{'fraction_non_alphanumeric': 0.030520014086160348, 'fraction_numerical': 0.021637907422623937, 'mean_word_length': 4.898453727209786, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 12, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Searching for the causal effects of BMI in over 300 000 individuals, using Mendelian randomization

Mendelian randomization (MR) has been used to estimate the causal effect of body mass index (BMI) on particular traits thought to be affected by BMI. However, BMI may also be a modifiable, causal risk factor for outcomes where there is no prior reason to suggest that a causal effect exists. We perform a MR phenome-wide association study (MR-pheWAS) to search for the causal effects of BMI in UK Biobank (n=334 968), using the PHESANT open-source phenome scan tool. Of the 20 461 tests performed, our MR-pheWAS identified 519 associations below a stringent P value threshold corresponding to a 5% estimated false discovery rate, including many previously identified causal effects. We also identified several novel effects, including protective effects of higher BMI on a set of psychosocial traits, identified initially in our preliminary MR-pheWAS and replicated in an independent subset of UK Biobank. Such associations need replicating in an independent sample.


INTRODUCTION
Body mass index (BMI), taken to be a general indicator of adiposity, has been associated with many traits and diseases [1][2][3] . These observational associations may be due to a causal effect of adiposity (as reflected in BMI), a causal effect of the phenotype on BMI, or confounding. Mendelian randomization (MR) estimates the causal effect of an exposure on an outcome using genetic variants as an instrumental variable for the exposure 4,5 . To date, MR has been used to assess whether BMI causally affects a vast array of phenotypes [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] . These results suggest that higher BMI leads to an earlier age at menarche 9 , a reduction in physical activity 6 , an increased risk of coronary heart disease 22 , cancer 10,18 and asthma 21 , a lower risk of Parkinson's disease 17 , and changes in metabolite concentrations 7 .
Hypothesis-free searching is an established method to identify novel associations, such as genetic variants associated with a particular phenotype in genome-wide association studies (GWAS) 23 . In contrast to hypothesis-driven analyses, a hypothesis-free analysis can identify novel associations where there is no prior expectation that an association might exist, and should help to avoid publication bias as all results are published, not just the most "statistically significant". Phenome scans are a class of hypothesis-free scan that test the association of a variable of interest with a potentially large array of phenotypes -the "phenome" 24 . Phenome scans commonly seek to identify phenotypes associated with a genetic variant (phenome-wide association studies -pheWAS) or an observed phenotype (environment-wide association studies -EnWAS). A third type of phenome scan, MR-pheWAS, uses MR to search for the causal effects of a particular exposure 25 .
To date, only a small number of phenome-wide scans of BMI have been published. We recently published a MR-PheWAS that searched for the causal effects of BMI in circa 8000 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, across 172 outcomes 25 . This study confirmed several known associations such as with leptin levels and blood pressure, and identified potentially novel associations, such as with a self-worth score. Cronin et al. performed a pheWAS of FTO genetic variants within electronic health record phenotypes, in circa 25 000 participants, and identified novel associations 26 . For instance, a genetic predisposition to a higher BMI was associated with a higher risk of fibrocystic breast disease and non-alcoholic liver disease 26 .
UK Biobank is a prospective cohort of circa 500 000 participants 27 , and several hypothesis-driven MR analyses of BMI have been performed in this study, to date 20,21,28,29 . The large sample size offsets the multiple testing burden of hypothesis-free analyses, providing an opportunity to search for causal effects with MR-pheWAS.
Recently, we published an open-source tool for performing phenome scans (including MR-pheWAS) in UK Biobank -the PHEnome Scan ANalysis Tool (PHESANT) 30 .
PHESANT enables comprehensive phenome scans to be performed across a large and diverse set of phenotypes -all continuous, integer and categorical fields in UK Biobank -where previously researchers would restrict their analysis to a homogeneous subset of phenotypes that could be processed in a consistent fashion 30 .
In this study we search for the causal effects of adiposity, using the PHESANT tool in UK Biobank. We use BMI as a surrogate measure of adiposity. In our presentation of PHESANT 30 we analyzed the initial non-random sample of circa 115 000 participants that was available in UK Biobank at that time. We found that participants with a genetic propensity to a higher BMI were less likely to perceive themselves as a nervous person or to call themselves tense or 'highly strung' 30 . In this work, we search for the causal effects of BMI in circa 330 000 participants in UK Biobank satisfying our inclusion criteria. BMI is a well-studied phenotype, hence this study serves as a model for future MR-pheWAS that may investigate phenotypes with much weaker priors regarding their causal effects.

