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Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes – Nature Communications

Studies and telomere length estimation

We incorporated four telomere-length GWAS with non-overlapping cohorts. Delgado et al. had 5075 samples from Bangladeshi individuals and telomere length was estimated using qPCR or Luminex-based assay. Dorajoo et al. had 23,096 samples from Singaporean Chinese individuals and telomere length was estimated using qPCR. Li et al. (2020) had 78,592 samples from European individuals and telomere length was estimated using qPCR. Taub et al. had 51,654 individuals of European ancestry, 5683 individuals of Asian ancestry, 29,260 individuals of African ancestry, and 18,019 individuals of Hispanic/Latino ethnicity. In that study telomere length was estimated bioinformatically from whole genome sequencing data using TelSeq62.


One concern with a meta-analysis approach was whether it is reasonable to compare summary statistics from GWAS where telomere length was estimated using different methods. Previous work determined that each method produces telomere length estimates that are highly correlated with Southern blot analysis24,63,64 and in each study telomere length estimates were standardized prior to running the GWAS. We used GWAMA65 to conduct a random effect meta-analysis that represents a total of 211,379 individuals. GWAMA automatically calculates the Cochran’s q statistic and I2 statistic for each SNP as estimates for heterogenity. We report these statistics for our lead SNPs in Supplementary Data 2 and they are available for all analyzed SNPs in our summary statistics file (see Data Availability). Taub et al. stratified individuals from the Trans-Omics for Precision Medicine (TOPMed) program cohorts by ancestry group where individuals were broadly categorized as European, African, Asian, or Hispanic/Latino using HARE and we maintain language used from that study here for clarity. That study also defined an “Other” group which was not included in our analysis. We provide a list of TOPMed cohorts whose data are represented in the meta-analysis and the broad ancestral groups individuals were categorized as (Supplementary Data 1). A detailed enumeration of individuals over ancestry by TOPMed cohort was previously published in Supplementary Table 1 of Taub et al. SNP positions were converted to hg38 using LiftOver prior to meta-analysis. The Delgado et al. summary statistics were harmonized to the forward strand and palindromic SNPs were removed from this dataset. Loci were considered novel if there were no other reported sentinels within 1 megabase of the lead SNP in the signal.

Lead SNPs were identified by minimum p-value within a 2 megabase window. We examined all loci with at least one variant that was genome-wide significant (p-value < 5 × 10−8) and had a minor allele frequency > 0.0001. This excluded loci where the lead SNPs were rs903494390, rs976923370, rs990671169, rs982808930, rs992178597, rs961617801, and rs1324702094. The signal led by rs3131064 is near the HLA locus and due to the extensive linkage disequilibrium in this region, we expanded the width of this signal to 4.2 megabases.

We considered a signal novel if the lead SNP was not within 1 megabase of a previously reported lead SNP in a telomere length GWAS6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24. One special circumstance arose for the signal led by rs3131064 which we report as a distinct signal from the signal led by rs1150748. However, SNPs in the signal led by rs3131064 were genome-wide significant (p-value < 5 × 10−8) in previous telomere length GWAS23 and therefore we do not consider this a novel signal in our meta-analysis. A second special circumstance arose for the signal led by rs12241155 which was genome-wide significant (p < 5 × 10−9) in the TOPMed pooled analysis24 but was not reported as a signal because it was not conditionally independent of the signal led by rs9420907 (data not shown in that manuscript). We report it as a signal here, but do not consider it a novel signal in our analysis.

Replication analysis

To determine whether novel association signals may be supported by other telomere length GWAS findings, we examined the highest powered telomere length GWAS not included in our dataset23. Of our five novel signals, four of the lead SNPs were evaluated in the replication dataset. The unexamined lead SNP, rs958919990, did not have any proxy SNPs that could be used as no SNPs in the region had r2 > 0.9 with rs958919990. r2 was calculated using a multi-ancestry group of all TOPMed individuals included in the meta-analysis. We considered a SNP replicated if it had an association p-value < 0.05/5 = 0.01 in the replication dataset.

