This study complies with all relevant ethical regulations. Ethical oversight is maintained for both UK Biobank and BRIDGES. UK Biobank data were accessed under application 102655, and access to BRIDGES data was approved by the BCAC Data Access Co-ordinating Committee.<\/p>\n
HDR library design and cloning<\/p>\n
BRCA1 exons, introns, nucleotide and amino acid positions are numbered according to RefSeq transcript NM_007294.4.<\/p>\n
Each SGE experiment requires a plasmid expressing a gRNA and an HDR plasmid library that provides a repair template for each SNV. Where possible, libraries were designed in Benchling to be compatible with two independent gRNAs. Protospacer adjacent motif (PAM)-blocking edits not impacting the amino acid sequence were included to prevent Cas9 recutting after successful HDR editing. For 5\u2032 UTR and promoter SGE regions, 1-bp and 5-bp deletions at every position were included in addition to SNVs. Flanking sequences were added to allow PCR amplification.<\/p>\n
Oligos were ordered in a pool from Twist Bioscience, amplified by PCR and purified using AMPure XP (Beckman Coulter). For each SGE region, a homology arm (HA) plasmid was created by PCR amplifying a genomic fragment containing 500\u20131,000\u2009bp of sequence on each side of the SGE region and cloning it into a linearized pUC19 vector using In-Fusion (Takara Bio). HA plasmids were linearized by PCR using primers to create 15\u201320\u2009bp of overlap with oligo pool amplification products, and treated with DpnI (New England Biolabs) to remove circular plasmid. Amplified oligo libraries were cloned into the linearized HA plasmids using In-Fusion. Stellar Competent Cells (Takara Bio) were transformed and 1% of each transformation was plated to check efficiency, with the rest cultured overnight in 150\u2009ml Luria broth containing 100\u2009\u00b5g\u2009ml\u22121 carbenicillin. From these cultures, HDR libraries were purified using ZymoPURE Maxipreps (Zymo Research).<\/p>\n
The exon 5 SGE library from 2018 (ref. 7<\/a>) included PAM-blocking edits (c.222A>G and c.297G>A) for two potential gRNAs, but only the 5\u2032 gRNA was used. We designed a new library replacing c.297G>A with another synonymous variant disrupting the 5\u2032 target (c.228T>C).<\/p>\n The original exon 17 library7<\/a> did not contain an SNV encoding p.R1699Q (c.5096G>A) because it overlapped a PAM-blocking edit. We therefore designed a 2-bp variant (c.5096_5097delinsAA) encoding p.R1699Q, which is predicted to block recutting. The exon 17 HA plasmid was modified using in vivo assembly61<\/a> to create an HDR template encoding this variant. This was mixed with the original HDR library at a molar ratio of 1:100 for SGE.<\/p>\n Cloning gRNA-expressing plasmids<\/p>\n For HAP1 experiments, gRNAs were cloned by ligating annealed oligos into BbsI-digested pX459 plasmid62<\/a>. For HMEC experiments, gRNAs were cloned into pGL3-U6-sgRNA-PGK-blasticidin, derived from pGL3-U6-sgRNA-PGK-puromycin63<\/a> (Addgene, 51133), as the inducible Cas9 HMEC line was puromycin resistant. Individual gRNAs were cloned by ligating annealed oligos into a BsaI-digested plasmid.<\/p>\n HAP1 cell culture<\/p>\n HAP1 cells with a frameshifting LIG4 mutation7<\/a> were cultured in Iscove\u2019s modified Dulbecco\u2019s medium (Gibco) completed with 10% FBS and 1% penicillin\u2013streptomycin (Gibco). To avoid reversion to diploid, cells were cultured in 2.5\u2009\u00b5M 10-deacetyl-baccatin-III (Stratech) and maintained below 80% confluency. To passage, cells were washed in DPBS (Gibco), trypsinized with 0.25% trypsin\u2013ethylenediaminetetraacetic acid (Gibco), neutralized with complete Iscove\u2019s modified Dulbecco\u2019s medium, centrifuged at 300g for 5\u2009min and resuspended.<\/p>\n HAP1 SGE protocol<\/p>\n The day before transfection, 18 million cells were seeded per 10-cm plate. Cells were transfected using Xfect (Takara Bio), with the following protocol modifications: 30\u2009\u00b5g gRNA-expressing plasmid, 10\u2009\u00b5g HDR library and 24\u2009\u00b5l Xfect polymer were combined in 800\u2009\u00b5l per transfection. A negative control using the HDR library and a pX459 plasmid expressing a gRNA targeting HPRT1, a nonessential gene, was performed for each experiment. Puromycin (Cayman Chemical) was added to media at 1\u2009\u00b5g\u2009ml\u22121 from days 1 to 4. At least 6 million cells were passaged on days 5, 8 and 11. On days 5 and 14, pellets of 10 million cells were collected. Negative controls were collected on day 5.