www.sciencemag.org/cgi/content/full/science.aaa3650/DC1

Supplementary Materials for

Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and

pathways

Elizabeth T. Cirulli, Brittany N. Lasseigne, Slavé Petrovski, Peter C. Sapp, Patrick A. Dion, Claire S. Leblond, Julien Couthouis, Yi-Fan Lu, Quanli Wang, Brian J. Krueger, Zhong Ren,

Jonathan Keebler, Yujun Han, Shawn E. Levy, Braden E. Boone, Jack R. Wimbish, Lindsay L. Waite, Angela L. Jones, John P. Carulli, Aaron G. Day-Williams, John F. Staropoli, Winnie W. Xin,

Alessandra Chesi, Alya R. Raphael, Diane McKenna-Yasek, Janet Cady, J. M. B. Vianney de Jong, Kevin P. Kenna, Bradley N. Smith, Simon Topp, Jack Miller, Athina Gkazi,

FALS Sequencing Consortium, Ammar Al-Chalabi, Leonard H. van den Berg, Jan Veldink, Vincenzo Silani, Nicola Ticozzi, Christopher E. Shaw, Robert H. Baloh, Stanley Appel, Ericka Simpson,

Clotilde Lagier-Tourenne, Stefan M. Pulst, Summer Gibson, John Q. Trojanowski, Lauren Elman, Leo McCluskey, Murray Grossman, Neil A. Shneider, Wendy K. Chung, John M. Ravits,

Jonathan D. Glass, Katherine B. Sims, Vivianna M. Van Deerlin, Tom Maniatis, Sebastian D. Hayes, Alban Ordureau, Sharan Swarup, John Landers, Frank Baas, Andrew S. Allen, Richard S. Bedlack,

J. Wade Harper, Aaron D. Gitler, Guy A. Rouleau, Robert Brown, Matthew B. Harms, Gregory M. Cooper, Tim Harris,* Richard M. Myers, David B. Goldstein

*Corresponding author. E-mail: tim.harris@biogenidec.com

Published 19 February 2015 on Science Express DOI: 10.1126/science.aaa3650

This PDF file includes: Materials and Methods

Supplementary Text

Figs. S1 to S4

Tables S1 to S5

References

Other supplementary material for this manuscript includes the following: Table S6 (Excel format)

Correction (26 March 2015): Data were changed throughout the supplement PDF and table S6 to reflect a reduction of 5 in the case sample size; the data now correspond to the final version of the main article.

2

Materials and Methods Samples

Subjects for whole exome analysis were drawn from IRB-approved genetic studies of

ALS subjects at Consortium member institutions: the Columbia University Medical

Center (which included Coriell samples), University of Massachusetts at Worchester,

Stanford University (which included contributions from Emory University School of

Medicine, the Johns Hopkins University School of Medicine, and the University of

California, San Diego), Massachusetts General Hospital Neurogenetics DNA Diagnostic

Lab Repository, Duke University, McGill University (which included contributions from

Saint-Luc and Notre-Dame Hospital of the Centre Hospitalier de l'Université de Montréal

(CHUM) (University of Montreal), Gui de Chauliac Hospital of the CHU de Montpellier

(Montpellier University), Pitié Salpêtrière Hospital, Fleurimont Hospital of the Centre

Hospitalier Universitaire de Sherbrooke (CHUS) (University of Sherbrooke), Enfant-

Jésus Hospital of the Centre hospitalier affilié universitaire de Québec (CHA) (Laval

University), and Montreal General Hospital and Montreal Neurological Institute and

Hospital of the McGill University Health Centre), and Washington University in St.

Louis (which included contributions from Houston Methodist Hospital, Virginia Mason

Medical Center, University of Utah, and Cedars Sinai Medical Center). Subjects for

follow-up sequencing came from the same centers plus the University of Pennsylvania

and University of Amsterdam. Genotypes for the 51 genes used in the replication analysis

were also provided for FALS exomes sequenced as previously described(17). All patients

were diagnosed according to El Escorial revised criteria as suspected, possible, probable,

or definite ALS by neuromuscular physicians. Subjects were considered sporadic if no

first or second-degree relatives had been diagnosed with ALS or died of an ALS-like

syndrome. Details are presented in Table S1.

All samples known to be carriers of the C9orf72 expansion were excluded from all

analyses. There were 883 case exomes used in the discovery phase that were not screened

for this variant. Additionally, prior to exome sequencing, some samples were screened

for variants in known ALS genes and were only sequenced if they were found to be

negative for a mutation in that gene. The number of pre-screened discovery samples for

3

each gene were 430 for SOD1, 334 for TARDBP and FUS, and 143 for VAPB, DCTN1,

ANG, FIG4, OPTN, VCP, UBQLN2, EWSR1, DAO, SQSTM1, SETX, and TAF15. The

543 exomes used in the replication stage were also screened for variants in TARDBP,

FUS, SOD1, VCP, PFN1, and TUBA4A prior to use in this study.

Control samples were sequenced as part of other studies at Duke University,

HudsonAlpha, and McGill University and were not enriched for (but not specifically

screened for) ALS or other neurodegenerative disorders. Control samples were matched

to case samples in terms of similar capture kits and coverage levels (Tables S2 and S3).

Except for the exome cases used in the replication phase, all samples used within each

analysis subset were processed using identical pipelines.

Only genetically European ethnicity samples were included in the analysis. Samples were

also screened with KING(55) to remove duplicate samples between the custom capture

and exome datasets and to remove second-degree or higher relatives in the exome

datasets; exomes with incorrect sexes according to X:Y coverage ratios were removed, as

were contaminated samples according to VerifyBamID(56).

Sequencing

Sequencing of DNA was performed at Duke University, McGill University, Stanford

University, HudsonAlpha, and University of Massachusetts, Worcester. Samples were

either exome sequenced using the Agilent All Exon (37MB, 50MB or 65MB) or the

Nimblegen SeqCap EZ V2.0 or 3.0 Exome Enrichment kit or whole-genome sequenced

using Illumina GAIIx or HiSeq 2000 or 2500 sequencers according to standard protocols

(see Table S2). Follow-up custom capture sequencing was performed using the same

methods as the exome sequencing with the Nimblegen SeqCap EZ Choice to an average

coverage of 144.60x within the capture regions, with an average of 99.37% bases covered

at least 5x.

Case and control samples were processed at Duke University (discovery Duke and

McGill/Stanford datasets and replication custom capture dataset), HudsonAlpha

(discovery HudsonAlpha dataset) and University of Massachusetts, Worcester

(replication exome dataset) as follows. The Illumina lane-level fastq files were aligned to

4

the Human Reference Genome (NCBI Build 37) using the Burrows-Wheeler Alignment

Tool (BWA)(57). We then used Picard software (http://picard.sourceforge.net) to remove

duplicate reads and process these lane-level SAM files, resulting in a sample-level BAM

file that is used for variant calling. We used GATK to recalibrate base quality scores,

realign around indels, and call variants(58). The Duke, McGill/Stanford, custom capture

and replication exome variants were required to have a quality score (QUAL) of at least

20 (30 for replication exomes), a genotype quality (GQ) score of at least 20, and at least

10x coverage. Additionally, Duke, McGill/Stanford and custom capture variants were

required to have a quality by depth (QD) score of at least 2 and a mapping quality (MQ)

score of at least 40. For Duke and McGill/Stanford exomes and custom capture samples,

indels were also required to have a maximum strand bias (FS) of 200 and a minimum

read position rank sum (RPRS) of -20. Other variants were restricted according to VQSR

tranche (calculated using the known SNV sites from HapMap v3.3, dbSNP, and the Omni

chip array from the 1000 Genomes Project): the cutoffs for Duke, McGill/Stanford and

custom capture variants were a tranche of 99.9% for SNVs in genomes and exomes and

99% for indels in genomes; the cutoffs for HudsonAlpha were a 99% tranche for SNVs

and 95% tranche for indels; and the cutoff for the replication exomes was a 97% tranche

for SNVs and indels. Variants were excluded if they were determined to be sequencing,

batch-specific or kit-specific artifacts; they were also excluded in the Duke,

McGill/Stanford and custom capture datasets if marked by EVS as being failures(59).

