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. 2020 Apr 28;15(4):e0232292. doi: 10.1371/journal.pone.0232292

Pleiotropy of polygenic factors associated with focal and generalized epilepsy in the general population

Costin Leu 1,2,3,*, Tom G Richardson 4, Tobias Kaufmann 5, Dennis van der Meer 6, Ole A Andreassen 6, Lars T Westlye 6,7, Robyn M Busch 8,9,10, George Davey Smith 4, Dennis Lal 1,2,8,11,*
Editor: Giuseppe Biagini12
PMCID: PMC7188256  PMID: 32343744

Abstract

Epilepsy is clinically heterogeneous, and neurological or psychiatric comorbidities are frequently observed in patients. It has not been tested whether common risk variants for generalized or focal epilepsy are enriched in people with other disorders or traits related to brain or cognitive function. Here, we perform two brain-focused phenome association studies of polygenic risk scores (PRS) for generalized epilepsy (GE-PRS) or focal epilepsy (FE-PRS) with all binary brain or cognitive function-related traits available for 334,310 European-ancestry individuals of the UK Biobank. Higher GE-PRS were associated with not having a college or university degree (P = 3.00x10-4), five neuroticism-related personality traits (P<2.51x10-4), and having ever smoked (P = 1.27x10-6). Higher FE-PRS were associated with several measures of low educational attainment (P<4.87x10-5), one neuroticism-related personality trait (P = 2.33x10-4), having ever smoked (P = 1.71x10-4), and having experienced events of anxiety or depression (P = 2.83x10-4). GE- and FE-PRS had the same direction of effect for each of the associated traits. Genetic factors associated with GE or FE showed similar patterns of correlation with genetic factors associated with cortical morphology in a subset of the UKB with 16,612 individuals and T1 magnetic resonance imaging data. In summary, our results suggest that genetic factors associated with epilepsies may confer risk for other neurological and psychiatric disorders in a population sample not enriched for epilepsy.

Introduction

More than 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally (www.who.int). Epilepsy is a heterogeneous neurological disorder characterized by an enduring predisposition to generate epileptic seizures [1]. Apart from seizure type and etiology, an array of neurological and psychiatric comorbidities contribute to the phenotypic heterogeneity of epilepsy [2]. Emerging evidence supports a widespread genetic sharing between all common brain disorders [3], including epilepsy and behavioral-cognitive traits.

Genome-wide association studies (GWAS) for common forms of epilepsy have recently identified common genetic risk variants for epilepsy and the two main epilepsy syndromes; generalized epilepsy (GE) and focal epilepsy (FE). The two main epilepsy syndromes differ by definition in their seizure semiology: FE is characterized by focal seizures originating from one cerebral hemisphere; GE is characterized by seizures involving both cerebral hemispheres [4]. Major depression and anxiety disorders are the most common psychiatric comorbidities, both reported at equivalent frequencies in all individuals with epilepsy (~20%) [5,6], and individuals with drug-resistant epilepsy (~50%) [7]. Epilepsy and comorbid disorders show strong heritability rates in family studies [8]. Subsequently, in recent years, common variants have been identified for epilepsy and virtually all comorbid brain disorders. Considering the substantial polygenicity of complex disorders [9], polygenic risk scores that represent a quantitative measure of an individual’s genetic risk towards disease can be used to explore pleiotropic effect between disorders, as shown for schizophrenia [10] and a selection of behavioral traits [11] in otherwise healthy individuals.

In this study, we use polygenic risk scores (PRS) to examine the pleiotropic effects of genetic factors associated with epilepsy using two approaches. First, we study whether PRS for GE or FE are associated with brain or cognitive function-related traits, by conducting a PRS-phenome association study in 334,310 European-ancestry individuals from the UK Biobank (UKB) [12]. Second, we investigate whether GE and FE are correlated at the level of genetic variants associated with brain morphology in a subset of the UKB with 16,612 European-ancestry individuals and T1-weighted magnetic resonance imaging (MRI) data.

