The genetics of obesityGoing beyond common variants and common

phenotypes

Ruth LoosProfessor, Preventive Medicine

Director, Genetics of Obesity and Related Metabolic Traits ProgramCharles Bronfman Institute for Personalized Medicine

Mindich Child Health and Development InstituteIcahn School of Medicine at Mount Sinai

New Yorkruth.loos@mssm.edu

18th Annual International Symposium of the Universite Laval Obesity Research Chair, Montreal, Canada, November 13 2015

Obesity is heritable

Borjeson Acta Paed Scand 1976

DZ

MZTwin studies

Family studies

h2 = 40-70%

Candidate gene studies

Linkage studies

Outline

• Common variation and common adiposity phenotypes

• Common variation and more refined adiposity phenotypes

• Low-frequency variation and common adiposity phenotypes

Waist-to-Hip Ratio

Common variation – common phenotypes

Common DNA variation (MAF >

5%)

Common Adiposity

traits

~2.5 million variantsmostly non-coding

Body Mass Index

+ Large sample sizes− Phenotype heterogeneity

Cumulative number of obesity-associated loci

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)

Larger GWAS samples size more (common) loci

BMI

Locke et al. Nature (2015)

FTO

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)

WHRadjBMI

Larger GWAS samples size more (common) loci

Shungin et al. Nature (2015)

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

Locke et al. Nature (In press)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0.000

0.200

0.400

0.600

0.800

1.000

1.200

BMI increasing allele

Effec

t on

wei

ght

(kg)

/ pe

r BM

I-in

crea

sing

alle

leAll variants are common and have modest effects

From common loci to causal/functional gene/variant

Locke et al. Nature (2015)

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)

Tissue enrichment analyses point towards different systems

BMI-associated loci

WHRadjBMI-associated loci

Is FTO the obesity gene ?

AdiposityER- breast cancer

FTO

Melanoma

Adiposity

FTO

RPGRIP1L IRX3

Is FTO the obesity gene ?

Common variation – More refined phenotypes

Common DNA variation (MAF >

5%)

~2.5 million variantsmostly non-coding Leptin levels

Refined adiposity

traits

Body Fat %

- Smaller sample sizes+ More accurate phenotype

Visceral and subcutaneous fat

Common variation – common phenotypes 166 loci

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

Body fat percentage more accurately assesses adiposity

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

12 loci for body fat percentage

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.090

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

CRTC1 (W)

FTO

IRS1

MC4RTMEM18

COBLL1

SPRY2

TOMM40

TUFM/SH2B1

IGF2BP1

SEC16B

PLA2G6 (M)

Effects on body fat % (SD/allele)

Effe

cts

on B

MI (

SD/a

llele

)

0.8

0.9

1

1.1

1.2All Men Women

Per-

alle

le c

hang

e in

risk

(O

R)

0.8

0.9

1

1.1

1.2All Men Women

-0.25

-0.2

-0.15

-0.1

-0.05

0All Men Women

Per-

alle

le c

hang

e in

bod

y fa

t (%

)

-0.15

-0.1

-0.05

0

0.05

0.1All Men Women

Per-

alle

le c

hang

e in

BM

I (k

g/m

2)

-0.1

0

0.1

0.2

0.3

0.4All Men Women

Per-

alle

le c

hang

e in

WH

R

The near-IRS1 locus & measures of body composition

P = 3.8x10-11

P = 2.9x10-11

P = 9x10-3

P = 0.32

P = 0.16

P = 0.79

P = 0.13

P = 0.40 P = 0.19

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

P = 0.67

P = 0.56

P = 0.19

P = 0.36

P = 0.04

P = 0.40

Body fat % BMI WHR

Overweight Obesity

Kilpeläinen et al Nature Genetics 2011

The near-IRS1 locus and disease risk

P = 1.5x10-5

P = 9.3x10-12

P = 3x10-9

P = 0.?P = 2.35x10-9

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

Leptin is secreted in proportion to total fat mass

The Fenland Study: n~5,000 population-based

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

GCKRIdentify candidate genes within each locus

Near-CCNL1

Near-SLC32A1

Explant knockdown strategy

80 mgWash in PBS AT +

siRNA

Plate in M199 media +10%FBS1.Collect media for leptin

content

2.Extract RNA for expression

12hrs With or without:

7nM Insulin25nM Dexamethasone

20 min Antibiotic-

AntimycoticPGAT

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 SkowronskiYiying Zhang

Charles LeDucAmanda Rosenbaum

Rudy Leibel

Cobll1 knockdown leads to decreased insulin/dexamethasone-stimulated Lep secretion

Near-SLC32A1

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

Waist-to-Hip Ratio

Low frequency variation – common phenotypesCommon Adiposity

traitsBody Mass Index

Low frequency DNA variation (MAF ≤ 5%)

ExomeChip ~250,00

coding variants

ExomeChip analyses: ~525,000 individuals, predominantly European ancestry~250,000 SNV’s of which 196,304 variants have a MAF <5%~20,000 genes

Low frequency variation (≤ 5%) and BMI

MC4RMAF= 0.01%

Effect ~ 8.4 kg/allele

Farooqi et al NEJM 2003

Low frequency variation (≤ 5%) and BMI

GPR61MAF= 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.

Low frequency variation (≤ 5%) and BMI

KSR2MAF = ~1%

Effect ~ 800 g/allele

Some variants are low-frequency and have intermediate effects

11 SNPs at P < 5x10-7

31 SNPs at P < 10-5

MC4R Y35X & D37V

KSR2 R554Q

• 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

Collaborators and acknowledgements

Erik Ingelsson

Kari North

Cecilia Lindgren

JoelHirschhorn

Karen Mohlke

Mike Boehnke

Ines Barroso

Cristen Willer

Peter Visscher

Goncalo Abecasis

Elizabeth Speliotes

Mark McCarthy

Tuomas KilpeläinenAssistant professor,

Novo Nordisk Foundation Center for Basic Metabolic Research

Copenhagen, Denmark

Rudy LeibelAlicia Skowronski

Jayne Martin