<\/p>\n HMEC cell culture<\/p>\n hTERT-immortalized HMECs with doxycycline-inducible Cas9 expression31<\/a> were cultured in HuMEC medium (Gibco) with 2% FBS and 1% penicillin\u2013streptomycin. To passage, cells were washed in DPBS, trypsinized with 0.25% trypsin\u2013ethylenediaminetetraacetic acid, neutralized with Defined Trypsin Inhibitor (Gibco), centrifuged at 180g for 5\u2009min and resuspended in media.<\/p>\n Generation of a BRCA1 To ensure each diploid cell would receive only one variant, we deleted one BRCA1 allele using two gRNAs to cut upstream and downstream of BRCA1. One day before transfection, cells were plated in 12-well plates with 1\u2009\u00b5g\u2009ml\u22121 doxycycline (Sigma-Aldrich) to induce Cas9 expression. Cells were transfected with 25\u2009nM of each crRNA:tracrRNA complex (Edit-R, Horizon Discovery) using DharmaFECT 4 (Horizon Discovery) according to the manufacturer\u2019s protocol. Doxycycline was removed from the media on day 2 and cells were expanded before limiting dilution. To identify lines with a BRCA1 deletion, gDNA was extracted using QuickExtract (Biosearch Technologies). PCR and Sanger sequencing were performed to identify lines with complete monoallelic BRCA1 deletions, and to check for mutations on remaining alleles (Extended Data Fig. 5a<\/a> and Supplementary Table 6<\/a>). A cell line with complete deletion of one BRCA1 allele and a 1-bp deletion upstream (NC_000017.11:g.43125556del) and 1-bp insertion downstream (NC_000017.11:g.43044294_43044295insG) on the remaining allele was used in SGE experiments.<\/p>\n HMEC SGE<\/p>\n The day before transfection, 4.2\u2009\u00d7\u2009105 cells were seeded per well in 12-well plates with 1\u2009\u00b5g\u2009ml\u22121 doxycycline. Cells were transfected using Xfect using 1.5\u2009\u00b5g of HDR library, 1.5\u2009\u00b5g gRNA plasmid and 1.2\u2009\u00b5l Xfect polymer per well. After 6\u2009h, cells were pooled separately for each replicate. To increase HDR editing efficiency, the DNA-PK inhibitor NU7441 (Stratech) was added to 300\u2009nM (SGE regions\u2014exons 6, 11 and 10 (3\u2032), intron 11) or 600\u2009nM (SGE regions\u2014promoter, exons 3 and 17). Blasticidin (Gibco) was included in the media from days 1 to 5 at 8\u2009\u00b5g\u2009ml\u22121. On day 7, doxycycline and NU7441 were removed. Half of the cells were collected, with the remainder divided between untreated and olaparib-treated conditions. Olaparib was included at 300\u2009nM in all HMEC experiments apart from exons 5 and 10 (5\u2032). For the 5\u2032 exon 12 region, untreated and olaparib-treated experiments were performed using two separate transfections. Cells were passaged every 3 days and collected on days 14 and 21.<\/p>\n Nucleic acid extraction<\/p>\n gDNA and RNA were extracted using the AllPrep DNA\/RNA Kit (Qiagen), using QIAshredder columns (Qiagen) to homogenize lysates. gDNA was eluted in 100\u2009\u00b5l nuclease-free water and quantified using the Qubit BR DNA Kit (Thermo Fisher Scientific). RNA was extracted from day 5 HAP1 SGE samples and quantified using NanoDrop UV spectrophotometry.<\/p>\n PCR1: amplifying gDNA<\/p>\n Each SGE region was PCR amplified from gDNA using a primer outside the HAs to prevent amplification of any remaining HDR library. Up to 2\u2009\u00b5g gDNA was used per 100\u2009\u00b5l reaction, with up to eight reactions per sample and one negative control reaction. KAPA HiFi HotStart ReadyMix (Roche) was used, with MgCl2 added to 5\u2009mM. SYBR Green (Thermo Fisher Scientific) was added to all reactions at 0.4\u00d7 to allow real-time monitoring and stopping upon completion.<\/p>\n PCR2: adding Nextera adaptors<\/p>\n PCR was used to add Nextera adaptors onto amplicons from PCR1. Each 25\u2009\u00b5l PCR2 reaction used 1\u2009\u00b5l of pooled PCR1 product as a template and was run for eight to ten cycles before AMPure XP purification.