'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.
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Ruth Loos-Lo último en obesidad
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,
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
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
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
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.
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
Notas del editor
1
Annotation of sequecing and filtering will provide better power (reduce number of vaiants).
More detailed phenotypes – phenotypes closer to DNA.
75 loci
To identify tissues in which genes within BMI-associated loci are preferentially 186 expressed, we first used large-scale gene expression data to connect genes with tissues, 187 and then tested for specific enrichment of tissues by comparing results with randomly 188 selected loci matched for gene density (implemented as part of DEPICT, see Methods).
We have developed a novel approach that integrates the associated loci with complementary data sets (including 77,840 gene expression microarrays, 169,810 experimentally-derived high-confidence protein-protein interactions, 211,882 gene-phenotype pairs from mouse knock-out studies, and 6,004 gene sets from pathway databases) and identified dozens of genes and pathways that are likely to be causal for one of the two phenotypes. Specifically, we found that pathways related to neuronal regulation (for example glutamate signaling) are likely to play key roles for BMI , while pathways that impact insulin resistance, lipid handling, glycogen levels, and blood pressure were significantly enriched in the WHR analysis. The marked difference in BMI and WHR pathways was further supported by our finding that genes in BMI-associated loci were significantly co-expressed in the central nervous system, while genes encoded in WHR-associated loci were significantly and exclusively co-expressed in adipose tissue.
16
Annotation of sequecing and filtering will provide better power (reduce number of vaiants).
More detailed phenotypes – phenotypes closer to DNA.
23
24
25
Stage 1: 22 GWAS: 32,061 individuals 10 loci
Satge 2: 12 GWAS: 22,000 individuals 6 loci
Using this technique, Adipogenin (Adig), implicated in one of the four novel regions that were found in this GWAS meta-analysis, was analyzed. Adig is a plasma membrane protein primarily expressed in white adipose tissue and has previously been shown to play a role in adipogenesis (Hong YH, et al, Mol Cell Biochem 2005 , Kim JY, et al, Biochem and Biophys Research Communications, 2005). Our results indicate that Adig is also involved in production of leptin in adipose tissue. Add to end note. Hong et al. Title: “Up-regulation of adipogenin, an adipocyte plasma transmembrane protein, during adipogenesis.”
Kim et al. title: “Cloning, expression, and differentiation-dependent regulation of SMAP1 in adipogenesis.”
Annotation of sequecing and filtering will provide better power (reduce number of vaiants).
More detailed phenotypes – phenotypes closer to DNA.