Variants were annotated to Ensembl 73 using SnpEff.

Predisposition analysis

This study first analyzed whole exome sequence data for discovery purposes and then

performed follow-up custom capture sequencing of 51 genes and interrogated these 51

genes in additional case exomes. We analyzed the discovery samples in separate groups

to control for differences in sequencing methods and coverage levels. The Duke analysis

used genomes and Nimblegen and Agilent 65MB exomes with at least 90% of the

consensus coding sequence (CCDS) bases covered to at least 10x, the HudsonAlpha

analysis used Nimbelgen exomes with at least 90% of the CCDS bases covered to at least

5

10x, and the McGill/Stanford University analysis used genomes and Agilent 37MB and

50MB exomes with 75% of the CCDS covered at least 10x.

Our study focused on gene-based collapsing analyses. First, the number of bases with at

least 10x coverage was calculated for each CCDS exon plus 10 bp into each intron for

each sample. Because differences in coverage can cause biased results, exons with

coverage differences (cutoff tailored to each analysis (see Table S3)) between cases and

controls were excluded from analysis.

For each gene, each sample was then indicated as carrying or not carrying a qualifying

variant. Qualifying variants were defined for dominant (one qualifying variant per gene;

minor allele frequency (MAF) cutoff of 0.05% internally and 0.005% in ExAC) and

recessive (two qualifying variants per gene, including homozygous and potentially

compound heterozygous samples as carriers; MAF cutoff 1%) models. These allele

frequency thresholds used a leave-one-out method for the combined sample of cases and

controls in each analysis group (where the MAF of each variant was calculated using all

samples except for the sample in question). Variants were also required to pass this MAF

cutoff in the publically available ExAC global frequencies; HudsonAlpha analyses

additionally required a MAF below 0.01 in the 1000 Genomes Data (50, 60). We

performed analyses of CCDS genes using three methods to identify qualifying variants:

1) all non-synonymous and canonical splice variants (coding model), 2) all non-

synonymous coding variants except those predicted by PolyPhen-2 HumVar(13) to be

benign (not benign model), and 3) only stop gain, frameshift and canonical splice variants

(loss-of-function [LoF] model). Qualifying variants were identified using Analysis Tools

for Annotated Variants (http://humangenome.duke.edu/software) at Duke and an in-house

pipeline at HudsonAlpha.

The total number of cases and controls with qualifying variants in each gene for each

model were calculated, and a Cochran-Mantel-Haenszel (CMH) test was performed in R

to generate a combined, stratified p-value across all three discovery groups. Genes were

only considered if they were assessed in all three discovery cohorts and had more than

one case or control sample with a genetic variant meeting the inclusion criteria for the

genetic model being tested. A Breslow-Day test was applied to assess homogeneity of

6

effects across different groups, and p>0.05 was required for the gene to be considered.

The adjusted alpha after correcting for the number of genes tested over all six genetic

models is p

7

samples(59). Linear regression analysis was performed to analyze age at onset, Firth

logistic regression was used to analyze the site of onset, and Cox Proportional-Hazards

Regression was used to analyze survival in R(62). These analyses always included sex,

analysis group and EIGENSTRAT axes (which were calculated for EIGENSTRAT-

pruned whites only and were created using the genotypes for variants from the Illumina

HumanCore chip that overlap exons and were not found to be influenced by sequencing

or genotyping method) as covariates, and the analysis of survival additionally included

age at onset as a covariate and required at least 1% of cases to have variants in a given

gene for it to be included in the analysis (with the exception of previously reported ALS

genes, which we analyzed separately and included regardless of the number of carriers).

Cell Culture, Transfection, and Reagents

All cell lines were grown in Dulbecco’s modified Eagle’s medium supplemented with

10% fetal calf serum (FBS) and maintained at 5% CO2/37oC. Plasmid were transfected

using lipid-based reagents (Lipofectamine 2000). Lentiviruses were made in 293T cells

and used to infect 293T cells followed by selection on puromycin.

Immunoprecipitation and Proteomic Analysis

AP-MS and CompPASS analysis using the (Comparative Proteomics Analysis Software

Suite) were performed as previously described(63, 64). Briefly, 107 cells were lysed in

lysis buffer [50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 1% NP-40 and

supplemented with protease inhibitors (Roche)] for 30 min on ice to obtain whole cell

extracts. Lysates were incubated with 30 l of anti-HA resin (Covance) and after

extensive washing with lysis buffer, proteins were eluted with HA peptide prior to

trichloroacetic acid precipitation, trypsinization, and stage tipping. Samples were ran in

technical duplicate on an LTQ Velos (Thermo) mass spectrometer, and spectra search

with Sequest prior to target-decoy peptide filtering, and linear discriminant analysis(65).

Protein Assembler was used to convert spectral counts to average protein spectral

matches (APSMs), which takes into account peptides, which match more than one protein

in the database. Peptides were identified with a false discovery rate of < 1.0% and the

protein false discovery rate was

8

used: Activation Type – Collision induced dissociation; Minimum Signal Required -

2000.0; Isolation width (m/z) - 1.00; Normalized Collision Energy - 35.0; Default Charge

State – 2; Activation Q - 0.250; Activation Time (ms) - 10.000. Peptide data (APSMs)

were uploaded into the CompPASS algorithm housed within the CORE environment. For

CompPASS analysis, we employed a stats table of 170 unrelated bait proteins analyzed in

an analogous manner, including deubiquitinating enzymes and autophagy

components(63, 64). The CompPASS system identifies high confidence candidate

interacting proteins (HCIPs) based on the normalized weighted D (NWD)-score, which

incorporates the frequency with which they identified within the stats table, the

abundance (APSMs, average peptide spectral matches) when found, and the

reproducibility of identification in technical replicates, and also determines a z-score

based on APSMs(63, 64). Proteins with NWD-scores >1.0 are considered HCIPs,

although we also note that some proteins that may be bona fide interacting proteins may

not reach the strict threshold set by a NWD-score of > 1.0.

For IP of endogenous NEK1, VAPB or ALS2 in NSC-34 cells expressing either HA-

ALS2, HA-VAPB or FLAG-NEK1, respectively, ~0.5 mg of lysate was incubated with

0.5 µg of the indicated antibody (anti-HA Abcam ab9110, anti-Nek1 Bethyl Labs A304-

570A, anti-FLAG Sigma F1804, anti-ALS2 Sigma SAB4200137, anti-VAPB Bethyl

Labs A302-894A) or control IgG (Cell Signaling Technologies) overnight at 4°C. Protein

G resin (25 µl) was then added to the IP reaction and incubated for a further 2 hours at

4°C. Beads were washed three times with lysis buffer. After washing, 4x SDS loading

buffer was added and the samples were boiled for 5 min. Samples were separated on a

SDS-PAGE gel prior to immunoblot analysis according to standard procedures using

primary antibodies at 1:1000 overnight at 4°C, HRP-conjugated secondary antibodies

(Promega) at 1:5000 for 1 hour at room temperature, and chemiluminescent detection

(PerkinElmer).

