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ABSTRACT Essential tremor (ET) is a prevalent neurological disorder with a largely unknown underlying biology. In this genome-wide association study meta-analysis, comprising 16,480 ET cases

and 1,936,173 controls from seven datasets, we identify 12 sequence variants at 11 loci. Evaluating mRNA expression, splicing, plasma protein levels, and coding effects, we highlight seven

putative causal genes at these loci, including _CA3_ and _CPLX1_. _CA3_ encodes Carbonic Anhydrase III and carbonic anhydrase inhibitors have been shown to decrease tremors. _CPLX1_,

encoding Complexin-1, regulates neurotransmitter release. Through gene-set enrichment analysis, we identify a significant association with specific cell types, including dopaminergic and

GABAergic neurons, as well as biological processes like Rho GTPase signaling. Genetic correlation analyses reveals a positive association between ET and Parkinson’s disease, depression, and

anxiety-related phenotypes. This research uncovers risk loci, enhancing our knowledge of the complex genetics of this common but poorly understood disorder, and highlights _CA3_ and _CPLX1_

as potential therapeutic targets. SIMILAR CONTENT BEING VIEWED BY OTHERS UPDATED MDSGENE REVIEW ON THE CLINICAL AND GENETIC SPECTRUM OF _LRRK2_ VARIANTS IN PARKINSON´S DISEASE Article Open

access 17 February 2025 MULTI-ANCESTRY GENOME-WIDE ASSOCIATION META-ANALYSIS OF PARKINSON’S DISEASE Article Open access 28 December 2023 EXPANDING CAUSAL GENES FOR PARKINSON’S DISEASE VIA

MULTI-OMICS ANALYSIS Article Open access 21 October 2023 INTRODUCTION Essential tremor (ET) is one of the most common neurological disorders, affecting up to 5% of the population1. However,

epidemiological studies show that the prevalence of the disorder is considerably underestimated as mildly affected individuals may not seek medical care2. ET is an isolated syndrome of

bilateral upper limb postural or kinetic tremor, that may be with or without tremor of head, voice, or lower limbs and without other neurological signs such as dystonia, ataxia, or

parkinsonism3. Although not life-threatening, the disorder can severely impact daily activities, reducing quality of life. Increasing age, European descent4,5,6 and family history7,8 are

considered risk factors for ET. Diagnosing ET can be challenging and often requires subspecialty consultation with a movement disorders neurologist. The diagnosis involves reviewing medical

and family history and conducting a thorough neurological examination, as a biomarker or diagnostic test is not available9. Although there is no cure for ET, several treatment options are

available to ease the symptoms. These include drug therapy (beta blockers, anti-epileptics, and tranquilizers), deep brain stimulation, and lifestyle modifications, such as avoiding triggers

that can increase the severity of the tremors. The cause of ET is not fully understood, but there is a growing support for the etiology of ET being partly related to abnormalities of the

cerebello-thalamo-cortical network, including loss of Purkinje cells and reduced γ-aminobutyric acid (GABA) receptor expression in the dentate nucleus10,11. Previously, the largest

genome-wide association study (GWAS) meta-analysis of ET reported five risk loci using data from 7177 cases and 475,877 controls from European populations12. Here, we more than double the ET

case number, combining 16,480 cases and 1,936,173 controls in a GWAS meta-analysis and find 12 independent sequence variants at 11 loci, of which 8 are novel. Our research presents new

genetic revelation regarding GABAergic dysfunction in ET, highlights the role of dopaminergic neurons, and provides further insight into the genetics of ET, offering clues that may lead to

novel future treatment options. RESULTS GWAS META-ANALYSIS In a meta-analysis of ET, we combined GWAS results from Iceland, Denmark, Estonia, Norway, UK, and USA (seven datasets) with

summary statistics from a reported GWAS12, resulting in 16,480 cases and 1,936,173 controls (Fig. 1 and Supplementary Data 1). Using a fixed-effect inverse variance model, we tested for

association, under an additive model, between ET and sequence variants with imputation information over 0.8 and minor allele frequency (MAF) over 0.01% in each dataset (except the Estonian

dataset and the reported GWAS, where variants with MAF over 1% were included). To account for multiple testing, we used weighted genome-wide significance thresholds based on the predicted

functional impacts of the associated variants (Supplementary Data 2). We uncovered association with ET at 11 loci (Supplementary Fig. 1 and Supplementary Data 3). Conditional analysis

revealed a secondary signal at one of the loci (Supplementary Data 4). In total, we uncovered 12 independent common variants, 8 of which are novel (Fig. 2). There was no evidence of

heterogeneity (all _P_-het > 0.05), indicating consistency of effects across the datasets (Supplementary Data 3). Five variants have been reported to associate with ET12 and we show

supportive evidence for all except one, at chromosome 1p13.1 (Supplementary Data 5). We report these signals directly or through a correlated variant (_r_2 ≥ 0.8) at the same locus

(Supplementary Data 5). At chromosome 4p15.2, we observed a previously reported variant as the primary signal, along with a novel variant at the same locus acting as the secondary signal

(_r_2 = 0.022, Supplementary Fig. 1c, f). POTENTIAL CAUSAL GENES We searched for causal genes at the ET loci by evaluating the affected amino acid sequence of the lead variants and highly

correlated variants (_r_2 ≥ 0.8), mRNA expression (expression quantitative trait loci [eQTLs]), splicing quantitative loci (sQTLs), and plasma protein levels (pQTLs) (Fig. 1). We found

coding variants at three of the ET associated loci, in _CA3_ (p.V31I, _r_2 = 1.00 with lead variant), _EHBP1_ (p.K720Q/K755Q, _r_2 = 0.96 with lead variant), and _GCKR_ (p.L446P, lead

variant) (Fig. 3 and Supplementary Data 6). We found eQTLs for _BACE2_, _CPLX1_, _OTX1_, _C2orf16_, and _CA3_ (Supplementary Data 7) and pQTLs for _CA3_ (Supplementary Data 8) and _GCKR_

(Supplementary Data 9). The lead ET variant at the _CA3_ locus confers protection against ET (rs955007-C, _P_ = 1.4 × 10−12, OR = 0.92, Fig. 3. and Supplementary Data 3) and is also highly

correlated (_r_2 = 0.97) with the primary _cis_-eQTL which decreases _CA3_ expression in skeletal muscles (rs10088136-A, _P_ = 8.7 × 10−13, β = -0.13, Supplementary Data 7). Using COLOC13,

we estimated that the posterior probability that the ET association and the eQTL are caused by the same variant is 89%. In addition, the lead variant is highly correlated (_r_2 = 1.00) with

the primary pQTL for carbonic anhydrase III and decreases its plasma levels (chr8:85445533, _P_ = 3.2 × 10−131, β = −0.22, Supplementary Data 8). Furthermore, rs955007-C also associates with

lower plasma levels of carbonic anhydrase XIII (located roughly 200KB upstream, _P_ = 6.3 × 10−195, β = −0.23). However, rs955007 is not in high LD (_r_2 = 0.14) with the primary pQTL at

the region (Supplementary Data 8). The lead intronic ET variant within _EHBP1_ is highly correlated (_r_2 = 0.91) with the top _cis-_eQTLs for _OTX1_ in whole blood (rs76298426-C, _P_ = 

10−1673, β = 1.15) and neutrophiles (rs146236066-CT, _P_ = 1.6 × 10−51, β = 0.98, Supplementary Data 7). We identified an intronic variant in _CPLX1_ that confers risk of ET (rs13128363-T,

_P_ = 9.0 × 10−15, OR = 1.14, Fig. 3 and Supplementary Data 3) and is the top _cis_-eQTL for the gene in whole blood (Supplementary Data 7). There were no sQTLs at the loci in whole blood.

Gene-set enrichment analysis in FUMA14 highlighted cell types in the human embryonic midbrain such as dopaminergic neurons, GABAergic neuroblasts and neurons, and mediolateral neuroblast

(Supplementary Data 10). In addition, the analysis revealed enrichment for pathways such as regulation of response to stress, cell adhesion, and Rho GTPase cycles (Supplementary Data 10).

