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We characterized the role of structural variants, a largely unexplored type of genetic variation, in two non-Alzheimer’s dementias, namely Lewy body dementia (LBD) and frontotemporal dementia (FTD)/amyotrophic lateral sclerosis (ALS). To do this, we applied an advanced structural variant calling pipeline (GATK-SV) to short-read whole-genome sequence data from 5,213 European-ancestry cases and 4,132 controls. We discovered, replicated, and validated a deletion in TPCN1 as a novel risk locus for LBD and detected the known structural variants at the C9orf72 and MAPT loci as associated with FTD/ALS. We also identified rare pathogenic structural variants in both LBD and FTD/ALS. Finally, we assembled a catalog of structural variants that can be mined for new insights into the pathogenesis of these understudied forms of dementia.

Introduction: Recent pan-cancer whole-genome sequencing studies revealed the rich landscape of structural variants (SV), from simpler indels to complex events involving multiple breakpoints and sequence gain/loss. SVs may contribute to tumorigenesis through direct modification of coding sequence or deregulation from copy number alterations, enhancer hijacking, or topological domain modification. A substantial part of the variation in the human genome is not accessible to short reads due to mapping ambiguities. Recent benchmarking studies reported that the best short-read methods only have 30-70% SV sensitivity. Long-read sequencing (such as PacBio or Oxford Nanopore) can overcome the limitations of short reads, however the current methods were not designed for the analysis of rearranged cancer genomes with complex copy number profiles. Methods: We developed BGA (Breakpoint Graph Assembler), a method that combines the ideas from long-read assembly and breakpoint graph frameworks. BGA detects abnormally mapped reads and builds a breakpoint graph that characterizes the structure of derived cancer karyotypes. Complex events with multiple breakpoints form connectivity clusters and are classified based on the subgraph properties. BGA also takes advantage of phased haplotypes and can incorporate multiple related datasets (such as in tumor-normal comparison or multi-site tumor sampling). BGA is freely available at: https://github.com/KolmogorovLab/BGA. Results: We first analyzed three cancer cell lines and corresponding matching normal DNAs (HCC1954, H2009, and COLO829). In each cell line, we identified 8-56 somatic rearrangement clusters involving more than two breakpoints and at least 1kb of sequence. In H2009, we identified a chromoplexy event involving chr13 and chr1, consistent with previous FISH experiments. COLO829 showed the lowest number of somatic rearrangement clusters (n=8), including translocation and inversion events between chr3, chr10, and chr12 within the RARB, BICC1, and TRHDE genes. Homologous recombination deficient HCC1954 has the highest number of complex events including the chr17q arm which hosts ERBB2, chromoplexy between chr8, chr5, and chromothripsis in chr21. In addition, we analyzed three other HPV-infected cell lines (CaSki, SCC152, SNU1000). In each of them, we observed complex clusters of HPV-HPV and HPV-human breakpoints that formed cycles, suggesting extrachromosomal amplification. The HPV fragments had many interactions with chromosomal DNA in CaSki and SC152, but not in mostly episomal SNU1000 cells. We also observed karyotype-scale changes that did not involve HPV sequences, such as the simultaneous exchange of six chromosome arms of chr2, chr7, and chr17 in CaSki. Citation Format: Ayse Keskus, Tanveer Ahmad, Ataberk Donmez, Yi Xie, Isabel Rodriguez, Rose Milano, Nicole Rossi, Hong Lou, Laksh Malik, Kimberley Billingsley, Cornelis Blauwendraat, Michael Dean, Mikhail Kolmogorov. Long-read, assembly-based characterization of rearranged cancer karyotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4289.

