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. 2017 Feb 9;542(7640):186-190.
doi: 10.1038/nature21039. Epub 2017 Feb 1.

Rare and Low-Frequency Coding Variants Alter Human Adult Height

Eirini Marouli  1 Mariaelisa Graff  2 Carolina Medina-Gomez  3   4 Ken Sin Lo  5 Andrew R Wood  6 Troels R Kjaer  7 Rebecca S Fine  8   9   10 Yingchang Lu  11   12   13 Claudia Schurmann  12   13 Heather M Highland  2   14 Sina Rüeger  15   16 Gudmar Thorleifsson  17 Anne E Justice  2 David Lamparter  16   18 Kathleen E Stirrups  1   19 Valérie Turcot  5 Kristin L Young  2 Thomas W Winkler  20 Tõnu Esko  8   10   21 Tugce Karaderi  22 Adam E Locke  23   24 Nicholas G D Masca  25   26 Maggie C Y Ng  27   28 Poorva Mudgal  27 Manuel A Rivas  8   29 Sailaja Vedantam  8   9   10 Anubha Mahajan  22 Xiuqing Guo  30 Goncalo Abecasis  23 Katja K Aben  31   32 Linda S Adair  33 Dewan S Alam  34 Eva Albrecht  35 Kristine H Allin  36 Matthew Allison  37 Philippe Amouyel  38   39   40 Emil V Appel  36 Dominique Arveiler  41   42 Folkert W Asselbergs  43   44   45 Paul L Auer  46 Beverley Balkau  47 Bernhard Banas  48 Lia E Bang  49 Marianne Benn  50   51 Sven Bergmann  16   18 Lawrence F Bielak  52 Matthias Blüher  53   54 Heiner Boeing  55 Eric Boerwinkle  56   57 Carsten A Böger  48 Lori L Bonnycastle  58 Jette Bork-Jensen  36 Michiel L Bots  59 Erwin P Bottinger  12 Donald W Bowden  27   28   60 Ivan Brandslund  61   62 Gerome Breen  63 Murray H Brilliant  64 Linda Broer  4 Amber A Burt  65 Adam S Butterworth  66   67 David J Carey  68 Mark J Caulfield  1   69 John C Chambers  70   71   72 Daniel I Chasman  8   73   74   75 Yii-Der Ida Chen  30 Rajiv Chowdhury  66 Cramer Christensen  76 Audrey Y Chu  74   77 Massimiliano Cocca  78 Francis S Collins  58 James P Cook  79 Janie Corley  80   81 Jordi Corominas Galbany  82 Amanda J Cox  27   28   83 Gabriel Cuellar-Partida  84   85 John Danesh  66   67   86   87 Gail Davies  80   81 Paul I W de Bakker  59   88 Gert J de Borst  89 Simon de Denus  5   90 Mark C H de Groot  91   92 Renée de Mutsert  93 Ian J Deary  80   81 George Dedoussis  94 Ellen W Demerath  95 Anneke I den Hollander  96 Joe G Dennis  97 Emanuele Di Angelantonio  66   67 Fotios Drenos  98   99 Mengmeng Du  100   101 Alison M Dunning  102 Douglas F Easton  97   102 Tapani Ebeling  103   104 Todd L Edwards  105 Patrick T Ellinor  106   107 Paul Elliott  108 Evangelos Evangelou  71   109 Aliki-Eleni Farmaki  94 Jessica D Faul  110 Mary F Feitosa  111 Shuang Feng  23 Ele Ferrannini  112   113 Marco M Ferrario  114 Jean Ferrieres  115 Jose C Florez  106   107   116 Ian Ford  117 Myriam Fornage  118 Paul W Franks  119   120   121 Ruth Frikke-Schmidt  51   122 Tessel E Galesloot  32 Wei Gan  22 Ilaria Gandin  123 Paolo Gasparini  123   124 Vilmantas Giedraitis  125 Ayush Giri  105 Giorgia Girotto  123   124 Scott D Gordon  85 Penny Gordon-Larsen  126   127 Mathias Gorski  20   48 Niels Grarup  36 Megan L Grove  56 Vilmundur Gudnason  128   129 Stefan