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[Preprint]. 2023 Jun 20:2023.06.14.23291322.
doi: 10.1101/2023.06.14.23291322.

Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Katherine A Kentistou  1 , Lena R Kaisinger  1 , Stasa Stankovic  1 , Marc Vaudel  2   3 , Edson M de Oliveira  4 , Andrea Messina  5 , Robin G Walters  6   7 , Xiaoxi Liu  8 , Alexander S Busch  9   10 , Hannes Helgason  11   12 , Deborah J Thompson  13 , Federico Santon  5 , Konstantin M Petricek  14 , Yassine Zouaghi  5 , Isabel Huang-Doran  4 , Daniel F Gudbjartsson  11   12 , Eirik Bratland  2   15 , Kuang Lin  6 , Eugene J Gardner  1 , Yajie Zhao  1 , Raina Jia  1 , Chikashi Terao  8   16   17 , Margie Riggan  18 , Manjeet K Bolla  13 , Mojgan Yazdanpanah  19 , Nahid Yazdanpanah  19 , Jonath P Bradfield  20   21 , Linda Broer  22   23 , Archie Campbell  24 , Daniel I Chasman  25 , Diana L Cousminer  26   27   28 , Nora Franceschini  29 , Lude H Franke  30 , Giorgia Girotto  31   32 , Chunyan He  33   34 , Marjo-Riitta Järvelin  35   36   37   38   39 , Peter K Joshi  40 , Yoichiro Kamatani  41 , Robert Karlsson  42 , Jian'an Luan  1 , Kathryn L Lunetta  43   44 , Reedik Mägi  45 , Massimo Mangino  46   47 , Sarah E Medland  48   49   50 , Christa Meisinger  51 , Raymond Noordam  52 , Teresa Nutile  53 , Maria Pina Concas  31 , Ozren Polašek  54   55 , Eleonora Porcu  56   57 , Susan M Ring  58   59 , Cinzia Sala  60 , Albert V Smith  61   62 , Toshiko Tanaka  63 , Peter J van der Most  64 , Veronique Vitart  65 , Carol A Wang  66   67 , Gonneke Willemsen  68 , Marek Zygmunt  69 , Thomas U Ahearn  70 , Irene L Andrulis  71   72 , Hoda Anton-Culver  73 , Antonis C Antoniou  13 , Paul L Auer  74 , Catriona Lk Barnes  40 , Matthias W Beckmann  75 , Amy Berrington  76 , Natalia V Bogdanova  77   78   79 , Stig E Bojesen  80   81 , Hermann Brenner  82   83   84 , Julie E Buring  25 , Federico Canzian  85 , Jenny Chang-Claude  86   87 , Fergus J Couch  88 , Angela Cox  89 , Laura Crisponi  56 , Kamila Czene  42 , Mary B Daly  90 , Ellen W Demerath  91 , Joe Dennis  13 , Peter Devilee  92   93 , Immaculata De Vivo  94   95 , Thilo Dörk  78 , Alison M Dunning  96 , Miriam Dwek  97 , Johan G Eriksson  98 , Peter A Fasching  75 , Lindsay Fernandez-Rhodes  99 , Liana Ferreli  56 , Olivia Fletcher  100 , Manuela Gago-Dominguez  101 , Montserrat García-Closas  70 , José A García-Sáenz  102 , Anna González-Neira  103 , Harald Grallert  104   105 , Pascal Guénel  106 , Christopher A Haiman  107 , Per Hall  42   108 , Ute Hamann  109 , Hakon Hakonarson  21   26   110   111 , Roger J Hart  112 , Martha Hickey  113 , Maartje J Hooning  114 , Reiner Hoppe  115   116 , John L Hopper  117 , Jouke-Jan Hottenga  68 , Frank B Hu  118   95 , Hanna Hübner  75 , David J Hunter  94   6 ; ABCTB Investigators; Helena Jernström  119 , Esther M John  120   121 , David Karasik  122   123 , Elza K Khusnutdinova  124   125 , Vessela