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Figure.  Network Showing Direct Protein-Protein Interactions Between Genes Harboring Common and De Novo Risk Variants in Bipolar Disorder (BD)
Network Showing Direct Protein-Protein Interactions Between Genes Harboring Common and De Novo Risk Variants in Bipolar Disorder (BD)

Gene list was derived from 218 BD-associated genes from 30 loci identified by the genome-wide association study by Stahl et al3 plus 112 genes with de novo variants identified in patients with BD (70 genes from Kataoka et al5 and 42 genes from the present study). Only direct interactions among proteins were considered. This top network (score 41) related to hereditary disorder, neurological disease, organismal injury, and abnormalities. Upregulatory effects are represented by outward pointing arrows, downregulatory effects are represented by outward ticks, and circular arrows indicate homotypic interactions.

Table.  List of 42 De Novo Variants Identified in 43 Affected Offspring From 18 Multiplex Families With BD
List of 42 De Novo Variants Identified in 43 Affected Offspring From 18 Multiplex Families With BD
1.
Torrico  B, Shaw  AD, Mosca  R,  et al.  Truncating variant burden in high-functioning autism and pleiotropic effects of LRP1 across psychiatric phenotypes.   J Psychiatry Neurosci. 2019;44(5):350-359. doi:10.1503/jpn.180184PubMedGoogle ScholarCrossref
2.
Fromer  M, Pocklington  AJ, Kavanagh  DH,  et al.  De novo mutations in schizophrenia implicate synaptic networks.   Nature. 2014;506(7487):179-184. doi:10.1038/nature12929PubMedGoogle ScholarCrossref
3.
Stahl  EA, Breen  G, Forstner  AJ,  et al; eQTLGen Consortium; BIOS Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium.  Genome-wide association study identifies 30 loci associated with bipolar disorder.   Nat Genet. 2019;51(5):793-803. doi:10.1038/s41588-019-0397-8PubMedGoogle ScholarCrossref
4.
Toma  C, Shaw  AD, Allcock  RJN,  et al.  An examination of multiple classes of rare variants in extended families with bipolar disorder.   Transl Psychiatry. 2018;8(1):65. doi:10.1038/s41398-018-0113-yPubMedGoogle ScholarCrossref
5.
Kataoka  M, Matoba  N, Sawada  T,  et al.  Exome sequencing for bipolar disorder points to roles of de novo loss-of-function and protein-altering mutations.   Mol Psychiatry. 2016;21(7):885-893. doi:10.1038/mp.2016.69PubMedGoogle ScholarCrossref
6.
Yuen  RK, Thiruvahindrapuram  B, Merico  D,  et al.  Whole-genome sequencing of quartet families with autism spectrum disorder.   Nat Med. 2015;21(2):185-191. doi:10.1038/nm.3792PubMedGoogle ScholarCrossref
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    Research Letter
    Psychiatry
    May 8, 2020

    De Novo Gene Variants and Familial Bipolar Disorder

    Author Affiliations
    • 1Neuroscience Research Australia, Sydney, New South Wales, Australia
    • 2School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
    • 3School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia
    • 4Black Dog Institute, Prince of Wales Hospital, Sydney, New South Wales, Australia
    • 5Garvan Institute of Medical Research, Sydney, New South Wales, Australia
    • 6St Vincent’s Clinical School, University of New South Wales, Sydney, New South Wales, Australia
    JAMA Netw Open. 2020;3(5):e203382. doi:10.1001/jamanetworkopen.2020.3382
    Introduction

    The detection of de novo variants (DNVs) by next-generation sequencing has facilitated the identification of candidate genes in psychiatric disorders.1,2 Spontaneous DNV mutations are estimated to explain approximately 5% of genetic liability in autism and schizophrenia.1,2 In bipolar disorder (BD), common genetic variants explain approximately 30% of the heritability,3 and rare inherited variants also contribute to disease risk.4 However, the involvement of DNVs in BD is largely unexplored, with only 1 independent study5 reporting 71 DNVs from 79 singleton BD families. We present a DNV study in 18 multiplex bipolar families, combining 32 individuals previously reported4 with 29 additional participants.

    Methods

    This study was approved by the University of New South Wales Ethics Committee. Participants provided written consent. This study follows the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guideline.