<\/p>\n cDNA preparation and amplification<\/p>\n For HAP1 day 5 samples, cDNA was prepared from 5\u2009\u00b5g RNA using SuperScript IV First-Strand Synthesis (Invitrogen) using a BRCA1-specific primer. cDNA was then amplified as described for PCR2 using primers spanning at least one exon junction where possible. For internal exon 10 regions not spanning exon junctions, RNA was first treated with ezDNAse (Invitrogen).<\/p>\n Indexing and sequencing<\/p>\n Each sample was dual indexed via PCR, purified and quantified using Qubit 1\u00d7 dsDNA High Sensitivity (Invitrogen). SGE samples were pooled, allocating gDNA samples 5\u2009\u00d7\u2009106 reads, cDNA samples 3\u2009\u00d7\u2009106 reads, HDR libraries 1\u2009\u00d7\u2009106 reads and negative controls 0.5\u2009\u00d7\u2009106 reads. Pools were sequenced on an Illumina NextSeq or NovaSeq using 300-cycle paired-end runs.<\/p>\n Processing SGE sequencing data<\/p>\n A published pipeline12<\/a> was modified to obtain variant counts for sequencing samples. Briefly, samples were demultiplexed using bcl2fastq2 (v2.17). SeqPrep was used to merge paired-end reads. Reads were aligned to a reference sequence for each amplicon using \u2018needleall\u2019 from EMBOSS (v6.6). A custom Python script was used to calculate variant frequencies, annotate editing events and add CADD (v1.6) annotations. To minimize PCR and sequencing errors, reads required one library variant and at least one PAM-blocking edit to be included for calculating function scores. Variant frequency was calculated by dividing variant reads by the total number of sample reads.<\/p>\n Deriving HAP1 function scores<\/p>\n Function scores were calculated as the log2 ratio of each SNV\u2019s day 14 to day 5 frequency. Variants with frequency of <10\u22124 in the HDR library or <10\u22125 on day 5 were filtered. Variants were also filtered if the difference in function score between replicates was >1.5, unless both scores were <\u2009\u22121.0. Variants resulting in a different amino acid change due to a PAM-blocking edit were excluded. Function scores were first averaged across replicates, then normalized across regions such that median nonsense and synonymous scores for each coding region matched the global medians. In cases where variants were initially assigned two scores due to SGE regions overlapping, scores from only one region were used in global analysis, although all scores are reported in Supplementary Table 1<\/a>. Scores from the promoter region were used for variants overlapping the 5\u2032-UTR region, and scores from the exon 12-3\u2032 region were used for variants overlapping the exon 12-5\u2032 region. Function scores for 1-bp and 5-bp deletions included in the HDR library were determined the same way.<\/p>\n Deriving HAP1 function classes<\/p>\n As experimental noise differs across SGE regions, region-specific null distributions were used to determine significance. For coding regions, null distributions included synonymous variants with RNA scores\u2009>\u2009\u22121.0. For noncoding regions, null distributions included all variants. The \u2018pnorm\u2019 function in R was used to model each null distribution and calculate P values, with Benjamini\u2013Hochberg adjustment (\u2018p.adjust\u2019). A q-value threshold of 0.01 distinguished significantly depleted and neutral variants. Depleted variants were categorized as \u2018LoF\u2019 or \u2018intermediate\u2019 based on whether function scores were significantly different from nonsense variants. As there are relatively few nonsense variants per region, we used the score distribution for 155 depleted nonsense variants across all regions. We again performed Benjamini\u2013Hochberg adjustment and set a q-value threshold of 0.01, corresponding to a function score threshold of \u22120.799, to distinguish \u2018intermediate\u2019 and \u2018LoF\u2019.<\/p>\n RNA scores<\/p>\n RNA scores were calculated for each coding variant scored in HAP1 as the log2 ratio of each SNV\u2019s cDNA frequency to its gDNA frequency on day 5. Variant frequencies were calculated from reads containing the variant and at least one PAM-blocking edit, in the correct reference transcript context. Splice-altering variants show reduced RNA scores independent of whether the resulting transcript is in-frame or frameshifted, reflecting relative depletion of correctly spliced transcript. RNA scores were normalized such that the median synonymous score equaled 0.0. RNA scores were not determined for noncoding variants absent from full-length mRNA, as these cannot be quantified in transcript sequencing, or for 5\u2032-UTR variants due to primer design constraints.