BackgroundExpansions of short tandem repeats are the cause of many neurogenetic disorders including familial amyotrophic lateral sclerosis, Huntington disease, and many others. Multiple methods have been recently developed that can identify repeat expansions in whole genome or exome sequencing data. Despite the widely-recognized need for visual assessment of variant calls in clinical settings, current computational tools lack the ability to produce such visualizations for repeat expansions. Expanded repeats are difficult to visualize because they correspond to large insertions relative to the reference genome and involve many misaligning and ambiguously aligning reads.ResultsWe implemented REViewer, a computational method for visualization of sequencing data in genomic regions containing long repeat expansions. To generate a read pileup, REViewer reconstructs local haplotype sequences and distributes reads to these haplotypes in a way that is most consistent with the fragment lengths and evenness of read coverage. To create appropriate training materials for onboarding new users, we performed a concordance study involving 12 scientists involved in STR research. We used the results of this study to create a user guide that describes the basic principles of using REViewer as well as a guide to the typical features of read pileups that correspond to low confidence repeat genotype calls. Additionally, we demonstrated that REViewer can be used to annotate clinically-relevant repeat interruptions by comparing visual assessment results of 44 FMR1 repeat alleles with the results of triplet repeat primed PCR. For 38 of these alleles, the results of visual assessment were consistent with triplet repeat primed PCR.ConclusionsRead pileup plots generated by REViewer offer an intuitive way to visualize sequencing data in regions containing long repeat expansions. Laboratories can use REViewer to assess the quality of repeat genotype calls as well as to visually detect interruptions or other imperfections in the repeat sequence and the surrounding flanking regions.

Background Expansions of short tandem repeats are the cause of many neurogenetic disorders including familial amyotrophic lateral sclerosis, Huntington disease, and many others. Multiple methods have been recently developed that can identify repeat expansions in whole genome or exome sequencing data. Despite the widely recognized need for visual assessment of variant calls in clinical settings, current computational tools lack the ability to produce such visualizations for repeat expansions. Expanded repeats are difficult to visualize because they correspond to large insertions relative to the reference genome and involve many misaligning and ambiguously aligning reads. Results We implemented REViewer, a computational method for visualization of sequencing data in genomic regions containing long repeat expansions and FlipBook, a companion image viewer designed for manual curation of large collections of REViewer images. To generate a read pileup, REViewer reconstructs local haplotype sequences and distributes reads to these haplotypes in a way that is most consistent with the fragment lengths and evenness of read coverage. To create appropriate training materials for onboarding new users, we performed a concordance study involving 12 scientists involved in short tandem repeat research. We used the results of this study to create a user guide that describes the basic principles of using REViewer as well as a guide to the typical features of read pileups that correspond to low confidence repeat genotype calls. Additionally, we demonstrated that REViewer can be used to annotate clinically relevant repeat interruptions by comparing visual assessment results of 44 FMR1 repeat alleles with the results of triplet repeat primed PCR. For 38 of these alleles, the results of visual assessment were consistent with triplet repeat primed PCR. Conclusions Read pileup plots generated by REViewer offer an intuitive way to visualize sequencing data in regions containing long repeat expansions. Laboratories can use REViewer and FlipBook to assess the quality of repeat genotype calls as well as to visually detect interruptions or other imperfections in the repeat sequence and the surrounding flanking regions. REViewer and FlipBook are available under open-source licenses at https://github.com/illumina/REViewer and https://github.com/broadinstitute/flipbook respectively.

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

Parkinson’s disease (PD) is a complex neurodegenerative disorder with a strong genetic component, where most known disease-associated variants are single nucleotide polymorphisms (SNPs) and small insertions and deletions (Indels). DNA repetitive elements account for >50% of the human genome, however little is known of their contribution to PD etiology. While select short tandem repeats (STRs) within candidate genes have been studied in PD, their genome-wide contribution remains unknown. Here we present the first genome-wide association study (GWAS) of STRs in PD. Through a meta-analysis of 16 imputed GWAS cohorts from the International Parkinson’s Disease Genomic Consortium (IPDGC), totalling 39,087 individuals (16,642 PD cases and 22,445 controls of European ancestry) we identified 34 genome-wide significant STR loci (p < 5.34×10-6), with the strongest signal located inKANSL1(chr17:44205351:[T]11, p=3×10-39, OR=1.31 [CI 95%=1.26-1.36]). Conditional-joint analyses suggested that 4 significant STRs mapping nearbyNDUFAF2, TRIML2, MIRNA-129-1andNCOR1were independent from known PD risk SNPs. Including STRs in heritability estimates increased the variance explained by SNPs alone. Gene expression analysis of STRs (eSTR) in RNASeq data from 13 brain regions, identified significant associations of STRs influencing the expression of multiple genes, including PD known genes. Further functional annotation of candidate STRs revealed that significant eSTRs withinNUDFAF2andZSWIM7overlap with regulatory features and are associated with change in the expression levels of nearby genes. Here we show that STRs at known and novel candidate PD loci contribute to PD risk, and have functional effects in disease-relevant tissues and pathways, supporting previously reported disease-associated genes and giving further evidence for their functional prioritization. These data represent a valuable resource for researchers currently dissecting PD risk loci.

