Gene-gene interactions between a LMNA variant and common polymorphisms drive early-onset atrial fibrillation.

Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death in young people. Although rare genetic variants are well-established contributors to HCM risk, common genetic variants have recently been implicated in disease pathogenesis. To assess the contributions of rare and common genetic variation to risk of HCM in the general population. This cohort study of the UK Biobank (data from 2006-2010) and the Mass General Brigham Biobank (2010-2019) assessed the relative and joint contributions of rare genetic variants and a common variant (polygenic) score to risk of HCM. Both rare and common variant predictors were then evaluated in the context of relevant clinical risk factors. Data analysis was conducted from May 2021 to February 2022. Pathogenic rare variants, common-variant (polygenic) score, and clinical risk factors. Risk of HCM. The primary study population comprised 184 511 individuals from the UK Biobank. Mean (SD) age was 56 (8) years, 83 690 (45%) of participants were men, and 204 (0.1%) participants had HCM. Of 51 genes included in clinical genetic testing panels for HCM, pathogenic or likely pathogenic variants in 14 core genes (designated by the American College of Medical Genetics and Genomics [ACMG]) were associated with 55-fold higher odds (95% CI, 35-83) of HCM, while those in the remaining 37 non-ACMG genes were not significantly associated with HCM (OR, 1.8; 95% CI, 0.6-4.0). ClinVar pathogenic or likely pathogenic mutations in MYBPC3 (OR, 72; 95% CI, 39-124) and MYH7 (OR, 61; 95% CI, 26-121) were strongly associated with HCM, as were loss-of-function variants in ALPK3 (OR, 13; 95% CI, 4.4-28). A polygenic score was strongly associated with HCM (OR per SD increase in score, 1.6; 95% CI, 1.4-1.8), with concordant results in the Mass General Brigham Biobank. Genetic factors enhanced clinical risk prediction for HCM: addition of rare variant carrier status and the polygenic score to clinical risk factors (obesity, hypertension, atrial fibrillation, and coronary artery disease) improved the area under the receiver operator characteristic curve from 0.71 (95% CI, 0.65-0.77) to 0.82 (95% CI, 0.77-0.87). Both rare and common genetic variants contribute substantially to HCM susceptibility in the general population and improve HCM risk prediction beyond that achieved with clinical factors.