The sex ratio in our study is close to 50% (52.9% females, Supplementary Data 1). When applying sex-specific models to the Icelandic, Danish, Norwegian, UK, and US-INTMT datasets for the 12

ET variants, none of the variants had an effect that significantly differed between the sexes, after accounting for multiple testing (_P_-het > 0.05/12 = 0.0042, Supplementary Data 11).

FAMILIAL CLUSTERING AND GENETIC VARIANCE EXPLAINED A close to complete genealogy exists for the Icelandic dataset. We did not find high-impact variants, including start-lost, stop-gain,

stop-lost, splice donor, splice acceptor, or frameshift, segregating among 53 large families with high incidence of ET (_N_ ≥ 5). In addition, we did not see a significant difference in

effects of 11 of the 12 lead variants between familial (_N_ = 1153) and sporadic cases (_P_-het > 0.05/12 = 0.0042, Supplementary Fig. 3). Using the 12 independent variants, we estimated

the genetic variance explained to be 4.4% (Supplementary Data 12). GENETIC CORRELATION Considering the epidemiology and positive genetic correlation that has been reported between ET and

Parkinson’s disease (PD) and depression12, we estimated the genetic correlation between ET and these two phenotypes using the most recent GWASs and cross-trait LD score regression. In line

with previous reports, we observed a positive genetic correlation between ET and these phenotypes (PD15, _r_g = 0.28, _P_ = 1.1 × 10−6; depression16, _r_g = 0.15, _P_ = 3.4 × 10−5,

Supplementary Data 13). In addition, we estimated the genetic correlation between ET and summary data from 1142 published GWASs (_P_-threshold ≤ 0.05/1142 = 4.4 × 10−5) and found that ET

correlates most strongly with anxiety-related phenotypes (e.g., feeling nervous, _r_g = 0.20, _P_ = 2.0 × 10−6, Supplementary Data 13). DISCUSSION We report a GWAS meta-analysis of ET that

combines 16,480 cases and expands results from previous GWASs by identifying 12 variants at 11 loci, of which 8 are novel. We leveraged mRNA expression, including splicing, plasma protein

measurements and predicted coding effects to highlight seven putative causal genes and the biological roles of some of the variants. Through gene-set enrichment analysis, we underscored the

involvement of dopaminergic and GABAergic neurons in ET, as well as the biological significance of the Rho GTPase cycle. We did not find high-impact variants segregating in families with

high prevalence of ET. We showed a positive genetic correlation between ET and PD, depression, and anxiety-related phenotypes. Based on functional annotation, we highlighted seven genes that

may participate in the pathogenesis of ET. One of the candidate causal genes is _CA3_ which encodes carbonic anhydrase III and is in close proximity ( >1 Mb) to other carbonic anhydrase

genes including _CA1_, _CA2_, and _CA13_. Carbonic anhydrase inhibitors represent a class of drugs that have demonstrated the ability to improve tremors, potentially through modulating brain

pH levels. Additionally, acidification facilitates GABA receptor potentiation which may facilitate the effect of the inhibitors, on tremor and seizures17,18. Primidone, an anti-epileptic

drug widely used to treat ET patients, has been shown to inhibit carbonic anhydrase II18. The lead variant at the _CA3_ locus confers protection against ET and is highly correlated with a

variant that associates (top _cis_-eQTLs) with decreased expression of _CA3_ in skeletal muscles. The variant also associates with decreased plasma levels of carbonic anhydrase III and XIII.

The high LD at the locus suggest a potentially shared biological mechanism or pathway through which the variants exert their effects. Further research is needed to identify the most likely

causal variants. Carbonic anhydrases are relevant proteins that may have an important role in the biology of ET. This notable finding lends support to the hypothesis that inhibiting carbonic

anhydrases could lead to improved tremor control. Thus, targeting the interplay of _CA3_ and its closely related enzymes might lead to the development of more targeted and effective

treatments for individuals suffering from ET. While ET may not be directly caused by imbalances in neurotransmitters, certain neurotransmitters have been implicated in its development and

severity, such as GABA10,19,20. _CPLX1_ encodes Complexin-1, a soluble presynaptic protein that specifically enhances transmitter release by increasing fusogenicity of synaptic vesicles21.

_CPLX1_ is overexpressed in substantia nigra from PD patients22 and dysregulation of the gene have been associated with neurogenetic disorders23, including myoclonic epilepsy24. In addition,

homozygous _Cplx1_ knockout mice have the earliest known onset of ataxia seen in a mouse model21,25,26. We identified an intronic variant in _CPLX1_ that increases the risk of ET and is the

top _cis_-eQTL for _CPLX1_ in blood. This finding aligns with previous research and strongly suggests the involvement of _CPLX1_ in the pathogenicity of ET. _OTX1_ is a homeodomain

transcription factor and is encoded by Orthodenticle homeobox 1. In the mammalian brain, _OTX1_ is expressed in the forebrain and midbrain during early stages of neural development27 and at

later stages and adulthood at high levels in layers 5 and 6, the deepest layers of the cortical plate28,29. Layer 5 neurons convey signals controlling motor behavior via their projections to

the colliculi, pons, and spinal cord30,31,32,33. _Otx1_ mutant mice have been reported to show spontaneous epileptic behavior and multiple abnormalities affecting certain brain regions34.

_Otx1_ mutant animal studies suggest that Otx1 is required for the development of normal axonal connectivity and the generation of coordinated motor behavior29. The ET associated variant in

_EHBP1_ is in high LD with a missense variant in the same gene and is, additionally, highly correlated with two intronic variants in _EHBP1_ and one variant in _OTX1_, all of which are top

_cis_-eQTLs for _OTX1_ expression in blood and neutrophiles. Based on these findings, our observations suggest that the intronic variant in _EHBP1_ may contribute to the risk of ET by

potentially upregulating the expression of _OTX1_. Consequently, _OTX1_ emerges as a promising candidate gene that could play a significant role in the underlying pathogenic mechanism of ET.

However, it is important to note that we did not investigate protein levels of OTX1, as it is neither measured on the Somalogic nor Olink platforms. _GCKR_ has been associated with high

serum uric acid and purine metabolism disorders35. Extrapyramidal signs such as tremor are often observed in these disorders36. Individuals with high serum uric acid might experience tremors

attributed to these metabolic issues, yet, due to the lack of routine serum uric acid testing, they could be incorrectly diagnosed with ET. Whether the tremor associated with the _GCKR_

variant is a phenocopy or typical ET needs to be investigated further. GABAergic dysfunction, consistently observed in ET patients10,19,37, is a focal point of research, but its genetic

underpinnings remain unidentified. Our gene-set enrichment analysis reinforces the proposed role of GABA in ET, yet the exact role of GABA in the causative framework requires further

elucidation. In addition, our analysis found enrichment for dopaminergic neurons, a crucial regulator of extrapyramidal movement. Given the shared phenotypic and genetic traits between ET

and PD, the association with dopaminergic neurons is anticipated, particularly as their selective degeneration in the substantia nigra pars compacta characterizes PD38. Notably, neurologist

have long suspected a potential link between ET and PD, but definitive evidence for this relationship has remained elusive39. Furthermore, our analysis underscores the significance of

various biological processes, most notably the Rho GTPase cycle. Rho GTPases regulate the actin cytoskeleton of dopaminergic neurons, thus influencing their degeneration40,41, and have been

implicated in PD41,42. Interestingly, our findings underscore the significance of stress response regulation, especially given the genetic overlap between ET and anxiety phenotypes. ET

patients exhibit intensified tremors under stress, and anti-anxiety medications, such as Clonazepam, prove highly effective in alleviating these tremors. Familial clustering of ET is well

recognized. However, estimates of the proportion with a family history is highly debated and ranges from as low as 17% to as high as 100%43. Linkage studies have identified susceptibility

loci on 3q1344 and 2p24.145 but others have shown absence of linkage at these loci46. The absence of a significant difference between the effects of sporadic and familial cases and the lack

of high-impact variants segregating in several large Icelandic families, underscores the potential that ET may be predominantly influenced by common variants or a combination of such

variants, rather than rare variants. To gain deeper insights into the contribution of common variants on ET risk, the creation of a polygenic risk score would be valuable. A PRS analysis, if

