Genetics of Central Obesity and Body Fat

Genetics of Central Obesity and Body Fat

C H A P T E R 14 Genetics of Central Obesity and Body Fat Yoriko Heianza*, Lu Qi*,† * Department of Epidemiology, School of Public Health and Tropi...

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C H A P T E R

14 Genetics of Central Obesity and Body Fat Yoriko Heianza*, Lu Qi*,†

*

Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States †Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States

O U T L I N E Introduction

153

Gene-Environment Interactions

164

Heritability

154

Sexual Dimorphism

166

Monogenic Obesity

155

Epigenetics

167

Genome-Wide Linkage Study

155

Conclusions

167

Candidate-Gene Association Study

156

References

168

Genome-Wide Association Study

157

INTRODUCTION Abdominal obesity, assessed by waist circumference (WC) or waist-hip ratio (WHR), is more closely associated with increased risks of cardiovascular events and mortality than general obesity, which is assessed by body mass index (BMI).1–4 Genetic predisposition to higher degrees of abdominal obesity is related to the risk of various metabolic diseases such as type 2 diabetes and coronary heart diseases.5,6 Classical genetic analyses in families, adoptees, and twins have confirmed the genetic contribution to the development of abdominal obesity. With the rapid advances in techniques, genome-wide association studies (GWASs) have also revealed the genetic architecture of “common (not rare)” types of abdominal obesity and

Nutrition in the Prevention and Treatment of Abdominal Obesity https://doi.org/10.1016/B978-0-12-816093-0.00014-8

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# 2019 Elsevier Inc. All rights reserved.

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14. GENETICS OF CENTRAL OBESITY AND BODY FAT

body fat, and increasing number of GWASs have successfully identified candidate genes (often expressed in the central nervous system) for BMI, WC, and WHR. On the other hand, the genetic variation may only explain a small proportion of the variations in adiposity measures,7 and the genetic predisposition to obesity has a greater effect in obesogenic environments.8 Epidemiological studies have identified diet and lifestyle risk factors for obesity, such as sugar-sweetened beverages, fried foods, poor diet quality, and physical inactivity.9 It is now widely accepted that such diet and lifestyle risk factors may modify the genetic risk in adiposity. This chapter summarizes major efforts in the past decades for genetic research on abdominal obesity and recently identified genetic markers, as well as interactions between genetic components and diet/lifestyle for the regulation of obesity.

HERITABILITY In classic genetic research, heritability (the proportion of the phenotypic variance accounted for by the genetic factors) is usually estimated from twin studies or family studies, showing high heritability estimates for overall adiposity >50%.10–14 Twin studies have provided a unique method for disentangling nature and nurture by taking advantage of the fact that monozygotic twins share all of their genes, whereas dizygotic twins on average share half of their segregating genes.15 If genes contribute to phenotype variance, the concordance in the phenotype would be high in monozygotic twins than in dizygotic twins. The classical twin model is based on the key assumption that both prenatal and postnatal environmental covariance are the same for monozygotic and dizygotic twin pairs. On the other hand, this assumption may be not true in some environments. A recent meta-analysis of twins from 40 cohorts suggested that environmental factors shared by co-twins affected childhood BMI, but there was little evidence for BMI in late adolescence.16 The heritability of BMI in twin studies may be affected by age.17 Of note, estimates of measures for abdominal obesity vary considerably across previous studies. For example, the heritability estimates for WC18–20 were reported from 37% in an Old Order Amish community to 81% in nondiabetic Pimas, from 6% to 30% in Taiwan Chinese for WHR,21–23 and from 35% in a Taiwan Chinese population to 63% for percentage body fat.23–26 A twin study and HERITAGE (HEalth, RIsk factors, exercise Training, And GEnetics) family study reported similar heritability of 63% and 62%, respectively.24,25 In an Indian population, a > 90% heritability has been shown for abdominal fat accumulation.27 Whereas the evidence from twin and family studies reported the high heritability, findings of a recent GWAS explained a small proportion of variations in phenotypic obesity, which is so-called “missing heritability.”28 The Twins Early Development Study (TEDS), a British twin birth cohort, investigated the mismatch of estimates from twin studies and GWASs by directly comparing the results of a standard twin analysis in the same families using a method that estimated the total additive genetic influence due to common SNPs on whole-genome arrays (in a software package called Genome-wide Complex Trait Analysis, GCTA).29 In results of their direct comparison, a standard twin analysis estimated the additive genetic influence as 82%, and the GCTA explained 30% of the variance in BMI, suggesting that 37% of the twin-estimated heritability (30/82%) can be explained by additive effects of multiple common SNPs.29 II. MECHANISMS OF OBESITY