Idiopathic pulmonary fibrosis (IPF) and telomere length are both strongly linked to rare and common genetic variants. Shortened telomere length might itself be causal for IPF. We aimed to evaluate whether rare and common variants compete or cooperate to confer genetic risk of IPF uniformly. In this genetic analysis, we used whole-genome sequencing (WGS) data from a discovery case-control cohort sequenced at Columbia University and validated findings using WGS data from Trans-Omics for Precision Medicine (TOPMed) and UK Biobank. In all cohorts, we identified rare damaging variants in disease-associated genes and computed control-normalised non-overlapping polygenic risk scores (PRS) for IPF and telomere length. We assessed the MUC5B rs35705950 single-nucleotide polymorphism (SNP), an IPF common risk variant with a large effect, independently from the polygenic scores. Telomere length in blood leukocytes was measured using a quantitative PCR assay for the discovery cohort and UK Biobank validation cohort. We conducted logistic regression (adjusting for age, sex, and principal components of ancestry) to evaluate the association between IPF risk and the MUC5B SNP, the IPF PRS excluding MUC5B (IPF-PRS-noMUC5B), and the PRS for telomere length in the overall cohort and analysed their effects in patient subgroups for IPF endotypes (carriers and non-carriers of rare variants stratified by telomere length cutoffs). To assess disease prediction, we calculated cross-validated area under the receiver operating receiver operating curve (AUC). We also compared the liability of IPF explained by genetic variables. The discovery cohort was recruited between April 23, 2003 and June 19, 2019 and included 777 patients with IPF and 2905 controls. We replicated the analyses in the TOPMed (1148 patients with IPF and 5202 controls) and UK Biobank (2739 patients with IPF and 395 331 controls) cohorts. 23-43% of patients with IPF had damaging rare variants or telomeres shorter than the tenth percentile. Analysis of the association of genetic variables with IPF diagnosis yielded odds ratios of 1·63 (95% CI 1·47-1·81) for telomere length PRS and 1·60 (1·44-1·77) for IPF-PRS-noMUC5B in the discovery cohort, with similar effect sizes for the two variables in the replication cohorts (1·47, 1·36-1·59 vs 1·37, 1·25-1·50 in TOPMed; 1·24, 1·19-1·29 vs 1·25, 1·21-1·30 in UK Biobank). The telomere length PRS had the greatest effect on disease risk in patients with IPF not harbouring rare variants and with telomere length shorter than the tenth percentile in the discovery cohort (2·02, 1·76-2·33) and UK Biobank replication cohort (1·70, 1·56-1·85). Accounting for clinical variables and all genetic variables (rare variants, MUC5B SNP, IPF PRS, and telomere length PRS) led to the best disease prediction in the discovery cohort (combined AUC 0·89), TOPMed cohort (0·89), and UK Biobank cohort (0·77). Rare and common variants contributed jointly to the genetic liability of IPF. The telomere length PRS accounted for 13% of the explained genetic liability of IPF in the discovery cohort and 8% and 13% in the TOPMed and UK Biobank cohorts, respectively. Common and rare genetic variation confer context-specific genetic risk in patients with IPF both competitively and cooperatively. In contrast to known IPF common risk variants, the telomere length PRS, which includes more than 180 genetic loci not previously associated with IPF, is associated with increased risk of disease in patients with specific IPF endotypes. Polygenic risk from telomere-associated common variants is a key feature of genetic heterogeneity in IPF. US National Institutes of Health, UK Medical Research Council, and UK National Institute for Health and Care Research.

Heart failure (HF) is the most concerning morbidity in atrial fibrillation (AF) through mutual influence on a poor prognosis. A polygenic risk score (PRS) has recently been proposed to improve the risk prediction for cardiovascular disease. The additive predictive role of PRS for incident HF in patients with AF who inherently carry a high risk of HF is unknown. From the UK Biobank, we identified 21 167 White Caucasian participants with newly diagnosed AF without a prior history of HF. The PRS for HF was constructed using genetic instruments from previous genome-wide association studies. The primary outcome was the occurrence of incident HF. The prediction of incident HF was evaluated using the tertile categorization of PRS for HF (low vs. moderate-high PRS) across the entire AF cohort, as well as within age subgroups (young AF, age <60 years; old AF, age ≥60 years, respectively). The mean age was 69.0 ± 6.9 years in the total population (55.2 ± 3.9 years in age <60 years; 70.7 ± 5.0 years in age ≥60 years group). During a median follow-up of 3.8 (1.4-7.2) years, the incidence rate (1000-patient year) of HF was 29.9. In the total population, AF patients with moderate-high PRS for HF were associated with a higher risk of HF than those with low PRS for HF [adjusted hazard ratio (HR) 1.18 (95% confidence interval (CI), 1.05-1.32), P = 0.005]. The higher risk of HF in the moderate-high PRS group was particularly accentuated in young AF patients: adjusted HR, 2.14 (95% CI 1.29-3.57) in young AF, and 1.13 (95% CI 1.01-1.27) in old AF, P-for-interaction = 0.015. In young AF, the onset of incident HF was earlier in those with the moderate-high PRS group [median time from AF diagnosis to incident HF, 4.2 (0.8-7.1) years in low PRS vs. 1.5 (0.3-4.7) years in the moderate-high PRS group, P = 0.001]. The prediction of HF was significantly improved by adding PRS to the clinical risk factors for HF, especially in young AF patients, with a net reclassification improvement of 29.7% (P = 0.003). PRS for HF can significantly improve the prediction of incident HF in patients with AF, especially in the young population, providing clinical utility of an individualized approach to integrated management of AF.

