Genetics of height and risk of atrial fibrillation: A Mendelian randomization study.

BACKGROUND: Observational studies have identified height as a strong risk factor for atrial fibrillation, but this finding may be limited by residual confounding. We aimed to examine genetic variation in height within the Mendelian randomization (MR) framework to determine whether height has a causal effect on risk of atrial fibrillation. METHODS AND
FINDINGS: In summary-level analyses, MR was performed using summary statistics from genome-wide association studies of height (GIANT/UK Biobank; 693,529 individuals) and atrial fibrillation (AFGen; 65,446 cases and 522,744 controls), finding that each 1-SD increase in genetically predicted height increased the odds of atrial fibrillation (odds ratio [OR] 1.34; 95% CI 1.29 to 1.40; p = 5 × 10-42). This result remained consistent in sensitivity analyses with MR methods that make different assumptions about the presence of pleiotropy, and when accounting for the effects of traditional cardiovascular risk factors on atrial fibrillation. Individual-level phenome-wide association studies of height and a height genetic risk score were performed among 6,567 European-ancestry participants of the Penn Medicine Biobank (median age at enrollment 63 years, interquartile range 55-72; 38% female; recruitment 2008-2015), confirming prior observational associations between height and atrial fibrillation. Individual-level MR confirmed that each 1-SD increase in height increased the odds of atrial fibrillation, including adjustment for clinical and echocardiographic confounders (OR 1.89; 95% CI 1.50 to 2.40; p = 0.007). The main limitations of this study include potential bias from pleiotropic effects of genetic variants, and lack of generalizability of individual-level findings to non-European populations.
CONCLUSIONS: In this study, we observed evidence that height is likely a positive causal risk factor for atrial fibrillation. Further study is needed to determine whether risk prediction tools including height or anthropometric risk factors can be used to improve screening and primary prevention of atrial fibrillation, and whether biological pathways involved in height may offer new targets for treatment of atrial fibrillation.

Introduction

Atrial fibrillation is a common cardiac arrhythmia, with a population prevalence of 0.5%, affecting more than 33 million individuals worldwide [1]. A number of risk factors are associated with atrial fibrillation, including chronic diseases like chronic kidney disease, heart failure, thyroid disease, obesity, obstructive sleep apnea, sleep apnea, and valvular heart disease, as well as cardiac surgery, smoking, and anthropometric factors [2-5]. Recent studies have identified both common and rare genetic variants at more than 100 independent loci contributing to the incidence of atrial fibrillation, and heritability is estimated at 20% [6-8]. Even with treatment, affected individuals are at risk of cardioembolic stroke, heart failure, and death [2].

Height has been identified as a risk factor for a number of cardiometabolic diseases, including coronary artery disease, atrial fibrillation, and venous thromboembolism [9,10]. The relationship between height and atrial fibrillation in particular has been identified in large observational studies, with greater height strongly associated with increased risk of atrial fibrillation [4,11-18]. These studies are limited in assessing the causality and potential mediators or confounders of this association by their observational design, and randomized controlled trials of anthropometric traits are not feasible. Although a number of factors influence height including childhood illness/development, diet/nutrition, and socioeconomic status, height has a strong genetic component. Large genome-wide association studies (GWASs) have provided heritability estimates of 60%–70%, and have identified more than 700 independent loci that contribute to height [19-22].

In the current study, we utilize human genetic data within the Mendelian randomization (MR) framework to evaluate a potential causal association between height and atrial fibrillation. MR analysis exploits the random assortment of genetic variants during meiosis as an instrumental variable to estimate the causal relationship between a trait and an outcome of interest. Here, we use summary data from large, multiethnic GWASs of height (693,529 individuals) and atrial fibrillation (65,446 cases and 522,744 controls) to estimate the effect of genetically predicted height on risk of atrial fibrillation [7,19]. We then use participant-level data from the Penn Medicine Biobank within the observational phenome-wide association study (PheWAS) framework to identify other clinical associations with height, and within the single-sample MR framework to further assess the impact of height on atrial fibrillation after adjustment for clinical risk factors.

Methods

This study is reported per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist). The study did not have a pre-registered or published analysis plan. All analyses were planned prior to study initiation.

Two-sample MR analysis

Summary-level data for height was obtained from a 2018 meta-analysis of GWASs of height [19]. This analysis was a fixed-effects meta-analysis combining results from a GWAS of height performed among 456,426 participants from the UK Biobank (adjusted for age, sex, recruitment center, genotyping batch, and 10 genetic principal components) and results from a 2014 GWAS published by the GIANT (Genetic Investigation of ANthropometric Traits) Consortium, which included 253,288 participants from 79 studies (adjusted for age, sex, and study-specific covariates). Genetic variants associated with height at genome-wide significance (p < 5 × 10−8) were then LD-pruned (distance threshold = 10,000 kb, r2 = 0.001) using the clump_data command in the TwoSampleMR package in R to identify an independent set of variants to serve as a genetic instrument for height [23]. The independent variants associated with height at genome-wide significance (p < 5 × 10−8) were then harmonized with variants from the 2018 Roselli et al. [7] atrial fibrillation GWAS from the AFGen (Atrial Fibrillation Genetics) Consortium, using the default settings of the harmonize_data command in the TwoSampleMR package in R to ensure that effect estimates were aligned to the same allele. This study included 65,446 atrial fibrillation cases and 522,744 controls from more than 50 studies (84.2% European, 12.5% Japanese, 2% African American, and 1.3% Brazilian and Hispanic), including participants from UK Biobank, Biobank Japan, other international biobanks, and international cardiovascular cohort studies (adjusted for age, sex, and study-specific covariates) [7]. In total, 707 independent SNPs associated with height were available in the atrial fibrillation outcome GWAS. These 707 SNPs accounted for 11.2% of the variability in height (S1 Table).

Inverse-variance-weighted 2-sample MR was used as the primary analysis, with weighted median, MR-Egger, and MR-PRESSO performed as sensitivity analyses to account for potential violations of the instrumental variable assumptions [24]. Further sensitivity analysis was performed using a genetic instrument for height constructed excluding any SNPs nominally (p < 0.05) associated with coronary artery disease, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, triglycerides, fasting glucose, fasting insulin, diabetes, BMI, waist-to-hip ratio, and systolic blood pressure, all identified using the MR-Base database [25-28].

