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Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture


The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer’s disease and Parkinson’s disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.

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Fig. 1: Analysis workflow.
Fig. 2: Genome-wide representation of common and rare variant associations in LBD.
Fig. 3: Regional association plots for eQTL and LBD GWAS colocalizations.
Fig. 4: Genetic risk scores from Alzheimer’s disease and Parkinson’s disease GWAS studies illustrate intersecting molecular genetic risk profiles with LBD.
Fig. 5: Insights into LBD pathways based on polygenic risk score enrichment analysis.

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Data availability

Individual-level sequence data for the resource genomes have been deposited at dbGaP (accession no. phs001963.v1.p1 NIA DementiaSeq). The GWAS summary statistics have been deposited in the GWAS catalog: eQTLGen data are available at PsychENCODE QTL data are available at Bulk-tissue RNA-seq data (GTEx v.8) are available at the Genotype-Tissue Expression consortium portal ( Human single-nucleus RNA-seq data are available at the Allen Institute for Brain Science portal ( Specificity values for the Allen Institute for Brain Science and GTEx data are available at:

Code availability

Analyses were performed using open-source tools, and code for analysis is available at the associated website of each software package. Genome sequence alignment and variant calling followed the implementation of the GATK Best Practices pipeline (v.2016-June) ( Contamination rates were assessed using VerifyBamID (v.1.1.3) ( Quality control checks, association analyses and conditional analyses were performed in PLINK2 (v.2.0-dev-20191128) ( Data formatting and visualizations were performed in R (v.3.5.2; using the following packages: MASS (v.7.3-51.4), tidyverse (v.1.2.1), stringr (v.1.4.0), ggrepel (v.0.8.1), data.table (v.1.12), viridis (v.0.5.1), ggplot2 (v.3.3.2), gridExtra (v.2.3) and grid (v.3.5.2). Imputation was performed using Minimac4 on data phased by Eagle (v.2.4) ( Meta-analysis was performed using METAL (v.2018-08-28) ( Heritability analysis was performed using GREML-LDMS in GCTA (v.1.26.0) ( Rare variant analysis was performed using RVTESTS (v.2.1.0) ( after annotation of variant files in ANNOVAR (v.2018-04/16) ( Genetic risk score analyses were performed in PLINK 1.9 (v.1.9.0-beta4.4) ( LBD summary statistics were converted from hg38 to hg19 using the R implementation of the LiftOver tool, which is available from the rtracklayer package (v.1.42.2) ( Colocalization analyses were performed in R-3.2 using the package coloc (v.4.0.1) ( Specificity values for the AIBS-derived dataset were generated using gene-level exonic reads and the ‘’ function of the EWCE package (v.0.99.2) ( Polygenic risk scores were constructed using PRSice-2 (v.2.1.1) ( Pathway enrichment analysis was performed using the R package gprofiler2 (v.0.2.0) (


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We thank contributors who collected samples used in this study, as well as patients and families whose help and participation made this work possible. This research was supported in part by the Intramural Research Program of the National Institutes of Health (National Institute on Aging and National Institute of Neurological Disorders and Stroke: (project nos. 1ZIAAG000935 (B.J.T.), 1ZIANS003154 (S.W.S.), 1ZIANS0030033 and 1ZIANS003034 (D.S.G.)). This work used the computational resources of the NIH HPC Biowulf cluster. ( A complete list of acknowledgments is given in the Supplementary Note.