Supplementary Text

FALS Sequencing Consortium

Other FALS Sequencing Consortium Members are as follows:

Orla Hardiman1, Russell L McLaughlin1, Letizia Mazzini2, Ian P Blair3, Kelly L Williams3, Garth A Nicholson4, Safa Al-Sarraj5, Andrew King5, Emma L Scotter5, Simon

9

Topp5, Claire Troakes5, Caroline Vance5, Sandra D'Alfonso6, Stefano Duga7, Lucia Corrado8, Anneloor LMA ten Asbroek9, Daniela Calini10, Claudia Colombrita11, Antonia Ratti11, Cinzia Tiloca12, Zheyang Wu13, Seneshaw Asress14, Meraida Polak14, Frank Diekstra15, Wouter van Rheenen15, Eric W Danielson16, Claudia Fallini16, Pamela Keagle16, Elizabeth A Lewis16, Jason Kost17, Gianni Sorarù18, Cinzia Bertolin18, Giorgia Querin18, Barbara Castellotti19, Cinzia Gellera19, Viviana Pensato19, Franco Taroni19, Cristina Cereda20, Stella Gagliardi20, Mauro Ceroni21, Giuseppe Lauria22, Jacqueline de Belleroche23, Giacomo P Comi24, Stefania Corti24, Roberto Del Bo24, Martin R Turner25, Kevin Talbot25, Hardev Pall26, Karen E Morrison27, Pamela J Shaw28, Jesús Esteban-Pérez29, Alberto García-Redondo29, José Luis Muñoz-Blanco30. 1Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Republic of Ireland. 2ALS Center, Department of Neurology, 'A. Avogadro' University of Eastern Piedmont, Novara, Italy. 3Australian School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia. 4Australian School of Advanced Medicine, Macquarie University, Sydney, NSW 2109, Australia; Northcott Neuroscience Laboratory, ANZAC Research Institute, Sydney, NSW 2139, Australia. 5Centre for Neurodegeneration Research, King’s College London, Department of Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, London, SE5 8AF, UK. 6Department of Health Sciences, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), ‘‘A. Avogadro’’ University, 28100 Novara, Italy. 7Humanitas University, Via Manzoni 113, 20089 Rozzano (Mi) – Italy; Humanitas Clinical and Research Center, Via Manzoni 56, 20089 Rozzano (Mi) – Italy. 8Department of Medical Sciences, 'A. Avogadro' University of Eastern Piedmont, Novara, Italy. 9Department of Neurogenetics and Neurology, Academic Medical Centre, Amsterdam, The Netherlands. 10Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy. 11Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy; Department of Pathophysiology and Transplantation, 'Dino Ferrari' Center - Università degli Studi di Milano, Milan 20122 Italy. 12Department of Neurology and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy; Doctoral School in Molecular Medicine, Department of Sciences and Biomedical Technologies, Universita` degli Studi di Milano, Milan 20122, Italy. 13Worcester Polytechnic Institute, Worcester, MA 01609, USA.

10

14Department of Neurology, Emory University, Atlanta, GA 30322, USA. 15Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, 3508 GA Utrecht, The Netherlands. 16Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA. 17Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, USA.; Worcester Polytechnic Institute, Worcester, MA 01609, USA. 18Department of Neurosciences, University of Padova, Padova, Italy. 19Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan 20133, Italy. 20Experimental Neurobiology Laboratory, IRCCS 'C. Mondino' National Neurological Institute, 27100 Pavia, Italy. 21Experimental Neurobiology Laboratory, IRCCS 'C. Mondino' National Neurological Institute, 27100 Pavia, Italy; Department of Neurological Sciences, University of Pavia, 27100 Pavia, Italy. 22Headache and Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milan 20133, Italy. 23Neurogenetics Group, Division of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London W12 0NN. 24Neurology Unit, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, Milan 20122, Italy; Department of Pathophysiology and Transplantation, 'Dino Ferrari' Center - Università degli Studi di Milano, Milan 20122 Italy. 25Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK. 26School of Clinical and Experimental Medicine, College of Medical and Dental Sciences, The University of Birmingham, Birmingham, UK. 27School of Clinical and Experimental Medicine, College of Medical and Dental Sciences, University of Birmingham, UK.; Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust UK. 28Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK. 29Unidad de ELA, Instituto de Investigación Hospital 12 de Octubre de Madrid, SERMAS, and Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER U-723), Madrid, Spain. 30Unidad de ELA, Instituto de Investigación Hospital Gregorio Marañón de Madrid, SERMAS, Spain.

Acknowledgements

11

The majority of the funding for the ALS patient sequencing performed in this study was

provided by Biogen Idec. P.C.S. was supported through the auspices of Dr. H. Robert

Horvitz, an Investigator at the Howard Hughes Medical Institute in the Department of

Biology at the Massachusetts Institute of Technology. J.W.H. was supported by a

research grant from Biogen-Idec and R37 NS083524. The Shaw laboratory is supported

by grants from the Wellcome Trust and Medical Research Council Grants

089701/Z/09/Z, G0900688 and MR/L021803/1, MND Association and American ALS

Association. Funding for the FALS Sequencing Consortium was provided by the

National Institutes of Health (NIH)/National Institute of Neurological Disorders and

Stroke (NINDS) (5RO1NS050557; 5R01 NS067206; 5R01NS065847; 1R01NS073873;

5R01NS079836 R01NS065847; and R01NS073873 [J.E.L., R.H.B.]), the American ALS

Association (N.T., V.S., C.E.S., J.E.L., R.H.B.), Project MinE, the MND Association

(N.T., V.S., A.A.C, C.E.S., J.E.L.), the Angel Fund (R.H.B.), Project ALS/P2ALS

(R.H.B.), the ALS Therapy Alliance (R.H.B., J.E.L.), the Pierre L. de Bourghknecht ALS

Research Foundation (R.H.B.), a Francesco Caleffi donation (N.T., V.S.), the Medical

Research Council, the Heaton-Ellis Trust and AriSLA - Italian Research Foundation for

ALS, cofinanced with support of ‘‘5 x 1000’’—Healthcare research of the Italian

Ministry of Health (grants EXOMEFALS 2009 and NOVALS 2012 [N.T., C.G., C.

Tiloca, V.S., J.E.L.]), the National Institute for Health Research (NIHR) Dementia

Biomedical Research Unit at South London (C.E.S., A.A.C.), Maudsley NHS Foundation

Trust (C.E.S., A.A.C.), King’s College London (C.E.S., A.C.), the Motor Neurone

Disease Research Institute of Australia (Leadership Grant to IPB and a Bill Gole

fellowship to K.L.W.), United Kingdom Medical Research Council under the aegis of

JPND – http://www.jpnd.eu (A.A.C.), the National Health and Medical Research Council

of Australia (1004670), Canadian Institutes of Health Research (208973, G.A.R.), Muscle

Dystrophy Association (153959, G.A.R.), Target ALS (C.L.T., A.D.G.), Instituto de

Salud Carlos III - ISCIII (grants EC08/00049; PI10/00092; PI12/ 03110 – Erare european

call – J.E.P., A.G.R.), FUNDELA (Spanish foundation for the development of ALS

research, J.E.P, A.G.R.) and the Mireia Barneda project ‘‘No llores, no te rindas’’ (J.E.P.,

A.G.R.), the Netherlands ALS Foundation, Telethon Genetic BioBank (GTB12001D) and

12

the Eurobiobank network. Research leading to these results has received funding from the

European Community's Health Seventh Framework Programme (FP7/2007-2013).

We acknowledge Jarod Shelton at Washington University St. Louis, Peggy Allred at

Cedars Sinai, Luis Lay and Sharon Halton at Houston Medical, Karla Figueroa at

University of Utah, Michael Baughn, Melissa McAlonis and Jonhatan W. Artates at

University of California, San Diego, and Marlyne Silver, Karen Grace, and Ken Cronin at

Duke University for recruiting subjects, preparing DNA, and maintaining clinical

databases. We acknowledge M.T. van Meegen and A.P. Prijs at University of Amsterdam

and Mei Liu and Paul Carmillo at Biogen Idec for technical assistance.

DNA panels from the NINDS Human Genetics Resource Center DNA and Cell Line

Repository (http://ccr.coriell.org/ninds) were used in this study, as well as clinical data.

The submitters that contributed samples are acknowledged in detailed descriptions of

each ALS1 sample.