For each variant included in the genetic instruments, variance (R2) was calculated using the formula R2 = 2 × MAF × (1−MAF) × beta2 (where MAF represents the effect allele frequency and beta represents the effect estimate of the genetic variant in the exposure GWAS). F-statistics were then calculated for each variant using the formula (where R2 represents the variance in exposure explained by the genetic variant, and N represents the number of individuals in the exposure GWAS) to assess the strength of the selected instruments [29]. Bias due to sample overlap between the exposure and outcome GWASs was estimated and found to be negligible (S1 Methods) [30]. The estimates for the effect of height on atrial fibrillation are reported per 1–standard deviation (SD) increase in height. A scaled estimate per 10-cm increase in height has also been calculated for the main analysis based on the February 2020 UK Biobank population standard deviation of height (9.28 cm).

To account for the possibility that the restrictive genetic instrument may introduce collider or ascertainment bias by conditioning on associated traits, multivariable MR was performed using the TwoSampleMR package in R. This method allows the direct effects of multiple traits on an outcome to be determined jointly. The effect of height on atrial fibrillation was estimated in analyses that individually adjusted for potential confounders, including coronary artery disease, HDL, LDL, total cholesterol, triglycerides, fasting glucose, fasting insulin, diabetes, BMI, waist-to-hip ratio, and systolic blood pressure, using genetic variants obtained from MR-Base as above. Potential confounders that had significant (p < 0.05) direct effects on atrial fibrillation in the individual multivariable models were then combined in a final model to jointly estimate their direct effects on atrial fibrillation.

Bidirectional MR was performed to assess for the possibility of reverse causality. A genetic instrument for atrial fibrillation containing 73 independent, genome-wide significant variants was constructed using the same methods as for the height instrument. Inverse-variance-weighted, weighted median, and MR-Egger analyses were performed.

Height genetic risk score

A standardized, weighted genetic risk score (GRS) for height was calculated for each individual in the Penn Medicine Biobank using imputed dosage information in an additive model, weighted by the effect size of each independent (distance threshold = 10,000 kb, r2 = 0.001), genome-wide significant (p < 5 × 10−8) variant from the 2018 Yengo et al. GWAS of height [19], using the clump_data command in the TwoSampleMR package in R. Scores were standardized, centered to mean = 0, and scaled.

PheWAS

PheWASs were performed to identify clinical diagnoses associated both measured height and the height GRS, using the default setting of the PheWAS package in R [31]. Briefly, international classification disease codes obtained from the electronic health record were mapped to “Phecodes,” and individuals were assigned case/control status or excluded using default mapping parameters. Association between height/height GRS and phenotypes was tested using logistic regression, adjusted for age, age2, sex, and 10 genetic principal components. The 10 genetic principal components included in the PheWAS analysis were selected a priori, without investigation of the variation of the data. For the more detailed individual-level MR analyses, 6 genetic principal components were identified qualitatively using a scree plot as explaining the majority of variation in the data (S4 Fig). The Bonferroni method was used to account for multiple testing (p = 0.05/1,816 phenotypes).

Individual-level MR

To estimate the population-averaged effect of increased height on risk of atrial fibrillation, individual-level MR was performed using the 2-stage method [32]. This analysis included 6,567 individuals with available height, genotype, and clinical covariate information. The instrumental variable was a GRS for height, computed from independent, genome-wide significant variants, weighted by effect on height in the 2018 GIANT/UK Biobank GWAS meta-analysis. Of the 707 genetic variants included in the main instrumental variable for height in the 2-sample MR analysis, 695 were available for use in the individual-level analysis (S3 Table). In the first stage of the 2-stage process, a linear regression was fitted with height as the dependent variable, and the GRS for height as the independent variable, among the subset of individuals without atrial fibrillation, adjusted for age, sex, and 6 genetic principal components. The 6 genetic principal components were selected qualitatively using a scree plot as explaining the majority of variation in the data (S4 Fig). In the second stage, a logistic regression model with robust standard errors was fit for both atrial fibrillation cases and controls, incorporating the fitted values of height from the first stage, adjusted for age, sex, and the 6 genetic principal components, with atrial fibrillation as the outcome. In additional models, each stage was adjusted for clinical diagnoses of hypertension, coronary artery disease, heart failure, hyperlipidemia, diabetes, chronic kidney disease, stroke, valvular heart disease, sleep apnea, thyroid disease, cardiac surgery, smoking, and echocardiographic left atrial size to account for potential cofounders/mediators of the height–atrial fibrillation relationship. These risk factors were selected for being common cardiometabolic risk factors or having prior associations with atrial fibrillation [2]. Sensitivity analysis was performed including only individuals who had available echocardiographic data.

MR assumptions

MR relies on the naturally random assortment of genetic variants at conception to provide unbiased estimates of the effect of an exposure (in this case height) on an outcome (atrial fibrillation). To provide unbiased estimates, MR depends on 3 key assumptions [33]. First, genetic instruments must be associated with the exposure of interest (relevance assumption). Weak instruments that are not strongly associated with the exposure of interest may result in biased estimates. To estimate the strength of the height genetic instrument, the F-statistic was calculated (S1 Table). The second assumption is that no confounders of the SNP–outcome association are present. This assumption is not readily testable because some confounders may not be measured, although at both the 2-sample and individual-level the MR analyses were adjusted for common cardiovascular risk factors. Further, because genetic variants are randomly allocated at conception, confounders of the SNP–outcome association should be distributed across the population. The third assumption is that the effect of the genetic instrument on atrial fibrillation is entirely through the effect on height (exclusion restriction). We performed a PheWAS using the GRS for height to assess for potentially pleiotropic effects of height. Further, we applied multiple MR methods including weighted median, MR-Egger, and MR-PRESSO, which are more robust to the presence of horizontal pleiotropy, a potential violation of the exclusion restriction assumption [24]. Finally, MR assumes that genetically mediated effects are similar to exogenous effects. In this case, height is highly heritable, although the effect of changes in stature related to environmental exposures (e.g., fetal growth restriction, malnutrition) may differ from the genetic effects assessed here.