conducted with larger and more diverse datasets, could offer further clarity on the polygenic nature of ET and thus, the assembly of larger cohorts in future research holds promise to

increasing our understanding of the complex genetics of ET. Another limitation lies in the lack of ethnic diversity within the datasets studied. As an increasing amount of genotypic and

phenotypic data becomes available for diverse ethnic backgrounds, the inclusion of greater diversity in future studies could be helpful in uncovering ethnicity-specific genetic contributions

and advancing our understanding of the genetic underpinnings of ET. In the previous GWAS conducted on ET, a subset consisting of 216 cases from the UK Biobank ET data was used. Regrettably,

we could not ascertain whether these particular cases overlap with the UK Biobank cases utilized in our current study. It is worth mentioning that even if all of these cases overlap with

our data, they represent only 2% of the total 9303 cases (excluding cases from the previous GWAS). Given their small proportion, the exclusion of these cases would not significantly impact

the results or conclusions of our study. To conclude, through a comprehensive GWAS meta-analysis and a multiomics approach using a substantial cohort, we have advanced our understanding of

the genetics and pathogenesis of ET. This progress not only enhances our knowledge of this complex and prevalent neurological disorder but can also form the basis for future investigations

into treatment strategies and personalized interventions. METHODS STUDY SAMPLE AND ETHICS STATEMENT In this study, ET cases were defined using International Classification of Diseases 10

(ICD-10) code G25.0 or ICD-9 code 333.1 in all datasets, as described in detail below, in addition to the sample defined by Liao et al.12. All ethical regulations relevant to human research

participants were followed. The data used in the GWAS meta-analysis were collected through studies approved by ethics committees governing each dataset and written informed consent was

obtained from all participants. Personal identifiers of participants’ data were encrypted for privacy protection purposes in accordance with the regulation in each country. Genetic ancestry

quality control was performed for all datasets47,48,49,50 and participants were genotypically verified as being of European descent. In total, we studied data from 16,480 ET cases and

1,936,173 controls (Fig. 1 and Supplementary Data 1). ICELAND – DECODE GENETICS A large fraction of the Icelandic population has participated in a research program at deCODE genetics.

Participants donated blood or buccal samples after signing a broad informed consent allowing the use of their samples and data in various projects approved by the National Bioethics

Committee (NBC). The data in this study was approved by the NBC (VSN-17-142-V5; VSNb2017060004/03.01) following review by the Icelandic Data Protection Authority. All personal identifiers of

the participants’ data were encrypted in accordance with the regulations of the Icelandic Data Protection Authority. The Icelandic ET cases were identified from medical records, filed from

1985 to 2022, through collaboration with physicians at Landspitali—National University Hospital in Reykjavik, the Registry of Primary Health Care Contacts, and the Registry of Contacts with

Medical Specialists in Private Practice. DENMARK – THE COPENHAGEN HOSPITAL BIOBANK AND THE DANISH BLOOD DONOR STUDY The Copenhagen Hospital Biobank (CHB) is a research biobank, which

contains samples obtained during diagnostic procedures on hospitalized and outpatients in the Danish Capital Region hospitals. Data analysis was performed under the Developing the basis for

personalized medicine in degenerative and episodic brain disorders protocol, approved by the National Committee on Health Research Ethics (H-21058057). The Danish Blood Donor Study (DBDS)

Genomic Cohort is a nationwide study of ~160,000 blood donors51. The Danish Data Protection Agency (P-2019-99) and the National Committee on Health Research Ethics (NVK-1700407) approved the

studies under which data on DBDS participants were obtained. The DBDS data requested for this study was approved by the DBDS steering committee. ESTONIA – ESTONIAN BIOBANK The Estonian

Biobank is a population-based cohort of approximately 210,000 participants, each accompanied by a variety of phenotypic and health-related data52. Upon recruitment, participants granted

permission through signed consents for subsequent linkage to their electronic health records, enabling the longitudinal accumulation of phenotypic details. The Estonian Biobank facilitates

access to the records from the National Health Insurance Fund Treatment Bills (since 2004), Tartu University Hospital (since 2008), and North Estonia Medical Center (since 2005). For each

participant, data is available on diagnoses coded in ICD-10 and drug dispensing records, including ATC codes, prescription statuses, and purchase dates (when available). The activities of

the EstBB are regulated by the Human Genes Research Act, which was adopted in 2000 specifically for the operations of the EstBB. Analysis of individual level data from the EstBB was carried

out under ethical approval 1.1-12/624 from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs), using data according to release application

[6-7/GI/29 977] from the Estonian Biobank. NORWAY – THE HORDALAND HEALTH STUDY The Hordaland Health Study (HUSK) is a community-based study in Western Norway conducted as a collaboration

between the University of Bergen, the Norwegian Health Screening Service and the Municipal Health Service in Hordaland (https://husk-en.w.uib.no/)53. In 1992–93 and 1997–99 participants were

invited based on year of birth and site of residence. Residents from Hordaland County born 1950–52 and residents from Bergen and three neighboring municipalities born 1925–27, in addition

to a random sample born 1926–49 were invited in 1992–93. In 1997–99, previous participants born 1950–51 and 1925–27 were reinvited, in addition to all residents in Hordaland County born

1953–57. In total, approximately 36,000 individuals participated in the study, 18,000 in 1992–93 and 26,000 in 1997–99, with some participating at both times. ET cases were identified

through diagnostic codes reported in the patient registry during 2008 to 2021. The HUSKment study is approved by the Regional Committee for Medical Research Ethics Western Norway, reference

2018/915. THE UK – THE UK BIOBANK The UK Biobank resource has collected extensive phenotype and genotype data from ~500,000 participants in the age range 40–69, from across the UK after

signing an informed consent for the use of their data in genetic studies54. The North West Research Ethics Committee reviewed and approved UK Biobank’s scientific protocol and operational

procedures (REC Reference Number: 06/MRE08/65). This study was conducted using the UK Biobank resource under application number 42256. ET cases were identified in General Practice clinical

event records (Field ID 42040) and UK hospital diagnoses (Field ID 41270 and 41271). THE US – INTERMOUNTAIN HEALTHCARE Participants, voluntary US residents over the age of 18 years, were

recruited by The Intermountain Inspire Registry and The HerediGene: Population study55, a large-scale collaboration between Intermountain Healthcare, deCODE genetics, and Amgen, Inc

(https://intermountainhealthcare.org). The Intermountain Healthcare Institutional Review Board approved this study, and all participants provided written informed consent prior to

enrollment. THE US – EMORY GENERAL CLINICAL RESEARCH CENTER The Clinical Research in Neurology (CRIN) provides an umbrella structure for subject enrollment in observational and genetic

studies in neurology, consent-approved data sharing across studies and disorders, and consistent sample processing. Participants were recruited under the CRIN protocol through support from

Emory Clinical Research Center NIH/NCRR M01 RR00039 (CRIN Infrastructure support). The study was approved by the Emory Institutional Review Board (IRB) and informed consent was obtained from

all subjects. ET genotyping work was done under specific IRB protocols. Samples were drawn from either review of previously enrolled subjects in the CRIN database, or prospective enrollment

of ET subjects into CRIN/ET observational and genetics work. All subjects underwent a basic structured interview for demographics and family history. A Folstein Mini Mental Status Exam was

administered to all CRIN subjects by trained CRIN personnel supervised by a neuropsychologist per published guidelines. All CRIN database subjects enrolled prior to January 2007 with a

reported diagnosis of 333.1 were reviewed. ET subjects were called in for full in-person assessments whenever possible. ET subjects mid-2006 onward were recruited through IRB-approved ads in

the Emory Movement Disorders and Neurosurgery deep brain stimulation group clinics, and ET community education events. ET subjects and family members were examined directly by at least one

movement disorders specialist; two independent exams were obtained whenever possible (a tremor rating scale derived from the Fahn-Tolosa-Marin scale and Tremor Research Group scale items,

the motor United Parkinson Disease Rating Scale, Tinetti gait and balance scales56, tandem gait57, and assessment for dystonia). Semi-structured interviews included ET specific questions

derived from the Fahn-Tolosa-Marin scale and WHIGET studies58. CRIN review and new enrollment subjects were given a research diagnosis of ET using Movement Disorders Society and Tremor