Background: Atrial fibrillation (AF) in valvular heart disease (VHD) is typically linked to pressure or volume overload resulting in atrial remodeling. However, the independent contribution of genetics to AF in VHD beyond abnormal hemodynamics and other risk factors remains unclear. Objective: To study the role of a previously developed Polygenic Risk Score (PRS) for AF in predicting AF across different VHDs in the UK Biobank cohort. Methods: We analyzed UK Biobank data between 2000 and 2022. ICD codes were used to define incident AF and VHD status, including mitral valve (MV) disease [prolapse (MVP), regurgitation (MR) and stenosis (MS)] and aortic (AV) disease [stenosis (AS) and regurgitation (AR)]. Prior AF was excluded. Participants were divided into quintiles based on the PRS: the top quintile was defined as high genetic AF risk, second through fourth as intermediate genetic risk, and bottom quintile as low genetic risk. Results: Among 452841 participants with available PRS, 2238 had at least one VHD (385 MVP, 1058 MR, 81 MS, 421 AR, and 509 AS). AF incidence was 9.9%, 13.6%, and 19% in the low, intermediate and high genetic risk groups respectively, compared to 1.4%, 2.8%, and 5.7% in the general population (all p&lt;0.05) - Figure 1A. Cox regression models adjusted for cardiovascular risk factors showed different genetic effects on AF across VHD types. Censoring patients at death, including valve intervention as a time-dependent variable, and excluding AF within 30 days post-intervention, PRS independently predicted AF in MV disease (hazard ratio [HR] 1.7, p=0.03 for the intermediate risk/mid PRS; HR 2.3, p=0.002 for the high risk PRS), particularly in those with MVP (HR 3.1, p=0.03 for the high PRS). However, in those with AV disease, PRS was not significantly associated with AF (p=0.93 and p=0.07) - Figure 1B. Conclusion: In patients with VHD, AF PRS is significantly associated with an increased risk of developing AF. However, the genetic impact varies across different VHD. In AV disease, there is a minimal genetic contribution, and the elevated AF risk may be predominantly due to pressure or volume overload. In contrast, in MV disease, particularly in MVP, there is a substantial genetic influence on AF risk.

Atrial fibrillation (AF) has a complex genetic architecture involving common, rare, and somatic variants. The association between these components requires further investigation. To examine the individual and combined contributions of polygenic, monogenic, and somatic genetic variants to AF incidence, and develop an integrated genomic model (IGM-AF) for improved risk prediction. This cohort study used whole-genome sequence data from participants of the UK Biobank, with follow-up for AF events through hospital records, death registries, and self-report. The UK Biobank recruited participants aged 40 to 69 years in the UK between 2006 and 2010. Study data were analyzed from August 2022 to November 2024. IGM-AF comprising an AF polygenic risk score (PRS), a composite rare variant gene set (AFgeneset), and somatic variants associated with clonal hematopoiesis of indeterminate potential (CHIP). Clinical AF risk was estimated using the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) score. The primary outcome was hazard ratios (HRs) for 5-year incident AF attributable to PRS, AFgeneset, CHIP, and their interactions. The predictive performance of IGM-AF and its components was quantified using HRs, C statistics, and reclassification indices. A total of 416 085 individuals (mean [SD] age, 56.6 [8.0] years; 224 642 female [54.0%]) with 30 797 AF cases were included. The PRS (HR per 1 SD, 1.65; 95% CI, 1.63-1.67; P < 1 × 10-8), AFgeneset (HR, 1.63; 95% CI, 1.52-1.75; P = 1.46 × 10-42), and CHIP (HR, 1.26; 95% CI, 1.15-1.38; P = 1.41 × 10-6) were associated with incident AF. The 5-year cumulative incidence of AF was at least 2-fold among individuals having all 3 genetic drivers (common, rare, and somatic drivers) compared with those with only 1 driver. Integration of IGM-AF with a clinical risk model (CHARGE-AF) showed higher predictive performance (C statistic, 0.80; 95% CI, 0.80-0.80) compared with IGM-AF and CHARGE-AF alone. The classification of the at-risk population for AF was improved when IGM-AF was added to CHARGE-AF (net reclassification index, 0.08; 95% CI, 0.07-0.09). Results of this cohort study demonstrated the complementary value of common, rare, and somatic variants in shaping genomic AF risk. Leveraging comprehensive genetic information may enhance screening and preventive interventions for AF.