N Kristensen  126   127 , James V Lacey  128   129 , Diether Lambrechts  130   131 , Lenore J Launer  132 , Penelope A Lind  48   133   50 , Annika Lindblom  134   135 , Patrik Ke Magnusson  42 , Arto Mannermaa  136   137 , Mark I McCarthy  138   139   140 , Thomas Meitinger  141 , Cristina Menni  46 , Kyriaki Michailidou  13   142 , Iona Y Millwood  6   7 , Roger L Milne  143   117 , Grant W Montgomery  144 , Heli Nevanlinna  145 , Ilja M Nolte  146 , Dale R Nyholt  147 , Nadia Obi  148 , Katie M O'Brien  149 , Kenneth Offit  150   151 , Albertine J Oldehinkel  152 , Sisse R Ostrowski  153   154 , Aarno Palotie  155   156   157   158   159   160 , Ole B Pedersen  161   154 , Annette Peters  105   162 , Giulia Pianigiani  31 , Dijana Plaseska-Karanfilska  163 , Anneli Pouta  164 , Alfred Pozarickij  6 , Paolo Radice  165 , Gad Rennert  166 , Frits R Rosendaal  167 , Daniela Ruggiero  53   168 , Emmanouil Saloustros  169 , Dale P Sandler  149 , Sabine Schipf  170 , Carsten O Schmidt  170 , Marjanka K Schmidt  171   172 , Kerrin Small  46 , Beatrice Spedicati  32 , Meir Stampfer  94   95 , Jennifer Stone  173   117 , Rulla M Tamimi  174   94 , Lauren R Teras  175 , Emmi Tikkanen  176   160 , Constance Turman  94   177 , Celine M Vachon  178 , Qin Wang  13 , Robert Winqvist  179   180 , Alicja Wolk  181 , Babette S Zemel  110   182 , Wei Zheng  183 , Ko W van Dijk  93   184 , Behrooz Z Alizadeh  146 , Stefania Bandinelli  185 , Eric Boerwinkle  186 , Dorret I Boomsma  68   187 , Marina Ciullo  53   168 , Georgia Chenevix-Trench  48 , Francesco Cucca  56   57 , Tõnu Esko  45 , Christian Gieger  104   105   188 , Struan Fa Grant  26   27   28   110   189 , Vilmundur Gudnason  61   62 , Caroline Hayward  65 , Ivana Kolčić  54   55 , Peter Kraft  94   177 , Deborah A Lawlor  58   59 , Nicholas G Martin  48 , Ellen A Nøhr  190 , Nancy L Pedersen  42 , Craig E Pennell  66   67   191 , Paul M Ridker  25 , Antonietta Robino  31 , Harold Snieder  146 , Ulla Sovio  35   192 , Tim D Spector  46 , Doris Stöckl  193 , Cathie Sudlow  24   194 , Nic J Timpson  58   59 , Daniela Toniolo  60 , André Uitterlinden  22   23 , Sheila Ulivi  31 , Henry Völzke  170 , Nicholas J Wareham  1 , Elisabeth Widen  160 , James F Wilson  40 ; Lifelines Cohort Study; Danish Blood Donor study; Ovarian Cancer Association Consortium; Breast Cancer Association Consortium; Biobank Japan Project; China Kadoorie Biobank Collaborative Group; Paul Dp Pharoah  13   96 , Liming Li  195   196 , Douglas F Easton  13   96 , Pål Njølstad  2   197 , Patrick Sulem  11 , Joanne M Murabito  44   198 , Anna Murray  199 , Despoina Manousaki  19   200   201 , Anders Juul  202   203   204 , Christian Erikstrup  205   206 , Kari Stefansson  11   62 , Momoko Horikoshi  207 , Zhengming Chen  6   7 , I Sadaf Farooqi  4 , Nelly Pitteloud  5   208 , Stefan Johansson  2   15 , Felix R Day  1 , John Rb Perry  1   209 , Ken K Ong  1   210
Affiliations

Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Katherine A Kentistou et al. medRxiv. .

Update in

  • Understanding the genetic complexity of puberty timing across the allele frequency spectrum.
    Kentistou KA, Kaisinger LR, Stankovic S, Vaudel M, Mendes de Oliveira E, Messina A, Walters RG, Liu X, Busch AS, Helgason H, Thompson DJ, Santoni F, Petricek KM, Zouaghi Y, Huang-Doran I, Gudbjartsson DF, Bratland E, Lin K, Gardner EJ, Zhao Y, Jia RY, Terao C, Riggan MJ, Bolla MK, Yazdanpanah M, Yazdanpanah N, Bradfield JP, Broer L, Campbell A, Chasman DI, Cousminer DL, Franceschini N, Franke LH, Girotto G, He C, Järvelin MR, Joshi PK, Kamatani Y, Karlsson R, Luan J, Lunetta KL, Mägi R, Mangino M, Medland SE, Meisinger C, Noordam R, Nutile T, Concas MP, Polašek O, Porcu E, Ring SM, Sala C, Smith AV, Tanaka T, van der Most PJ, Vitart V, Wang CA, Willemsen G, Zygmunt M, Ahearn TU, Andrulis IL, Anton-Culver H, Antoniou AC, Auer PL, Barnes CLK, Beckmann MW, Berrington de Gonzalez A, Bogdanova NV, Bojesen SE, Brenner H, Buring JE, Canzian F, Chang-Claude J, Couch FJ, Cox A, Crisponi L, Czene K, Daly MB, Demerath EW, Dennis J, Devilee P, De Vivo I, Dörk T, Dunning AM, Dwek M, Eriksson JG, Fasching PA, Fernandez-Rhodes L, Ferreli L, Fletcher O, Gago-Dominguez M, García-Closas M, García-Sáenz JA, González-Neira A, Grallert H, Guénel P, Haiman CA, Hall P, Hamann U, Hakonarson H, Hart RJ, H… See abstract for full author list ➔ Kentistou KA, et al. Nat Genet. 2024 Jul;56(7):1397-1411. doi: 10.1038/s41588-024-01798-4. Epub 2024 Jul 1. Nat Genet. 2024. PMID: 38951643 Free PMC article.

Abstract

Pubertal timing varies considerably and has been associated with a range of health outcomes in later life. To elucidate the underlying biological mechanisms, we performed multi-ancestry genetic analyses in ~800,000 women, identifying 1,080 independent signals associated with age at menarche. Collectively these loci explained 11% of the trait variance in an independent sample, with women at the top and bottom 1% of polygenic risk exhibiting a ~11 and ~14-fold higher risk of delayed and precocious pubertal development, respectively. These common variant analyses were supported by exome sequence analysis of ~220,000 women, identifying several genes, including rare loss of function variants in ZNF483 which abolished the impact of polygenic risk. Next, we implicated 660 genes in pubertal development using a combination of in silico variant-to-gene mapping approaches and integration with dynamic gene expression data from mouse embryonic GnRH neurons. This included an uncharacterized G-protein coupled receptor GPR83, which we demonstrate amplifies signaling of MC3R, a key sensor of nutritional status. Finally, we identified several genes, including ovary-expressed genes involved in DNA damage response that co-localize with signals associated with menopause timing, leading us to hypothesize that the ovarian reserve might signal centrally to trigger puberty. Collectively these findings extend our understanding of the biological complexity of puberty timing and highlight body size dependent and independent mechanisms that potentially link reproductive timing to later life disease.

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Conflict of interest statement

Competing interests J.R.B.P. and E.J.G. are employed by Adrestia Therapeutics. D.J.T. is employed by Genomics PLC. D.L.C. and J.P.B. are employed by GSK. E.T. is employed by Pfizer. D.A.L. has received support from Roche Disgnostics and Medtroic Ltd for work unrelated to the research in this paper. T.D.S. is co-founder and stakeholder of Zoe Global Ltd. P.A.F. conducts research funded by Amgen, Novartis and Pfizer, he received Honoraria from Roche, Novartis and Pfizer. M.W.B. conducts research funded by Amgen, Novartis and Pfizer.

