Genetics of Obesity and Its Measures in India

Journal of Genetics, Vol. 97, No. 4, September 2018, pp. 1047–1071 © Indian Academy of Sciences https://doi.org/10.1007/s12041-018-0987-8

REVIEW ARTICLE

Genetics of obesity and its measures in India

SIMMI SAINI1, GAGANDEEP KAUR WALIA2, MOHINDER PAL SACHDEVA1 and VIPIN GUPTA1∗

1Department of Anthropology, University of Delhi, Delhi 110 007, India 2Public Health Foundation of India, Gurugram 122 002, India *For correspondence. E-mail: [email protected].

Received 14 September 2017; accepted 27 February 2018; published online 14 September 2018

Abstract. Obesity is one of the largest global health problems associated with increased morbidity and mortality mediated by its association with several other metabolic disorders. The interaction between the genes and environment plays an important role in the manifestation of obesity. Despite a high heritability (40–70%) of obesity, the search for genetic variants associated with obesity susceptibility has been a challenging task. To date, limited studies have been conducted in India, restricted to the validation of few genetic variants identified by genomewide association studies. In this critical review, we sought to examine the current knowledge of genetic basis of obesity and its measures in the Indian population. A comprehensive literature search was performed using ‘PubMed’, ‘Medline’ and ‘IndMed’ databases to search for citations published until 31st May 2017, using the key terms as ‘Genetics’ AND ‘obesity’ AND ‘India’. We identified 48 potential studies which fulfilled the eligibility criteria. The findings indicated that FTO, MC4R, TNF-α, PPAR-γ , UCP1, UCP2, LPL, LEPR, AMD1, IL6, APOE, ADIPOQ, DOK5, INSIG2, PBEF1, IL6R, Myostatin, CXCR4, HHEX, IRX3, POMC, NGN3, FOXA2, MTR, TCN and CHDH are some of the important genes studied among the Indian population. Importantly, the role of sexual dimorphism in the genetic regulation of obesity and body fat distribution was also reported in a few studies. Further, seven biological pathways have been identified that contribute to obesity pathogenesis in India. In conclusion, further exploration of pathway-based research on genetics of obesity can be useful for better understanding the pathophysiology of obesity in India.

Keywords. obesity; body fat distribution; genetics; genomewide association study; India.

Introduction school-aged children worldwide, between 5 and 17 years, are either overweight or obese (Kalra and Unnikrishnan Obesity is one of the common public-health conditions 2012). A recent systematic review has reported a combined that has a huge epidemiological burden across low-, prevalence of 19.3% of childhood overweight and obesity middle-income and high-income countries. The global in India which is a signiﬁcant increase from the earlier burden of overweight/obesity increased to 36.9% in men prevalence of 16.3% reported in 2001–2005 (Ranjani et al. and 38% in women in 2013 (Ng et al. 2014). Rising preva- 2016). lence of overweight and obesity is the major driving factor Obesity is broadly measured through body mass index of noncommunicable chronic diseases (NCDs), account- (BMI), which is a composite parameter of weight–height ing for 44% of diabetes, 23% of ischaemic heart disease and that indicates the amount of body fat and also used to between 7 and 41% of certain cancer burden (WHO 2013). classify overweight and obesity in adults (Chris 2004). In The prevalence of overweight/obesity range from 1.50 comparison with Western countries, a lower cut-off for to 45.6% among the Indian population (Kshatriya and BMI (≥23 kg/m2 instead of ≥25 kg/m2) has been proposed Acharya 2016; NFHS-4 2016, http://rchiips.org/NFHS/ to deﬁne overweight/obesity in South Asians including factsheet_NFHS-4.shtml). Indians (Misra and Khurana 2011). The risks for type- Childhood obesity is another rapidly growing public 2 diabetes (T2D) and cardiovascular diseases (CVDs) are health concern worldwide (Lobstein et al. 2004) and is one associated with a lower BMI among South Asians as they of the major determinants of onset of NCDs in adulthood have a higher total and central adiposity for a given body (Biro and Wien 2010). It has been estimated that 10% of weight when compared with matched white populations

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(Sniderman et al. 2007; Misra and Khurana 2011). Fur- of relevant search terms such as: ‘genetics’ OR ‘genetic ther, higher rate of metabolic syndrome in South Asians association studies’ OR ‘single-nucleotide polymorphisms’ is mostly attributed to the increased prevalence of central AND ‘obesity’ OR ‘adiposity’ OR ‘body fat’ OR ‘seg- adiposity (Ramachandran and Snehalatha 2010). Waist mental body fat’ OR ‘central obesity’ AND ‘measures’ circumference (WC), hip circumference (HC) and waist– OR ‘body mass index’ OR ‘waist circumference’ OR ‘hip hip ratio (WHR) are the other indicators which measure circumference’ OR ‘waist hip ratio’ OR ‘skinfolds’ AND regional fat distribution (Avenell et al. 2004). A higher ‘risk’ AND ‘India’. We included all the studies that were prevalence of T2D and CVDs in Asian populations, espe- published until 31st May 2017. Bibliographies and cita- cially in women could be attributed to the higher WC tion sections of the retrieved articles were also reviewed (Sniderman et al. 2007; Kaur et al. 2012). for additional related studies. Obesity is an outcome of complex, heritable and mul- tifactorial interactions of multiple genes, environmental factors and behavioural traits that make the management Selection strategy and prevention challenging in human populations (Rao et al. 2014). Globally, the changing patterns of food intake, The inclusion criteria followed were: (i) studies published both in quality and quantity (nutrition transition), an in English language journals, (ii) studies related to humans, increase in physical