Gustafsson  130 Torben Hansen  36 Kathleen Mullan Harris  126   131 Tamara B Harris  132 Andrew T Hattersley  133 Caroline Hayward  134 Liang He  135   136 Iris M Heid  20   35 Kauko Heikkilä  136   137 Øyvind Helgeland  138   139 Jussi Hernesniemi  140   141   142 Alex W Hewitt  143   144   145 Lynne J Hocking  146   147 Mette Hollensted  36 Oddgeir L Holmen  148 G Kees Hovingh  149 Joanna M M Howson  66 Carel B Hoyng  96 Paul L Huang  106 Kristian Hveem  150 M Arfan Ikram  3   151   152 Erik Ingelsson  130   153 Anne U Jackson  23 Jan-Håkan Jansson  154   155 Gail P Jarvik  65   156 Gorm B Jensen  157 Min A Jhun  52 Yucheng Jia  30 Xuejuan Jiang  158   159 Stefan Johansson  139   160 Marit E Jørgensen  161   162 Torben Jørgensen  51   163   164 Pekka Jousilahti  165 J Wouter Jukema  166   167 Bratati Kahali  168   169   170 René S Kahn  171 Mika Kähönen  172 Pia R Kamstrup  50 Stavroula Kanoni  1 Jaakko Kaprio  136   137   165 Maria Karaleftheri  173 Sharon L R Kardia  52 Fredrik Karpe  174   175 Frank Kee  176 Renske Keeman  177 Lambertus A Kiemeney  32 Hidetoshi Kitajima  22 Kirsten B Kluivers  32 Thomas Kocher  178 Pirjo Komulainen  179 Jukka Kontto  165 Jaspal S Kooner  70   72   180 Charles Kooperberg  181 Peter Kovacs  53 Jennifer Kriebel  182   183   184 Helena Kuivaniemi  68   185 Sébastien Küry  186 Johanna Kuusisto  187 Martina La Bianca  188 Markku Laakso  187 Timo A Lakka  179   189 Ethan M Lange  190 Leslie A Lange  190 Carl D Langefeld  191 Claudia Langenberg  192 Eric B Larson  65   193   194 I-Te Lee  195   196   197 Terho Lehtimäki  141   142 Cora E Lewis  198 Huaixing Li  199 Jin Li  200 Ruifang Li-Gao  93 Honghuang Lin  201 Li-An Lin  118 Xu Lin  199 Lars Lind  202 Jaana Lindström  165 Allan Linneberg  51   164   203 Yeheng Liu  30 Yongmei Liu  204 Artitaya Lophatananon  205 Jian'an Luan  192 Steven A Lubitz  106   107 Leo-Pekka Lyytikäinen  141   142 David A Mackey  144 Pamela A F Madden  206 Alisa K Manning  106   107   116 Satu Männistö  165 Gaëlle Marenne  86 Jonathan Marten  134 Nicholas G Martin  85 Angela L Mazul  2 Karina Meidtner  182   207 Andres Metspalu  21 Paul Mitchell  208 Karen L Mohlke  190 Dennis O Mook-Kanamori  93   209 Anna Morgan  123 Andrew D Morris  210 Andrew P Morris  22   79 Martina Müller-Nurasyid  35   211   212 Patricia B Munroe  1   69 Mike A Nalls  213 Matthias Nauck  214   215 Christopher P Nelson  25   26 Matt Neville  174   175 Sune F Nielsen  50   51 Kjell Nikus  216 Pål R Njølstad  138   139 Børge G Nordestgaard  50   51 Ioanna Ntalla  1 Jeffrey R O'Connel  217 Heikki Oksa  218 Loes M Olde Loohuis  219 Roel A Ophoff  171   219 Katharine R Owen  174   175 Chris J Packard  117 Sandosh Padmanabhan  117 Colin N A Palmer  220 Gerard Pasterkamp  221   222 Aniruddh P Patel  8   75   106 Alison Pattie  81 Oluf Pedersen  36 Peggy L Peissig  64 Gina M Peloso  106   107 Craig E Pennell  223 Markus Perola  165   224   225 James A Perry  217 John R B Perry  192 Thomas N Person  64 Ailith Pirie  102 