Research Group criteria. ET cases with either Parkinson’s disease or dystonia were excluded. Subjects were excluded based on a number of criteria; if an in-person exam and re-interview

determined a different diagnosis, if movement disorders clinical notes listed an uncertain or different final diagnosis (i.e., medication induced tremor), if there was an incomplete

examination, lack of medication response, or other data to clearly establish an ET research diagnosis. GENOTYPING AND IMPUTATION ICELAND – DECODE GENETICS The genomes of 63,460 Icelanders

were whole genome sequenced (WGS)47,59 using GAIIx, HiSeq, HiSeqX, and NovaSeq Illumina technology to a mean depth of 38×. Genotypes of single nucleotide polymorphisms (SNPs) and

insertions/deletions (indels) were identified and called jointly with Graphtyper60,61. Over 173,000 Icelanders (including all WGS Icelanders) were genotyped using various Illumina SNP

arrays47,59. The genotypes were long-range phased62, which allows for improving genotype calls using haplotype sharing information. Subsequently, extensive encrypted genealogic information

was used to impute variants into the chip-typed Icelanders, as well as ungenotyped close relatives63 to increase the sample size and power for association analysis. DENMARK – THE COPENHAGEN

HOSPITAL BIOBANK AND THE DANISH BLOOD DONOR STUDY The Danish samples from the CHB and DBDS were genotyped using Illumina Global Screening Array, and long-range phased together with 270,627

genotyped samples from North-western Europe using Eagle264. Samples and variants with less than 98% yield were excluded. A haplotype reference panel was prepared in the same manner as for

the Icelandic data47,62 by phasing genotypes of 25,215 WGS individuals (sequenced with NovaSeq Illumina technology to a mean depth of 20×) from North-western Europe, including 8,360 Danes,

using the phased chip data. Graphtyper60,61 was used to call the genotypes which were subsequently imputed into the phased chip data. WGS, chip-typing, quality control, long-range phasing,

and imputation from which the data for this analysis were generated was performed at deCODE genetics. ESTONIA – ESTONIAN BIOBANK The samples from the Estonian Biobank were genotyped at the

Genotyping Core Facility of the Institute of Genomics at the University of Tartu, using the Illumina Global Screening Array. In total, 212,955 samples passed quality control. Samples were

excluded from the analysis if their call-rate was below 95% or if the gender, identified by the heterozygosity of the X chromosome, did not align with the gender documented in phenotype

data. Variants were excluded if the call-rate was below 95% or if the HWE p-value was less than 1e-4 (only autosomal variants). In addition, SNPs that showed potential traces of batch bias

were removed. Two batch bias control steps were performed: 1) SNPs that showed poor cluster separation results among any of Estonian Biobank genotyping experiments were removed. The

threshold for SNP removal was Illumina GenTrain score <0.6 and/or cluster separation score <0.4. 2) SNPs that showed inconsistent allele frequency among genotyping experiments were

excluded. First, allele frequency was calculated for each SNP for each genotyping experiment with more than 10,000 samples. Next, mean allele frequency was calculated. Finally, if SNP allele

frequency was more than 5% away from the mean in any of genotyping experiments, the SNP was excluded from the merged dataset. Prior to imputation, variants with MAF less than 1% and indels

were removed. The Eagle v2.4.164 was used for prephasing and imputation was executed using Beagle v5.4 (beagle.22Jul22.46e)65. An imputation reference, specific to the Estonian population,

consisting of 2056 WGS samples was used66. Participants with non-European assigned group ancestry were removed, leaving a total of 206,162 samples. NORWAY – THE HORDALAND HEALTH STUDY The

Norwegian dataset was genotyped using Illumina SNP arrays (either OmniExpress or Global Screening Array). The chip-genotyping quality control and imputation were performed at deCODE

genetics, where the same methods used for the Danish sample were applied. The imputation process relied on the same haplotype reference panel as the Danish sample, a panel composed of phased

genotypes of 25,215 WGS samples of European ancestry, including 3,336 samples of Norwegian origin. THE UK – THE UK BIOBANK The samples from the UK Biobank were genotyped using two different

Affymetrix chips – the UK BiLEVE Axiom in the first 50,000 individuals67, and the Affymetrix UK Biobank Axiom array68 in the remaining participants. In total, 428,864 participants have been

genotyped and 131,272 WGS. Samples with variant yield below 98% were filtered out and any duplicate samples were removed. High-quality sequence variants and indels to a mean depth of at

least 20× were identified using Graphtyper60,61. Quality-controlled chip-genotype data were phased using Shapeit469 and variants where at least 50% of the samples had a genotype quality

score above 0 were used to prepare a haplotype reference panel using in-house tools and the long-range phased chip data. The variants in the haplotype reference panel were imputed into the

chip-genotyped samples using the same in-house tools and methods described for the Icelandic data47,62. THE US – INTERMOUNTAIN HEALTHCARE The Intermountain dataset was genotyped using

Illumina Global Screening Array chips (_N_ = 76,660) and WGS with NovaSeq Illumina technology (_N_ = 20,632). The samples were filtered on 98% variant yield and duplicates removed.

High-quality sequence variants and indels with at least a mean depth of 20× were identified with Graphtyper60,61. Quality-controlled genotype data were phased with Shapeit469. A phased

haplotype reference panel was prepared with the same in-house tools and methods described for the Icelandic data47,62. THE US – EMORY GENERAL CLINICAL RESEARCH CENTER The genotyping of the

Emory dataset has been described previously50. In short, the Emory dataset was genotyped using three types of chips from Illumina (HumanHap300, HumanHap300-Duo and HumanCNV370-Duo). These

chips have 314,125 SNPs in common. Prior to analysis, certain SNPs were excluded based on the following criteria; being monomorphic, having less than 95% yield in either cases or controls,

deviating from Hardy-Weinberg equilibrium, or displaying divergent allele frequencies between the chips. Additionally, samples with a call-rate less than 98% were excluded. STATISTICS AND

REPRODUCIBILITY We applied logistic regression assuming an additive model using the expected allele counts as covariates, and combined the results with the available GWAS summary statistics

of 10,000 variants12 to test for association between sequence variants and ET. The covariates we used in the association analysis are described in Supplementary Data 14 for the datasets. We

used LD score regression to account for distribution inflation due to cryptic relatedness and population stratification70 and used the intercepts as correction factors. We combined the

results from the association analysis of all of the datasets together with the summary statistics (only the top 10,000 variants)12 using a fixed-effects inverse variance method71 based on

effect estimates and standard errors in which each dataset was assumed to have a common OR but allowed to have different population frequencies for alleles and genotypes. Sequence variants

were mapped to NCBI Build38 and matched on position and alleles to harmonize the datasets. The genome-wide significance threshold was corrected for multiple testing using a weighted

Bonferroni adjustment that controls for the family-wise error rate. Variants were weighted based on predicted functional impact72 (Supplementary Data 2). In a random-effects method, a

likelihood ratio test was performed in all genome-wide associations to test the heterogeneity of the effect estimate in the datasets; the null hypothesis is that the effects are the same in

all datasets and the alternative hypothesis is that the effects differ between datasets. The primary signal at each locus was defined as the sequence variant with the lowest

Bonferroni-adjusted _P_-value using the adjusted significance thresholds (Supplementary Data 2). To identify secondary signals at each locus (defined as 1 Mb from the index variants), we

performed conditional association analyses using the true imputed genotype data of each dataset except the Estonian and US-EMORY datasets and the summary statistics where an approximate

conditional analysis implemented in the GCTA software73 was used. LD between variants was estimated using a set of 5,000 WGS Icelanders. After adjusting for all variants in high LD (_r_2 

> 0.8) and vice versa, the _P_-values were combined for all datasets to identify the most likely causal variant at each locus and any secondary signals. Based on the number of variants

tested, we chose a conservative _P_-value threshold of <5 × 10−8 for secondary signals. Manhattan plots were generated using the qqman package in R74. FUNCTIONAL DATA To highlight

potential causal genes associating with ET, we annotated the variants associating with ET or variants in high LD (_r_2 ≥ 0.8 and within ± 1 Mb) that are predicted to affect coding or

splicing of a protein (variant effect predictor using Refseq gene set), mRNA expression (top local expression quantitative trait loci [_cis_-eQTL]) in multiple tissues from deCODE, GTEx