inactivity due to increasing sedentary (iii) original research studies and (iv) studies conducted lifestyle, changing modes of transportation and increasing exclusively in India. Studies were excluded if they were (i) urbanization (WHO 2013) are contributing to the ris- duplicated studies, (ii) reviews (iii) based on gene expres- ing burden of obesity. Biologically, obesity is an adverse sion, (iv) methylation and (v) case only studies (in the case consequence of energy imbalance between the calories of other metabolic phenotypes such as T2D, insulin resis- consumed and calories expended. Family studies includ- tance and hyperinsulinaemia). ing twin and adoption studies indicated that adiposity is As a result of the initial search, we identified 919 poten- highly heritable with the estimated genetic contribution tial articles for inclusion. After excluding articles not ranging from 65 to 80% for body weight (Stunkard et al. related to humans, 628 papers were left for examination. 1986; Malis et al. 2005), 40–77% for BMI (Maes et al. 1997; Further, screening for duplicates left a total of 133 studies. Atwood et al. 2002; Schousboe et al. 2003; Wardle et al. Preliminary assessment of titles and abstracts was carried 2008) and 31–76% for WC and WHR (Selby et al. 1990; out to determine the objectives and relevance of studies, Nelson et al. 1999; Souren et al. 2007) even after account- which resulted in the exclusion of 70 articles. The full ing for the BMI (Rose et al. 1998; Nelson et al. 2002). texts of 63 articles were read to extract information on Genomewide association studies (GWASs) have the topic of interest, of which 15 articles were excluded. changed the genetic landscape of common traits which The excluded articles did not fit the inclusion criteria. The were earlier restricted to the linkage and candidate gene- remaining 48 articles fulfilled eligibility criteria. based association studies. More than 80 genomewide linkage studies have been carried out so far, identifying Data extraction more than 300 chromosomal loci showing some evidence of linkage with obesity (Loos 2012). On the other hand, After the final inclusion, identifying information (such as since 2007, several waves of GWASs have been conducted research setting, study design, phenotype, gene, genetic in Western countries, and have discovered more than 100 variant and effect size) was extracted from each article and loci associated with obesity and related traits (Frayling is presented in tables 1–4. et al. 2007; Scuteri et al. 2007; Chambers et al. 2008; Heid et al. 2010; Speliotes et al. 2010; Locke et al. 2015; Shun- gin et al. 2015). Indian-specific GWASs related to obesity Genetic associations with obesity and its measures are absent and to date no published findings are available. To address the clinical and public health Of the 25 genetic studies exclusively on obesity in the implications of the alarming obesity epidemic in India, a Indian population only 15 had observed significant asso- comprehensive understanding of the genetic architecture ciations of genetic variants with obesity in pooled sample of obesity and related traits is demanded. Therefore, the size of 22,383 participants (table 2). Similar to the studies purpose of this review is to assess the current knowledge on Western populations (Frayling et al. 2007; Loos et al. and understanding of genetics of obesity and its measures 2008; Lindgren et al. 2009), FTO and MC4R loci have been in the Indian population. widely studied in India (table 2) as they are the major obesity determining genes even in younger age groups (Zhao Search strategy et al. 2011; Melka et al. 2012). Both the genes are highly expressed in the central nervous system that has shown to A comprehensive search was conducted using ‘PubMed’, play a significant role in control of the well-known regula- ‘Medline’ and ‘IndMed’ databases using a combination tory pathways of energy homeostasis (Beckers et al. 2009). Genetics of obesity and its measures in India 1049 ) ) ); ) ) ) ) ) Bhatt 2008 ( 2016b 2012a 2011 ( ); ) ) ( ( ) 2012 ) 2012 2011 ) ) ( ( ( 2016 ( et al. 2010 et al. 2012 2011 2014 ( ( 2012a 2012 et al. 2013 ( et al. ( ( ( ( et al. et al. et al. et al. ) et al. et al. et al. b et al. et al. et al. et al. , Srivastava Prakash Prakash Chauhan ) Chauhan ) Tabassum ); Dhall Prakash ); ); ) ); ) Bhatt 2006a ); Vimaleswaran Vasan ), Ramya ( Gupta Kumar ); ) ); ) ) ) ) ); ), ) ); Sanghera ) ); ) ); ) ); 2015 ) ( 2010 2008 2008 2010 2012b 2014 ) ); 2016a ( ( ( ( ( 2010 ( 2012 ( 2013 2012 2013 2010 ( 2017 ( 2011 et al. ( ( 2011 ( ( ( 2007 2013 2010 ( 1999 2012 ( 2007 2009 ( 2011 2011 2012 ( ( ( 2013 ( ( ( ( ( ( ) ( 2010 2012 et al. ( ( et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. 2012b et al. et al. ( Sharma Srivastava Prakash Dasgupta Bhagat Moore Cassell Been Ramya Dwivedi Vimaleswaran Gupta Kumar Taylor Mahajan Tabassum Madeshiya Tabassum Radha Been Srivastava Srivastava Yajnik Dwivedi Tabassum et al. Bhaskar Vikram Vasan Sharma Janipalli Gupta Punjab and Uttarakhand Himachal Pradesh, Uttar Pradesh, and Bengaluru (Indian Migration Study Sites) Epidemiology Study (CURES) 48) Areas covered References = n 1 Mysuru, Karnataka 1 Punjab 1 Delhi and Trivandrum 5 Delhi, Haryana and Jammu and Kashmir 6 Chennai urban and rural 1 North India (area not specified) 7 New Delhi 2 Lucknow, Nagpur, Hyderabad 3 Punjab (Sikh Diabetes Study) 4 Lucknow 1 Pune and Mysuru 1 South India (area not specified) 1Pune 2 Lucknow Study designs adopted for studying obesity and its measures in India. Hospital-based case control 1 Lucknow Table 1. Study designPopulation-based cross-sectional 2 No. of studies ( Chennai population-based controls Hospital-based cases and 5 Delhi School-based case control 3 Delhi Population-based case control 1 South India (Kota tribe) 1050 Simmi Saini et al. 3 − 10 7 × − value 8 . 