Author information

Authors and Affiliations




C.L.D., B.J.T. and S.W.S. conceptualized and supervised the study. M.S.S., S.A., R.L.W., J.T.G. and Y.A. performed sample preparations. C.V. performed library preparations and genome sequencing. J.D., A.M., J.R.G. and C.L.D. performed genome sequence alignment, variant calling and initial quality control checks. R.C., S.W.S. and B.J.T. curated the data. R.C. performed quality control checks and genome-wide association analysis, and Z.S. contributed to this analysis. R.C. also led the genome-wide, gene-based rare variant analysis, with contributions from M.B.M., M.D.-F. and C.B. M.S.S. performed the heritability analysis. S.B.-C. performed the genetic risk score analysis. S.S.-A. computed polygenic risk scores and performed enrichment analyses. R.H.R., E.G. and M.R. performed eQTL analyses. M.A.N. consulted on the statistical analysis. M.K.P. performed validation experiments. R.H.R., M.R., M.G., A. Calvo, G.M., A. Canosa, G.F., R.C.B., F.B., Z.G.-O., P.M., R.K., D.S.G., G.L., N.T., E.S., L.N.-K., J.-A.P., H.K., V.G.S., M.P., K.L.N., E.M., R.C.K., C.A.C., E.S.M., M.B., T.M.D., L.S.R., M.S.A., O.P., J.C.T., M.E.F., Q.M., E.H.B., E.R.-R., J.I., C.L., I.G.-A., P.S.-J., B.G., J.K., S.E.B., M.M., E.R., C.D., A.B., S.L., G.X., M.J.B., B.S.T., S.G., G.L., G.E.S., T.G.B., I.G.M., A.J.T., J.A., C.M.M., L.P., S.L., C.T., S.A.-S., A.K.H., D. Aarsland, G.K., S.M.K., R.W., P.P., L.M. Bekris, J.B.L., L.M. Besser, A.K., A.E.R., A.G., D.A.B., C.R.S., H.R.M., R.F., D. Albani, S.P.-B., K.F., W.A.K., E.M.-R., A.L., J.F., D. Alcolea, J.C., L.F., S.M.R., T.T., T.M.F., N.R.G.-R., Z.K.W., T.F., B.F.B., J.A.H., D.W.D., A.B.S., A. Chiò, O.A.R., B.J.T. and S.W.S. provided biospecimens and clinical data. D.J.S., J.E., L.P., A.B.S., J.A.H., O.A., L.C., L.S.H., K. Marder, A.L., P. St.G.-H., E.L., K. Morgan, T.L., T.T.W., Z.J., D.G., I.S., P.J.T., L.M., M.O., N.J.C., J.C.M., G.M.H., V.M.V.D., J.Q.T., T.G., C.S., F.L., E.S., D.C., A. Chiò, B.J.T. and S.W.S. provided replication data. A.T., E.J.T., D.G.H., J.R.G. and A.B.S. provided convenience control genomes. S.W.S. wrote the initial manuscript. All authors critically reviewed and edited the article.

Corresponding author

Correspondence to Sonja W. Scholz.