Colocalization analysis

All colocalization analysis was conducted using the coloc package30 using the coloc.abf() command with the prior probability that the SNP is shared between the two traits (p12) set to 1e−6 and that there were at least 1000 shared variants between the two datasets. For GTEx_v831 colocalization we evaluated all genes for which the lead SNP was a significant QTL in any of the 49 GTEx_v8 tissues (FDR < 0.05) (Supplementary Data 45). For colocalization with eQTLGen cis-eQTLs (version available 2019-12-11)33 (Supplementary Data 6) and DICE cis-eQTLs (version available 2019-06-07)34 (Supplementary Data 7) we evaluated all genes within a 2 megabase window centered on the lead SNP and the meta-analysis summary statistics were lifted down to hg19 using LiftOver to compare SNPs based on chromosome and position. The X-chromsome signals could not be evaluated for colocalization with eQTLGen data as that dataset is limited to autosomes. Colocalization was conducted using minor allele frequency, p-value, and the number of samples for eQTLGen. Minor allele frequency was estimated from TOPMed pooled across ancestries. For all other colocalization analyses effect size estimates and their standard errors were used. We report the posterior probability that there are two signals but they do not share a causal signal (PPH3) and the posterior probability that there are two signals and they do share a causal signal (PPH4) within the text, figures, and figure legends. Posterior probabilities for the cases that there is no signal in one or either of the datasets (PPH0, PPH1, and PPH2) are reported in the appropriate Supplementary Data (47). We considered cases where PPH4 > 0.7 to be colocalized except for colocalization analysis with DICE cis-eQTLs where we reduced this threshold to PPH4 > 0.5 to account for the reduced power in the dataset. For Manhattan plots colored by linkage disequilibrium, r2 was calculated using a multi-ancestry group of all TOPMed individuals included in the meta-analysis.

Visualizing sQTLs

RNA alignment information for each individual was extracted using SAMtools (version 1.16) in the GTEx_v8 cultured fibroblast samples on AnVIL. We extracted genotype information from GTEx_v8 for the corresponding individuals and plotted the average alignment depth at each base position (hg38) stratified by genotype using Matplotlib. Visualization of LeafCutter66 splicing clusters was produced using LeafCutter exon-exon junction quantifications generated by GTEx_v831.


TWAS was conducted using FUSION37 using a pre-trained weight model from the Young Finns Cohort36 which was trained on whole blood samples (N = 1264) available from the Gusev lab ( The X-chromosome was excluded from this analysis. The pre-trained model evaluated 4700 genes and a significance threshold of 0.05/4,700 = 1.06 × 10−5 was used.

Variant fine-mapping

Due to the multi-ancestry nature of our meta-analysis we used individual-level data from TOPMed individuals spanning all four ancestries represented in our meta-analysis (European, Asian, African, and Hispanic/Latino) as our linkage disequilibrium reference. Despite the fact that TOPMed individuals represent the largest group in the meta-analysis, the mismatch between the linkage disequilibrium reference and meta-analysis summary statistics was problematic for SuSiE (susieR_0.12.16)40. Therefore, we used summary statistics from the pooled TOPMed GWAS24 to estimate credible sets for all meta-analysis signals since this was an exact match (Supplementary Data 8) and generated a genotype correlation matrix using a random subset, preserving the proportion of ancestries, of 15,000 TOPMed individuals to manage SNP density. We did not use a minor allele frequency threshold for SNP inclusion. At two loci the signal was over 1 megabase wide and calculating the genetic correlation matrix exceeded the ability of computational resources on the premises. At 16 loci there was not sufficient signal in the TOPMed GWAS to predict a credible set. CAVIAR58 requires specification of the assumed number of causal signals whereas SuSiE jointly models the likelihood of varying numbers of causal signals and converges on the highest likelihood case. Due to this assumption and the computational burden of running CAVIAR, we only ran CAVIAR on the POP5 and KBTBD6/KBTBD7 loci.