0.005 0.004 P 1.3 (1.1–1.6) 1.3 (1.1–1.6) –T93G–53G-C 1.77 (1.19–2.63) 0.561 (0.03–0.99) 0.005 0.05 (+10211) T/G 1.57 (1.34–1.84) 10 rs6064099 NR 9 rs2229094 rs1800630 Gly318Ala 1.46 (1.05–2.03) 0.024 rs12970134 1.24 0.012 -866 G/A 2.84 (1.55–5.19) 0.001 Pro12Ala 1.74 (1.03–2.93) 0.038 α - LPL ADIPOQ DOK5 TNF TNFA MC4R UCP2 PPARG ) ) ) ) ) ) 2 ) 2 2 2 2 2 2 23 kg/m 23 kg/m 25 kg/m 30 kg/m 25 kg/m 25 kg/m 25 kg/m ≥ ≥ ≥ ≥ ≥ > ( hyperinsulinaemia ( ( ( ( ( > Phenotype (obesity cut-off) Genes SNPs studied OR (95% CI) 0Obesity/T2D 9 controls: 7 . . . 12 14 17 1212 Obesity/T2D Obesity/T2D ± ± ± 1 5 5 ± ± SD) . . . Age in years (range/mean/mean ± Sample size total (M/F) (843/1157) (606/400) Obese: 201 48 (392/391) Obese: 200 Nonobese: 240 ( Thin: 143 39 ChennaiChennai 731North India 2000 783 49 Delhi 39 51 1006Lucknow 440 50Delhi Not specified 1006 Obesity/T2D Obesity/ 50 T2D/obesity Punjab 344 Cases: Obesity ∗ ∗ ∗ ∗ ∗ ∗ based case control cases and population- based controls cases and population- based controls based case control based case control cases and population- based controls based case control Population- Hospital-based Hospital-based Population- Population- Hospital-based Population- ) et al. et al. et al. Genetic variants associated with obesity in India. et al. 2008 et al. ) ) ) ) ) ) ( et al. 2007 2010 2010 2010 2010 2010 ( ( et al. ( ( ( ( Table 2. Authors (year) Study designRadha Location Been Srivastava Vimaleswaran Tabassum Mahajan Bhagat Genetics of obesity and its measures in India 1051 value P T) 1.75 (1.16–2.64) 0.008 > T 5.6 (2.5–12.2) 0.001 > rs8050136 1.17 (1.01–1.36) 0.04 1908C rs8050136rs1588413 NR 1.53 (1.05–2.22) 0.002 K153R 0.0001 3.2 (1.2–12.9) NR rs17817449 (G rs17782313 1.19 (1.00–1.40) 0.05 rs1421085 1.22 (1.05–1.41) 0.009 Pro12Ala 3.2 (1.2–12.9) NR 2 γ LMNA FTO Myostatin FTO MC4R FTO ) ) ) ) ) 2 2 2 2 2 ) ) 2 2 30 kg/m 25 kg/m 23 kg/m 25 kg/m 25 kg/m 25 kg/m 25 kg/m ≥ > ≥ ≥ > ( ( ( ( ( ≥ ≥ Phenotype (obesity cut-off) Genes SNPs studied OR (95% CI) 7 3( . . 8 8 ± 1 Obesity PPAR- 7Obesity ± . . 9 9 8 8 . . ± ± 4 2 . . 0 Females: 13 Obesity 9 . . . 7 0 6 14 Obesity/T2D ± ± ± 7 2 0 ± SD) . . . Age in years (range/mean/mean ± 40 38 (1017/831) (4301/2479) subjects: 333 309 Sample size total (M/F) 2854 Stage 1: 50 Obesity/T2D Stage 1: 1006 Stage 2: 52 ( (606/400) Stage 2: 1848 (279/216) Nonobese: 40 (238/97) 38 (269/260) Non obese: 38 6780 Nonobese Obese subjects: (areas around Delhi) Nagpur, Hyderabad and Bengaluru New Delhi 335 Males: Obesity Lucknow 642 Not specified Obesity ChennaiNorth 1001 India (418/583) 43 New Delhi 495 Obese: 38 Lucknow, New Delhi 529 Obese: 40 ∗ ∗ based cross- sectional cases and population- based controls based case control based cross- sectional based cross- sectional based cross- sectional cases and population- based controls Population- Hospital-based Population- Population- Population- Population- Hospital-based ) et al. contd et al. et al. ( ) ) et al. et al. ) ) ) ) ) et al. et al. 2012a 2011 2011 2012b 2011 2011 2011 ( ( ( ( ( ( ( Table 2 Authors (year) Study designPrakash Location Bhatt Ramya Sharma Bhatt Taylor Chauhan 1052 Simmi Saini et al. 4 3 5 3 5 6 − − − − − − 10 10 10 10 10 10 × × × × × × value 3 3 5 0 9 9 ...... P rs8050136 1.19 (1.05–1.35) 5 rs9939609 1.21 (1.07–1.37) 2 rs7514452 1.19 0.011 rs2069845 1.37 2 rs16834521rs4563403 0.82 (0.68–0.99) 0.04 0.69 (0.52–0.92) 0.01 rs1137100rs3801266 1.39 1.35 3 4 rs2796749 1.35 (1.19–1.52) 1 rs1801198 1.24 (1.04–1.48) 0.02 FTO IL6R IL6 MTR CHDH LEPR PBEF1 AMD1 TCN2 ) ) ) ) 2 Cole Cole Cole 2000 2000 2000 23 kg/m criteria, et al. criteria, et al. criteria, et al. ≥ Obesity/T2D Phenotype (obesity cut-off) Genes SNPs studied OR (95% CI) Obesity (IOTF SD) 50 children: 13.2 children 13.00 Age in years (range/mean/mean ± Stage 2: NW: 13.00 OW/OB: 13.2 Stage 1: NW: 14.00 OW/OB OW and OB: (305/591)) (370/464) (173/279) (132/312) and OB: (173/279) (420/979), OW and OB: (132/312) Sample size total (M/F) (NW: (789/1441) Stage 1:NW children: OW and OB: Stage 2: NW: 14.00.NW: OW/OB: (420/979) 13.00 OW and OB: Obese: 562 ( Stage 1:NW: (370/464) OW Stage 2: NW: Stage 2: NW: 13.00 OW/OB 3168 Normal weight: 436 Delhi 3126 13.50 Obesity (IOTF Delhi 3168 Stage 1: Obesity (IOTF New Delhi 998 Obese and normal weight: Delhi case control case control case control case control School-based School-based Hospital-based School-based ) et al. et al. et al. contd et al. ( ) ) ) ) 2012 2012b 2012 2012a ( ( ( ( Table 2 Authors (year) Study designDwivedi Location Chauhan Tabassum Tabassum Genetics of obesity and its measures in India 1053 6 − 6 5 10 − − 10 10 × × × value 016 6 9 . . . 0.005 7 6 0.015 0.03775 0.003 0.008391 4 0.006 P (1.07–1.44) (1.27–2.05) (1.36–2.19) (1.11–2.65) 3.261) (1.09–1.48) (1.352–7.78) 11.74) 2.370) 1.24 1.62 1.73 1.27 1.65 (1.155– (in adults) (in children) (in children) (in adults) rs1801282 rs12970134 rs12970134 rs4731426 5.63 (2.701– rs17782313 rs9939609 1.71 rs17782313 Pro12Ala rs7799039 1.837 (1.035– γ - FTO LEPR MC4R PPAR ) ) ) ) 2 2 2 2 30 kg/m 30 kg/m 25 kg/m 27.5 kg/m > ≥ ≥ ( ( ( ≥ Phenotype (obesity cut-off) Genes SNPs studied OR (95% CI) 03 ( . 60 . 2 81 Obesity/T2D . 16 . 96 Obesity 2 10 1 . ± 4Obesity ± ± ± . 11 2 4 88 . . ± 65 96 . . ± 37 8 . . SD) (adults): 53 Age in years (range/mean/mean ± Nonobese: 35 Nondiabetic patient Children: 13 controls: 333 (347/295) 309 patient (adults): 2028 (1111/917) (620/742) 613 Obese: 46 Sample size total (M/F) 642 Obese: 36 Nonobese Obese cases: Obese: 304 Nonobese: 46 Nondiabetic Nonobese: 309 rs2167270 3.243 Obese: 309 Nonobese: 333 Karnataka Haryana, Jammu and Kashmir, Himachal Pradesh, Uttar Pradesh, Punjab and Uttarakhand Mysuru, Lucknow 642 19-60 Obesity Delhi, Delhi Children: 1362 ∗ based case control cases and population- based controls based case control based case control Population- Hospital-based Population- Population- ) et al. contd et al. et al. et al. ( ) ) ) ) 2014 2012 2016 2013 ( ( ( ( Dasgupta Table 2 Authors (year) Study designPrakash Location Prakash Dwivedi 1054 Simmi Saini et al. 001 . value 0 < P o; 95% CI, 95% rs1042571Hha1 4.0 (1.1–14.1) 0.03 5.0 (1.4–17.2) 0.011 rs1421085rs9939609rs17817449 3.0 (1.8–5.0)rs3751723 4.2 (2.5–7.3) 3.8 (1.2–11.8) 0.0001 3.3 (1.8–3.6) 0.0001 0.021 0.012 rs8050136 3.1 (1.9–5.2) 0.0001 rs17782313 2.9 (1.8–4.7) 0.0001 rs7566605 3.82 (1.95–7.48) POMC APOE IRX3 FTO MC4R INSIG2 ) ) ) 2 2 2 30 kg/m 30 kg/m 30 kg/m ≥ ≥ ≥ ( ( ( Phenotype (obesity cut-off) Genes SNPs studied OR (95% CI) 15 . 