RESULTS
We checked the strength of the association between the BMI allele score and BMI, and found a standard deviation (SD) increase in BMI allele score was associated with a 0.63 kg/m 2 increase in BMI (95% confidence interval (CI): 0.62, 0.65, F statistic=6052.88).

Results of MR-pheWAS analysis
The results of our MR-pheWAS include 20 461 tests ranked by P value, given in Supplementary data file 1. Figure 2 shows the number of fields reaching each stage of the PHESANT automated pipeline. A QQ plot is given in Figure 3, and the PHESANTviz visualization can be found at [http://www.datamining.org.uk/PHESANT/resultsbmi.html]. We identified 519 results at a false discovery rate of 5% (using a P value threshold of 0.05×519/20461 = 1.27x10 ./ ), given in Supplementary table 4, and of these, 259 results had a P value lower than a stringent Bonferroni corrected threshold of 2.44x10 -6 (0.05/20461).
Our MR-pheWAS identified several known effects of BMI. For example, a genetic predisposition to a higher BMI was associated with an increased risk of diabetes 19 (field ID (FID)=2443), hypertension 40 (FID=41204 value I10, and FID=4079), a higher bone mineral density 8 (FID=3148) and an earlier age of puberty in both sexes 9 (FIDs={2714, 2375, 2385}). Figure 4 shows that, when restricting to psychosocial traits, a subset is associated more strongly than would be expected by chance including several nervousness / anxiety traits.

PHESANT sensitivity analysis
Our tests of association of assessment centre with the BMI genetic score found that, while adjusting for the genetic principal components attenuated the association, an association remained (likelihood ratio test P values of 6.49x10 -26 , 4.44x10 -10 and 6.15x10 -6 when adjusting for age and sex only, and additionally the first 10 and first 40 genetic principal components, respectively). Supplementary figure 1 compares the results of our main MR-pheWAS analysis, adjusting for age, sex and the first 10 genetic principal components, with our sensitivity analysis, additionally adjusting for assessment centre and genetic batch. Large differences between these results often occurred for categorical outcomes with small numbers in a certain category (for multinomial logistic model this can cause a zero P value when the likelihood of the model increases to a limit 41 ; see examples in Supplementary table 5).