Penn Medicine Biobank

The Penn Medicine Biobank is a longitudinal genomics and precision medicine study in which participants consent to linkage of genomic information and biospecimens to the electronic health record. More than 60,000 individuals are currently enrolled. This study included 6,567 individuals of European ancestry (genetically determined) who underwent genotyping and had available electronic health record data. For individual-level analyses in the Penn Medicine Biobank, continuous demographic variables were summarized by mean and standard deviation, with categorical variables summarized by count and group percent. The Wilcoxon rank sum test and Fisher’s exact test, respectively, were used to assess for significant differences between groups.

Phenotype ascertainment

For individual-level analyses in Penn Medicine Biobank, phenotypes were determined by querying the electronic health record. International Classification of Diseases (ICD)–9/10 and Current Procedural Terminology (CPT) codes, in addition to laboratory measurements and vital signs, were used to identify height, weight, BMI, smoking status, diagnoses of heart failure, hypertension, diabetes mellitus, chronic kidney disease, sleep apnea, stroke, thyroid disease, valvular heart disease, and cardiac surgery. We identified 6,567 participants with available high-quality genotype and electronic health record phenotype data, with transthoracic echocardiogram data available for a subset of 2,842 individuals. Atrial fibrillation was defined using ICD-9/10 codes 427.31, I48.0, I48.1, I48.2, and I48.91. Atrial fibrillation ascertainment in the 2018 Roselli et al. GWAS meta-analysis was study-specific [7]. Data were extracted as of January 2017 (S1 Methods).

Ethical approval

The investigators from the Penn Medicine Biobank (MGL, RJ, MV, SSV, YB, MDR, DJR, BFV, SMD) have received approval from the University of Pennsylvania Institutional Review Board. No additional approval was required for analyses of publicly available summary statistics.

Statistical analysis

All statistical analyses were performed using R version 3.5.1 [34]. For statistical analyses, p < 0.05 was considered statistically significant.

Results

Population-level MR

To characterize the relationship between increasing height and risk of atrial fibrillation, we constructed a genetic instrument for height using 707 independent SNPs associated with height at a genome-wide level of significance (p < 5 × 10−8), which accounted for 11.2% of the variability in height (S1 Table). The mean F-statistic was 110 (range 22–948), suggesting the risk of weak instrument bias was low [30]. We performed 2-sample MR using summary statistics from a GWAS of atrial fibrillation including 65,446 atrial fibrillation cases and 522,744 controls. Inverse-variance-weighted modeling identified a significant association between increasing height and atrial fibrillation (odds ratio [OR] 1.34 per 1-SD increase in height; 95% CI 1.29 to 1.40; p = 5 × 10−42) (Fig 1). This corresponds to an OR of 1.37 (95% CI 1.31 to 1.44) per 10-cm increase in height among UK Biobank participants. The intercept from Egger regression was −0.001 (p = 0.13), thus not providing evidence for significant pleiotropic bias. Similar estimates were obtained in sensitivity analyses from weighted median, MR-Egger, and MR-PRESSO models.

Fig 1

Two-sample Mendelian randomization (MR).

Two-sample MR was performed using a genetic instrument containing 707 independent SNPs associated with height. (A) Each point represents the SNP effects on height and atrial fibrillation. Colored lines represent inverse-variance-weighted (red), weighted median (green), and MR-Egger (blue) estimates of the association between a 1-SD increase in height and risk of atrial fibrillation. (B) Odds ratios (ORs), 95% confidence intervals (CIs), and p-values for MR estimates.

An additional genetic instrument for height was constructed, excluding variants nominally associated (p < 0.05) with potentially pleiotropic risk factors for atrial fibrillation. The selection of SNPs using a nominal p-value association of 0.05 was an arbitrarily chosen threshold to more liberally account for potentially pleiotropic effects of height-associated SNPs. The restrictive instrument consisted of 224 independent SNPs, in sum explaining 2.8% of the variance in height (S2 Table). The mean F-statistic was 88 (range 22–867). MR results using this restrictive genetic instrument were similar (S1 Fig).

We next performed multivariable MR. Height remained significantly associated with atrial fibrillation after adjustment for the effect of genetic variants separately on each of coronary artery disease, HDL, LDL, total cholesterol, triglycerides, fasting glucose, fasting insulin, diabetes, BMI, waist-to-hip ratio, and systolic blood pressure (S4 Table). This analysis identified significant associations of BMI, systolic blood pressure, total cholesterol, and coronary artery disease with atrial fibrillation after adjustment for height. The effect of height on risk of atrial fibrillation was similar in models accounting for these risk factors individually, and in a combined model jointly considering genetic variants associated with height, BMI, systolic blood pressure, total cholesterol, and coronary artery disease (Fig 2 and S5 Table).

Fig 2

Multivariable Mendelian randomization analysis.

Multivariable Mendelian randomization was performed to jointly consider the effect of genetic variants for cardiometabolic traits and height on atrial fibrillation. The effect of a 1-SD increase in height on risk of atrial fibrillation estimated by each model is displayed. ORs per 1-SD increase in height, 95% confidence intervals (CIs), and p-values for Mendelian randomization estimates are displayed.

To assess for the possibility of reverse causation, where genetic variants primarily associated with increased risk of atrial fibrillation may represent invalid instruments for height, a bidirectional MR analysis was performed. Using a genetic instrument including 73 independent, genome-wide significant variants associated with atrial fibrillation, there was no evidence for a causal association with height (S2 Fig).

Demographics

Individual-level analysis focused on 6,567 European-ancestry individuals from the Penn Medicine Biobank with available genotype data linked to the electronic health record, recruited between 2008 and 2015. Individuals with atrial fibrillation were older (mean 66 versus 60 years; p < 0.001) than individuals without atrial fibrillation. Individuals with atrial fibrillation were taller (mean 174 versus 170 cm; p < 0.001), and significantly more likely to have been diagnosed with chronic kidney disease, coronary artery disease, heart failure, hyperlipidemia, hypertension, prior cardiac surgery, sleep apnea, smoking, stroke, or valvular heart disease, compared to individuals without atrial fibrillation (Table 1 and S6–S8). Among the subset of individuals with echocardiograms, individuals with atrial fibrillation had larger left atrial diameter (mean 4.39 versus 3.92 cm; p < 0.001).

Table 1

Demographics.