2 39 Obesity ± . 2 44 . ± 78 . SD) Nonobese: 35 Age in years (range/mean/mean ± 396 controls: 300 controls: 333 (347/295) 309 642 Obese: 36 600 20–42 Obesity 696 20-42 Obesity Sample size total (M/F) Obese cases: Nonobese (375/321) Nonobese Obese cases: (350/250) Jammu and Kashmir, Himachal Pradesh, Uttar Pradesh, Punjab and Uttarakhand Jammu and Kashmir, Himachal Pradesh, Uttar Pradesh, Jammu and Kashmir, Himachal Pradesh, Uttar Pradesh, Punjab and Uttarakhand Uttarakhand Delhi, Haryana, Delhi, Haryana, Delhi, Haryana, Punjab and case control case control case control Population-based Population-based Population-based ) et al. et al. contd et al. ( ) ) ) 2016a 2017 2016b ( ( ( In case–control design based on metabolic disorders other than obesity, effect sizes noted from control participants only. Prakash Table 2 Authors (year) Study designSrivastava Location F, female; M, male; IOTF, International Obesity∗ Task Force; NW, normal weight; NGT, normal glucose tolerant; OW/OB, overweight/obese; OR, odds rati confidence interval; NR, not reported. Srivastava Genetics of obesity and its measures in India 1055 3 − 10 001 . value 0 × < 0 P . 95% CI BMI β T NR NR 0.001 > Exon 8 NR NR 93 T to G NR NR 0.003 Pro12Ala NR NR 0.01 1908C rs9939609 0.08 0.02–0.14 0.009 rs2229094 NR NR 0.008 rs6064099 NR NR 7 Gly318Ala NR NR 0.01 rs12970134 NR NR 0.002 − α - LPL FTO Gene SNPs studied UCP2 TNFA DOX5 MC4R TNF LMNA PPARG Primary outcome 0) 7) 7) 7) . . . . 13) 8 8 . 17 14 0 ± ± 12) 12) ± ± ± 9 2 . . 5 5 7 ± ± . . . (38 (40 (39 6170 Obesity (49 765 Obesity/T2D (51 Non obese: Obese: (40 Sample size/range (mean age) (50.0) (50.0) rs1800630 0.01 (45 Migration study Study (Punjab) Punjab 344 Obesity Indian ChennaiChennai 255Sikh Diabetes 731Delhi BMI/T2D Obesity/T2D 1006Delhi Obesity/T2D 1006 Obesity/T2D New Delhi 529 Obesity ∗ ∗ ∗ ∗ ∗ based cross- sectional based case control cases and population- based controls based cross- sectional based cross- sectional based case control cases and population- based controls based case control Population- Population- Hospital-based Population- Population- Population- Hospital-based Population- et al. et al. Genetic variants associated with BMI in India. et al. et al. et al. et al. et al. ) ) ) ) ) ) ) ) et al. 1999 2010 2010 2011 2011 2010 2010 2007 ( ( ( ( ( ( ( ( Sharma Table 3. ReferenceCassell Study design Location Bhagat Mahajan Taylor Been Tabassum Radha 1056 Simmi Saini et al. 7 6 5 7 6 4 − − − − − − 10 10 10 10 10 10 value × × × × × × 5 2 6 7 2 7 P ...... 95% CI BMI β A55T NR NR 0.04 rs9930506 NR NR 0.04 rs1137100 0.15 NR 3 rs8050136 0.14 0.08–0.19 4 rs1421085 NR NR 0.0002 rs3801266 0.17 NR 1 rs9939609 0.14 0.09–0.19 1 rs2796749 0.13 NR 2 rs9939609 0.015 0.003–0.026 0.01 rs2069845 0.12 NR 5 IL6 FTO FTO FTO Gene SNPs studied LEPR AMD1 PBEF1 Myostatin Primary outcome 9) . 1) . 66 1 ± ± 0 3 . . (52.0) rs8050136 NR NR 0.002 Sample size/range (mean age) 1627–1671 Obesity/T2D (38 (13.0) (13.0) (13.50) 2060 Obesity (28 (areas around Delhi) Nadu North India New Delhi 335 Obesity Delhi 3126 Obesity Delhi 3168 Obesity Delhi 3168 Obesity Vellore Tamil ∗ based cross- sectional cases and population- based controls case control cross- sectional case control based cross- sectional study Population- Hospital-based School-based School-based School-based Population- ) et al. et al. et al. contd et al. ( ) ) ) ) ) ) et al. et al. 2012a 2012a 2012b 2011 2012 2012 ( ( ( ( ( ( Tabassum Table 3 ReferenceChauhan Study design Location Tabassum Bhatt Vasan Dwivedi Genetics of obesity and its measures in India 1057 4 4 9 11 − − − − 10 10 10 10 value × × × × 1 1 7 P 5 . . . . 95% CI BMI β rs932206 0.13 NR 0.001 Pro12Ala NR NR 0.02 rs5015480 0.09 NR 0.002 rs3751812 0.55 0.14–0.96 0.008 rs12970134 0.43 0.19–0.66 4 rs17782313 0.45 0.21–0.68 2 rs17782313 (in adults)rs12970134 (in adults) 0.08 0.08 0.01–0.14 0.01–0.14 0.027 0.018 rs12970134 (in children) 0.22 0.14–0.29 6 rs17782313 (in children) 0.24 0.17–0.32 8 2 γ - FTO Gene SNPs studied MC4R MC4R HHEX CXCR4 PPAR T2D Primary outcome 60) . 1) 3) 3) 81) . . . . 2) 9) 9 8 . . 1 10 10 9 9 ± ± ± ± ± ± ± 8 4 . . 6 7 1 96 65 . . . . . (40 (38 618 specified) patient (adults): 2028 Nonobese: Obese: (39 Trivandrum: (48 Sample size/range (mean age) 1129 Obesity New Delhi: 511 (47 5056 Obesity 1362 (13 Nondiabetic (53 Trivandrum Migration Study New Delhi and New Delhi 495 Obesity Indian Delhi Children: Obesity/T2D Pune 1549 (not ∗ based cross- sectional based cross- sectional based cross- sectional based case control based case control Population- Population- Population- Population- Population- ) et al. contd et al. ( ) et al. et al. ) ) ) ) et al. 2012 2012b 2013 2013 2012 ( ( ( ( ( Moore Janipalli Table 3 ReferenceBhatt Study design Location Gupta Dwivedi 1058 Simmi Saini et al. 001 . value 0 < P 95% CI BMI β rs2167270rs4731426 1.304 1.46 NR NR 0.0068 0.0001 rs7799039 0.604 NR 0.0501 rs7566605 NR NR Gene SNPs studied LEPR INSIG2 Primary outcome 96) 03) . . 15) 39) . . 2 11 16 2 ± ± ± ± 37 88 78 44 . . . . 309 controls: 333 Obese: 304 (46 613 Obesity Nonobese: 309 (46 Sample size/range (mean age) 642 Obesity Obese cases: (36 Nonobese (35 Karnataka Haryana, Jammu and Kashmir, Himachal Pradesh, Uttar Pradesh, Punjab and Uttarakhand Mysuru, Delhi, , beta coefficient; NR, not reported. based case control based case control β Population- Population- ) et al. contd et al. ( ) ) 2014 2017 ( ( In case–control design based on metabolic disorders other than obesity, effect sizes noted from control participants only. Table 3 ReferenceDasgupta Study design Location T2D, type-2 diabetes; ∗ Prakash Genetics of obesity and its measures in India 1059 033 013 01 01 . . . . 