(https://gtexportal.org), and other public datasets (Supplementary Data 15), and/or plasma protein levels (top protein quantitative trait loci [pQTL]) in large proteomic datasets from

Iceland and the UK. RNA sequencing was performed on whole blood (_N_ = 17,848) and subcutaneous adipose tissue (_N_ = 769). RNA isolation was performed using RNAzol RT according to

manufacturer’s protocol (Molecular Research Center RN 190). We isolated RNA using Chemagic Total RNA Kit special (PerkinElmer) in whole blood and RNAzol RT in adipose tissue, according to

the manufacturer’s instructions (Molecular Research Center, RN190). The concentration and quality of the RNA were determined with an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA was

prepared and sequenced on the Illumina HiSeq 2500 and Illumina Novaseq systems according to the manufacturer’s recommendation. RNA-seq reads were aligned to personalized genomes using the

STAR software package v.2.5.3 with Ensembl v.87 gene annotations75,76. Gene expression was computed based on personalized transcript abundances using kallisto77. Association between sequence

variants and gene expression (_cis_-eQTL) was estimated using a generalized linear regression, assuming additive genetic model and quantile-normalized gene expression estimates, adjusting

for measurements of sequencing artefacts, demographic variables, blood composition, and PCs78. The gene expression PCs were computed per chromosome using a leave-one-chromosome-out method.

Quantification of alternative RNA splicing in whole blood was done using LeafCutter79. The _cis_ association between sequence variants and quantified splicing (_cis_-sQTL) was estimated

using linear regression assuming an additive genetic model and quantile-normalized percentage-spliced-in (PSI) values of each splice junction, adjusting for measurements of sequencing

artefacts, demography variables, and 15 leave-one-chromosome-out PCs of the quantile-normalized PSI matrix. All variants with MAF > 0.2% within 30 Kb of each LeafCutter cluster were

tested. Icelandic plasma samples were collected through two main projects: the Icelandic Cancer Project (52% of participants; samples collected from 2001 to 2005) and various genetic

programs at deCODE genetics, mainly the population-based deCODE Health study. The average participant age was 55 years (SD = 17 years) and 57% were women. In the case of repeated samples for

an individual, one was randomly selected. This left measurements for 39,155 individuals. Of these, 35,892 Icelanders were used in the protein GWASs, because they also had genotype

information80. The plasma samples were measured with SomaScan v4 assay (SomaLogic®). The assay scanned 4,907 aptamers that measure 4719 proteins. Plasma protein levels were standardized and

adjusted for year of birth, sex, and year of sample collection. The plasma levels of a subset of 47,150 individuals in the UK Biobank were measured with the Olink Explore 1536 platform as a

part of the UKB–Pharma Proteomics Project (UK Biobank application no. 65851)81 at Olink’s facilities in Uppsala, Sweden. The majority of the samples were randomly selected across the UK

Biobank. Plasma protein levels were standardized to a normal distribution. We performed gene-based enrichment analysis using the GENE2FUNC tool in FUMA14. The genes on the loci (closest

protein coding gene per locus was prioritized) that met traditional genome-wide significance (_P_ ≤ 5 × 10−8) in the ET meta-analysis were tested for over-representation in different gene

sets, including Gene Ontology biological processes (MsigDB c5), Reactome (MsigDB c2) and Cell type signatures (MsigDB c8). A Bonferroni test was used for multiple comparison correction.

FAMILIAL CLUSTERING We used family-based method to test rare coding variants for segregation within a pedigree. We focused our search on rare (carried by <30 whole-genome-sequenced

individuals), high-penetrance coding variants that could account for the familial clustering. To test for association, we created a scoring function based on the coding effect of the variant

and its cosegregation with ET, inside and outside of the pedigrees, and used genome-wide simulations to estimate the significance. This method has been described in detail elsewhere82.

ESTIMATION OF GENETIC VARIANCE EXPLAINED We calculated the variance explained (_h_2) using the β and EAF from the ET meta-analysis of each of the independent and significant variant with the

formula _h_2 = β2 × (1-EAF) × 2EAF83. GENETIC CORRELATION Cross-trait LD score regression70 was used to estimate the genetic correlation between the ET meta-analysis and GWAS meta-analyses

of other neurological phenotypes, namely Parkinson’s disease and major depressive disorder. We also estimated the genetic correlation between the ET meta-analysis and 1152 previously

published GWAS traits (_P_ ≤ 3.8 × 10−5) each with an effective sample size over 5,000 for an unbiased estimate of genetic correlation and heritability. To avoid bias due to sample overlap,

we excluded the UK dataset from the ET meta-analysis. We used results for about 1.2 million well imputed variants, and for LD information we used precomputed LD scores for European

populations (downloaded from: https://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2). REPORTING SUMMARY Further information on research design is available in the Nature

Portfolio Reporting Summary linked to this article. DATA AVAILABILITY The GWAS summary statistics for the ET meta-analysis are available at https://www.decode.com/summarydata/. Other data

generated or analyzed in this study are included in the article and Supplementary data and information. CODE AVAILABILITY GraphTyper (v2.0-beta, GNU GPLv3 license) at

https://github.com/DecodeGenetics/graphtyper Svimmer (v0.1, GNU GPLv3 license), the structural variant merging software at https://github.com/DecodeGenetics/svimmer SHAPEIT4 (v4.2.2) at

https://odelaneau.github.io/shapeit4/ Eagle2 (v2.4.1) at http://www.hsph.harvard.edu/alkes-price/software/ Beagle (v5.4) at https://faculty.washington.edu/browning/beagle/beagle.html GCTA

(v1.93.3beta2) at https://yanglab.westlake.edu.cn/software/gcta/#Overview STAR (v2.5.3) at http://star.mit.edu/ Kallisto at https://pachterlab.github.io/kallisto/ LeafCutter at

https://davidaknowles.github.io/leafcutter/ LD score regression (first release) at https://github.com/bulik/ldsc qqman package (v0.1.6) at https://github.com/stephenturner/qqman Axiom

genotyping algorithm (v1) at https://www.thermofisher.com/is/en/home.html FUMA at https://fuma.ctglab.nl/ REFERENCES * Louis, E. D. & Ferreira, J. J. How common is the most common adult

movement disorder? Update on the worldwide prevalence of essential tremor. _Mov. Disord._ 25, 534–541 (2010). Article  PubMed  Google Scholar  * Lorenz, D., Poremba, C., Papengut, F.,

Schreiber, S. & Deuschl, G. The psychosocial burden of essential tremor in an outpatient- and a community-based cohort. _Eur. J. Neurol._ 18, 972–979 (2011). Article  CAS  PubMed  Google

Scholar  * Bhatia, K. P. et al. Consensus Statement on the Classification of Tremors. From the Task Force on Tremor of the International Parkinson and Movement Disorder Society. _Mov.

Disord._ 33, 75 (2018). Article  PubMed  Google Scholar  * Haerer, A. F., Anderson, D. W. & Schoenberg, B. S. Prevalence of Essential Tremor: Results From the Copiah County Study. _Arch.

Neurol._ 39, 750–751 (1982). Article  CAS  PubMed  Google Scholar  * Louis, E. D. et al. Differences in the prevalence of essential tremor among elderly African Americans, whites, and

Hispanics in northern Manhattan, NY. _Arch. Neurol._ 52, 1201–1205 (1995). Article  CAS  PubMed  Google Scholar  * Louis, E. D., Fried, L. P., Fitzpatrick, A. L., Longstreth, W. T. &

Newman, A. B. Regional and racial differences in the prevalence of physician-diagnosed essential tremor in the United States. _Mov. Disord._ 18, 1035–1040 (2003). Article  PubMed  Google

Scholar  * Tanner, C. M. et al. Essential tremor in twins: an assessment of genetic vs environmental determinants of etiology. _Neurology_ 57, 1389–1391 (2001). Article  CAS  PubMed  Google

Scholar  * Louis, E. D. Etiology of essential tremor: should we be searching for environmental causes? _Mov. Disord._ 16, 822–829 (2001). Article  CAS  PubMed  Google Scholar  * Deuschl, G.,

Raethjen, J., Hellriegel, H. & Elble, R. Treatment of patients with essential tremor. _Lancet Neurol._ 10, 148–161 (2011). Article  PubMed  Google Scholar  * Paris-Robidas, S. et al.