Ozren Polasek  210   226 Danielle Posthuma  227   228 Olli T Raitakari  229   230 Asif Rasheed  231 Rainer Rauramaa  179   232 Dermot F Reilly  233 Alex P Reiner  181   234 Frida Renström  119   235 Paul M Ridker  74   75   236 John D Rioux  5   237 Neil Robertson  22   174 Antonietta Robino  188 Olov Rolandsson  154   238 Igor Rudan  210 Katherine S Ruth  6 Danish Saleheen  231   239 Veikko Salomaa  165 Nilesh J Samani  25   26 Kevin Sandow  30 Yadav Sapkota  85 Naveed Sattar  117 Marjanka K Schmidt  177 Pamela J Schreiner  240 Matthias B Schulze  182   207 Robert A Scott  192 Marcelo P Segura-Lepe  71 Svati Shah  241 Xueling Sim  23   242 Suthesh Sivapalaratnam  106   243   244 Kerrin S Small  245 Albert Vernon Smith  128   129 Jennifer A Smith  52 Lorraine Southam  22   86 Timothy D Spector  245 Elizabeth K Speliotes  168   169   170 John M Starr  80   246 Valgerdur Steinthorsdottir  17 Heather M Stringham  23 Michael Stumvoll  53   54 Praveen Surendran  66 Leen M 't Hart  247   248   249 Katherine E Tansey  250   251 Jean-Claude Tardif  5   237 Kent D Taylor  30 Alexander Teumer  252 Deborah J Thompson  97 Unnur Thorsteinsdottir  17   128 Betina H Thuesen  164 Anke Tönjes  253 Gerard Tromp  68   254 Stella Trompet  166   255 Emmanouil Tsafantakis  256 Jaakko Tuomilehto  165   257   258   259 Anne Tybjaerg-Hansen  51   122 Jonathan P Tyrer  102 Rudolf Uher  260 André G Uitterlinden  3   4 Sheila Ulivi  188 Sander W van der Laan  222 Andries R Van Der Leij  261 Cornelia M van Duijn  3 Natasja M van Schoor  247 Jessica van Setten  43 Anette Varbo  50   51 Tibor V Varga  119 Rohit Varma  159 Digna R Velez Edwards  262 Sita H Vermeulen  32 Henrik Vestergaard  36 Veronique Vitart  134 Thomas F Vogt  263 Diego Vozzi  124 Mark Walker  264 Feijie Wang  199 Carol A Wang  223 Shuai Wang  265 Yiqin Wang  199 Nicholas J Wareham  192 Helen R Warren  1   69 Jennifer Wessel  266 Sara M Willems  192 James G Wilson  267 Daniel R Witte  268   269 Michael O Woods  270 Ying Wu  190 Hanieh Yaghootkar  6 Jie Yao  30 Pang Yao  199 Laura M Yerges-Armstrong  217   271 Robin Young  66   117 Eleftheria Zeggini  86 Xiaowei Zhan  272 Weihua Zhang  70   71 Jing Hua Zhao  192 Wei Zhao  239 Wei Zhao  52 He Zheng  199 Wei Zhou  168   169 EPIC-InterAct ConsortiumCHD Exome+ ConsortiumExomeBP ConsortiumT2D-Genes ConsortiumGoT2D Genes ConsortiumGlobal Lipids Genetics ConsortiumReproGen ConsortiumMAGIC InvestigatorsJerome I Rotter  30 Michael Boehnke  23 Sekar Kathiresan  8   75   106 Mark I McCarthy  22   174   175 Cristen J Willer  168   169   273 Kari Stefansson  17   128 Ingrid B Borecki  111 Dajiang J Liu  274 Kari E North  275 Nancy L Heard-Costa  77   276 Tune H Pers  36   277 Cecilia M Lindgren  22   278 Claus Oxvig  7 Zoltán Kutalik  15   16 Fernando Rivadeneira  3   4 Ruth J F Loos  12   13   279 Timothy M Frayling  6 Joel N Hirschhorn  8   10   280 Panos Deloukas  1   281 Guillaume Lettre  5   237
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Free PMC article

