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SUPPLEMENTARY INFORMATION
Exome Sequencing for Bipolar Disorder Points to Roles of De Novo Loss-of-function and Protein-altering MutationsRunning title: Exome sequencing points to de novo mutations in BD
Muneko Kataoka, MD1,2,6, Nana Matoba, MS1,3,6, Tomoyo Sawada, PhD1, An-a Kazuno, MS1, Mizuho Ishiwata1, Kumiko Fujii, MD, PhD1, 4, Koji Matsuo, MD, PhD5, Atsushi Takata, MD, PhD1,7
and Tadafumi Kato, MD, PhD 1,7
Author affiliation:1Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Brain Science Institute, Saitama, 351-0198, Japan2Department of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan3Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8561, Japan4Department of Psychiatry, Dokkyo Medical University School of Medicine, Tochigi, 321-0193, Japan5Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, 755-8505, Japan6These authors contributed equally to this work and are listed in an alphabetical order7Co-corresponding authors
TABLE OF CONTENTSSUPPLEMENTARY MATERIALS AND METHODS..........................................................................3
Studied Subjects.......................................................................................................................... 3Library Preparation and Whole Exome Sequencing.....................................................................3Sequence Read Mapping and Variants Calling............................................................................4Identification of De Novo Point Mutations..................................................................................4Identification of De Novo Copy Number Variations.....................................................................5Data of De Novo Mutations in Controls and Patients with Schizoaffective Disorder...................6Enrichment Analysis of Loss-of-function and Protein-altering De Novo Mutations in Case Subjects....................................................................................................................................... 7Ages of Onset in Probands with or without De Novo Protein-Altering Mutations.......................7Gene Ontology Enrichment Analysis of Genes with Protein-Altering De Novo Mutations...........7Estimation of the Proportion of Genuine Disease-associated Mutations from Ascertainment Differentials.................................................................................................................................8Genes Hit by De Novo Protein-altering Mutations in BD and Also in Schizophrenia....................9Integrative Gene Ontology Enrichment Analysis of BD Candidate Genes....................................9Generation of Cells with the Frameshift Mutation in EHD1.........................................................9Immunoblot Analysis.................................................................................................................10
SUPPLEMENTARY FIGURES......................................................................................................11Figure S1. Sequencing Coverage in Each Individual and Each Trio.............................................11Figure S2. The De Novo 3q29 Deletion in BD.............................................................................12Figure S3. The De Novo Frameshift Mutation in EHD1 and Its Functional Consequence...........13
SUPPLEMENTARY TABLES........................................................................................................14Table S1. Detailed Information for the Studied Subjects and Sequencing Performance...........14Table S2. List of De Novo Mutations with Detailed Annotations...............................................15
SUPPLEMENTARY REFERENCES...............................................................................................17
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SUPPLEMENTARY MATERIALS AND METHODSStudied Subjects
Participants were recruited through the Bipolar Disorder Research Network Japan or at
Yamaguchi University Hospital. All the probands are diagnosed with bipolar I or II disorder (BDI
or BDII) based on the DSM (Diagnostic and Statistical Manual of Mental Disorders) IV criteria by
trained psychiatrists. All the parents were screened for mental disorders by structured interview
using M.I.N.I. (Mini International Neuropsychiatric Interview)1. M.I.N.I. alone cannot make a
lifetime diagnosis of major (unipolar) depression and BDII, because it does not include questions
to screen past major depressive episodes. Therefore, additional questions to verify past history
of major depressive episodes were asked to the participants. We used DNA samples from 79
proband-parents trios in this study. We did not include families with a parent affected by bipolar
disorder (BD; BDI or BDII), schizophrenia or schizoaffective disorder. Because unipolar depression
is highly prevalent2, we did not exclude families with a history of unipolar depression. The
studied probands consisted of 32 males and 47 females, and 56 and 23 probands were
diagnosed with BDI and BDII, respectively. The average age at recruitment was 36.9 ± 9.2 (16-56)
years old. All the participants gave written informed consent for the study. The study was
approved by the First Committee of Research Ethics of RIKEN Wako Institute.