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Competing interests

T.G.B. is a consultant for Prothena Biosciences, Vivid Genomics and Avid Radiopharmaceutical, and is a scientific advisory board member for Vivid Genomics. J.A.H., H.R.M., S.P.-B., P.J.T. and B.J.T. hold US, EU and Canadian patents on the clinical testing and therapeutic intervention for the hexanucleotide repeat expansion of C9orf72. H.R.M. reports paid consultancy from Biogen, Biohaven, Lundbeck, UCB and Denali and lecture fees/honoraria from the Wellcome Trust and Movement Disorders Society. H.R.M. received research grants from Parkinson’s UK, Cure Parkinson’s Trust, PSP Association, CBD Solutions, Drake Foundation and the Medical Research Council. H.R.M. is a coapplicant on a patent application related to C9orf72—Method for diagnosing a neurodegenerative disease (no. PCT/GB2012/052140). J.E. was an employee of a for-profit company (Merck) at the time of the collaboration. M.A.N.‘s participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA; as a possible conflict of interest, M.A.N. also consults for Neuron23 Inc., Lysosomal Therapeutics Inc., Illumina Inc., the Michael J. Fox Foundation and Vivid Genomics among others. A.B.S. is an associate editor for the journals Brain, Movement Disorders and npj Parkinson’s Disease. H.K. is Editor-in-Chief of Clinical Autonomic Research, serves as PI of a clinical trial sponsored by Biogen MA, Inc. (TRACK MSA, no. S19-01846) and received consultancy fees from Lilly USA LLC, Biohaven Pharmaceuticals Inc., Takeda Pharmaceutical Company Ltd, Ono Pharma UK Ltd, Lundbeck LLC and Theravance Biopharma US Inc. J.-A.P. is an editorial board member of Movement Disorders, Parkinsonism & Related Disorders, BMC Neurology and Clinical Autonomic Research. B.F.B., J.B.L. and S.W.S. serve on the Scientific Advisory Council of the Lewy Body Dementia Association. S.W.S. is an editorial board member for the Journal of Parkinson’s Disease and JAMA Neurology. B.J.T. is an editorial board member for JAMA Neurology, Journal of Neurology, Neurosurgery and Psychiatry, Brain and Neurobiology of Aging. D.A. is associate editor of the Journal of Alzheimer’s Disease. R.K. is Coordinator of the National Centre for Excellence in Research on Parkinson’s disease and received speaker’s honoraria and/or travel grants from Abbvie, Zambon and Medtronic, and he participated as PI or site-PI for industry-sponsored clinical trials without receiving honoraria. Z.K.W. serves as a principal investigator or coprincipal investigator on Biogen, Inc. (no. 228PD201), Biohaven Pharmaceuticals, Inc. (nos. BHV4157-206 and BHV3241-301) and Neuraly, Inc. (no. NLY01-PD-1) grants. Z.K.W. also serves as the coprincipal investigator of the Mayo Clinic Florida American Parkinson Disease Association Center for Advanced Research. Z.G.-O. received consultancy fees from Lysosomal Therapeutics Inc., Idorsia, Prevail Therapeutics, Inceptions Sciences (now Ventus), Ono Therapeutics, Denali, Deerfield, Neuron23 and Handle Therapeutics. Z.G.-O. is an Associate Editor of the Journal of Parkinson’s Disease and editorial board member in Parkinsonism and Related Disorders. A.T. serves on the scientific advisory board for Vivid Genomics. All other authors report no competing interests.

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Peer review information Nature Genetics thanks Sudha Seshadri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 BIN1 and TMEM175 genotype-phenotype analysis.

Relationship between BIN1 and TMEM175 genotypes and the presence of Alzheimer’s disease co-pathology in definite LBD cases. The color gradation refers to semi-quantitative pathological measures of neuritic plaques (assessed by CERAD method) and neurofibrillary tangles (assessed by Braak stage). Darker colors refer to higher burden of pathology. Homozygous BIN1 risk allele carriers (TT) were found to have significantly increased neurofibrillary tangle pathology compared to homozygous major allele carriers (CC; Fisher’s exact test P-value on Braak staging = 0.0002). Although the proportion of LBD cases that had high neuritic plaque burden was higher in homozygous risk allele carries compared to homozygous major allele carries, the difference between these groups was not statistically significant (P = 0.23). There was no association of TMEM175 risk allele dosage and Alzheimer’s disease co-pathology, though a trend toward lower Alzheimer’s disease co-pathology was observed among homozygous TMEM175 risk allele carriers.

Extended Data Fig. 2 Regional association plots.

a-g, Regional association plots, local linkage disequilibrium, and recombination rates at the significantly associated LBD GWAS risk signals. Regional associations are plotted as a function of their genomic position, denoting the index variant by a red diamond. Single nucleotide variants or indels surrounding the index variant are color-coded to reflect the strength of linkage disequilibrium with the index variant based on pairwise r2-values in the study cohort (red, 1.0 ≥ r2 ≥ 0.8; orange, 0.8 > r2 ≥ 0.6; green 0.6 > r2 ≥ 0.4; light blue, 0.4 > r2 ≥ 0.2; dark blue, 0.2 > r2 ≥ 0; gray, no r2 value available). Transcript annotations according to the University of California Santa Cruz genome browser are depicted under each association plot.

Extended Data Fig. 3 Conditional analysis.

a-f, Conditional analyses for all genome-wide significant GWAS signals are depicted. For each panel, the x-axis denotes the chromosomal position in build 38, and the y-axis indicates the association P-values on a -log10 scale. The unconditioned GWAS signal is shown in the upper pane of each panel, while the lower pane illustrates the association results after correction for the index variant(s) at each respective signal. This analysis demonstrated two signals at the APOE locus (e, f). The locus name is based on the closest gene to the index variant.