Rare and Low-Frequency Coding Variants Alter Human Adult Height

Eirini Marouli et al. Nature. .
Free PMC article

Abstract

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

Figures

Extended Data Figure 1
Extended Data Figure 1
Flowchart of the GIANT ExomeChip height study design.
Extended Data Figure 2
Extended Data Figure 2
Height ExomeChip association results. (A) Quantile-quantile plot of ExomeChip variants and their association to adult height under an additive genetic model in individuals of European ancestry. We stratified results based on allele frequency. (B) Manhattan plot of all ExomeChip variants and their association to adult height under an additive genetic model in individuals of European ancestry with a focus on the 553 independent SNPs, of which 469 have MAF>5% (grey), 55 have MAF between 1 and 5% (green), and 29 have MAF<1% (blue). (C) Linkage disequilibrium (LD) score regression analysis for the height association results in European-ancestry studies. In the plot, each point represents an LD Score quantile, where the x-axis of the point is the mean LD Score of variants in that quantile and the y-axis is the mean χ2 statistic of variants in that quantile. The LD Score regression slope of the black line is calculated based on Equation 1 in Bulik-Sullivan et al. which is estimated upwards due to the small number of common variants (N=15,848) and the design of the ExomeChip. The LD score regression intercept is 1.4, the λGC is 2.7, the mean χ2 is 7.0, and the ratio statistic of (intercept -1) / (mean χ2 -1) is 0.067 (standard error=0.012). (D) Scatter plot comparison of the effect sizes for all variants that reached significance in the European-ancestry discovery results (N=381,625) and results including only studies with sample sizes >5000 individuals (N=241,453).
Extended Data Figure 3
Extended Data Figure 3
Height ExomeChip association results in African-ancestry populations. Among the all-ancestry results, we found eight variants for which the genetic association with height is mostly driven by individuals of African ancestry. The minor allele frequency of these variants is <1% (or monomorphic) in all ancestries except African-ancestry individuals. In individuals of African ancestry, the variants had allele frequencies between 9 and 40%.
Extended Data Figure 4
Extended Data Figure 4
Concordance between direct conditional effect sizes using UK Biobank (x-axis) and conditional analysis performed using a combination of imputation-based methodology and approximate conditional analysis (SSimp, y-axis). The Pearson's correlation coefficient is r=0.85. The dashed line indicates the identity line. The 95% confidence interval is indicated in both directions. Red, SNPs with Pcond>0.05 in the UK Biobank; Green, SNPs with Pcond≤0.05 in the UK Biobank.
Extended Data Figure 5
Extended Data Figure 5
Heritability estimated for all known height variants in the first release of the UK Biobank dataset. (A) We observed a weak but significant positive trend between minor allele frequency (MAF) and heritability explained (P=0.012). (B) Average heritability explained per variant when stratifying the analyses by allele frequency or genomic annotation. For heritability estimations in UKBB, variants were pruned to r2< 0.2 in the 1000 Genomes Project data set, and the heritability figures are based on h2=80% for height.
Extended Data Figure 6
Extended Data Figure 6
Comparison of DEPICT gene set enrichment results based on coding variation from ExomeChip (EC) or non-coding variation from genome-wide association study data (GWAS). The x-axis indicates the P-value for enrichment of a given gene set using DEPICT adapted for EC data, where the input to DEPICT is the genes implicated by coding EC variants that are independent of known GWAS signals. The y-axis indicates the P-value for gene set enrichment using DEPICT, using as input the GWAS loci that do not overlap the coding signals. Each point represents a meta-gene set, and the best P-value for any gene set within the meta-gene set is shown. Only significant (false discovery rate < 0.01) gene set enrichment results are plotted. Colors correspond to whether the meta-gene set was significant for EC only (blue), GWAS only (green), both but more significant for EC (purple), or both but more significant for GWAS (orange), and the most significant gene sets within each category are labeled. A line is drawn at x = y for ease of comparison.
Extended Data Figure 7
Extended Data Figure 7
Heat map showing entire DEPICT gene set enrichment results (analogous to Fig. 2 in the main text). For any given square, the color indicates how strongly the corresponding gene (shown on the x-axis) is predicted to belong to the reconstituted gene set (y-axis). This value is based on the gene's Z-score for gene set inclusion in DEPICT's reconstituted gene sets, where red indicates a higher Z-score and blue indicates a lower one. The proteoglycan binding pathway was uniquely implicated by coding variants (as opposed to common variants) by both DEPICT and the Pascal method. To visually reduce redundancy and increase clarity, we chose one representative “meta-gene set” for each group of highly correlated gene sets based on affinity propagation clustering (see Methods and Supplementary Information). Heat map intensity and DEPICT p-values correspond to the most significantly enriched gene set within the meta-gene set; meta-gene sets are listed with their database source. Annotations for the genes indicate whether the gene has OMIM annotation as underlying a disorder of skeletal growth (black and grey) and the minor allele frequency of the significant EC variant (shades of blue; if multiple variants, the lowest-frequency variant was kept). Annotations for the gene sets indicate if the gene set was also found significant for EC by the Pascal method (yellow and grey) and if the gene set was found significant by DEPICT for EC only or for both EC and GWAS (purple and green). Abbreviations: GO: Gene Ontology; KEGG: Kyoto encyclopedia of genes and genomes; MP: mouse phenotype in the Mouse Genetics Initiative; PPI: protein-protein interaction in the InWeb database.