Methods

UK Biobank resource

The UKB is an open-access population-based prospective study with over 500,000 participants aged 40–69 years who were recruited in 2006–2010 [13]. Phenotypic data for >2000 traits & disorders were collected from questionnaires, physical measures, sample assays, accelerometry, multimodal imaging (e.g., MRI), and health records. All individuals of the UKB are genotyped with the Axiom array [12]. Of note, despite efforts to ensure a broad distribution across all health outcomes, there is evidence of enrichment of the UKB with healthy volunteers [14]. The healthy volunteer selection bias is likely to be particularly strong for mental disorders, where disorder status or symptoms may influence participation in research [15]. For this study, we used all individuals of European ancestry of the UKB that had genotypic data (N = 334,310).

PRS-phenome association

We made use of the rich variety of phenotypic information in the UKB [12] to determine if GE- and FE-PRS are associated with traits and disorders related to any brain or cognitive function, using a phenome-association method described by Richardson et al. (2019) [16]. Out of all UKB phenotypes (>2000), we found 42 brain or cognitive function-related phenotypes (i.e. neurological, neurodegenerative, and psychiatric disorders, personality traits, and educational attainment) that were binary and heritable (P<0.05 in LD score regression as calculated previously: https://nealelab.github.io/UKBB_ldsc/). Focusing on binary phenotypes (i.e. excluding continuous and ordinal phenotypes) enabled comparison of the final effect estimates across all tested traits.

We then generated polygenic risk scores for generalized (GE-PRS) and focal epilepsy (FE-PRS) for all UKB individuals using single-nucleotide polymorphism (SNP) weights derived from summary statistics of the International League Against Epilepsy (ILAE) Consortium on Complex Epilepsies GWAS for GE and FE [17]. The SNPs were pruned based on P<0.5. All remaining SNPs were extracted from the UKB imputed genotype data based on the Haplotype Reference Consortium r1.1 [18] and 1000 genomes [19] phase 3 reference panels. When a GWAS SNP was not present in the UKB genotype data, we attempted to identify a proxy SNP in high linkage disequilibrium with r2≥0.8. The remaining SNPs were clumped to a subset of weakly correlated SNPs (based on r2<0.1 within 500kb from the SNP with the lowest P-value at each locus). PRS for 334,310 individuals enrolled in the UKB study were generated using the allelic scoring function, as implemented in PLINKv1.9 [20]. Individual PRS were calculated as the sum of weighted effect alleles divided by the number of SNPs in the analysis. We excluded all individuals that were included in the GWAS studies used for PRS development using KING (kinship coefficient >0.0442) [21].

We used logistic regression, adjusted for sex, and the first four principal components of ancestry (PCs) to test for association between GE- or FE-PRS and the 42 brain or cognitive function-related traits of the UKB. We selected only four PCs, following the example in Khera et al. (2018) [22], and to avoid overfitting when too many PCs are included association model [23,24]. The threshold to reject the null hypothesis was set to α = 5.95x10-4, after the Bonferroni correction method for multiple testing (2x42 tests).

Vertex-wise genetic correlation

Vertex-wise genetic correlation maps with brain morphology were built following the methods presented by Kaufmann et al. (2018) [25]. Cortical reconstruction was performed in a UKB [12] subset of 16,612 predominantly healthy [26] individuals of European ancestry with T1-weighted MRI data, using Freesurfer [27]. Of note, the impact of confounding factors such as comorbid disorders or medication on cortical morphology was reduced by the MRI data acquisition from predominantly healthy participants in the UKB [25]. We computed surface maps for cortical thickness and cortical surface area, registered to fsaverage4 space (2,562 vertices), smoothed using a kernel with full width at half maximum of 15 mm. GWASs of each vertex’s thickness and area were performed using PLINKv1.9 [20], with adjustment for age, age2, sex, scanning site, and the first four genetic principal components of ancestry. Next, we estimated the genetic correlation between each vertex’s thickness and surface area and the GWAS summary statistics for GE and FE [17], using LD-score regression [28]. The resulting vertex-wise genetic correlation maps of cortical morphology with epilepsy were tested for correlation with each other using Spearman correlation. Statistical testing of the correlations was performed using spin-rotation based permutation testing with 10,000 permutations [29].