0 0 0 0 < < = = P P P P value 03 NS 02 NS 040 NR . . . P 0 0 0 NRNR NR NR = = = P P P (95% CI), 4 4 − − β 03 NR NR 10 10 009 . 01 NR 05 NR . . . 0 0 0 0 × × WC HC WHR 4 4 < < = = P P P = = P P P 866 G/A NR NR 93 T to G Pro12Ala rs9939609 NS rs1800630 rs1055080 NR NR rs2229094 − Gly318Ala rs12495941 NS rs12970134 − α - ADIPOQ UCP2 FTO MC4R TNFA LPL FOXA2 TNF PPARG hyperinsulinaemia Primary outcome Obesity and 0) 4) 6) 7) . . . . 16 15 14 17 12) ± ± ± ± 0 8 5 5 ± . . . . specified) 960765500 Obesity/T2D Obesity/T2D T2D (37 (49 (51 Sample size/range (mean age) (51 (50.0) (50.0) (39 Mysuru Study (Punjab) study (Punjab) ChennaiNew 731 Delhi 625Pune Obesity/T2D and Sikh Diabetes T2D Sikh Diabetes Lucknow 240 (not Delhi 1006 Obesity/T2D Punjab 344 Obesity ∗ ∗ ∗ ∗ ∗ ∗ ∗ based case control cases and population- based controls based case control case control cases and population- based controls based case control cases and population- based controls based case control Population- Hospital-based Population- Hospital-based Hospital-based Population- Hospital-based Population- et al. et al. et al. et al. Genetic variants associated with other obesity measures in India. et al. et al. et al. ) ) ) ) ) ) ) ) et al. 2007 2010 2010 2009 2008 2010 2010 2010 ( ( ( ( ( ( ( ( Table 4. ReferenceRadha Study design Location Gene SNPs studied Tabassum Been Srivastava Sanghera Yajnik Mahajan Bhagat 1060 Simmi Saini et al. 3 − 10 03 02 . . 001 × 0 0 . 0 2 . = = < 9 P P P = P 4 4 − − value 10 10 P 03 002 NS . . × × 0 0 3 5 . . = = 1 1 P P (0.01–0.12) = = (95% CI), P P β 5 6 − − 10 10 05 NS NS 0202 NS NS NS 001 001 NS NS . . . . . × × 0 0 0 0 0 7 9 . . WC HC WHR = = = = = 1 7 P P P P P (0.07–0.18) (0.05–0.16) (0.02–0.13) = = P P T > rs17782313 NS 0.06 NS rs8050136 G-174C NS NS 1908C rs8050136 0.12 0.11 0.07 rs9930506 rs9939609 rs1421085 rs9939609 0.12 0.11 0.06 MC4R IL6 LMNA FTO FTO syndrome Primary outcome 86) 51) . . 7) 7) 5 . 6 . 13) 8 8 . ± ± 0 ± ± ± 9 2 12 04 . . . . 7 . (38 (40 (29 (28 Nonobese: Obese: Obese: (40 6168 Obesity 1627–1671 Obesity/T2D Nonobese: Sample size/range (mean age) (52.0) (13.50) (0.07–0.17) (0.05–0.16) (0.01–0.12) Migration Study (places in and around Delhi) New Delhi 529 Obesity Indian North India 178 Metabolic North India Delhi 3126 Obesity ∗ ∗ based case control cases and population- based controls based cross- sectional based cross- sectional case control Population- Hospital-based Population- Population- School-based ) et al. contd et al. et al. ( et al. et al. ) ) ) ) ) 2011 2011 2011 2011 2012 ( ( ( ( ( Chauhan Sharma Gupta Table 4 ReferenceTaylor Study design Location Gene SNPs studied Dwivedi Genetics of obesity and its measures in India 1061 05 01 . . 0 0 = = P P 7 − value 10 03 NS P 011 039 . . . × 0 0 0 7 . = = = 3 P P P = (0.002–0.012) P (95% CI), β 7 − 10 04 NS NS 04 NR NR 002 . . . × 0 0 0 2 . WC HC WHR = = = 3 P P P = P K153R Pro12Ala NS rs9939609 0.013 0.007 0.005 rs2796749 0.14 0.16 0.06 rs3751812 rs17782313 NS 0.005 NS 2 γ - FTO MC4R AMD1 PPAR Myostatin FTO Primary outcome 1) 3) 9) . . . 1) (0.005–0.021) (0.002–0.013) (0.001–0.0008) 9) 2) 9 . 8 . . 1 66 9 9 ± ± ± ± ± ± 8 4 . . 3 0 1 7 . . . . (40 (38 Trivandrum: 618 2065 Obesity (28 Sample size/range (mean age) (13.0) (38 Nonobese: Obese: New Delhi: 511 (47 (48 1129 Obesity (Vellore) Trivandrum Delhi 3168 Obesity Tamil Nadu New Delhi 495 Obesity New Delhi 335 Obesity New Delhi and case control based cross- sectional based cross- sectional based cross- sectional based cross- sectional School-based Population- Population- Population- Population- ) et al. contd ( ) ) ) et al. ) ) et al. et al. et al. 2012b 2012b 2012a 2012 2012 ( ( ( ( ( Table 4 ReferenceTabassum Study design Location Gene SNPs studied Vasan Bhatt Bhatt Moore 1062 Simmi Saini et al. 01 . 0 021 . = 0 P = P (0.001–0.012) 3 3 − − value 10 10 P × × 0 0 . . 1 2 = = P P (95% CI), β 3 3 − − 10 10 × × 0 0 . . WC HC WHR 1 2 (0.38–1.74) (0.31–1.41) (0.52–1.87) (0.34–1.44) = = P P rs9939609 NS NS 0.006 rs10823406 NS NS 0.08 rs12970134 1.06 0.86 NR rs17782313 1.19 0.89 NR NGN3 MC4R FTO T2D Primary outcome 3) . 9) . 10 1 ± ± 6 1 . . specified) 5056 Obesity (39 (17 Sample size/range (mean age) Migration study Indian Pune 1549 (not South India 1230 Obesity ∗ based cross- sectional based case control based cross- sectional Population- Population- Population- ) et al. contd ( et al. ) ) ) et al. 2013 2012 2013 ( ( ( Gupta Table 4 ReferenceJanipalli Study design LocationVasan Gene SNPs studied Genetics of obesity and its measures in India 1063 4 − 10 002 . × 0 0 . = 2 P = P value 01 NR P 001 NR . . 0 0 = = P P (95% CI), 10 12 β − − 10 10 05 NR NR 001 NS NR 034 . . . 0 × × 0 0 3 8 WC HC WHR = . . = = 3 4 P P P = = P P +276G/T NS (in adults) (in adults) (0.01–0.14) rs12970134 0.24 NR 0.11 rs17782313 0.07 NR NR rs17782313 0.26 NR 0.13 rs12970134 (in children) (0.16–0.31) (0.04–0.18) 11365C/G (rs266729) NS –3971A/G (rs822396) ADIPOQ MC4R , beta coefficient; NS, nonsignificant; NR, not reported. β Primary outcome 60) . 81) (in children) (0.19–0.34) (0.06–0.20) . 1 10 ± ± 13) (rs1501299) 96 65 ± . . patient (adults): 2028 1100 Obesity/T2D (13 Nondiabetic Sample size/range (mean age) (53 (41 Himachal Pradesh, Delhi and Jammu and Kashmir Delhi Children: 1362 Obesity/T2D Haryana, ∗ based case control based case control Population- Population- ) contd et al. ( et al. ) ) 2013 2013 ( ( In case–control design based on metabolic disorders other than obesity, effect sizes noted from control participants only. Table 4 ReferenceDwivedi Study design LocationWC, waist circumference; HC,∗ hip Gene circumference; WHR, waist–hip ratio; SNPs studied Ramya 1064 Simmi Saini et al.