    We performed whole-genome sequencing of 9 multiplex families (47 participants, including 29 offspring; there were 8 unaffected offspring and 21 case offspring with BD type I, BD type II, or schizoaffective-disorder bipolar type) in this case-control study. Identification of DNVs used 2 variant calling software packages, GATK version 3.4.0 (Broad Institute) and RTG-Core sequencing software version 3.7.1 (Real Time Genomics Ltd). All variants were validated by Sanger sequencing and were combined with variants previously identified by whole-exome sequencing in 32 offspring,4 for a final sample of 18 multiplex families (99 individuals, including 61 offspring; there were 43 cases and 18 unaffected offspring individuals). The DNA samples were prepared and whole-genome sequencing was performed on the HiSeq X platform at The Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Sydney, Australia. Whole-exome sequencing was previously performed at the Lottery State Biomedical Genomics Facility, University of Western Australia.

    For each gene harboring DNVs, a gene-based association study was performed using the multi snp-wise model in MAGMA statistical software version 1.06b (VU University, Amsterdam, The Netherlands) using a threshold of P <.05 and genome-wide association study summary statistics from the Psychiatric Genomics Consortium. For BD, there were 20 352 cases and 31 358 controls3; for schizophrenia, there were 40 675 cases and 64 643 controls; and for major depression, there were 170 756 cases and 329 443 controls. Protein interaction network analysis was performed using IPA statistical software release date February 8, 2019 (Qiagen Digital Insights). Data were analyzed from February 2017 to June 2019.

    Results

    Thirty-two DNVs from 12 female and 17 male offspring were validated and combined with 31 DNVs from our previous report,4 for a combined analysis of 63 coding DNVs from 61 white participants (30 male participants [50%]; mean [SD] age, 38.4 [8.5] years). In total, 42 DNVs were identified in cases (Table) and 21 variants were identified in unaffected offspring. The overall DNV mutation rate was no different between cases and unaffected relatives (42 of 43 [0.98] and 21 of 18 [1.17], respectively; P = .65, 2-tailed χ2 statistic). However, missense and gene-truncating DNVs were statistically significantly more frequent in cases compared with unaffected relatives (32 of 42 [75%] vs 10 of 21 [47%]; P = .01, 1-tailed test of proportions). Interestingly, the rate for potentially disrupting DNVs in brain-expressed genes (missense, classified as pathogenic in both SIFT statistical software version 5.0.2 [Bioinformatics Institute] and PolyPhen-2 statistical software version 2.2.2 [Harvard Medical School], and nonsense and gene-truncating indels)1,4 was higher in participants with BD compared with unaffected relatives (14 of 42 [33%] vs 3 of 21 [14%]), which is comparable to the previous BD report (30%)5 and rates observed in autism (36%) and schizophrenia (34%).2

    Gene-based association tests for the 42 DNV-bearing genes implicated haptoglobin (HP) and pyruvate carboxylase (PC), 2 brain-expressed genes associated across 3 psychiatric disorders that carry potentially pathogenic DNVs. The PC gene encodes a mitochondrial enzyme involved in glucose metabolism and neurotransmitter synthesis, which lies within one of 30 BD-associated loci recently reported,3 and it had the strongest gene-based association (P = 7.61 × 10−7; z statistic = 4.808; Cohen d = 0.042) (Table).

    To identify relevant networks, 218 genes from BD-significant loci3 were combined with 112 genes carrying DNVs from the current and previous BD studies.4,5 Protein-protein interaction network analysis implicated 4 genes: microtubule associated protein 4 (MAP4), which promotes microtubule assembly and is reported to carry DNV in autism; WD repeat and HMG-box DNA binding protein 1 (WDHD1), a DNA replication initiation factor; eukaryotic translation initiation factor 4E (EIF4E), which interacts with cytoplasmic Fragile X Mental Retardation interaction protein and is implicated in autism; and striatin (STRN), a largely unknown calmodulin-binding protein with a potential role in dendritic Ca2+ signaling and striatal neuron maturation (Figure). Of these 4 genes, MAP4 and WDHD1 harbored predicted-pathogenic variants with nominal gene-based association in schizophrenia (Table).