Detailed follow-up of potentially novel results
Our hypothesis-free scan found that a genetic predisposition to a higher BMI was associated with a decreased reporting of being a nervous person (FID=1970, p=2.92x10 -24 ), being tense or 'highly strung' (FID=1990, p=4.47x10 -12 ), suffering from 'nerves' (FID=2010, p=5.47x10 -10 ), or being a worrier (FID=1980, p=4.42x10 -7 ). Table   1  The results of these four nervousness/anxiety traits do not provide a form of replication for each other because these traits are highly correlated (see Supplementary table 7; all accuracy between 57.59% and 83.49%) and so are more likely to agree compared to tests using independent traits or a replication on an independent dataset.
We searched for other nervousness related traits by searching for the terms: "worr", "nerv", "tens", "anxi", in our results listing, to determine if other related traits were tested and an association not identified, to assess the strength of the evidence when considering results for all similar phenotypes. This identified three self-reported Our results include associations consistent with previous MR studies, such as adverse effects of higher BMI on risk of diabetes 19 and hypertension 40 , a higher bone mineral density 8 and an earlier age of puberty in both sexes 9 . Consistent with our preliminary results 30 , we identified an association with a set of nervousness phenotypes, where a genetic predisposition to a higher BMI was associated with a person being less likely to call themselves a nervous person, tense or highly strung, a worrier, or to report they suffer from 'nerves'. We followed up this analysis to estimate the causal effect and found that each 1 kg/m 2 decrease in BMI caused a 6.1% [95% CI: 5.0%, 7.2%] increase in likelihood of self-reporting being a nervous person. The causal estimates were more extreme than the observational estimates for three of these outcomes. Furthermore, causal estimates using the discovery and replication subsamples of UK Biobank were consistent for these four nervousness/anxiety phenotypes.
We searched our results for other nervousness traits to determine the strength of the evidence in the context of the results of related phenotypes, which, while not a form of replication, provides an unbiased view of the evidence using the UK Biobank cohort.
We identified two phenotypes detailing treatment by a psychiatrist or a doctor, respectively, for nerves, anxiety, tension or depression, but we did not find evidence of a causal effect of BMI on these phenotypes. A third result, the frequency of tenseness / restlessness in the last 2 weeks, was weakly associated in a direction consistent with the other nervousness associations we identified, although it did not pass the P value threshold corresponding to an estimated FDR of 5%.
Several previous observational studies have reported the association of anxiety and BMI. The prevalence of anxiety has been shown to be higher in obese compared with non-obese people 43 49 and the other using an anxiety measure defined using the Hospital Anxiety and Depression Scale (HADS) 50 ), did not find evidence of a causal effect, although this may be due to insufficient statistical power 49,50 . Two recent MR studies provided evidence that an increase in BMI adversely affects risk of depression symptoms 51 and major depressive disorder 52 . Furthermore, it has been shown that the mechanism through which BMI-associated genetic variants affect risk of obesity may involve regions of the brain such as the basal ganglia 53 , which are plausibly involved in emotional processes 54 . Hence, estimates of association between two phenotypes -such as our BMI genetic score and a given outcome in our study -can be biased, if inclusion in the study is affected by both phenotypes.
We found that our BMI genetic score was associated with assessment centre, even after adjustment for the first 40 principal components. This may indicate that the genetic principal components are not fully accounting for genetic population differences.
However, this may also be due to selection induced collider bias, because both BMI and location are related to selection into the sample 56 . If both BMI and assessment centre affect participation in the study, then an association may be induced between the BMI genetic score and assessment centre. For example, the South West region had the highest participation rate of the regions sampled, such that living in this region is associated with a higher chance of participating compared to the other regions 56 . Since BMI is negatively associated with participation in UK Biobank, we would expect (under most realistic assumptions about the association between BMI, location and participation in UK Biobank) to see a positive association between the BMI genetic risk score, and participating in the South West region compared to other regions if collider bias is the cause, and (using attendance at the Bristol assessment centre as a proxy for region of residence) this is indeed what we see (see illustration shown in Supplementary figure 8). Furthermore, the West Scotland region has the lowest participation rate, and attending the Glasgow assessment centre compared to other centres is associated with a lower BMI genetic risk score, which is expected if this relationship is due to selection induced collider bias.
We now discuss some further limitations of this work. PHESANT uses a rule-based method to automatically determine how to test each outcome, and it is possible that this may deal with some variables inappropriately. Also, PHESANT tests the linear association of the genetic score with the set of outcomes, and it is possible that nonlinear associations may exist. For instance, it is possible that BMI has a non-linear effect on nervousness/anxiety (for example low and high BMI may cause higher levels of anxiety compared with those in the normal range) and in follow-up analyses this should be investigated. Ranking the associations means that we should expect the true strength of the associations to be less than we reported due to the winner's curse. We used stringent P value thresholds, which, although reducing the type I error rate, is likely to also increase the type II error rate. We used BMI as a surrogate measure for adiposity, but the effect of adiposity estimated using observational data is not the true effect of an intervention that modifies BMI, because this will depend on the particular intervention used. The causal effects we have estimated may be the result of changes of other aspects of BMI in addition to or instead of changes in adiposity (e.g. changes in lean mass versus fat mass) 59 . Similarly, our BMI genetic score is an instrument for life-long BMI, hence we cannot say that it is BMI at a specific age (e.g. the age of the UK Biobank cohort) that affects an outcome. However, searching for potential causal effects using MR is useful to identify outcomes that may be modified through interventions on modifiable determinants of adiposity (e.g. diet or physical activity) 59 .
PHESANT is the first tool to perform comprehensive phenome scans, where previously the set of outcomes tested would be restricted to a homogeneous subset 30 . In this study, we have presented the first comprehensive phenome scan, using the PHESANT tool to search for the causal effects of BMI. This MR-pheWAS confirmed several established effects of BMI, and also identified potentially interesting novel results, such as a potential causal effect of BMI on feelings of nervousness. This work demonstrates how MR-pheWAS can be applied in UK Biobank and can serve as a model for future studies.
There is much potential to use MR-pheWAS to search for the causal effects of phenotypes where, compared to BMI, much weaker priors regarding their causal effects. Phenome-wide scans are a hypothesis generation approach and hence identified associations should be followed-up in an independent sample.