We would like to acknowledge the following individuals for the contributions of control

samples: Dr. Joseph McEvoy, Dr. Anna Need, Mr. Jordan Silver and Ms. Marlyne Silver;

Dr. Deborah Koltai Attix, and Ms. Jill McEvoy, Dr. Kenneth Schmader, Dr. Shelley

McDonald, Dr. Heidi K. White, Dr. Mamata Yanamadala, and the Carol Woods and

Crosdaile Retirement Communities; Dr. Gianpiero Cavalleri, Dr. Norman Delanty, and

Dr. Chantal Depondt; Dr. William B. Gallentine, Dr. Erin L. Heinzen , Dr. Aatif M.

Husain, Ms. Kristen N. Linney, Dr. Mohamad A. Mikati, Dr. Rodney A. Radtke, Dr.

Saurabh R. Sinha, and Ms. Nicole M. Walley; Dr. Ruth Ottman; Dr. Julie Hoover-Fong,

Dr. Nara L. Sobreira and Dr. David Valle; Dr. Yong-Hui Jiang; Dr. Demetre Daskalakis;

Dr. William L. Lowe; Dr. James Burke, Dr. Christine Hulette, and Dr. Kathleen Welsh-

Bohmer; Dr. Vandana Shashi and Ms. Kelly Schoch; Mr. David H. Murdock and The

MURDOCK Study Community Registry and Biorepository; Dr. Scott M. Palmer; Dr. Zvi

Farfel, Dr. Doron Lancet, and Dr. Elon Pras; Mr. Arthur Holden and Dr. Elijah Behr; Dr.

Annapurna Poduri; Dr. M Chiara Manzini; Dr. Nicole Calakos; Dr. Patricia Lugar; Dr.

Doug Marchuk; Dr. Qian Zhao; Dr. Sarah Kerns and Dr. Harriet Oster; Dr. Rasheed

Gbadegesin and Dr. Michelle Winn; Dr. Francis J. McMahon and Nirmala Akula; Dr. Eli

13

J. Holtzman; Dr. Joshua Milner; Dr. Deborah Levy; Dr. Ann Pulver; and Dr. Michael

Hauser.

The collection of control samples was funded in part by Bryan ADRC NIA P30

AG028377, NIMH Award RC2MH089915, NINDS Award RC2NS070344, NIAID

Award R56AI098588, the Ellison Medical Foundation New Scholar award AG-NS-0441-

08, an award from SAIC-Frederick, Inc. (M11-074), NIMH Award K01MH098126,

MH057314 and MH068406 to Ann E. Pulver, Sc.D. (Johns Hopkins University School of

Medicine), and the Epi4K Gene Discovery in Epilepsy study (NINDS U01-NS077303)

and the Epilepsy Genome/Phenome Project (EPGP - NINDS U01-NS053998). The

collection of control samples was supported in part by funding from the Division of

Intramural Research, NIAID, NIH, and with federal funds by the Center for HIV/AIDS

Vaccine Immunology ("CHAVI") under a grant from the National Institute of Allergy

and Infectious Diseases, National Institutes of Health, Grant Number UO1AIO67854.

The authors would like to thank the Exome Aggregation Consortium and the groups that

provided exome variant data for comparison. A full list of contributing groups can be

found at http://exac.broadinstitute.org/about.

14

Fig. S1. QQ plot of discovery results for dominant not benign model

Shown are the results for the analysis of 2,869 case and 6,405 control exomes. There

were 16,339 covered genes passing QC with more than one case or control carrier for this

test, and the genes with the top 10 associations are labeled. The lambda quantifying

inflation is 1.058 The association with SOD1 passes correction for multiple tests.

15

Fig. S2 QQ plot for the discovery results from the dominant LoF model

Shown are the results for the analysis of 2,869 case and 6,405 control exomes. There

were 9,817 covered genes passing QC with more than one case or control carrier for this

test, and the genes with the top 10 associations are labeled. The lambda quantifying

inflation is 0.956.

16

Fig. S3 Variants in NEK1 and ALS2

Dominant LoF variants are shown for NEK1 (combined dataset), and recessive coding

variants are shown for ALS2 (discovery dataset). LoF variants are filled in red, non-

synonymous variants are filled in blue, and splice variants are filled in purple. Case

variants are shown with red lines, control variants are shown with blue lines, and variants

found in both cases and controls are shown with dashed lines.

17

Fig. S4 NEK1 interacts with ALS2 and VAPB

NEK1 interacts with ALS2 and VAPB. (A,B) HEK293T cells stably expressing HA-

NEK1 or HA-NEK1K33R (K/R) were subjected to AP-MS analysis using the CompPASS

platform and proteins with a normalized weighted D (NWD)-score > 1.0 identified.

Among the 51 proteins identified with NEK1 and the 38 proteins identified with

NEK1K33R, 17 were in common (panel A). The major classes of interacting proteins found

with both bait proteins are shown in panel B. Proteins indicated with asterisks were

identified but with a sub-threshold NWD-score. (C) HEK293T cells stably expressing

either HA-ALS2 or ALS2-HA were subjected to AP-MS and NEK1 as well as the NEK1

associated protein C21orf2 were identified. APSM, average peptide spectral matches.

(D,E) NCS-34 neuronal cells expressing either HA-ALS2, HA-VAPB, or FLAG-NEK1

were subjected to immunoprecipitation using either anti-NEK1, anti-VAPB or anti-

ALS2, as indicated, to immunoprecipitate the endogenous protein and complexes then

immunoblotted with the indicated antibodies. Similarly, anti-FLAG or anti-HA

immunoprecipitations were performed to demonstrate reciprocal interactions.

18

Table S1. Patient Demographics for Discovery Samples

Total ALS Patients Analyzed 2,869

Family History of ALS 6.7% (104/1560)

Male Sex 58.8% (1687/2869)

Bulbar symptom onset 26.8% (661/2465)

If limb onset, proportion upper limb onset 52.0% (903/1735)

Cognitive impairment noted at any time 14.6% (178/1221)

Mean age at symptom onset in years (Stdev, n) 57.1 (13.0,2519)

Range of ages at symptom onset 13-90

Median disease duration in months (IQR, n) 36 (32, 678)

Because data collection varied across centers, the numerators and denominators are

shown. Disease duration was only calculated for subjects with complete follow-up and

known durations to death or full-time positive pressure ventilation. All patients analyzed

were of white ethnicity.

19

Table S2. Sequencing methods and groups

Kit 37MB 50MB 65MB Nimblegen Genome

Analysis group Cases/Ctrls Cases/Ctrls Cases/Ctrls Cases/Ctrls Cases/Ctrls

Duke University 0/0 0/0 0/676 1137/2915 36/423

McGill/Stanford

University

0/335 248/227 0/165 1/1 2/61

HudsonAlpha 0/0 0/0 0/0 1445/1602 0/0

20

Table S3. Number of CCDS bases covered in each analysis

Analysis group Cases Ctrls Cutoff

Duke University 32,233,687

(91%)

32,165,079

(91%)

5%

McGill/Stanford

University

28,272,224

(80%)

27,737,938

(78%)

27%

HudsonAlpha 30,969,984

(88%)

31,367,279

(90%)

20%

Replication- Duke

University custom

capture

111,821 112,423 5%

Replication-

University of

Massachusetts

exomes

102,359 N/A N/A*

Shown is the average number of bases covered at least 10x. Numbers are n(%). The

cutoff refers to the difference allowed between cases and controls in their average exonic

coverage; exons with differences above this value were not included in the analysis.

*Exomes used in the replication dataset were restricted to the same exons used in the

custom capture samples.

21

Table S4. The 51 genes chosen for targeted follow-up based on initial exome

sequencing results.