In the FOUNdational Data INitiative for Parkinson’s Disease (FOUNDIN-PD) we sought to produce a multi-layered molecular dataset in a large cohort of 95 Induced pluripotent stem cells (iPSC) lines at multiple timepoints during differentiation to dopaminergic (DA) neurons, a major affected cell type in Parkinson’s Disease (PD). The lines are derived from the Parkinson’s Progression Markers Initiative (PPMI) study that includes both people with PD and unaffected individuals across a wide range of polygenic risk scores (PRS) with both risk variants identified by genome-wide association studies (GWAS), and monogenic causal alleles. We generated genetic, epigenetic, regulatory, transcriptomic, proteomic, and longitudinal cellular imaging data from iPSC-derived DA neurons to understand key molecular relationships between disease associated genetic variation and proximate molecular events in a PD relevant cell-type. Analyses of all data modalities collected in FOUNDIN-PD suggest that the differentiation to DA neurons, while not fully mature, was successful and robust. Interrogation of PD genetic risk in this relevant cellular context may elucidate the functional effects of some of these risk variants alone or in combination with other variants. These data reveal that DA neurons derived from human iPSC provide a valuable cellular context and foundational atlas for modeling PD-related genetic risk. In addition to making the data and analyses for this molecular atlas readily available, we have integrated these data into the browsable FOUNDIN-PD data portal (https://www.foundinpd.org) to be used as a resource for understanding the molecular pathogenesis of PD.

Parkinson’s disease (PD) is a complex disorder underpinned by both environmental and genetic factors. The latter only began to be understood around two decades ago, but since then great inroads have rapidly been made into deconvoluting the genetic component of PD. In particular, recent large-scale projects such as genome-wide association (GWA) studies have provided insight into the genetic risk factors associated with genetically ‘’complex’’ PD (PD that cannot readily be attributed to single deleterious mutations). Here, we discuss the plethora of genetic information provided by PD GWA studies and how this may be utilized to generate polygenic risk scores (PRS), which may be used in the prediction of risk and trajectory of PD. We also comment on how pathway-specific genetic profiling can be used to gain insight into PD-related biological pathways, and how this may be further utilized to nominate causal PD genes and potentially druggable therapeutic targets. Finally, we outline the current limits of our understanding of PD genetics and the potential contribution of variation currently uncaptured in genetic studies, focusing here on uncatalogued structural variants.

