'Lo último en obesidad'. Este es el título del Simposio Internacional que organizamos en la Fundación Ramón Areces los días 1 y 2 de diciembre de 2015. En colaboración con la Fundación General CSIC, reunió a algunos de los mayores expertos en la materia para analizar cómo reducir este grave problema de salud pública.

1. The genetics of obesity
Going beyond common variants and common phenotypes
Ruth Loos
Professor, Preventive Medicine
Director, Genetics of Obesity and Related Metabolic Traits Program
Charles Bronfman Institute for Personalized Medicine
Mindich Child Health and Development Institute
Icahn School of Medicine at Mount Sinai
New York
ruth.loos@mssm.edu
18th Annual International Symposium of the Universite Laval Obesity Research Chair, Montreal, Canada,

2. Obesity is heritable
Borjeson Acta Paed Scand 1976
DZ
MZ
Twin studies
Family studies
h2 = 40-70%
Candidate gene studies
Linkage studies

3. Outline
• Common variation and common adiposity phenotypes
• Common variation and more refined adiposity phenotypes
• Low-frequency variation and common adiposity phenotypes

4. Waist-to-Hip Ratio
Common variation – common phenotypes
Common DNA
variation (MAF > 5%)
Common
Adiposity traits
~2.5 million variants
mostly non-coding
Body Mass Index
+ Large sample sizes
− Phenotype heterogeneity

5. 0
20
40
60
80
100
120
140
160
180
2007 2008 2009 2010 2011 2012 2013 2015
Cumulativenumberofobesityloci
Childhood Obesity
WHR and BMI tails/ categories
Extreme and early onset obesity
Waist and WHRadjBMI
BMI in African ancestry
BMI in East Asians
BMI in Europeans
Cumulative number of obesity-associated loci

6. Larger GWAS samples size  more (common) loci
123,865 individuals of
European descent
from 46 GWAS
103,046 individuals of mainly
European descent
from 43 MetaboChip studies
112,366 individuals of mainly
European descent
from 36 GWAS
339,277 individuals of mainly European decent from 125 studies.
Association of SNPs in 97 loci reach P<5x10-8,
including the 31 established BMI loci, 10 established “other obesity traits” loci and 56 new BMI loci
BMI
Locke et al. Nature (2015)
GANTGANTC O N S O R T I U M

7. Larger GWAS samples size  more (common) loci
BMI
Locke et al. Nature (2015)
FTO

8. 77,167 individuals of
European descent
from 32 GWAS
67,326 individuals of mainly
European descent
from 40 MetaboChip studies
65,695 individuals of mainly
European descent
from 25 GWAS
210,088 individuals of mainly European decent from 87 studies.
Association of SNPs in 49 loci reach P<5x10-8,
including the 14 established WHR loci and 35 new WHR loci
WHRadjBMI
Larger GWAS samples size  more (common) loci
Shungin et al. Nature (2015)
GANTGANTC O N S O R T I U M

9. WHRadjBMI
Larger GWAS samples size  more (common) loci
Shungin et al. Nature (2015)

10. 0
20
40
60
80
100
120
140
160
180
2007 2008 2009 2010 2011 2012 2013 2015
Cumulativenumberofobesityloci
Childhood Obesity
WHR and BMI tails/ categories
Extreme and early onset obesity
Waist and WHRadjBMI
BMI in African ancestry
BMI in East Asians
BMI in Europeans
GIANT BMI meta-analysis:
N=339,247
 56 new BMI loci
GIANT WHR meta-analysis:
N=211,221
 35 new WHR loci
Common variation – common phenotypes  166 loci

11. Locke et al. Nature (In press)
0.000
0.200
0.400
0.600
0.800
1.000
1.200
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Effectonweight(kg)/perBMI-increasingallele
BMI increasing allele
All variants are common and have modest effects

12. From common loci to causal/functional gene/variant
Locke et al. Nature (2015)

13. Analyses to decipher each locus
• Cross-phenotype associations with cardiometabolic traits and diseases
• Cross-ancestry associations
• Fine-mapping analyses
• eQTL analyses for cis-association
• ENCODE annotation to identify regulatory marks
• Pathway analyses (MAGENTA and DEPICT)