TET3

ALYREF

TAF6

NEK1

ATP6V1F

ZNF296

UBE2D2

DOCK3

TBK1

TRAF4

OPTN

GRID2IP

C16orf11

PFKFB1

BTBD11

ENAH

TBC1D30

TNNT3

TMEM55B

CYGB

CYP17A1

CEL

PCDHGA9

LGALSL

PDLIM2

LENG9

C19orf25

PAMR1

SPSB3

DNMT3A

SH3KBP1

SPG11

ZNF432

AP1G2

MADD

GPR162

ADCYAP1R1

YBX3

KCNT1

CAMK2A

LRRC73

S100A2

HAS2

ZNF837

IL5

MPL

SLC15A2

PPCS

HIVEP3

TGM3

SCEL

22

Table S5. NEK1-Interacting proteins

INTERACTOR NEK1 (APSM) NEK1K33R (APSM)

NEK1 (NWD) NEK1K33R (NWD)

ZXDC 10 4 2.75 1.56

VPS29 1 3 0.08 1.35

VPS26B 2 7 1.06 2.19

VAPA 9 4 1.5 1.04

SGPL1 21 4 1.74 0.39

RPS6KA3 4 1 3 0.12

PIPSL 2 0 2.12 0

PDF 1 3 0.12 2.71

NOSIP 2 1 1.06 0.78

NEK1 592 269 17.15 11.95

MYO5A 2 0 1.06 0

MAP4 2 1 1.06 0.78

LRP2 1 2 0.08 1.1

KIFC1 4 1 3 0.12

KIF2C 13 11 3.27 3.06

KIF2A 25 15 2.35 1.72

KIAA0562 15 8 2.77 1.78

KATNB1 2 3 1.06 1.35

KATNA1 4 1 3 0.12

JUN 4 4 1.5 1.56

CREB5 5 0 1.68 0

CEP97 2 1 2.12 1.56

CEP78 5 1 1.12 0.52

CEP290 24 8 3.75 1.78

23

CAMK2G 4 6 1.5 1.93

CAMK2D 7 10 1.19 1.7

CAMK2B 4 4 1 1.04

C21orf2 10 8 4.74 4.42

ATF7 5 1 3.35 0.12

ATF2 6 1 1.86 0.08

ANKRD27 0 3 0 2.71

ALS2 19 23 2.52 3.01

ALG11 1 0 1.5 0

VPS35 7 13 0.23 0.4

YWHAG 12 13 0.2 0.21

YWHAZ 27 23 0.2 0.19

YWHAQ 22 10 0.15 0.1

YWHAE 104 91 0.48 0.46

YWHAB 28 24 0.46 0.43

YWHAH 10 16 0.36 0.48

References

1. L. Poppe, L. Rué, W. Robberecht, L. Van Den Bosch, Translating biological findings into new

treatment strategies for amyotrophic lateral sclerosis (ALS). Exp. Neurol. 262, 138–151

(2014). Medline

2. S. Chen, P. Sayana, X. Zhang, W. Le, Genetics of amyotrophic lateral sclerosis: An update.

Mol. Neurodegener. 8, 28 (2013). Medline doi:10.1186/1750-1326-8-28

3. A. E. Renton, A. Chiò, B. J. Traynor, State of play in amyotrophic lateral sclerosis genetics.

Nat. Neurosci. 17, 17–23 (2014). Medline doi:10.1038/nn.3584

4. A. Al-Chalabi, A. Jones, C. Troakes, A. King, S. Al-Sarraj, L. H. van den Berg, The genetics

and neuropathology of amyotrophic lateral sclerosis. Acta Neuropathol. 124, 339–352

(2012). Medline doi:10.1007/s00401-012-1022-4

5. M. Neumann, Frontotemporal lobar degeneration and amyotrophic lateral sclerosis: Molecular

similarities and differences. Rev. Neurol. 169, 793–798 (2013). Medline

doi:10.1016/j.neurol.2013.07.019

6. R. A. Smith, T. M. Miller, K. Yamanaka, B. P. Monia, T. P. Condon, G. Hung, C. S. Lobsiger,

C. M. Ward, M. McAlonis-Downes, H. Wei, E. V. Wancewicz, C. F. Bennett, D. W.

Cleveland, Antisense oligonucleotide therapy for neurodegenerative disease. J. Clin.

Invest. 116, 2290–2296 (2006). Medline doi:10.1172/JCI25424

7. See supplementary materials on Science Online.

8. J. Sasabe, T. Chiba, M. Yamada, K. Okamoto, I. Nishimoto, M. Matsuoka, S. Aiso, D-serine

is a key determinant of glutamate toxicity in amyotrophic lateral sclerosis. EMBO J. 26,

4149–4159 (2007). Medline doi:10.1038/sj.emboj.7601840

9. M. Thompson, J. C. Marecki, S. Marinesco, V. Labrie, J. C. Roder, S. W. Barger, J. P. Crow,

Paradoxical roles of serine racemase and D-serine in the G93A mSOD1 mouse model of

amyotrophic lateral sclerosis. J. Neurochem. 120, 598–610 (2012). Medline

doi:10.1111/j.1471-4159.2011.07601.x

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25017368&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23941283&dopt=Abstracthttp://dx.doi.org/10.1186/1750-1326-8-28http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24369373&dopt=Abstracthttp://dx.doi.org/10.1038/nn.3584http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22903397&dopt=Abstracthttp://dx.doi.org/10.1007/s00401-012-1022-4http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24011641&dopt=Abstracthttp://dx.doi.org/10.1016/j.neurol.2013.07.019http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=16878173&dopt=Abstracthttp://dx.doi.org/10.1172/JCI25424http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=17762863&dopt=Abstracthttp://dx.doi.org/10.1038/sj.emboj.7601840http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22117694&dopt=Abstracthttp://dx.doi.org/10.1111/j.1471-4159.2011.07601.x

10. P. Paul, J. de Belleroche, The role of D-amino acids in amyotrophic lateral sclerosis

pathogenesis: A review. Amino Acids 43, 1823–1831 (2012). Medline

doi:10.1007/s00726-012-1385-9

11. J. Cady, P. Allred, T. Bali, A. Pestronk, A. Goate, T. M. Miller, R. D. Mitra, J. Ravits, M. B.

Harms, R. H. Baloh, Amyotrophic lateral sclerosis onset is influenced by the burden of

rare variants in known amyotrophic lateral sclerosis genes. Ann. Neurol. 77, 100–113

(2015). Medline doi:10.1002/ana.24306

12. P. D. Stenson et al.., Human Gene Mutation Database (HGMD): 2003 update. Hum. Mutat.

21, 577–581 (2003).

13. I. A. Adzhubei, S. Schmidt, L. Peshkin, V. E. Ramensky, A. Gerasimova, P. Bork, A. S.

Kondrashov, S. R. Sunyaev, A method and server for predicting damaging missense

mutations. Nat. Methods 7, 248–249 (2010). Medline doi:10.1038/nmeth0410-248

14. A. Iida, N. Hosono, M. Sano, T. Kamei, S. Oshima, T. Tokuda, M. Nakajima, M. Kubo, Y.

Nakamura, S. Ikegawa, Novel deletion mutations of OPTN in amyotrophic lateral

sclerosis in Japanese. Neurobiol. Aging 33, 1843.e19–1843.e24 (2012). Medline

doi:10.1016/j.neurobiolaging.2011.12.037

15. M. van Blitterswijk, P. W. van Vught, M. A. van Es, H. J. Schelhaas, A. J. van der Kooi, M.

de Visser, J. H. Veldink, L. H. van den Berg, Novel optineurin mutations in sporadic

amyotrophic lateral sclerosis patients. Neurobiol. Aging 33, 1016.e1–1016.e7 (2012).

Medline doi:10.1016/j.neurobiolaging.2011.05.019

16. H. Daoud, S. Zhou, A. Noreau, M. Sabbagh, V. Belzil, A. Dionne-Laporte, C. Tranchant, P.

Dion, G. A. Rouleau, Exome sequencing reveals SPG11 mutations causing juvenile ALS.

