk factors. The heritability analyses suggest that most of the contribution of PTVs is mediated through the five genes BRCA1, BRCA2, ATM, CHEK2 and PALB2, commonly tested for in clinical cancer genetics33. These analyses assume dominant inheritance and recessive genes may also contribute to the familial risk, while subsets of missense variants may also make important contributions (exemplified by CDKN2A and SAMHD1). However, these results suggest that the majority of the ‘missing’ heritability is likely to be found in the noncoding genome.

Methods

UKB

The UKB is a population-based prospective cohort study of more than 500,000 subjects. More detailed information on the UKB is given elsewhere34,35. The study received ethics approval from the North West Multi-center Research Ethics Committee. All participants signed written informed consent before participating. WES data for 450,000 subjects were released in October 2021 and accessed via the UKB DNA Nexus platform36. QC metrics were applied to Variant Call Format (VCF) files as discussed in ref. 37, including genotype level filters for depth and genotype quality.

Samples with disagreement between genetically determined and self-reported sex, sex aneuploidy or excess relatives in the dataset were excluded. Excess relatives were identified by considering pairs of individuals with kinship >0.17. If one individual in a pair was a case and one was a control then the case was preferentially selected; otherwise, one individual was selected at random. Genetic ancestry was estimated using genetic principal components and the Gilbert–Johnson–Keerthi distance algorithm38. If genetic principal components were not available, self-reported ancestry was used. Samples of ancestry other than European were excluded. The final dataset for analysis included 419,307 samples with 227,393 females. Cases were defined by having invasive breast cancer (International Classification of Diseases (ICD)-10 code C50) or carcinoma in situ (D05), as determined by linkage to the National Cancer Registration and Analysis Service (NCRAS), or self-reported breast cancer. Both prevalent and incident cases were included. Only breast cancers that were an individual’s first or second diagnosed cancer were included as cases. By this definition, 17,958 female and 94 male cases were included.

For structural variants, we accessed the structural variant population VCF files for the initial release of UKB whole-genome sequencing via the DNA Nexus platform. These deletions, duplications and insertions were called using GraphTyper (2.7.1) (refs.39,40). We identified any structural variant that passed the GraphTyper QC filters and overlapped an exon of the MANE transcripts of each gene. The samples were filtered using the above exclusions leaving 64,264 samples (4,847 female breast cancer cases and 59,417 female controls). The frequency of structural variants was then calculated for each gene and used to adjust the PTV frequency (Supplementary Methods).

The BCAC datasets

The BRIDGES and PERSPECTIVE samples were from studies in the BCAC (BRIDGES: eight studies, PERSPECTIVE: three studies; Supplementary Table 1). All studies were approved by ethical review boards (Supplementary Table 17). All subjects provided written informed consent. Most samples were previously included in a targeted panel sequencing project1. Phenotype data were based on the BCAC database v14. Samples were oversampled for early-onset (age of diagnosis below 50 years) or family history of breast cancer. Cases were preferentially selected to have information on tumor pathology. Samples with previously identified pathogenic mutations in BRCA1, BRCA2 or PALB2 (348 cases, 176 controls) were not included.

For BRIDGES, library preparation was conducted in the three laboratories using the Nextera DNA Exome kit (Illumina) for tagmentation, barcoding and amplification steps. Subsequently, 500 ng of DNA per sample was pooled in 12-plex and concentrated using a vacuum system. Afterward, hybridization capture reagents for DNA libraries were used for overnight hybridization with the xGen Exome Research panel (Integrated DNA Technologies), capture and amplification. Barcoded pooled libraries of 96 samples were sequenced on each lane of a NovaSeq 6000 S4 flowcell (Illumina) using NovaSeq XP 4-Lane Kit (2 × 100 bp).

For PERSPECTIVE, library preparation was conducted using Agilent SureSelect Human all exon V7 (48.2 Mb). Barcoded libraries of 88 samples were sequenced on a NovaSeq 6000 S4 flowcell (Illumina) using NovaSeq XP 4-Lane Kit (2 × 100 bp).