International Parkinson's Disease Genomics Consortium (IPDGC)., [Background] Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome‐wide association studies. The most recent large‐scale PD genome‐wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome‐wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus., [Methods] We included all significant genome‐wide signals from multiple recent PD genome‐wide association studies including themost recent PD risk genome‐wide association study, age‐at‐onset genome‐wide association study, progression genome‐wide association study, and Asian population PD risk genome‐wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self‐ranked criteria. Multiple databases were queried for each gene to collect additional causal data., [Results] We created a PD genome‐wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow‐up functional studies to identify potential therapeutic targets., [Conclusions] Our PD genome‐wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large‐scale PD genome‐wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA., This work was supported in part by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute on Aging (NIA), and the National Institute of Environmental Health Sciences, both part of the National Institutes of Health, Department of Health and Human Services (project numbers 1ZIA‐NS003154, Z01‐AG000949‐02, and Z01‐ES101986). We thank the research participants and employees of 23andMe for making this work possible. C.W. is supported by the UK Dementia Research Institute funded by the Medical Research Council (MRC), Alzheimer's Society and Alzheimer's Research UK. C.S. is supported by the Ser Cymru II program, which is partly funded by Cardiff University and the European Regional Development Fund through the Welsh Government. Data were generated as part of the PsychENCODE Consortium supported by: U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877, and P50MH106934 awarded to Schahram Akbarian (Icahn School of Medicine at Mount Sinai), Gregory Crawford (Duke), Stella Dracheva (Icahn School of Medicine at Mount Sinai), Peggy Farnham (USC), Mark Gerstein (Yale), Daniel Geschwind (UCLA), Thomas M. Hyde (LIBD), Andrew Jaffe (LIBD), James A. Knowles (USC), Chunyu Liu (UIC), Dalila Pinto (Icahn School of Medicine at Mount Sinai), Nenad Sestan (Yale), Pamela Sklar (Icahn School of Medicine at Mount Sinai), Matthew State (UCSF), Patrick Sullivan (UNC), Flora Vaccarino (Yale), Sherman Weissman (Yale), Kevin White (UChicago), and Peter Zandi (JHU). The Genotype‐Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from the GTEx Portal on February 12, 2020. Molecular data for the Trans‐Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Genome sequencing for “NHLBI TOPMed: Atherosclerosis Risk in Communities (ARIC)” (phs001211.v2.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1)and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for the “NHLBI TOPMed: Cleveland Clinic Atrial Fibrillation (CCAF) Study” (phs001189.v1.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: Trans‐Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Project: Cardiovascular Health Study (phs001368.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for “NHLBI TOPMed: Partners HealthCare Biobank” (phs001024.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)” (phs001402.v1.p1) was performed at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for “NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women” (phs001040.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: The Genetics and Epidemiology of Asthma in Barbados” (phs001143.v2.p1) was performed by Illumina Genomic Services (3R01HL104608‐04S1). Genome sequencing for “NHLBI TOPMed: The Vanderbilt Genetic Basis of Atrial Fibrillation” (phs001032.v4.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1). Genome sequencing for “NHLBI TOPMed: Heart and Vascular Health Study (HVH)” (phs000993.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1) and at the Baylor Human Genome Sequencing Center (3U54HG003273‐12S2, HHSN268201500015C). Genome sequencing for “NHLBI TOPMed: Genetic Epidemiology of COPD (COPDGene)” (phs000951.v3.p3) was performed at the University of Washington Northwest Genomics Center (3R01HL089856‐08S1) and at the Broad Institute of MIT and Harvard (HHSN268201500014C). Genome sequencing for “NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Ablation Registry” (phs000997.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2, 3U54HG003067‐13S1). Genome sequencing for “NHLBI TOPMed: The Jackson Heart Study” (phs000964.v3.p1) was performed at the University of Washington Northwest Genomics Center (HHSN268201100037C). Genome sequencing for “NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish” (phs000956.v3.p1) was performed at the Broad Institute of MIT and Harvard (3R01HL121007‐01S1). Genome sequencing for “NHLBI TOPMed: Massachusetts General Hospital Atrial Fibrillation (MGH AF) Study” (phs001062.v3.p2) was performed at the Broad Institute of MIT and Harvard (3R01HL092577‐06S1, 3U54HG003067‐12S2, 3U54HG003067‐13S1, 3UM1HG008895‐01S2). Genome sequencing for “NHLBI TOPMed: The Framingham Heart Study” (phs000974.v3.p2) was performed at the Broad Institute of MIT and Harvard (3U54HG003067‐12S2). Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample‐identity QC, and general program coordination were provided by the TOPMed Data Coordinating Center (R01HL‐120393; U01HL‐120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institute of Health, Department of Health and Human Services, under contract numbers (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, and HHSN268201700005I). The authors thank the staff and participants of the ARIC study for their important contributions. The research reported in this article was supported by grants from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute grants R01 HL090620 and R01 HL111314, the NIH National Center for Research Resources for Case Western Reserve University and Cleveland Clinic Clinical and Translational Science Award (CTSA) UL1‐RR024989, the Department of Cardiovascular Medicine philanthropic research fund, Heart and Vascular Institute, Cleveland Clinic, the Fondation Leducq grant 07‐CVD 03, and the Atrial Fibrillation Innovation Center, state of Ohio. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01‐HC85079, N01‐HC‐85080, N01‐HC‐85081, N01‐HC‐85082, N01‐HC‐85083, N01‐HC‐85084, N01‐HC‐85085, N01‐HC‐85086, N01‐HC‐35129, N01‐HC‐15103, N01‐HC‐55222, N01‐HC‐75150, N01‐HC‐45133, and N01‐ HC‐85239; grant numbers U01 HL080295 and U01 HL130014 from the National Heart, Lung, and Blood Institute, and R01 AG023629 from the National Institute on Aging, with additional contribution from the National Institute of Neurological Disorders and Stroke. A full list of principal CHS investigators and institutions can be found at https://chs-nhlbi.org/pi. This article was not prepared in collaboration with CHS investigators and does not necessarily reflect the opinions or views of CHS or the NHLBI. We thank the Broad Institute for generating high‐quality sequence data supported by NHLBI grant 3R01HL092577‐06S1 to Dr. Patrick Ellinor. Funded in part by grants from the National Institutes of Health, National Heart, Lung, and Blood Institute (HL66216 and HL83141), and the National Human Genome Research Institute (HG04735). The Women's Genome Health Study (WGHS) is supported by HL 043851 and HL099355 from the National Heart, Lung, and Blood Institute and CA 047988 from the National Cancer Institute, the Donald W. Reynolds Foundation with collaborative scientific support and funding for genotyping provided by Amgen. AF end‐point confirmation was supported by HL‐093613 and a grant from the Harris Family Foundation and Watkin's Foundation. The Genetics and Epidemiology of Asthma in Barbados is supported by National Institutes of Health (NIH) National Heart, Lung, and Blood Institute TOPMed (R01 HL104608‐S1), and R01 AI20059, K23 HL076322, and RC2 HL101651. The research reported in this article was supported by grants from the American Heart Association to Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (HL092217), and Dr. Roden (U19 HL65962, and UL1 RR024975). This project was also supported by a CTSA award (UL1TR000445) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences of the NIH. The research reported in this article was supported by grants HL068986, HL085251, HL095080, and HL073410 from the National Heart, Lung, and Blood Institute. This article was not prepared in collaboration with Heart and Vascular Health (HVH) Study investigators and does not necessarily reflect the opinions or views of the HVH Study or the NHLBI. This research used data generated by the COPDGene study, which was supported by NIH grants U01 HL089856 and U01 HL089897. The COPDGene project is also supported by the COPD Foundation through contributions made by an Industry Advisory Board composed of Pfizer, AstraZeneca, Boehringer Ingelheim, Novartis, and Sunovion. Centralized read mapping and genotype calling, along with variant quality metrics and filtering were provided by the TOPMed Informatics Research Center (3R01HL‐117626‐02S1; contract HHSN268201800002I). Phenotype harmonization, data management, sample‐identity QC, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL‐120393‐02S1; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. This study is part of the Centers for Common Disease Genomics (CCDG) program, a large‐scale genome sequencing effort to identify rare risk and protective alleles that contribute to a range of common disease phenotypes. The CCDG program is funded by the National Human Genome Research Institute (NHGRI) and the National Heart, Lung, and Blood Institute (NHLBI). Sequencing was completed at the Human Genome Sequencing Center at Baylor College of Medicine under NHGRI grant UM1 HG008898. The research reported in this article was supported by grants from the American Heart Association to Dr. Shoemaker (11CRP742009) and Dr. Darbar (EIA 0940116N), and grants from the National Institutes of Health (NIH) to Dr. Darbar (R01 HL092217) and Dr. Roden (U19 HL65962 and UL1 RR024975). The project was also supported by a CTSA award (UL1 TR00045) from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the NIH. The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I/HHSN26800001), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The authors also thank the staffs and participants of the JHS. The Amish studies on which these data are based were supported by NIH grants R01 AG18728, U01 HL072515, R01 HL088119, R01 HL121007, and P30 DK072488. See publication PMID: 18440328. The research reported in this article was supported by NIH grants K23HL071632, K23HL114724, R21DA027021, R01HL092577, R01HL092577S1, R01HL104156, K24HL105780, and U01HL65962. The research has also been supported by an Established Investigator Award from the American Heart Association (13EIA14220013) and by support from the Fondation Leducq (14CVD01). This article was not prepared in collaboration with MGH AF Study investigators and does not necessarily reflect the opinions or views of the MGH AF Study investigators or the NHLBI. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (contract nos. N01‐HC‐25195, HHSN268201500001I, and 75N92019D00031). This article was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.