Figures

Figure 1 |
Figure 1 |. Age at menarche GWAS and gene prioritisation.
(a) Miami plot showing signals from the European meta-analysis for age at menarche (upper panel) and genome-wide G2G scores with names of the top 50 genes annotated (lower panel). The upper panel Y-axis is capped at –log10(1x10−150) for visibility. (b) The 50 top scoring genes implicated by G2G, annotated by their sources of evidence. Relevant data are included in Supplementary Tables 2 and 13-16.
Figure 2 |
Figure 2 |. Exome-wide rare (MAF <0.1%) variant associations with age at menarche.
(a) Manhattan plot showing gene burden test results for age at menarche. Genes passing exome-wide significance (P<1.54×10−6) are highlighted; in addition, KDM5B shows a sub-threshold association (P=2.6×10−6). Point shapes indicate variant predicted functional class (DMG, damaging; HC PTV, high confidence protein truncating). (b) QQ plot for gene burden tests. (c) Comparison of gene burden associations for age at menarche (female participants, years) and age at voice breaking (men, 3 categories). Relevant data are included in Supplementary Table 5.
Figure 3 |
Figure 3 |. Epistatic interactions between rare coding variants and common genetic susceptibility on age at menarche in UK Biobank.
(a) Interaction effects (95% CI) on age at menarche between a GWAS polygenic score (PGS) and carriage of qualifying rare variants in seven exome-highlighted genes. Predicted mean (95% CI) age at menarche in (b) non-carriers (black) and carriers (light blue) of rare variants in six genes without significant interaction effects and (c) in non-carriers (left panel) and carriers (right) of rare variants in ZNF483 which shows significant interaction. In (c) points show individual age at menarche values. (d) Plot of individual rare damaging (DMG) variant associations with age at menarche by ZNF483 functional domains. The coding part of ZNF483 is depicted by the horizontal black line. Included damaging variants had a minor allele frequency (MAF) <0.1% and were annotated to either be high-confidence protein truncating variants or missense variants with CADD score >=25. Relevant data are included in Supplementary Table 12.
Figure 4 |
Figure 4 |. Stratification of age at menarche signals and biological pathway enrichments by their influence on early childhood weight.
(a) Proportion of GWAS signals for age at menarche by early childhood weight trajectory. (b) Biological pathways enriched for high confidence age at menarche genes, plus enrichment within early childhood weight trajectories. Row names describe pathway clusters. Strength of associations with individual pathways are indicated by circles. Circle size reflects the proportion of pathway genes that are high confidence age at menarche genes. The right-hand panel indicates whether each pathway cluster remains enriched for age at menarche genes when stratified by early childhood weight trajectory. Extended data are included in Supplementary Tables 21 and 23-26.
Figure 5 |
Figure 5 |. Enrichment of gene drivers of GnRH migration and maturation in the age at menarche GWAS.
(a) Schematic representation of the stages of GnRH neuron migration during embryonic development. Using RNAseq data, Pitteloud and colleagues [manuscript in preparation] grouped differentially-expressed genes into 23 expressional trajectories based on their comparative level of expression during the Early (yellow), Intermediate (amber) and Late (red) stages of GnRH migration. (b) Genome-wide MAGMA enrichment for age at menarche associations within each expression trajectory. (c) Trajectories significantly enriched at the genome-wide level in (b) show significant overlap with the 660 high-confidence age at menarche genes. Extended data are included in Supplementary Tables 18 and 27.
Figure 6 |
Figure 6 |. Interactions between G protein-coupled receptors (GPCRs) on age at menarche.
(a) 24 brain-expressed GPCRs implicated in age at menarche by G2G analysis of white European GWAS data (b) Time-resolved NDP-αMSH-stimulated cAMP production in HEK293 cells expressing MC3R-alone or with both MC3R and GPR83. Data are mean (standard error) % of the maximal MC3R-alone response (from 6 independent experiments). (c) Predicted mean (95% CI) age at menarche according to dosage of MC3R function-increasing C alleles at rs3746619 (X-axis in each panel) and GPR83 expression-increasing T alleles at rs592068 (panels). Betainteraction= −0.034 ± 0.015 years, Pinteraction=0.02. Extended data are included in Supplementary Tables 28 and 30.
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