Polygenic risk scores (PRS), commonly referred to as genetic or genomic risk scores, aggregate the effects of multiple genetic variants into a single composite estimate of genetic risk. PRS scores are typically used to predict the risk of developing a disease or to explain the phenotypic variation, and are derived from the effect sizes observed in large-scale genome-wide association studies (GWAS). Unlike rare monogenic diseases such as cystic fibrosis, which are attributable to genetic variants in single genes, with large effects on disease status, common complex diseases such as type 2 diabetes mellitus (T2DM) are polygenic, with risk contributed by a panel of genetic variants present throughout the genome. The concept of integrating information from these multiple genetic variants into a single metric of genetic risk was initially proposed in the shape of genetic risk scores, which generally limited the score to include single-nucleotide polymorphisms (SNPs) that were common and reached genome-wide significance in the initial GWASs. In contrast, PRS incorporates information from a much larger set of genetic variants, typically hundreds of thousands, including SNPs below the threshold for genome-wide statistical significance, and often with much more modest effect sizes. Indeed, recent findings have pointed to how polygenic background could also increase the accuracy of risk estimation for individuals with monogenic risk variant in conditions such as familial hypercholesterolaemia, hereditary breast and ovarian cancer, and Lynch syndrome.

Patients with atrial fibrillation (AF), a common morbid arrhythmia, are more likely to carry rare genetic variants associated with inherited cardiomyopathies. Prior studies on rare pathogenic variants in AF relied on small, hospital referral populations, and knowledge on clinical outcomes remains limited. To evaluate the prevalence and prognostic implications of cardiomyopathy-associated pathogenic or likely pathogenic (CMP-PLP) genetic variants in patients with AF. In 2 prospective cohort studies, the prevalence of CMP-PLP variants was assessed in the population of patients with AF and early-onset AF. The association between carrying a CMP-PLP variant and the risk of incident cardiomyopathy or heart failure (CMP/HF) after AF diagnosis was evaluated. Finally, the joint contributions of CMP-PLP variants, clinical risk, and polygenic risk were assessed. Included in this study were 2 large longitudinal cohort studies, the UK Biobank (UKB) (data 2006-2023) and the All of Us Research Program (AllofUs) (2018-2022). The UKB and AllofUs cohorts, respectively, contained 393 768 and 193 232 unrelated genotyped participants. CMP-PLP variants. Prevalence of CMP-PLP variants and risk of incident CMP/HF after AF diagnosis. In the UKB cohort, 32 281 participants (8%) had AF (mean [SD] age, 62 [6] years; 20 459 male [63.4%]). In the AllofUs cohort, 11 901 participants (6%) had AF (mean [SD] age, 67 [12] years; 6576 male [55.3%]). Compared with the biobank populations, CMP-PLP variants were twice as prevalent in patients with AF (UKB, 2.04%; 95% CI, 1.89%-2.20%; AllofUs, 2.52%; 95% CI, 2.25%-2.82%) and 5 times as prevalent in AF with onset before age 45 years (UKB, 4.99%; 95% CI, 3.07%-7.91%; AllofUs, 4.66%; 3.40%-6.32%). Cumulative incidence of CMP/HF was high in patients with AF (18%) compared with patients without AF (3%). Still, among patients with AF without prior CMP/HF (UKB, 20 226; AllofUs, 8330), carrying a CMP-PLP variant was associated with 1.6-fold risk of incident CMP/HF (meta-analysis, 95% CI, 1.32-1.90). Finally, CMP-PLP variants, a polygenic score, and clinical risk factors were independent estimators of CMP/HF. Results of this cohort study suggest that the prevalence of CMP-PLP variants was substantial in patients with early-onset AF. Patients with AF carrying a CMP-PLP variant had an associated increased risk of future CMP/HF, independent of clinical and polygenic risk. These results indicate that genetic testing in patients with AF may identify individuals at higher risk for developing CMP/HF.