Genomic wide association studies identified the CACNA1C locus as genetically associated with both schizophrenia and bipolar affective disorder. CACNA1C encodes Cav1.2, one of four subunits of L-type voltage gated calcium channels. Variation resides in non-coding regions of CACNA1C which interact with the promoter and are validated expression quantitative trait loci. Using reporter gene constructs we demonstrate the CACNA1C promoter is a major mediator of inducible regulation of CACNA1C activity in the SH-SY5Y neuroblastoma cell line. Exposure of SH-SY5Y cells to lithium and cocaine modulated both the endogenous CACNA1C gene and the promoter in reporter gene constructs. Deletion analysis of the promoter demonstrated the actions of both lithium and cocaine were mediated by the proximal promoter. Initial interrogation of ENCODE ChIP-seq data over the CACNA1C promoter indicated binding of the transcription factor ‘Enhancer of zeste homolog 2’ (EZH2), which was consistent with our data that overexpression of EZH2 repressed CACNA1C promoter reporter gene expression. Array data from the Human Brain Transcriptome demonstrated that EZH2 was highly expressed across the developing brain, but subsequently maintained at low levels after birth and adulthood. RNA-seq data obtained from PD_NGSAtlas, a reference database for epigenomic and transcriptomic data for psychiatric disorders, demonstrated a 3-fold increase in EZH2 expression in the anterior cingulate cortex of individuals with schizophrenia compared to controls. We propose that EZH2 may contribute to schizophrenia risk at two distinct time points either through disruption in development leading to neurodevelopmental changes, or through anomalous reactivation of expression in the adult brain.