<\/p>\n Deriving HMEC function scores and classes<\/p>\n HMEC function scores were calculated as the mean of the log2 ratios of day 14 to day 7 and day 21 to day 14 variant frequencies. The same frequency filters were applied as for HAP1. Variants were excluded if replicate scores differed by >2.0, unless both were <\u22121.0 or >\u22121.0. Scores were normalized between experiments as for HAP1, and the same approach was used to define significantly depleted variants, deemed \u2018LoF\u2019. (No intermediate class was defined.)<\/p>\n Deriving combined HAP1\u2013HMEC function classes<\/p>\n Four combined HAP1\u2013HMEC classes were defined\u2014\u2018concordant-neutral\u2019, \u2018HAP1-intermediate, HMEC-neutral\u2019, \u2018HAP1-LoF, HMEC-neutral\u2019 and \u2018concordant-LoF\u2019. A small number of HMEC-depleted variants scoring as neutral (n\u2009=\u20099) or intermediate (n\u2009=\u20092) in HAP1 were excluded from analyses of combined function classes due to insufficient power. Discordantly scored variants are those that were significantly depleted in HAP1 but not HMECs (HAP1-LoF, HMEC-neutral and HAP1-intermediate, HMEC-neutral).<\/p>\n Structural analysis<\/p>\n BRCA1 protein domains were defined as follows: p.2\u2013101, RING domain; p.1392\u20131422, CC domain; p.1646\u20131863, BRCT domain. The mean function score for missense variants at each amino acid position was mapped onto the BRCA1\u2013PALB2 CC-domain structure (Protein Data Bank (PDB): 7K3S<\/a>) using PyMOL (v2.6.0).<\/p>\n FoldX (v5.0; https:\/\/foldxsuite.crg.eu\/<\/a>) was used to calculate \u2206\u2206G predictions for missense variants scored in the RING, CC and BRCT domains. Protein structures from the PDB included the BRCA1 RING domain in complex with BARD1 (PDB: 1JM7<\/a>), the BRCT domain (PDB: 1T15<\/a>) and the CC domain in complex with PALB2 (mouse; PDB: 7K3S<\/a>). FoldX \u2018RepairPDB\u2019 and \u2018PositionScan\u2019 were used to generate \u2206\u2206G predictions. Predictions for 3 of 28 CC domain residues that differ between mouse and human were excluded.<\/p>\n Predicting transcription factor binding<\/p>\n In the promoter region, JASPAR predicts a binding site for DP1 (encoded by TFDP1). The JASPAR scan tool (https:\/\/jaspar.elixir.no\/<\/a>) was used to predict binding scores for SNVs within the 8-bp DP1 binding motif, using matrix profile MA1122.2.<\/p>\n Comparisons with computational predictions<\/p>\n CADD scores (v1.6) were obtained for all SNVs, with consequence labels from transcript CCDS11456.2. \u2018Splice region\u2019 describes variants 3\u20138\u2009bp into introns and 1\u20133\u2009bp into exons that do not alter amino acid sequence.<\/p>\n For missense variants, EVE scores64<\/a> (https:\/\/evemodel.org\/<\/a>; 24 October 2024) and AlphaMissense pathogenicity scores65<\/a> (https:\/\/alphamissense.hegelab.org\/<\/a>; 21 December 2023) were obtained. SpliceAI masked scores were generated by running SpliceAI28<\/a> with a maximum distance of 500\u2009bp for SNVs within the BRCA1 transcript (ENST00000357654.9). \u2018Maximum SpliceAI score\u2019 is the maximum of \u2018acceptor gain\u2019, \u2018donor gain\u2019, \u2018acceptor loss\u2019 and \u2018donor loss\u2019 scores.<\/p>\n Comparison with other BRCA1 functional assays<\/p>\n Outcomes for missense variants in BRCA1 assays collated in ref. 19<\/a> were obtained. For each variant scored in both HAP1 and HMEC, the proportion of other assays for which the variant was deemed functionally abnormal was calculated. Variants were analyzed only if classified by \u22653 assays. Prior SGE data7<\/a> were excluded.