For the signal led by rs1411041, which we attributed to KBTBD6 and targeted for CRISPR/Cas9 editing, we further fine-mapped the locus by intersecting the credible set SNPs with ATAC-seq peaks and with ChIP-seq data from Roadmap Epigenomics. ATAC-seq data were downloaded from ENCODE (identifiers: ENCFF058UYY, ENCFF333TAT, ENCFF421XIL, ENCFF470YYO, ENCFF558BLC, ENCFF748UZH, ENCFF751CLW, ENCFF788BUI, and ENCFF867TMP) or from ATACdb (Sample_1195, Sample_1194, Sample_1175, Sample_1171, Sample_1020, Sample_1021, Sample_1209, and Sample_1208). BEDTools was used to identify intersecting regions. Roadmap Epigenomic ChIP-seq data was visualized using the WashU Epigenome browser.

GO enrichment analysis

All gene ontology (GO) enrichment analysis was conducted using PANTHER44 overrepresentation test with the GO Ontology database (released on 2022-07-01) with the all Homo sapiens gene set list as the reference list. PANTHER GO biological process complete terms were tested for enrichment using a Fisher’s exact test with false discovery rate correction. Proximal genes were assigned as the gene with minimal distance to the gene body in the UCSC genome browser.

Transcription factor binding site analyses

To assess the enrichment of 95% credible set SNPs with transcription factor and chromatin regulator DNA binding sites, we downloaded the ENCODE regulation track transcription factor binding site cluster ChIP-seq index file to report data for 330 DNA binding proteins spanning 129 cell types. The intersection of variants with transcription factor binding sites was performed by BEDTools v2.29.2. We computed the enrichment of 95% credible set SNPs in transcription factor binding sites using a GREGOR Perl based pipeline67. Briefly, this pipeline sums independent binomial random variables for the number of index SNPs falling in a single feature and calculates the enrichment p-value using a saddlepoint approximation method. The SNPs are considered to have a positional overlap if the input SNP, or variants in high linkage disequilibrium (r2) with the input SNP (r2 > 0.7, linkage disequilibrium window size = 1 megabase), fall within the regulatory features or overlap by ≥ 1 base pair. The pairwise linkage disequilibrium was computed using the 1000 Genomes European reference panel. Transcription factor binding site fold enrichment is measured as the fraction of index SNPs (or SNPs in linkage disequilibrium) overlapping the feature (as observed) over the mean number of overlaps with the control set of SNPs (as expected). Control SNPs are matched based on the number of variants in linkage disequilibrium, minor allele frequency, and distance to the nearest gene of the index SNPs. We also performed the enrichment analysis of 95% credible set SNPs with 1210 DNA-associated factors spanning across 737 cell-tissue types using the peak bed files downloaded from the ReMap 2022 database using the same pipeline. In addition, we performed both the ENCODE and ReMap enrichment analyses using only the lead SNP at each signal (Supplementary Fig. 4B, C). In addition to the enrichment analysis, we identified transcription factor binding sites overlapping the lead SNP for each meta-analysis association signal by searching the rsID on the UCSC genome browser and identified overlapping binding sites using the JASPAR 2022 track with default settings. We identified transcription factors with known roles in telomere length regulation by searching PubMed. Publication references supporting known roles for these transcription factors are indicated in Supplementary Data 10.

Telomere length GWAS with an age × genotype interaction term

We repeated the pooled analysis from Taub et al. (2022) using all 109,122 TOPMed individuals with telomere length estimates. We ran the GWAS including an interaction term for genotype and age in addition to cohort, sequencing center, sex, age at sample collection, and 11 genotype PCs as covariates on Analysis Commons.