Characteristic	Statistic	No atrial fibrillationN = 3,538	Atrial fibrillationN = 3,029	p-Value
Age (years)	Mean (SD)	60 (14)	66 (12)	<0.001
Sex (female)	n (%)	1,577 (45%)	930 (31%)	<0.001
BMI (kg/m2)	Mean (SD)	29 (6)	29 (6)	<0.001
Height (cm)	Mean (SD)	170 (11)	174 (11)	<0.001
Weight (kg)	Mean (SD)	83 (21)	88 (22)	<0.001
Hypertension	n (%)	1,799 (51%)	2,117 (70%)	<0.001
Coronary artery disease	n (%)	1,747 (49%)	1,764 (58%)	<0.001
Heart failure	n (%)	971 (27%)	1,690 (56%)	<0.001
Hyperlipidemia	n (%)	1,829 (52%)	1,946 (64%)	<0.001
Diabetes	n (%)	596 (17%)	497 (16%)	0.7
Chronic kidney disease	n (%)	400 (11%)	577 (19%)	<0.001
Sleep apnea	n (%)	411 (12%)	634 (21%)	<0.001
Stroke	n (%)	575 (16%)	724 (24%)	<0.001
Thyroid disease	n (%)	71 (2.0%)	78 (2.6%)	0.14
Cardiac surgery	n (%)	830 (23%)	2,063 (68%)	<0.001
Valve disease	n (%)	902 (25%)	1,407 (46%)	<0.001
Smoking	n (%)	1,654 (47%)	1,526 (50%)	0.004
Left atrial diameter (cm)*	Mean (SD)	3.92 (0.76)	4.39 (0.81)	<0.001

Demographics of individuals in Penn Medicine Biobank cohort with and without atrial fibrillation.

*Left atrial diameter available for 2,842 participants.

PheWASs of height and height GRS

To determine the association between genetically predicted height and clinical diagnoses, we first constructed a GRS for height using weights derived from the 2018 GIANT/UK Biobank height GWAS meta-analysis. PheWASs were performed to identify clinical diagnoses associated with both measured height and the height GRS across the Penn Medicine Biobank. Each 1-SD increase in measured height was associated with increased risk of atrial fibrillation (OR 1.55; 95% CI 1.41 to 1.71; p = 3.6 × 10−18) and decreased risk of coronary atherosclerosis (OR 0.66; p = 6.9 × 10−18). Each 1-SD increase in height GRS was associated with increased risk of atrial fibrillation and flutter (OR 1.16; 95% CI 1.09 to 1.23; p = 7 × 10−6) (Fig 3).

Fig 3

Phenome-wide associations of clinical diagnoses with height and height genetic risk score (GRS).

Phenome-wide association studies were performed in Penn Medicine Biobank participants to identify clinical phenotypes associated with increased (A) height and (B) height GRS. The horizontal red line denotes the Bonferroni-adjusted level of significance (0.05/1,816 phenotypes), the blue line denotes the nominal level of significance (0.05), and triangles denote the direction of association between increasing height and risk of the phenotype (pointing upward = increased risk; pointing downward = decreased risk).

Individual-level MR was performed in Penn Medicine Biobank participants to further assess the association between height and atrial fibrillation. Using the height GRS as an instrumental variable, height was significantly associated with atrial fibrillation (OR 1.75 per 1-SD increase in height; 95% CI 1.53 to 2.0; p = 6 × 10−5) after adjusting for age, sex, and 6 genetic principal components (Fig 4). This corresponds to an OR of 1.66 (95% CI 1.46 to 1.86) per 10-cm increase in height among Penn Medicine Biobank participants. Results were similar in sex-stratified analysis. Height remained associated with atrial fibrillation after adjustment for weight, hypertension, coronary artery disease, heart failure, hyperlipidemia, diabetes, chronic kidney disease, sleep apnea, stroke, thyroid disease, smoking, cardiac surgery, and valvular heart disease. After further adjustment for left atrial size, height remained significantly associated with atrial fibrillation (OR 1.83; 95% CI 1.44 to 2.31; p = 0.01). When individual-level MR was restricted to the subset of individuals with complete data, the effect estimates were similar, with modest attenuation with sequential adjustment for clinical risk factors and left atrial size (S3 Fig).

Fig 4

Individual-level instrumental variable analysis.

Individual-level instrumental variable analysis was performed in Penn Medicine Biobank participants, using a weighted genetic risk score for height as an instrumental variable for measured height. The base model was adjusted for age, sex, and 6 genetic principal components. Model 1 was additionally adjusted for weight, hypertension, coronary artery disease, heart failure, hyperlipidemia, diabetes, chronic kidney disease, sleep apnea, stroke, thyroid disease, smoking, cardiac surgery, and valvular heart disease. Model 2 was additionally adjusted for left atrial size as measured on transthoracic echocardiogram. Odds ratios (ORs) are reported per 1-SD increase in height.

Discussion

In this study we used both population- and individual-level genetic information to test the association between height and atrial fibrillation. At the population level, there was a strong causal association between genetic determinants of height and risk of atrial fibrillation. This finding was robust to multiple sensitivity analyses of the MR methods and the genetic instrument for height. Observational analysis at the individual level identified a strong association between height and increased risk of atrial fibrillation, and decreased risk of coronary artery disease. MR analysis at both the population and individual levels suggested that height remains a causal risk factor for atrial fibrillation even after adjustment for other traditional risk factors.

Our findings are consistent with prior observational analyses that have identified height as a risk factor for atrial fibrillation [4,11-18]. These studies, including a large Swedish national cohort study of 1.5 million military conscripts recruited over the course of 28 years, have consistently identified a strong association between height and atrial fibrillation [4]. Observational designs have limited these studies, due to the possibility of residual confounding. The Helsinki Birth Cohort Study partially addressed this limitation by considering the effect of maternal height on risk of atrial fibrillation in offspring, but was limited by a small, homogenous sample [35]. We further build on those prior findings using the MR framework, considering both summary-level and individual-level genetic data, examining genetic variants associated with height and atrial fibrillation to provide a causal estimate for the effect of increasing height on risk of atrial fibrillation. Our population-level MR analysis reinforces a prior MR estimate for association between increasing height and atrial fibrillation (OR 1.33; 95% CI 1.26 to 1.40) using updated GWAS summary statistics for height and atrial fibrillation [9]. By including a large multiethnic study of atrial fibrillation, we increase the generalizability of the prior MR findings, which were limited only to white participants of the UK Biobank.