14. Tissue enrichment analyses point towards different systems
BMI-associated loci
WHRadjBMI-associated loci

15. Is FTO the obesity gene ?
Adiposity
ER- breast cancer
FTO
Melanoma

16. Adiposity
FTO
RPGRIP1L IRX3
Is FTO the obesity gene ?

17. Common variation – More refined phenotypes
Common DNA
variation (MAF > 5%)
~2.5 million variants
mostly non-coding Leptin levels
Refined adiposity
traits
Body Fat %
- Smaller sample sizes
+ More accurate phenotype
Visceral and
subcutaneous fat

18. 0
20
40
60
80
100
120
140
160
180
2007 2008 2009 2010 2011 2012 2013 2015
Cumulativenumberofobesityloci
Childhood Obesity
WHR and BMI tails/ categories
Extreme and early onset obesity
Waist and WHRadjBMI
BMI in African ancestry
BMI in East Asians
BMI in Europeans
Common variation – common phenotypes  166 loci

19. GIANT BMI meta-analysis:
N=339,247
 56 new BMI loci
GIANT WHR meta-analysis:
N=211,221
 35 new WHR loci
Common variation – more refined phenotypes  177 loci
0
20
40
60
80
100
120
140
160
180
2007 2008 2009 2010 2011 2012 2013 2015
Cumulativenumberofobesityloci
Circulating Leptin
Bariatric surgery weight loss
VAT/ SAT
Body fat%
Childhood Obesity
WHR and BMI tails/ categories
Extreme and early onset obesity
Waist and WHRadjBMI
BMI in African ancestry
BMI in East Asians
BMI in Europeans

20. Body fat percentage more accurately assesses adiposity
0
10
20
30
40
50
60
15 20 25 30 35 40 45 50 55
BMI (kg.m-2)
BodyfatpercentagebyDEXA(%)
Women
Men

21. 24,582 individuals of 13
MetaboChip studies
37,562 individuals of
from 28 new GWAS
100,706 individuals of mainly European decent from 56 studies.
Association of SNPs in 12 loci reach P<5x10-8,
including the 2 established BF% loci, 6 established BMI loci and 4 new BF% loci
38,562 individuals
from 15 GWAS used
previously
Common variation for body fat percentage

22. 12 loci for body fat percentage
FTO
IRS1
MC4R
TMEM18
COBLL1
SPRY2
TOMM40
TUFM/SH2B1
IGF2BP1
SEC16B
PLA2G6 (M)
CRTC1 (W)
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
EffectsonBMI(SD/allele)
Effects on body fat % (SD/allele)

23. 0.9
1
1.1
1.2
All Men Women
Per-allelechangeinrisk(OR)
0.9
1
1.1
1.2
All Men Women
-0.25
-0.2
-0.15
-0.1
-0.05
0
All Men Women
Per-allelechangeinbodyfat(%)
-0.15
-0.1
-0.05
0
0.05
0.1
All Men Women
Per-allelechangeinBMI(kg/m2)
-0.1
0
0.1
0.2
0.3
0.4
All Men Women
Per-allelechangeinWHR
The near-IRS1 locus & measures of body composition
N 43,291 24,731 18,560 N 21.832 10,602 11,230N 43,291 24,731 18,560
N 42,551 24,557 17,944 N 26,009 13,518 12,491
Body fat % BMI WHR
Overweight Obesity
Kilpeläinen et al Nature Genetics 2011

24. The near-IRS1 locus and disease risk
P = 1.5x10-5
P = 9.3x10-12
P = 3x10-9
P = 0.?
P = 2.35x10-9

25. The near-IRS1 locus and fat distribution (CT data)
Fat%-decreasing allele ... Men Women
(n = 4,997) (n = 5,560)
Subcutaneous fat (SAT) P = 0.0018 P = 0.063
Visceral fat (VAT) P = 0.95 P = 0.63
VAT/SAT P = 6.1x10-6 P = 0.31
GWAS of CT data Personal communication with Caroline Fox
The ‘body fat% decreasing allele’ leads to …
 reduced storage of fat subcutaneously, but not viscerally
 ectopic fat deposition ?
 insulin resistance and dyslipidemia ?
Kilpeläinen et al Nature Genetics 2011