Dystonia is a neurological disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and postures, often occurring in absence of any structural brain abnormality. Psychiatric comorbidities, including anxiety, depression, obsessive-compulsive disorder and schizophrenia, are frequent in patients with dystonia. While mutations in a fast-growing number of genes have been linked to Mendelian forms of dystonia, the cellular, anatomical, and molecular basis remains unknown for most genetic forms of dystonia, as does its genetic and biological relationship to neuropsychiatric disorders. Here we applied an unbiased systems-biology approach to explore the cellular specificity of all currently known dystonia-associated genes, predict their functional relationships, and test whether dystonia and neuropsychiatric disorders share a genetic relationship. To determine the cellular specificity of dystonia-associated genes in the brain, single-nuclear transcriptomic data derived from mouse brain was used together with expression-weighted cell-type enrichment. To identify functional relationships among dystonia-associated genes, we determined the enrichment of these genes in co-expression networks constructed from 10 human brain regions. Stratified linkage-disequilibrium score regression was used to test whether co-expression modules enriched for dystonia-associated genes significantly contribute to the heritability of anxiety, major depressive disorder, obsessive-compulsive disorder, schizophrenia, and Parkinson’s disease. Dystonia-associated genes were significantly enriched in adult nigral dopaminergic neurons and striatal medium spiny neurons. Furthermore, 4 of 220 gene co-expression modules tested were significantly enriched for the dystonia-associated genes. The identified modules were derived from the substantia nigra, putamen, frontal cortex, and white matter, and were all significantly enriched for genes associated with synaptic function. Finally, we demonstrate significant enrichments of the heritability of major depressive disorder, obsessive-compulsive disorder and schizophrenia within the putamen, frontal cortex and white matter modules, and nominal enrichment of the heritability of Parkinson’s disease within the substantia nigra module. In conclusion, multiple dystonia-associated genes interact and contribute to pathogenesis likely through dysregulation of synaptic signalling in striatal medium spiny neurons, adult nigral dopaminergic neurons and frontal cortical neurons. Furthermore, the enrichment of the heritability of psychiatric disorders in the co-expression modules enriched for dystonia-associated genes indicates that psychiatric symptoms associated with dystonia are likely to be intrinsic to its pathophysiology., See Busch and Klein (doi:10.1093/brain/awaa253) for a scientific commentary on this article. Mencacci et al. show that multiple dystonia genes are functionally related and likely contribute to modulation of synaptic signalling in striatal, dopaminergic and frontal cortical neurons. They also demonstrate a genetic relationship between dystonia and psychiatric disorders including depression, OCD and schizophrenia.