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MONOGENIC OBESITY More direct evidence supporting the genetic contribution to obesity-related traits comes from identification of the monogenic form of obesity. Monogenic obesity is a kind of Mendelian disorder that is caused by mutations in genes that encode proteins mainly playing roles in energy intake or expenditure and appetite regulation. The known monogenic forms of obesity can be divided into three broad categories. The first category is obesity caused by mutations in genes that have a physiologic role in the hypothalamic leptin-melanocortin system of energy balance. Mutations in human genes coding for leptin (LEP),30 leptin receptor (LEPR),31 proopiomelanocortin (POMC),32 melanocortin 4 receptor (MC4R),33 and prohormone convertase 1/3 gene (proprotein convertase subtilisin/kexin type 1, PCSK1),34 have been associated with juvenile onset morbid obesity. The mutations in the gene that encodes MC4R account for the frequent autosomal-dominant forms of obesity.33,35 MC4R deficiency represents the most common monogenic obesity disorder that has been identified so far, and occurs in 1%–6% of obese individuals from different ethnic groups.36 The second category is obesity resulting from mutations in the three genes necessary for the development of the hypothalamus: single-minded homolog 1 (SIM1), brain-derived neurotrophic factor (BDNF), and NTRK-like family member 1 (NTRK).37 These genes have important roles during hypothalamic development and lead to severe obesity when mutated. The third category is obesity presenting as part of a complex syndrome caused by mutations in genes whose functional relationship to obesity is also unclear. The presence of mental retardation distinguishes most of these obesity syndromes. There are about 30 Mendelian disorders in which obesity or abdominal obesity is a clinical feature, often associated with mental retardation, dysmorphic features, and organ-specific developmental abnormalities, such as Bardet-Biedl Syndrome, Albright Hereditary Osteodystrophy, Fragile X Syndrome, etc.38

GENOME-WIDE LINKAGE STUDY Genetic linkage analysis is one of the principal approaches used to identify genomic regions that contain genes predisposing to disease. Linkage analysis is often performed as the first stage in the genetic investigation of a trait, as it can be used to identify broad genomic regions that might contain a disease gene, even in the absence of previous biologically driven hypotheses.15 A logarithm of the odds (LOD) score is usually used to indicate the significance level of the linkage; and LOD  3 is widely accepted as the cutpoint for genome-wide significance. This score was first proposed by Morton in 1955.39 It is a function of the recombination fraction (θ) or chromosomal position measured in cM. Large positive scores are evidence for linkage (or cosegregation), and negative scores are evidence against linkage.15 A genome region identified from linkage analysis is termed as a quantitative trait locus (QTL). The vast majority of these linkage analyses focused on overall adiposity, and a few of the studies have found evidence of linkage with measures related to abdominal obesity, such as WC or WHR.40–43 For instance, QTL 1q21-q25 in the Hong Kong Family Diabetes Study, and QTL 6q23-25 in the Framingham Heart Study were found to be in linkage with WC.40,43 Suggestive linkage was found in European Americans and African Americans, both with LOD scores of 2.7 at the II. MECHANISMS OF OBESITY

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14. GENETICS OF CENTRAL OBESITY AND BODY FAT

Xp21.3 and Xp11.3 regions.41 Some studies have reported regions in linkage with percentage body fat,44–48 including two studies performed in non-Hispanic Whites and EuropeanAmerican families; both observed a LOD score of 3.8 in chromosome 12q24.46,47 Several limitations of linkage studies of obesity-related phenotypes should be acknowledged. Firstly, the relatively small study sample size may limit the power of genome scans to detect relative moderate genetic effects. Secondly, multiple tests performed in each study increased type 1 error; and correction for multiple testing is necessary to claim significant levels. Thirdly, the heterogeneity of the study populations makes it difficult to detect true linkages that could be validated across studies. The lack of replication of the findings from genome-wide linkage studies has been a major concern.

CANDIDATE-GENE ASSOCIATION STUDY Before the genome-wide association approach was first introduced in the field, candidate gene association study was a predominant method to detect genetic variants for complex disorders including abdominal obesity. The design of the candidate gene association study is simple: including identification of genes that are relevant to the outcome phenotype of interest; selection of polymorphic markers within the candidate genes; and analyses of associations between the genetic markers with the outcomes in a suitable set of subjects. Identification of the potential candidate genes is the main stumbling block. There are two major types of candidate genes that are considered: functional and positional.49 Functional candidates are genes with products that are in some way involved in the pathogenesis of disease. Clearly, this is highly dependent on the current state of knowledge about disease. In the case of obesity or abdominal obesity, there is confirmed evidence that genes influencing energy homeostasis and thermogenesis, adipogenesis, leptin-insulin signaling transduction, and hormonal signaling peptides play critical roles.50 Positional candidates are genes that are identified by linkage or association studies, or by the detection of chromosomal translocations that disrupt the gene. The number of studies reporting associations between DNA sequence variation in specific genes and obesity phenotypes has increased considerably, with 426 findings of positive associations in 127 candidate genes by October 2005. A promising observation is that 22 genes are each supported by at least five positive studies.37 Most of the candidate genes are found to be associated with overall obesity. It is worth mentioning that several candidate genes have shown biological effects underlying the genetical association. In addition to the nuclear receptor peroxisome proliferative activated receptor-γ (PPARG),51–53 the POMC gene54 and MC4R gene55 are associated with common obesity. There are also genes that have been related to WC, WHR, or abdominal fat, such as the b2 and b3-adrenergic receptor gene (ADRB2 and ADRB3),56,57 UCP1, UCP3,58 ADRA2A,59 angiotensin I converting enzyme (ACE),60 APOA2,61 FABP2,62 lymphotoxinalpha gene (LTA),63 microsomal triglyceride transfer protein gene (MTTP),64 PLIN,65 PPARG,66 and ACDC.67 The ADRB3 gene is predominantly expressed in adipose tissue and regulates lipid metabolism and thermogenesis,68 and genetic variants at ADRB3 locus have been associated with body weight across diverse populations. A metaanalysis including 31 studies with >9000 individuals demonstrated a significant association of the Trp64Arg