26. Leptin is secreted in proportion to total fat mass
The Fenland Study: n~5,000 population-based

27. 20,278 individuals of
from 12 GWAS +
MetaboChip
52,339 individuals of European decent from 34 studies.
Association of SNPs in 6 loci reach P<5x10-8
LEP, FTO, CCNL1, GCKR, COBLL1, SLC32A1
32,061 individuals
from 22 GWAS
Common variation for circulating leptin levels

28. GCKR
Identify candidate genes within each locus
Near-CCNL1
Near-SLC32A1
0
2
4
6
8
10
-log10(p−value)
0
20
40
60
80
100
Recombinationrate(cM/Mb)
rs780093
0.2
0.4
0.6
0.8
r2
6 genes
omitted
ABHD1
PREB
C2orf53
TCF23
SLC5A6
ATRAID
CAD
SLC30A3
DNAJC5G
TRIM54
UCN
MPV17
GTF3C2
LOC100505624
EIF2B4
SNX17
ZNF513
PPM1G
FTH1P3
NRBP1
KRTCAP3
IFT172
FNDC4
GCKR
C2orf16
ZNF512
CCDC121
GPN1
SUPT7L
SLC4A1AP
MRPL33
RBKS
BRE−AS1
BRE
27.4 27.6 27.8 28
Position on chr2 (Mb)
Plotted SNPs
0
2
4
6
8
10
-log10(p−value)
0
20
40
60
80
100
Recombinationrate(cM/Mb)
rs6738627
0.2
0.4
0.6
0.8
r2
GRB14 COBLL1
SNORA70F
SLC38A11 SCN3A
165.2 165.4 165.6 165.8
Position on chr2 (Mb)
Plotted SNPs
0
2
4
6
8
10
-log10(p−value)
0
20
40
60
80
100
Recombinationrate(cM/Mb)
rs6071166
0.2
0.4
0.6
0.8
r2
4 genes
omitted
BPI
LBP
LOC388796
SNORA71B
SNORA71A
SNORA71C
SNORA71D
SNHG11
SNORA39
SNORA60
ADIG
ARHGAP40
SLC32A1
ACTR5
PPP1R16B
FAM83D
DHX35
37 37.2 37.4 37.6
Position on chr20 (Mb)
Plotted SNPs
0
2
4
6
8
10
-log10(p−value)
0
20
40
60
80
100
Recombinationrate(cM/Mb)
rs900400
0.2
0.4
0.6
0.8
r2
TIPARP−AS1
TIPARP
LOC730091
PA2G4P4
LEKR1
LOC339894
LOC100498859
CCNL1 VEPH1
PTX3
C3orf55
156.4 156.6 156.8 157 157.2
Position on chr3 (Mb)
Plotted SNPs

29. Explant knockdown strategy
80 mg
Wash in PBS
AT +
siRNA
Plate in M199 media +10%FBS
1. Collect media for leptin content
1. Extract RNA for expression
12hrs
With or without:
7nM Insulin
25nM Dexamethasone
20 min
Antibiotic-
Antimycotic
PGAT
Change
media
after 20hrs
Electroporation
(n=3/condition)
Puri, et al. J Lip Res. 2007, Lee, et al. AJP-Endocrinol Metab. 2007
Explant knockdown strategy to identify causal gene Jayne Martin
Alicja Skowronski
Yiying Zhang
Charles LeDuc
Amanda Rosenbaum
Rudy Leibel