The same pipeline for variant calling was applied to both the BRIDGES and PERSPECTIVE data and followed the Genome Analysis Toolkit (GATK) best practices. Briefly, raw sequence data (FASTQ format) were preprocessed to produce BAM files. This involved alignment to the reference genome (hg38) using BWA (v0.7.17) and the sorting and indexing of the reads using samtools (v1.10). Identification and removal of duplicate read pairs from the same DNA fragments were performed using Picard’s MarkDuplicates (v2.1.1). Base recalibration included the generation of a base quality score recalibration table with the GATK BaseRecalibrator software (v4.1.4.1), later applied to the read bases to adjust their quality scores and increase the accuracy of the variant calling algorithms with the GATK BQSR (v4.1.4.1). An intermediate and informal QC was performed for a sanity check, including coverage and alignment mapping metrics using samtools flagstat software (v1.10) and Picard (v2.22.2). Variants were then called using GATK HaplotypeCaller (v4.1.4.1). The GATK GenotypeGVCFs (v4.1.4.1) tool was used for the joint genotyping step on each genomic database. The variants with excess heterozygosity were filtered out using GATK VariantFiltration (v4.1.4.1) and GATK MakeSitesOnlyVcf (v4.1.4.1). The GATK VariantRecalibrator (v4.1.4.1) software was used to produce tranches files on SNPs and indels separately. Finally, the tranches files were used to apply the recalibration using the GATK ApplyVQSR (v4.1.4.1). Further details are provided in Supplementary Methods.

For the final dataset, similar QC filtering as for the UKB was applied, using VCFtools (v0.1.15), BCFtools (v1.9), Picard (v2.22.2) and PLINK (v1.90b). At the genotype level, SNPs were excluded with sequencing depth <7 or heterozygous allele balance <0.15 or >0.85. Indels were excluded with sequencing depth <10 or allele balance <0.2 or >0.8. On males’ X chromosomes, depth filters were reduced to 5 for SNPs and 7 for indels. Samples with missing calls for >15% and variants with missing calls for >15% of samples were excluded. Variants with Hardy–Weinberg equilibrium P value 10−15 were also removed. We also excluded samples where the genotypes were inconsistent with previous array genotyping or targeted sequencing data1,2.

The BRIDGES study sequenced 6,912 samples, of which 3,461 cases and 3,200 controls remained in the final dataset after QC. The PERSPECTIVE study sequenced 10,523 samples, of which 4,777 cases and 5,210 remained in the final dataset.

Data preparation

For both the UKB and BCAC datasets, Ensembl Variant Effect Predictor (VEP) v101.0 was used to annotate variants41. Annotations included the 1000 genomes phase 3 allele frequency, sequence ontology variant consequences and exon/intron number. For each gene, the MANE Select42 transcript was used if it was available for that gene, or the RefSeq Select transcript43. Annotation files were used to identify PTVs and rare (allele frequency <0.001 in both the 1000 genomes dataset and the current dataset) missense variants. PTVs in the last exon of each gene and the last 50 bp of the penultimate exon were excluded as these are generally predicted to escape nonsense-mediated mRNA decay. VEP was also used to annotate missense variants by CADD score (v1.6) (ref. 6). Here CADD ≥ 20 was used to define variants predicted to be deleterious. We also defined deleterious missense variants using Helix scores (v4.4.1) (ref. 7).

Burden test analysis

Association analyses were carried out for each gene separately for PTVs, rare missense variants and predicted deleterious rare missense variants (defined by CADD score ≥ 20 or Helix score > 0.5) and PTVs combined. The main association analyses were burden tests in which genotypes were collapsed to a 0/1 variable based on whether samples carried a variant of the given class. That is, \({G}_{i}=1\,{\mathrm{if}}{\,\sum }_{j=1}^{p}{\,g}_{{ij}} > 0\,{\mathrm{and}}\,0\,{\mathrm{if}}{\,\sum }_{j=1}^{p}{\,g}_{{ij}}=0\), where gij = 0, 1, 2 is the number of minor alleles observed for sample i at variant j, and p is the number of variants in the gene (thus, heterozygous and homozygous carriers were combined). All P values were two-sided.