Several mechanisms have been proposed to explain the relationship between height and atrial fibrillation. Increased left atrial size is correlated with height, and has been identified as an independent predictor of atrial fibrillation [17,18,36,37]. Consistent with findings from the Cardiovascular Health Study, however, we found that the effect of height on risk of atrial fibrillation was not attenuated after adjustment for left atrial diameter [14]. It is possible that other, more nuanced markers of left atrial structure and function that have been associated with severity of atrial fibrillation, such as left atrial volume index, emptying fraction, expansion index, and contractile function, may also be affected by height and may better explain the association between height and atrial fibrillation [38]. Similarly, 2-dimensional echocardiography significantly underestimates left atrial size compared to MRI assessment [39]. Investigation of these factors in the 2-sample setting is limited by the lack of genetic studies of cardiac structure/function by echocardiography, MRI, or cardiac CT, and study of these parameters in large cohorts with genetics and imaging data is warranted. Bioimpedance and dual-energy X-ray absorptiometry measures of body composition have also been associated with atrial fibrillation [40]. While the current study focused primarily on common cardiometabolic risk factors used in clinical practice, the possibility remains that more advanced anthropometric screening beyond height, weight, body mass index, and waist-to-hip ratio may explain some of the effect of increased height on atrial fibrillation.

MR has previously identified an association between shorter stature and increased risk of coronary artery disease, mediated in part by increases in LDL cholesterol and triglyceride levels [10]. Our PheWAS of measured height similarly identified an association between height and decreased risk of coronary artery disease, and our PheWAS of a height GRS identified a nominally (p < 0.05) protective association between height and coronary artery disease. In the current MR analysis, we were unable to detect significant associations between these lipid traits and risk of atrial fibrillation when considered in multivariable models alongside height. Similarly, in multivariable models considering height alongside common cardiometabolic risk factors for atrial fibrillation, effect estimates were all similar, suggesting these factors may not substantially mediate the effect of height on risk of atrial fibrillation.

Although we detected no evidence of horizontal pleiotropy and our results remained robust to extensive sensitivity analyses, height is a highly polygenic trait, and the possibility remains that pleiotropic pathways mediate the association between height and atrial fibrillation. A recent GWAS found genes at atrial-fibrillation-associated loci to be enriched in pathways important for tissue formation [41]. Coupled with the finding that genes at height-associated loci are enriched for cardiovascular and endocrine tissue types, these results raise the possibility of a more complex shared genetic architecture affecting both height and atrial fibrillation [22]. Thus, we cannot exclude the possibility that genetic variants broadly associated with growth and development may simultaneously affect height and establish structural cardiovascular changes that may predispose to atrial fibrillation.

The findings of this study have several clinical implications. Anthropometric characteristics have been included in clinical models that predict incident atrial fibrillation, including BMI in a model derived from the Framingham Heart Study, and height in the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology–Atrial Fibrillation) model from the CHARGE-AF Consortium [42-44]. While height is not a readily modifiable risk factor, the recognition that taller individuals have increased risk of atrial fibrillation may prompt more aggressive management of modifiable cardiovascular risk factors like overweight/obesity, prediabetes/diabetes, hypertension, and alcohol/tobacco use. As both height and atrial fibrillation are heritable, it is possible that height might act as a surrogate for family history of atrial fibrillation when this information is not readily available. Further study may clarify the risks of incident atrial fibrillation attributable to height and family history. Similarly, further mechanistic investigation of the height–atrial fibrillation link may identify novel modifiable pathways for intervention. Future study is needed to determine whether risk prediction tools including height or other anthropometric factors can be used to improve screening and primary prevention of atrial fibrillation.

Our study has several limitations. First, observational analyses from the Penn Medicine Biobank may not be generalizable, as this population represents a cohort within a single academic health system. Second, despite our use of multiethnic GWASs of height and atrial fibrillation, the underlying studies focused primarily on individuals of European ancestry. Genetic studies in broader populations are warranted to further improve the generalizability of these findings. Third, our population-level MR analyses relied on publicly available summary statistics that contain some overlapping samples/cohorts. Two-sample MR tends to bias the causal effect estimates to the null, but sample overlap may make the estimate susceptible to weak instrument bias. In this study, however, despite overlapping cohorts, simulation suggests that the large sample sizes of the height and atrial fibrillation GWASs, and large F-statistics, make the risk of weak instrument bias low [30].

In conclusion, we find that increased height is associated with increased risk of atrial fibrillation, and this relationship is likely to be causal. These results raise the possibility of investigating height/growth-related pathways as a means for gaining novel mechanistic insights into atrial fibrillation, as well as the possibility of incorporating height into larger targeted screening strategies for atrial fibrillation.

Two-sample MR sensitivity analysis.

Two-sample MR was performed using a genetic instrument containing 224 independent SNPs associated with height, excluding SNPs nominally associated (p < 0.05) with traditional atrial fibrillation risk factors: coronary artery disease, HDL, LDL, total cholesterol, triglycerides, fasting glucose, fasting insulin, diabetes, BMI, waist-to-hip ratio, and systolic blood pressure. (A) Each point represents the SNP effects on height and atrial fibrillation. Colored lines represent inverse-variance-weighted (red), weighted median (green), and MR-Egger (blue) estimates of the association between a 1-SD increase in height and risk of atrial fibrillation. (B) Odds ratios (ORs), 95% confidence intervals (CIs), and p-values for MR estimates.

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Two-sample MR directionality sensitivity analysis.

Two-sample MR was performed using a genetic instrument containing 73 independent SNPs associated with atrial fibrillation. (A) Each point represents the SNP effects on atrial fibrillation and height. Colored lines represent inverse-variance-weighted (red), weighted median (green), and MR-Egger (blue) estimates of the association between a 1-SD increase in height and risk of atrial fibrillation. (B) Odds ratios (ORs), 95% confidence intervals (CIs), and p-values for MR estimates.

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Individual MR sensitivity analysis.

Individual-level instrumental variable analysis was performed in the subset of Penn Medicine Biobank participants with clinically obtained echocardiogram data, using a GRS for height as an instrumental variable for measured height. The base model was adjusted for age, sex, and 6 genetic principal components. Model 1 was additionally adjusted for weight, hypertension, coronary artery disease, heart failure, hyperlipidemia, diabetes, chronic kidney disease, sleep apnea, stroke, thyroid disease, smoking, cardiac surgery, and valvular heart disease. Model 2 was additionally adjusted for left atrial size as measured on transthoracic echocardiogram. Odds ratios (ORs) are reported per 1-SD increase in height.