Retrotransposons can alter the regulation of genes both transcriptionally and post-transcriptionally, through mechanisms such as binding transcription factors and alternative splicing of transcripts. SINE-VNTR-Alu (SVA) retrotransposons are the most recently evolved class of retrotransposable elements, found solely in primates, including humans. SVAs are preferentially found at genic, high GC loci, and have been termed &ldquo, mobile CpG islands&rdquo, We hypothesise that the ability of SVAs to mobilise, and their non-random distribution across the genome, may result in differential regulation of certain pathways. We analysed SVA distribution patterns across the human reference genome and identified over-representation of SVAs at zinc finger gene clusters. Zinc finger proteins are able to bind to and repress SVA function through transcriptional and epigenetic mechanisms, and the interplay between SVAs and zinc fingers has been proposed as a major feature of genome evolution. We describe observations relating to the clustering patterns of both reference SVAs and polymorphic SVA insertions at zinc finger gene loci, suggesting that the evolution of this network may be ongoing in humans. Further, we propose a mechanism to direct future research and validation efforts, in which the interplay between zinc fingers and their epigenetic modulation of SVAs may regulate a network of zinc finger genes, with the potential for wider transcriptional consequences.

Repetitive elements (RE) constitute the majority of the human genome and have a range of functions both structural and regulatory on genomic function and gene expression. RE overexpression has been observed in several neurodegenerative diseases, consistent with the observation of aberrant expression of RE posing a mutagenic threat. Despite reports that associate RE expression with PD no study has comprehensively analysed the role of these elements in the disease. This study presents the first genome-wide analysis of RE expression in PD to date. Analysis of RNA-sequencing data of 12 PD patients and 12 healthy controls identified tissue-specific expression differences and more significantly, differential expression of four satellite elements; two simple satellite III (repName = CATTC_n and _GAATG_n) a high-copy satellite II (HSATII) and a centromeric satellite (ALR_Alpha) in the blood of PD patients. In support of the growing body of recent evidence associating REs with neurodegenerative disease, this study highlights the potential importance of characterization of RE expression in such diseases.

Mitochondrial dysfunction has been implicated in the aetiology of monogenic Parkinson’s disease (PD). Yet the role that mitochondrial processes play in the most common form of the disease; sporadic PD, is yet to be fully established. Here we comprehensively assessed the role of mitochondrial function associated genes in sporadic PD by leveraging improvements in the scale and analysis of PD GWAS data with recent advances in our understanding of the genetics of mitochondrial disease. First, we identified that a proportion of the “missing heritability” of the PD can be explained by common variation within genes implicated in mitochondrial disease (primary gene list) and mitochondrial function (secondary gene list). Next we calculated a mitochondrial-specific polygenic risk score (PRS) and showed that cumulative small effect variants within both our primary and secondary gene lists are significantly associated with increased PD risk. Most significantly we further report that the PRS of the secondary mitochondrial gene list was significantly associated with later age at onset. Finally, to identify possible functional genomic associations we implemented Mendelian randomisation, which showed that 14 of these mitochondrial function associated genes showed functional consequence associated with PD risk. Further analysis suggested that the 14 identified genes are not only involved in mitophagy but implicate new mitochondrial processes. Our data suggests that therapeutics targeting mitochondrial bioenergetics and proteostasis pathways distinct from mitophagy could be beneficial to treating the early stage of PD.

Over the last two decades, we have witnessed a revolution in the field of Parkinson’s disease (PD) genetics. Great advances have been made in identifying many loci that confer a risk for PD, which has subsequently led to an improved understanding of the molecular pathways involved in disease pathogenesis. Despite this success, it is predicted that only a relatively small proportion of the phenotypic variability has been explained by genetics. Therefore, it is clear that common heritable components of disease are still to be identified. Dissecting the genetic architecture of PD constitutes a critical effort in identifying therapeutic targets and although such substantial progress has helped us to better understand disease mechanism, the route to PD disease-modifying drugs is a lengthy one. In this review, we give an overview of the known genetic risk factors in PD, focusing not on individual variants but the larger networks that have been implicated following comprehensive pathway analysis. We outline the challenges faced in the translation of risk loci to pathobiological relevance and illustrate the need for integrating big-data by noting success in recent work which adopts a broad-scale screening approach. Lastly, with PD genetics now progressing from identifying risk to predicting disease, we review how these models will likely have a significant impact in the future.