Defective dentate nucleus GABA receptors in essential tremor. _Brain_ 135, 105–116 (2012). Article  PubMed  Google Scholar  * Louis, E. D. et al. Neuropathological changes in essential

tremor: 33 cases compared with 21 controls. _Brain_ 130, 3297–3307 (2007). Article  PubMed  Google Scholar  * Liao, C. et al. Association of Essential Tremor With Novel Risk Loci: A

Genome-Wide Association Study and Meta-analysis. _JAMA Neurol._ 79, 185–193 (2022). Article  PubMed  Google Scholar  * Giambartolomei, C. et al. Bayesian Test for Colocalisation between

Pairs of Genetic Association Studies Using Summary Statistics. _PLoS Genet._ 10, e1004383 (2014). Article  PubMed  PubMed Central  Google Scholar  * Watanabe, K., Taskesen, E., van Bochoven,

A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. _Nat. Commun._ 8, 1–11 (2017). Article  CAS  Google Scholar  * Nalls, M. A. et al. Identification

of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. _Lancet Neurol._ 18, 1091–1102 (2019). Article  CAS 

PubMed  PubMed Central  Google Scholar  * Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal

brain regions. _Nat. Neurosci._ 22, 343–352 (2019). Article  CAS  PubMed  PubMed Central  Google Scholar  * Ondo, W. Essential Tremor: What We Can Learn from Current Pharmacotherapy. _Tremor

Other Hyperkinetic Mov._ 6, 356 (2016). Article  Google Scholar  * Magheru, C. et al. Antiepileptic Drugs and Their Dual Mechanism of Action on Carbonic Anhydrase. _J. Clin. Med._ 11, 2614

(2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Mouginot, D. & Gähwiler, B. H. Presynaptic GABAB receptors modulate IPSPs evoked in neurons of deep cerebellar nuclei in

vitro. _J. Neurophysiol._ 75, 894–901 (1996). Article  CAS  PubMed  Google Scholar  * Shill, H. A. et al. Brain biochemistry in autopsied patients with essential tremor. _Mov. Disord._ 27,

113 (2012). Article  CAS  PubMed  Google Scholar  * Chang, S. et al. Complexin stabilizes newly primed synaptic vesicles and prevents their premature fusion at the mouse calyx of held

synapse. _J. Neurosci._ 35, 8272–8290 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Basso, M. et al. Proteome analysis of human substantia nigra in Parkinson’s disease.

_Proteomics_ 4, 3943–3952 (2004). Article  CAS  PubMed  Google Scholar  * Karaca, E. et al. Genes that affect brain structure and function identified by rare variant analyses of Mendelian

neurologic disease. _Neuron_ 88, 499 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  * Redler, S. et al. Variants in CPLX1 in two families with autosomal-recessive severe

infantile myoclonic epilepsy and ID. _Eur. J. Hum. Genet._ 25, 889 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Glynn, D., Drew, C. J., Reim, K., Brose, N. & Morton,

A. J. Profound ataxia in complexin I knockout mice masks a complex phenotype that includes exploratory and habituation deficits. _Hum. Mol. Genet._ 14, 2369–2385 (2005). Article  CAS  PubMed

  Google Scholar  * Glynn, D., Sizemore, R. J. & Morton, A. J. Early motor development is abnormal in complexin 1 knockout mice. _Neurobiol. Dis._ 25, 483–495 (2007). Article  CAS 

PubMed  Google Scholar  * Simeone, A., Acampora, D., Gulisano, M., Stornaiuolo, A. & Boncinelli, E. Nested expression domains of four homeobox genes in developing rostral brain. _Nature_

358, 687–690 (1992). Article  CAS  PubMed  Google Scholar  * Frantz, G. D., Weimann, J. M., Levin, M. E. & McConnell, S. K. Otx1 and Otx2 define layers and regions in developing

cerebral cortex and cerebellum. _J. Neurosci._ 14, 5725–5740 (1994). Article  CAS  PubMed  PubMed Central  Google Scholar  * Weimann, J. M. et al. Cortical Neurons Require Otx1 for the

Refinement of Exuberant Axonal Projections to Subcortical Targets. _Neuron_ 24, 819–831 (1999). Article  CAS  PubMed  Google Scholar  * O’Leary, D. D. M. & Koester, S. E. Development of

projection neuron types, axon pathways, and patterned connections of the mammalian cortex. _Neuron_ 10, 991–1006 (1993). Article  PubMed  Google Scholar  * Bates, C. A. & Killackey, H.

P. The emergence of a discretely distributed pattern of corticospinal projection neurons. _Dev. Brain Res._ 13, 265–273 (1984). Article  Google Scholar  * Wise, S. P. & Jones, E. G.

Cells of origin and terminal distribution of descending projections of the rat somatic sensory cortex. _J. Comp. Neurol._ 175, 129–157 (1977). Article  CAS  PubMed  Google Scholar  *

Gilbert, C. D. & Kelly, J. P. The projections of cells in different layers of the cat’s visual cortex. _J. Comp. Neurol._ 163, 81–105 (1975). Article  CAS  PubMed  Google Scholar  *

Acampora, D. et al. Epilepsy and brain abnormalities in mice lacking the Otx1 gene. _Nat. Genet._ 14, 218–222 (1996). Article  CAS  PubMed  Google Scholar  * Sandoval-Plata, G., Morgan, K.

& Abhishek, A. Variants in urate transporters, ADH1B, GCKR and MEPE genes associate with transition from asymptomatic hyperuricaemia to gout: results of the first gout versus

asymptomatic hyperuricemia GWAS in Caucasians using data from the UK Biobank. _Ann. Rheum. Dis._ 80, 1220–1226 (2021). Article  CAS  PubMed  Google Scholar  * Kamatani, N., Jinnah, H. A.,

Hennekam, R. C. M. & Van Kuilenburg, A. B. P. Purine and Pyrimidine Metabolism. _Emery Rimoin’s Princ. Pract. Med. Genet. Genomics Metab. Disord_. 183–234.

https://doi.org/10.1016/B978-0-12-812535-9.00006-6 (2021). * Málly, J., Baranyi, M. & Vizi, E. S. Change in the concentrations of amino acids in CSF and serum of patients with essential

tremor. _J. Neural Transm._ 103, 555–560 (1996). Article  PubMed  Google Scholar  * Kalia, L. V. & Lang, A. E. Parkinson’s disease. _Lancet_ 386, 896–912 (2015). Article  CAS  PubMed 

Google Scholar  * Tarakad, A. & Jankovic, J. Essential Tremor and Parkinson’s Disease: Exploring the Relationship. _Tremor Other Hyperkinetic Mov._ 8, 589 (2018). Article  Google Scholar

  * Hall, A. & Lalli, G. Rho and Ras GTPases in Axon Growth, Guidance, and Branching. _Cold Spring Harb. Perspect. Biol_. 2, a001818 (2010). * Zhou, Z. et al. Rho GTPase regulation of

α-synuclein and VMAT2: Implications for pathogenesis of Parkinson’s disease. _Mol. Cell. Neurosci._ 48, 29–37 (2011). Article  CAS  PubMed  PubMed Central  Google Scholar  * Musilli, M. et

al. Therapeutic effects of the Rho GTPase modulator CNF1 in a model of Parkinson’s disease. _Neuropharmacology_ 109, 357–365 (2016). Article  CAS  PubMed  Google Scholar  * Louis, E. D.