Study population
UK Biobank sampled 503 325 men and women in the UK aged between 37-73 years (99.5% were between 40 and 69 years) 31 . This cohort includes a large and diverse range of data from blood, urine and saliva samples and health and lifestyle questionnaires 27 .
Of the 487 406 participants with genetic data, we removed 373 with genetic sex different to reported sex, and 471 with sex chromosome aneuploidy (identified as putatively carrying sex chromosome configurations that are not either XX or XY). We found no outliers in heterozygosity and missing rates, which would indicate poor quality of the genotypes. We removed 78 309 participants not of white British ancestry 32 . We removed 73 277 participants who were identified as being related, having a kinship coefficient denoting a third degree (or closer) relatedness 32 . We removed 8 individuals with withdrawn consent, giving a sample of 334 968 participants. A participant flow diagram is given in Figure 1.

BMI allele score
We created an allele score from 96 genetic variants previously found to be associated with BMI, in a recent GWAS meta-analysis by the GIANT consortium 33 . The score was calculated as a sum of the number of BMI-increasing alleles, weighted by the effect size as reported in the GIANT GWAS (reported as a SD change of BMI per dosage increase) 33 (see Supplementary table 1), such that a higher allele score corresponds to a higher BMI, and was standardized to have a mean of zero and SD of 1.

Outcomes
The Biobank data showcase allows researchers to identify variables based on the field type (http://biobank.ctsu.ox.ac.uk/showcase/list.cgi). At the time of initial data download there were 2143 fields of the following types: integer, continuous, categorical (single) and categorical (multiple) (identified in March 2016, and updated in November 2017 for fields where data accrual was still underway).
We excluded 70 fields a priori, given in Supplementary table 2, for the following reasons. We removed one field denoting the assessment centre. We removed 2 fields described by UK Biobank as 'polymorphic', containing values with mixed data types.
We removed 14 fields that, although listed in the data showcase, were not currently available. We removed 13 genetic descriptor fields, 1 sex field and 4 age fields. We removed 17 variables describing the assessment centre environment. We removed 18 categorical (single) fields with more than one value recorded per person.
We assigned 92 outcomes that denote adiposity or some aspect of weight, fat mass or height as 'exposure phenotypes' -to be included in our MR-pheWAS analysis so that we can assess the strength of outcome associations in relation to these, while this a priori assignment means they will not contribute to the multiple testing burden (see Supplementary table 3).
This resulted in a set of 1981 UK Biobank fields (263 integer, 969 continuous, 649 categorical (single) and 100 categorical (multiple)), referred to hereafter as the outcome dataset (because they are tested as an outcome irrespective of whether this is biologically plausible).

Observed BMI
Weight and height were measured at the initial UK Biobank assessment centre -weight in light clothing and unshod was measured using Tanita BC418MA body composition analyser to the nearest 100g, and height to the nearest cm using at Seca 202 device.
These were used to calculate BMI (kg/m 2 ).

Covariates
We include age and sex as covariates in our models to reduce the variation in our outcomes. Age when participants attended the UK Biobank assessment centre was derived from their date of birth and the date of their assessment centre visit. Sex was self-reported during the touchscreen questionnaire (and validated using the genomewide data). We adjust for the first 10 genetic principal components to control for confounding via population stratification. Genetic variants are set at conception, and after conception they cannot be affected by traditional confounding factors, therefore we did not adjust for any further covariates.