Proteomic and genomic evaluation of plasma protein biomarkers for atrial fibrillation

In this the second of an anticipated four papers, we examine polygenic risk scores from a quantitative genetics perspective. In its most simplistic form, a polygenic risk score (PRS) analysis involves estimating the genetic effects of alleles in one study and then using those estimates to predict phenotype in another sample of individuals. Almost since the first application of these types of analyses it has been noted that PRSs often give unexpected and difficult-to-interpret results, particularly when applying effect-size estimates taken from individuals with ancestry very different than those to whom it is applied (applying PRSs across differing populations). To understand these seemingly perplexing observations, we deconstruct the effects of applying valid statistical estimates taken from one population to another when the two populations have differing allele frequencies at the sites contributing effect, when alleles with effects in one population are absent from the other, and finally when there is differing linkage disequilibrium (LD) patterns in the two populations. It will be shown that many of the seemingly most confusing results in the field are natural consequences of these factors. Given our best current understanding of human demographic history, most of the patterns seen in PRS analysis can be predicted as resulting from systematic differences in allele frequency and LD. Put the other way around, the most challenging and confusing results seen in cross population application of PRSs are likely to be the result of allele frequency and LD differences, not differences in the genetic effects of individual alleles. PRS analysis is an important tool both for understanding the genetic basis of complex phenotypes and, potentially, for identifying individuals at risk of developing disease before such disease manifests. As such it has the potential to be among the most important analysis frameworks in human genetics. Nevertheless, when a PRS is trained in people with one ancestry and then applied to people with another, the PRS’s behavior is often unpredictable, and sometimes is seemingly perverse. PRS distributions are often nearly non-overlapping between individuals with differing ancestry, i.e., odds ratios for unaffected people with one ancestry might be vastly larger than affected individuals from another. The correlation between a PRS and known phenotype might differ substantially, and sometimes the correlation is higher among people with ancestry different than the one used to create the PRS. Naively, one might conclude from these observations that the genetic basis of traits differs substantially among people of differing ancestry, and that the behavior of a PRS is difficult to predict when applied to new study populations. Differing definitions of genetic effect sizes are discussed, and key observations are made. It is shown that when populations differ in allele frequency, a locus affecting phenotype could have equal differences in allelic (additive) effects or equal additive variances, but not both. They cannot have equal additive effects, equal allelic penetrances, or equal odds ratios. PRS is defined, and its moments are derived. The effect of differing allele frequency and LD patterns is described. Perplexing PRS observations are discussed in light of theory and human demographic history. Suggestions for best practices for PRS construction are made. The most confusing results seen in cross population application of PRSs are often the predictable result of allele frequency and LD differences. There is relatively little evidence for systematic differences in the genetic basis of disease in individuals of differing ancestry, other than that which results from environmental, allele frequency, and LD differences.

Incident atrial fibrillation according to gender in patients with ischemic stroke: A nationwide cohort study

Introduction: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable, however current risk stratification tools (CHA 2 DS 2 -VASc) don’t include family history or genetic risk. Hypothesis: A polygenic risk scores (PRS) is both independently, and in integrated with clinical risk factors, predictive of ischemic stroke in patients with Atrial Fibrillation. Methods: Using data from the largest available GWAS in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank (UK Biobank), both independently and integrated with clinical risk factors. Results: The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Compared with the currently recommended risk tool (CHA 2 DS 2 -VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.04 to 1.21)). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson’s correlation coefficient: -0.018). Conclusions: In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors, however the prediction of stroke remains challenging.