Library Preparation and Whole Exome Sequencing
Genomic DNA samples were obtained from either peripheral blood or saliva (Table S1). To
extract DNA from saliva, we used the Oragene kit (DNA Genotek Inc., Ontario, Canada)
according to the manufacturer’s instruction. Target capture of exome regions was performed on
individual samples using SureSelectXT Human All Exon kits V4, V5 or V5 + mitochondria (Agilent
Technologies, Inc., Santa Clara, CA). Whole exome sequencing (WES) was performed by using
either the HiSeq2000 or HiSeq2500 (Illumina, San Diego, CA) with paired-end 101bp reads.
Barcoded 5 or 6 libraries were pooled and sequenced in one lane of the HiSeq. Fastq files were
produced by the Illumina CASAVA pipeline. Raw sequence data will be available in dbGaP
(www.ncbi.nlm.nih.gov/gap).
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Sequence Read Mapping and Variants Calling
We first excluded all the low quality reads (more than 20 % of bases failed Q20) from the fastq
files using the FASTQ Quality Filter module of FASTX-Toolkits-0.0.13.2 followed by compfast_pe.
(http://compbio.brc.iop.kcl.ac.uk/software/cmpfastq_pe.php). Then reads were mapped to the
Human 1kg Reference (GRCh37 + decoy) by using BWA-MEM3 (version 0.7.5a). Generated SAM
(sequence alignment/map) files were converted into the BAM (binary alignment/map) format
using SAMtools4. PCR duplicates were flagged by Picard (version 1.92,
http://picard.sourceforge.net/). Reads around known or private (family specific)
insertion/deletion (indel) sites were re-aligned locally using IndelRealigner module of the GATK 5
(version 2.6-4). GATK BaseRecalibrator was used to recalibrate base quality score. Single
nucleotide variants (SNVs) and indels were called by GATK UnifiedGenotyper from combined
bams of each trio. The standard parameters were used for hard filtering according to the GATK
best practices recommendations6. Only the positions with ≥ 20X coverage in all three family
members were considered for detection of de novo and transmittable variants.
Identification of De Novo Point Mutations
From the list of variant calls generated as above, we considered those with 1) eight or more
reads for the variant allele in the proband and 2) 95% or more reads for the reference allele in
both of the parents as candidates for de novo mutations except that they are within the HLA
locus. From the list of candidates for de novo mutations, we removed those found with a minor
allele frequency (MAF) greater than 0.01 in either of the following databases; 1) dbSNP
(http://www.ncbi.nlm.nih.gov/SNP/) build138 (except for those with clinical information), 2) the
NHLBI Exome Variant Server (http://evs.gs.washinton.edu.EVS/), 3) the 1000 Genomes Project
(http://www.1000genomes.org/) dataset and 4) the Exome Aggression Consortium (ExAC)
(http://exac.broadinstitute.org/) dataset. We also removed candidates that were found in
multiple families or found as a homozygous variant in one or more unaffected parents (likely
these variants are non-pathogenic or false positives). We finally performed manual inspection of
the candidates by using Integrative Genomics Viewer (IGV)7, and excluded four candidates that
are likely due to contamination of bacterial genomes according to the results of BLAST
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(http://blast.ncbi.nlm.nih.gov/Blast.cgi) search. After performing these procedures, we
identified 78 candidates for de novo mutations. These candidates were verified by Sanger
sequencing with a standard protocol, and 91.0 % of them (71/78) were validated. We annotated
these validated de novo mutations by ANNOVAR (2015Mar22)8 with Ensembl Genes for
transcript mapping. If multiple annotations were assigned for one mutation, we used the
annotation with the most severe impact on protein (i.e., nonsense, canonical splice site,
frameshift indels > missense, inframe indels > synonymous). Based on these annotations we
classified the mutations into loss-of-function (LOF) mutations (nonsense, canonical splice site,
frameshift indels mutations), protein-altering mutations (LOF, missense and inframe indel
mutations) and synonymous mutations.