We used logistic regression analysis to test for an association between carriers of variants within a gene and breast cancer status. We incorporated family history as a surrogate for disease status, similar to the method presented in ref. 44. This markedly improves power because susceptibility variants will also be associated with family history; in particular, it allows information on males in the cohort with a family history of female breast cancer to be used. To do this, we treated genotype (0/1) as the dependent variable and family history weighted disease status as the covariate; the latter is defined as d + 1/2 f, where d = 0, 1 was the disease status of the genotyped individual and f = 0 or 1 according to whether the individual reported a positive first-degree family history. The rationale for this weighting is that, for small effect sizes, the log-OR associated with a positive first-degree relative is approximately ½ that associated with the disease. (The approach of using family history as a surrogate was suggested in ref. 44. This method differs in that family history is included with a weight of ½ rather than 1, or using a 2-degree freedom test.) For the BCAC dataset, we adjusted for country and library preparation method (BRIDGES versus PERSPECTIVE). Ancestry was not adjusted for as within each country ancestry was constant. For the UKB dataset, we adjusted for the first ten principal components and sex. For genes on chromosome X, only females were used in the analysis. When looking at case–control analyses for subtypes of the disease, for example ER status, in the BCAC dataset logistic regression was also used.

NUDT11 was excluded because missing genotypes (which were treated as noncarriers) led to spurious associations, although the variants passed QC filtering. AFF1 was also removed as the PTV frequency was high in PERSPECTIVE but rare in BRIDGES and UKB. This was likely due to a single PTV artifact within the PERSPECTIVE dataset.

To combine the results from the BCAC and UKB datasets in a meta-analysis, the association tests for each gene were converted to z scores. The combined z score was defined as \({z}_{M}=\frac{\sum _{j}{w}_{j}{z}_{j}}{\sqrt{{w}_{j}^{2}}}\). Here zM is the combined z score, zj is the z score for study j and wj is the weight associated with study j.

A standard meta-analysis would define the weights wj using inverse variance or effective sample sizes. However, the effect sizes from the BCAC and UKB may not be comparable, because the BCAC studies oversampled for family history and early age at onset, which may have increased the estimated effect. Therefore, we defined weights by using the associations in the known risk gene CHEK2 as a standard—we rationalized that the CHEK2 PTVs provided the best standard as the association is well-established and the OR is highly reproducible1,5,45. Moreover, the OR (~2 to 3) was representative of the size of effects we hoped to detect for other genes. Thus, we defined \(\left({w}_{1},{w}_{2}\right)=\left(\frac{{z}_{1}}{{z}_{2}},1\right)\), where \(\frac{{z}_{1}}{{z}_{2}}\) is the ratio of z scores for CHEK2 for the BCAC dataset and UKB. The approach is equivalent to a meta-analysis of risk per unit dose in studies with different levels of exposure or dose (with dose here being the log-OR for CHEK2)46,47. As a sensitivity analysis, we also derived weightings based on a combined analysis of the five known genes ATM, BRCA1, BRCA2, CHEK2 and PALB2. This gives slightly more weight to UKB (BCAC: UKB 0.307 versus 0.473) but does not change the genes reaching exome-wide significance (the ten most significant genes for PTVs were identical; Supplementary Table 18). The same weights were applied for the meta-analysis of the other variant categories. The zM scores were plotted in Manhattan plots, and associated P values were plotted in quantile–quantile plots. For PTVs, the lambda statistic for inflation in the test statistics (based on the median chi-squared statistic) was 0.766 for UKB, 0.688 for BCAC and 0.725 for the meta-analysis, indicating that the tests were somewhat conservative on average.