PHESANT MR-pheWAS
We test the direct association of the BMI genetic score with each of the outcome variables using the PHESANT package (version 0.10). A description of PHESANT's automated rule-based method is given in detail elsewhere 30  PHESANT estimates the bivariate association of the BMI genetic score with each outcome variable. The BMI genetic score and outcome variables are the independent (exposure) and dependent (outcome) variables in the regression model, respectively.
Outcome variables with continuous, binary, ordered categorical and unordered categorical data types, are tested using linear, logistic, ordered logistic, and multinomial logistic regression, respectively. Prior to testing, an inverse normal rank transform is applied to variables of the continuous data type, to ensure they are normally distributed.
All analyses are adjusted for covariates as described above.
We correct for multiple testing by controlling for the expected proportion of false positive results. After ranking the results by P value, we identify the largest rank position with a P value less than 5 6789:7;<= = 0.05×rank/n, where n is the total number of tests in the phenome scan. P threshold is the P value threshold resulting in a false discovery rate of 5% 34 . We also calculate a stringent Bonferroni corrected P-value threshold, by dividing 0.05 by the number of tests performed, which assumes each test is independent.

Results visualization with PHESANT-viz
We use PHESANT-viz, a D3 Javascript visualization tool included in the PHESANT package, to visualize our results as a graph, using the Biobank assigned category structure (http://biobank.ctsu.ox.ac.uk/showcase/label.cgi). This enables interpretation of the identified associations with consideration of the results for related variables. For example, the estimated effects of BMI on depression are grouped with the results of other psychosocial phenotypes.

PHESANT sensitivity analysis
We re-run our phenome scan to assess residual confounding, additionally adjusting for both assessment centre and genetic batch.
Testing for residual population stratification We test the extent that genetic principal components account for genetic differences across the population. We test the association of assessment centre with the BMI genetic score (as the independent and dependent variables, respectively), adjusting for: 1) Age and sex.
2) Age, sex and the first 10 genetic principal components.
3) Age, sex and the first 40 genetic principal components.
We use a likelihood ratio test to determine the strength of the association of the assessment centres collectively, with the genetic score. An association between the assessment centres and the BMI genetic score, after adjusting for the genetic principal components, may indicate that the genetic principal components are not fully accounting for population stratification.

Follow-up analysis of identified associations
We identified associations with a related set of psychosocial traits and generate a QQplot restricting to the psychosocial UK Biobank category only (category ID=100059), to determine whether these results have an association stronger than expected by chance, given the results of related phenotypes. We perform a formal instrumental variable analysis for a set of binary outcomes, using two-stage IV probit regression (the Stata ivprobit command, with conditional maximum-likelihood estimation), adjusting for age, sex and the first 10 genetic principal components. We also adjust for age, sex and the first 40 genetic principal components, as a sensitivity analysis. We take the exponent of 1.6 times the estimates, to approximate the association in terms of the change of odds 35 . We also test the observational association of BMI with each phenotype using logistic regression (Stata logistic command).
We compare estimates within the initial discovery sample (used in our initial presentation of PHESANT 30 ), with estimates using the additional participants used in this study, as a replication. We remove all participants from our replication sample who were identified as being related to a participant in the discovery sample, having a kinship coefficient denoting a third degree (or closer) relatedness.

Follow-up sensitivity analyses
We performed sensitivity analyses to investigate whether our genetic score, and its constituent genetic variants, may be invalid instruments for BMI. First, we compared the effect estimates using two independent instrumental variables: 1) SNP rs1558902 (the SNP most strongly associated with BMI, at the FTO locus), and 2) the remaining 95 genetic variants.
Second, we also estimated causal effects using three alternative MR approaches -MR- Directional pleiotropy is identified where the intercept estimate is not consistent with the null. MR-Egger assumes that the SNP-exposure associations are measured without error, known as the NOME assumption 39 . We determine the degree to which the NOME assumption is violated, using the 0 12 3 statistic, which is an adaption of the I 2 statistic used in the field of meta-analysis 39  Furthermore, we performed an MR-Egger analysis with bias adjustment using SIMEX, to estimate the causal effect when the NOME assumption is violated 39 . In brief, SIMEX works by learning models with increasing violations of the NOME assumption (i.e. increasing error in the SNP-exposure associations). The estimates from these models are then treated as a set of data points and a model is learnt across these. This 'meta' model is then used to extrapolate back to the estimate that would have occurred if the NOME assumption was satisfied.
The weighted median approach estimates a consistent causal effect under the assumption that less than 50% of the genetic variants are invalid instruments 37