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polymorphism of the ADRB3 gene with BMI.69 The ADRB3 genotype has also been related to WC, a measure of abdominal obesity. The b3-adrenergic receptor is expressed in visceral fat in humans70 and is responsible for increases in lipolysis and the delivery of free fatty acids into the portal vein. In one study of 335 subjects from western Finland (207 without diabetes and 128 with diabetes), the Trp64Arg allele of the ADRB3 gene was associated with abdominal obesity.57 Many studies have suggested associations of uncoupling protein (UCP) family genes with obesity and fat distribution. The major function of UCPs is to uncouple oxidative phosphorylation of adenosine diphosphate to adenosine triphosphate, leading to the generation of heat.71 The UCP family includes three different proteins, uncoupling protein 1 (UCP-1, expressed in brown adipose tissue), uncoupling protein 2 (UCP-2, most tissues including white adipose tissue), and uncoupling protein 3 (UCP-3, expressed in skeletal muscle). UCP2 and UCP3 genes are located on chromosome 11q13 adjacent to each another.72 The G866A polymorphism of the UCP-2 gene has been related in Chinese and Indian men73 and Finnish overweight individuals74 to increased risk of central obesity and overall obesity.75,76 However, the association was not consistently observed.77 The Neuropeptide Y (NPY) gene, containing four exons, is located on chromosome 7p15.1 and codes for a 36-amino acid peptide that is secreted by neurons in the hypothalamus.78 The neuropeptide has orexigenic effects, affecting appetite and food intake in animals. Injection of this peptide directly into the central nervous systems of animals leads to obesity through an increase in feeding.79 In addition, maternal low-protein diet upregulates the neuropeptide system in visceral fat and leads to abdominal obesity and glucose intolerance in a sex- and time-specific manner.80 Although candidate-gene association studies have suggested the genetic variants in several biologically relevant genes might be related to abdominal obesity, it is also notable that most of the findings are not reproducible. In fact, no loci have been convincingly confirmed for their associations with abdominal obesity to date. Of note, most of the candidate-gene association studies are relatively small in size and were performed without replications.81 In addition, candidate-gene association studies usually focus on limited variants in the studied regions, failing to capture the overall genetic variance.82 Moreover, the hypothesis-led nature of the candidate-gene approaches places a heavy dependence on existing knowledge in the field, seriously limiting the power for detection of novel genetic variations affecting the outcomes of interest.

GENOME-WIDE ASSOCIATION STUDY Genetic research into complex diseases has achieved a remarkable leap since the application of the genome-wide association approach in 2006.83 Such revolutionary progress in the field is largely due to the completion of Human Genome Project and breakthrough in highthroughput, genome-wide genotyping technology. Different from candidate-gene association studies, GWAS are conducted without a prior hypothesis. The widely used genome-wide scan platforms cover up to several millions of genetic variants—single nucleotide polymorphisms (SNPs) or structural variants such as copy number variants (CNVs)—over the human

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genome. Another significant improvement in a study design is that, all the GWAS take replication mechanism, and typically include large sample size. The number of susceptibility loci for obesity has grown dramatically. Given the high correlation between BMI and WHR/WC, the GWAS for indicators of abdominal obesity, WHR or WC, should consider the independent genetic determinates after adjustment of BMI. In 2009, the first round of GWAS was performed by using a metaanalysis of 16 studies in 38,580 participants with replication in up to 70,689 individuals.84 The study identified two loci (TFAP2B and MSRA) associated with WC, and a locus near LYPLAL1 associated with WHR only in women.84 Heid et al. performed a subsequent metaanalysis of 32 GWASs for WHR adjusted for BMI within the Genetic Investigation of Anthropometric Traits (GIANT) consortium in 2010.85 The study identified 13 novel loci (in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4) and the previously identified signal at LYPLAL1.85 In a genome-wide association of abdominal adipose depots [quantified using computed tomography (CT) scans] to identify novel loci for body fat distribution among participants of European ancestry,86 authors confirmed 14 previously published loci for WHR adjusted for BMI, and found nominal associations for 7 loci, most notably for the visceral adipose tissue (VAT)/subcutaneous adipose tissue (SAT) ratio.86 The study86 uncovered a new locus for VAT at THNSL2 in women, but not in men, and found a genome-wide significance for rs11118316 at LYPLAL1 for the VAT/SAT ratio, which was previously identified in a GWAS85 for WHR in the GIANT consortium, although the lead SNP was only in moderate LD with the SNP identified by GIANT. In an updated meta-GWAS analysis for anthropometric traits, another 11 new loci were identified for anthropometric traits including clinical obesity (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3, and ZZZ3).87 Four SNPs that reached genome-wide significance (P < 5  108) have previously been identified as WHR-associated loci in the general population.85 The authors concluded that a large overlap was found in genetic structure and the distribution of variants between traits based on extremes and the general population, and little etiological heterogeneity between obesity subgroups.87 In 2015, three papers were published from GIANT. Each study examined associations of BMI,88 WHR after adjustment for BMI89 and differences in age and sex on the genetic associations.90 The GWAS for BMI identified 97 loci, 56 of which were novel and 41 were previously associated with one or more obesity measures. In their metaanalysis of Europeandescent individuals (n ¼ 322,152), a total of 77 genome-wide significant loci were identified (Table 1). An additional inclusion of 17,072 non-European-descent individuals (total n ¼ 339,224) identified 10 more loci, while secondary analyses identified another 10 genome-wide significant loci.88 Although the GWAS successfully identified many loci for BMI, the study also estimated that the 97 loci accounted for only 2.7% of BMI variation.88 In the GWAS for WHR after adjustment for BMI, a total of 49 loci were identified, 33 of which were novel89 (Table 2). In their analysis, a European ancestry (n ¼ 210,088) sex-combined analysis identified 39 of the 49 loci, and European ancestry sex-specific analyses identified 9 additional loci, 8 of which were new and significant only in women. The addition of 14,371 individuals of non-European ancestry identified 1 additional locus in women, with no evidence of heterogeneity across ancestries.89 A study examining the 49 loci and other previously suggested loci has reported that numerous genes in these loci associated with body fat distribution may be linked to specific alterations in adipose tissue morphology and function.91 A more recent genome-wide analysis from GIANT also newly identified genetic variants II. MECHANISMS OF OBESITY