We compared this approach to the method outlined in ref. 48. This method is similar to a random-effect meta-analysis but assumes no heterogeneity under the null hypothesis. When heterogeneity is present, this method can achieve greater power than traditional random-effect methods that do not normally achieve greater power than fixed-effect methods. We tested this method for genes with P < 1 × 10−4 from the PTV meta-analysis as described above. P values using this method were only smaller for the genes BRCA1, BRCA2 and PALB2. Furthermore, tau, the estimated amount of total heterogeneity, was estimated to be 0 for all genes apart from BRCA1 and BRCA2, suggesting that for most genes this method is not an improvement to the method above using CHEK2 PTV z scores as weights in a fixed-effect approach (Supplementary Table 19).

To investigate the joint effect of PTVs in MAP3K1 and common susceptibility variants in the region identified through GWAS, we accessed imputed genotype data from UKB for the lead SNPs as identified through previous fine-mapping analyses13,14. We fitted logistic regression models including covariates for PTVs and the lead SNPs and compared the fit of the model and effect sizes, with the model in which the PTVs or the lead SNPs were excluded.

Data on clinicopathological characteristics of cases in the BCAC dataset were also accessed, and the proportion of individuals with specific pathologic features, for example stage and grade, were compared between carriers of variants in a specific gene, for example, MAP3K1 PTV carriers, and the overall dataset.

Pathway analysis

We performed GSEA49 to evaluate the enrichment of genes in KEGG50, Reactome51 and BioCarta52 pathways in breast cancer risk using the R package clusterProfiler53. Pathway lists were accessed using MSigDB54,55. We used z scores from the PTV meta-analysis to create an ordered gene list. We did this using all genes in each pathway and excluding BRCA1, BRCA2, CHEK2, PALB2 and ATM. Results were presented in terms of false discovery rates (q).

Contribution of PTVs to the FRR

We estimated the overall contribution of PTVs to the FRR of breast cancer using an empirical Bayesian approach. Given the aggregate frequency pj of PTVs in a gene is rare, and all PTVs confer the same relative risk \({e}^{{\beta }_{j}}\), the FRR due to one gene, given pj and \({e}^{{\beta }_{j}}\), is1

$${\lambda }_{j}=1+\frac{{p}_{j}{({e}^{{\beta }_{j}}-1)}^{2}}{{\left(2{p}_{j}\left({e}^{{\beta }_{j}}-1\right)+1\right)}^{2}}$$

Under the additional assumption that the risks conferred by variants in different genes are additive, the total contribution over J genes is given by

$$\lambda =1+\mathop{\sum }\limits_{j=1}^{J}\left({\lambda }_{j}-1\right)$$

We ignored recessive effects in this analysis—because PTV homozygotes are extremely rare for most genes their effect is difficult to estimate. These results can therefore be interpreted as the contribution to the FRR to the offspring of affected individuals. However, there is limited evidence of a higher familial risk of breast cancer to siblings that would indicate an important rare recessive component. We assumed a prior distribution for effect sizes (log-OR) in which a proportion α of genes are risk associated and the estimated log-OR, β, for associated genes have an exponential distribution with parameter η; this distribution was chosen because the distribution of effect sizes is likely to be skewed, with only a small number of genes have a large effect size and most undiscovered genes having smaller effect sizes. An approximate likelihood of the observed carrier count data, by gene, was derived, summed over all genes and maximized numerically to estimate α and η, and hence posterior effect size distributions given the data. We estimated pj for each gene using the PTV carrier counts and then updated this to additionally account for the structural variant frequency in the gene. The total contribution to the FRR was estimated by integrating the FRR estimates given βj over the posterior distribution. Further details for the methods are given in Supplementary Methods.

We calculated the contribution of PTVs to the FRR for all genes in the dataset and also for subsets of genes including breast cancer driver genes and target genes of GWAS signals identified in ref. 13, the list of cancer predisposition genes identified in ref. 30, COSMIC TSGs31 and the top pathways identified by GSEA.

Statistics and reproducibility

No statistical method was used to predetermine the sample size. The experiments were not randomized, and we did not use blinding. Some samples were excluded for reasons as described in the methods above, for example, for sex discrepancies, excess relatives or discrepancies with previous genotyping. The analyses were conducted as meta-analyses combining the BCAC and UKB datasets.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.