    Discussion

    Examination of DNVs in psychiatric disorders has traditionally focused on singleton families. These findings suggest that DNVs may also contribute to mutational load in multiplex BD families, as previously observed for multiplex autism families.6 Although this study is limited by the small sample size, the overall de novo mutation rate was comparable in cases and unaffected offspring, whereas deleterious DNVs were observed more frequently in participants with BD, which is consistent with previous reports in autism and schizophrenia.2 This study highlighted HP, PC, MAP4, and WDHD1 as potential susceptibility genes for BD. Additional sequencing studies in larger cohorts are needed to further delineate the impact of DNVs in BD.

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    Article Information

    Accepted for Publication: February 24, 2020.

    Published: May 8, 2020. doi:10.1001/jamanetworkopen.2020.3382

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Toma C et al. JAMA Network Open.

    Corresponding Author: Janice M. Fullerton, PhD, Neuroscience Research Australia, Margarete Ainsworth Bldg, Barker St, Randwick, Sydney, NSW 2031, Australia (j.fullerton@neura.edu.au).

    Author Contributions: Drs Toma and Fullerton had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Toma, Schofield, Fullerton.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Toma, Schofield, Fullerton.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Toma, Overs.

    Obtained funding: Schofield, Cooper, Fullerton.

    Administrative, technical, or material support: Toma, Overs, Schofield, Cooper, Fullerton.

    Supervision: Toma, Schofield, Fullerton.

    Conflict of Interest Disclosures: Dr Mitchell reported receiving personal fees from Sanofi (China) outside the submitted work. No other disclosures were reported.

    Funding/Support: Whole-genome sequencing was funded by the NAB Foundation. The whole-exome sequencing was funded by project grant 1063960 from the Australian National Medical and Health Research Council (NHMRC). DNA was extracted by Genetic Repositories Australia, an Enabling Facility that was supported by NHMRC enabling grant 401184. This study was also supported by NHMRC project grant 1066177 and program grant 1037196. Dr Fullerton also received salary support from the Janette Mary O’Neil Research Fellowship.

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: This research was carried out using RTG-Core (www.realtimegenomics.com/products/rtg-core) under an Academic Non-Commercial License granted by Real Time Genomics Ltd. We thank the participants and their families, as well as clinical collaborators involved in collecting and phenotyping these families. Anna Heath, BSc (NeuRA, Sydney), performed de novo variant validation using the facilities at Ramaciotti Centre for Genomics (UNSW Sydney) and received a salary for this work. Barbara Toson, MSc (NeuRA, Sydney), provided support in statistical analyses and received a salary for this work.

    References
    1.
    Torrico  B, Shaw  AD, Mosca  R,  et al.  Truncating variant burden in high-functioning autism and pleiotropic effects of LRP1 across psychiatric phenotypes.   J Psychiatry Neurosci. 2019;44(5):350-359. doi:10.1503/jpn.180184PubMedGoogle ScholarCrossref
    2.
    Fromer  M, Pocklington  AJ, Kavanagh  DH,  et al.  De novo mutations in schizophrenia implicate synaptic networks.   Nature. 2014;506(7487):179-184. doi:10.1038/nature12929PubMedGoogle ScholarCrossref
    3.
    Stahl  EA, Breen  G, Forstner  AJ,  et al; eQTLGen Consortium; BIOS Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium.  Genome-wide association study identifies 30 loci associated with bipolar disorder.   Nat Genet. 2019;51(5):793-803. doi:10.1038/s41588-019-0397-8PubMedGoogle ScholarCrossref
    4.
    Toma  C, Shaw  AD, Allcock  RJN,  et al.  An examination of multiple classes of rare variants in extended families with bipolar disorder.   Transl Psychiatry. 2018;8(1):65. doi:10.1038/s41398-018-0113-yPubMedGoogle ScholarCrossref
    5.
    Kataoka  M, Matoba  N, Sawada  T,  et al.  Exome sequencing for bipolar disorder points to roles of de novo loss-of-function and protein-altering mutations.   Mol Psychiatry. 2016;21(7):885-893. doi:10.1038/mp.2016.69PubMedGoogle ScholarCrossref
    6.
    Yuen  RK, Thiruvahindrapuram  B, Merico  D,  et al.  Whole-genome sequencing of quartet families with autism spectrum disorder.   Nat Med. 2015;21(2):185-191. doi:10.1038/nm.3792PubMedGoogle ScholarCrossref
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