Age-stratified GWAS

We divided the 109,122 TOPMed individuals with telomere length estimates into three age bins: ages 0–43 years old, ages 43.1–61 years old, and 61.1–98 years old. We ran the GWAS including cohort, sequencing center, sex, age at sample collection, and 11 genotype PCs as covariates on Analysis Commons. TOPMed cohorts included in this analysis are indicated in Supplementary Data 1. There were 36,980 individuals in the [0,43] group, 37,470 individuals in the (43,61] group, and 34,671 individuals in the (61,98] group. Any peak that cleared genome-wide significance (p < 5 × 10−8) in at least one age group was considered. We then required that the lead SNP in the signal was evaluated in all three age groups. To ensure a reasonable comparison between groups, we required that the minor allele count for the SNP was at least half of the maximum group minor allele count in each group. Then we identified loci where the effect size estimate confidence interval was non-overlapping in at least one age group. Finally, we examined loci that had a genotype × age interaction p-value < 5 × 10−5 and had a meta-analysis association p-value < 5 × 10−8.

Enrichment of meta-analysis signals in chromatin states

We estimated the enrichment of lead meta-analysis signal SNPs across each state of the 25-state chromatin state model from Roadmap Epigenomics29 across all 127 Roadmap Epigenomics samples (Supplementary Data 14). Similarly, Roadmap Epigenomics consolidated narrowPeak files for H3K4me1 and H3K27ac from 98 and 127 samples, respectively (Supplementary Data 14), were used to compute the enrichment of lead SNPs in ChIP-seq peak regions for these histone modifications. Control SNPs were randomly selected from the genome and matched for the number of linkage disequilibrium proxy SNPs, the minor allele frequency, and the distance to the nearest gene. The same GREGOR Perl script pipeline67 used to evaluate transcription factor binding site enrichment (above) was used for these analyses. This script sums binomial random variables corresponding to the count of index SNPs located within any given states/features, followed by the computation of enrichment p-values via saddlepoint approximation.

To identify the cell type group with the strongest enrichment for each chromatin state we used Fisher’s method to calculate a combined chi-squared statistic for the samples in each cell type group. We then identified the group with the strongest enrichment for each chromatin state as the group with the smallest p-value. Briefly, active chromatin states may be considered states 1-19. For a full description of the chromatin states see the section on the 25 state model

Partitioned heritability across cell types (S-LDSC)

We limited our analysis to European individuals because the accuracy of this method depends upon an accurate match with the linkage disequilibrium reference panel. Therefore, we meta-analyzed the European individuals from two studies included in our meta-analysis22,24 using GWAMA as described above and ran stratified linkage disequilibrium score regression (S-LDSC, 1.0.1) using the cell-type specific analyses pipeline. We directly used the 1000 Genomes European baseline files, multi-tissue gene expression counts, and multi-tissue chromatin marker data generated as part of the S-LDSC pipeline28.

Molecular cloning

Gibson assembly primers were designed using Snapgene software (GSL Biotech) and sequencing primers were identified using the GenScript sequencing primer tool. All primers were synthesized by IDT. Primer sequence and a brief description of their use are provided in Supplementary Data 15. Polymerase chain reaction products were amplified using Phusion HS II DNA polymerase (F549; Thermo Fisher). Gibson Assembly was conducted using Gibson Assembly Master Mix (E2611; NEB) according to the recommended protocol. Plasmids were transformed into NEB5α cells (C2987; NEB), prepared using the QIAprep Miniprep Kit (27104; Qiagen) or the Qiagen Plasmid Midiprep Kit (12143; Qiagen), and sequence verified using the Sanger method at the Johns Hopkins School of Medicine Synthesis & Sequencing Facility.