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TABLE 1

Loci Reaching Genome-Wide Significance (P < 5  108) for BMI in Europeans

SNP

Nearest Gene

Chr:Position (bp)

Alleles Effect/Other

β  SE

rs657452

AGBL4

1:49,362,434

A/G

0.023  0.003

rs12286929

CADM1

11:114,527,614

G/A

0.022  0.003

rs7903146

TCF7L2

10:114,748,339

C/T

0.023  0.003

rs10132280

STXBP6

14:24,998,019

C/A

0.023  0.003

rs17094222

HIF1AN

10:102,385,430

C/T

0.025  0.004

rs7599312

ERBB4

2:213,121,476

G/A

0.022  0.003

rs2365389

FHIT

3:61,211,502

C/T

0.020  0.003

rs2820292

NAV1

1:200,050,910

C/A

0.020  0.003

rs12885454

PRKD1

14:28,806,589

C/A

0.021  0.003

rs16851483

RASA2

3:142,758,126

T/G

0.048  0.008

rs1167827

HIP1

7:75,001,105

G/A

0.020  0.003

rs758747

NLRC3

16:3,567,359

T/C

0.023  0.004

rs1928295

TLR4

9:119,418,304

T/C

0.019  0.003

rs9925964

KAT8

16:31,037,396

A/G

0.019  0.003

rs11126666

KCNK3

2:26,782,315

A/G

0.021  0.003

rs2650492

SBK1

16:28,240,912

A/G

0.021  0.004

rs6804842

RARB

3:25,081,441

G/A

0.019  0.003

rs4740619

C9orf93

9:15,624,326

T/C

0.018  0.003

rs13191362

PARK2

6:162,953,340

A/G

0.028  0.005

rs3736485

DMXL2

15:49,535,902

A/G

0.018  0.003

rs17001654

SCARB2

4:77,348,592

G/C

0.031  0.005

rs11191560

NT5C2

10:104,859,028

C/T

0.031  0.005

rs1528435

UBE2E3

2:181,259,207

T/C

0.018  0.003

rs1000940

RABEP1

17:5,223,976

G/A

0.019  0.003

rs2033529

TDRG1

6:40,456,631

G/A

0.019  0.003

rs11583200

ELAVL4

1:50,332,407

C/T

0.018  0.003

rs9400239

FOXO3

6:109,084,356

C/T

0.019  0.003

rs10733682

LMX1B

9:128,500,735

A/G

0.017  0.003

rs11688816

EHBP1

2:62,906,552

G/A

0.017  0.003

rs11057405

CLIP1

12:121,347,850

G/A

0.031  0.006 Continued

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TABLE 1 Loci Reaching Genome-Wide Significance (P < 5  108) for BMI in Europeans—Cont’d SNP

Nearest Gene

Chr:Position (bp)

