Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

TLDR: Exome-wide analysis identifies rare and low-frequency coding variants associated with body mass index that confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Abstract: Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Figures

Table 2. Genes significantly associated with BMI in a gene-based meta-analyses, aggregating R/LF coding SNVs

Table 1 Rare and low-frequency coding variants significantly associated with BMI

Full text

Protein-altering variants associated with body mass index implicate
pathways that control energy intake and expenditure in obesity
Turcot, V., Lu, Y., Highland, H. M., Schurmann, C., Justice, A. E., Fine, R. S., Kee, F., Hirschhorn, J. N., & Loos,
R. J. F. (2018). Protein-altering variants associated with body mass index implicate pathways that control energy
intake and expenditure in obesity.
Nature Genetics
,
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Download date:11. Aug. 2022

Page 1 of 54
Protein-altering variants associated with body mass index implicate pathways that
control energy intake and expenditure underpinning obesity
Valérie Turcot
1*
, Yingchang Lu
2,3,4*
, Heather M Highland
5,6*
, Claudia Schurmann
3,4*
, Anne E Justice
5*
,
Rebecca S Fine
7,8,9*
, Jonathan P Bradfield
10,11
, Tõnu Esko
7,9,12
, Ayush Giri
13
, Mariaelisa Graff
5
, Xiuqing
Guo
14
, Audrey E Hendricks
15,16
, Tugce Karaderi
17,18
, Adelheid Lempradl
19
, Adam E Locke
20,21
, Anubha
Mahajan
17
, Eirini Marouli
22
, Suthesh Sivapalaratnam
23,24,25
, Kristin L Young
5
, Tamuno Alfred
3
, Mary F
Feitosa
26
, Nicholas GD Masca
27,28
, Alisa K Manning
7,24,29,30
, Carolina Medina-Gomez
31,32
, Poorva
Mudgal
33
, Maggie CY Ng
33,34
, Alex P Reiner
35,36
, Sailaja Vedantam
7,8,9
, Sara M Willems
37
, Thomas W
Winkler
38
, Goncalo Abecasis
20
, Katja K Aben
39,40
, Dewan S Alam
41
, Sameer E Alharthi
22,42
, Matthew
Allison
43
, Philippe Amouyel
44,45,46
, Folkert W Asselbergs
47,48,49
, Paul L Auer
50
, Beverley Balkau
51
, Lia E
Bang
52
, Inês Barroso
15,53,54
, Lisa Bastarache
55
, Marianne Benn
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, Sven Bergmann
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, Lawrence F
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, Matthias Blüher
61,62
, Michael Boehnke
20
, Heiner Boeing
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, Eric Boerwinkle
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, Carsten A
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, Jette Bork-Jensen
67
, Michiel L Bots
68
, Erwin P Bottinger
3
, Donald W Bowden
33,34,69
, Ivan
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70,71
, Gerome Breen
72,73
, Murray H Brilliant
74
, Linda Broer
32
, Marco Brumat
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, Amber A Burt
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,
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77,78
, Peter T Campbell
79
, Stefania Cappellani
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, Eulalia Catamo
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,
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, John C Chambers
83,84,85
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7,86,87,88
, Yii-Der Ida Chen
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Page 2 of 54
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Page 3 of 54
AFFILIATIONS
1. Montreal Heart Institute, Universite de Montreal, Montreal, Quebec, H1T 1C8, Canada
2. Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt
Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN, 37203, USA
3. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai,
New York, NY, 10029, USA
4. The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount
Sinai, New York, NY, 10069, USA
5. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, 27514, USA
6. Human Genetics Center, The University of Texas School of Public Health, The University of Texas
MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, The University of
Texas Health Science Center at Houston, Houston, TX, 77030, USA
7. Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
8. Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA
9. Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston
Children's Hospital, Boston, MA, 02115, USA
10. Center for Applied Genomics, Division of Human Genetics, The Children's Hospital of Philadelphia,
Philadelphia, PA, 19104, USA
11. Quantinuum Research LLC, San Diego CA, 92101, USA
12. Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia
13. Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health,
Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, 37203, USA
14. Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical
Center, Torrance, CA, 90502, USA
15. Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK
16. Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO, 80204,
USA
17. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
18. Department of Biological Sciences, Faculty of Arts and Sciences, Eastern Mediterranean University,
Famagusta, Cyprus
19. Max Planck Institute of Immunobiology and Epigenetics, Freiburg, 79108, Germany
20. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor,
MI, 48109, USA
21. McDonnell Genome Institute, Washington University School of Medicine, Saint Louis, MO, 63108,
USA
22. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen
Mary University of London, London, EC1M 6BQ, UK
23. Department of Vascular Medicine, AMC, Amsterdam, 1105 AZ, The Netherlands
24. Massachusetts General Hospital, Boston, MA, 02114, USA
25. Department of Haematology, University of Cambridge, Cambridge, CB2 0PT, UK
26. Division of Statistical Genomics, Department of Genetics, Washington University School of
Medicine, St. Louis, MO, 63108, USA
27. Department of Cardiovascular Sciences, Univeristy of Leicester, Glenfield Hospital, Leicester, LE3
9QP, UK
28. NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, LE3 9QP,
UK
29. Department of Medicine, Harvard University Medical School, Boston, MA, 02115, USA
30. Medical and Population Genetics Program, Broad Institute, Cambridge, MA, 02141, USA
31. Department of Epidemiology, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands
32. Department of Internal Medicine, Erasmus Medical Center, Rotterdam, 3015 GE, The Netherlands
33. Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA

Page 4 of 54
34. Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine,
Winston-Salem, NC, 27157, USA
35. Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA
36. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle WA, 98109,
USA
37. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of
Metabolic Science, Cambridge, CB2 0QQ, UK
38. Department of Genetic Epidemiology, University of Regensburg, Regensburg, D-93051, Germany
39. Netherlands Comprehensive Cancer Organisation, Utrecht, 3501 DB, The Netherlands
40. Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, 6500 HB,
The Netherlands
41. School of Kinesiology and Health Science, Faculty of Health, York University, Toronto
42. Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-
HD), King Abdulaziz University, Jeddah, 21589, Saudi Arabia
43. Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, CA,
92093, USA
44. INSERM U1167, Lille, F-59019, France
45. Institut Pasteur de Lille, U1167, Lille, F-59019, France
46. Universite de Lille, U1167 - RID-AGE - Risk factors and molecular determinants of aging-related
diseases, Lille, F-59019, France
47. Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht, The
Netherlands
48. Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The
Netherlands
49. Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College
London, London, UK
50. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, USA
51. INSERM U1018, Centre de recherche en Épidemiologie et Sante des Populations (CESP), Villejuif,
France
52. Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, 2100,
Denmark
53. Metabolic Research Laboratories, University of Cambridge, Cambridge, CB2 0QQ, UK
54. NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science,
Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
55. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37203, USA
56. Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University
Hospital, Herlev, 2730, Denmark
57. Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen,
Copenhagen, 2200, Denmark
58. Department of Computational Biology, University of Lausanne, Lausanne, 1011, Switzerland
59. Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
60. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI,
48109, USA
61. IFB Adiposity Diseases, University of Leipzig, Leipzig, 04103, Germany
62. University of Leipzig, Department of Medicine, Leipzig, 04103, Germany
63. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE),
Nuthetal, 14558, Germany
64. School of Public Health, Human Genetics Center, The University of Texas Health Science Center at
Houston, Houston, TX, 77030, USA
65. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030 USA
66. Department of Nephrology, University Hospital Regensburg, Regensburg, 93042, Germany

Frequently asked questions

Q1. Why did the authors conduct the conditional analyses in the UK Biobank dataset?

Due to the selective coverage of variants on the ExomeChip, the authors also conducted the respectiveconditional analyses in the UK Biobank dataset that included 847,441 genome-wide genotyped markers,and 72,355,667 variants imputed against UK10k haplotype reference panel, merged with the 1000 GenomesPhase 3 reference panel. 

Q2. Why did the authors exclude more variants from the analysis?

To try to include morevariants in the analysis, the authors also generated null ExomeChip data based on the UK Biobank, whichresulted in the exclusion of fewer BMI-associated variants (due to the much larger sample size ofthe UK Biobank data). 

Q3. What is the meta-analysis used to combine the discovery and follow-up results?

The authors used the inverse-variance weightedfixed effects meta-analysis in METAL108, to combine the discovery and follow-up association results. 

Q4. What was the protocol used for the studies?

The majority of studies followed a standardized protocol and performed genotype calling using thedesignated manufacturer software, which was then followed by zCall97. 

Q5. What is the effect of removing established loci?

After removing established loci (+/- 1Mb), the excess of significant associations is markedlyreduced and inflation reduced (Supplementary Figures 2c and 2d). 

Q6. How many rare SNVs are associated with obesity?

Effects of rare SNVs range between 0.06 and0.54 SD per allele, equivalent to 0.26 to 2.44 kg/m2 or 0.74 kg to 7.05 kg per allele (Table 1, Figure 1). 

Q7. What is the reason for the exclusion of rare variants?

The exclusion of the rarest variants is expected due to themuch smaller sample size of the null cohorts relative to the BMI data. 

Q8. What is the effect of the inclusion of rare variants in the analysis?

This result suggests that 1) the rarestvariants are more likely to have a lower true positive rate and/or 2) the heterogeneity of theunderlying biology increases with the inclusion of very rare variants. 

Q9. What was the P-value for each meta-gene set?

For each meta-gene set, the member gene set with the best P-value was used as representative for purposes of visualization(Supplementary Note).