Identifying candidate genes for overexpression experiments

We identified genes with eQTLs in any GTEx tissue that colocalized with our meta-analysis signals at a reduced threshold of PPH4 > 0.5. Elsewhere in this manuscript we used a threshold of PPH4 > 0.7, which we would consider to be strong colocalization. However, since we planned to experimentally validate the genes, we lowered the threshold to expand our candidate gene list. Next we required that tested allele of the lead SNP at the meta-analysis signal also have a significant effect (FDR < 0.05 in GTEx) on the expression of the candidate gene and be associated with increasing gene expression as the allele copy number increased in GTEx (the eSNP estimated effect size must be positive). In cases where the meta-analysis signal colocalized with the eQTL of a gene in multiple GTEx tissues, we examined the estimated effect size in the tissue where colocalization was strongest (greatest PPH4). Finally, to implement our overexpression experiment we would need to clone the gene into a plasmid, which limited the genes to those with a single protein isoform or with a gene length of less than 15 kilobases. We manually queried each gene on the NCBI RefSeq database and identified the number of known protein isoforms and obtained the gene length from the UCSC genome browser. Some genes, such as CBX1 and POP5, had multiple transcriptional isoforms that diverged in the untranslated regions. Because we planned to only overexpress the coding sequence, we counted these cases as a single protein isoform. Of the genes that met these criteria, we conducted a literature search on PubMed to determine whether the candidate genes had known roles in telomere biology or related processes and chose five. A detailed walkthrough of this filtering process is described in Supplementary Note 2.

Overexpression constructs

All cDNA sequences were ordered through GenScript (OHu26641, OHu13170, OHu31184, OHu26125, OHu108607) with the coding sequence subcloned into a pcDNA3.1/C-DYK vector. We added the FLAG tag to the N- or C-terminus in accordance with precedent in the literature: CBX1 C-terminus68, PSMB4 C-terminus69, POP5 N-terminus70, OBFC1 N-terminus71, and KBTBD6 N-terminus72. We used Gibson Assembly to add a 3x FLAG tag to the appropriate end and insert the tagged coding sequence into a pcDNA5/FRT vector (Thermo Fisher). We note that we overexpressed the propeptide of PSMB4 (removing amino acids 2–45). Plasmid maps are available at Zenodo (doi: 10.5281/zenodo.10476137).

Cell culture

HeLa-FLP cells were generated from HeLa cells using the FLP-in system and were cultured in 1x Dulbecco’s modified Eagle’s medium (11965118; Thermo Fisher). K562 cells were purchased from ATCC (CCL-243) and were cultured in 1x RPMI medium (11875119; Thermo). Cells were cultured in the indicated media supplemented with 10% heat-inactivated fetal bovine serum (16140071; Thermo Fisher) and 1% Penicillin-Streptomycin-Glutamine (10378016; Thermo Fisher).

Overexpression experiments and passaging

For overexpression experiments, 100 ng of the indicated overexpression construct and 900 ng of the pOG44 flippase plasmid were co-transfected into HeLa-FLP cells by the use of the FLP-in system using Lipofectamine 3000 (L3000008; Invitrogen) with the recommended protocol and hygromycin resistant (550 μg/mL; 30-240-CR; Corning) cells were examined. The GFP overexpression plasmid (pAMP0605) was previously generated73. For each construct, we used one pool of HeLa-FLP cells to conduct multiple independent transfections, which we refer to as independent clones. Twice a week, cells were treated with 0.05% trypsin-EDTA (25300054; Invitrogen), washed in 1x PBS (10010049; LifeTech), and counted using a Luna II Automated Cell Counter (Logos Biosystems). The number of population doublings for each passage was estimated as the number of cells counted divided by the number of cells seeded for that passage.