Neurobiol. Aging 33, 839.e5–839.e9 (2012). Medline

doi:10.1016/j.neurobiolaging.2011.11.012

17. B. N. Smith, N. Ticozzi, C. Fallini, A. S. Gkazi, S. Topp, K. P. Kenna, E. L. Scotter, J. Kost,

P. Keagle, J. W. Miller, D. Calini, C. Vance, E. W. Danielson, C. Troakes, C. Tiloca, S.

Al-Sarraj, E. A. Lewis, A. King, C. Colombrita, V. Pensato, B. Castellotti, J. de

Belleroche, F. Baas, A. L. ten Asbroek, P. C. Sapp, D. McKenna-Yasek, R. L.

McLaughlin, M. Polak, S. Asress, J. Esteban-Pérez, J. L. Muñoz-Blanco, M. Simpson,

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22890612&dopt=Abstracthttp://dx.doi.org/10.1007/s00726-012-1385-9http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25382069&dopt=Abstracthttp://dx.doi.org/10.1002/ana.24306http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=20354512&dopt=Abstracthttp://dx.doi.org/10.1038/nmeth0410-248http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22402017&dopt=Abstracthttp://dx.doi.org/10.1016/j.neurobiolaging.2011.12.037http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21802176&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21802176&dopt=Abstracthttp://dx.doi.org/10.1016/j.neurobiolaging.2011.05.019http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22154821&dopt=Abstracthttp://dx.doi.org/10.1016/j.neurobiolaging.2011.11.012

W. van Rheenen, F. P. Diekstra, G. Lauria, S. Duga, S. Corti, C. Cereda, L. Corrado, G.

Sorarù, K. E. Morrison, K. L. Williams, G. A. Nicholson, I. P. Blair, P. A. Dion, C. S.

Leblond, G. A. Rouleau, O. Hardiman, J. H. Veldink, L. H. van den Berg, A. Al-Chalabi,

H. Pall, P. J. Shaw, M. R. Turner, K. Talbot, F. Taroni, A. García-Redondo, Z. Wu, J. D.

Glass, C. Gellera, A. Ratti, R. H. Brown Jr., V. Silani, C. E. Shaw, J. E. Landers, Exome-

wide rare variant analysis identifies TUBA4A mutations associated with familial ALS.

Neuron 84, 324–331 (2014). Medline doi:10.1016/j.neuron.2014.09.027

18. J. O. Johnson, E. P. Pioro, A. Boehringer, R. Chia, H. Feit, A. E. Renton, H. A. Pliner, Y.

Abramzon, G. Marangi, B. J. Winborn, J. R. Gibbs, M. A. Nalls, S. Morgan, M. Shoai, J.

Hardy, A. Pittman, R. W. Orrell, A. Malaspina, K. C. Sidle, P. Fratta, M. B. Harms, R. H.

Baloh, A. Pestronk, C. C. Weihl, E. Rogaeva, L. Zinman, V. E. Drory, G. Borghero, G.

Mora, A. Calvo, J. D. Rothstein, C. Drepper, M. Sendtner, A. B. Singleton, J. P. Taylor,

M. R. Cookson, G. Restagno, M. Sabatelli, R. Bowser, A. Chiò, B. J. Traynor, Mutations

in the Matrin 3 gene cause familial amyotrophic lateral sclerosis. Nat. Neurosci. 17, 664–

666 (2014). Medline

19. H. M. Kaneb, A. W. Folkmann, V. V. Belzil, L. E. Jao, C. S. Leblond, S. L. Girard, H.

Daoud, A. Noreau, D. Rochefort, P. Hince, A. Szuto, A. Levert, S. Vidal, C. André-

Guimont, W. Camu, J. P. Bouchard, N. Dupré, G. A. Rouleau, S. R. Wente, P. A. Dion,

Deleterious mutations in the essential mRNA metabolism factor, hGle1, in amyotrophic

lateral sclerosis. Hum. Mol. Genet. 24, 1363–1373 (2015). Medline

doi:10.1093/hmg/ddu545

20. A. Chaussenot, I. Le Ber, S. Ait-El-Mkadem, A. Camuzat, A. de Septenville, S. Bannwarth,

E. C. Genin, V. Serre, G. Augé, A. Brice, J. Pouget, V. Paquis-Flucklinger, Screening of

CHCHD10 in a French cohort confirms the involvement of this gene in frontotemporal

dementia with amyotrophic lateral sclerosis patients. Neurobiol. Aging 35, 2884.e1–

2884.e4 (2014). Medline doi:10.1016/j.neurobiolaging.2014.07.022

21. S. Bannwarth, S. Ait-El-Mkadem, A. Chaussenot, E. C. Genin, S. Lacas-Gervais, K. Fragaki,

L. Berg-Alonso, Y. Kageyama, V. Serre, D. G. Moore, A. Verschueren, C. Rouzier, I. Le

Ber, G. Augé, C. Cochaud, F. Lespinasse, K. N’Guyen, A. de Septenville, A. Brice, P.

Yu-Wai-Man, H. Sesaki, J. Pouget, V. Paquis-Flucklinger, A mitochondrial origin for

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25374358&dopt=Abstracthttp://dx.doi.org/10.1016/j.neuron.2014.09.027http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24686783&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25343993&dopt=Abstracthttp://dx.doi.org/10.1093/hmg/ddu545http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25155093&dopt=Abstracthttp://dx.doi.org/10.1016/j.neurobiolaging.2014.07.022

frontotemporal dementia and amyotrophic lateral sclerosis through CHCHD10

involvement. Brain 137, 2329–2345 (2014). Medline doi:10.1093/brain/awu138

22. J. Couthouis, A. R. Raphael, R. Daneshjou, A. D. Gitler, Targeted exon capture and

sequencing in sporadic amyotrophic lateral sclerosis. PLOS Genet. 10, e1004704 (2014).

Medline doi:10.1371/journal.pgen.1004704

23. H. Maruyama, H. Morino, H. Ito, Y. Izumi, H. Kato, Y. Watanabe, Y. Kinoshita, M.

Kamada, H. Nodera, H. Suzuki, O. Komure, S. Matsuura, K. Kobatake, N. Morimoto, K.

Abe, N. Suzuki, M. Aoki, A. Kawata, T. Hirai, T. Kato, K. Ogasawara, A. Hirano, T.

Takumi, H. Kusaka, K. Hagiwara, R. Kaji, H. Kawakami, Mutations of optineurin in

amyotrophic lateral sclerosis. Nature 465, 223–226 (2010). Medline

doi:10.1038/nature08971

24. S. L. Rea, V. Majcher, M. S. Searle, R. Layfield, SQSTM1 mutations—bridging Paget

disease of bone and ALS/FTLD. Exp. Cell Res. 325, 27–37 (2014). Medline

doi:10.1016/j.yexcr.2014.01.020

25. H. Maruyama, H. Kawakami, Optineurin and amyotrophic lateral sclerosis. Geriatr.

Gerontol. Int. 13, 528–532 (2013). Medline doi:10.1111/ggi.12022

26. M. Thomas, J. Alegre-Abarrategui, R. Wade-Martins, RNA dysfunction and aggrephagy at

the centre of an amyotrophic lateral sclerosis/frontotemporal dementia disease

continuum. Brain 136, 1345–1360 (2013). Medline doi:10.1093/brain/awt030

27. D. Kachaner, P. Génin, E. Laplantine, R. Weil, Toward an integrative view of Optineurin

functions. Cell Cycle 11, 2808–2818 (2012). Medline doi:10.4161/cc.20946

28. B. A. Keller, K. Volkening, C. A. Droppelmann, L. C. Ang, R. Rademakers, M. J. Strong,

Co-aggregation of RNA binding proteins in ALS spinal motor neurons: Evidence of a