Alleles Effect/Other

β  SE

rs11727676

HHIP

4:145,878,514

T/C

0.036  0.006

rs3849570

GBE1

3:81,874,802

A/C

0.019  0.003

rs6477694

EPB41L4B

9:110,972,163

C/T

0.017  0.003

rs7899106

GRID1

10:87,400,884

G/A

0.040  0.007

rs2176598

HSD17B12

11:43,820,854

T/C

0.020  0.004

rs2245368

PMS2L11

7:76,446,079

C/T

0.032  0.006

rs17724992

PGPEP1

19:18,315,825

A/G

0.019  0.004

rs7243357

GRP

18:55,034,299

T/G

0.022  0.004

rs2033732

RALYL

8:85,242,264

C/T

0.019  0.004

rs1558902

FTO

16:52,361,075

A/T

0.082  0.003

rs6567160

MC4R

18:55,980,115

C/T

0.056  0.004

rs13021737

TMEM18

2:622,348

G/A

0.060  0.004

rs10938397

GNPDA2

4:44,877,284

G/A

0.040  0.003

rs543874

SEC16B

1:176,156,103

G/A

0.048  0.004

rs2207139

TFAP2B

6:50,953,449

G/A

0.045  0.004

rs11030104

BDNF

11:27,641,093

A/G

0.041  0.004

rs3101336

NEGR1

1:72,523,773

C/T

0.033  0.003

rs7138803

BCDIN3D

12:48,533,735

A/G

0.032  0.003

rs10182181

ADCY3

2:25,003,800

G/A

0.031  0.003

rs3888190

ATP2A1

16:28,796,987

A/C

0.031  0.003

rs1516725

ETV5

3:187,306,698

C/T

0.045  0.005

rs12446632

GPRC5B

16:19,842,890

G/A

0.040  0.005

rs2287019

QPCTL

19:50,894,012

C/T

0.036  0.004

rs16951275

MAP2K5

15:65,864,222

T/C

0.031  0.004

rs3817334

MTCH2

11:47,607,569

T/C

0.026  0.003

rs2112347

POC5

5:75,050,998

T/G

0.026  0.003

rs12566985

FPGT-TNNI3K

1:74,774,781

G/A

0.024  0.003

rs3810291

ZC3H4

19:52,260,843

A/G

0.028  0.004

rs7141420

NRXN3

14:78,969,207

T/C

0.024  0.003

rs13078960

CADM2

3:85,890,280

G/T

0.030  0.004

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TABLE 1

Loci Reaching Genome-Wide Significance (P < 5  108) for BMI in Europeans—Cont’d

SNP

Nearest Gene

Chr:Position (bp)

Alleles Effect/Other

β  SE

rs10968576

LINGO2

9:28,404,339

G/A

0.025  0.003

rs17024393

GNAT2

1:109,956,211

C/T

0.066  0.009

rs12429545

OLFM4

13:53,000,207

A/G

0.033  0.005

rs13107325

SLC39A8

4:103,407,732

T/C

0.048  0.007

rs11165643

PTBP2

1:96,696,685

T/C

0.022  0.003

rs17405819

HNF4G

8:76,969,139

T/C

0.022  0.003

rs1016287

FLJ30838

2:59,159,129

T/C

0.023  0.003

rs4256980

TRIM66

11:8,630,515

G/C

0.021  0.003

rs12401738

FUBP1

1:78,219,349

A/G

0.021  0.003

rs205262

C6orf106

6:34,671,142

G/A

0.022  0.004

rs12016871

MTIF3

13:26,915,782

T/C

0.030  0.005

rs12940622

RPTOR

17:76,230,166

G/A

0.018  0.003

rs11847697

PRKD1

14:29,584,863

T/C

0.049  0.008

rs2075650

TOMM40

19:50,087,459

A/G

0.026  0.005

rs2121279

LRP1B

2:142,759,755

T/C

0.025  0.004

rs29941

KCTD15

19:39,001,372

G/A

0.018  0.003

rs1808579

C18orf8

18:19,358,886

C/T

0.017  0.003

SNP positions are reported according to Build 36 and their alleles are coded based on the positive strand. Effect alleles, allele frequencies, betas (β), standard errors (SE) are based on the metaanalysis of GWAS I + II + Metabochip association data from the European all dataset.

TABLE 2

Loci Reaching Genome-Wide Significance (P < 5  108) for WHR in Europeans

SNP

Chr

Nearest Gene

Effect Allele (EA)

Effect Allele Frequency

β

rs905938

1

DCST2

T

0.74

0.025

rs10919388

1

GORAB

C

0.72

0.024

rs2645294

1

TBX15-

T

0.58

0.031

rs714515

1

DNM3-PIGC

G

0.43

0.027

rs2820443

1

LYPLAL1

T

0.72

0.035

rs1385167

2

MEIS1

G

0.15

0.029

rs1569135

2

CALCRL

A

0.53

0.021

rs10195252

2

GRB14-COBLL1

T

0.59

0.027

rs10804591

3

PLXND1

A

0.79

0.025 Continued

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TABLE 2 Loci Reaching Genome-Wide Significance (P < 5  108) for WHR in Europeans—Cont’d SNP

Chr

Nearest Gene

Effect Allele (EA)