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Genetic principal component scree plot for Penn Medicine Biobank.

Proportion of variance explained for each genetic principal component among European-ancestry participants of Penn Medicine Biobank.

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Supplemental methods.

(DOCX)

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STROBE checklist.

(DOCX)

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Full genetic instrument for height used in 2-sample MR analysis including independent (distance threshold = 10,000 kb, r2 = 0.001), genome-wide significant (p < 5 × 10−8) variants.

(XLSX)

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Restrictive genetic instrument for height used in 2-sample MR analysis including independent (distance threshold = 10,000 kb, r2 = 0.001), genome-wide significant (p < 5 × 10−8) variants, excluding those SNPs nominally (p < 0.05) associated with coronary artery disease, HDL, LDL, total cholesterol, triglycerides, fasting glucose, fasting insulin, diabetes, BMI, waist-to-hip ratio, and systolic blood pressure in the MR-Base database.

(XLSX)

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Full genetic instrument for height used in individual-level MR analysis including independent (distance threshold = 10,000 kb, r2 = 0.001), genome-wide significant (p < 5 × 10−8) variants.

(XLSX)

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Results of bivariate multivariable MR analysis considering the effect of height and each of coronary artery disease, HDL, LDL, total cholesterol, triglycerides, fasting glucose, fasting insulin, diabetes, BMI, waist-to-hip ratio, and systolic blood pressure on atrial fibrillation.

(XLSX)

Click here for additional data file.

Results of combined multivariable MR analysis considering the effects of height, body mass index, total cholesterol, systolic blood pressure, and coronary heart disease.

(XLSX)

Click here for additional data file.

Summary demographics for Penn Medicine Biobank participants overall.

(XLSX)

Click here for additional data file.

Summary demographics for Penn Medicine Biobank participants stratified by quartile of height GRS.

(XLSX)

Click here for additional data file.

Summary demographics for Penn Medicine Biobank participants limited to subset of individuals with complete phenotype and echocardiographic data.

(XLSX)

Click here for additional data file.

13 Feb 2020

Dear Dr. Damrauer,

Thank you very much for submitting your manuscript "Genetics of Height and Risk of Atrial Fibrillation: A Mendelian Randomization Study" (PMEDICINE-D-20-00041) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Abstract-“as well as incorporating height into population screening strategies for atrial fibrillation” I’m not sure how screening strategies fit into your study so I would remove this from the conclusions

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Comments from the reviewers:

Reviewer #1: In the manuscript entitled " Genetics of Height and Risk of Atrial Fibrillation: A Mendelian Randomization Study " the authors investigated the association of height with incidence of atrial fibrillation.

They performed a two-step approach with an initial analysis using summary-level data for GWAS of height and atrial fibrillation. In a second step they verified the results using individual-participant data from the Penn Medicine Biobank and found similar results. Even after adjusting for relevant clinical factors the results remained consistent. The authors conclude that height is causally associated with high risk of atrial fibrillation. The study size is adequate and the statistics used are appropriate. The manuscript is well written and the topic is of scientific relevance and interest. However, I have a few major concerns and comments:

1. One concern is the missing link to the clinical relevance of the findings. How can these findings add to diagnosis, prevention or therapies in patients with AF in a clinically routine? e.g. a risk score? Should tall people try to lose weight? Can we try to compensate our stature? I think the manuscript would benefit from a section on this.

2. The finding that height and atrial fibrillation are causally related is interesting. Similar, fat-free mass has been described as an independent risk factor for atrial fibrillation (Tikkanen et al EHJ 2019). Regarding height and traits like fat-free mass, is there a genetic overlap? Do some of these variants affect both - height and fat-free mass?

3. Did the authors see any gender differences between men and women in their analyses?

4. If data on LA-size is available, why was this not included in the baseline table?

Reviewer #2: General comments

Interesting and well conducted study that investigates the evidence for causality between height and incident atrial fibrillation. The authors state that their findings raises the possibility of investigating pathways of height.

The authors also state that their findings suggest to use height in screening strategies for atrial fibrillation. That is not part of the research described in this paper, and seems overinterpretation of their results.

Specific comments

Introduction

The background and aim of the study are discussed. Clear overview of the manuscript is given.

Materials and methods

Methods are discussed in a well-arranged manner. According to the supplementary data atrial fibrillation is identified by ICD codes in the Penn dataset. However, atrial fibrillation ascertainment should be added in the materials and methods section of the paper itself.

To accommodate the readers, please align the order of analyses in the methods and results section. In the results section the text starts with data of the individual-level analyses while the methods section starts with the summary-level two-sample MR. The same applies to the discussion section where the analyses on population-level are discussed first; try to arrange a certain order to describe the results.

Results

In most literature about height 'centimeter' instead of 'inches' is used. If the authors chose to use centimeters instead of inches, the data is easier comparable with existing literature. It may be worth using the SI system or both to accommodate worldwide readers.

In the individual-level Mendelian randomization adjustment for 5 genetic principal components was choosen. Did the authors make a screen plot or was it an arbitrary estimate? Please provide a screen plot in supplementary data if possible.

Discussion

The authors also state that their findings suggest to use height in screening strategies for atrial fibrillation. That is not part of the research described in this paper, and seems overinterpretation of their results. Please remove.

Reviewer #3: The main claim of the paper is that developing atrial fibrillation is causally related to height. The main result is from a two-sample mendelian randomisation model, with data from two large GWAS studies whose summary data is publicly available. A clear scientific background is given, along with a rationale for investigating the association between height and atrial fibrillation. Due to the implausibility of conducting a randomised controlled trial with height as an exposure, Mendelian Randomisation is a sensible approach to take to investigate this association.

This claim is not novel, there was a large study that demonstrated a very similar relationship (Lai FY, Nath M, Hamby SE, et al. Adult height and risk of 50 diseases: a combined epidemiological and genetic analysis. BMC Med 2018;16:187. doi:10.1186/s12916-018-1175-7). The claims made by that paper are properly placed in the context of previous literature. That paper looked only at people of European ancestry and did not do the level of sensitivity analysis shown here. This paper gives strong evidence to support this earlier paper, while also showing some generalisability to non-European populations. It also uses a secondary analysis on individual level data from Penn Medicine Biobank to support the main result and investigate some potential mechanisms.