Results

Epilepsy-PRS are associated with brain or cognitive function-related traits

We found seven traits associated with GE-PRS (surpassing Bonferroni correction for 2x42 tests, a = 5.95x10-4): not having a college or university degree (P = 3.00x10-4; Fig 1), five personality traits associated with neuroticism (sensitivity / hurt feelings, P = 1.65x10-6; worry too long after embarrassment, P = 1.24x10-5; mood swings, P = 2.27x10-5; miserableness, P = 2.27x10-5; worrier / anxious feelings, P = 2.51x10-4; Fig 2), and having ever smoked (P = 1.27x10-6; Fig 3). The largest effect sizes for high GE-PRS were observed in association with neuroticism-related personality traits and smoking. In a separate analysis, we found six traits associated with high FE-PRS: several measures indicating low educational attainment (not having a college or university degree, P = 2.34x10-15; not having a General Certificate of Education Level A/AS [UK equivalent of a US associate degree], P = 2.92x10-7; not having any other professional qualifications, P = 4.87x10-5; Fig 1), one personality trait associated with neuroticism (mood swings, P = 2.33x10-4; Fig 2), having ever smoked (P = 1.71x10-4; Fig 3), and having experienced events of anxiety or depression (P = 2.83x10-4; Fig 4). The largest effect sizes for high FE-PRS were observed in association with low educational attainment. Out of the 42 traits related to cognitive functioning, seven showed association with either GE-PRS or FE-PRS and three with both types of epilepsy-PRS. Notably, the PRS had the same direction of effect for all ten traits associated with GE- or FE-PRS, including the not associated epilepsy subtype. Additional not associated traits are shown in the supplementary material (S1 Fig–S3 Fig).

Fig 1. Association between genetic risk for GE and FE and educational attainment phenotypes.

Fig 1

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Plotted are the PRS-phenome association results for GE and FE for all binary educational attainment traits in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex, and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, A/AS: British General Certificate of Education (GCE) advanced/advanced supplementary level equivalent to the US associate degree, O: GCE lowest pass grade at ordinary level, NVQ: National Vocational Qualifications / British work-based awards, HND: British Higher National Diploma, HNC: British Higher National Certificate, SE: standard error, *: P-value is surpassing the Bonferroni corrected threshold to reject the null hypothesis.

Fig 2. Association between genetic risk for GE and FE and neuroticism-related traits.

Fig 2

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Plotted are the PRS-phenome association results for GE and FE for all binary neuroticism-related traits in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex, and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error, *: P-value is surpassing the Bonferroni corrected threshold to reject the null hypothesis.

Fig 3. Association between genetic risk for GE and FE and mental and behavioral disorders due to substance abuse.

Fig 3

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Plotted are the PRS-phenome association results for GE and FE for all binary mental and behavioral disorders due to psychoactive substance abuse in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex, and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error, *: P-value is surpassing the Bonferroni corrected threshold to reject the null hypothesis.

Fig 4. Association between genetic risk for GE and FE and nonpsychotic mental disorders.

Fig 4

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Plotted are the PRS-phenome association results for GE and FE for all binary nonpsychotic mental disorders in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex, and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error, *: P-value is surpassing the Bonferroni corrected threshold to reject the null hypothesis.

GE and FE show similar patterns of genetic correlation at vertex-level with cortical thickness and cortical surface area

We used a recent cortico-genetic mapping approach [25] to explore whether GE and FE are genetically correlated at the level of brain morphology. We performed vertex-level genetic correlation analyses between epilepsy (GE and FE) and cortical surface area and thickness. The genetic correlation results for each vertex were mapped onto the brain. Each map reflects the overlap between the genetic architectures of cortical morphology at the vertex level with each of the two epilepsy syndromes (Fig 5). The maps for GE were weakly correlated with the maps for FE for both parameters: cortical thickness (correlation coefficient rho = 0.24, P<10−4, Fig 5C) and cortical surface area (correlation coefficient rho = 0.17, P = 4x10-4, Fig 5D).

Fig 5. Vertex-wise genetic correlation between cortical morphology and epilepsy.

Fig 5

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(a) Genetic correlation between GWAS summary statistics for cortical thickness and focal or generalized epilepsy at the vertex level. (b) Same as panel a, but for cortical surface area. (c) Spearman correlation between the two cortical thickness maps from panel a. (d) Spearman correlation between the two cortical area maps from panel b. Legend: Rg: genetic correlation, rho: Spearman’s correlation coefficient, Pperm: P-value after 10k permutations.