The associations of genetic variants of FTO (rs9939609 et al. 2011). In both studies, samples were collected from and rs8050136) and MC4R (rs17782313 and rs12970134) multiple ethnicities and using them for combined analysis with obesity are relatively well studied in the Indian popu- was the major limitation. lation. Fourteen studies have made an attempt to validate Further, Dwivedi et al. (2012) had found the these genes in the Indian populations. Of these, 10 studies association of FTO (rs9939609, OR = 1.21 and rs8050136, have found significant associations of FTO variants (OR OR = 1.19) with childhood obesity in urban India. range: 1.17–4.2) (Chauhan et al. 2011; Prakash et al. 2011; They had also found age-dependent influence of MC4R Ramya et al. 2011; Dwivedi et al. 2012; Janipalli et al. (rs17782313 and rs12970134) with higher effect size in chil- 2012; Moore et al. 2012; Vasan et al. 2012, 2013; Prakash dren compared with adults (Dwivedi et al. 2013)(table2). et al. 2016; Srivastava et al. 2016a), and comparatively,only The reported risk of obesity in Indian children for MC4R four studies identified associations of MC4R variants (OR (rs17782313, OR = 1.73) was higher in comparison with range: 1.19–2.9) with obesity (Been et al. 2010; Taylor et al. European children (OR range: 1.20–1.40), in spite of a 2011; Dwivedi et al. 2013; Srivastava et al. 2016b)(table2). similar mean BMI in the obese category in both studies Taylor et al. (2011) had conducted the largest popu- (29.5–33.0 kg/m2 in European versus 30.14 kg/m2 in Indi- lation-based study on obesity and related traits on 3390 sib ans) (Loos et al. 2008; Dwivedi et al. 2013). Altogether, pairs from four Indian cities. They had reported weak evi- nine genetic variants in seven genes have shown associa- dence of association of MC4R (rs17782313) with obesity tions with childhood obesity in India. (OR=1.19, P=0.05) and no association was observed for All the genetic studies which have been conducted so FTO (rs9939609). Sib-pair design was the major strength far in relation to childhood obesity have used case–control of this study due to its resistance to population strat- design and restricted to a single geographical location (i.e. ification which reduces the possibility of false-positive Delhi) which may be biased given the high level of cultural associations (Taylor et al. 2011). Further, Chauhan et al. and biological diversity in India. For better understanding, (2011) had reported association of two variants of FTO these loci need to be validated on children from different (rs1421085 and rs8050136) with obesity after evaluat- ethnic groups representing the socio-cultural diversity of ing eight genetic variants of FTO (rs1421085, rs8050136, India. rs9939609, rs9930506, rs1861867, rs9926180, rs2540769 The obesity associated SNPs within FTO are and rs708277) in 2854 nondiabetic control subjects from functionally connected with regulation of IRX3 expression north India (table 2). which is an important determinant of body mass and com- The studies with largest effect sizes for FTO and MC4R position (Ragvina et al. 2010; Smemo et al. 2014). Recently, were primarily based on obesity and have used case– Srivastava et al. (2016a) explored the associations of FTO control study design with relatively small sample size (rs8050136, rs9939609, rs1421085 and rs17817449) and (n range: 600–696) (table 2)(Srivastava et al. 2016a, b), IRX3 (rs3751723) variants with obesity in the north Indian indicating the need for larger sample size to observe an population (table 2). They have found that these variants unbiased true effect size. were associated with obesity risk and were in high linkage Indian genetic studies (n=4) with relatively large sample disequilibrium (r2 = 0.81–0.91) with each other, support- size (n range: 3126–3390) are actually related to child- ing the concept of genetic connectivity between the FTO hood obesity (Dwivedi et al. 2012, 2013; Tabassum et al. and IRX3 loci (Srivastava et al. 2016a). Further studies 2012a, b). An attempt was made by Tabassum et al. (2012a) with fairly large sample sizes are necessary to confirm these in assessing the association of 125 common variants findings. from 21 genes, encoding adipocytokines and inflammatory In India, research on genetics of obesity is generally markers in 1325 urban Indian children. They had repli- performed along with other metabolic disorders. For cated the top four loci in 1843 Indian children, and finally example, eight studies have reported significant associ- showed association of four variants: PBEF1 (rs3801266, ations of SNPs with obesity when studied in samples OR = 1.35), IL6 (rs2069845, OR = 1.37), LEPR primarily collected for T2D, hyperinsulinaemia and insulin (rs1137100, OR = 1.39) and IL6R (rs7514452, OR = resistance (Radha et al. 2007; Vimaleswaran et al. 2008; 1.19) after correction for multiple testing (Tabassum et al. Been et al. 2010; Mahajan et al. 2010; Srivastava et al. 2012a). These loci are known to play an important role 2010; Tabassum et al. 2010; Chauhan et al. 2011; Ramya in energy homeostasis, metabolic processes and regulation et al. 2011; Chauhan et al. 2012; Ramya et al. 2013). Radha of body (Tilg and Moschen 2006). They also examined the et al. (2007) had examined the association of the LPL contribution of single-nucleotide polymorphisms (SNPs) variant (–T93G, OR=1.77, 95% CI: 1.19–2.63, P=0.005) to homocysteine pathway genes in relation to obesity with obesity, whereas the other variants (–G53C) of the susceptibility (Tabassum et al. 2012b) and identified asso- same gene appears to be protective (OR = 0.561, 95% CI: ciation of the AMD1 variant (rs2796749, OR = 1.35) with 0.03–0.99, P=0.05) against obesity in a Chennai Urban obesity in urban Indian children (table 2). It was proposed Rural Epidemiology Study. Another study from Chen- that AMD1 influences the susceptibility to obesity by mod- nai showed association of a novel variant (+10211 T/G, ulating either the polyamine metabolism or DNA (Park OR = 1.57, 95% CI: 1.34–1.84, P = 10−7) in the first Genetics of obesity and its measures in India 1065 exon of ADIPOQ (Vimaleswaran et al. 2008). It was times higher than that reported for adult BMI variance proposed that the ADIPOQ gene enhances insulin sen- (0.20%) in South Asians (Li et al. 2012). These findings sitivity and functions in regulating homeostatic control of have clearly indicated higher impact of FTO variants in glucose, lipids and energy metabolism (Hu et al. 1996; Díez children than in adults. and Iglesias 2003)(table2). Further, Chauhan et al. (2012) The association of MC4R variant (rs17782313) with had observed nominal associations of CHDH (rs4563403, BMI is relatively well studied in Indian children and had OR = 0.69 (95% CI: 0.52–0.92), P = 0.01), TCN2 shown ∼2.5 kg/m2 increased BMI in comparison with wild (rs1801198, OR = 1.24 (95% CI: 1.04–1.48), P = 0.02) genotypes (Dwivedi et al. 2013). Similar association has and MTR (rs16834521, OR = 0.82 (95% CI: 0.68–0.99), been reported in adults, i.e. ∼0.8 kg/m2 increased BMI P = 0.04) in the discovery phase, but no association was among homozygous adults for effect allele in compar- observed after meta-analyses (table 2). ison with common allele (Dwivedi et al. 2013). Indian The associations of several other genes such as PPARG, children with risk allele of MC4R have ∼2-fold higher TNF-α, Myostatin, DOK5, UCP2, LMNA, IRX3, POMC, BMI (Z score=0.24) when compared with European chil- APOE and INSIG2 with obesity have been reported in dren (Z score=0.01–0.13) (Loos et al. 2008; Dwivedi et al. studies with relatively small sample size (n range: 335– 2013). The risk alleles of MC4R variants (rs17782313 and 1006) with an OR range of 1.3–5.6 in different Indian rs12970134) are more prevalent in Indians (∼36–40%) population groups (Bhagat et al. 2010; Mahajan et al. compared with Europeans (∼27–31%), Asians (∼18–24%) 2010; Srivastava et al. 2010; Tabassum et al. 2010; Sharma and Africans (∼13–31%) (HapMap release no. 27), sug- et al. 2011; Bhatt et al. 2012a, b; Srivastava et al. 2016a, b; gesting possible higher population attributable risk for Prakash et al. 2017) (table 2). More comprehensive stud- obesity in Indians. Further, a school-based case–control ies are needed before ruling out the role of these candidate study has evaluated the associations of variants in PBEF1 genes in predisposition of obesity. (rs3801266, β = 0.17), IL6 (rs2069845, β = 0.12) and LEPR (rs1137100, β = 0.15) with BMI in urban Indian children, suggesting the role of inflammatory genes in pre- Genetic associations with BMI disposition to obesity in childhood (Tabassum et al. 2012a) (table 3). The categories of BMI are widely used for assessing the Gupta et al. (2013) conducted the second largest