& Ottman, R. How familial is familial tremor? _Neurology_ 46, 1200 (1996). Article  CAS  PubMed  Google Scholar  * Gulcher, J. R. et al. Mapping of a familial essential tremor gene,

FET1, to chromosome 3q13. _Nat. Genet._ 17, 84–87 (1997). Article  CAS  PubMed  Google Scholar  * Higgins, J. J., Loveless, J. M., Jankovic, J. & Patel, P. I. Evidence that a gene for

essential tremor maps to chromosome 2p in four families. _Mov. Disord._ 13, 972–977 (1998). Article  CAS  PubMed  Google Scholar  * Kovach, M. J. et al. Genetic heterogeneity in autosomal

dominant essential tremor. _Genet. Med._ 3, 197–199 (2001). Article  CAS  PubMed  Google Scholar  * Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic

population. _Nat. Genet._ 47, 435–444 (2015). Article  CAS  PubMed  Google Scholar  * Skuladottir, A. T. et al. A genome-wide meta-analysis uncovers six sequence variants conferring risk of

vertigo. _Commun. Biol._ 4, 1–9 (2021). Article  Google Scholar  * Skuladottir, A. T. et al. A meta-analysis uncovers the first sequence variant conferring risk of Bell’s palsy. _Sci. Rep._

11, 4188 (2021). Article  CAS  PubMed  PubMed Central  Google Scholar  * Stefansson, H. et al. Variant in the sequence of the LINGO1 gene confers risk of essential tremor. _Nat. Genet._ 41,

277 (2009). Article  CAS  PubMed  PubMed Central  Google Scholar  * Erikstrup, C. et al. Cohort Profile: The Danish Blood Donor Study. _Int. J. Epidemiol._ 52, e162 (2023). Article  PubMed 

Google Scholar  * Leitsalu, L., Alavere, H., Tammesoo, M. L., Leego, E. & Metspalu, A. Linking a Population Biobank with National Health Registries—The Estonian Experience. _J. Pers.

Med._ 5, 96–106 (2015). Article  PubMed  PubMed Central  Google Scholar  * Refsum, H. et al. The Hordaland Homocysteine Study: a community-based study of homocysteine, its determinants, and

associations with disease. _J. Nutr._ 136, 1731S–1740S (2006). Article  CAS  PubMed  Google Scholar  * Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data.

_Nature_ 562, 203–209 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Azriel, E. et al. Utilizing Public Health Frameworks and Partnerships to Ensure Equity in DNA-Based

Population Screening. _Front. Genet._ 13, 886755 (2022). Article  CAS  PubMed  PubMed Central  Google Scholar  * Tinetti, M. E. Performance-Oriented Assessment of Mobility Problems in

Elderly Patients. _J. Am. Geriatr. Soc._ 34, 119–126 (1986). Article  CAS  PubMed  Google Scholar  * Kieburtz, K. Unified Huntington’s disease rating scale: Reliability and consistency.

_Mov. Disord._ 11, 136–142 (1996). Article  Google Scholar  * Louis, E. D., Ford, B., Lee, H., Andrews, H. & Cameron, G. Diagnostic Criteria for Essential Tremor: A Population

Perspective. _Arch. Neurol._ 55, 823–828 (1998). Article  CAS  PubMed  Google Scholar  * Jónsson, H. et al. Data Descriptor: Whole genome characterization of sequence diversity of 15,220

Icelanders. _Sci. Data_ 4, 1–9 (2017). Article  Google Scholar  * Eggertsson, H. P. et al. Graphtyper enables population-scale genotyping using pangenome graphs. _Nat. Genet._ 49, 1654–1660

(2017). Article  CAS  PubMed  Google Scholar  * Eggertsson, H. P. et al. GraphTyper2 enables population-scale genotyping of structural variation using pangenome graphs. _Nat. Commun._ 10,

5402 (2019). Article  PubMed  PubMed Central  Google Scholar  * Kong, A. et al. Detection of sharing by descent, long-range phasing and haplotype imputation. _Nat. Genet._ 40, 1068–1075

(2008). Article  CAS  PubMed  PubMed Central  Google Scholar  * Gudbjartsson, D. F. et al. Sequence variants from whole genome sequencing a large group of Icelanders. _Sci. Data_ 2, 1–11

(2015). Article  Google Scholar  * Loh, P. R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. _Nat. Genet._ 48, 1443–1448 (2016). Article  CAS  PubMed  PubMed

Central  Google Scholar  * Browning, S. R. & Browning, B. L. Rapid and Accurate Haplotype Phasing and Missing-Data Inference for Whole-Genome Association Studies By Use of Localized

Haplotype Clustering. _Am. J. Hum. Genet._ 81, 1084–1097 (2007). Article  CAS  PubMed  PubMed Central  Google Scholar  * Mitt, M. et al. Improved imputation accuracy of rare and

low-frequency variants using population-specific high-coverage WGS-based imputation reference panel. _Eur. J. Hum. Genet._ 25, 869–876 (2017). Article  PubMed  PubMed Central  Google Scholar

  * Wain, L. V. et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): A genetic association study in UK Biobank.

_Lancet Respir. Med._ 3, 769–781 (2015). Article  PubMed  PubMed Central  Google Scholar  * Welsh, S., Peakman, T., Sheard, S. & Almond, R. Comparison of DNA quantification methodology

used in the DNA extraction protocol for the UK Biobank cohort. _BMC Genomics_ 18, 26 (2017). Article  PubMed  PubMed Central  Google Scholar  * Delaneau, O., Zagury, J. F., Robinson, M. R.,

Marchini, J. L. & Dermitzakis, E. T. Accurate, scalable and integrative haplotype estimation. _Nat. Commun._ 10, 1–10 (2019). Article  CAS  Google Scholar  * Bulik-Sullivan, B. et al. LD

score regression distinguishes confounding from polygenicity in genome-wide association studies. _Nat. Genet._ 47, 291–295 (2015). Article  CAS  PubMed  PubMed Central  Google Scholar  *

Mantel, N. & Haenszel, W. Statistical aspects of the analysis of data from retrospective studies of disease. _J. Natl Cancer Inst._ 22, 719–748 (1959). CAS  PubMed  Google Scholar  *

Sveinbjornsson, G. et al. Weighting sequence variants based on their annotation increases power of whole-genome association studies. _Nat. Genet._ 48, 314–317 (2016). Article  CAS  PubMed 

Google Scholar  * Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: A tool for genome-wide complex trait analysis. _Am. J. Hum. Genet._ 88, 76–82 (2011). Article  CAS  PubMed

  PubMed Central  Google Scholar  * Turner, S. qqman: an R package for visualizing GWAS results using Q-Q. _Manhattan plots. J. Open Source Softw._ 3, 731 (2018). Article  Google Scholar  *

Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. _Bioinformatics_ 29, 15–21 (2013). Article  CAS  PubMed  Google Scholar  * Howe, K. L. et al. Ensembl 2021. _Nucleic Acids Res_

49, D884–D891 (2021). Article  CAS  PubMed  Google Scholar  * Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. _Nat. Biotechnol._

34, 525–527 (2016). Article  CAS  PubMed  Google Scholar  * Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to

obtain increased power and interpretability of gene expression analyses. _Nat. Protoc._ 7, 500–507 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Li, Y. I. et al.

Annotation-free quantification of RNA splicing using LeafCutter. _Nat. Genet._ 50, 151–158 (2018). Article  CAS  PubMed  Google Scholar  * Ferkingstad, E. et al. Large-scale integration of

the plasma proteome with genetics and disease. _Nat. Genet._ 53, 1712–1721 (2021). Article  CAS  PubMed  Google Scholar  * Sun, B. B. et al. Genetic regulation of the human plasma proteome

in 54,306 UK Biobank participants. Prepr. https://doi.org/10.1101/2022.06.17.496443v1 20, (2022) * Steinberg, S. et al. Truncating mutations in RBM12 are associated with psychosis. _Nat.

Genet. 2017 498_ 49, 1251–1254 (2017). CAS  Google Scholar  * Alves, A. C. et al. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult

BMI. _Sci. Adv._ 5, 1–17 (2019). Google Scholar  * Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. _Nat. Genet._ 46, 310–315

(2014). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank the participants in this study for their valuable contribution to research. We

thank all investigators and colleagues who contributed to data collection, phenotypic characterization of clinical samples, genotyping, and analysis of the whole-genome association data. The

Estonian dataset was funded by European Union through the European Regional Development Fund Project No. 2014-2020.4.01.15-0012 GENTRANSMED and by Estonian Research Council grant PRG1291.