<\/p>\n Generating HMEC lines with BRCA1 variants<\/p>\n The BRCA1+\/\u2212 HMEC SGE line was edited as previously described to derive clonal lines with the following variants on the nondeleted allele: CC-domain variants p.M1411T (c.4232T>C) and p.L1414R (c.4241T>G), and exon 10 splice donor variant c.4096+1G>A. To generate HDR templates, HA plasmids were mutated using two rounds of in vivo assembly61<\/a> to introduce the PAM-blocking edit and the variant of interest. After editing, monoclonal lines were generated and sequenced using the MiniSeq (Illumina). A line with the two synonymous variants (c.4260G>A and c.4263T>C) used to prevent Cas9 recutting after CC-domain editing was isolated as a control.<\/p>\n Testing gene essentiality<\/p>\n BRCA1 essentiality was established in BRCA1+\/\u2212 HMECs by analyzing indel depletion after editing the exon 12-5\u2032 region according to the HMEC SGE protocol. Indel function scores were defined as the log2 ratio of indel frequency on day 14 over day 7.<\/p>\n PALB2 and RNF168 were edited in exons 4 and 1, respectively, through transfection of synthetic gRNAs to BRCA1+\/\u2212 HMECs. Cells were sampled on days 2 and 14, and amplicon sequencing of gDNA was performed. For indels at a frequency\u2009>5\u2009\u00d7\u200910\u22124 on day 2, an indel function score was calculated as the log2 ratio of day 14 frequency to day 2 frequency, normalized such that the median score for in-frame indels equaled 0.0.<\/p>\n RNF168 essentiality was assessed in the same way, except cells were sampled on days 5 and 14. Indel function scores were normalized such that the median of the parental cell line equaled 0.0.<\/p>\n Performing SGE in RNF168 A synthetic gRNA was used to target the coding sequence in RNF168 exon 1 in BRCA1+\/\u2212 HMECs. Clonal lines were generated and Sanger sequenced, with DECODR66<\/a> used to deconvolute traces. A line with biallelic frameshift mutations (c.163_175del and c.166_175del) deemed \u2018RNF168\u2212\/\u2212\u2019 was selected for SGE of the exon 12-5\u2032 region. To compare between parental and RNF168\u2212\/\u2212 lines, function scores were normalized so that the median nonsense and median synonymous scores from each line matched.<\/p>\n Testing olaparib sensitivity in HMEC lines<\/p>\n HMECs were cultured in white, flat-bottom 96-well plates (Greiner CELLSTAR, Sigma-Aldrich) in olaparib concentrations between 1\u2009nM and 30\u2009\u00b5M for 7 days. Three technical replicates were seeded per concentration per edited line, with two replicates performed for parental cells. The CellTiter-Glo (v2.0) viability assay (Promega) was then performed. Luminescence was measured using a Spark multimode microplate reader (Tecan). For each plate, the mean background signal derived from wells containing media only was subtracted from luminescence values before normalization to the DMSO-only sample for each line. Dose\u2013response curves and IC50 values were generated by fitting a four-parameter logistic model using the drc package (v3.0-1) in R.<\/p>\n RNA sequencing of cell lines<\/p>\n RNA was extracted using the AllPrep DNA\/RNA Kit, with integrity assessed using TapeStation RNA ScreenTape (Agilent) and concentration measured using the Qubit RNA HS Assay (Thermo Fisher Scientific). Polyadenylated RNA was enriched from 200\u2009ng total RNA in duplicate for each cell line using the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs), and used to prepare libraries with the NEBNext Ultra II RNA Library Prep Kit and NEBNext Multiplex Oligos for Illumina. Libraries were sequenced on an Illumina NovaSeq X using 100\u2009bp paired-end reads to a depth of 60 million reads per sample.<\/p>\n Reads were aligned to GRCh38 using Bowtie 2 (v2.4.5), and BAM files were sorted and indexed with SAMtools (v1.18). Reads mapping to annotated exons were quantified at the gene level using featureCounts (Subread; v1.6.4). Genes with \u22645 reads across all samples were filtered. Differential expression analysis was performed with DESeq2 (v1.42.1) in R. Genes were annotated using BioMart, and log2 fold change estimates were shrunk using \u2018lfcShrink\u2019 with the \u2018apeglm\u2019 method67<\/a>. Unshrunk and shrinkage-adjusted log2 fold change values are provided in Supplementary Table 7<\/a>. Genes with an adjusted P\u2009<\u20090.05 were considered differentially expressed.