Introduction: Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with substantial morbidity and mortality. AF is known to have a heritable component, with &gt;100 associated common variant loci. Rare variant studies have yielded limited robust associations for AF. We aimed to utilize large genome and exome sequencing data to discover rare genetic variants conferring large effects on AF risk. Methods: We meta-analyzed genome and exome sequencing data from 36 studies, including TOPMed, CCDG, UK Biobank and FOURIER. We performed exome-wide gene burden testing of rare (MAF&lt;0.1%) loss-of-function and deleterious missense variants, and single variant testing of low-frequency and rare (MAF&lt;1%) coding variants. Within genome sequenced samples, we performed gene burden testing of rare structural variants. Novel signals were replicated in MyCode. Finally, we functionally validated a novel gene by siRNA knockdown in pluripotent-induced atrial cardiomyocytes. Results: We included 52,416 AF cases and 277,762 controls, of which 49.6% were female, 83.4% were of European ancestry, and the mean baseline age was 56 years. In analysis of rare coding variation, we identified 4 novel genes associated with AF, including MYBPC3 (OR 3.5, P =2.1x10 -15 ), LMNA (OR 5.7, P =8.8x10 -11 ), PKP2 (OR 1.9, P =5.2x10 -8 ) and KDM5B (OR 2.3, P =3.0x10 -6 ). These signals were robust to removal of heart failure and cardiomyopathy cases and were replicated in independent datasets. Single variant analysis identified 2 novel signals in FAM189A2 (OR 3.9, P =7.86x10 -8 ) and ZFC3H1 (OR 5.9, P =9.7x10 -8 ). Rare deletions in CTNNA3 (OR 4.5, P =7.0x10 -9 ) were associated with increased AF risk and were supported by independent coding variant analyses, while duplications of GATA4 (OR 0.24, P =2.1x10 -5 ) were associated with reduced AF risk. In functional studies, knockdown of KDM5B resulted in shortening of the atrial action potential duration. Conclusions: Our analyses show the contribution of rare coding and structural variants to AF risk, highlight the shared genetic pathways underlying cardiomyopathy and AF, and implicate the histone demethylase gene KDM5B in AF susceptibility. In sum, we expanded our understanding of the rare variant architecture of this common arrhythmia.

Atrial fibrillation (AF) has a substantial genetic component. The importance of polygenic risk is well established, while the contribution of rare variants to disease risk warrants characterization in large cohorts. To identify rare predicted loss-of-function (pLOF) variants associated with AF and elucidate their role in risk of AF, cardiomyopathy (CM), and heart failure (HF) in combination with a polygenic risk score (PRS). This was a genetic association and nested case-control study. The impact of rare pLOF variants was evaluated on the risk of incident AF. HF and CM were assessed in cause-specific Cox regressions. End of follow-up was July 1, 2022. Data were analyzed from January to October 2023. The UK Biobank enrolled 502 480 individuals aged 40 to 69 years at inclusion in the United Kingdom between March 13, 2006, and October 1, 2010. UK residents of European ancestry were included. Individuals with prior diagnosis of AF were excluded from analyses of incident AF. Rare pLOF variants and an AF PRS. Risk of AF and incident HF or CM prior to and subsequent to AF diagnosis. A total of 403 990 individuals (218 489 [54.1%] female) with a median (IQR) age of 58 (51-63) years were included; 24 447 were diagnosed with incident AF over a median (IQR) follow-up period of 13.3 (12.4-14.0) years. Rare pLOF variants in 6 genes (TTN, RPL3L, PKP2, CTNNA3, KDM5B, and C10orf71) were associated with AF. Of these, TTN, RPL3L, PKP2, CTNNA3, and KDM5B replicated in an external cohort. Combined with high PRS, rare pLOF variants conferred an odds ratio of 7.08 (95% CI, 6.03-8.28) for AF. Carriers with high PRS also had a substantial 10-year risk of AF (16% in female individuals and 24% in male individuals older than 60 years). Rare pLOF variants were associated with increased risk of CM both prior to AF (hazard ratio [HR], 3.13; 95% CI, 2.24-4.36) and subsequent to AF (HR, 2.98; 95% CI, 1.89-4.69). Rare and common genetic variation were associated with an increased risk of AF. The findings provide insights into the genetic underpinnings of AF and may aid in future genetic risk stratification.