Data analysis of the Estonian dataset was carried out in part in the High-Performance Computing Center of University of Tartu. This research was conducted using the UK Biobank Resource

(application number 42256). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * deCODE genetics/Amgen Inc., Reykjavik, Iceland Astros Th. Skuladottir, Lilja Stefansdottir, Gisli H. Halldorsson, 

Olafur A. Stefansson, Thorgeir E. Thorgeirsson, G. Bragi Walters, Rosa S. Gisladottir, Gyda Bjornsdottir, Gudrun A. Jonsdottir, Patrick Sulem, Daniel F. Gudbjartsson, Daniel Gudbjartsson, 

Hreinn Stefánsson, Unnur Þorsteinsdóttir, Hreinn Stefansson & Kari Stefansson * Faculty of Medicine, University of Iceland, Reykjavik, Iceland Astros Th. Skuladottir, Palmi Jonsson, 

Unnur Þorsteinsdóttir & Kari Stefansson * Heilsuklasinn Clinic, Reykjavik, Iceland Anna Bjornsdottir * Department of Geriatric Medicine, Landspitali University Hospital, Reykjavik,

Iceland Palmi Jonsson * Department of Internal Medicine, Landspitali University Hospital, Reykjavik, Iceland Vala Palmadottir * Faculty of Icelandic and Comparative Cultural Studies,

University of Iceland, Reykjavik, Iceland Rosa S. Gisladottir * Faculty of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland Daniel F. Gudbjartsson & Daniel

Gudbjartsson * Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, USA Kirk U. Knowlton * Precision Genomics, Intermountain Healthcare, Saint George, Utah, UK David

A. Jones & Lincoln D. Nadauld * Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia Aigar Ottas, Tõnu Esko, Reedik Mägi, Mari Nelis, Georgi Hudjashov &

 Lili A. Milani * Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark Ole B. Pedersen, Jakob Bay, Thorsten Brodersen & Ole Birger Pedersen * Department of

Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Ole B. Pedersen, Ole Birger Pedersen, Thomas Werge & Sisse Rye Ostrowski *

Department of Clinical Immunology, Copenhagen University Hospital, Righospitale, Copenhagen, Denmark Maria Didriksen, Joseph Dowsett, Bjarke Feenstra, Frank Geller, Rikke Louise Jacobsen, 

Margit Anita Hørup Larsen, Christina Mikkelsen, Ioanna Nissen, Michael Schwinn, Erik Sørensen, Lise Wegner Thørner & Sisse Rye Ostrowski * Novo Nordisk Foundation Center for Protein

Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Søren Brunak, Karina Banasik, Kristoffer Burgdorf, Ioannis Louloudis, Agnete Lundgaard, 

Alexander Pil Henriksen & David Westergaard * Danish Headache Center, Department of Neurology, Copenhagen University Hospital, Righospitalet-Glostrup, Copenhagen, Denmark Thomas Folkmann

Hansen, Mona Ameri Chalmer & Lisette Kogelman * Department of Clinical Immunology, Aarhus University Hospital, Righospitalet, Copenhagen, Denmark Christian Erikstrup, Jens Kjærgaard

Boldsen, Khoa Manh Dinh, Lotte Hindhede, Katrine Kaspersen, Bertram Dalskov Kjerulff & Susan Mikkelsen * Department of Clinical Medicine, Faculty of Health and Medical Sciences, Aarhus

University, Aarhus, Denmark Christian Erikstrup * Department of Biomedicine, University of Bergen, Bergen, Norway Jan Haavik * Bergen Center of Brain Plasticity, Division of Psychiatry,

Haukeland University Hospital, Bergen, Norway Jan Haavik * Institute of Clinical Medicine, University of Oslo, Oslo, Norway Ole A. Andreassen * NORMENT, Division of Mental Health and

Addiction, Oslo University Hospital, Oslo, Norway Ole A. Andreassen * Emory Department of Neurology, Wesley Woods Health Center, Atlanta, GA, USA David Rye * Department of Global Public

Health and Primary Care, University of Bergen, Bergen, Norway Jannicke Igland * Department of Health and Caring sciences, Western Norway University of Applied Sciences, Bergen, Norway

Jannicke Igland * Stanford University, School of Medicine, Stanford, CA, USA Lincoln D. Nadauld * Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark Bjarke

Feenstra, Frank Geller & Klaus Rostgaard * Danish Cancer Society Research Center, Copenhagen, Denmark Henrik Hjalgrim & Klaus Rostgaard * Department of Dermatology, Zealand

University hospital, Roskilde, Denmark Gregor Jemec * Department of Clinical Immunology, Aalborg University Hospital, Aalborg, Denmark Bitten Aagaard Jensen * Department of Health Science

and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark Mette Nyegaard & Palle Duun Rohde * Department of Clinical Immunology, Odense University Hospital, Odense,

Denmark Mie Topholm Bruun * Statens Serum Institut, Copenhagen, Denmark Henrik Ullum * Institute of Biological Psychiatry, Mental Health Centre, Sct. Hans, Copenhagen University Hospital,

Roskilde, Denmark Thomas Werge Authors * Astros Th. Skuladottir View author publications You can also search for this author inPubMed Google Scholar * Lilja Stefansdottir View author

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Scholar CONSORTIA ESTONIAN BIOBANK * Tõnu Esko * , Reedik Mägi * , Mari Nelis *  & Georgi Hudjashov DBDS GENOMIC CONSORTIUM * Karina Banasik * , Jakob Bay * , Jens Kjærgaard Boldsen * ,

 Thorsten Brodersen * , Søren Brunak * , Kristoffer Burgdorf * , Mona Ameri Chalmer * , Maria Didriksen * , Khoa Manh Dinh * , Joseph Dowsett * , Christian Erikstrup * , Bjarke Feenstra * , 

Frank Geller * , Daniel Gudbjartsson * , Thomas Folkmann Hansen * , Lotte Hindhede * , Henrik Hjalgrim * , Rikke Louise Jacobsen * , Gregor Jemec * , Bitten Aagaard Jensen * , Katrine

Kaspersen * , Bertram Dalskov Kjerulff * , Lisette Kogelman * , Margit Anita Hørup Larsen * , Ioannis Louloudis * , Agnete Lundgaard * , Susan Mikkelsen * , Christina Mikkelsen * , Ioanna

Nissen * , Mette Nyegaard * , Sisse Rye Ostrowski * , Ole Birger Pedersen * , Alexander Pil Henriksen * , Palle Duun Rohde * , Klaus Rostgaard * , Michael Schwinn * , Kari Stefansson * , 

Hreinn Stefánsson * , Erik Sørensen * , Unnur Þorsteinsdóttir * , Lise Wegner Thørner * , Mie Topholm Bruun * , Henrik Ullum * , Thomas Werge *  & David Westergaard CONTRIBUTIONS A.T.S.,

H.S., and K.S. designed the study. A.T.S., L.S., G.H.H., O.A.S., H.S., and K.S. analyzed the data and interpreted the results. Data collection and subject ascertainment and recruitment was

carried out by A.T.S., A.B., P.J., V.P., and H.S. for the Icelandic dataset, L.A.M. and A.O. for the Estonian dataset, K.U.K., D.A.J., and L.D.N. for the US-INTMT dataset, J.I., J.H., and

O.A.A. for the Norwegian dataset, D.R. for the US-EMORY dataset, and S.R.O., O.B.P., M.D., S.B., K.B., T.F.H., C.E. for the Danish dataset. A.T.S. drafted the manuscript with input and

comments from G.H.H., A.B., G.B.W., R.S.G., G.B., T.E.T., G.A.J., P.S., D.F.G., S.R.O., L.A.M., J.H., O.A.A., J.I., H.S., and K.S. All authors read the final version of the manuscript.

CORRESPONDING AUTHORS Correspondence to Astros Th. Skuladottir or Kari Stefansson. ETHICS DECLARATIONS COMPETING INTERESTS A.T.S., L.S., G.H.H., O.A.S., G.B.W., R.S.G., G.B., T.E.T., G.A.J.,

P.S., D.F.G., H.S., and K.S. are employees of deCODE genetics/Amgen Inc. The remaining authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature

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reveals key risk loci in essential tremor pathogenesis. _Commun Biol_ 7, 504 (2024). https://doi.org/10.1038/s42003-024-06207-4 Download citation * Received: 04 December 2023 * Accepted: 17

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