Identification of De Novo Copy Number Variations
We called copy number variations (CNVs) from our WES data using eXome Hidden-Markov
Model (XHMM)9-11 and Copy Number Inference From Exome Reads (CoNIFER)12. We only
subjected the genomic regions overlapped between the targets for SureSelect V4 and V5 to
these analyses. For the XHMM analysis, we first counted local read depth by using GATK
DepthOfCoverage function with the BAM files generated as above. Then the mean coverage of
the target regions for all exomes was merged into a matrix file. GC-rich regions, repeat
sequences and regions with outlier read-depth were excluded from the analysis. CNVs were
called by XHMM discover and genotype function with default parameters. For the CoNIFER
analysis, we first generated RPKM files from the BAM files and then called CNVs based on SVD-
ZRPKM values. From both of the datasets generated by the two software we excluded CNV calls
identified twice or more in our unaffected parental population and those not supported by the
coverage data of three or more genomic regions targeted by exome capture probes using
PLINK13. Then candidates for de novo CNVs were identified using PLINK/SEQ
(http://atgu.mgh.harvard.edu/plinkseq/). Among these candidates we subjected those called by
both software to validation experiments using the Human Genome CGH Microarrays (Agilent),
according to the results of a recent study demonstrating that most of the validated CNVs were
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called by both software14.
Data of De Novo Mutations in Controls and Patients with Schizoaffective Disorder
We used the data of de novo mutations (SNVs and indels) in controls and patients with
schizoaffective disorder (SAD) in the following studies for comparison with our own data of BD:
controls; Iossifov et al.15 (# of trios =1,911), schizoaffective disorder; Xu et al.16 and McCarthy et
al.17 (# of trios = 64). We are aware that several previous studies suggested that schizoaffective
disorder, bipolar, is genetically more related to BD than schizoaffective disorder, depressed.
Thus, it would be better to include schizoaffective disorder, bipolar, only. However, in these
papers, no information of the subtype of schizoaffective disorder is listed. Several family studies,
however, support that depressive subtype of schizoaffective disorder is also associated with
elevated familial risk of BD18, 19. Therefore, we included both subtypes of schizoaffective disorder
to combine with BD. To compare the datasets from different studies in an equivalent condition,
we re-annotated and filtered all the de novo mutations from published studies with the same
pipeline used for our own data based on the genomic positions and the non-reference alleles of
mutations. It might be noteworthy that the rate of de novo LOF mutations among the control
subjects in Iossifov et al.15 could be slightly inflated because in their study candidates for de novo
LOF mutations were subjected to validation experiments more aggressively than others (for LOF
mutations they subjected candidates with the “strong” and “weak” tags for validation, while
they only considered the ones with the “strong” tag for other types of mutations).
Enrichment Analysis of Loss-of-function and Protein-altering De Novo Mutations in Case Subjects
To test enrichment of loss-of-function and protein-altering de novo mutations in BD, we
performed one-tailed Fisher’s exact tests with the following 2×2 table: columns; BD and controls
(1,911 unaffected siblings in Iossifov et al.15 as described above), rows; the number of de novo
LOF or protein-altering mutations and the number of synonymous mutations. This method
should be resistant to potential artifacts caused by comparison of data from different studies
(e.g. mutation detection rates may vary across studies) because synonymous mutations, whose
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enrichment was not observed in a comparison between ASD and controls15, were used as the
internal control. Comparison between BD+SAD or BDI+SAD and controls was performed by using
the same procedures.
Ages of Onset in Probands with or without De Novo Protein-Altering Mutations
We compared average ages of onset between probands with one or more de novo protein-
altering mutations and those without mutations using two-tailed Student’s t-test. We also
analyzed averages ages of ascertainment between these two groups using two-tailed Student’s t-
test, to test whether significant difference in ages of onset can be explained by this factor.