<\/p>\n Gene set enrichment analysis68<\/a> was performed using clusterProfiler (v4.10.1) in R. Genes were ranked using the DESeq2 Wald statistic, and enrichment was assessed using Gene Ontology Biological Process69<\/a> or Reactome70<\/a> gene sets from the Molecular Signatures Database via msigdbr (v25.1.1). Enrichment scores were normalized to account for gene set size, yielding normalized enrichment scores (NES), with positive and negative values indicating enrichment in HAP1 and HMEC, respectively. P values were generated by permutation testing and adjusted using the BH method. Gene set enrichment analysis data are provided in Supplementary Table 8<\/a>.<\/p>\n Assessing HDR via SSTR in HMEC lines<\/p>\n To assess HDR editing via SSTR in HMEC lines, a synonymous PAM-blocking edit was introduced into HPRT1. HMECs were transfected with a synthetic gRNA targeting exon 3 and a modified single-stranded DNA HDR template (Horizon Discovery), containing the synonymous SNV and 40-bp HAs (Supplementary Table 6<\/a>), at 50\u2009nM. After 5 days, gDNA was extracted and HPRT1 amplicon sequencing was performed. HDR rate was defined as the percentage of reads with a correctly edited synonymous SNV, and was normalized to total editing (percentage of reads not matching the reference) to account for variable transfection efficiency. Each cell line was tested in duplicate.<\/p>\n ClinVar analysis<\/p>\n ClinVar data were downloaded on 13 September 2024. \u2018Benign\u2019 and \u2018likely benign\u2019 were combined as \u2018benign\/likely benign\u2019 (B\/LB) and \u2018pathogenic\u2019 and \u2018likely pathogenic\u2019 were combined as \u2018pathogenic\/likely pathogenic\u2019 (P\/LP). VUS and variants with conflicting interpretations of pathogenicity were grouped together as \u2018VUS\u2019. ROC curves were generated in R using \u2018geom_ROC\u2019 from plotROC (v2.3.3), using B\/LB and P\/LP variants with a minimum one-star review status. To compare classification performance across lines, prior HAP1 SGE data for exon 3 were included. For analyses of VUS, all variants were included independent of review status.<\/p>\n Population sequencing data<\/p>\n Allele counts from gnomAD (v4.1.0) were accessed on 12 February 2025 (https:\/\/gnomad.broadinstitute.org\/<\/a>). All of Us data were accessed on 26 June 2024 (https:\/\/allofus.nih.gov\/<\/a>).<\/p>\n Estimating breast cancer risk from case\u2013control studies<\/p>\n The BRIDGES dataset from the BCAC used in this study included BRCA1 sequencing from 46,306 breast cancer cases and 43,481 controls. The UK Biobank whole-exome sequencing dataset included 20,553 female breast cancer cases and 227,708 female controls. Dataset quality control and filtering criteria are described elsewhere47<\/a>. Logistic regression was used to estimate ORs and 95% CIs for breast cancer associated with any variant in a given SGE-defined function class. Associations were adjusted for age and study country for the BCAC dataset, and age and genetic ancestry for the UK Biobank dataset. Analyses were performed separately for each dataset, then combined in a fixed-effects, inverse-variance meta-analysis using the \u2018metafor\u2019 R package to test overall association.<\/p>\n Assigning the American College of Medical Genetics and Genomics\/Association for Molecular Pathology evidence codes<\/p>\n Truth sets were defined separately for HAP1 and HMEC SGE datasets and comprised all ClinVar variants assayed with a minimum two-star review status (Extended Data Fig. 10f,g<\/a>). Benign variants with SpliceAI scores of >0.2 were excluded. A two-component GMM was fitted using mclust (v6.1.1) in R and the \u2018predict\u2019 function to assign a posterior probability of pathogenicity for each variant. Odds of pathogenicity were calculated as follows: (P2\u2009\u00d7\u2009(1\u2009\u2212\u2009P1)\/9(1\u2009\u2212\u2009P2)\u2009\u00d7\u2009P1), where P1 and P2 are the prior and posterior probabilities of pathogenicity, respectively. P1 was set uniformly at 0.10, as established14<\/a>. PS3\/BS3 evidence codes and corresponding point values49<\/a> were assigned according to the American College of Medical Genetics and Genomics\/Association for Molecular Pathology framework71<\/a> using a maximum evidence strength of \u2018strong\u2019, thus capping the points at \u00b14 per assay.<\/p>\n
\n +\/\u2212 HMEC line for SGE<\/p>\n
\n \u2212\/\u2212 HMECs<\/p>\n