Extended Data Figure 8
Extended Data Figure 8
Heatmaps showing associations of the height variants to other complex traits; −log10(P-values) are oriented with beta effect direction for the alternate allele, white are missing values, yellow are non-significant (P>0.05), green to blue shading for hits with positive beta in the other trait and P-values between 0.05 and <2×10-7 and, orange to red shading for hits with negative beta in the other trait and P-values between 0.05 to <2×10-7. Short and tall labels are given for the minor alleles. Clustering is done by the complete linkage method with Euclidean distance measure for the loci. Clusters highlight SNPs that are more significantly associated with the same set of traits. (A) Variants for which the minor allele is the height-decreasing allele. (B) Variants for which the minor allele is the height-increasing allele.
Figure 1
Figure 1
Variants with a larger effect size on height variation tend to be rarer. We observed an inverse relationship between the effect size (from the combined “discovery+validation” analysis, in cm on the y-axis) and the minor allele frequency (MAF) for the height variants (x-axis, from 0 to 50%). We included in this figure the 606 height variants with P<2×10-7.
Figure 2
Figure 2
Heat map showing subset of DEPICT gene set enrichment results. The full heat map is available as Extended Data Fig. 7. For any given square, the color indicates how strongly the corresponding gene (shown on the x-axis) is predicted to belong to the reconstituted gene set (y-axis). This value is based on the gene's Z-score for gene set inclusion in DEPICT's reconstituted gene sets, where red indicates a higher Z-score and blue indicates a lower one. The proteogly can binding pathway (bold) was uniquely implicated by coding variants by DEPICT and PASCAL. To visually reduce redundancy and increase clarity, we chose one representative “meta-gene set” for each group of highly correlated gene sets based on affinity propagation clustering (Supplementary Information). Heat map intensity and DEPICT P-values correspond to the most significantly enriched gene set within the meta-gene set; meta-gene sets are listed with their database source. Annotations for the genes indicate whether the gene has OMIM annotation as underlying a disorder of skeletal growth (black and grey) and the minor allele frequency of the significant ExomeChip (EC) variant (shades of blue; if multiple variants, the lowest-frequency variant was kept). Annotations for the gene sets indicate if the gene set was also found significant for EC by PASCAL (yellow and grey) and if the gene set was found significant by DEPICT for EC only or for both EC and GWAS (purple and green). Abbreviations: GO: Gene Ontology; MP: mouse phenotype in the Mouse Genetics Initiative; PPI: protein-protein interaction in the In Web database.
Figure 3
Figure 3
STC2 mutants p.Arg44Leu (R44L) and p.Met86Ile (M86I) show compromised proteolytic inhibition of PAPP-A. (A) Schematic representation of the role of STC2 in IGF-1 signaling. Partial inactivation of STC2 by height-associated DNA sequence variation could increase bioactive IGF-1 through reduced inhibition of PAPP-A. (B) Western blot analysis of recombinant STC2 wild-type and variants R44L and M86I. (C) Covalent complex formation between PAPP-A and STC2 wild-type or variants R44L and M86I. Separately synthesized proteins were analyzed by PAPP-A Western blotting following incubation for 8 h. In the absence of STC2 (Mock lane), PAPP-A appears as a single 400 kDa band (*). Following incubation with wild-type STC2, the majority of PAPP-A is present as the approximately 500 kDa covalent PAPP-A:STC2 complex (#), in which PAPP-A is devoid of proteolytic activity towards IGFBP-4. Under similar conditions, incubation with variants R44L or M86I appeared to cause less covalent complex formation with PAPP-A. The gels are representative of at least three independent experiments. (D) PAPP-A proteolytic cleavage of IGFBP-4 following incubation with wild-type STC2 or variants for 1-24 h. Wild-type STC2 causes reduction in PAPP-A activity, with complete inhibition of activity following 24 h incubation. Both STC2 variants show increased IGFBP-4 cleavage (i.e. less inhibition) for all time points analyzed. Mean and standard deviations of three independent experiments are shown. One-way repeated measures analysis of variance followed by Dunnett's post-test showed significant differences between STC2 wild-type and variants R44L (P<0.001) and M86I (P<0.01).

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