Gene Ontology Enrichment Analysis of Genes with Protein-Altering De Novo Mutations
We performed a gene ontology (GO) enrichment analysis of genes hit by de novo protein-
altering mutations in BDI and SAD using Database for Annotation, Visualization and Integrated
Discovery (DAVID, v6.7)20, 21. We did not include the genes disrupted by the de novo CNV
identified in our cohort in the list of input genes because we cannot equally weight the CNV
disrupting multiple genes and a de novo mutation affecting a single gene. It is known that target
capture efficiency varies for different genes in exome sequencing22. This capture bias potentially
affects the results of GO enrichment analyses. We therefore used a custom background gene list
that does not include genes with poor coverage for DAVID analyses. To prepare the list, we first
defined “poor-coverage” regions using DiagnoseTargets module of GATK23 with default
parameters except for the minimum coverage set as 20 (same to the threshold used for variant
calling). We then calculated the proportion of exonic regions that overlap with the “poor-
coverage” regions for each gene using BEDTools24, and generated the custom list of background
genes by excluding the genes of which > 30% of the exonic regions were included in the “poor-
coverage” regions. Among the 17,392 genes assigned for any of the GO terms, 226 genes were
excluded by this procedure. We excluded extremely large (including > 5,000 genes) and small GO
terms (hit count less than five) from the result because these terms are in general less
informative. For the nine GO terms with nominal significance (P < 0.05) in this GO enrichment
analysis using DAVID, we further tested their specificity by performing a simulation analyses
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randomly selecting 75 de novo protein-altering mutations (equal to the number of mutations in
BDI and SAD) in controls15 10,000 times. We considered that the GO terms that are not
significantly enriched in this simulation analysis do not represent the terms truly enriched
among the genes hit by protein-altering mutations in BDI and SAD, rather the enrichment of
these terms in our initial DAVID analysis can be explained by general properties of genes
preferentially hit by de novo mutations.
Estimation of the Proportion of Genuine Disease-associated Mutations from Ascertainment Differentials
Using our data of de novo mutations in BD and the data for controls in Iossifov et al.15, we
calculated per-individual mutation rates as follows; per-individual rates for de novo LOF
mutations, 0.114 in BD and 0.089 in controls, per-individual rates for de novo protein-altering
mutations, 0.722 in BD and 0.657 in controls. By using these numbers and the procedures
described in Iossifov et al.15, we calculated ascertainment differentials as (per-individual rates of
de novo LOF or protein-altering mutations in our BD cohort) - (per-individual rates of de novo
LOF or protein-altering mutations in controls). Based on these ascertainment differentials we
roughly estimated the proportion of the mutations contributing to the disease risks as follows:
LOF mutations; (0.114 - 0.089) / 0.114 = 0.22, protein-altering; (0.722 - 0.657) / 0.722 = 0.09.
Genes Hit by De Novo Protein-altering Mutations in BD and Also in Schizophrenia
The list of de novo mutations in schizophrenia was obtained from Refs 16, 17, 25-28. From this list we
identified five genes that are hit by de novo protein-altering mutations in BD and also in
schizophrenia. An expected number of de novo protein-altering mutations for each gene was
calculated from per-gene mutation rates provided in Supplementary Table 1 of Samocha et al.29
Then we invoked Poisson distribution probabilities to determine the significance for the
observed number of mutations (note that these P values are not subjected to genome-wide
correction for multiple testing).
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Integrative Gene Ontology Enrichment Analysis of BD Candidate Genes
To perform an integrative gene ontology enrichment analysis of BD candidate genes we
included the following gene lists in the input; 1) genes with de novo protein-altering mutations
in our WES study (# = 56), 2) genes with SNPs associated with BD at P < 1 × 10 -4 in a large-scale
GWAS30, (# = 54) and 3) genes included in CNVs that showed nominally significant association
with BD (Table 1 and Supplementary Table S4 of Green et al.31, # = 120). By using these input
genes (# = 229 after excluding overlaps) we performed an enrichment analysis using DAVID.