common pathogenic mechanism. Acta Neuropathol. 124, 733–747 (2012). Medline

doi:10.1007/s00401-012-1035-z

29. E. L. Scotter, C. Vance, A. L. Nishimura, Y. B. Lee, H. J. Chen, H. Urwin, V. Sardone, J. C.

Mitchell, B. Rogelj, D. C. Rubinsztein, C. E. Shaw, Differential roles of the ubiquitin

proteasome system and autophagy in the clearance of soluble and aggregated TDP-43

species. J. Cell Sci. 127, 1263–1278 (2014). Medline doi:10.1242/jcs.140087

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24934289&dopt=Abstracthttp://dx.doi.org/10.1093/brain/awu138http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25299611&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25299611&dopt=Abstracthttp://dx.doi.org/10.1371/journal.pgen.1004704http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=20428114&dopt=Abstracthttp://dx.doi.org/10.1038/nature08971http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24486447&dopt=Abstracthttp://dx.doi.org/10.1016/j.yexcr.2014.01.020http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23279185&dopt=Abstracthttp://dx.doi.org/10.1111/ggi.12022http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23474849&dopt=Abstracthttp://dx.doi.org/10.1093/brain/awt030http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22801549&dopt=Abstracthttp://dx.doi.org/10.4161/cc.20946http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22941224&dopt=Abstracthttp://dx.doi.org/10.1007/s00401-012-1035-zhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24424030&dopt=Abstracthttp://dx.doi.org/10.1242/jcs.140087

30. S. Morton, L. Hesson, M. Peggie, P. Cohen, Enhanced binding of TBK1 by an optineurin

mutant that causes a familial form of primary open angle glaucoma. FEBS Lett. 582,

997–1002 (2008). Medline doi:10.1016/j.febslet.2008.02.047

31. M. Pilli, J. Arko-Mensah, M. Ponpuak, E. Roberts, S. Master, M. A. Mandell, N. Dupont, W.

Ornatowski, S. Jiang, S. B. Bradfute, J. A. Bruun, T. E. Hansen, T. Johansen, V. Deretic,

TBK-1 promotes autophagy-mediated antimicrobial defense by controlling

autophagosome maturation. Immunity 37, 223–234 (2012). Medline

doi:10.1016/j.immuni.2012.04.015

32. C. E. Gleason, A. Ordureau, R. Gourlay, J. S. Arthur, P. Cohen, Polyubiquitin binding to

optineurin is required for optimal activation of TANK-binding kinase 1 and production of

interferon β. J. Biol. Chem. 286, 35663–35674 (2011). Medline

doi:10.1074/jbc.M111.267567

33. P. Wild, H. Farhan, D. G. McEwan, S. Wagner, V. V. Rogov, N. R. Brady, B. Richter, J.

Korac, O. Waidmann, C. Choudhary, V. Dötsch, D. Bumann, I. Dikic, Phosphorylation

of the autophagy receptor optineurin restricts Salmonella growth. Science 333, 228–233

(2011). Medline doi:10.1126/science.1205405

34. J. Korac, V. Schaeffer, I. Kovacevic, A. M. Clement, B. Jungblut, C. Behl, J. Terzic, I. Dikic,

Ubiquitin-independent function of optineurin in autophagic clearance of protein

aggregates. J. Cell Sci. 126, 580–592 (2013). Medline doi:10.1242/jcs.114926

35. Y. C. Wong, E. L. Holzbaur, Optineurin is an autophagy receptor for damaged mitochondria

in parkin-mediated mitophagy that is disrupted by an ALS-linked mutation. Proc. Natl.

Acad. Sci. U.S.A. 111, E4439–E4448 (2014). Medline doi:10.1073/pnas.1405752111

36. J. R. Buchan, R. M. Kolaitis, J. P. Taylor, R. Parker, Eukaryotic stress granules are cleared

by autophagy and Cdc48/VCP function. Cell 153, 1461–1474 (2013). Medline

doi:10.1016/j.cell.2013.05.037

37. M. Ramaswami, J. P. Taylor, R. Parker, Altered ribostasis: RNA-protein granules in

degenerative disorders. Cell 154, 727–736 (2013). Medline

doi:10.1016/j.cell.2013.07.038

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=18307994&dopt=Abstracthttp://dx.doi.org/10.1016/j.febslet.2008.02.047http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22921120&dopt=Abstracthttp://dx.doi.org/10.1016/j.immuni.2012.04.015http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21862579&dopt=Abstracthttp://dx.doi.org/10.1074/jbc.M111.267567http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21617041&dopt=Abstracthttp://dx.doi.org/10.1126/science.1205405http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23178947&dopt=Abstracthttp://dx.doi.org/10.1242/jcs.114926http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25294927&dopt=Abstracthttp://dx.doi.org/10.1073/pnas.1405752111http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23791177&dopt=Abstracthttp://dx.doi.org/10.1016/j.cell.2013.05.037http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23953108&dopt=Abstracthttp://dx.doi.org/10.1016/j.cell.2013.07.038

38. M. Komatsu, S. Kageyama, Y. Ichimura, p62/SQSTM1/A170: Physiology and pathology.

Pharmacol. Res. 66, 457–462 (2012). Medline doi:10.1016/j.phrs.2012.07.004

39. K. Clark, M. Peggie, L. Plater, R. J. Sorcek, E. R. Young, J. B. Madwed, J. Hough, E. G.

McIver, P. Cohen, Novel cross-talk within the IKK family controls innate immunity.

Biochem. J. 434, 93–104 (2011). Medline doi:10.1042/BJ20101701

40. S. Sharma, B. R. tenOever, N. Grandvaux, G. P. Zhou, R. Lin, J. Hiscott, Triggering the

interferon antiviral response through an IKK-related pathway. Science 300, 1148–1151

(2003). Medline doi:10.1126/science.1081315

41. K. A. Fitzgerald, S. M. McWhirter, K. L. Faia, D. C. Rowe, E. Latz, D. T. Golenbock, A. J.

Coyle, S. M. Liao, T. Maniatis, IKKε and TBK1 are essential components of the IRF3

signaling pathway. Nat. Immunol. 4, 491–496 (2003). Medline doi:10.1038/ni921

42. H. Hemmi, O. Takeuchi, S. Sato, M. Yamamoto, T. Kaisho, H. Sanjo, T. Kawai, K. Hoshino,

K. Takeda, S. Akira, The roles of two IκB kinase-related kinases in lipopolysaccharide

and double stranded RNA signaling and viral infection. J. Exp. Med. 199, 1641–1650

(2004). Medline doi:10.1084/jem.20040520

43. S. Liu, X. Cai, J. Wu, Q. Cong, X. Chen, T. Li, F. Du, J. Ren, Y. Wu, N. Grishin, Z. J. Chen,

Phosphorylation of innate immune adaptor proteins MAVS, STING, and TRIF induces

IRF3 activation. Science 10.1126/science.aaa2630 (2015). Medline

44. M. G. Wathelet, C. H. Lin, B. S. Parekh, L. V. Ronco, P. M. Howley, T. Maniatis, Virus

infection induces the assembly of coordinately activated transcription factors on the IFN-

β enhancer in vivo. Mol. Cell 1, 507–518 (1998). Medline doi:10.1016/S1097-

2765(00)80051-9

45. C. K. Glass, K. Saijo, B. Winner, M. C. Marchetto, F. H. Gage, Mechanisms underlying

inflammation in neurodegeneration. Cell 140, 918–934 (2010). Medline

doi:10.1016/j.cell.2010.02.016

46. Y. Yang, A. Hentati, H. X. Deng, O. Dabbagh, T. Sasaki, M. Hirano, W. Y. Hung, K.

Ouahchi, J. Yan, A. C. Azim, N. Cole, G. Gascon, A. Yagmour, M. Ben-Hamida, M.