Effect Allele Frequency

β

rs17451107

3

LEKR1

T

0.61

0.026

rs17819328

3

PPARG

G

0.43

0.021

rs2276824

3

PBRM1{

C

0.43

0.024

rs2371767

3

ADAMTS9

G

0.72

0.036

rs3805389

4

NMU

A

0.28

0.012

rs9991328

4

FAM13A

T

0.49

0.019

rs303084

4

SPATA5-FGF2

A

0.8

0.023

rs9687846

5

MAP3K1

A

0.19

0.024

rs6556301

5

FGFR4

T

0.36

0.022

rs1045241

5

TNFAIP8-

C

0.71

0.019

rs7705502

5

CPEB4

A

0.33

0.027

rs7759742

6

BTNL2

A

0.51

0.023

rs1776897

6

HMGA1

G

0.08

0.03

rs1294410

6

LY86

C

0.63

0.031

rs1358980

6

VEGFA

T

0.47

0.039

rs1936805

6

RSPO3

T

0.51

0.043

rs7801581

7

HOXA11

T

0.24

0.027

rs10245353

7

NFE2L3

A

0.2

0.035

rs7830933

8

NKX2–6

A

0.77

0.022

rs12679556

8

MSC

G

0.25

0.027

rs10991437

9

ABCA1

A

0.11

0.031

rs7917772

10

SFXN2

A

0.62

0.014

rs11231693

11

MACROD1-VEGFB

A

0.06

0.041

rs4765219

12

CCDC92

C

0.67

0.028

rs10842707

12

ITPR2-

T

0.23

0.032

rs1443512

12

HOXC13

A

0.24

0.028

rs8042543

15

KLF13

C

0.78

0.026

rs8030605

15

RFX7

A

0.14

0.03

rs1440372

15

SMAD6

C

0.71

0.024

rs2925979

16

CMIP

T

0.31

0.018

rs4646404

17

PEMT

G

0.67

0.027

II. MECHANISMS OF OBESITY

163

GENOME-WIDE ASSOCIATION STUDY

TABLE 2

Loci Reaching Genome-Wide Significance (P < 5  108) for WHR in Europeans—Cont’d

SNP

Chr

Nearest Gene

Effect Allele (EA)

Effect Allele Frequency

β

rs8066985

17

KCNJ2

A

0.5

0.018

rs12454712

18

BCL2

T

0.61

0.016

rs12608504

19

JUND

A

0.36

0.022

rs4081724

19

CEBPA

G

0.85

0.035

rs979012

20

BMP2

T

0.34

0.027

rs224333

20

GDF5

G

0.62

0.02

rs6090583

20

EYA2

A

0.48

0.022

rs2294239

22

ZNRF3

A

0.59

0.025

associated with different body shapes based on a combination of multiple anthropometric traits such as BMI, height, weight, WC, and hip circumference.92 In a different GWAS of 100,716 individuals for body fat percentage,93 a total of 12 loci were identified, of which eight loci were previously associated with increased overall adiposity, and four loci (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, and CRTC1) were novel93 (Table 3). When they compared effects of the 12 loci on body fat percentage and BMI, they TABLE 3

SNP

Loci Reaching Genome-Wide Significance (P < 5  108) for Body Fat % in Europeans

Chromosome

Positions (bp)

Nearest Gene

Other Nearby Genes of Interest

Body Fat% Increasing Allele

rs543874

1

176,156,103

SEC16B

G

rs2943652

2

226,816,690

IRS1

C

rs6755502

2

625,721

TMEM18

C

rs6738627

2

165,252,696

COBLL1

rs693839

13

79,856,289

SPRY2z

C

rs1558902

16

52,361,075

FTO

A

rs4788099

16

28,763,228

TUFM

rs9906944

17

44,446,419

IGF2BP1

C

rs6567160

18

55,980,115

MC4R

C

rs6857

19

50,084,094

TOMM40

GRB14

ATXN2L, SBK1, SULT1A2

APOE, APOC1

A

G

C

SH2B1, APOB48R, rs757318

19

18,681,308

CRTC1

rs3761445

22

36,925,357

PLA2G6

C PICK1

II. MECHANISMS OF OBESITY

G

164

14. GENETICS OF CENTRAL OBESITY AND BODY FAT

found that seven loci (TOMM40/APOE, IRS1, SPRY2, COBLL1/GRB14, IGF2BP1, PLA2G6, and CRCT1) had a larger effect on body fat percentage than on BMI, suggesting that these variants may primarily associate with adiposity. The remaining five loci (FTO, TMEM18, MC4R, SEC16B, and TUFM/SH2B1) showed larger effects on BMI than on body fat percentage, suggesting association with both fat and lean mass.93 Most of the loci for abdominal obesity have been identified in large-scale GWASs of European-descent individuals, whereas a GWAS in East Asian populations94 also identified 4 novel loci (near the EFEMP1, ADAMTSL3, CNPY2, and GNAS) that were associated with WC after adjustment for BMI; 2 loci (near NID2 and HLA-DRB5) associated with WHR after adjustment for BMI; and 3 loci (near CEP120, TSC22D2, and SLC22A2) associated with WC without adjustment for BMI.94 In addition, a recent GWAS in Japanese (n ¼ 173,430) found 85 loci (51 were novel) for BMI, which accounted for 3% of the phenotypic differences.7 They also conducted trans-ancestral metaanalyses by integrating these results with the results from a GWAS of Europeans and identified 61 additional new loci. In total, >200 BMI-associated loci have been shown in the study.7 A multiethnic genome-wide metaanalysis of ectopic fat depots in 9594 women and 8738 men of European, African, Hispanic, and Chinese ancestry has been performed. The study found a total of seven new loci associated with ectopic-fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3, and ENSA), and functional analysis of these genes their roles in adipocyte development and differentiation.95 Whereas the GWASs have identified and replicated loci for abdominal obesity, the significant SNPs are rarely causal for a common type of obesity, and also further functional analyses are needed to understand the roles of candidate genes in the development of adiposity. Nonetheless, findings of the GWASs led to creating genetic risk scores (GRSs) based on the sum of the risk alleles, and utility of GRSs has been demonstrated to understand genetic effects in response to diet and exercise. In addition, epidemiological studies have introduced GRSs on obesity to investigate whether obesity is causally associated with comorbidities, based on the Mendelian randomization principle.

