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Comparative Study
. 2016 Sep;48(9):1043-8.
doi: 10.1038/ng.3622. Epub 2016 Jul 25.

Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis

Wouter van Rheenen  1 , Aleksey Shatunov  2 , Annelot M Dekker  1 , Russell L McLaughlin  3 , Frank P Diekstra  1 , Sara L Pulit  4 , Rick A A van der Spek  1 , Urmo Võsa  5 , Simone de Jong  6   7 , Matthew R Robinson  8 , Jian Yang  8 , Isabella Fogh  2   9 , Perry Tc van Doormaal  1 , Gijs H P Tazelaar  1 , Max Koppers  1   10 , Anna M Blokhuis  1   10 , William Sproviero  2 , Ashley R Jones  2 , Kevin P Kenna  11 , Kristel R van Eijk  1 , Oliver Harschnitz  1   10 , Raymond D Schellevis  1 , William J Brands  1 , Jelena Medic  1 , Androniki Menelaou  4 , Alice Vajda  12   13 , Nicola Ticozzi  9   14 , Kuang Lin  2 , Boris Rogelj  15   16 , Katarina Vrabec  17 , Metka Ravnik-Glavač  17   18 , Blaž Koritnik  19 , Janez Zidar  19 , Lea Leonardis  19 , Leja Dolenc Grošelj  19 , Stéphanie Millecamps  20 , François Salachas  20   21   22 , Vincent Meininger  23   24 , Mamede de Carvalho  25   26 , Susana Pinto  25   26 , Jesus S Mora  27 , Ricardo Rojas-García  28   29 , Meraida Polak  30   31 , Siddharthan Chandran  32   33 , Shuna Colville  32 , Robert Swingler  32 , Karen E Morrison  34 , Pamela J Shaw  35 , John Hardy  36 , Richard W Orrell  37 , Alan Pittman  36   38 , Katie Sidle  37 , Pietro Fratta  39 , Andrea Malaspina  40   41 , Simon Topp  2 , Susanne Petri  42 , Susanne Abdulla  43 , Carsten Drepper  44 , Michael Sendtner  44 , Thomas Meyer  45 , Roel A Ophoff  46   47   48 , Kim A Staats  48 , Martina Wiedau-Pazos  49 , Catherine Lomen-Hoerth  50 , Vivianna M Van Deerlin  51 , John Q Trojanowski  51 , Lauren Elman  52 , Leo McCluskey  52 , A Nazli Basak  53 , Ceren Tunca  53 , Hamid Hamzeiy  53 , Yesim Parman  54 , Thomas Meitinger  55 , Peter Lichtner  55 , Milena Radivojkov-Blagojevic  55 , Christian R Andres  56 , Cindy Maurel  56 , Gilbert Bensimon  57   58   59 , Bernhard Landwehrmeyer  60 , Alexis Brice  61   62   63   64   65 , Christine A M Payan  57   59 , Safaa Saker-Delye  66 , Alexandra Dürr  67 , Nicholas W Wood  68 , Lukas Tittmann  69 , Wolfgang Lieb  69 , Andre Franke  70 , Marcella Rietschel  71 , Sven Cichon  72   73   74   75   76 , Markus M Nöthen  72   73 , Philippe Amouyel  77 , Christophe Tzourio  78 , Jean-François Dartigues  78 , Andre G Uitterlinden  79   80 , Fernando Rivadeneira  79   80 , Karol Estrada  79 , Albert Hofman  80   81 , Charles Curtis  6   7 , Hylke M Blauw  1 , Anneke J van der Kooi  82 , Marianne de Visser  82 , An Goris  83 , Markus Weber  84 , Christopher E Shaw  2 , Bradley N Smith  2 , Orietta Pansarasa  85 , Cristina Cereda  85 , Roberto Del Bo  86 , Giacomo P Comi  86 , Sandra D'Alfonso  87 , Cinzia Bertolin  88 , Gianni Sorarù  88 , Letizia Mazzini  89 , Viviana Pensato  90 , Cinzia Gellera  90 , Cinzia Tiloca  9 , Antonia Ratti  9   14 , Andrea Calvo  91   92 , Cristina Moglia  91   92 , Maura Brunetti  91   92 , Simona Arcuti  93 , Rosa Capozzo  93 , Chiara Zecca  93 , Christian Lunetta  94 , Silvana Penco  95 , Nilo Riva  96 , Alessandro Padovani  97 , Massimiliano Filosto  97 , Bernard Muller  98 , Robbert Jan Stuit  98 ; PARALS Registry; SLALOM Group; SLAP Registry; FALS Sequencing Consortium; SLAGEN Consortium; NNIPPS Study Group; Ian Blair  99 , Katharine Zhang  99 , Emily P McCann  99 , Jennifer A Fifita  99 , Garth A Nicholson  99   100 , Dominic B Rowe  99 , Roger Pamphlett  101 , Matthew C Kiernan  102 , Julian Grosskreutz  103 , Otto W Witte  103 , Thomas Ringer  103 , Tino Prell  103 , Beatrice Stubendorff  103 , Ingo Kurth  104 , Christian A Hübner  104 , P Nigel Leigh  105 , Federico Casale  91 , Adriano Chio  91   92 , Ettore Beghi  106 , Elisabetta Pupillo  106 , Rosanna Tortelli  93 , Giancarlo Logroscino  107   108 , John Powell  2 , Albert C Ludolph  60 , Jochen H Weishaupt  60 , Wim Robberecht  83   109   110 , Philip Van Damme  83   109   110 , Lude Franke  5 , Tune H Pers  111   112   113   114   115 , Robert H Brown  11 , Jonathan D Glass  30   31 , John E Landers  11 , Orla Hardiman  12   13 , Peter M Andersen  60   116 , Philippe Corcia  56   117   118 , Patrick Vourc'h  56 , Vincenzo Silani  9   14 , Naomi R Wray  8 , Peter M Visscher  8   119 , Paul I W de Bakker  4   120 , Michael A van Es  1 , R Jeroen Pasterkamp  10 , Cathryn M Lewis  6   121 , Gerome Breen  6   7 , Ammar Al-Chalabi  2 , Leonard H van den Berg  1 , Jan H Veldink  1
Affiliations
Comparative Study

Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis

Wouter van Rheenen et al. Nat Genet. 2016 Sep.

Abstract

To elucidate the genetic architecture of amyotrophic lateral sclerosis (ALS) and find associated loci, we assembled a custom imputation reference panel from whole-genome-sequenced patients with ALS and matched controls (n = 1,861). Through imputation and mixed-model association analysis in 12,577 cases and 23,475 controls, combined with 2,579 cases and 2,767 controls in an independent replication cohort, we fine-mapped a new risk locus on chromosome 21 and identified C21orf2 as a gene associated with ALS risk. In addition, we identified MOBP and SCFD1 as new associated risk loci. We established evidence of ALS being a complex genetic trait with a polygenic architecture. Furthermore, we estimated the SNP-based heritability at 8.5%, with a distinct and important role for low-frequency variants (frequency 1-10%). This study motivates the interrogation of larger samples with full genome coverage to identify rare causal variants that underpin ALS risk.

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Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1
Comparison of imputation accuracy. (a,b) Aggregate r2 values between imputed and sequenced genotypes on chromosome 20 are shown when using different reference panels for imputation. Allele frequencies were calculated from the Dutch samples included in the Genome of the Netherlands (GoNL) cohort. The highest imputation accuracy was achieved when imputing from the merged custom and 1000 Genomes Project (1000GP) panel. The difference in accuracy was most pronounced for low-frequency alleles (frequency 0.5–10%) in ALS cases from both the Netherlands (a) and the UK (b).
Figure 2
Figure 2
Meta-analysis and LMM associations. (a) Manhattan plot for the meta-analysis results. This approach yielded four genome-wide-significant associations. The associated SNP in C21orf2 is a nonsynonymous variant not found to be associated in previous GWAS. (b) Manhattan plot for the LMM results. This analysis yielded three loci in addition to those identified by meta-analysis with associations that reached genome-wide significance (MOBP, LOC101927815 and SCFD1). The association for SNPs in the previously identified ALS risk gene TBK1 approached genome-wide significance (P = 6.6 × 10−8). As the C21orf2 SNP was removed from a Swedish stratum because of MAF <1%, this SNP was tested separately, but it is presented here together with all SNPs with MAF >1% in all strata. LOC101927815 is shown in gray because the association for this locus could not be replicated. Loci are labeled by the name of the nearest gene. The dotted lines correspond to the significance threshold of P = 5 × 10−8.
Figure 3
Figure 3
Partitioned heritability. (a) Heritability estimates for each chromosome were significantly correlated with chromosome length (P = 4.9 × 10−4). (b) For ALS, there was a clear trend where more heritability was explained by the low-frequency alleles. This effect was still observed when, for a fair comparison between ALS and a previous study partitioning heritability for schizophrenia (SCZ) using identical methods22, SNPs present in HapMap 3 (HM3) were included. Error bars correspond to standard errors.
See this image and copyright information in PMC

References

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