The prior study also found a multitude of other diseases associated with genetically determined height such as reduced CAD and increased hip fracture. This paper is interesting in that it didn't find any significant confirmation of those other results as part of its PheWAS, potentially due to a higher standard of correction for multiple testing being applied. Sadly, the authors do not go into much detail about which of these earlier results appear on the PheWAS at a lower threshold of significance or discuss whether they have any evidence to contradict the results of the earlier paper in these diseases.

The main result supports the causal claim in the abstract, and the number and consistency of the sensitivity analyses give reassurance as to the robustness of these results. I have some concerns about some of the secondary analyses. I detail these below. The selection of the genetic variables is sensible and well-described; both in the primary analysis and the sensitivity analysis with a reduced genetic instrument. The choice of analyses to perform is comprehensive and well-chosen, and for the most part, reasonably well explained. The discussion of the limitations of this study was appropriate. The results are mostly correctly interpreted and compared sensibly with other relevant studies. The conclusion of this study was appropriate. The potential for clinical relevance is limited, due to the impracticalities of intervening on height, but the potential for investigating novel pathways or screening strategies is present.

This study follows most of the relevant guidelines in MR-STROBE; there are some specific and easy to fix queries detailed below. The paper is reasonably well-organised and should be accessible to non-specialists.

The underlying research question is an important one, but given the earlier paper, it is not clear that these results are a substantial advance on existing knowledge. The results will be of interest to policymakers and clinicians, particularly those with an interest in screening strategies for atrial fibrillation.