Discussion

We investigated the role of genetic risk for GE and FE across 42 brain or cognitive function-related traits in 334,310 individuals of the UKB cohort. Our results suggest that high epilepsy PRS are associated with low educational attainment, personality traits associated with neuroticism, and smoking behavior in a population sample not enriched for epilepsy. Our results confirm the genetic correlation between intelligence (as a proxy for educational attainment) and epilepsy, which is the only genetic correlation of epilepsy shown as significant in several studies [3,17]. The genetic pleiotropy between epilepsy and neuroticism observed in this study was missed in a larger study that employed linkage disequilibrium (LD) score regression [3], showing the value of examining pleiotropy with different methods. Currently, there is no single method that performs best for all possible trait pairs and study parameters [30]. Our novel findings are in line with previous evidence of neuroticism being genetically correlated to educational attainment [31] and major depression disorder [32], one of the most frequent comorbidities in individuals with epilepsy [5]. In addition, neuroticism is phenotypically correlated with smoking behavior [33]. Out of ten traits associated with either GE-PRS or FE-PRS, three were associated with both types of epilepsy-PRS. GE- and FE-PRS had the same direction of effect for all ten traits, including the not associated epilepsy subtype. To further explore the relationship between GE and FE, we investigated the correlation between maps of cortical morphologies of GE and FE, derived from correlation analyses with genetic factors associated with cortical morphology in a subset of 16,612 UKB individuals with T1 MRI data. The cortico-genetic mapping showed weak correlations between the genetic architecture of cortical morphology of GE with the genetic architecture of cortical morphology of FE. These results are in line with a recent large epilepsy structural brain imaging study, which identified distinct and also shared brain abnormalities in individuals with different epilepsy syndromes [34].

The use and generation of PRS is a rapidly developing field, with no established best-practice method. We selected the LD-clumping and P-value thresholding method to generate PRS based on a known excellent performance in neurological and psychiatric disorders [35–37]. A commonly used alternative is LDpred, a method that accounts for LD between SNPs and thus allows joint modeling with the potential of improvements in the prediction power [38]. However, the most significant improvements by joint modeling tend to be in diseases with multiple variants in LD that have independent effects (e.g., multiple sclerosis, rheumatoid arthritis, type I diabetes with associated HLA variants) [39]. In epilepsy, variants with independent effects within one LD region have not been demonstrated [17]. Our results should be interpreted in light of PRS generated from GWASs with a strong European bias. Therefore, our analyses are restricted to individuals of European descent, and the generalizability to individuals of non-European ancestry remains to be determined. Although our analyses comprise a large testing cohort for epilepsy PRS, additional phenotypes may uncover other association leads in future larger-scale phenome association studies.

Our PRS-phenome approach illustrates new possibilities for the shared genetic basis of epilepsy and comorbid traits. Comorbidities in epilepsy are common but poorly understood [40]. The quality of life of individuals with epilepsy is reduced by comorbid conditions that include neurological and psychiatric disorders. Possibly, genetic factors associated with epilepsy may contribute directly to the neurological and psychiatric comorbidities observed in individuals with or without epilepsy. Future research is needed to deepen our understanding of pleiotropic effects shared between epilepsy and the various neurological and psychiatric traits.

Using genetics to dissect the heterogeneous clinical representation of individuals with epilepsy represents a new research area. Potentially, polygenic risk for epilepsy includes genetic factors that predispose to a general vulnerability for altered brain function, which is shared with epilepsy comorbid disorders. Our results provide an initial indication of the opportunities and limitations using PRS research in epilepsy. The ongoing growth of large-scale hospital and nation-wide biobanks, which generate genetic data and collect clinical data, will set the stage for future well-powered studies dissecting the interplay of genetic and environmental factors in the etiology of epilepsy and related disorders.

Supporting information

S1 Fig. Association between genetic risk for GE and FE and mood disorders.

Plotted are the PRS-phenome association results for GE and FE for all binary mood affective disorders in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error.

(DOCX)

Click here for additional data file. (220.9KB, docx)
S2 Fig. Association between genetic risk for GE and FE and other diseases of the nervous system.

Plotted are the PRS-phenome association results for GE and FE for all binary diseases of the nervous system in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error.

(DOCX)

Click here for additional data file. (206.5KB, docx)
S3 Fig. Association between genetic risk for GE and FE and adult personality / behavior disorders.

Plotted are the PRS-phenome association results for GE and FE for all binary adult personality / behavior disorders in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error.