Generation of Cells with the Frameshift Mutation in EHD1
HEK293T (RIKEN Cell Bank) cells were maintained at 37°C with 5% CO2 atmosphere in DMEM
(Wako Pure Chemical Industries, Ltd., Osaka, Japan) supplemented with 10% FBS (Life
Technologies Japan, Tokyo, Japan). Plasmids were transfected using Lipofectamine 2000 (Life
Technologies Japan) according to manufacturer’s instructions. Human cDNA for EHD1 was
purchased from Kazusa DNA Res. Inst. and cloned into pcDNA3-Myc vector. The 1414 delG
mutation was introduced into hEHD1 by PCR-based mutagenesis using 5’-
TGAAGTCCAAGCTCCCCAAC-3’ and 5’- ATCTCCTTCTTGGCGTTGG-3’ as primers. The full-length
sequence of hEHD1 1414delG was confirmed by Sanger sequencing.
Immunoblot Analysis
Cells were harvested 28 hrs post-transfection and lysed in 1% Triton X-100 -based lysis buffer
(10 mM Tris-HCl [pH 7.4], 120 mM NaCl, 5 mM EDTA, 1% Triton X-100 and protease inhibitor
[Roche Diagnostics, Tokyo Japan]). Cell lysates were subjected to Western blot analysis with
detection reagents (Termo Fisher Scientific, Waltham, MA, USA). Antibodies used in this study
are as follows: anti-Myc (4A6) (Merck Millipore, Billerica, MA, USA), anti-EHD1 (ab75886 and
ab109747) (Abcam, Cambridge, UK), anti-β-actin (AC-15) (Sigma-Aldrich Japan, Tokyo, Japan),
goat anti-Mouse IgG-HRP and goat anti-Rabbit IgG-HRP (SantaCruz Biotechnology, Santa Cruz,
CA, USA).
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SUPPLEMENTARY FIGURES
Figure S1. Sequencing Coverage in Each Individual and Each Trio
Proportion of the exome target regions with joint coverage (the coverage of the least well
covered individual in the trio) ≥ 20 was plotted as red circles in the order of the trio rank (trios
with the lowest coverage on the left and the highest on the right). Proportion of the target
regions covered by ≥ 20 reads at the individual level (blue open circles) and on average in the
trio (blue circles) was also plotted.
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Figure S2. The De Novo 3q29 Deletion in BD
(A) Visualization of the output from CoNIFER12 for the 3q29 region. Red, blue and green lines
indicate normalized coverage in the proband, father and mother, respectively. (B) Signals from
array CGH visualized by using the Agilent Genomic Workbench. Reduction of signals only in the
proband indicating existence of a de novo deletion was detected in the same region in (A) and
(B). (C) A cytoband image for the human chromosome 3. A red line indicates the locus where the
deletion was detected.
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Figure S3. The De Novo Frameshift Mutation in EHD1 and Its Functional Consequence
(A) Electropherogram of Sanger sequencing of EHD1. The heterozygous 1414G deletion in EHD1
was detected only in the proband. (B) Schematic representation of wild-type and 1414delG
mutant EHD1 protein. The de novo frameshift mutation directory introducing a stop codon
results in truncation of the C-terminal of the protein. (C) Western blot analysis showing the
expression of wild-type and 1414delG mutant EHD1. Although anti-Myc antibody or anti-EHD1
antibody against a.a.390-415 detected both wild-type and mutant EHD1, mutant EHD1 was not
detected by anti-EHD1 antibody against a.a.500-534, suggesting expression of the truncated
protein.
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SUPPLEMENTARY TABLES
Table S1. Detailed Information for the Studied Subjects and Sequencing PerformanceThis table is provided as a separate spreadsheet
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Table S2. List of De Novo Mutations with Detailed AnnotationsThis table is provided as a separate spreadsheet
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Table S3. List of 75 Genes Subjected to Gene Ontology Enrichment Analyses
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