obesity status. An elevated BMI increases the risk of population-based study on obesity-related traits on 2528 mortality and is associated with several adverse health sib-pairs recruited from four Indian cities. They had exam- outcomes, like T2D, CVDs, and continues to remain as a ined the influence of 25 T2D-associated loci on obesity significant public health problem (Misra and Shrivastava risk using sib-pair design which is resistant to population 2013). A total of 32 studies have made an attempt to exam- stratification and decreases the likelihood of false-positive ine the roles of previously known genetic polymorphisms associations. They had found associations of CXCR4 in relation to BMI among Indians. Of these, 21 studies (rs932206, β = 0.13) and HHEX (rs5015480, β = 0.09) showed significant associations with BMI (table 3). with higher BMI suggesting the role of T2D-associated Taylor et al. (2011) had studied the effects of FTO loci in influencing the measures of obesity in the Indian and MC4R variants in 3390 sib-pairs recruited from population (Gupta et al. 2013)(table3). four Indian cities, and showed associations of FTO (Z In studies primarily based on T2D samples, score=0.08, 95% CI: 0.02–0.14, P = 0.009) with BMI, analysis for obesity traits was conducted on control sam- and no such association was observed for MC4R. They ples. For instance, seven Indian studies have reported had also performed an interaction analysis between the significant associations of SNPs with BMI in control FTO and MC4R loci and rural/urban dwelling in asso- subjects. The association of FTO with T2D is mediated ciation with BMI, but no strong evidence was detected through BMI is well-known among Europeans (Frayling (Taylor et al. 2011). In a population-based cross-sectional et al. 2007). Yajnik et al. (2009) had reported weaker asso- study from the rural and urban regions of south India, ciation between the FTO variant (rs9939609) and BMI FTO (rs9939609) was shown to be associated with BMI (Z score=0.06, 95% CI: 0.01–0.10) among controls of only in adulthood, and not at younger ages (Vasan et al. Indo-European and Dravidian ancestry than the previ- 2012). On comparing the effect sizes of two SNPs of FTO ously reported effect in Europeans (Z score=0.1, 95% CI: (rs9939609 and rs17782313) on BMI in rural and urban 0.09–0.12) (Frayling et al. 2007). Similarly, associations of groups, the carriers of FTO risk allele was associated with SNPs near MC4R (rs12970134 and rs17782313) with BMI 1% increase in BMI (β = 0.020 SD/allele, P = 0.026) in the (p = 4.1×10−4 and 2.1×10−4, respectively) was reported urban group than in the rural group, and were suggested in 1549 control subjects of Indo-European ethnicity (Jani- to be influenced by urban living (Vasan et al. 2012). Inter- palli et al. 2012). Further, two studies with a relatively large estingly, in a school-based case–control study, the FTO sample size (N = 1006) had reported significant associa- variant (rs9939609) showed 0.88% BMI variance in urban tions of variants in DOX5 (rs6064099, P = 7.0 × 10−3) Indian children (Dwivedi et al. 2012) which is almost four (Tabassum et al. 2010)andTNF-α (rs2229094, P = 0.008 1066 Simmi Saini et al. and rs1800630, P = 0.01) (Mahajan et al. 2010) with The widely studied FTO variant (rs9939609) is not only BMI among controls of north India belonging to Indo- associated with WC and WHR but evidence of its asso- European ethnicity (table 3). ciation was also found with skinfold measures: abdomen In addition, several studies with a relatively small (P = 0.014), triceps (P = 0.003), biceps (P = 0.004), sample size (n range: 255–642) had reported the associ- subscapular (P = 0.003), thigh (P = 0.042) and body ations of genetic variants of UCP2, LPL, LMNA, Myo- fat percentage (BF%) (P = 0.005) in individuals recruited statin, PPAR-γ 2 and INSIG2 (Cassell et al. 1999; Radha from the Vellore birth cohort (Vasan et al. 2012). Other et al. 2007; Sharma et al. 2011; Bhatt et al. 2012a, b; variants of FTO (rs1421085, rs9930506 and rs3751812) are Prakash et al. 2017) with BMI in different population not well studied in the Indian population, only a few stud- groups of India. A total of 23 variants in 16 genes have ies have shown significant associations of these variants reported associations with BMI in the Indian population with WC (Chauhan et al. 2011; Moore et al. 2012) (table 4). (table 3). More studies with large sample size are needed to Further, association of MC4R (rs17782313 and validate these loci on anthropologically well-defined pop- rs12970134) with adiposity measures has been indicated ulations of India. that these variants might mediate susceptibility to obesity GWASs conducted in European populations have through overall body size (Hardy et al. 2010). In compar- identified more than 100 genetic variants that influence ison with wild genotypes, both homozygous children and BMI (Locke et al. 2015). Of these, only very few have been adults for effect allele (rs17782313) had ∼6.4 and ∼1.5 validated in Indian populations. Since the distribution of cm increased WC, respectively (Dwivedi et al. 2013). In body fat in India is different from Europeans (Rush et al. contrast, Vasan et al. (2012) failed to detect any associa- 2009), the identification of genetic variants related to BMI tion of variants near MC4R with adiposity measures (WC at a genomewide scale is required for the Indian population and HC) after adjusting for height, suggesting an associa- with emphasis on exploring gene environmental interaction with a larger body frame than obesity related traits in tions in predisposing increased adiposity levels. younger age groups. In comparison with WC and WHR, very few studies (n = 11) have shown genetic associations with HC (Yajnik et al. 2009; Been et al. 2010; Sanghera et al. 2010; Sharma et al. 2011; Taylor et al. 2011; Bhatt et al. 2012b; Dwivedi et al. 2012; Janipalli et al. 2012; Genetics of body fat distribution Tabassum et al. 2012b; Vasan et al. 2012; Ramya et al. 2013). It was reported that each additional copy of the risk Genetics of body fat distribution is relatively less allele at the rs17782313 of the MC4R gene was associated investigated, around the world, in comparison with obe- with a 0.06 Z score increase in HC among the individu- sity and BMI, and generally restricted to two measures, i.e. als recruited from four Indian cities (Taylor et al. 2011) WC and WHR and only a few attempts have been made to (table 4). study genetic variants of HC and body composition mea- In addition, some studies have used more precise sures. A total of 23 genetic studies in India had reported the methods for assessing regional deposition of fat such as association of studied markers with WC or WHR (table 4). computed tomography scan (Vimaleswaran et al. 2006b), Association of the FTO locus has been studied in dual-energy X-ray absorptiometry scan (Vimaleswaran different Indian populations, not only with BMI but also et al. 2006a; Sharma et al. 2011, 2013; Vikram et al. with other measures of adiposity. The observed effect sizes 2011; Bhatt et al. 2012a), magnetic resonance imaging of FTO variants (rs9939609 and rs8050136) and their con- (Sharma et al. 2011, 2013; Vikram et al. 2011) and bio- tribution to variance of adiposity traits (WC, HC and electric impedance (Bhagat et al. 2010; Dhall et al. 2012; WHR) in Indian children are higher than adults (Li et al. Prakash et al. 2012). The first study quantifying regional 2012). For adiposity parameters (WC and WHR), age- fat deposition had reported the association of PPARGC1A dependent effects of FTO have been suggested with higher variant (Gly482Ser) with visceral fat (P = 0.001), sub- contribution to the variance in children (0.54–0.65%) than cutaneous fat (P = 0.001), abdominal fat (P = 0.004), South Asian adults (0.03–0.10%) (Dwivedi et al. 2012). central abdominal fat (P<0.0001) and nonabdominal fat The effect of rs9939609 was also examined in Indian ado- (P<0.0001) among the normal glucose tolerant (NGT) in lescents and was found that carriers of homozygous risk the south Indian population (Vimaleswaran et al. 2006a). allele displayed a 0.007 unit increase in the WHR with each Vikram et al. (2011) had explored the association of TNF-α copy of the FTO risk allele (Vasan et al. 2013)evenafter with body fat distribution among north Indians and failed adjusting for BMI (β = 0.006, 95% CI: 0.001–0.012, P = to detect any relationship. 0.021) which may predispose to future metabolic risk in Variants in several other genes such as FOXA2 adulthood (Vasan et al. 2013)(table4). The WHR also cor- (Tabassum et al. 2008), UCP2 (Srivastava et al. 2010), relates strongly with insulin resistance and dyslipidaemia ADIPOQ (Sanghera et al. 2010), IL6 (Gupta et al. 2011), among Indians and other ethnic groups independent of LMNA (Sharma et al. 2011), Myostatin (Bhatt et al. overall obesity (Dhawan et al. 1994). 