Telomere Southern blot analysis

For each time point, (2–4) × 106 cells were collected, washed in 1x PBS (10010049; LifeTech), and pellets stored at −80 °C. Genomic DNA was isolated using the Promega Wizard gDNA kit (A1120; Promega) as directed. Genomic DNA was quantified using the broad range double-stranded DNA kit (Q32853; Thermo Fisher) for QuBit 3.0 (Thermo Fisher). Approximately 1 μg of genomic DNA was restricted with HinfI (R0155M; NEB) and RsaI (R0167L; NEB) and resolved by 0.8% Tris-acetate-EDTA (TAE) agarose gel electrophoresis. 10 ng of a 1kB Plus DNA ladder (N3200; NEB) was included on either side of the Southern as a size reference. Following denaturation (0.5 M NaOH, 1.5 M NaCl) and neutralization (1.5 M NaCl, 0.5 M Tris-HCL, pH 7.4), the DNA was transferred in 10x SSC (3 M NaCl, 0.35 M NaCitrate) to a Nylon membrane (RPN303B; GE Healthcare) by vacuum blotting (Boekel Scientific). The membrane was UV crosslinked (Stratagene), prehybridized in Church buffer (0.5 M Na2HP04, pH7.2, 7% SDS, 1 mM EDTA, 1% BSA), and hybridized overnight at 65 °C using a radiolabelled telomere fragment and ladder, as previously described74. Briefly, a 100x human telomere repeat fragment is excised by EcoRI restriction digest from JHU821 (aka pBLRep4) (Supplemental Data 15) and used for random isotope labeling with αP32 dGTP or dCTP (Thermo Fisher). The membrane was washed twice with a high salt buffer (2x SSC, 0.1% SDS) and twice with a low salt buffer (0.5X SSC, 0.1% SDS) at 65 °C, exposed to a Storage Phosphor Screen (GE Healthcare), and scanned on a Storm 825 imager (GE Healthcare). The images were copied from ImageQuant TL (GE Life Sciences) to Adobe PhotoShop CS6, signal was adjusted across the image using the curves filter, and the image was saved as a.tif file. Minimum, maximum and median telomere length was estimated in ImageQuant TL using the original, unedited scan from the Phosphor Screen and accounted for differences in DNA migration across the gel by including the 1 kB Plus ladder on either side of the Southern blot.

Western blot analysis

2 × 106 cells were collected, washed in 1x PBS (10010049; LifeTech), resuspended in 1x sample buffer (1x NuPAGE loading buffer (NP0008; Thermo Fisher), 50 μM DTT) and stored at −80 °C. Samples were thawed on wet ice, lysed by sonication, and boiled at 65 °C for 10 min. Proteins were resolved using recommended parameters on 4–12% Bis–Tris NuPAGE pre-cast gels (NP0321BOX; Invitrogen) and Precision Plus Dual Color protein ladder (161-0374; BioRad) was run for comparison. Proteins were transferred to a PVDF membrane (170-4273; BioRad) using a Trans-Blot Turbo Transfer System (BioRad). The membrane was blocked in 5% milk-TBST (w/v powdered milk (170-6404; BioRad) resuspended in 1x Tris Buffered Saline, pH 7.4 (351-086-101CS; Quality Biological), 0.01% Tween-10 (P1379-100ML; Sigma) for 1 h at room temperature. Primary antibodies were diluted in blocking buffer and incubated at room temperature for 1 h with mild agitation (M2 FLAG 1:2,000 (F1804-5MG; Sigma), tubulin 1:5,000 (ab6046; Abcam)). Blots were washed in 1x TBST with mild agitation before incubation with horseradish peroxidase-conjugated secondary antibodies diluted in blocking buffer (α-mouse 1:10,000 (170-6516; BioRad), α-rabbit 1:10,000 (170-6515; BioRad)). Blots were washed in 1x TBST with mild agitation, incubated with Forte horseradish peroxidase substrate (WBLUF0100; Millipore) for 5 min with agitation, and imaged on an ImageQuant LAS 4000 mini biomolecular imager (GE Healthcare). Image files were copied from ImageQuant TL software to Adobe PhosShop CS6, the curves filter was applied across the image, and then saved as a.tif file. To reprobe a membrane with the loading control, the membrane was incubated with Restore Western Blot Stripping Buffer (21059; Thermo Fisher) for 30 min, washed in 1x TBST, and processed as described above.

CRISPR editing constructs

We sequence verified the CRISPR target regions in our K562 cells and selected gRNA sequences with a high likelihood of on-target editing (and a low likelihood of off-target editing) using We subcloned the guides into px458 as previously described75. To edit both the POP5 and KBTBD6/KBTBD7 regions we chose one guide to each side of the target region (Supplementary Fig. 9C, D). For guide sequence and genome coordinates (hg38), see Supplementary Data 15.