Extended Data Fig. 4 Sensitivity analyses.

a,b, Sensitivity analyses of colocalization between eQTLs regulating TMEM175 expression and LBD GWAS signals (a) and SNCA-AS1 expression and LBD GWAS signals (b). eQTLs for TMEM175 were derived from eQTL-Gen, while eQTLs for SNCA-AS1 were derived from PsychENCODE. Plots of prior (left) and posterior (right) probabilities for H0-H4 hypotheses across varying p12 priors are shown. A dashed vertical line indicates the value of p12 used in the initial analysis (p12 = 5 x 10−6). The green shaded areas in these plots show the regions for which the posterior probability of H4 ≥ 0.90 would still be supported. Abbreviations: H0, hypothesis 0 (no association with either trait); H1, hypothesis 1 (association with trait 1, not with trait 2); H2, hypothesis 2 (association with trait 2, not with trait 1); H3, hypothesis 3 (association with trait 1 and trait 2, two independent SNPs); H4, hypothesis 4 (association with trait 1 and trait 2, one shared SNP).

Extended Data Fig. 5 GWAS variants correlate with increased SNCA-AS1 expression.

Shown here are genome-wide significant SNPs that decrease risk for LBD and their correlation with increased SNCA-AS1 expression. a, Scatterplot of beta coefficients and association P-values (on a -log10 scale) for SNPs shared between the LBD GWAS (left) and PsychENCODE (right). The SNPs represented in this plot are those that are eQTLs regulating SNCA-AS1 expression. The top SNP in the LBD GWAS (as determined by the lowest association test P-value) is indicated in both scatterplots by a red point. The dashed line represents the cut-off for genome-wide significance (5 x 10-8). b, Scatterplot of SNPs shared between the LBD GWAS and PsychENCODE, which pass genome-wide significance in the LBD GWAS. Spearman’s rho (R) and associated P-value are displayed.

Extended Data Fig. 6 Tissue and cell-type specificity of SNCA-AS1 and TMEM175.

a,b, Plot of SNCA-AS1 and TMEM175 specificity in 35 human tissues (GTEx dataset) (a) and seven broad categories of cell types derived from human middle temporal gyrus (Allen Institute for Brain Science dataset) (b). Tissues are colored by whether they belong to the brain. In all plots, tissues and cell types have been ordered by specificity.

Extended Data Fig. 7 Tissue and cell-specificity of SNCA-AS1 and SNCA.

a,b, Plots of SNCA-AS1 and SNCA specificity in 35 human tissues (GTEx dataset) (a) and seven broad categories of cell types derived from human middle temporal gyrus (Allen Institute for Brain Science dataset) (b). Tissues are colored by whether they belong to the brain. In all plots, tissues and cell types have been ordered by specificity.

Extended Data Fig. 8 LBD polygenic risk score is associated with dementia severity.

Dementia severity score proportions (measured by the Clinical Dementia Rating scale) at baseline evaluation relative to LBD polygenic risk score quintiles. LBD patients in the highest quintile had significantly more severe cognitive impairment at baseline compared to cases in the lowest quintile (χ2 = 5.60, df = 1, test P-value = 0.009).

Extended Data Fig. 9 Principal components analysis and QQ plot.

Quality control metrics of GWAS data. a, Population structure is shown by plotting the first two principal components of the study cohorts (n = 2,591 LBD cases and n = 4,027 controls) compared to the HapMap3 Genome Reference panel. b, Quantile-quantile (QQ) plot of single-variant associations depicting observed (y-axis) versus expected P-values (x-axis). The sample size adjusted genomic inflation factor λ1000 was 1.004.

Extended Data Fig. 10 Quality control metrics.

This figure depicts quality control metrics of the genome data across study cohorts. a, Heterozygous-to-homozygous single nucleotide variant (SNV) ratios. b, Mean coverage across the study cohorts.

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Chia, R., Sabir, M.S., Bandres-Ciga, S. et al. Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture. Nat Genet 53, 294–303 (2021).

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