GENE-ENVIRONMENT INTERACTIONS The GWASs have shown tremendous success in the identification of more and more genetic variants for general and abdominal obesity. However, it is notable that the established genetic markers only account for a small proportion of the variance (e.g., <5%) in the phenotype, leaving considerable “missing heritability” unexplained. Several reasons have been proposed to explain why missing heritability may exit. For example, the GWAS focus on common variants but not variants with low frequency; the structural variants are not extensively examined, and the sample sizes may be still not enough to discover even more moderate genetic effects. As we and others described previously,96–99 the importance of geneenvironment interaction has been well recognized, and the missing heritability of obesity could be partly due to interactions between the genetic variations and environmental factors such as lifestyle and dietary factors (Table 4). Epidemiological studies have demonstrated that sugar-sweetened beverages,100–102 fried food consumption,103 physical inactivity, and sedentary lifestyles106–109 modified the genetic

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165

GENE-ENVIRONMENT INTERACTIONS

TABLE 4 Lifestyle and Dietary Factors That May Modify the Risk of Obesity Among Individuals Genetically at High Risk Factors

References

Sugar-sweetened beverages

100–102

Fried foods

103

Saturated fatty acids intake

104

Unhealthy dietary habit (such as low healthy eating index)

105

A sedentary lifestyle (indicated by prolonged TV watching)

106, 107

Physically inactive lifestyle

106–110

Sleep characteristics

111

effect in associations of obesity and weight gain. One of the most replicated findings is that physical activity may modify the genetic risk of obesity.106–108, 112, 113 For example, higher physical activity attenuated the genetic effect of the FTO gene on obesity.112 A sedentary lifestyle, as assessed by prolonged hours of TV watching, strengthened the genetic effect of obesity.107 The associations have been replicated in a different population.106 Wang and Qi et al., compared associations of GRS for BMI (77 SNPs) and body fat percentage (12 SNPs) in long-term changes in body weight and BMI over 20 years of follow-up time among US men and women.109 Both GRSs for BMI or for body fat were significantly predictive of long-term increases in BMI and body weight. Interestingly, the body fat GRS showed stronger associations than BMI GRS. In addition, changes in physical activity significantly interacted with the body-fat-GRS for changes in BMI, showing that the genetic associations of body fat percentage were attenuated by higher physical activity.109 Another well-replicated evidence is an intake of sugary drinks and the genetic risk of obesity. Intake of free sugars or sugary drinks is closely associated with gaining body weight,114,115 and the genetic risk of obesity may be significantly modified by intake of sugar-sweetened beverages.102 An independent cohort of US and Swedish adults reported similar findings showing the association of sugar-sweetened beverage intake with BMI was stronger in people genetically predisposed to obesity.101,102 Further, another different study also showed interactions between the obesity GRS and intake of soft drinks for changes in BMI.100 A study combining 18 cohort studies of European ancestry investigated associations of a healthy dietary habit (which was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds and red/processed meats, sweets, sugar-sweetened beverages, and fried potatoes) and GRSs for BMI or WHR (32 SNPs for BMI; 14 SNPs for WHR).116 Their results suggested that associations between genetic predisposition and obesity traits were stronger among individuals with a healthier diet assessed by the diet score.116 A more recent study of US men and women examined changes in dietary patterns assessed by the Alternate Healthy Eating Index 2010 (AHEI-2010), Dietary Approach to Stop Hypertension (DASH), and Alternate Mediterranean Diet (AMED).105 The study identified and replicated findings that improving adherence to healthy dietary patterns attenuated the genetic

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14. GENETICS OF CENTRAL OBESITY AND BODY FAT

risk of obesity for long-term weight gain.105 The beneficial effect of improved diet quality on weight management was particularly pronounced among individuals with higher genetic risk.105 In addition to observational studies, gene-diet interactions have also been reported in randomized diet intervention trials.117–140 A unique strength of testing gene-diet interaction in diet intervention trials is that such studies may provide more direct evidence to instruct genetic-targeted diet intervention in public health practice. For example, the effect of the FTO variant rs1558902 on 2-year changes in body fat composition and fat distribution were significantly modified dietary protein intake in overweight and obese adults.137 Interestingly, the authors indicated that carriers of the risk allele (A allele) had a greater reduction in weight, body composition, and fat distribution in response to a high-protein diet, whereas an opposite genetic effect was observed on changes in fat distribution in response to a low-protein diet during 2 years of follow-up. In addition, the FTO genotype also interacted with dietary protein in changes in appetite control in the trial.120 Another study also supports the concept that dietary protein intake may modify the effect of obesity genes such as MC4R in the regulation of appetite during the weight-loss diet intervention.141 The NPY rs16147 genotype is also related to long-term changes in central adiposity and the deposition of abdominal fat among overweight and obese adults.121 Interestingly, dietary fat intake significantly modified the genetic effects, and individuals carrying the C allele of the NPY rs16147 genotype benefited more by eating a high-fat/low-carbohydrate diet.121