(DOCX)

Click here for additional data file. (93.4KB, docx)

Acknowledgments

This research has been conducted using the UK Biobank Resource under Application Numbers 8786, 15825, and 27412.

Data Availability

The epilepsy GWAS summary statistics underlying the results presented in the study are available from the ILAE Consortium on Complex Epilepsies: http://www.epigad.org/gwas_ilae2018_16loci.html. The study samples are available from the UK Biobank: http://www.ukbiobank.ac.uk/register-apply.

Funding Statement

This work was supported by the Integrative Epidemiology Unit, which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/1). TGR is a UKRI Innovation Research Fellow (MR/S003886/1). RMB received support from the NIH/NCATS, CTSA UL1TR000439, Cleveland, Ohio. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Giuseppe Biagini

3 Feb 2020

PONE-D-19-34427

Pleiotropy of polygenic factors associated with focal and generalized epilepsy in the general population

PLOS ONE

Dear Dr. Leu,

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #1: I Don't Know

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is the report of a wide retrospective study of pleiotropy of poligenic factors associated with focal and generalized epilepsy. The authors used data from an existing biobank including 334,310 individuals of European ancestry and found high polygenic risk scores for both epilepsies, with generalized epilepsies showing the largest effect sizes for personality traits associated with neuroticism and smoking and focal epilepsies inversely associated with education. Genetic factors associated with the two epilepsies showed similar patterns of correlation with the genetics of cortical morphology in a subsample (16,612 individuals).

The study findings are interesting and add to the existing knowledge on the complexity of the association between genetic and environmental factors in epilepsy. I have, however, a few major queries for the authors to address to provide convincing evidence on these purported associations. More specifically:

1. There is paucity of details on the individuals included in the biobank; the purposes of the biobank are not illustrated and there are no data on the way the information on each individual was collected; the Methods should be expanded to include these details;

2. The diagnosis of focal vs generalized epilepsy has been made according to the 2017 ILAE Classification; however, there is no indication of the criteria used and applied; the validity of this information must be also illustrated;

3. As specified by the authors, “… individuals with a diagnosed brain disorder were excluded.” These exclusion criteria should be specified to put focal epilepsy (largely attributable to structural CNS injuries) in a more correct perspective.

Minor points:

1. Introduction, lines 75 and 76: The sentence in question should be accompanied by a reference;

2. Methods, line 113: SNP and ILAE should be spelled out;

3. Methods, line 116: “a SNP” should perhaps read “an SNP”;

4. Results, lines 145-147: The two sentences are a repetition with what said in the Methods and should be deleted;

5. Results, line 215: “parameter” should read “parameters”;

6. References, lines 331 and 360: Is the journal’s name correct?

Reviewer #2: The authors present interesting analyses that demonstrate pleiotropic effects at the genome-wide level between PRS for generalized and focal epilepsy and a number of behavioral/cognitive traits. These analyses are valuable because they further our understanding of how genetic risk for epilepsy may play a role in other brain or cognitive phenotypes. In general, the article is well written, and analyses are performed to a high technical standard. However, I have a few major and minor comments that I would like addressed before I can recommend the article for publication.

Comments (in no particular order):

1. Can the authors distinguish their work more from the Brainstorm Consortium paper in Science? This paper also looks at genetic correlations between focal and generalized epilepsy and a number of phenotypes. Some of the results overlap with this paper (e.g., educational attainment), and some do not (e.g., neuroticism). The authors are using different methods to examine pleiotropy (PRS vs. LDSC regression), but it seems like results between the papers should overlap more. Along these lines, what are the advantages of using PRS over LDSC regression to examine pleiotropic effects?

2. Can the authors provide evidence that their results are robust to other clumping/pruning parameters or to the use of PRS created using LD Pred? There is no standard in the field for best methods to use when creating PRSs so I think some robustness checks along these lines are needed to show that the results are not sensitive to how the PRS were constructed.

3. Why do the authors only include 4 PCs in their analysis? Are the results robust to the inclusion of more PCs (10 seems to be more common in the literature)?

4. Why do the authors control for sex but not age?

5. In the figures that display the results, it would be helpful for the reader if the results that pass Bonferroni correction are noted in the figure somehow.