Polygenic risk score for hypertrophic cardiomyopathy predicts population disease risk, penetrance in sarcomeric rare variant carriers and survival in cases

SummaryBackground:Idiopathic pulmonary fibrosis (IPF) and telomere length (TL) are both strongly linked to rare and common genetic variation. Shortened TL itself may be causal for IPF. Whether rare and common variants compete or cooperate to confer genetic risk of IPF uniformly is unknown.Methods:We used whole genome sequencing (WGS) data from a discovery case-control cohort sequenced at Columbia (777 IPF, 2905 controls) and validated findings using WGS data from Trans-Omics for Precision Medicine (TOPMed, 1148 IPF, 5202 controls) and the UK Biobank (UKBB, 2739 IPF, 395331 controls). In all cohorts, we identified rare damaging variants in disease-associated genes and computed control-normalized polygenic risk scores for IPF (IPF-PRS) and telomere length (TL-PRS). Telomere length of blood leukocytes was measured using a qPCR assay for two cohorts. We determined the association of the MUC5B rs35705950 polymorphism, an IPF-PRS excluding MUC5B (IPF-PRS-noMUC5B), and a TL-PRS with IPF risk in the overall cohort and in subgroups stratified by genetic endotypes (rare variant carriers, non-carriers stratified by TL cutoffs). We calculated cross-validated area under the receiver operator curve (AUC) and compared the liability of IPF explained by genetic variables.Findings:We identified independent associations between IPF risk and rare variants, the MUC5B SNP, and both polygenic scores in the discovery cohort and replicated these findings in the TOPMed and UKBB cohorts. The adjusted effect size of the TL-PRS, which includes >180 SNPs not previously associated with IPF, was comparable to the IPF-PRS-noMUC5B in the discovery (ORTL-PRS 1.63 [95% CI 1.47, 1.81] vs. ORIPF-PRS 1.60 [1.44, 1.77]) and replication cohorts (TOPMed ORTL-PRS 1.47 [1.36, 1.59] vs. ORIPF-PRS 1.37 [1.25, 1.50]; UKBB ORTL-PRS 1.24 [1.19, 1.29] vs. ORIPF-PRS 1.25 [1.21, 1.30]). The TL-PRS incrementally improved disease prediction beyond known IPF common and rare genetic predictors and clinical variables in discovery (combined AUC: 0.89, pDelong = 0.006), TOPMed (combined AUC: 0.89, pDelong = 0.01), and UKBB cohorts (combined AUC: 0.77, pDelong = 0.03). Rare and common variants jointly contributed to genetic liability of IPF. The TL-PRS increased liability of IPF explained by 13% in the discovery cohort and 8% and 13% in the TOPMed and UKBB cohorts, respectively. In IPF subjects with damaging rare variants, the TL-PRS was consistently associated with disease risk whereas the IPF-PRS-noMUC5B was not. The TL-PRS also conferred nominally greater odds of disease risk than the IPF-PRS-noMUC5B in patients with shorter TL, in the discovery and UKBB cohorts. Together, 23–43% of IPF cases have damaging rare variants or telomeres <10th percentile, where the TL-PRS represents a major unrecognized genetic risk factor.Interpretation:Common and rare genetic variation confer context-specific genetic risk in IPF competitively and cooperatively. In contrast to known IPF common risk variants, the TL-PRS, which includes >180 genetic loci not previously associated with IPF, increases the risk of disease specifically in certain IPF endotypes. Polygenic risk from telomere-associated common variants is a key feature of IPF genetic heterogeneity.Funding:National Institutes of Health (NIH), Medical Research Council (MRC), National Institute for Health and Care Research (NIHR)