SEXUAL DIMORPHISM Women have more subcutaneous fat, whereas men have more visceral fat, a genetic predisposition may be other reason for this sex-specific fat distribution. The previous study has demonstrated that genetic variance was significantly higher in women for the waist, hip, and thigh circumference, and WHR, implying that genes account for more variance of fat distribution in women than in men.142 In addition, by examining morbidly obese US subjects in “affected clusters,” that is, groups of three or more closely related individuals with BMI 40—Stone et al. identified a female-only predisposition locus at chromosome 4p15-p14, although the authors made no claims that this was a genuine sex-specific effect.143 In a genome-wide linkage study performed in non-Hispanic whites and African Americans— the HyperGEN study, a sex-specific linkage was observed for human fatness.46 Lewis et al. found that a QTL influencing percentage of body fat in women was detected on chromosome12q (12q24.3-12q24.32, maximum empirical LOD score ¼ 3.8); and a QTL influencing this phenotype in men was found on chromosome 15q (15q25.3, maximum empirical LOD score ¼ 3.0). These QTLs were detected both in African-American and white women (12q) and men (15q). Several GWASs have revealed sexual dimorphism in the genetic basis of fat distribution with showing genetic variants with a stronger effect on adiposity measures in women than in men.85,89,144, 145 In the GIANT consortium, sex-specific analysis revealed seven of these loci (those near RSPO3, VEGFA, GRB14, LYPLAL1, HOXC13, ITPR2-SSPN, and ADAMTS9) exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than in men.85 Further, in a metaanalysis of 114 studies (up to 320,485

II. MECHANISMS OF OBESITY

CONCLUSIONS

167

individuals of European descent) that examined sex-specific effects of genetic variants on BMI and WHR (adjusted for BMI), the authors identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes.90 Of note, the study did not find sex-dependent effects for BMI. Similarly, in the GWAS of WHR, 20 of the 49 WHR (adjusted for BMI) loci showed significant sexual dimorphism, 19 of which displayed a stronger effect in women.89

EPIGENETICS Epigenetics means “on top of genetics” and can be defined as heritable changes that affect gene expression through mechanisms not associated with alterations in the DNA sequence. The most-studied epigenetic modification is methylation of cytosine in CpG dinucleotides in DNA. Recent epigenome-wide association studies have identified methylation at numerous CpG sites associated with adiposity.146–150 The alterations in CpG sites may be predominantly the consequence of adiposity (rather than the cause).149 In the first epigenome-wide analysis of methylation at CpG sites in relation to BMI among European populations,146 elevated BMI was associated with increased methylation at the HIF3A locus in blood cells and adipose tissue.146 In addition, the DNA methylation variant in the HIF3A locus showed significant associations for BMI changes in US men and women, through interactions with dietary intake of total or supplemental vitamin B2, vitamin B12, and folate.151 A study of African American adults confirmed previously identified methylation loci suggested to be associated with obesity and related traits (CPT1A, ABCG1, and HIF3A) and identified numerous additional novel loci relating DNA methylation variation in both blood and adipose tissue that were associated with adiposity traits.148 The latest epigenome-wide association study identified changes in DNA methylation in 187 genetic loci, and the disturbances in DNA methylation (assessed by a methylation risk score) were also predictive of future type 2 diabetes.149 In the epigenome-wide association study, the NFATC2IP genetic polymorphism (rs11150675), cisDNA methylation (DNAm) at cg26663590 CpG sites was recently identified to be causally related to BMI.149 A randomized diet intervention trial among overweight and obese adults suggests potentially causal effects of genetic, epigenetic, and transcriptional variations at the NFATC2IP locus on adiposity changes in response to dietary fat intake.152

CONCLUSIONS Obesity has become an epidemic in the United States and worldwide in the past three decades, which is one of the greatest challenges in public health care and research today. Evidence from genetic research, especially those collected from GWAS in the past few years, has indicated the importance of the human genome in determining susceptibility to central obesity including WHR, body fat percentage, and body fat distribution. The significant sex differences in the genetic associations with adiposity have repeatedly been reported in recent GWASs.85,89,90, 144, 145 Unhealthy diet, physical inactivity, and sedentary lifestyle are

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modifiable risk factors of obesity, and data on gene-environment interaction suggest that these factors may not affect obesity in themselves, but due to interactions with humans’ genomic makeup. Even though the supporting evidence is still weedy at this time, the available studies have indicated considerable diversity in being obese may exist at individual levels in response to obesogenic risk factors. Individual “personalized” diet and lifestyle interventions have long been proposed as an attractive target in future prevention efforts tailored to the reduction of obesity. A thorough understanding of the genetic basis and gene-environment interactions underlying obesity may foster more effective intervention strategies and thus may assist in improving the quality of life for affected individuals. Advance in high-throughput genotyping technology is facilitating the offer of direct-to-consumer genetic testing services and raised great hope and expectations that genetic testing will pave the way to personalized prevention. In the light of such a trend, debates about how to establish preventive genomics-based genetic testing in a medically and socially responsible way have just begun.

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