2012a), AMD1 (Tabassum et al. 2012b)andNGN3 (Gupta Genetics of obesity and its measures in India 1067 et al. 2013) were also associated with measures of body fat (2011) had studied GxE interactions, among rural/urban distribution (table 4). dwellers of Indian Migration Study to investigate whether Since body fat distribution reflects regional adiposity the urban or rural environment modifies genetic risk of and its pattern is different in the Indian population when obesity. They had found stronger association of FTO with compared with Europeans (Sniderman et al. 2007), there weight in urban dwellers (Z scores=0.15, 95% CI: 0.01– is a need for genetic studies on body fat distribution mea- 0.29) as they were found to be physically inactive and sured in detail using advance imaging techniques in India. consuming higher levels of dietary fat intake than rural dwellers. Bhagat et al. (2010) have also reported that subjects with PPARG AB allele were less physically active and Sexual dimorphism had a greater intake of calories and fats. Large-scale studies with detailed information on lifestyle To dissect the genetic architecture of sexual dimorphism and dietary intake are needed for identifying GxE inter- in obesity, very few studies with obesity as a primary out- actions as this may facilitate the choice of more effective come of interest, have performed sex stratified analysis measures in prevention of obesity based on the individu- and showed different effect sizes in males and females. alized genetic make-up. Gupta et al. (2013) had made an attempt to understand the genetics associated with sexual dimorphism in sib-pairs (males = 436 pairs and females = 331 pairs). They iden- Biological pathways tified a variant in CXCR4 (rs932206) showing association with overweight in both sexes (OR = 1.80), but the effect The biological pathways related to genes studied in the was observed only in males, which may be due to smaller Indian population supported strongly the role of cen- sample size of females. Other variants in TCF2 (rs757210, tral nervous system in obesity susceptibility. Several genes OR = 0.57) and LOC646279 (rs1256517, OR = 0.29) have been identified but the functional role could be were shown to be associated with protective effects against assigned to handful of them involved in the central neu- overweight in females with twice the effect size compared ronal signalling pathway (NPY and MC4R)(Bhaskar with males (Gupta et al. 2013). et al. 2010; Dwivedi et al. 2013), energy metabolism and Sexual dimorphism is a well-marked feature of body fat thermogenesis (UCP1 andUCP2)(Srivastava et al. 2010; distribution. Both males and females have different pat- Dhall et al. 2012), homocysteine metabolism/one carbon terns of body fat distribution which defines their body metabolism (AMD1)(Tabassum et al. 2012b), adipoge- shape (Wells 2007). Genetic variants associated with sexual nesis (LPL, LMNA, PPAR-γ 2, ADIPOQ, APOE and dimorphism play a vital role in regulation of body fat distri- INSIG2)(Radha et al. 2007; Sharma et al. 2011; Bhatt et al. bution traits (Heid et al. 2010; Shungin et al. 2015). Sexual 2012b; Ramya et al. 2013; Srivastava et al. 2016b; Prakash dimorphism in body fat distribution is not well studied in et al. 2017), the insulin signalling pathway (PPARGC1A) the Indian population. Vikram et al. (2011) had investi- (Vimaleswaran et al. 2006a), the leptin insulin signalling gated the association of variants in TNF-α (–308G>A) pathway (LEPR and resistin)(Gupta et al. 2011; Tabas- with subscapular skinfold in males and total BF% in sum et al. 2012b; Dasgupta et al. 2014) and inflammatory females. It was found that the females with at least one cytokine (TNF-α and IL6)(Vikram et al. 2011; Tabassum single effect allele of TNF-α (–308G>A) had significantly et al. 2012a). To understand the biological processes con- high BF% and total skinfold, whereas higher values of trolled by other identified genes leading to obesity, there WHR were observed in males, suggesting a gender-specific is a need for pathway-based validation of genetic poly- role of this polymorphism in body fat distribution (Sharma morphisms, identified by GWASs related to obesity in the et al. 2013). Low statistical power due to small sample Indian population. size make these studies inconclusive, and demands more research using large sample sizes to confirm these asso- Limitations of the studies ciations. For better understanding of adiposity, research exclusively based on sexual dimorphism in body fat dis- Most of the studies conducted in India lack information tribution among Indians is needed as it can yield insights on lifestyle risk factors like unhealthy dietary intake and into the gender-specific risk factors and causes of overall physical inactivity which can influence the effects of genetic obesity. variants on obesity and its measures. A large number of studies have used case–control design, even for studying Lifestyle factors quantitative traits like BMI and WHR, where cases were defined as T2D or related metabolic disorders. For study- Interplay between the genetic and environmental factors ing obesity, very few studies have taken cases defined by plays a vital role in modulating predisposition to obesity. obesity which is why the majority of findings from Indian In India, limited studies (n = 2) have investigated gene– studies are based on control samples only, and may be environment (GxE) interactions. For instance, Taylor et al. considered as drastically biased. Two studies have not 1068 Simmi Saini et al. reported whether the investigated polymorphisms were Been L. F., Hatfield J. L., Shankar A., Aston C. E., Ralhan in the Hardy–Weinberg equilibrium or not, which is an S.,WanderG.S.et al. 2012 A low frequency variant within important quality control measure (Cassell et al. 1999; the GWAS locus of MTNR1B affects fasting glucose concen- trations: genetic risk is modulated by obesity. Nutr. Metab. Gupta et al. 2012). The availability of the limited num- Cardiovasc. Dis. 22, 944–951. ber of large population-based resources in India is one of Bhagat N., Agrawal M., Luthra K., Vikram N. K., Misra A. and the reasons that very few studies have explored genetics of Gupta R. 2010 Evaluation of single nucleotide polymorphisms sexual dimorphism with body fat distribution. Moreover, of Pro12Ala in peroxisome proliferator-activated receptor-γ α validations of findings of GWASs conducted on Western and gly308Ala in tumor necrosis factor- genes in obese Asian Indians: a population-based study. Diabetes, Metab. Syndr. populations are limited to few genetic variants in India. So Obes. 3, 349–356. far, there has been only a single meta-analysis related to Bhaskar L. V., Thangaraj K., Pardhasaradhi G., Kumar K. P., obesity and its measures were published from India (Vasan Singh L. and Rao V. R. 2010 Neuropeptide Y gene poly- et al. 2014). Differences in sample size, study design and morphisms are not associated with obesity in a South Indian SNPs studied made it difficult to perform meta-analyses population. Eur. J. Clin. Nutr. 64, 868–872. Bhatt S. P., Nigam P., Misra A., Guleria R., Luthra K., Jain S. for the FTO gene. K. et al. 2012a Association of the myostatin gene with obesity, In conclusion, the compilation of genetic studies related abdominal obesity and low lean body mass and in non-diabetic to obesity and its measures summarized in our critical Asian Indians in north India. PLoS ONE 7, e40977. review provides a comprehensive update on the current Bhatt S. P.,Misra A., Sharma M., Luthra K., Guleria R., Pandey knowledge on genetic basis of obesity and related mea- R. M. et al. 2012b Ala/Ala genotype of Pro12Ala polymorphism in the peroxisome proliferator-activated receptor-γ2 sures among Indian populations. There are some research gene is associated with obesity and insulin resistance in Asian gaps which pose a challenge in understanding general Indians. Diabetes Technol. Ther. 14, 828–834. pathways underlying obesity susceptibility such as small Biro F. M. and Wien M. 2010 Childhood obesity and adult mor- sample size, differences in methodology used across stud- bidities. Am. J. Clin. Nutr. 91, 1499S–1505S. ies, limited information on obesity susceptibility loci and Cassell P. G., Neverova M., Janmohamed S., Uwakwe N., Qureshi A., McCarthy M. I. et al. 1999 An uncoupling protein lack of validation studies that may reflect differences in the 2 gene variant is associated with a raised body mass index but genetic background. Moreover, given the high heritabil- not type II diabetes. 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Corresponding editor: Shantanu Sengupta