Pericak-Vance, F. Hentati, T. Siddique, The gene encoding alsin, a protein with three

guanine-nucleotide exchange factor domains, is mutated in a form of recessive

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22841931&dopt=Abstracthttp://dx.doi.org/10.1016/j.phrs.2012.07.004http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21138416&dopt=Abstracthttp://dx.doi.org/10.1042/BJ20101701http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12702806&dopt=Abstracthttp://dx.doi.org/10.1126/science.1081315http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12692549&dopt=Abstracthttp://dx.doi.org/10.1038/ni921http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=15210742&dopt=Abstracthttp://dx.doi.org/10.1084/jem.20040520http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=25636800&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9660935&dopt=Abstracthttp://dx.doi.org/10.1016/S1097-2765(00)80051-9http://dx.doi.org/10.1016/S1097-2765(00)80051-9http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=20303880&dopt=Abstracthttp://dx.doi.org/10.1016/j.cell.2010.02.016

amyotrophic lateral sclerosis. Nat. Genet. 29, 160–165 (2001). Medline

doi:10.1038/ng1001-160

47. J. Li, J. Huang, J. H. Jeong, S. J. Park, R. Wei, J. Peng, Z. Luo, Y. T. Chen, Y. Feng, J. L.

Luo, Selective TBK1/IKKi dual inhibitors with anticancer potency. Int. J. Cancer 134,

1972–1980 (2014). Medline doi:10.1002/ijc.28507

48. M. Kato, T. W. Han, S. Xie, K. Shi, X. Du, L. C. Wu, H. Mirzaei, E. J. Goldsmith, J.

Longgood, J. Pei, N. V. Grishin, D. E. Frantz, J. W. Schneider, S. Chen, L. Li, M. R.

Sawaya, D. Eisenberg, R. Tycko, S. L. McKnight, Cell-free formation of RNA granules:

Low complexity sequence domains form dynamic fibers within hydrogels. Cell 149, 753–

767 (2012). Medline doi:10.1016/j.cell.2012.04.017

49. E. B. Lee, V. M. Lee, J. Q. Trojanowski, Gains or losses: Molecular mechanisms of TDP43-

mediated neurodegeneration. Nat. Rev. Neurosci. 13, 38–50 (2012). Medline

50. Exome Aggregation Consortium (ExAC); http://exac.broadinstitute.org.

51. P. Van Damme, W. Robberecht, Clinical implications of recent breakthroughs in

amyotrophic lateral sclerosis. Curr. Opin. Neurol. 26, 466–472 (2013). Medline

doi:10.1097/WCO.0b013e328364c063

52. C. Münch, A. Rolfs, T. Meyer, Heterozygous S44L missense change of the spastin gene in

amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 9, 251–253 (2008). Medline

doi:10.1080/17482960801900172

53. T. Meyer, A. Schwan, J. S. Dullinger, J. Brocke, K. T. Hoffmann, C. H. Nolte, A. Hopt, U.

Kopp, P. Andersen, J. T. Epplen, P. Linke, Early-onset ALS with long-term survival

associated with spastin gene mutation. Neurology 65, 141–143 (2005). Medline

doi:10.1212/01.wnl.0000167130.31618.0a

54. A. Chesi, B. T. Staahl, A. Jovičić, J. Couthouis, M. Fasolino, A. R. Raphael, T. Yamazaki, L.

Elias, M. Polak, C. Kelly, K. L. Williams, J. A. Fifita, N. J. Maragakis, G. A. Nicholson,

O. D. King, R. Reed, G. R. Crabtree, I. P. Blair, J. D. Glass, A. D. Gitler, Exome

sequencing to identify de novo mutations in sporadic ALS trios. Nat. Neurosci. 16, 851–

855 (2013). Medline

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11586297&dopt=Abstracthttp://dx.doi.org/10.1038/ng1001-160http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=24150799&dopt=Abstracthttp://dx.doi.org/10.1002/ijc.28507http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22579281&dopt=Abstracthttp://dx.doi.org/10.1016/j.cell.2012.04.017http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=22127299&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23945281&dopt=Abstracthttp://dx.doi.org/10.1097/WCO.0b013e328364c063http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=18608088&dopt=Abstracthttp://dx.doi.org/10.1080/17482960801900172http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=16009903&dopt=Abstracthttp://dx.doi.org/10.1212/01.wnl.0000167130.31618.0ahttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23708140&dopt=Abstract

55. A. Manichaikul, J. C. Mychaleckyj, S. S. Rich, K. Daly, M. Sale, W. M. Chen, Robust

relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873

(2010). Medline doi:10.1093/bioinformatics/btq559

56. G. Jun, M. Flickinger, K. N. Hetrick, J. M. Romm, K. F. Doheny, G. R. Abecasis, M.

Boehnke, H. M. Kang, Detecting and estimating contamination of human DNA samples

in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012).

Medline doi:10.1016/j.ajhg.2012.09.004

57. H. Li, R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler transform.

Bioinformatics 25, 1754–1760 (2009). Medline doi:10.1093/bioinformatics/btp324

58. A. McKenna, M. Hanna, E. Banks, A. Sivachenko, K. Cibulskis, A. Kernytsky, K.

Garimella, D. Altshuler, S. Gabriel, M. Daly, M. A. DePristo, The Genome Analysis

Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data.

Genome Res. 20, 1297–1303 (2010). Medline doi:10.1101/gr.107524.110

59. Exome Variant Server (NHLBI GO Exome Sequencing Project, Seattle, WA);

http://evs.gs.washington.edu/EVS.

60. G. R. Abecasis, A. Auton, L. D. Brooks, M. A. DePristo, R. M. Durbin, R. E. Handsaker, H.

M. Kang, G. T. Marth, G. A. McVean, An integrated map of genetic variation from 1,092

human genomes. Nature 491, 56–65 (2012). Medline

61. S. Petrovski, Q. Wang, E. L. Heinzen, A. S. Allen, D. B. Goldstein, Genic intolerance to

functional variation and the interpretation of personal genomes. PLOS Genet. 9,

e1003709 (2013). Medline doi:10.1371/journal.pgen.1003709

62. R Core Team, “A language and environment for statistical computing” (R Foundation for

Statistical Computing, Vienna, 2013); www.R-project.org.

63. C. Behrends, M. E. Sowa, S. P. Gygi, J. W. Harper, Network organization of the human

autophagy system. Nature 466, 68–76 (2010). Medline doi:10.1038/nature09204

64. M. E. Sowa, E. J. Bennett, S. P. Gygi, J. W. Harper, Defining the human deubiquitinating

enzyme interaction landscape. Cell 138, 389–403 (2009). Medline

doi:10.1016/j.cell.2009.04.042

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=20926424&dopt=Abstracthttp://dx.doi.org/10.1093/bioinformatics/btq559http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23103226&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23103226&dopt=Abstracthttp://dx.doi.org/10.1016/j.ajhg.2012.09.004http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=19451168&dopt=Abstracthttp://dx.doi.org/10.1093/bioinformatics/btp324http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=20644199&dopt=Abstracthttp://dx.doi.org/10.1101/gr.107524.110http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23128226&dopt=Abstracthttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=23990802&dopt=Abstracthttp://dx.doi.org/10.1371/journal.pgen.1003709http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=20562859&dopt=Abstracthttp://dx.doi.org/10.1038/nature09204http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=19615732&dopt=Abstracthttp://dx.doi.org/10.1016/j.cell.2009.04.042

65. E. L. Huttlin, M. P. Jedrychowski, J. E. Elias, T. Goswami, R. Rad, S. A. Beausoleil, J.

Villén, W. Haas, M. E. Sowa, S. P. Gygi, A tissue-specific atlas of mouse protein

phosphorylation and expression. Cell 143, 1174–1189 (2010). Medline

doi:10.1016/j.cell.2010.12.001

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21183079&dopt=Abstracthttp://dx.doi.org/10.1016/j.cell.2010.12.001

Cirulli-SM.page.1-REVISED.pdfExome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways

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