CRISPR editing experiments

Low-passage K562 cells were cultured to a density of 3 × 105 cells/mL in media without antibiotics, but otherwise as described above, two days prior to nucleofection. Cells were electroporated using the SF Cell Line 4D-Nucleofector X Kit (V4XC-2012; Lonza) with 8 μg of each guide plasmid and the K562 cell line recommended protocol (FF-120). Cells were cultured in antibiotic-free media for 24 h to allow for GFP expression before being single-cell sorted in a 96-well plate at the Johns Hopkins Ross Flow Cytometry Core. Each sample had 1–10% GFP-positive cells. Plates were expanded clonally using media described above. After approximately two weeks cell concentration was estimated using the Luna II Automated Cell Counter (Logos Biosystems), 4×104 cells were collected, and genomic DNA was extracted using QuickExtract DNA Extraction Solution (QE09050; Epicentre) following the protocol recommended in the Alt-R genomic editing detection kit (1075931; IDT). Target editing regions were amplified (primers described in Supplementary Data 15, diagrams in Supplementary Fig. 9) and confirmed by Sanger sequencing. Sequencing reads were aligned in Snapgene (GSL Biotech) and we considered a clone to at least be heterozygous for editing if the alignment began on one side of the deletion, failed across the intended deletion, but resumed across the deletion. Because the POP5 locus deletion was so extensive, we did two separate PCRs on each sample: one that would amplify if the deletion was present (RK236 + RK231) and one that would amplify if a wildtype allele was present (RK236 + RK234) (Supplementary Fig. 9C). All POP5 edited clones were confirmed to be heterozygous.

RNA extraction and qPCR

2 × 106 cells were collected, washed in 1x PBS (10010049; LifeTech), and RNA was purified using a QIAshreddar column (79656; Qiagen) and RNeasy kit (74104; Qiagen) following the recommended protocols, including DNase digestion of RNA prior to RNA cleanup (79254; Qiagen). RNA concentration was estimated using a high sensitivity RNA kit (Q32852; Thermo Fisher) for QuBit 3.0 (Thermo Fisher). cDNA was generated with random hexamers using a SuperScript IV First Strand Synthesis kit (18091050; Thermo Fisher). qPCR primers were designed using the GenScript RT-PCR primer design tool and a standard reference plasmid was generated by amplifying genomic DNA from K562 cells with each primer pair followed by TA cloning the amplicon into a pCR2.1 vector (Supplementary Data 15) using a TA cloning kit (451641; Thermo Fisher). TA cloning was conducted using the recommended protocol and plasmids were transformed into TOP10 cells (C404003; Invitrogen). Each qPCR reaction included approximately 10 ng of cDNA, 1x iQ SYBER Green Super Mix (1708882; BioRad), and 0.25 μM of each primer; qPCR was conducted on a CFX96 real-time qPCR system (BioRad). KBTBD6 and KBTBD7 expression was measured in the POP5-edited clones as CRISPR/Cas9-edited controls and POP5 expression was measured in the KBTBD6/KBTBD7-edited clones as CRISPR/Cas9-edited controls. Samples were analyzed in triplicate and instances where the Cq range was greater than 1 were excluded from further analysis. Standard plasmids were analyzed in duplicate on each plate at a range of 0.001–100 ng as a quality control measure and plates where the standards Cq had an R2 < 0.98 were excluded from further analysis. Plates that passed this threshold were used to estimate the efficiency of the qPCR primers (ACTB = 1.90, KBTBD6 = 1.98, KBTBD7 = 1.92, and POP5 = 1.80). Because the range of efficiency between measured genes was greater than 10%, we analyzed our qPCR results with the Pfaffl method. A one-sided t-test was used to compare experimental to control samples.

Reporting summary

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

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