This paper is well done, but I think it is held back from being outstanding by the combination of its overall similarity to an earlier study and the lower statistical rigour in the more novel parts. It would be improved by a higher focus on the individual level analysis and the potential mechanisms for the effect.

~~~

Major Concerns

I am not convinced by the statistical rigour of the secondary analysis on the individual level data. This is a shame, as the inclusion of this data and the opportunity it affords for investigation of the potential mediators and mechanisms of the effect under investigation is one of the more novel parts of this paper.

The model is not sufficiently explained; I am somewhat confused as to what exactly has been done and what conclusions have been drawn from it. It would be helpful for the authors to expand this section, to clarify the hypothesis being tested and how they are interpreting the resulting odds ratios.

[p9] "In the first stage of the two-stage process, a linear regression was conducted with standardized height as the dependent variable, and the

standardized genetic risk score for height as the independent variable. In the second stage, a logistic regression model with robust standard errors was fit, incorporating both the residuals from the first stage and scaled height, with atrial fibrillation as the outcome."

It is not clear whether the first stage was assessed only in controls, or if the cases were included as well. Including cases in this estimate could introduce bias to the model. I am unsure what scaled height refers to in the second stage but using an adjusted two-stage approach makes the odd ratio is very complex to interpret. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5642006/ does not recommend adjustment on first-stage residuals, and it would be useful to the reader to have greater clarity on how conditioning the IV estimate on the residual should be interpreted as a result.

[p9] "In additional models, the second stage was adjusted for clinical diagnoses and left atrial size."

This implies that the first stage was not adjusted for these covariates. In addition to the concerns above, this raises some questions over what the ORs in these additional models are representing when the second stage, but not the first is adjusted for covariates. Could the authors please clarify what hypotheses are being tested here? A better alternative would be to use the same covariates in the first- and second-stage IV regressions. If a specific conditional estimate is intended, then it needs much more explanation to the reader. See http://www.phpc.cam.ac.uk/ceu/files/2012/11/letterthirdone010611-1.pdf & https://www.bmj.com/content/362/bmj.k601 for further detail on this issue.

It is also worth noting that the samples used for this MR analysis were also used in determining the instrument (as part of the "Multi-ethnic genome-wide association study for atrial fibrillation")

[P15] "Using individual-level MR analysis, we found that the relationship between height and atrial fibrillation appears to be independent of traditional clinical and echocardiographic risk factors for atrial fibrillation."

This is too strong a statement. I am unconvinced that this claim can be justified from the paper as written. Either the authors need to expand and explain the results that have led to this, or they need to soften this conclusion.

Minor Concerns

MR Assumptions:

There is no discussion of the underlying MR assumptions & how they are justified. While some tests of the assumptions have been performed (e.g. MR-Egger to check for heterogeneity), these tests aren't explicitly linked to the underlying MR assumptions. It is critical for these assumptions & the evidence for them to be explicit so that the reader can use them to evaluate the validity of the model.

Information on the data sources for the two-sample MR:

The first line of the methods section says "Summary-level data for GWAS of height and atrial fibrillation were obtained." but it requires some effort from the reader to discover exactly where this data comes from. Based on the references in the previous section, the AF associations comes from the Nature paper "Multi-ethnic genome-wide association study for atrial fibrillation", which has data from UK Biobank, Biobank Japan, & others - including Penn Medicine Biobank. The height associations come from "Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry", which shows that the data for height comes from a meta-analysis of data from UK Biobank & Wood et. al (which takes data from 79 smaller studies).

Firstly, the reader should not have to read 3-4 references to determine the underlying population of the study. Please provide a clear account of the source of the data, it's underlying population etc. This is particularly relevant as the multi-ethnicity of the populations is one of the strengths of the study. Please include results on the heterogeneity of the different populations of your various data sources - the table for the Penn Medicine Biobank is excellent and it would be helpful to have as similar as possible for the populations that the AF & height data came from. A table in on the individual level data comparing the 25th and 75th percentile of your genetic risk score for the Penn Medicine Biobank data could also provide useful context for considering the multivariate genetic effects.

Also, UK Biobank's European participants are included in both studies, giving an overlap of at least 50%. This is briefly mentioned in the limitations section of the discussion, but a clear statement as to the level of the overlap & its estimate on the impact of this overlap should be given. https://sb452.shinyapps.io/overlap/ may be useful here.

There is no comment on how the summary statistics were calculated for each data source - were similar covariates included in both calculations?

Multiple definitions of AF

Different definitions of AF are used by different data sources. e.g. from "Multi-ethnic genome-wide study...": "Ascertainment of AF in the UKBB includes samples with one or more of the following codes: non-cancer illness code, self-reported (1471, 1483); operation code (1524); diagnoses - main/secondary ICD10 (I48, I48.0-4, I48.9); underlying (primary/secondary) cause of death: ICD10 (I48, I48.0-4, I48.9); diagnoses - main/secondary ICD9 (4273); operative procedures - main/secondary OPCS (K57.1, K62.1-4)" whereas the definition used in the Penn Medicine Biobank definition is "Atrial fibrillation was defined using ICD9/10 codes: 427.31, I48.0, I48.1, I48.2, I48.91.". I do not think this is problematic, but it should be made clear to the reader.

Typos/Corrections

In methods, it says "The instrumental variable was a standardized genetic risk score for height, computed from

independent, genome-wide significant variants, weighted by the effect on height in the GIANT

GWAS (see Height Genetic Risk Score above)." (P9) but the section on Height Genetic Risk does not appear until P11.

Multiple testing was managed via Bonferroni correction in the PheWAS analysis, but this is only stated on the relevant figure. Please make it clear in the body of the text also.

Figure 3 is a little unclear. I think it is plotting the OR of Height on AF but there is no scale provided (presumably per 1S.D. change in height?) from the Multivariable MR that also includes the variables named under covariate. It feels odd to describe the other risk factors includes in the multivariable MR as covariates - that implies more a measured confounder to my ears. The OR for the other risk factors should also be reported (some are in ST4, but not for the combined model)

p13 says "Inverse variance weighted modelling identified a significant association between increasing height and atrial fibrillation (OR 1.34; 95% CI 1.29 to 1.40; p = 5x10-42)" could the authors first clarify what the scale of height is for this OR - I am presuming per S.D. increase as with the Phenome-wide association studies, but it would be useful to have it explicitly. Could the authors also please translate this into a clinically understandable result - e.g. what is the increased risk for a patient who is 10cm taller.

ST2-4: please include s.e. and p-value for each odd ratio, not just the 95% confidence interval.

Any attachments provided with reviews can be seen via the following link:

[LINK]

28 Feb 2020

Submitted filename: Afib-Height_response-to-reviewers_20200213.docx

Click here for additional data file.

7 Jul 2020

Dear Dr. Damrauer,

Thank you very much for re-submitting your manuscript "Genetics of Height and Risk of Atrial Fibrillation: A Mendelian Randomization Study" (PMEDICINE-D-20-00041R1) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two of the original reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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We look forward to receiving the revised manuscript by Jul 14 2020 11:59PM.

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------------------------------------------------------------

Requests from Editors:

1- Please consider different wording than “leverage(d)” in the Abstract and elsewhere (lines 42, 70, 105, 229).

2- In the Abstract, please provide the years of enrollment for all of the cohorts, as well as additional demographic information (e.g., sex).

3- Thank you for including the study limitations in the Abstract. Please remove the word “may”. Also, “*lack of* generalizability”?

4- Please expand the Abstract Conclusions to include the potential implications. While true that these findings do not provide evidence for incorporating height into screening strategies, you could note that that they suggest future studies should investigate “whether risk-prediction tools including height or other anthropometric factors can be used to improve screening and primary prevention of atrial fibrillation”, as noted in the Discussion.

5- Please also edit the Abstract Conclusions to limit the conclusions to the current study. "In this study, we observed ..." may be useful.

6- Thank you for adding an Author Summary. On line 66, please replaces “actually causes” with “elevates the risk of”

7- On line 73, please edit to: “Genetic variants associated with taller stature *were also associated with* increased risk of atrial fibrillation.”

8- Thank you for providing your STROBE statement. Please replace the page numbers with paragraph numbers per section (e.g. "Methods, paragraph 1"), since the page numbers of the final published paper may be different from the page numbers in the current manuscript.

9- Is it possible to include the graph from your response to reviewer 2, comment 6 as a supplemental figure?

10- Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

11- Please remove the “Transparency” section.

12- Please move the Ethical approval statement to the Methods section. Was approval sought or required for the specific analyses in this study? If so please specify in this section. Additionally, it seems odd that the “biobank” received approval, do you mean the researchers at the biobank, including the authors of this study?

13- Please check the formatting for reference 3

14- References 19 and 23 seem like different versions the same paper, one of which is a preprint. Please reconcile.

Comments from Reviewers:

Reviewer #1: The authors have addressed all of my concerns satisfactorily with great benefit for the manuscript. I am happy to recommend the study for publication in Plos Medicine.

Reviewer #4: As the fourth reviewer, I have seen the revised version of the manuscript and only have a few minor comments which appears to much more detailed and improved than the original.

The methods are very complete, designed to test to the robustness of the authors assumptions and results. The separate components of the study (MR two stage, individual-level MR, and PheWAS) all confirmed consistency in results. From this analysis the conclusion that height is causally associated with AF is confirmed.

Minor comments:

Lines 131 - 133: Can the authors describe what method they used for allele harmonisation to ensure exposure and the effect of that SNP on the outcome corresponds to the same allele

Lines 141 - 144: Whats the rationale for excluding SNPS using a nominal p-value of 0.05?

Lines 186-187: was 10 PCs determined a priori or through investigation of the variation of the data.

Line 211: How were potential confounders/mediators selected?

Lines 419-421: In terms of clinical implications for risk prediction models for AF - many do already include anthropometric factors, including the recent study in JACC (https://pubmed.ncbi.nlm.nih.gov/31706453/) which includes both height and weight. I guess one could say that the results confirm that this is the correct choice to include as a potential a priori factor - but how much height contributes to discrimination of the outcome is likely to be pretty modest. I think the key clinical implication still remains that this study opens the door for mechanistic work to understanding the biological pathways which more has implications in furthering therapeutics development

Final comment: What do the authors think about family history of AF - if height is causally associated with AF, and height has inheritability, it would make sense that a taller person may have more likelihood of family history of AF. Any implications for the study results in terms simply assessing family history for AF.

Any attachments provided with reviews can be seen via the following link:

[LINK]

31 Aug 2020

Submitted filename: Afib-Height_response-to-reviewers_20200731.docx

Click here for additional data file.

3 Sep 2020

Dear Dr. Damrauer,

On behalf of my colleagues and the academic editor, Dr. Michiel Rienstra, I am delighted to inform you that your manuscript entitled "Genetics of Height and Risk of Atrial Fibrillation: A Mendelian Randomization Study" (PMEDICINE-D-20-00041R2) has been accepted for publication in PLOS Medicine.

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PLOS Medicine

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