6. Can the authors test whether their findings are due to biological or mediated pleiotropy by controlling for whether or not an individual has seizures or has been diagnosed with epilepsy? For example, if correlations between the PRS and educational attainment are not significant after controlling for the epilepsy phenotype, this suggests mediated pleiotropy (e.g., see Schmitz et al., 2019). I think this is useful for clarifying whether or not the genetic risk is independent or working through the phenotype. Particularly in the case of educational attainment, an individual may not have completed much schooling because they struggle with seizures, rather than because genetic variants are independently affecting both phenotypes. Same for neuroticism--if an individual has epilepsy, they may be worried about have seizures in dangerous or inappropriate places and isolate themselves more as a result.

7. The PRS analyses are interesting, but they leave me wondering what genes may be driving these associations. Can the authors test for pleiotropic effects at the gene level for a few key epilepsy genes that have been implicated in the literature? In particular, the PRS results do not tell us much about the underlying biology behind the associations that may be useful from a clinical standpoint. In addition, even within ancestry, the weights from GWAS used to construct the PRS are likely biased by population stratification, assortative mating, or differences in environmental variance between groups (e.g., see Mostafavi et al., 2020). Because of these limitations, analyses at the gene region level would substantially enhance the findings reported in this paper.

Cites:

Mostafavi et al., 2020. Variable prediction accuracy of polygenic scores within an ancestry group eLife; 9:e48376.

Schmitz et al., 2019. Examining sex differences in pleiotropic effects for depression and smoking using polygenic and gene-region aggregation techniques. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 180B: 448-468.

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PLoS One. 2020 Apr 28;15(4):e0232292. doi: 10.1371/journal.pone.0232292.r002

Author response to Decision Letter 0

19 Mar 2020

Response to the reviewers provided in the uploaded file "EPI-PRS-UKB_Response_to_Reviewers_March19.docx".

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Submitted filename: EPI-PRS-UKB_Response_to_Reviewers_March19.docx

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Decision Letter 1

Giuseppe Biagini

13 Apr 2020

Pleiotropy of polygenic factors associated with focal and generalized epilepsy in the general population

PONE-D-19-34427R1

Dear Dr. Leu,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Giuseppe Biagini, MD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: None. All queries have been addressed. There are no remaining concerns to address on my side. The manuscript as revised is to me acceptable for publication.

Reviewer #2: (No Response)

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Reviewer #1: No

Reviewer #2: No

Acceptance letter

Giuseppe Biagini

17 Apr 2020

PONE-D-19-34427R1

Pleiotropy of polygenic factors associated with focal and generalized epilepsy in the general population

Dear Dr. Leu:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Giuseppe Biagini

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Association between genetic risk for GE and FE and mood disorders.

    Plotted are the PRS-phenome association results for GE and FE for all binary mood affective disorders in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error.

    (DOCX)

    Click here for additional data file. (220.9KB, docx)
    S2 Fig. Association between genetic risk for GE and FE and other diseases of the nervous system.

    Plotted are the PRS-phenome association results for GE and FE for all binary diseases of the nervous system in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error.

    (DOCX)

    Click here for additional data file. (206.5KB, docx)
    S3 Fig. Association between genetic risk for GE and FE and adult personality / behavior disorders.

    Plotted are the PRS-phenome association results for GE and FE for all binary adult personality / behavior disorders in the UKB. The betas for GE-PRS are highlighted in blue, and for FE-PRS in red. P-values were calculated using a logistic regression model, adjusted for sex and the first four principal components of ancestry. The threshold to reject the null hypothesis was set to α = 5.95x10-4 after Bonferroni correction for 84 tests. Legend: UKB: UK Biobank, SE: standard error.

    (DOCX)

    Click here for additional data file. (93.4KB, docx)
    Attachment

    Submitted filename: PLOS_ONE_Review_1-20.docx

    Click here for additional data file. (15.5KB, docx)
    Attachment

    Submitted filename: EPI-PRS-UKB_Response_to_Reviewers_March19.docx

    Click here for additional data file. (1.1MB, docx)

    Data Availability Statement

    The epilepsy GWAS summary statistics underlying the results presented in the study are available from the ILAE Consortium on Complex Epilepsies: http://www.epigad.org/gwas_ilae2018_16loci.html. The study samples are available from the UK Biobank: http://www.ukbiobank.ac.uk/register-apply.

    Articles from PLoS ONE are provided here courtesy of PLOS

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