30. Cobll1 knockdown leads to decreased insulin/dexamethasone-
stimulated Lep secretion
s iC tr s iA d ig
0 .0
0 .2
0 .4
0 .6
0 .8
1 .0
A d ig m R N AArbitraryUnits
****
s iC tr s iA d ig
0
5
1 0
1 5
L e p m R N A
ArbitraryUnits
*
s iC tr s iA d ig
0
1 0
2 0
3 0
S e c re te d L e p tin
ng/ml
*
Near-SLC32A1
0
2
4
6
8
10
-log10(p−value)
0
20
40
60
80
100
Recombinationrate(cM/Mb)
rs6071166
0.2
0.4
0.6
0.8
r2
4 genes
omitted
BPI
LBP
LOC388796
SNORA71B
SNORA71A
SNORA71C
SNORA71D
SNHG11
SNORA39
SNORA60
ADIG
ARHGAP40
SLC32A1
ACTR5
PPP1R16B
FAM83D
DHX35
37 37.2 37.4 37.6
Position on chr20 (Mb)
otted SNPs
Explant knockdown strategy to identify causal gene
ADIG  Adipogenin
- Involved in adipogenesis and adipose tissue
development
- Highly expressed in white adipose tissue
- Upregulated in mice on high-fat diet
- Plasma membrane protein
 Involved in production of leptin in adipose tissue

31. Waist-to-Hip Ratio
Low frequency variation – common phenotypes
Common
Adiposity traits
Body Mass Index
Low frequency DNA
variation (MAF ≤ 5%)
ExomeChip
~250,00 coding
variants
GANTGANTC O N S O R T I U M
ExomeChip analyses:
~525,000 individuals, predominantly European ancestry
~250,000 SNV’s of which 196,304 variants have a MAF <5%
~20,000 genes

32. Low frequency variation (≤ 5%) and BMI
MC4R
MAF= 0.01%
Effect ~ 8.4 kg/allele
Farooqi et al NEJM 2003

33. Low frequency variation (≤ 5%) and BMI
GPR61
MAF= 3%
Effect ~ 850g/allele
- Highly expressed in the brain
- KO mice gain weight faster and eat more
RAPGEF3 (EPAC1)
MAF= 1 %
Effect ~ 1 kg/allele
- KO mice develop diet-induced obesity,
hyperglycemia, b-cell dysfunction, and other
metabolic defects.

34. Low frequency variation (≤ 5%) and BMI
KSR2
MAF = ~1%
Effect ~ 800 g/allele

35. Some variants are low-frequency and have intermediate effects
11 SNPs at P < 5x10-7
31 SNPs at P < 10-5
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
0.0% 0.1% 1.0% 10.0%
Effectonweight(kg)/perBMI-increasingallele
Minor alele frequency
GWAS-identified loci
ExomeChip sub-significant
ExomChip Significant
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
0.0% 0.1% 1.0% 10.0%
Effectonweight(kg)/perBMI-increasingallele
Minor alele frequency
GWAS-identified loci
ExomeChip sub-significant
ExomChip Significant
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
0.0% 0.1% 1.0% 10.0%
Effectonweight(kg)/perBMI-increasingallele
Minor alele frequency
GWAS-identified loci
ExomeChip sub-significant
ExomChip Significant
MC4R Y35X & D37V
KSR2 R554Q

36. • GWAS has been successful in identifying >170 genetic variants associated
with adiposity traits. However, identifying the causal genes proves to be
challenging, because
• The vast majority of variants locate in intergenic and intronic regions.
• The majority of adiposity phenotypes studies represent heterogeneous
outcomes.
Conclusions & near future directions
Follow-up analyses aim at identifying regulatory regions that target the “causal”
gene, requires accurate annotation of the whole genome at a tissue-specific level.
 Maybe first focussing on the exome only provides an easier way “in”.
These heterogeneous phenotypes are the result of a diverse range of biological causes
 Phenotypes that are more refined and closer to the “biology” of the outcome might
help point towards the causal gene

37. GANTGANTC O N S O R T I U M
Collaborators and acknowledgements
Erik
Ingelsson
Kari North
Cecilia
Lindgren
Joel
Hirschhorn
Karen
Mohlke
Mike
Boehnke
Ines Barroso
Cristen
Willer
Peter
Visscher
Goncalo
Abecasis
Elizabeth
Speliotes
Mark
McCarthy
Tuomas Kilpeläinen
Assistant professor,
Novo Nordisk Foundation
Center for Basic Metabolic Research
Copenhagen, Denmark
Rudy LeibelAlicia SkowronskiJayne Martin